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dev/nisse:main dev/nisse:a17-knowledge-store
dev/nisse:v0.1.0
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dev/nisse:v0.1.0
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Author SHA1 Message Date
dongarandClaude Opus 5.5 277bff6fcd trace-bug: say how much precision the rates need
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Details 
Calibration showed Haiku fixing the right constant but keeping only four
decimal places, which passes the visible test and still loses the JPY
rate's precision. An explicit plan should say so.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
 2026-09-27 22:43:39 +00:00
dongarandClaude Opus 5.5 25fd57351d Add four harder benchmark tasks 
All seven newer local models passed the original four tasks 20 of 20,
so the benchmark no longer told them apart. The new tasks keep plans
explicit, as the planner is told to write them, and make the work
itself harder:

- trace-bug: a wrong multi-currency total whose cause is a shared
  rate helper; discounts and tax go through it too, so a fix at the
  call site fails the check.
- refactor-rename: rename load_config across a ten-module package,
  leaving load_config_defaults and reload_config alone.
- spec-parser: implement parse_duration from an eight-rule SPEC.md
  given three examples, and write tests for every rule.
- long-milestone: eight dependent steps adding due dates to a task
  tracker, from the model through storage versions to the CLI.

Self-tests prove that a call-site fix, a blind text replace and a
partial rename all fail their checks.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
 2026-09-27 22:43:39 +00:00
dongar f2c48d8b16 Merge pull request 'Keep runs with different tuning apart in the report, and raise max_turns to 40' (#52) from report-keeps-tuning-apart into main
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Details 
Reviewed-on: dongar/nisse#52
Reviewed-by: Don Garrett <dgarrett@acm.org>
 2026-09-27 22:43:26 +00:00
dongarandClaude Opus 5.5 f881a18ca4 Keep runs with different tuning apart in the report, and raise max_turns to 40
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Details 
The report grouped runs by model, serving note and task version, so
runs made at different tuning were silently averaged together. Tuning
is now part of the key, and a tuning column shows how a row's settings
differ from today's defaults; it appears only when some row needs it.

tuning.executor.max_turns goes from 24 to 40. On the harder benchmark
tasks Haiku, the calibration ceiling, hit 24 on both refactor-rename
runs and both long-milestone runs, one of which had finished the work.
The cap guards against a model trying forever; it isn't a budget.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
 2026-09-27 22:25:14 +00:00
dev-agent a77c84c128 Merge pull request 'Render one assistant message per turn, and make the request timeout a setting' (#51) from fix-transcript-and-timeout into main
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Details
2026-09-27 22:24:50 +00:00
dongarandClaude Opus 5.5 d49435d4e5 Render one assistant message per turn, and make the request timeout a setting
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Details 
Two setup failures found while calibrating the new benchmark tasks,
both of which would also fail real milestones.

Devstral's chat template (Mistral) rejected any turn in which the model
wrote some text before a tool call: the OpenAI renderer made one message
per event, so the turn became a text message and a tool-call message in
a row, and llama.cpp answered 500. The renderer now folds consecutive
user or assistant messages into one, which is the OpenAI format for a
turn anyway. Checked against the live server: the captured failing
request no longer occurs, over six real runs.

LiteLLMClient gave up on any request after a fixed 120 s. A dense 27B
model writing a whole module in one turn takes longer, so the executor
escalated work that was going well. llm.request_timeout_s now sets it
for both clients, default 600.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
 2026-09-27 22:15:30 +00:00
dev-agent f41cbb00cf Merge pull request 'Record rate-limit waits in benchmark runs, and leave them out of time' (#50) from record-rate-limit-retries into main
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Details
2026-09-27 21:23:56 +00:00
dongarandClaude Opus 5.5 4e45dfe06a Record rate-limit waits in benchmark runs, and leave them out of time
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Details 
The client waits RATE_LIMIT_RETRY_S and retries once on a 429, silently.
A benchmark run that waited looked slower than the model is: on
2026-09-27, fast models under the unit-test key's 200k tokens/minute
limit had 20-45 s runs that were probably waits.

Both clients now report last_rate_limit_wait_s after every call, even
one that then fails. The benchmark sums it per run into
rate_limit_wait_s, the report's time column leaves it out, and a new
429 column counts the runs that waited. Results stored before this
load unchanged.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
 2026-09-27 20:57:15 +00:00
dev-agent a716d5ec8d Merge pull request 'Benchmark seven new RTX 4090 models, and local-fast on the 4090' (#49) from bench-4090 into main
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2026-09-27 20:31:46 +00:00
dev-agent 1cc8df8dab Merge pull request 'Point the example config at Opus 5.5, with Haiku as consultant' (#48) from example-opus-5-5 into main
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2026-09-27 20:31:34 +00:00
dongarandClaude Opus 5.5 82f171c2f5 Benchmark seven new RTX 4090 models, and local-fast on the 4090
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Details 
Five runs per task for qwen3.8-27b, qwen3.6-35b-a3b, gemma-4-31b,
gemma-4-26b-a4b, gemma-4-12b, mimo-v2.6-9b and granite-4.2-8b, all
served by llama.cpp b11206 through llama-swap. Every one passed 20 of
20. local-fast (vLLM 0.30.0) passed 3 of 20.

Runs were made under a 350 W power cap, recorded in each note, after
the uncapped card throttled for heat. granite-4.2-8b also has one
uncapped set, kept apart by its note.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
 2026-09-27 18:43:55 +00:00
dongarandClaude Opus 5.5 3cb70c0656 Point the example config at Opus 5.5, with Haiku as consultant
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Details 
Planner and delegate use claude-opus-5-5 for the high-end judgement
those roles need. The consultant answers single questions with no tools,
so it uses claude-haiku-4-5. Sonnet has no role.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
 2026-09-25 20:22:49 +00:00
dongar 8a8d8dc27f Merge pull request 'Add an executor benchmark that keeps its results' (#47) from benchmark into main
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Details 
Reviewed-on: dongar/nisse#47
Reviewed-by: Don Garrett <dgarrett@acm.org>
 2026-09-25 02:23:57 +00:00
dongarandClaude Opus 5.5 0cccfe1e94 Add the local baseline: qwen3-8b-awq and qwen3-14b, 5 runs per task
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Details 
The 4070's two local models with default tuning, as the baseline the
4090's candidates will be compared with. Every one of the 22 failures
is an escalation from failure_limit: 2; there were no prose turns,
invented tools, context trims, or finished-but-wrong results.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
 2026-09-25 01:25:13 +00:00
dongarandClaude Opus 5.5 50b46821d7 Add an executor benchmark that keeps its results
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Details 
Choosing a local executor model, and tuning its knobs, needs measured
success rates across repeated runs, comparable with every model
measured before.

- Four canned milestones under nisse_dev/bench/tasks: write a function,
  fix a bug a failing test exposes, thread an option through several
  files, and find one failure in output far past max_output_chars. Each
  is an approved plan over a seed project, judged by a hidden check the
  model never sees; a reference solution proves each is solvable and
  that its check tells the difference
- Runs use the agent's own client wiring, now shared as
  make_llm_clients, with delegation and ask_expert off: an escalation is
  a failure of the model under test
- Each run is stored as one JSON line in bench/results/, committed, with
  the model's backing name and declared facts, a note on how it was
  served, the task's content hash, the agent's commit and tuning, the
  outcome, turns, misbehaviour counts, context trims, tokens, time and
  LiteLLM's cost; a fallback answer can never count as a success
- The report rebuilds the comparison from every stored run, with Wilson
  95% intervals, marking runs against older task versions
- scripts/benchmark.py runs and reports; scripts/evaluate_model.sh takes
  a new model through the context probe, the capability matrix and the
  benchmark in one command
- Clients keep LiteLLM's per-call cost, and BudgetedClient counts the
  requests it had to trim
- The tuning guide gains "Benchmarking models"

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
 2026-09-25 00:35:24 +00:00
dongar b2bbf70b7b Merge pull request 'Enforce the tool-call contract, asking servers only where it works' (#45) from enforce-tool-calls into main
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Details 
Reviewed-on: dongar/nisse#45
Reviewed-by: Don Garrett <dgarrett@acm.org>
 2026-09-25 00:05:46 +00:00
dongarandClaude Opus 5.5 b934c46714 Enforce the tool-call contract, asking servers only where it works
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Details 
The executor ended a milestone on any reply without a tool call, and
nothing held a model to the offered tools. Measured on the 4070:
tool_choice "required" is enforced by vLLM (local-fast) and Anthropic,
but qwen3-14b (local-coder) under it cannot stop and writes until the
output ceiling, a known model bug. So enforcement is asked for per
model, and the loop holds the contract itself everywhere.

- The catalog reads supports_tool_choice from LiteLLM's model_info;
  BudgetedClient sends tool_choice only to models declared true, as
  "required" on the normalized route and {"type": "any"} on the
  Anthropic passthrough, and only when tools are offered
- The executor gains an explicit finish tool, the only way to end a
  milestone. Finishing with nothing changed is refused once; a prose
  reply gets one nudge, then escalates; a call to a tool not offered
  counts toward failure_limit, like a failed command; calls after
  finish are recorded as not run
- Clients record the model that actually answered. ModelFacts.answered
  recognises a proxy fallback, which once credited Claude's answers to
  a local model that could not call tools at all
- The context probe reports a fallback instead of trusting it; rerun,
  both local models now refuse over-long prompts, as they should
- The capability matrix checks every answer came from the model asked,
  that models call only offered tools, and that a declared
  supports_tool_choice is true
- Spec A15 and the tuning guide say how enforcement is requested,
  declared and backed up

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
 2026-09-24 22:54:49 +00:00
dongar 389b71a5d1 Merge pull request 'Fit every model request to its context window' (#44) from context-budget into main
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Details 
Reviewed-on: dongar/nisse#44
Reviewed-by: Don Garrett <dgarrett@acm.org>
 2026-09-24 13:41:15 +00:00
dongarandClaude Opus 5.5 494715efa9 Fit every model request to its context window
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Details 
Every turn re-sent the whole transcript with nothing checking it against
the model's window. On a local model that fails silently: Ollama drops
the start of an over-long prompt, which is where the system prompt and
the plan are. Measured on the Jetson's llama3.2, cut at 4096 tokens with
no error.

- The model catalog reads max_input_tokens and max_output_tokens from
  LiteLLM's model_info, and startup refuses a role on a model with no
  declared window
- BudgetedClient wraps both routes. It fits the canonical events before
  rendering: older tool results cut to head and tail, then the oldest
  turns dropped with a note, keeping calls with their results; a request
  that still cannot fit fails with ContextOverflow and is never sent.
  The output ceiling is the smaller of config and the model's limit
- Tokens are estimated from characters, pessimistically, and the
  estimate is corrected from reported prompt sizes, only ever upward
- The executor takes its scope from the log: the latest plan, which is
  the approved milestone plan, goes in its system prompt, and it sees
  only events after it
- Tool output keeps its head and, mostly, its tail, so a test runner's
  failure summary survives truncation
- tuning.context.keep_recent_turns and safety_margin are new knobs
- scripts/probe_context.py checks a declared window against what the
  server does; it confirmed llama3.2 cuts at the declared 4096
- The tuning guide covers the budget, declaring a local model's limits,
  and the probe

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
 2026-09-24 00:26:30 +00:00
dongar 31851b7396 Merge pull request 'Move configuration to YAML, and make the tuning knobs configurable' (#43) from yaml-config into main
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Details 
Reviewed-on: dongar/nisse#43
Reviewed-by: Don Garrett <dgarrett@acm.org>
 2026-09-23 23:49:44 +00:00
dongarandClaude Opus 5.5 e1034a2cb7 Show which config key refers to each example secret
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Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
 2026-09-23 23:45:02 +00:00
dongarandClaude Opus 5.5 58b439088f Pass the capability matrix's models as a pytest option
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NISSE_CAPABILITY_MODELS was neither a secret nor configuration: which
models to measure is a question for one run, so it is now an argument,
--capability-models=a,b, declared in tests/conftest.py where pytest
reads options at startup. With no models the matrix skips as before, so
CI is unchanged. The last non-secret NISSE_* variable other than
NISSE_CONFIG and NISSE_ENV is gone.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
 2026-09-23 23:32:44 +00:00
dongarandClaude Opus 5.5 15d69799bf Make the config file the only home for settings
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Details 
With no production instance yet, there is no reason to carry two ways
of configuring the agent. Every environment variable that duplicated a
config key is gone; the environment now supplies only the secrets the
file references by ${…}, plus NISSE_CONFIG and NISSE_ENV to find the
files.

- AgentConfig loads from a file (load, from_file) or a parsed tree
  (from_tree, for tests); a missing file is an error naming the example
- The from_env constructors on LiteLLMClient, AnthropicPassthroughClient,
  GiteaClient, MatrixConfig and RoleMap are removed, with the env
  reading for max output tokens; clients take their settings from config
- The live integration tests share a committed agent config,
  tests/integration/nisse-test.yaml, aimed at test resources, and one
  conftest that loads it and builds clients. NISSE_LIVE_PLANNER_MODEL
  becomes its planner role; NISSE_CAPABILITY_MODELS stays as a per-run
  switch
- A missing secret now fails the live tests instead of skipping them
- CI passes only the three tokens
- .env.example is secrets only; spec A5 says config comes from the file

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
 2026-09-23 23:25:58 +00:00
dongarandClaude Opus 5.5 df449c7e34 Move configuration to a YAML file, and make the tuning knobs configurable
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Details 
The thresholds Part H says to tune on the real inference hardware were
hardcoded, so every adjustment would have been a PR. They now live in a
`tuning:` section of a YAML config file, which also replaces flat
environment variables as the home for structure (#32).

- The file (NISSE_CONFIG, default ./nisse-dev.yaml) carries structure
  and holds no secrets: credentials are ${VAR} references resolved from
  the environment, and a reference to an unset variable is an error
- The environment still wins: every existing NISSE_* variable overrides
  its key, and the file is optional, so .env and CI work unchanged
- Validation stays fail-fast and reports every problem at once; unknown
  keys are errors, so a misspelt knob fails at startup instead of
  silently doing nothing
- The config file joins the state store and .env in the check that
  refuses to start when they sit inside the workspace clone
- Tuning covers the executor's turn and failure limits, the consult
  budget, the delegation caps, the command timeout and the tool output
  limit, which Workspace now takes as a setting; the defaults are the
  values the agent already ran with
- llm.max_output_tokens reaches both LLM clients from the config

docs/Tuning-Guide.md explains the knobs for someone learning local
model tuning: how weights, quantization and the KV cache spend VRAM,
what each knob does and what goes wrong at either extreme, and an order
to tune in. nisse-dev.example.yaml documents every key and is checked
by a test so it cannot drift.

Closes #32

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
 2026-09-23 23:14:07 +00:00
dongar 4a9354ae3b Merge pull request 'Add the operator kill switch, stop (A4)' (#41) from stop-command into main
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Reviewed-on: dongar/nisse#41
Reviewed-by: Don Garrett <dgarrett@acm.org>
 2026-09-23 22:50:46 +00:00
dongarandClaude Opus 5.5 abb7fa0216 Add the operator kill switch, stop (A4)
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Details 
`stop` could not have worked by adding the command alone. Planning
turns, milestones and review responses ran synchronously on the thread
that delivered the message, so the Matrix sync loop read nothing until
the work finished. The state store was also opened on the main thread
and used from the transport and poller threads, which SQLite refuses
with its default same-thread check; the in-memory test transport never
showed it.

A single worker thread now runs every handler and owns the store:

- The worker opens the store, so the same-thread check stays on and
  fails loudly if anything touches the store from elsewhere
- Matrix messages are handed to the worker; pollers hand over and wait,
  so their cursor never passes an unhandled comment
- A message that arrives during long work gets an immediate "busy; say
  'stop' to halt" reply and is recorded, not applied later in a state
  the operator was not answering
- Tests keep running inline through InlineWorker, which defers a job
  submitted from inside another as the real worker would queue it

`stop` sets a shared Cancel flag straight from the transport's thread.
The executor and delegation loops check it before each model call and
between tool calls; the planner checks it before each turn. Calls left
unrun get a result saying so, so the transcript still renders. A
stopped delegation is discarded like a failed one. The orchestrator
moves to AWAIT_INPUT and asks why the work was stopped. The reply is
recorded for the model, and a stopped planning turn resumes with it as
the answer. `stop` with nothing running says so and changes nothing; at
a gate it is no longer taken as rejection feedback.

Closes #40

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
 2026-09-23 22:47:29 +00:00
dongar d2fe3d5642 Merge pull request 'Gate ops writes on approved step sequences' (#39) from ops-gating into main
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Reviewed-on: dongar/nisse#39
Reviewed-by: Don Garrett <dgarrett@acm.org>
 2026-09-23 22:26:12 +00:00
dongarandClaude Opus 5.5 019fa24517 Wait a grace period before switching away from a waiting item
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Details 
Switching as soon as the active item posts a plan or question means the
operator sees the request and the switch notice seconds apart. A prompt
approval then waits until the other item waits or closes, which can be
a whole milestone. A configurable grace period (default 10 minutes)
lets a watching operator answer before the agent moves on, and still
switches when they are away.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
 2026-09-23 22:16:40 +00:00
dongarandClaude Opus 5.5 a91252cb77 Let the agent also switch on its own when the active item closes
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Details 
0.2 previously allowed an autonomous switch only while the active item
waits on the operator. Ready work should not sit behind an idle agent
either, so closing an item (completed or abandoned) is now the second
case. This also defines "ready": the agent can act on the item without
the operator, because it has not started or because intake arrived
while it was parked that answers what it was waiting for. A9's
work_item.switch allows a closed or null `from`, and B2 no longer says
only the operator selects the active project.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
 2026-09-23 22:09:39 +00:00
dongarandClaude Opus 5.5 5b163f0c2e Give calls outside any work item an agent-level scope
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Details 
Letting the model choose which ready item to switch to adds a model
call that belongs to no work item. Duty runs before a finding already
had the same gap. A10 now defines an agent-level scope for these calls,
kept apart from work item transcripts in both directions. The switch
call sees only code-rendered summaries of the ready items, runs as the
executor, and falls back to the oldest item if it fails. A9 gains a
work_item.switch event, and A18 accepts that an injected alert can at
worst reorder work.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
 2026-09-23 21:57:05 +00:00
dongarandClaude Opus 5.5 854bddc680 Let the model choose which ready work item to switch to
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Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
 2026-09-23 21:54:18 +00:00
dongarandClaude Opus 5.5 b8df5d2066 Gate ops writes on approved step sequences
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Details 
C2's per-tool allow/deny/confirm taxonomy would have to be written in
advance for a tool catalog that will grow large, and every mistake in it
is friction or a hole. An ops write plan is now a list of literal steps
(tool, target, arguments, optional precondition and expected outcome,
retry allowance), and one approval covers exactly that sequence:

- Code renders the approval message from the steps, flagging
  irreversible steps, protected targets, and steps that take down the
  agent's own host
- A matcher checks every call: read-only is free, an elevated call must
  exactly match the next approved step (arguments normalized against the
  tool's schema), and anything else halts to AWAIT_INPUT
- Approvals expire if the plan does not start in time
- Classification shrinks to a versioned policy file: instance privilege,
  protected targets, and each elevated tool's target argument, all
  failing safe; MCP annotations may only tighten
- Bounded matching and standing approvals are deferred, with reasons

Supporting changes:

- A3: an approved sequence resumes after the agent's own restart only on
  a verified expected outcome for the step in flight
- A4: an operator kill switch, `stop`, for every agent
- 0.2: the agent may switch items on its own while the active one waits
  on the operator; parking unmounts elevated instances; a Matrix reply
  to a parked item binds to that item
- A17: step preconditions replace the staleness bounds, the log-checked
  verification precondition and the boot-time re-fetch
- A18 (new): prompt injection, designed around limiting what a
  compromised model can do rather than detecting injection
- Part H records the decision and parks the deferred items

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
 2026-09-23 21:50:15 +00:00
dongar 182216296d Merge pull request 'Define the work model: duties, work items, scopes' (#38) from work-model into main
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Details 
Reviewed-on: dongar/nisse#38
Reviewed-by: Don Garrett <dgarrett@acm.org>
 2026-09-23 20:31:29 +00:00
dongarandClaude Opus 5.5 2c9a082e5b Define the work model: duties, work items, scopes
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Details 
"Project" was carrying several things at once: the repo, the effort
through arch plan and milestones, and the transcript's scope. The
Sysadmin Agent needs the same distinctions in a different shape, with
standing duties that produce many efforts over one scope. New section
0.2 names them:

- Duty: a standing responsibility that produces work items, and owns
  deduplication of its findings
- Work item: the unit of state, identified by a locally minted id
  (A9's task_id), optionally linked to an external record
- Scope: what a work item operates on, shared across work items
- Park-and-switch: one active work item, switching only at safe
  points, nothing lost while parked; the schema does not assume one

0.1 records this as a bounded exception to extract-after-it-works:
identity is designed ahead, behaviour is not. A2, A4, A9, A10, B1/B2
and C4 now speak in these terms; the transcript is scoped per work
item, and a milestone no longer mints a task_id. Part H parks
concurrency with its costs, and leaves open when a work item must
have an external record.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
 2026-09-23 20:26:24 +00:00
dongar 889615e3e2 Merge pull request 'Record the MCP server deployment decisions in the spec' (#36) from mcp-deployment into main
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Details 
Reviewed-on: dongar/nisse#36
Reviewed-by: Don Garrett <dgarrett@acm.org>
 2026-09-23 02:03:59 +00:00
dongarandClaude Opus 5.5 3dbfbc8cf0 Name the site domains the identifiers are stems of
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Details 
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
 2026-09-23 02:00:43 +00:00
dongarandClaude Opus 5.5 cdc3881deb Fold the MCP deployment amendment into the spec
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Details 
Part F is restructured around rules that now apply to every agent:

- F1: every agent gets its own VM, for inter-agent isolation and
  per-agent credential attribution
- F2: a declared, per-instance default-deny egress allowlist that
  covers MCP servers' reach, with the tailnet's 100.64.0.0/10 called
  out
- F3: MCP servers per agent VM and per (service, site, privilege),
  plus the rejected routed-meta-tools alternative
- F4: the existing image pipeline, with third-party MCP images
  digest-pinned and excluded from Watchtower

Consequences land where they belong: site identifiers in Part 0, a
machine account per MCP server instance in A7, B3 and C5 recast as
instances of F1/F2, plan approval required for every write in C2, MCP
as the primary infrastructure path in C3, the network-gear browser
item narrowed to fallback access, and stateful-server relocation
parked in Part H.

The standalone amendment is removed now that its content lives here,
as with the Tiered Inference amendment; it remains in the previous
commit.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
 2026-09-23 01:56:30 +00:00
dongarandClaude Opus 5.5 31bef59151 Add the MCP deployment amendment 
Records a design session on MCP server deployment as a standalone
amendment, ahead of folding it into the spec:

- Every agent gets its own VM, justified by inter-agent isolation and
  per-agent credential attribution; Docker-in-Docker stays as an extra
  Dev Agent reason rather than the rule's foundation
- Egress is a declared, per-agent-instance default-deny allowlist,
  covering what MCP servers reach on the agent's behalf, with the
  tailnet's 100.64.0.0/10 called out as outside RFC1918
- MCP servers run per agent VM, one instance per (service, site,
  privilege), with site in the server identity rather than a tool
  argument
- Third-party MCP images are digest-pinned and excluded from Watchtower
- Routed meta-tools rejected; stateful-server relocation parked

Two consequences settled with the operator while drafting: each MCP
server instance gets its own Vaultwarden machine account (A7), and any
write, reversible or not, requires an approved plan (C2).

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
 2026-09-23 01:55:31 +00:00
dongar b0d7adb70d Merge pull request 'Discover a model's vendor from LiteLLM instead of its name' (#35) from alias-discovery into main
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Details 
Reviewed-on: dongar/nisse#35
Reviewed-by: Don Garrett <dgarrett@acm.org>
 2026-09-23 01:49:36 +00:00
dongarandClaude Opus 5 e38adf40ca Discover a model's vendor from LiteLLM instead of its name
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Details 
Route selection sniffed model names: anything starting "claude-" or
"anthropic/" took the vendor-native passthrough, everything else took the
normalized route. That works only while an alias names its vendor. A
semantic role alias like "remote-architecture" is meant to point at
Anthropic today and somewhere else tomorrow, and the name says nothing
either way -- so it would have taken the normalized route regardless of
what was behind it, silently losing the fidelity A13 wants for delegations.

LiteLLM already knows the answer. /model/info reports each alias's backing
model, provider, and declared tool support, so the agent asks at startup and
routes on the provider it discovers. That keeps vendor knowledge where A5
already put it -- in the proxy -- rather than duplicating it as name patterns
here.

There is deliberately no name-based fallback: PASSTHROUGH_PREFIXES and
has_native_adapter are gone, and select_client now requires a catalog. A
second heuristic could only ever disagree with the proxy. An unknown model
raises instead of guessing a route.

Misconfiguration is therefore loud and early. build_agent validates every
role against the catalog before it clones anything, naming all the bad ones
at once. Discovery is per key -- /model/info returns only what the presented
token may use -- so the same check catches a role pointed at a real model
this key cannot reach. An unreachable proxy is reported separately, since an
outage and a typo call for different responses from the operator.

Two things now get recorded rather than inferred: the resolved model is
pinned alongside the role mapping at task start, for the reason A11 already
pins roles, and DelegationStarted carries backing_model so A16 eval history
is not averaged across whatever an alias happened to point at.

A model LiteLLM declares tool-less warns at startup rather than refusing;
A15 expects weak local models to be accommodated, and they are observed
driving tool loops anyway.

Verified live: all four aliases the production key can see route correctly
through discovery, and each chosen route works.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-09-23 01:47:58 +00:00
dongar 231b406393 Merge pull request 'Choose the LLM route by model, and add a capability matrix' (#34) from model-routing into main
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Details 
Reviewed-on: dongar/nisse#34
Reviewed-by: Don Garrett <dgarrett@acm.org>
 2026-09-23 01:47:34 +00:00
dongarandClaude Opus 5 8cfa116aa1 Preserve delegation work through any upstream outage, not just budget
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Details 
An exhausted budget was a special case of something more general: the
work is not at fault. A network blip, a timeout, a 5xx from the proxy,
or a rate limit that did not clear all leave a delegation branch that
may be perfectly good, and discarding it would throw away work for a
reason that has nothing to do with the work.

UpstreamUnavailable now names that family, with BudgetExhausted a
member of it — distinguishable where the difference matters, since a
budget will not clear within the hour while a network blip will. A
delegation stopped by any of them is "interrupted": branch kept,
intent left pending, and A3's resume rule picks it up.

The benefit of the doubt stops at the server's own door. A 4xx that is
not a rate limit means the request itself was wrong, and preserving
branches on the strength of one would hide a real bug — so those still
fail and clean up.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-09-22 23:57:05 +00:00
dongarandClaude Opus 5 8cfb45aed3 Enforce the token cap, and treat a spent budget as an interruption
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Details 
Exceeding a LiteLLM spend limit takes external models away for the
rest of the day, which makes three existing gaps consequential rather
than untidy.

- A13 calls the delegation caps hard on "turns, tokens, and wall
  clock", and the module docstring repeated it, but the token cap was
  decoration: nothing counted, and result.tokens was always zero. It
  is now metered from the usage the provider reports, so a confused
  delegate cannot spend a day's allowance. A5 keeps the *money* cap on
  the LiteLLM key, which is a different thing; this is a scope cap
- The normalized route sent no ceiling at all, so truncation detection
  there had nothing to detect against. Both routes now send one
- A budget refusal is recognised distinctly. It is not a rate limit:
  retrying cannot help, so the planner no longer burns a second call
  to fail identically

And a delegation cut off by an exhausted budget is now "interrupted"
rather than "failed", which preserves its branch and leaves its intent
pending. The work on that branch may be perfectly good; discarding it
because the budget ran out would throw away work for a reason that has
nothing to do with the work. A3's resume rule already knows how to
pick such a delegation back up.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-09-22 23:54:57 +00:00
dongarandClaude Opus 5 249bcb10fc Detect truncated responses, and raise the output ceiling
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Details 
Measuring Opus found a bug that had been silent all along: nothing
checked stop_reason or finish_reason, so a response cut off mid-answer
was parsed as a complete one.

The symptom was a submit_plan tool call whose required `content` field
was simply absent — structurally valid, substantively empty, and
indistinguishable from a model that chose to send nothing. The cause
was our max_tokens of 4096: Opus writing an architecture plan
routinely spends 3,000-4,000 output tokens, and was observed at 4,064
against that ceiling. Roughly one run in three crossed it.

Worse than the empty plan is what the same failure does elsewhere: a
truncated write_file writes half a file and reports success.

- Both routes now raise TruncatedResponse when the model was cut off.
  It subclasses LLMError, so the planner's retry and the executor's
  and delegation's gating already handle it without learning a new
  type: a cut-off turn is a failed turn
- The default ceiling is 16384 and configurable via
  NISSE_MAX_OUTPUT_TOKENS. Output is billed by what is produced, not
  by the ceiling, so a low limit buys nothing and costs silent
  truncation

Verified: three consecutive Opus planning turns now return plans of
5,700-6,200 characters where one in three previously returned nothing.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-09-22 23:45:28 +00:00
dongarandClaude Opus 5 ed2f5dfc7f Choose the LLM route by model, and add a capability matrix
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Details 
The arrival of non-Anthropic models made a latent bug observable: the
delegate's client was hardcoded to the Anthropic passthrough, so
pointing NISSE_ROLE_DELEGATE at gpt-6-astra or a local model would
have sent Anthropic-shaped /v1/messages bodies to something that does
not speak them. A5 promises the frontier tier is swappable "as a
configuration change, not a refactor", and it was not.

select_client now picks the route from the model: the vendor-native
adapter where one exists, LiteLLM's normalization otherwise — which is
exactly what A13 prescribes for a vendor whose renderer adapter has
not been written, pending validation against the A16 eval set.

Also adds the capability matrix Part H asks for. It says the
thresholds that depend on local capability — A12's failure count,
A14's consult budget, A15's manifest size — must be tuned "on the real
hardware", and that needs measurement rather than reputation. The
harness probes each model for the things that actually break: whether
it calls a tool at all, whether the plan it submits has substance
(llama3.2 emitted "{"), whether it can consume its own tool result,
and whether prompt-level JSON holds. Opt in with
NISSE_CAPABILITY_MODELS; it spends real tokens across several models.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-09-22 23:20:38 +00:00
dongar 1da94d2581 Merge pull request 'Add the composition root and a runnable entry point' (#31) from agent-entrypoint into main
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Details 
Reviewed-on: dongar/nisse#31
Reviewed-by: Don Garrett <dgarrett@acm.org>
 2026-09-08 22:33:45 +00:00
dongarandClaude Opus 5 dfa574aeec Refuse to start with the state store or .env inside the clone
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Details 
Answering "is the workspace where the agent runs, or what it works
on?" — it is what it works on, and conflating the two is dangerous
rather than merely untidy.

The agent commits its workspace with git add -A and pushes it, so
anything inside the clone reaches the project repository. A .env
placed there would publish the LiteLLM, Matrix and Gitea tokens as a
side effect of the agent working correctly: silently, once, and
probably unnoticed. Startup now refuses both that and a state store
inside the clone, and .env.example documents the intended layout.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-09-08 22:31:39 +00:00
dongarandClaude Opus 5 2fb25b8195 Add the composition root and a runnable entry point
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Details 
Nothing had ever assembled the agent. main.py was still the hello-world
placeholder from the scaffold, and no code outside the test suite
constructed an Orchestrator — which is how two fully built, fully
tested components came to be reachable from nothing: the delegation
harness, and the Gitea comment poller. Unit tests that construct a
component directly cannot catch that; only assembly can.

- config.py validates every variable before anything is built and
  names all of them at once, rather than failing at first use twenty
  minutes into a milestone on the call that mattered. Whitespace-only
  counts as missing, since a token of spaces passes a truthiness check
  and fails at the first request instead
- agent.py builds the whole graph and owns startup and shutdown order:
  reconcile before accepting commands, and on stop, stop watching,
  then stop talking, then close the store last
- The Gitea account is read via whoami rather than configured: it names
  the identity every permission is evaluated against, and proves the
  token works before anything depends on it
- Poller lifecycle is now a state-machine concern: watch the plan issue
  while a gate stands, the PR while it is open, and stop on leaving.
  The orchestrator owns when to watch; an injected factory owns how to
  build one
- The executor reports each turn, because it runs synchronously inside
  the transport callback: without it the agent is silent for a whole
  milestone and a working run looks exactly like a hung one
- main.py loads .env, builds, and shuts down cleanly on SIGINT/SIGTERM

The entry-point tests changed shape: nisse-dev no longer greets, it
refuses to start unconfigured and says everything that is missing. Both
tests now run with a deliberately empty environment and a .env path
that does not exist, because the working directory's .env holds live
credentials and a test must never be one cwd away from starting a real
agent.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-09-08 22:26:26 +00:00
dongar 6331fcafd9 Merge pull request 'Wire delegation to A12's trigger, and implement A3's resume rule' (#25) from delegation-resume into main
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Details 
Reviewed-on: dongar/nisse#25
Reviewed-by: Don Garrett <dgarrett@acm.org>
 2026-09-08 21:46:26 +00:00
dongarandClaude Opus 5 ce436262f4 Wire delegation to A12's trigger, and implement A3's resume rule
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Details 
Two gaps found by auditing spec against code, the second created by
7b landing the harness without its boot-time half.

**The harness was not connected to anything.** Only a live test
constructed a Delegation; A12's failure-count trigger still gated to
the human, which A13 describes as the fallback rather than the path.
Consecutive command failures now hand the sub-task to the delegate,
with the failures in the brief so it need not rediscover what the
local model learned the hard way. A usable handback lets the local
loop carry on; anything else escalates, because a cap breach leaves a
half-edited tree.

**A3 step 5 was unimplemented.** A crash mid-delegation failed safe —
boot gated on "restarted mid-EXECUTING" — but always gated, even on
the clean cut the spec says should resume, and left the branch orphaned
because _abandon only runs while the process lives.

- A delegation now records an intent before the branch exists, and
  settles it whichever way the delegation ends; an aborted delegation
  is a finished one, and leaving it pending would make the next boot
  believe work was still in flight
- delegation_verdict implements the clean-cut test as an observable
  check, never model self-assessment: settled if a completion follows
  the start, absent if the branch was never created, gate on a
  tool_call with no recorded result, resume otherwise
- recover() sweeps at boot, announcing a resume with a notice rather
  than a question, since a clean cut needs no decision from the
  operator, and leaving an unclean one pending so the generic sweep
  gates on it
- resume() continues the same delegation rather than starting a
  second: no new branch, no second delegation_started, because A10
  defines a session as the delegation, not the process lifetime

Honest limit, stated in the code: "the branch sits at the expected
commit" is approximated by the branch existing and carrying the work,
since per-turn shas are not recorded in the log, so a branch rewritten
underneath us would go undetected.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-09-08 20:03:46 +00:00
dongar 608a3a5bfb Merge pull request 'Add ask_expert and eval capture (A14/A16) — step 7c' (#24) from ask-expert into main
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Details 
Reviewed-on: dongar/nisse#24
Reviewed-by: Don Garrett <dgarrett@acm.org>
 2026-09-08 19:53:30 +00:00
dongarandClaude Opus 5 2b7651fb37 Add ask_expert and eval capture (A14/A16) — step 7c
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Details 
Completes step 7.

- expert.py: one stateless call for a question needing no repo access.
  Structured output is contracted at the prompt level, since vendors
  differ on JSON enforcement, so the reply is parsed and validated
  locally with one retry — tolerating a fenced code block, because
  models wrap JSON habitually, but not missing fields
- The per-task budget is counted from the canonical log rather than
  memory, so it survives a restart and cannot hand a circling executor
  a fresh allowance. Exhaustion raises rather than answering: a caller
  out of consults should get on with the work or escalate
- Consults are recorded as delegation_started/completed with role
  consultant. A16 asks for a "consult/delegation eval set", and
  reusing the composite events gets consult evaluation for free rather
  than inventing a parallel record
- evals.py pairs starts with completions into eval records, with
  verdicts kept outside the canonical log: the log records what
  happened, a verdict is a later judgement about it, and judgements
  get revised. Schema v3 adds the outcomes table, so a v1 store now
  traverses two migrations — a better exercise of the hook than one
- ask_expert is offered to the executor as a tool only when a desk is
  configured, in addition to the observable escalation triggers and
  never instead of them (A12). Consulting is not mutating, so a
  completion claim backed only by consults is still challenged

Found live: passing an empty message list and putting the question in
the system prompt fails outright — the Messages API requires at least
one non-system message. The question now travels as a synthesised user
message that is never appended to the log, which keeps the consult
stateless while remaining a valid request.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-09-08 19:48:13 +00:00
dongar dd22eda528 Merge pull request 'Map A17 onto Outline's actual primitives, and record the MCP decision' (#23) from a17-outline-mapping into main
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Details 
Reviewed-on: dongar/nisse#23
Reviewed-by: Don Garrett <dgarrett@acm.org>
 2026-09-08 19:38:29 +00:00
dongar f2ba932744 Merge pull request 'Add A17, the knowledge store contract (Outline)' (#22) from a17-knowledge-store into main
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Details 
Reviewed-on: dongar/nisse#22
Reviewed-by: Don Garrett <dgarrett@acm.org>
 2026-09-08 19:25:00 +00:00
171 changed files with 11817 additions and 581 deletions
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.env.example
+15 -34
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@@ -1,36 +1,17 @@
# Copy to .env (chmod 600). Real environment variables win over this file.
# .env is gitignored; never commit real values.
# Secrets only. Copy to .env (chmod 600); it is gitignored, so never commit
# real values. Real environment variables win over this file.
#
# Everything else lives in the YAML config (see nisse-dev.example.yaml),
# which refers to these by name, e.g. `token: ${NISSE_LLM_TOKEN}`. The live
# integration tests use the same names, from tests/integration/nisse-test.yaml.
# --- LLM access (A5): the LiteLLM proxy is the single LLM destination ---
NISSE_LLM_BASE_URL=http://litellm.example:4000
NISSE_LLM_TOKEN=sk-your-litellm-virtual-key
# llm.token: the LiteLLM virtual key (A5)
NISSE_LLM_TOKEN=replace-me
# matrix.token: the Matrix access token for the agent's account (A1)
NISSE_MATRIX_TOKEN=replace-me
# gitea.token: the Gitea service account's scoped token (B2)
NISSE_GITEA_TOKEN=replace-me
# --- Role mapping (A11): roles resolve to concrete model strings ---
NISSE_ROLE_EXECUTOR=anthropic/claude-haiku-4-5-20251001
NISSE_ROLE_PLANNER=anthropic/claude-opus-5
NISSE_ROLE_DELEGATE=anthropic/claude-opus-5
NISSE_ROLE_CONSULTANT=anthropic/claude-opus-5
# Opt-in for the live planning test: a model the virtual key can reach that
# holds the tool-call contract. Planning escalates unconditionally (A12), and
# small local models do not hold the contract reliably — llama3.2 returns
# truncated tool arguments, Haiku-class and up are solid.
NISSE_LIVE_PLANNER_MODEL=claude-haiku-4-5
# --- Matrix transport (A1), used by nisse-dev's MatrixTransport ---
NISSE_MATRIX_HOMESERVER=https://matrix.example
NISSE_MATRIX_USER_ID=@nisse-dev:example
NISSE_MATRIX_TOKEN=syt_your_matrix_access_token
NISSE_MATRIX_ROOM_ID=!roomid:example
# Dedicated room for the live integration test; never the production room.
NISSE_MATRIX_TEST_ROOM_ID=!testroomid:example
NISSE_MATRIX_OPERATORS=@you:example
# --- Gitea (B2): service account for branches, PRs, and comment intake ---
NISSE_GITEA_URL=https://gitea.example
NISSE_GITEA_TOKEN=your-scoped-service-account-token
# Sandbox repo for live tests; never the real project repo.
NISSE_GITEA_TEST_REPO=owner/sandbox-repo
# Gitea logins allowed to drive the agent via PR comments (A4 per-channel
# allowlist; a Matrix MXID and a Gitea login are different names).
NISSE_GITEA_OPERATORS=your-gitea-login
# Where the config file is, if not ./nisse-dev.yaml. Where this file is, if
# not ./.env, is NISSE_ENV, which has to be set in the real environment.
# NISSE_CONFIG=~/nisse-demo-workspace/nisse-dev.yaml
.gitea/workflows/ci.yml
+3 -11
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@@ -34,19 +34,11 @@ jobs:
run: uv run pytest -m "not integration"
- name: Integration tests
# Live-path credentials come from repo Actions secrets; the tests
# skip-guard on these, so a missing secret or unreachable service
# degrades to "skipped", never a failure on unrelated work.
# Everything but the secrets is in tests/integration/nisse-test.yaml,
# which refers to these by name. A missing secret fails the live
# tests that need it; it is never quietly turned into a skip.
env:
NISSE_LLM_BASE_URL: ${{ secrets.NISSE_LLM_BASE_URL }}
NISSE_LLM_TOKEN: ${{ secrets.NISSE_LLM_TOKEN }}
NISSE_ROLE_EXECUTOR: ${{ secrets.NISSE_ROLE_EXECUTOR }}
NISSE_LIVE_PLANNER_MODEL: ${{ secrets.NISSE_LIVE_PLANNER_MODEL }}
NISSE_MATRIX_HOMESERVER: ${{ secrets.NISSE_MATRIX_HOMESERVER }}
NISSE_MATRIX_USER_ID: ${{ secrets.NISSE_MATRIX_USER_ID }}
NISSE_MATRIX_TOKEN: ${{ secrets.NISSE_MATRIX_TOKEN }}
NISSE_MATRIX_TEST_ROOM_ID: ${{ secrets.NISSE_MATRIX_TEST_ROOM_ID }}
NISSE_GITEA_URL: ${{ secrets.NISSE_GITEA_URL }}
NISSE_GITEA_TOKEN: ${{ secrets.NISSE_GITEA_TOKEN }}
NISSE_GITEA_TEST_REPO: ${{ secrets.NISSE_GITEA_TEST_REPO }}
run: uv run pytest -m integration -rs
.gitignore
+1
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@@ -6,3 +6,4 @@ __pycache__/
.pytest_cache/
.ruff_cache/
dist/
nisse-dev.yaml
bench/results/.gitkeep
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Whitespace-only changes.
bench/results/2026-09-25-local-coder.jsonl
+20
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@@ -0,0 +1,20 @@
{"backing": "openai/qwen3-14b", "check_output": "...... [100%]\n6 passed in 0.01s\n", "commit": "50b4682+dirty", "cost": 0.0, "failed_commands": 1, "fallbacks": [], "invented_tools": 0, "model": "local-coder", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11028, Qwen3-14B Q4_K_M, q8 KV, 32k", "outcome": "completed", "outcome_note": "Fixed total_value to multiply unit price by quantity. All tests now pass.", "prose_turns": 0, "provider": "openai", "success": true, "task": "fix-bug", "task_hash": "b78248aff00ddc39", "tokens_in": 5328, "tokens_out": 1492, "trimmed_requests": 0, "ts": "2026-09-25T01:03:24+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 5, "wall_s": 46.4}
{"backing": "openai/qwen3-14b", "check_output": "\n==================================== ERRORS ====================================\n_________________ ERROR collecting .bench_check/test_hidden.py _________________\nImportError while importing test module '/tmp/tmpr1q1pe0r/work/.bench_check/test_hidden.py'.\nHint: make sure your test modules/packages have valid Python names.\nTraceback:\n/usr/lib/python3.12/importlib/__init__.py:90: in import_module\n return _bootstrap._gcd_import(name[level:], package, level)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n.bench_check/test_hidden.py:4: in <module>\n from inventory import Item, low_stock, total_value\nE ImportError: cannot import name 'Item' from 'inventory' (/tmp/tmpr1q1pe0r/work/inventory.py)\n=========================== short test summary info ============================\nERROR .bench_check/test_hidden.py\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\n1 error in 0.10s\n", "commit": "50b4682+dirty", "cost": 0.0, "failed_commands": 2, "fallbacks": [], "invented_tools": 0, "model": "local-coder", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11028, Qwen3-14B Q4_K_M, q8 KV, 32k", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 2\nstdout:\n\n==================================== ERRORS ====================================\n______________________ ERROR collecting test_inventory.py ______________________\nImportError while importing test module '/tmp/tmpr1q1pe0r/work/test_inventory.py'.\nHint: make sure your test modules/packages have valid Python names.\nTraceback:\n/usr/lib/python3.12/importlib/__init__.py:90: in import_module\n return _bootstrap._gcd_import(name[level:], packa", "prose_turns": 0, "provider": "openai", "success": false, "task": "fix-bug", "task_hash": "b78248aff00ddc39", "tokens_in": 2560, "tokens_out": 1136, "trimmed_requests": 0, "ts": "2026-09-25T01:03:49+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 3, "wall_s": 24.3}
{"backing": "openai/qwen3-14b", "check_output": "...... [100%]\n6 passed in 0.01s\n", "commit": "50b4682+dirty", "cost": 0.0, "failed_commands": 1, "fallbacks": [], "invented_tools": 0, "model": "local-coder", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11028, Qwen3-14B Q4_K_M, q8 KV, 32k", "outcome": "completed", "outcome_note": "Fixed the total_value function to multiply unit price by quantity, passing all tests.", "prose_turns": 0, "provider": "openai", "success": true, "task": "fix-bug", "task_hash": "b78248aff00ddc39", "tokens_in": 5318, "tokens_out": 1623, "trimmed_requests": 0, "ts": "2026-09-25T01:04:23+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 5, "wall_s": 34.5}
{"backing": "openai/qwen3-14b", "check_output": "\n==================================== ERRORS ====================================\n_________________ ERROR collecting .bench_check/test_hidden.py _________________\n.bench_check/test_hidden.py:4: in <module>\n from inventory import Item, low_stock, total_value\ninventory.py:1: in <module>\n def total_value(items: list[Item]) -> float:\n ^^^^\nE NameError: name 'Item' is not defined\n=========================== short test summary info ============================\nERROR .bench_check/test_hidden.py - NameError: name 'Item' is not defined\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\n1 error in 0.11s\n", "commit": "50b4682+dirty", "cost": 0.0, "failed_commands": 2, "fallbacks": [], "invented_tools": 0, "model": "local-coder", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11028, Qwen3-14B Q4_K_M, q8 KV, 32k", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 2\nstdout:\n\n==================================== ERRORS ====================================\n______________________ ERROR collecting test_inventory.py ______________________\ntest_inventory.py:1: in <module>\n from inventory import Item, low_stock, total_value\ninventory.py:1: in <module>\n def total_value(items: list[Item]) -> float:\n ^^^^\nE NameError: name 'Item' is not defined\n=========================== short ", "prose_turns": 0, "provider": "openai", "success": false, "task": "fix-bug", "task_hash": "b78248aff00ddc39", "tokens_in": 2565, "tokens_out": 958, "trimmed_requests": 0, "ts": "2026-09-25T01:04:45+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 3, "wall_s": 20.6}
{"backing": "openai/qwen3-14b", "check_output": "\n==================================== ERRORS ====================================\n_________________ ERROR collecting .bench_check/test_hidden.py _________________\n.bench_check/test_hidden.py:4: in <module>\n from inventory import Item, low_stock, total_value\ninventory.py:1: in <module>\n def total_value(items: list[Item]) -> float:\n ^^^^\nE NameError: name 'Item' is not defined\n=========================== short test summary info ============================\nERROR .bench_check/test_hidden.py - NameError: name 'Item' is not defined\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\n1 error in 0.09s\n", "commit": "50b4682+dirty", "cost": 0.0, "failed_commands": 2, "fallbacks": [], "invented_tools": 0, "model": "local-coder", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11028, Qwen3-14B Q4_K_M, q8 KV, 32k", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 2\nstdout:\n\n==================================== ERRORS ====================================\n______________________ ERROR collecting test_inventory.py ______________________\ntest_inventory.py:1: in <module>\n from inventory import Item, low_stock, total_value\ninventory.py:1: in <module>\n def total_value(items: list[Item]) -> float:\n ^^^^\nE NameError: name 'Item' is not defined\n=========================== short ", "prose_turns": 0, "provider": "openai", "success": false, "task": "fix-bug", "task_hash": "b78248aff00ddc39", "tokens_in": 2565, "tokens_out": 1051, "trimmed_requests": 0, "ts": "2026-09-25T01:05:07+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 3, "wall_s": 22.6}
{"backing": "openai/qwen3-14b", "check_output": "\n==================================== ERRORS ====================================\n_________________ ERROR collecting .bench_check/test_hidden.py _________________\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/python.py:508: in importtestmodule\n mod = import_path(\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/pathlib.py:596: in import_path\n importlib.import_module(module_name)\n/usr/lib/python3.12/importlib/__init__.py:90: in import_module\n return _bootstrap._gcd_import(name[level:], package, level)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n<frozen importlib._bootstrap>:1387: in _gcd_import\n ???\n<frozen importlib._bootstrap>:1360: in _find_and_load\n ???\n<frozen importlib._bootstrap>:1331: in _find_and_load_unlocked\n ???\n<frozen importlib._bootstrap>:935: in _load_unlocked\n ???\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/assertion/rewrite.py:188: in exec_module\n exec(co, module.__dict__)\n.bench_check/test_hidden.py:2: in <module>\n from rates import RATES\nE File \"/tmp/tmpq8iz1j8z/work/rates.py\", line 56\nE [... 1445 characters omitted ...]\nE ^^^^^^^^\nE SyntaxError: invalid syntax. Perhaps you forgot a comma?\n=========================== short test summary info ============================\nERROR .bench_check/test_hidden.py\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\n1 error in 0.14s\n", "commit": "50b4682+dirty", "cost": 0.0, "failed_commands": 2, "fallbacks": [], "invented_tools": 0, "model": "local-coder", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11028, Qwen3-14B Q4_K_M, q8 KV, 32k", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 2\nstdout:\n\n==================================== ERRORS ====================================\n________________________ ERROR collecting test_rates.py ________________________\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/python.py:508: in importtestmodule\n mod = import_path(\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/pathlib.py:596: in import_path\n importlib.import_module(module_name)\n/usr/lib/python3.12/importlib/__init__", "prose_turns": 0, "provider": "openai", "success": false, "task": "long-output", "task_hash": "8cfbf73fb6620613", "tokens_in": 13616, "tokens_out": 3186, "trimmed_requests": 0, "ts": "2026-09-25T01:06:21+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 4, "wall_s": 72.8}
{"backing": "openai/qwen3-14b", "check_output": ": 'R271'\nFAILED .bench_check/test_hidden.py::test_rate[272] - KeyError: 'R272'\nFAILED .bench_check/test_hidden.py::test_rate[273] - KeyError: 'R273'\nFAILED .bench_check/test_hidden.py::test_rate[274] - KeyError: 'R274'\nFAILED .bench_check/test_hidden.py::test_rate[275] - KeyError: 'R275'\nFAILED .bench_check/test_hidden.py::test_rate[276] - KeyError: 'R276'\nFAILED .bench_check/test_hidden.py::test_rate[277] - KeyError: 'R277'\nFAILED .bench_check/test_hidden.py::test_rate[278] - KeyError: 'R278'\nFAILED .bench_check/test_hidden.py::test_rate[279] - KeyError: 'R279'\nFAILED .bench_check/test_hidden.py::test_rate[280] - KeyError: 'R280'\nFAILED .bench_check/test_hidden.py::test_rate[281] - KeyError: 'R281'\nFAILED .bench_check/test_hidden.py::test_rate[282] - KeyError: 'R282'\nFAILED .bench_check/test_hidden.py::test_rate[283] - KeyError: 'R283'\nFAILED .bench_check/test_hidden.py::test_rate[284] - KeyError: 'R284'\nFAILED .bench_check/test_hidden.py::test_rate[285] - KeyError: 'R285'\nFAILED .bench_check/test_hidden.py::test_rate[286] - KeyError: 'R286'\nFAILED .bench_check/test_hidden.py::test_rate[287] - KeyError: 'R287'\nFAILED .bench_check/test_hidden.py::test_rate[288] - KeyError: 'R288'\nFAILED .bench_check/test_hidden.py::test_rate[289] - KeyError: 'R289'\nFAILED .bench_check/test_hidden.py::test_rate[290] - KeyError: 'R290'\nFAILED .bench_check/test_hidden.py::test_rate[291] - KeyError: 'R291'\nFAILED .bench_check/test_hidden.py::test_rate[292] - KeyError: 'R292'\nFAILED .bench_check/test_hidden.py::test_rate[293] - KeyError: 'R293'\nFAILED .bench_check/test_hidden.py::test_rate[294] - KeyError: 'R294'\nFAILED .bench_check/test_hidden.py::test_rate[295] - KeyError: 'R295'\nFAILED .bench_check/test_hidden.py::test_rate[296] - KeyError: 'R296'\nFAILED .bench_check/test_hidden.py::test_rate[297] - KeyError: 'R297'\nFAILED .bench_check/test_hidden.py::test_rate[298] - KeyError: 'R298'\nFAILED .bench_check/test_hidden.py::test_rate[299] - KeyError: 'R299'\n247 failed, 54 passed in 0.49s\n", "commit": "50b4682+dirty", "cost": 0.0, "failed_commands": 2, "fallbacks": [], "invented_tools": 0, "model": "local-coder", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11028, Qwen3-14B Q4_K_M, q8 KV, 32k", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 1\nstdout:\n.....................................................FFFFFFFFFFFFFFFFFFF [ 24%]\nFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF [ 48%]\nFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF [ 72%]\nFFFFFFFFFFFFFFFFFFFFF.FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF [ 96%]\nFFFFFFFFFFFF [100%]\n=================================== FAILU", "prose_turns": 0, "provider": "openai", "success": false, "task": "long-output", "task_hash": "8cfbf73fb6620613", "tokens_in": 12726, "tokens_out": 2366, "trimmed_requests": 0, "ts": "2026-09-25T01:07:16+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 4, "wall_s": 54.4}
{"backing": "openai/qwen3-14b", "check_output": "\n==================================== ERRORS ====================================\n_________________ ERROR collecting .bench_check/test_hidden.py _________________\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/python.py:508: in importtestmodule\n mod = import_path(\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/pathlib.py:596: in import_path\n importlib.import_module(module_name)\n/usr/lib/python3.12/importlib/__init__.py:90: in import_module\n return _bootstrap._gcd_import(name[level:], package, level)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n<frozen importlib._bootstrap>:1387: in _gcd_import\n ???\n<frozen importlib._bootstrap>:1360: in _find_and_load\n ???\n<frozen importlib._bootstrap>:1331: in _find_and_load_unlocked\n ???\n<frozen importlib._bootstrap>:935: in _load_unlocked\n ???\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/assertion/rewrite.py:188: in exec_module\n exec(co, module.__dict__)\n.bench_check/test_hidden.py:2: in <module>\n from rates import RATES\nE File \"/tmp/tmphac71897/work/rates.py\", line 56\nE [... 1445 characters omitted ...]\nE ^^^^^^^^\nE SyntaxError: invalid syntax. Perhaps you forgot a comma?\n=========================== short test summary info ============================\nERROR .bench_check/test_hidden.py\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\n1 error in 0.15s\n", "commit": "50b4682+dirty", "cost": 0.0, "failed_commands": 2, "fallbacks": [], "invented_tools": 0, "model": "local-coder", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11028, Qwen3-14B Q4_K_M, q8 KV, 32k", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 2\nstdout:\n\n==================================== ERRORS ====================================\n________________________ ERROR collecting test_rates.py ________________________\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/python.py:508: in importtestmodule\n mod = import_path(\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/pathlib.py:596: in import_path\n importlib.import_module(module_name)\n/usr/lib/python3.12/importlib/__init__", "prose_turns": 0, "provider": "openai", "success": false, "task": "long-output", "task_hash": "8cfbf73fb6620613", "tokens_in": 13614, "tokens_out": 3000, "trimmed_requests": 0, "ts": "2026-09-25T01:08:25+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 4, "wall_s": 69.2}
{"backing": "openai/qwen3-14b", "check_output": "\n==================================== ERRORS ====================================\n_________________ ERROR collecting .bench_check/test_hidden.py _________________\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/python.py:508: in importtestmodule\n mod = import_path(\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/pathlib.py:596: in import_path\n importlib.import_module(module_name)\n/usr/lib/python3.12/importlib/__init__.py:90: in import_module\n return _bootstrap._gcd_import(name[level:], package, level)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n<frozen importlib._bootstrap>:1387: in _gcd_import\n ???\n<frozen importlib._bootstrap>:1360: in _find_and_load\n ???\n<frozen importlib._bootstrap>:1331: in _find_and_load_unlocked\n ???\n<frozen importlib._bootstrap>:935: in _load_unlocked\n ???\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/assertion/rewrite.py:188: in exec_module\n exec(co, module.__dict__)\n.bench_check/test_hidden.py:2: in <module>\n from rates import RATES\nE File \"/tmp/tmp0vs0w4kr/work/rates.py\", line 56\nE [... 1445 characters omitted ...]\nE ^^^^^^^^\nE SyntaxError: invalid syntax. Perhaps you forgot a comma?\n=========================== short test summary info ============================\nERROR .bench_check/test_hidden.py\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\n1 error in 0.14s\n", "commit": "50b4682+dirty", "cost": 0.0, "failed_commands": 2, "fallbacks": [], "invented_tools": 0, "model": "local-coder", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11028, Qwen3-14B Q4_K_M, q8 KV, 32k", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 2\nstdout:\n\n==================================== ERRORS ====================================\n________________________ ERROR collecting test_rates.py ________________________\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/python.py:508: in importtestmodule\n mod = import_path(\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/pathlib.py:596: in import_path\n importlib.import_module(module_name)\n/usr/lib/python3.12/importlib/__init__", "prose_turns": 0, "provider": "openai", "success": false, "task": "long-output", "task_hash": "8cfbf73fb6620613", "tokens_in": 13607, "tokens_out": 3223, "trimmed_requests": 0, "ts": "2026-09-25T01:09:40+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 4, "wall_s": 73.8}
{"backing": "openai/qwen3-14b", "check_output": "\n==================================== ERRORS ====================================\n_________________ ERROR collecting .bench_check/test_hidden.py _________________\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/python.py:508: in importtestmodule\n mod = import_path(\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/pathlib.py:596: in import_path\n importlib.import_module(module_name)\n/usr/lib/python3.12/importlib/__init__.py:90: in import_module\n return _bootstrap._gcd_import(name[level:], package, level)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n<frozen importlib._bootstrap>:1387: in _gcd_import\n ???\n<frozen importlib._bootstrap>:1360: in _find_and_load\n ???\n<frozen importlib._bootstrap>:1331: in _find_and_load_unlocked\n ???\n<frozen importlib._bootstrap>:935: in _load_unlocked\n ???\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/assertion/rewrite.py:188: in exec_module\n exec(co, module.__dict__)\n.bench_check/test_hidden.py:2: in <module>\n from rates import RATES\nE File \"/tmp/tmp_pxm0ldr/work/rates.py\", line 56\nE [... 1445 characters omitted ...]\nE ^^^^^^^^\nE SyntaxError: invalid syntax. Perhaps you forgot a comma?\n=========================== short test summary info ============================\nERROR .bench_check/test_hidden.py\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\n1 error in 0.15s\n", "commit": "50b4682+dirty", "cost": 0.0, "failed_commands": 2, "fallbacks": [], "invented_tools": 0, "model": "local-coder", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11028, Qwen3-14B Q4_K_M, q8 KV, 32k", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 2\nstdout:\n\n==================================== ERRORS ====================================\n________________________ ERROR collecting test_rates.py ________________________\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/python.py:508: in importtestmodule\n mod = import_path(\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/pathlib.py:596: in import_path\n importlib.import_module(module_name)\n/usr/lib/python3.12/importlib/__init__", "prose_turns": 0, "provider": "openai", "success": false, "task": "long-output", "task_hash": "8cfbf73fb6620613", "tokens_in": 13609, "tokens_out": 3180, "trimmed_requests": 0, "ts": "2026-09-25T01:10:53+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 4, "wall_s": 73.0}
{"backing": "openai/qwen3-14b", "check_output": "..... [100%]\n5 passed in 0.04s\n", "commit": "50b4682+dirty", "cost": 0.0, "failed_commands": 2, "fallbacks": [], "invented_tools": 0, "model": "local-coder", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11028, Qwen3-14B Q4_K_M, q8 KV, 32k", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 1\nstdout:\n..FF [100%]\n=================================== FAILURES ===================================\n_________________________ test_cli_prints_the_greeting _________________________\n\ncapsys = <_pytest.capture.CaptureFixture object at 0x777eb60e3e90>\n\n def test_cli_prints_the_greeting(capsys) -> None:\n> assert main([\"Bob\"])\nE AssertionError: assert 0\nE + wh", "prose_turns": 0, "provider": "openai", "success": true, "task": "multi-file", "task_hash": "76bc0dcf36a72599", "tokens_in": 13753, "tokens_out": 5755, "trimmed_requests": 0, "ts": "2026-09-25T01:12:55+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 10, "wall_s": 121.2}
{"backing": "openai/qwen3-14b", "check_output": "..... [100%]\n5 passed in 0.04s\n", "commit": "50b4682+dirty", "cost": 0.0, "failed_commands": 1, "fallbacks": [], "invented_tools": 0, "model": "local-coder", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11028, Qwen3-14B Q4_K_M, q8 KV, 32k", "outcome": "completed", "outcome_note": "Added --shout option to greeter, updated core and cli code, added tests, and verified with pytest.", "prose_turns": 0, "provider": "openai", "success": true, "task": "multi-file", "task_hash": "76bc0dcf36a72599", "tokens_in": 21373, "tokens_out": 9952, "trimmed_requests": 0, "ts": "2026-09-25T01:16:25+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 13, "wall_s": 210.0}
{"backing": "openai/qwen3-14b", "check_output": "..... [100%]\n5 passed in 0.04s\n", "commit": "50b4682+dirty", "cost": 0.0, "failed_commands": 2, "fallbacks": [], "invented_tools": 0, "model": "local-coder", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11028, Qwen3-14B Q4_K_M, q8 KV, 32k", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 1\nstdout:\n..FF [100%]\n=================================== FAILURES ===================================\n_________________________ test_cli_prints_the_greeting _________________________\n\ncapsys = <_pytest.capture.CaptureFixture object at 0x7a2788fdbe30>\n\n def test_cli_prints_the_greeting(capsys) -> None:\n> assert main([\"Bob\"])\nE AssertionError: assert 0\nE + wh", "prose_turns": 0, "provider": "openai", "success": true, "task": "multi-file", "task_hash": "76bc0dcf36a72599", "tokens_in": 9432, "tokens_out": 6691, "trimmed_requests": 0, "ts": "2026-09-25T01:18:47+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 8, "wall_s": 142.1}
{"backing": "openai/qwen3-14b", "check_output": "..... [100%]\n5 passed in 0.04s\n", "commit": "50b4682+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "local-coder", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11028, Qwen3-14B Q4_K_M, q8 KV, 32k", "outcome": "completed", "outcome_note": "Added --shout option to greeter, updated core and cli code, added tests, and verified with pytest.", "prose_turns": 0, "provider": "openai", "success": true, "task": "multi-file", "task_hash": "76bc0dcf36a72599", "tokens_in": 9104, "tokens_out": 3626, "trimmed_requests": 0, "ts": "2026-09-25T01:20:03+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 8, "wall_s": 75.8}
{"backing": "openai/qwen3-14b", "check_output": "..... [100%]\n5 passed in 0.04s\n", "commit": "50b4682+dirty", "cost": 0.0, "failed_commands": 2, "fallbacks": [], "invented_tools": 0, "model": "local-coder", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11028, Qwen3-14B Q4_K_M, q8 KV, 32k", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 1\nstdout:\n..FF [100%]\n=================================== FAILURES ===================================\n_________________________ test_cli_prints_the_greeting _________________________\n\ncapsys = <_pytest.capture.CaptureFixture object at 0x76eac82e3da0>\n\n def test_cli_prints_the_greeting(capsys) -> None:\n> assert main([\"Bob\"])\nE AssertionError: assert 0\nE + wh", "prose_turns": 0, "provider": "openai", "success": true, "task": "multi-file", "task_hash": "76bc0dcf36a72599", "tokens_in": 9382, "tokens_out": 7755, "trimmed_requests": 0, "ts": "2026-09-25T01:22:48+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 8, "wall_s": 164.5}
{"backing": "openai/qwen3-14b", "check_output": "........ [100%]\n8 passed in 0.01s\n", "commit": "50b4682+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "local-coder", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11028, Qwen3-14B Q4_K_M, q8 KV, 32k", "outcome": "completed", "outcome_note": "Added slugify function and passed all tests.", "prose_turns": 0, "provider": "openai", "success": true, "task": "write-function", "task_hash": "ec722fb0f389a6f4", "tokens_in": 1769, "tokens_out": 1300, "trimmed_requests": 0, "ts": "2026-09-25T01:23:16+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 2, "wall_s": 27.7}
{"backing": "openai/qwen3-14b", "check_output": "........ [100%]\n8 passed in 0.01s\n", "commit": "50b4682+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "local-coder", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11028, Qwen3-14B Q4_K_M, q8 KV, 32k", "outcome": "completed", "outcome_note": "Added slugify function with tests; all tests passed.", "prose_turns": 0, "provider": "openai", "success": true, "task": "write-function", "task_hash": "ec722fb0f389a6f4", "tokens_in": 1782, "tokens_out": 1239, "trimmed_requests": 0, "ts": "2026-09-25T01:23:43+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 2, "wall_s": 26.2}
{"backing": "openai/qwen3-14b", "check_output": "........ [100%]\n8 passed in 0.01s\n", "commit": "50b4682+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "local-coder", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11028, Qwen3-14B Q4_K_M, q8 KV, 32k", "outcome": "completed", "outcome_note": "Added slugify function and passed all tests.", "prose_turns": 0, "provider": "openai", "success": true, "task": "write-function", "task_hash": "ec722fb0f389a6f4", "tokens_in": 3627, "tokens_out": 1332, "trimmed_requests": 0, "ts": "2026-09-25T01:24:11+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 4, "wall_s": 28.1}
{"backing": "openai/qwen3-14b", "check_output": "........ [100%]\n8 passed in 0.01s\n", "commit": "50b4682+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "local-coder", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11028, Qwen3-14B Q4_K_M, q8 KV, 32k", "outcome": "completed", "outcome_note": "Added slugify function and passed all tests.", "prose_turns": 0, "provider": "openai", "success": true, "task": "write-function", "task_hash": "ec722fb0f389a6f4", "tokens_in": 734, "tokens_out": 653, "trimmed_requests": 0, "ts": "2026-09-25T01:24:25+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 1, "wall_s": 13.9}
{"backing": "openai/qwen3-14b", "check_output": "........ [100%]\n8 passed in 0.01s\n", "commit": "50b4682+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "local-coder", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11028, Qwen3-14B Q4_K_M, q8 KV, 32k", "outcome": "completed", "outcome_note": "Added slugify function and passed all tests.", "prose_turns": 0, "provider": "openai", "success": true, "task": "write-function", "task_hash": "ec722fb0f389a6f4", "tokens_in": 733, "tokens_out": 523, "trimmed_requests": 0, "ts": "2026-09-25T01:24:37+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 1, "wall_s": 11.3}
bench/results/2026-09-25-local-fast.jsonl
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@@ -0,0 +1,20 @@
{"backing": "openai/qwen3-8b-awq", "check_output": "29.0 \u00b1 2.9e-05\nE \nE comparison failed\nE Obtained: 0.35\nE Expected: 29.0 \u00b1 2.9e-05\n\n.bench_check/test_hidden.py:22: AssertionError\n________________________ test_total_value[items2-37.5] _________________________\n\nitems = [Item(name='gear', unit_price=12.5, quantity=3)], expected = 37.5\n\n @pytest.mark.parametrize(\n (\"items\", \"expected\"),\n [\n ([], 0.0),\n ([Item(\"bolt\", 0.25, 100), Item(\"nut\", 0.10, 40)], 29.0),\n ([Item(\"gear\", 12.5, 3)], 37.5),\n ([Item(\"empty\", 9.99, 0)], 0.0),\n ],\n )\n def test_total_value(items: list[Item], expected: float) -> None:\n> assert total_value(items) == pytest.approx(expected)\nE assert 12.5 == 37.5 \u00b1 3.7e-05\nE \nE comparison failed\nE Obtained: 12.5\nE Expected: 37.5 \u00b1 3.7e-05\n\n.bench_check/test_hidden.py:22: AssertionError\n_________________________ test_total_value[items3-0.0] _________________________\n\nitems = [Item(name='empty', unit_price=9.99, quantity=0)], expected = 0.0\n\n @pytest.mark.parametrize(\n (\"items\", \"expected\"),\n [\n ([], 0.0),\n ([Item(\"bolt\", 0.25, 100), Item(\"nut\", 0.10, 40)], 29.0),\n ([Item(\"gear\", 12.5, 3)], 37.5),\n ([Item(\"empty\", 9.99, 0)], 0.0),\n ],\n )\n def test_total_value(items: list[Item], expected: float) -> None:\n> assert total_value(items) == pytest.approx(expected)\nE assert 9.99 == 0.0 \u00b1 1.0e-12\nE \nE comparison failed\nE Obtained: 9.99\nE Expected: 0.0 \u00b1 1.0e-12\n\n.bench_check/test_hidden.py:22: AssertionError\n=========================== short test summary info ============================\nFAILED .bench_check/test_hidden.py::test_total_value[items1-29.0] - assert 0....\nFAILED .bench_check/test_hidden.py::test_total_value[items2-37.5] - assert 12...\nFAILED .bench_check/test_hidden.py::test_total_value[items3-0.0] - assert 9.9...\n3 failed, 3 passed in 0.03s\n", "commit": "50b4682", "cost": 0.0, "failed_commands": 2, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.29, Qwen3-8B-AWQ, fp8 KV, 32k", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 1\nstdout:\nF. [100%]\n=================================== FAILURES ===================================\n______________________ test_total_value_counts_every_unit ______________________\n\n def test_total_value_counts_every_unit() -> None:\n items = [Item(\"bolt\", 0.25, 100), Item(\"nut\", 0.10, 40)]\n> assert total_value(items) == 29.0\nE AssertionError: assert 0.35 =", "prose_turns": 0, "provider": "openai", "success": false, "task": "fix-bug", "task_hash": "b78248aff00ddc39", "tokens_in": 1578, "tokens_out": 50, "trimmed_requests": 0, "ts": "2026-09-25T01:00:41+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 2, "wall_s": 63.2}
{"backing": "openai/qwen3-8b-awq", "check_output": "29.0 \u00b1 2.9e-05\nE \nE comparison failed\nE Obtained: 0.35\nE Expected: 29.0 \u00b1 2.9e-05\n\n.bench_check/test_hidden.py:22: AssertionError\n________________________ test_total_value[items2-37.5] _________________________\n\nitems = [Item(name='gear', unit_price=12.5, quantity=3)], expected = 37.5\n\n @pytest.mark.parametrize(\n (\"items\", \"expected\"),\n [\n ([], 0.0),\n ([Item(\"bolt\", 0.25, 100), Item(\"nut\", 0.10, 40)], 29.0),\n ([Item(\"gear\", 12.5, 3)], 37.5),\n ([Item(\"empty\", 9.99, 0)], 0.0),\n ],\n )\n def test_total_value(items: list[Item], expected: float) -> None:\n> assert total_value(items) == pytest.approx(expected)\nE assert 12.5 == 37.5 \u00b1 3.7e-05\nE \nE comparison failed\nE Obtained: 12.5\nE Expected: 37.5 \u00b1 3.7e-05\n\n.bench_check/test_hidden.py:22: AssertionError\n_________________________ test_total_value[items3-0.0] _________________________\n\nitems = [Item(name='empty', unit_price=9.99, quantity=0)], expected = 0.0\n\n @pytest.mark.parametrize(\n (\"items\", \"expected\"),\n [\n ([], 0.0),\n ([Item(\"bolt\", 0.25, 100), Item(\"nut\", 0.10, 40)], 29.0),\n ([Item(\"gear\", 12.5, 3)], 37.5),\n ([Item(\"empty\", 9.99, 0)], 0.0),\n ],\n )\n def test_total_value(items: list[Item], expected: float) -> None:\n> assert total_value(items) == pytest.approx(expected)\nE assert 9.99 == 0.0 \u00b1 1.0e-12\nE \nE comparison failed\nE Obtained: 9.99\nE Expected: 0.0 \u00b1 1.0e-12\n\n.bench_check/test_hidden.py:22: AssertionError\n=========================== short test summary info ============================\nFAILED .bench_check/test_hidden.py::test_total_value[items1-29.0] - assert 0....\nFAILED .bench_check/test_hidden.py::test_total_value[items2-37.5] - assert 12...\nFAILED .bench_check/test_hidden.py::test_total_value[items3-0.0] - assert 9.9...\n3 failed, 3 passed in 0.03s\n", "commit": "50b4682", "cost": 0.0, "failed_commands": 2, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.29, Qwen3-8B-AWQ, fp8 KV, 32k", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 1\nstdout:\nF. [100%]\n=================================== FAILURES ===================================\n______________________ test_total_value_counts_every_unit ______________________\n\n def test_total_value_counts_every_unit() -> None:\n items = [Item(\"bolt\", 0.25, 100), Item(\"nut\", 0.10, 40)]\n> assert total_value(items) == 29.0\nE AssertionError: assert 0.35 =", "prose_turns": 0, "provider": "openai", "success": false, "task": "fix-bug", "task_hash": "b78248aff00ddc39", "tokens_in": 1576, "tokens_out": 50, "trimmed_requests": 0, "ts": "2026-09-25T01:00:43+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 2, "wall_s": 1.4}
{"backing": "openai/qwen3-8b-awq", "check_output": "29.0 \u00b1 2.9e-05\nE \nE comparison failed\nE Obtained: 0.35\nE Expected: 29.0 \u00b1 2.9e-05\n\n.bench_check/test_hidden.py:22: AssertionError\n________________________ test_total_value[items2-37.5] _________________________\n\nitems = [Item(name='gear', unit_price=12.5, quantity=3)], expected = 37.5\n\n @pytest.mark.parametrize(\n (\"items\", \"expected\"),\n [\n ([], 0.0),\n ([Item(\"bolt\", 0.25, 100), Item(\"nut\", 0.10, 40)], 29.0),\n ([Item(\"gear\", 12.5, 3)], 37.5),\n ([Item(\"empty\", 9.99, 0)], 0.0),\n ],\n )\n def test_total_value(items: list[Item], expected: float) -> None:\n> assert total_value(items) == pytest.approx(expected)\nE assert 12.5 == 37.5 \u00b1 3.7e-05\nE \nE comparison failed\nE Obtained: 12.5\nE Expected: 37.5 \u00b1 3.7e-05\n\n.bench_check/test_hidden.py:22: AssertionError\n_________________________ test_total_value[items3-0.0] _________________________\n\nitems = [Item(name='empty', unit_price=9.99, quantity=0)], expected = 0.0\n\n @pytest.mark.parametrize(\n (\"items\", \"expected\"),\n [\n ([], 0.0),\n ([Item(\"bolt\", 0.25, 100), Item(\"nut\", 0.10, 40)], 29.0),\n ([Item(\"gear\", 12.5, 3)], 37.5),\n ([Item(\"empty\", 9.99, 0)], 0.0),\n ],\n )\n def test_total_value(items: list[Item], expected: float) -> None:\n> assert total_value(items) == pytest.approx(expected)\nE assert 9.99 == 0.0 \u00b1 1.0e-12\nE \nE comparison failed\nE Obtained: 9.99\nE Expected: 0.0 \u00b1 1.0e-12\n\n.bench_check/test_hidden.py:22: AssertionError\n=========================== short test summary info ============================\nFAILED .bench_check/test_hidden.py::test_total_value[items1-29.0] - assert 0....\nFAILED .bench_check/test_hidden.py::test_total_value[items2-37.5] - assert 12...\nFAILED .bench_check/test_hidden.py::test_total_value[items3-0.0] - assert 9.9...\n3 failed, 3 passed in 0.03s\n", "commit": "50b4682", "cost": 0.0, "failed_commands": 2, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.29, Qwen3-8B-AWQ, fp8 KV, 32k", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 1\nstdout:\nF. [100%]\n=================================== FAILURES ===================================\n______________________ test_total_value_counts_every_unit ______________________\n\n def test_total_value_counts_every_unit() -> None:\n items = [Item(\"bolt\", 0.25, 100), Item(\"nut\", 0.10, 40)]\n> assert total_value(items) == 29.0\nE AssertionError: assert 0.35 =", "prose_turns": 0, "provider": "openai", "success": false, "task": "fix-bug", "task_hash": "b78248aff00ddc39", "tokens_in": 1576, "tokens_out": 50, "trimmed_requests": 0, "ts": "2026-09-25T01:00:44+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 2, "wall_s": 1.4}
{"backing": "openai/qwen3-8b-awq", "check_output": "29.0 \u00b1 2.9e-05\nE \nE comparison failed\nE Obtained: 0.35\nE Expected: 29.0 \u00b1 2.9e-05\n\n.bench_check/test_hidden.py:22: AssertionError\n________________________ test_total_value[items2-37.5] _________________________\n\nitems = [Item(name='gear', unit_price=12.5, quantity=3)], expected = 37.5\n\n @pytest.mark.parametrize(\n (\"items\", \"expected\"),\n [\n ([], 0.0),\n ([Item(\"bolt\", 0.25, 100), Item(\"nut\", 0.10, 40)], 29.0),\n ([Item(\"gear\", 12.5, 3)], 37.5),\n ([Item(\"empty\", 9.99, 0)], 0.0),\n ],\n )\n def test_total_value(items: list[Item], expected: float) -> None:\n> assert total_value(items) == pytest.approx(expected)\nE assert 12.5 == 37.5 \u00b1 3.7e-05\nE \nE comparison failed\nE Obtained: 12.5\nE Expected: 37.5 \u00b1 3.7e-05\n\n.bench_check/test_hidden.py:22: AssertionError\n_________________________ test_total_value[items3-0.0] _________________________\n\nitems = [Item(name='empty', unit_price=9.99, quantity=0)], expected = 0.0\n\n @pytest.mark.parametrize(\n (\"items\", \"expected\"),\n [\n ([], 0.0),\n ([Item(\"bolt\", 0.25, 100), Item(\"nut\", 0.10, 40)], 29.0),\n ([Item(\"gear\", 12.5, 3)], 37.5),\n ([Item(\"empty\", 9.99, 0)], 0.0),\n ],\n )\n def test_total_value(items: list[Item], expected: float) -> None:\n> assert total_value(items) == pytest.approx(expected)\nE assert 9.99 == 0.0 \u00b1 1.0e-12\nE \nE comparison failed\nE Obtained: 9.99\nE Expected: 0.0 \u00b1 1.0e-12\n\n.bench_check/test_hidden.py:22: AssertionError\n=========================== short test summary info ============================\nFAILED .bench_check/test_hidden.py::test_total_value[items1-29.0] - assert 0....\nFAILED .bench_check/test_hidden.py::test_total_value[items2-37.5] - assert 12...\nFAILED .bench_check/test_hidden.py::test_total_value[items3-0.0] - assert 9.9...\n3 failed, 3 passed in 0.03s\n", "commit": "50b4682", "cost": 0.0, "failed_commands": 2, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.29, Qwen3-8B-AWQ, fp8 KV, 32k", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 1\nstdout:\nF. [100%]\n=================================== FAILURES ===================================\n______________________ test_total_value_counts_every_unit ______________________\n\n def test_total_value_counts_every_unit() -> None:\n items = [Item(\"bolt\", 0.25, 100), Item(\"nut\", 0.10, 40)]\n> assert total_value(items) == 29.0\nE AssertionError: assert 0.35 =", "prose_turns": 0, "provider": "openai", "success": false, "task": "fix-bug", "task_hash": "b78248aff00ddc39", "tokens_in": 1574, "tokens_out": 50, "trimmed_requests": 0, "ts": "2026-09-25T01:00:46+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 2, "wall_s": 1.4}
{"backing": "openai/qwen3-8b-awq", "check_output": "29.0 \u00b1 2.9e-05\nE \nE comparison failed\nE Obtained: 0.35\nE Expected: 29.0 \u00b1 2.9e-05\n\n.bench_check/test_hidden.py:22: AssertionError\n________________________ test_total_value[items2-37.5] _________________________\n\nitems = [Item(name='gear', unit_price=12.5, quantity=3)], expected = 37.5\n\n @pytest.mark.parametrize(\n (\"items\", \"expected\"),\n [\n ([], 0.0),\n ([Item(\"bolt\", 0.25, 100), Item(\"nut\", 0.10, 40)], 29.0),\n ([Item(\"gear\", 12.5, 3)], 37.5),\n ([Item(\"empty\", 9.99, 0)], 0.0),\n ],\n )\n def test_total_value(items: list[Item], expected: float) -> None:\n> assert total_value(items) == pytest.approx(expected)\nE assert 12.5 == 37.5 \u00b1 3.7e-05\nE \nE comparison failed\nE Obtained: 12.5\nE Expected: 37.5 \u00b1 3.7e-05\n\n.bench_check/test_hidden.py:22: AssertionError\n_________________________ test_total_value[items3-0.0] _________________________\n\nitems = [Item(name='empty', unit_price=9.99, quantity=0)], expected = 0.0\n\n @pytest.mark.parametrize(\n (\"items\", \"expected\"),\n [\n ([], 0.0),\n ([Item(\"bolt\", 0.25, 100), Item(\"nut\", 0.10, 40)], 29.0),\n ([Item(\"gear\", 12.5, 3)], 37.5),\n ([Item(\"empty\", 9.99, 0)], 0.0),\n ],\n )\n def test_total_value(items: list[Item], expected: float) -> None:\n> assert total_value(items) == pytest.approx(expected)\nE assert 9.99 == 0.0 \u00b1 1.0e-12\nE \nE comparison failed\nE Obtained: 9.99\nE Expected: 0.0 \u00b1 1.0e-12\n\n.bench_check/test_hidden.py:22: AssertionError\n=========================== short test summary info ============================\nFAILED .bench_check/test_hidden.py::test_total_value[items1-29.0] - assert 0....\nFAILED .bench_check/test_hidden.py::test_total_value[items2-37.5] - assert 12...\nFAILED .bench_check/test_hidden.py::test_total_value[items3-0.0] - assert 9.9...\n3 failed, 3 passed in 0.03s\n", "commit": "50b4682", "cost": 0.0, "failed_commands": 2, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.29, Qwen3-8B-AWQ, fp8 KV, 32k", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 1\nstdout:\nF. [100%]\n=================================== FAILURES ===================================\n______________________ test_total_value_counts_every_unit ______________________\n\n def test_total_value_counts_every_unit() -> None:\n items = [Item(\"bolt\", 0.25, 100), Item(\"nut\", 0.10, 40)]\n> assert total_value(items) == 29.0\nE AssertionError: assert 0.35 =", "prose_turns": 0, "provider": "openai", "success": false, "task": "fix-bug", "task_hash": "b78248aff00ddc39", "tokens_in": 1578, "tokens_out": 50, "trimmed_requests": 0, "ts": "2026-09-25T01:00:48+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 2, "wall_s": 1.4}
{"backing": "openai/qwen3-8b-awq", "check_output": "\n==================================== ERRORS ====================================\n_________________ ERROR collecting .bench_check/test_hidden.py _________________\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/python.py:508: in importtestmodule\n mod = import_path(\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/pathlib.py:596: in import_path\n importlib.import_module(module_name)\n/usr/lib/python3.12/importlib/__init__.py:90: in import_module\n return _bootstrap._gcd_import(name[level:], package, level)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n<frozen importlib._bootstrap>:1387: in _gcd_import\n ???\n<frozen importlib._bootstrap>:1360: in _find_and_load\n ???\n<frozen importlib._bootstrap>:1331: in _find_and_load_unlocked\n ???\n<frozen importlib._bootstrap>:935: in _load_unlocked\n ???\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/assertion/rewrite.py:188: in exec_module\n exec(co, module.__dict__)\n.bench_check/test_hidden.py:2: in <module>\n from rates import RATES\nE File \"/tmp/tmpl4mdto5g/work/rates.py\", line 1\nE \"R237\": 0.12,\nE IndentationError: unexpected indent\n=========================== short test summary info ============================\nERROR .bench_check/test_hidden.py\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\n1 error in 0.14s\n", "commit": "50b4682", "cost": 0.0, "failed_commands": 3, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.29, Qwen3-8B-AWQ, fp8 KV, 32k", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 2\nstdout:\n\n==================================== ERRORS ====================================\n________________________ ERROR collecting test_rates.py ________________________\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/python.py:508: in importtestmodule\n mod = import_path(\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/pathlib.py:596: in import_path\n importlib.import_module(module_name)\n/usr/lib/python3.12/importlib/__init__", "prose_turns": 0, "provider": "openai", "success": false, "task": "long-output", "task_hash": "8cfbf73fb6620613", "tokens_in": 11000, "tokens_out": 183, "trimmed_requests": 0, "ts": "2026-09-25T01:00:52+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 6, "wall_s": 4.2}
{"backing": "openai/qwen3-8b-awq", "check_output": "\n==================================== ERRORS ====================================\n_________________ ERROR collecting .bench_check/test_hidden.py _________________\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/python.py:508: in importtestmodule\n mod = import_path(\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/pathlib.py:596: in import_path\n importlib.import_module(module_name)\n/usr/lib/python3.12/importlib/__init__.py:90: in import_module\n return _bootstrap._gcd_import(name[level:], package, level)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n<frozen importlib._bootstrap>:1387: in _gcd_import\n ???\n<frozen importlib._bootstrap>:1360: in _find_and_load\n ???\n<frozen importlib._bootstrap>:1331: in _find_and_load_unlocked\n ???\n<frozen importlib._bootstrap>:935: in _load_unlocked\n ???\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/assertion/rewrite.py:188: in exec_module\n exec(co, module.__dict__)\n.bench_check/test_hidden.py:2: in <module>\n from rates import RATES\nE File \"/tmp/tmpsqvx_94y/work/rates.py\", line 1\nE \"R237\": 0.12,\nE IndentationError: unexpected indent\n=========================== short test summary info ============================\nERROR .bench_check/test_hidden.py\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\n1 error in 0.14s\n", "commit": "50b4682", "cost": 0.0, "failed_commands": 3, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.29, Qwen3-8B-AWQ, fp8 KV, 32k", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 2\nstdout:\n\n==================================== ERRORS ====================================\n________________________ ERROR collecting test_rates.py ________________________\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/python.py:508: in importtestmodule\n mod = import_path(\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/pathlib.py:596: in import_path\n importlib.import_module(module_name)\n/usr/lib/python3.12/importlib/__init__", "prose_turns": 0, "provider": "openai", "success": false, "task": "long-output", "task_hash": "8cfbf73fb6620613", "tokens_in": 11000, "tokens_out": 183, "trimmed_requests": 0, "ts": "2026-09-25T01:00:57+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 6, "wall_s": 4.2}
{"backing": "openai/qwen3-8b-awq", "check_output": "\n==================================== ERRORS ====================================\n_________________ ERROR collecting .bench_check/test_hidden.py _________________\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/python.py:508: in importtestmodule\n mod = import_path(\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/pathlib.py:596: in import_path\n importlib.import_module(module_name)\n/usr/lib/python3.12/importlib/__init__.py:90: in import_module\n return _bootstrap._gcd_import(name[level:], package, level)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n<frozen importlib._bootstrap>:1387: in _gcd_import\n ???\n<frozen importlib._bootstrap>:1360: in _find_and_load\n ???\n<frozen importlib._bootstrap>:1331: in _find_and_load_unlocked\n ???\n<frozen importlib._bootstrap>:935: in _load_unlocked\n ???\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/assertion/rewrite.py:188: in exec_module\n exec(co, module.__dict__)\n.bench_check/test_hidden.py:2: in <module>\n from rates import RATES\nE File \"/tmp/tmpe3eakwxj/work/rates.py\", line 1\nE \"R237\": 0.12,\nE IndentationError: unexpected indent\n=========================== short test summary info ============================\nERROR .bench_check/test_hidden.py\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\n1 error in 0.14s\n", "commit": "50b4682", "cost": 0.0, "failed_commands": 3, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.29, Qwen3-8B-AWQ, fp8 KV, 32k", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 2\nstdout:\n\n==================================== ERRORS ====================================\n________________________ ERROR collecting test_rates.py ________________________\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/python.py:508: in importtestmodule\n mod = import_path(\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/pathlib.py:596: in import_path\n importlib.import_module(module_name)\n/usr/lib/python3.12/importlib/__init__", "prose_turns": 0, "provider": "openai", "success": false, "task": "long-output", "task_hash": "8cfbf73fb6620613", "tokens_in": 11013, "tokens_out": 183, "trimmed_requests": 0, "ts": "2026-09-25T01:01:01+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 6, "wall_s": 4.2}
{"backing": "openai/qwen3-8b-awq", "check_output": "\n==================================== ERRORS ====================================\n_________________ ERROR collecting .bench_check/test_hidden.py _________________\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/python.py:508: in importtestmodule\n mod = import_path(\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/pathlib.py:596: in import_path\n importlib.import_module(module_name)\n/usr/lib/python3.12/importlib/__init__.py:90: in import_module\n return _bootstrap._gcd_import(name[level:], package, level)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n<frozen importlib._bootstrap>:1387: in _gcd_import\n ???\n<frozen importlib._bootstrap>:1360: in _find_and_load\n ???\n<frozen importlib._bootstrap>:1331: in _find_and_load_unlocked\n ???\n<frozen importlib._bootstrap>:935: in _load_unlocked\n ???\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/assertion/rewrite.py:188: in exec_module\n exec(co, module.__dict__)\n.bench_check/test_hidden.py:2: in <module>\n from rates import RATES\nE File \"/tmp/tmprlm2camm/work/rates.py\", line 1\nE \"R237\": 0.12,\nE IndentationError: unexpected indent\n=========================== short test summary info ============================\nERROR .bench_check/test_hidden.py\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\n1 error in 0.14s\n", "commit": "50b4682", "cost": 0.0, "failed_commands": 3, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.29, Qwen3-8B-AWQ, fp8 KV, 32k", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 2\nstdout:\n\n==================================== ERRORS ====================================\n________________________ ERROR collecting test_rates.py ________________________\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/python.py:508: in importtestmodule\n mod = import_path(\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/pathlib.py:596: in import_path\n importlib.import_module(module_name)\n/usr/lib/python3.12/importlib/__init__", "prose_turns": 0, "provider": "openai", "success": false, "task": "long-output", "task_hash": "8cfbf73fb6620613", "tokens_in": 10987, "tokens_out": 183, "trimmed_requests": 0, "ts": "2026-09-25T01:01:06+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 6, "wall_s": 4.2}
{"backing": "openai/qwen3-8b-awq", "check_output": "\n==================================== ERRORS ====================================\n_________________ ERROR collecting .bench_check/test_hidden.py _________________\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/python.py:508: in importtestmodule\n mod = import_path(\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/pathlib.py:596: in import_path\n importlib.import_module(module_name)\n/usr/lib/python3.12/importlib/__init__.py:90: in import_module\n return _bootstrap._gcd_import(name[level:], package, level)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n<frozen importlib._bootstrap>:1387: in _gcd_import\n ???\n<frozen importlib._bootstrap>:1360: in _find_and_load\n ???\n<frozen importlib._bootstrap>:1331: in _find_and_load_unlocked\n ???\n<frozen importlib._bootstrap>:935: in _load_unlocked\n ???\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/assertion/rewrite.py:188: in exec_module\n exec(co, module.__dict__)\n.bench_check/test_hidden.py:2: in <module>\n from rates import RATES\nE File \"/tmp/tmpvzw7f5tn/work/rates.py\", line 1\nE \"R237\": 0.12,\nE IndentationError: unexpected indent\n=========================== short test summary info ============================\nERROR .bench_check/test_hidden.py\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\n1 error in 0.14s\n", "commit": "50b4682", "cost": 0.0, "failed_commands": 3, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.29, Qwen3-8B-AWQ, fp8 KV, 32k", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 2\nstdout:\n\n==================================== ERRORS ====================================\n________________________ ERROR collecting test_rates.py ________________________\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/python.py:508: in importtestmodule\n mod = import_path(\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/pathlib.py:596: in import_path\n importlib.import_module(module_name)\n/usr/lib/python3.12/importlib/__init__", "prose_turns": 0, "provider": "openai", "success": false, "task": "long-output", "task_hash": "8cfbf73fb6620613", "tokens_in": 11000, "tokens_out": 183, "trimmed_requests": 0, "ts": "2026-09-25T01:01:11+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 6, "wall_s": 4.2}
{"backing": "openai/qwen3-8b-awq", "check_output": "..... [100%]\n5 passed in 0.04s\n", "commit": "50b4682", "cost": 0.0, "failed_commands": 3, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.29, Qwen3-8B-AWQ, fp8 KV, 32k", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 1\nstdout:\n...F [100%]\n=================================== FAILURES ===================================\n_________________________ test_cli_prints_the_greeting _________________________\n\ncapsys = <_pytest.capture.CaptureFixture object at 0x713c88ce3c20>\n\n def test_cli_prints_the_greeting(capsys) -> None:\n> assert main([\"Bob\"])\nE AssertionError: assert 0\nE + wh", "prose_turns": 0, "provider": "openai", "success": true, "task": "multi-file", "task_hash": "76bc0dcf36a72599", "tokens_in": 13039, "tokens_out": 566, "trimmed_requests": 0, "ts": "2026-09-25T01:01:19+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 10, "wall_s": 8.4}
{"backing": "openai/qwen3-8b-awq", "check_output": "..... [100%]\n5 passed in 0.04s\n", "commit": "50b4682", "cost": 0.0, "failed_commands": 2, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.29, Qwen3-8B-AWQ, fp8 KV, 32k", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 1\nstdout:\n..FF [100%]\n=================================== FAILURES ===================================\n________________________________ test_cli_shout ________________________________\n\n def test_cli_shout() -> None:\n> from pytest import capsys\nE ImportError: cannot import name 'capsys' from 'pytest' (/root/nisse/.venv/lib/python3.12/site-packages/pytest/__init__.py)", "prose_turns": 0, "provider": "openai", "success": true, "task": "multi-file", "task_hash": "76bc0dcf36a72599", "tokens_in": 11243, "tokens_out": 715, "trimmed_requests": 0, "ts": "2026-09-25T01:01:29+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 9, "wall_s": 9.9}
{"backing": "openai/qwen3-8b-awq", "check_output": "\n==================================== ERRORS ====================================\n_________________ ERROR collecting .bench_check/test_hidden.py _________________\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/python.py:508: in importtestmodule\n mod = import_path(\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/pathlib.py:596: in import_path\n importlib.import_module(module_name)\n/usr/lib/python3.12/importlib/__init__.py:90: in import_module\n return _bootstrap._gcd_import(name[level:], package, level)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n<frozen importlib._bootstrap>:1387: in _gcd_import\n ???\n<frozen importlib._bootstrap>:1360: in _find_and_load\n ???\n<frozen importlib._bootstrap>:1331: in _find_and_load_unlocked\n ???\n<frozen importlib._bootstrap>:935: in _load_unlocked\n ???\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/assertion/rewrite.py:188: in exec_module\n exec(co, module.__dict__)\n.bench_check/test_hidden.py:4: in <module>\n from greeter.cli import main\nE File \"/tmp/tmp3f09u7jk/work/greeter/cli.py\", line 11\nE print(greet(args.name, shout=args.--shout))\nE ^\nE SyntaxError: invalid syntax\n=========================== short test summary info ============================\nERROR .bench_check/test_hidden.py\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\n1 error in 0.14s\n", "commit": "50b4682", "cost": 0.0, "failed_commands": 2, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.29, Qwen3-8B-AWQ, fp8 KV, 32k", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 1\nstdout:\n\nstderr:\nTraceback (most recent call last):\n File \"<frozen runpy>\", line 198, in _run_module_as_main\n File \"<frozen runpy>\", line 88, in _run_code\n File \"/tmp/tmp3f09u7jk/work/greeter/__main__.py\", line 1, in <module>\n from greeter.cli import main\n File \"/tmp/tmp3f09u7jk/work/greeter/cli.py\", line 11\n print(greet(args.name, shout=args.--shout))\n ^\nSyntaxError: invalid syntax\n", "prose_turns": 0, "provider": "openai", "success": false, "task": "multi-file", "task_hash": "76bc0dcf36a72599", "tokens_in": 9358, "tokens_out": 450, "trimmed_requests": 0, "ts": "2026-09-25T01:01:56+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 8, "wall_s": 26.5}
{"backing": "openai/qwen3-8b-awq", "check_output": "..... [100%]\n5 passed in 0.04s\n", "commit": "50b4682", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.29, Qwen3-8B-AWQ, fp8 KV, 32k", "outcome": "completed", "outcome_note": "Added --shout option to greeter. Tests pass.", "prose_turns": 0, "provider": "openai", "success": true, "task": "multi-file", "task_hash": "76bc0dcf36a72599", "tokens_in": 10784, "tokens_out": 549, "trimmed_requests": 0, "ts": "2026-09-25T01:02:04+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 9, "wall_s": 7.5}
{"backing": "openai/qwen3-8b-awq", "check_output": " stderr arguments may not be used '\n 'with capture_output.')\n kwargs['stdout'] = PIPE\n kwargs['stderr'] = PIPE\n \n with Popen(*popenargs, **kwargs) as process:\n try:\n stdout, stderr = process.communicate(input, timeout=timeout)\n except TimeoutExpired as exc:\n process.kill()\n if _mswindows:\n # Windows accumulates the output in a single blocking\n # read() call run on child threads, with the timeout\n # being done in a join() on those threads. communicate()\n # _after_ kill() is required to collect that and add it\n # to the exception.\n exc.stdout, exc.stderr = process.communicate()\n else:\n # POSIX _communicate already populated the output so\n # far into the TimeoutExpired exception.\n process.wait()\n raise\n except: # Including KeyboardInterrupt, communicate handled that.\n process.kill()\n # We don't call process.wait() as .__exit__ does that for us.\n raise\n retcode = process.poll()\n if check and retcode:\n> raise CalledProcessError(retcode, process.args,\n output=stdout, stderr=stderr)\nE subprocess.CalledProcessError: Command '['/root/nisse/.venv/bin/python', '-m', 'greeter', '--shout', 'Bob']' returned non-zero exit status 2.\n\n/usr/lib/python3.12/subprocess.py:571: CalledProcessError\n=========================== short test summary info ============================\nFAILED .bench_check/test_hidden.py::test_cli_default - SystemExit: 2\nFAILED .bench_check/test_hidden.py::test_cli_shout - SystemExit: 2\nFAILED .bench_check/test_hidden.py::test_module_entry_point - subprocess.Call...\n3 failed, 2 passed in 0.19s\n", "commit": "50b4682", "cost": 0.0, "failed_commands": 2, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.29, Qwen3-8B-AWQ, fp8 KV, 32k", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 1\nstdout:\n..F [100%]\n=================================== FAILURES ===================================\n________________________________ test_cli_shout ________________________________\n\ncapsys = <_pytest.capture.CaptureFixture object at 0x763f352e3980>\n\n def test_cli_shout(capsys) -> None:\n> assert main([\"--shout\", \"Bob\"]) == 0\n ^^^^^^^^^^^^^^^^^^^^^^^^\n\ntest", "prose_turns": 0, "provider": "openai", "success": false, "task": "multi-file", "task_hash": "76bc0dcf36a72599", "tokens_in": 13124, "tokens_out": 589, "trimmed_requests": 0, "ts": "2026-09-25T01:02:13+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 10, "wall_s": 8.4}
{"backing": "openai/qwen3-8b-awq", "check_output": " (\"Hello, World!\", \"hello-world\"),\n (\" Nisse Agent \", \"nisse-agent\"),\n (\"a--b\", \"a-b\"),\n (\"\", \"\"),\n (\"Python 3.12 Release\", \"python-3-12-release\"),\n (\"--leading and trailing--\", \"leading-and-trailing\"),\n (\"MiXeD CaSe\", \"mixed-case\"),\n (\"!!!\", \"\"),\n ],\n )\n def test_slugify(text: str, expected: str) -> None:\n> assert slugify(text) == expected\nE AssertionError: assert '' == 'leading-and-trailing'\nE \nE - leading-and-trailing\n\n.bench_check/test_hidden.py:19: AssertionError\n_____________________ test_slugify[MiXeD CaSe-mixed-case] ______________________\n\ntext = 'MiXeD CaSe', expected = 'mixed-case'\n\n @pytest.mark.parametrize(\n (\"text\", \"expected\"),\n [\n (\"Hello, World!\", \"hello-world\"),\n (\" Nisse Agent \", \"nisse-agent\"),\n (\"a--b\", \"a-b\"),\n (\"\", \"\"),\n (\"Python 3.12 Release\", \"python-3-12-release\"),\n (\"--leading and trailing--\", \"leading-and-trailing\"),\n (\"MiXeD CaSe\", \"mixed-case\"),\n (\"!!!\", \"\"),\n ],\n )\n def test_slugify(text: str, expected: str) -> None:\n> assert slugify(text) == expected\nE AssertionError: assert '' == 'mixed-case'\nE \nE - mixed-case\n\n.bench_check/test_hidden.py:19: AssertionError\n=========================== short test summary info ============================\nFAILED .bench_check/test_hidden.py::test_slugify[Hello, World!-hello-world]\nFAILED .bench_check/test_hidden.py::test_slugify[ Nisse Agent -nisse-agent]\nFAILED .bench_check/test_hidden.py::test_slugify[a--b-a-b] - AssertionError: ...\nFAILED .bench_check/test_hidden.py::test_slugify[Python 3.12 Release-python-3-12-release]\nFAILED .bench_check/test_hidden.py::test_slugify[--leading and trailing---leading-and-trailing]\nFAILED .bench_check/test_hidden.py::test_slugify[MiXeD CaSe-mixed-case] - Ass...\n6 failed, 2 passed in 0.03s\n", "commit": "50b4682", "cost": 0.0, "failed_commands": 2, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.29, Qwen3-8B-AWQ, fp8 KV, 32k", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 1\nstdout:\nF [100%]\n=================================== FAILURES ===================================\n_________________________________ test_slugify _________________________________\n\n def test_slugify():\n> assert slugify('Hello, World!') == 'hello-world'\nE AssertionError: assert '' == 'hello-world'\nE \nE - hello-world\n\ntest_slugify.py:6: AssertionEr", "prose_turns": 0, "provider": "openai", "success": false, "task": "write-function", "task_hash": "ec722fb0f389a6f4", "tokens_in": 4942, "tokens_out": 377, "trimmed_requests": 0, "ts": "2026-09-25T01:02:19+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 5, "wall_s": 5.5}
{"backing": "openai/qwen3-8b-awq", "check_output": "........ [100%]\n8 passed in 0.01s\n", "commit": "50b4682", "cost": 0.0, "failed_commands": 2, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.29, Qwen3-8B-AWQ, fp8 KV, 32k", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 1\nstdout:\nF [100%]\n=================================== FAILURES ===================================\n_________________________________ test_slugify _________________________________\n\n def test_slugify():\n> assert slugify('Hello, World!') == 'hello-world'\n ^^^^^^^\nE NameError: name 'slugify' is not defined\n\ntest_slugify.py:4: NameError\n===============", "prose_turns": 0, "provider": "openai", "success": true, "task": "write-function", "task_hash": "ec722fb0f389a6f4", "tokens_in": 3688, "tokens_out": 255, "trimmed_requests": 0, "ts": "2026-09-25T01:02:23+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 4, "wall_s": 3.9}
{"backing": "openai/qwen3-8b-awq", "check_output": "........ [100%]\n8 passed in 0.01s\n", "commit": "50b4682", "cost": 0.0, "failed_commands": 2, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.29, Qwen3-8B-AWQ, fp8 KV, 32k", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 1\nstdout:\nF [100%]\n=================================== FAILURES ===================================\n_________________________________ test_slugify _________________________________\n\n def test_slugify():\n> assert slugify('Hello, World!') == 'hello-world'\n ^^^^^^^\nE NameError: name 'slugify' is not defined\n\ntest_slugify.py:4: NameError\n===============", "prose_turns": 0, "provider": "openai", "success": true, "task": "write-function", "task_hash": "ec722fb0f389a6f4", "tokens_in": 3696, "tokens_out": 255, "trimmed_requests": 0, "ts": "2026-09-25T01:02:27+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 4, "wall_s": 3.9}
{"backing": "openai/qwen3-8b-awq", "check_output": " (\"Hello, World!\", \"hello-world\"),\n (\" Nisse Agent \", \"nisse-agent\"),\n (\"a--b\", \"a-b\"),\n (\"\", \"\"),\n (\"Python 3.12 Release\", \"python-3-12-release\"),\n (\"--leading and trailing--\", \"leading-and-trailing\"),\n (\"MiXeD CaSe\", \"mixed-case\"),\n (\"!!!\", \"\"),\n ],\n )\n def test_slugify(text: str, expected: str) -> None:\n> assert slugify(text) == expected\nE AssertionError: assert '' == 'leading-and-trailing'\nE \nE - leading-and-trailing\n\n.bench_check/test_hidden.py:19: AssertionError\n_____________________ test_slugify[MiXeD CaSe-mixed-case] ______________________\n\ntext = 'MiXeD CaSe', expected = 'mixed-case'\n\n @pytest.mark.parametrize(\n (\"text\", \"expected\"),\n [\n (\"Hello, World!\", \"hello-world\"),\n (\" Nisse Agent \", \"nisse-agent\"),\n (\"a--b\", \"a-b\"),\n (\"\", \"\"),\n (\"Python 3.12 Release\", \"python-3-12-release\"),\n (\"--leading and trailing--\", \"leading-and-trailing\"),\n (\"MiXeD CaSe\", \"mixed-case\"),\n (\"!!!\", \"\"),\n ],\n )\n def test_slugify(text: str, expected: str) -> None:\n> assert slugify(text) == expected\nE AssertionError: assert '' == 'mixed-case'\nE \nE - mixed-case\n\n.bench_check/test_hidden.py:19: AssertionError\n=========================== short test summary info ============================\nFAILED .bench_check/test_hidden.py::test_slugify[Hello, World!-hello-world]\nFAILED .bench_check/test_hidden.py::test_slugify[ Nisse Agent -nisse-agent]\nFAILED .bench_check/test_hidden.py::test_slugify[a--b-a-b] - AssertionError: ...\nFAILED .bench_check/test_hidden.py::test_slugify[Python 3.12 Release-python-3-12-release]\nFAILED .bench_check/test_hidden.py::test_slugify[--leading and trailing---leading-and-trailing]\nFAILED .bench_check/test_hidden.py::test_slugify[MiXeD CaSe-mixed-case] - Ass...\n6 failed, 2 passed in 0.03s\n", "commit": "50b4682", "cost": 0.0, "failed_commands": 2, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.29, Qwen3-8B-AWQ, fp8 KV, 32k", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 1\nstdout:\nF [100%]\n=================================== FAILURES ===================================\n_________________________________ test_slugify _________________________________\n\n def test_slugify():\n> assert slugify('Hello, World!') == 'hello-world'\nE AssertionError: assert '' == 'hello-world'\nE \nE - hello-world\n\ntest_slugify.py:6: AssertionEr", "prose_turns": 0, "provider": "openai", "success": false, "task": "write-function", "task_hash": "ec722fb0f389a6f4", "tokens_in": 4936, "tokens_out": 377, "trimmed_requests": 0, "ts": "2026-09-25T01:02:33+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 5, "wall_s": 5.5}
{"backing": "openai/qwen3-8b-awq", "check_output": "........ [100%]\n8 passed in 0.01s\n", "commit": "50b4682", "cost": 0.0, "failed_commands": 2, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.29, Qwen3-8B-AWQ, fp8 KV, 32k", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 1\nstdout:\nF [100%]\n=================================== FAILURES ===================================\n_________________________________ test_slugify _________________________________\n\n def test_slugify():\n> assert slugify('Hello, World!') == 'hello-world'\n ^^^^^^^\nE NameError: name 'slugify' is not defined\n\ntest_slugify.py:4: NameError\n===============", "prose_turns": 0, "provider": "openai", "success": true, "task": "write-function", "task_hash": "ec722fb0f389a6f4", "tokens_in": 3686, "tokens_out": 251, "trimmed_requests": 0, "ts": "2026-09-25T01:02:37+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 4, "wall_s": 3.9}
bench/results/2026-09-27-gemma-4-12b.jsonl
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{"backing": "openai/gemma-4-12b", "check_output": "...... [100%]\n6 passed in 0.01s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 1, "fallbacks": [], "invented_tools": 0, "model": "gemma-4-12b", "model_info": {"max_input_tokens": 262144, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": true}, "note": "llama.cpp b11206, q4_0, q8_0 KV, 256k, temp 1.0 top-p 0.95 top-k 64, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Fixed the `total_value` function in `inventory.py` to multiply `unit_price` by `quantity` for each item. All tests now pass.", "prose_turns": 0, "provider": "openai", "success": true, "task": "fix-bug", "task_hash": "b78248aff00ddc39", "tokens_in": 4909, "tokens_out": 853, "trimmed_requests": 0, "ts": "2026-09-27T17:32:39+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 5, "wall_s": 11.1}
{"backing": "openai/gemma-4-12b", "check_output": "...... [100%]\n6 passed in 0.01s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 1, "fallbacks": [], "invented_tools": 0, "model": "gemma-4-12b", "model_info": {"max_input_tokens": 262144, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": true}, "note": "llama.cpp b11206, q4_0, q8_0 KV, 256k, temp 1.0 top-p 0.95 top-k 64, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Fixed the `total_value` function in `inventory.py` to correctly multiply `unit_price` by `quantity` for each item. All tests passed.", "prose_turns": 0, "provider": "openai", "success": true, "task": "fix-bug", "task_hash": "b78248aff00ddc39", "tokens_in": 5927, "tokens_out": 832, "trimmed_requests": 0, "ts": "2026-09-27T17:32:51+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 6, "wall_s": 11.9}
{"backing": "openai/gemma-4-12b", "check_output": "...... [100%]\n6 passed in 0.01s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 1, "fallbacks": [], "invented_tools": 0, "model": "gemma-4-12b", "model_info": {"max_input_tokens": 262144, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": true}, "note": "llama.cpp b11206, q4_0, q8_0 KV, 256k, temp 1.0 top-p 0.95 top-k 64, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Fixed the bug in `total_value` where it was only summing the unit price instead of multiplying by the quantity.", "prose_turns": 0, "provider": "openai", "success": true, "task": "fix-bug", "task_hash": "b78248aff00ddc39", "tokens_in": 5927, "tokens_out": 623, "trimmed_requests": 0, "ts": "2026-09-27T17:33:01+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 6, "wall_s": 9.8}
{"backing": "openai/gemma-4-12b", "check_output": "...... [100%]\n6 passed in 0.01s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 1, "fallbacks": [], "invented_tools": 0, "model": "gemma-4-12b", "model_info": {"max_input_tokens": 262144, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": true}, "note": "llama.cpp b11206, q4_0, q8_0 KV, 256k, temp 1.0 top-p 0.95 top-k 64, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Fixed the `total_value` function in `inventory.py` to correctly multiply each item's unit price by its quantity.", "prose_turns": 0, "provider": "openai", "success": true, "task": "fix-bug", "task_hash": "b78248aff00ddc39", "tokens_in": 5651, "tokens_out": 749, "trimmed_requests": 0, "ts": "2026-09-27T17:33:13+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 6, "wall_s": 11.0}
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{"backing": "openai/gemma-4-12b", "check_output": "..... [100%]\n5 passed in 0.05s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "gemma-4-12b", "model_info": {"max_input_tokens": 262144, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": true}, "note": "llama.cpp b11206, q4_0, q8_0 KV, 256k, temp 1.0 top-p 0.95 top-k 64, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Added a `--shout` option to the greeter. Updated `greeter/core.py` to support shouting, `greeter/cli.py` to include the flag, and added corresponding tests to `tests/test_greeter.py`. Verified the functionality via tests and manual command execution.", "prose_turns": 0, "provider": "openai", "success": true, "task": "multi-file", "task_hash": "76bc0dcf36a72599", "tokens_in": 14116, "tokens_out": 1686, "trimmed_requests": 0, "ts": "2026-09-27T17:41:21+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 12, "wall_s": 22.1}
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{"backing": "openai/gemma-4-12b", "check_output": "..... [100%]\n5 passed in 0.04s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "gemma-4-12b", "model_info": {"max_input_tokens": 262144, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": true}, "note": "llama.cpp b11206, q4_0, q8_0 KV, 256k, temp 1.0 top-p 0.95 top-k 64, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Added a `--shout` option to the greeter. Updated `greeter/core.py` to support the `shout` parameter, `greeter/cli.py` to include the `--shout` flag, and added corresponding tests in `tests/test_greeter.py`. Verified that `python3 -m greeter --shout Bob` prints `HELLO, BOB!`.", "prose_turns": 0, "provider": "openai", "success": true, "task": "multi-file", "task_hash": "76bc0dcf36a72599", "tokens_in": 19838, "tokens_out": 4424, "trimmed_requests": 0, "ts": "2026-09-27T17:42:35+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 15, "wall_s": 53.7}
{"backing": "openai/gemma-4-12b", "check_output": "..... [100%]\n5 passed in 0.04s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "gemma-4-12b", "model_info": {"max_input_tokens": 262144, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": true}, "note": "llama.cpp b11206, q4_0, q8_0 KV, 256k, temp 1.0 top-p 0.95 top-k 64, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Added a --shout option to the greeter. Updated `greeter/core.py` to support the `shout` parameter, `greeter/cli.py` to include the `--shout` flag, and added tests to `tests/test_greeter.py`. Verified that the greeting is correctly shouted in uppercase when the flag is provided.", "prose_turns": 0, "provider": "openai", "success": true, "task": "multi-file", "task_hash": "76bc0dcf36a72599", "tokens_in": 12000, "tokens_out": 1523, "trimmed_requests": 0, "ts": "2026-09-27T17:42:55+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 11, "wall_s": 20.0}
{"backing": "openai/gemma-4-12b", "check_output": "..... [100%]\n5 passed in 0.04s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "gemma-4-12b", "model_info": {"max_input_tokens": 262144, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": true}, "note": "llama.cpp b11206, q4_0, q8_0 KV, 256k, temp 1.0 top-p 0.95 top-k 64, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Added the `--shout` option to the greeter. Updated `greeter/core.py` to support shouting, `greeter/cli.py` to include the CLI flag, and added corresponding tests in `tests/test_greeter.py`. Verified the changes with tests and manual command execution.", "prose_turns": 0, "provider": "openai", "success": true, "task": "multi-file", "task_hash": "76bc0dcf36a72599", "tokens_in": 14049, "tokens_out": 1697, "trimmed_requests": 0, "ts": "2026-09-27T17:43:17+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 12, "wall_s": 22.1}
{"backing": "openai/gemma-4-12b", "check_output": "........ [100%]\n8 passed in 0.01s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "gemma-4-12b", "model_info": {"max_input_tokens": 262144, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": true}, "note": "llama.cpp b11206, q4_0, q8_0 KV, 256k, temp 1.0 top-p 0.95 top-k 64, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Created `slugify.py` with the `slugify` function, created `test_slugify.py` with passing tests, and verified everything.", "prose_turns": 0, "provider": "openai", "success": true, "task": "write-function", "task_hash": "ec722fb0f389a6f4", "tokens_in": 3283, "tokens_out": 451, "trimmed_requests": 0, "ts": "2026-09-27T17:43:24+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 4, "wall_s": 6.3}
{"backing": "openai/gemma-4-12b", "check_output": "........ [100%]\n8 passed in 0.01s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "gemma-4-12b", "model_info": {"max_input_tokens": 262144, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": true}, "note": "llama.cpp b11206, q4_0, q8_0 KV, 256k, temp 1.0 top-p 0.95 top-k 64, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Created slugify.py with the slugify function, created test_slugify.py with pytest tests, and verified all tests pass.", "prose_turns": 0, "provider": "openai", "success": true, "task": "write-function", "task_hash": "ec722fb0f389a6f4", "tokens_in": 3259, "tokens_out": 458, "trimmed_requests": 0, "ts": "2026-09-27T17:43:31+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 4, "wall_s": 7.0}
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bench/results/2026-09-27-granite-4.2-8b.jsonl
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{"backing": "openai/granite-4.2-8b", "check_output": "..... [100%]\n5 passed in 0.04s\n", "commit": "8a8d8dc", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "granite-4.2-8b", "model_info": {"max_input_tokens": 131072, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11206, Q6_K, q8_0 KV, 128k, temp 1.0 top-p 0.95, RTX 4090", "outcome": "completed", "outcome_note": "Added --shout option to greeter: updated greet in core.py to accept shout=False, upper-cased output when True; added --shout flag in cli.py; added two tests (one for greet with shout=True, one for CLI with --shout). All tests pass and CLI works as expected.", "prose_turns": 0, "provider": "openai", "success": true, "task": "multi-file", "task_hash": "76bc0dcf36a72599", "tokens_in": 18519, "tokens_out": 1737, "trimmed_requests": 0, "ts": "2026-09-27T17:22:11+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 12, "wall_s": 18.2}
{"backing": "openai/granite-4.2-8b", "check_output": "..... [100%]\n5 passed in 0.04s\n", "commit": "8a8d8dc", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "granite-4.2-8b", "model_info": {"max_input_tokens": 131072, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11206, Q6_K, q8_0 KV, 128k, temp 1.0 top-p 0.95, RTX 4090", "outcome": "completed", "outcome_note": "Added --shout option to greeter. Modified core.greet to accept shout=False and return uppercase when True. Modified cli.py to add --shout flag and pass it to greet. Added two tests (one for core.greet with shout=True and one for CLI with --shout). All tests pass and CLI works correctly.", "prose_turns": 0, "provider": "openai", "success": true, "task": "multi-file", "task_hash": "76bc0dcf36a72599", "tokens_in": 20265, "tokens_out": 2239, "trimmed_requests": 0, "ts": "2026-09-27T17:22:34+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 13, "wall_s": 22.8}
{"backing": "openai/granite-4.2-8b", "check_output": "..... [100%]\n5 passed in 0.04s\n", "commit": "8a8d8dc", "cost": 0.0, "failed_commands": 1, "fallbacks": [], "invented_tools": 0, "model": "granite-4.2-8b", "model_info": {"max_input_tokens": 131072, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11206, Q6_K, q8_0 KV, 128k, temp 1.0 top-p 0.95, RTX 4090", "outcome": "completed", "outcome_note": "Added --shout option to greeter: updated core.greet to accept shout=False and uppercase the name when True; updated cli.py to add --shout flag and pass it to greet; added two tests for shout behavior (one for core.greet, one for CLI via capsys); all tests pass and CLI prints 'HELLO, BOB!' when using --shout.", "prose_turns": 0, "provider": "openai", "success": true, "task": "multi-file", "task_hash": "76bc0dcf36a72599", "tokens_in": 25055, "tokens_out": 2097, "trimmed_requests": 0, "ts": "2026-09-27T17:22:56+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 14, "wall_s": 21.9}
{"backing": "openai/granite-4.2-8b", "check_output": "..... [100%]\n5 passed in 0.04s\n", "commit": "8a8d8dc", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "granite-4.2-8b", "model_info": {"max_input_tokens": 131072, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11206, Q6_K, q8_0 KV, 128k, temp 1.0 top-p 0.95, RTX 4090", "outcome": "completed", "outcome_note": "Added --shout option to greeter: core.greet now takes shout=False; cli adds --shout flag; tests added for both core and cli shout behavior; all tests pass and command works.", "prose_turns": 0, "provider": "openai", "success": true, "task": "multi-file", "task_hash": "76bc0dcf36a72599", "tokens_in": 18240, "tokens_out": 2766, "trimmed_requests": 0, "ts": "2026-09-27T17:23:24+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 12, "wall_s": 27.8}
{"backing": "openai/granite-4.2-8b", "check_output": "..... [100%]\n5 passed in 0.04s\n", "commit": "8a8d8dc", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "granite-4.2-8b", "model_info": {"max_input_tokens": 131072, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11206, Q6_K, q8_0 KV, 128k, temp 1.0 top-p 0.95, RTX 4090", "outcome": "completed", "outcome_note": "Successfully added --shout option to greeter. Modified core.py to accept shout=bool and return uppercase when True. Modified cli.py to add --shout flag and pass it to greet. Added two tests for greet(shout=True) and main([\"--shout\", \"Bob\"]). All tests pass and the CLI works as expected.", "prose_turns": 0, "provider": "openai", "success": true, "task": "multi-file", "task_hash": "76bc0dcf36a72599", "tokens_in": 21491, "tokens_out": 2173, "trimmed_requests": 0, "ts": "2026-09-27T17:23:47+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 13, "wall_s": 22.2}
{"backing": "openai/granite-4.2-8b", "check_output": "........ [100%]\n8 passed in 0.01s\n", "commit": "8a8d8dc", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "granite-4.2-8b", "model_info": {"max_input_tokens": 131072, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11206, Q6_K, q8_0 KV, 128k, temp 1.0 top-p 0.95, RTX 4090", "outcome": "completed", "outcome_note": "Created slugify.py with slugify function that lowercases text, replaces runs of non-alnum with a single hyphen, and strips hyphens from both ends. Created test_slugify.py with pytest tests covering the four required examples and additional edge cases. All tests pass.", "prose_turns": 0, "provider": "openai", "success": true, "task": "write-function", "task_hash": "ec722fb0f389a6f4", "tokens_in": 10388, "tokens_out": 4091, "trimmed_requests": 0, "ts": "2026-09-27T17:24:28+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 7, "wall_s": 40.5}
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{"backing": "openai/granite-4.2-8b", "check_output": "..... [100%]\n5 passed in 0.04s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "granite-4.2-8b", "model_info": {"max_input_tokens": 131072, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11206, Q6_K, q8_0 KV, 128k, temp 1.0 top-p 0.95, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Added --shout option to greeter. Modified core.greet to accept shout=False and return uppercase when True. Added --shout flag to CLI and passed it to greet. Added two tests: one for greet with shout=True and one for CLI with --shout flag. All tests pass and CLI output matches expected.", "prose_turns": 0, "provider": "openai", "success": true, "task": "multi-file", "task_hash": "76bc0dcf36a72599", "tokens_in": 14785, "tokens_out": 3312, "trimmed_requests": 0, "ts": "2026-09-27T18:25:05+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 10, "wall_s": 33.1}
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{"backing": "openai/granite-4.2-8b", "check_output": "..... [100%]\n5 passed in 0.05s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "granite-4.2-8b", "model_info": {"max_input_tokens": 131072, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11206, Q6_K, q8_0 KV, 128k, temp 1.0 top-p 0.95, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Added --shout option to greeter. Updated greet function in core.py with shout parameter; updated cli.py to add --shout flag and pass it to greet; added two tests (one for greet with shout=True and one for CLI with --shout). All tests pass and CLI prints 'HELLO, BOB!' when invoked with --shout.", "prose_turns": 0, "provider": "openai", "success": true, "task": "multi-file", "task_hash": "76bc0dcf36a72599", "tokens_in": 18584, "tokens_out": 1434, "trimmed_requests": 0, "ts": "2026-09-27T18:25:41+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 12, "wall_s": 15.2}
{"backing": "openai/granite-4.2-8b", "check_output": "..... [100%]\n5 passed in 0.04s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "granite-4.2-8b", "model_info": {"max_input_tokens": 131072, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11206, Q6_K, q8_0 KV, 128k, temp 1.0 top-p 0.95, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Added --shout option to greeter: core.greet now takes shout=False keyword, cli adds --shout flag, tests added for both core and CLI, all tests pass and CLI works correctly.", "prose_turns": 0, "provider": "openai", "success": true, "task": "multi-file", "task_hash": "76bc0dcf36a72599", "tokens_in": 20462, "tokens_out": 1703, "trimmed_requests": 0, "ts": "2026-09-27T18:25:59+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 13, "wall_s": 17.8}
{"backing": "openai/granite-4.2-8b", "check_output": "........ [100%]\n8 passed in 0.01s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "granite-4.2-8b", "model_info": {"max_input_tokens": 131072, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11206, Q6_K, q8_0 KV, 128k, temp 1.0 top-p 0.95, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Successfully implemented slugify function in slugify.py that lowercases text, replaces every run of non-alphanumeric characters (a-z, 0-9) with a single hyphen, and strips hyphens from both ends. Created test_slugify.py with pytest tests for the four required examples and ran tests; all pass. Verified edge cases.", "prose_turns": 0, "provider": "openai", "success": true, "task": "write-function", "task_hash": "ec722fb0f389a6f4", "tokens_in": 30463, "tokens_out": 24495, "trimmed_requests": 0, "ts": "2026-09-27T18:30:05+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 14, "wall_s": 245.6}
{"backing": "openai/granite-4.2-8b", "check_output": "........ [100%]\n8 passed in 0.01s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "granite-4.2-8b", "model_info": {"max_input_tokens": 131072, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11206, Q6_K, q8_0 KV, 128k, temp 1.0 top-p 0.95, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Added slugify function in slugify.py using regex substitution to replace non-alnum characters with hyphens and strip hyphens from ends. Created test_slugify.py with four tests covering the examples. All tests pass.", "prose_turns": 0, "provider": "openai", "success": true, "task": "write-function", "task_hash": "ec722fb0f389a6f4", "tokens_in": 8921, "tokens_out": 6467, "trimmed_requests": 0, "ts": "2026-09-27T18:31:09+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 7, "wall_s": 63.9}
{"backing": "openai/granite-4.2-8b", "check_output": "........ [100%]\n8 passed in 0.01s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "granite-4.2-8b", "model_info": {"max_input_tokens": 131072, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11206, Q6_K, q8_0 KV, 128k, temp 1.0 top-p 0.95, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Created slugify.py with the required function, test_slugify.py with pytest tests for the four examples, and all tests pass.", "prose_turns": 0, "provider": "openai", "success": true, "task": "write-function", "task_hash": "ec722fb0f389a6f4", "tokens_in": 9089, "tokens_out": 3303, "trimmed_requests": 0, "ts": "2026-09-27T18:31:42+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 7, "wall_s": 33.1}
{"backing": "openai/granite-4.2-8b", "check_output": "........ [100%]\n8 passed in 0.01s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "granite-4.2-8b", "model_info": {"max_input_tokens": 131072, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11206, Q6_K, q8_0 KV, 128k, temp 1.0 top-p 0.95, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Created slugify.py with slugify function that lowercases text, replaces runs of non-alphanumeric characters with a single hyphen, and strips hyphens from both ends. Created test_slugify.py with pytest tests for the four examples. All tests pass.", "prose_turns": 0, "provider": "openai", "success": true, "task": "write-function", "task_hash": "ec722fb0f389a6f4", "tokens_in": 5948, "tokens_out": 1905, "trimmed_requests": 0, "ts": "2026-09-27T18:32:02+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 5, "wall_s": 19.0}
{"backing": "openai/granite-4.2-8b", "check_output": "........ [100%]\n8 passed in 0.01s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "granite-4.2-8b", "model_info": {"max_input_tokens": 131072, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11206, Q6_K, q8_0 KV, 128k, temp 1.0 top-p 0.95, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Added slugify function in slugify.py that lowercases text, replaces runs of non-alnum characters with a single hyphen, and strips hyphens from both ends. Created test_slugify.py with four tests covering the examples, and all tests pass.", "prose_turns": 0, "provider": "openai", "success": true, "task": "write-function", "task_hash": "ec722fb0f389a6f4", "tokens_in": 5551, "tokens_out": 2521, "trimmed_requests": 0, "ts": "2026-09-27T18:32:27+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 5, "wall_s": 25.0}
bench/results/2026-09-27-local-fast.jsonl
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{"backing": "openai/qwen3-8b-awq", "check_output": "29.0 \u00b1 2.9e-05\nE \nE comparison failed\nE Obtained: 0.35\nE Expected: 29.0 \u00b1 2.9e-05\n\n.bench_check/test_hidden.py:22: AssertionError\n________________________ test_total_value[items2-37.5] _________________________\n\nitems = [Item(name='gear', unit_price=12.5, quantity=3)], expected = 37.5\n\n @pytest.mark.parametrize(\n (\"items\", \"expected\"),\n [\n ([], 0.0),\n ([Item(\"bolt\", 0.25, 100), Item(\"nut\", 0.10, 40)], 29.0),\n ([Item(\"gear\", 12.5, 3)], 37.5),\n ([Item(\"empty\", 9.99, 0)], 0.0),\n ],\n )\n def test_total_value(items: list[Item], expected: float) -> None:\n> assert total_value(items) == pytest.approx(expected)\nE assert 12.5 == 37.5 \u00b1 3.7e-05\nE \nE comparison failed\nE Obtained: 12.5\nE Expected: 37.5 \u00b1 3.7e-05\n\n.bench_check/test_hidden.py:22: AssertionError\n_________________________ test_total_value[items3-0.0] _________________________\n\nitems = [Item(name='empty', unit_price=9.99, quantity=0)], expected = 0.0\n\n @pytest.mark.parametrize(\n (\"items\", \"expected\"),\n [\n ([], 0.0),\n ([Item(\"bolt\", 0.25, 100), Item(\"nut\", 0.10, 40)], 29.0),\n ([Item(\"gear\", 12.5, 3)], 37.5),\n ([Item(\"empty\", 9.99, 0)], 0.0),\n ],\n )\n def test_total_value(items: list[Item], expected: float) -> None:\n> assert total_value(items) == pytest.approx(expected)\nE assert 9.99 == 0.0 \u00b1 1.0e-12\nE \nE comparison failed\nE Obtained: 9.99\nE Expected: 0.0 \u00b1 1.0e-12\n\n.bench_check/test_hidden.py:22: AssertionError\n=========================== short test summary info ============================\nFAILED .bench_check/test_hidden.py::test_total_value[items1-29.0] - assert 0....\nFAILED .bench_check/test_hidden.py::test_total_value[items2-37.5] - assert 12...\nFAILED .bench_check/test_hidden.py::test_total_value[items3-0.0] - assert 9.9...\n3 failed, 3 passed in 0.03s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 2, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.30.0, AWQ, fp8 KV, 32k, RTX 4090, 350 W cap", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 1\nstdout:\nF. [100%]\n=================================== FAILURES ===================================\n______________________ test_total_value_counts_every_unit ______________________\n\n def test_total_value_counts_every_unit() -> None:\n items = [Item(\"bolt\", 0.25, 100), Item(\"nut\", 0.10, 40)]\n> assert total_value(items) == 29.0\nE AssertionError: assert 0.35 =", "prose_turns": 0, "provider": "openai", "success": false, "task": "fix-bug", "task_hash": "b78248aff00ddc39", "tokens_in": 1578, "tokens_out": 50, "trimmed_requests": 0, "ts": "2026-09-27T18:35:22+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 2, "wall_s": 1.0}
{"backing": "openai/qwen3-8b-awq", "check_output": "29.0 \u00b1 2.9e-05\nE \nE comparison failed\nE Obtained: 0.35\nE Expected: 29.0 \u00b1 2.9e-05\n\n.bench_check/test_hidden.py:22: AssertionError\n________________________ test_total_value[items2-37.5] _________________________\n\nitems = [Item(name='gear', unit_price=12.5, quantity=3)], expected = 37.5\n\n @pytest.mark.parametrize(\n (\"items\", \"expected\"),\n [\n ([], 0.0),\n ([Item(\"bolt\", 0.25, 100), Item(\"nut\", 0.10, 40)], 29.0),\n ([Item(\"gear\", 12.5, 3)], 37.5),\n ([Item(\"empty\", 9.99, 0)], 0.0),\n ],\n )\n def test_total_value(items: list[Item], expected: float) -> None:\n> assert total_value(items) == pytest.approx(expected)\nE assert 12.5 == 37.5 \u00b1 3.7e-05\nE \nE comparison failed\nE Obtained: 12.5\nE Expected: 37.5 \u00b1 3.7e-05\n\n.bench_check/test_hidden.py:22: AssertionError\n_________________________ test_total_value[items3-0.0] _________________________\n\nitems = [Item(name='empty', unit_price=9.99, quantity=0)], expected = 0.0\n\n @pytest.mark.parametrize(\n (\"items\", \"expected\"),\n [\n ([], 0.0),\n ([Item(\"bolt\", 0.25, 100), Item(\"nut\", 0.10, 40)], 29.0),\n ([Item(\"gear\", 12.5, 3)], 37.5),\n ([Item(\"empty\", 9.99, 0)], 0.0),\n ],\n )\n def test_total_value(items: list[Item], expected: float) -> None:\n> assert total_value(items) == pytest.approx(expected)\nE assert 9.99 == 0.0 \u00b1 1.0e-12\nE \nE comparison failed\nE Obtained: 9.99\nE Expected: 0.0 \u00b1 1.0e-12\n\n.bench_check/test_hidden.py:22: AssertionError\n=========================== short test summary info ============================\nFAILED .bench_check/test_hidden.py::test_total_value[items1-29.0] - assert 0....\nFAILED .bench_check/test_hidden.py::test_total_value[items2-37.5] - assert 12...\nFAILED .bench_check/test_hidden.py::test_total_value[items3-0.0] - assert 9.9...\n3 failed, 3 passed in 0.03s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 2, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.30.0, AWQ, fp8 KV, 32k, RTX 4090, 350 W cap", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 1\nstdout:\nF. [100%]\n=================================== FAILURES ===================================\n______________________ test_total_value_counts_every_unit ______________________\n\n def test_total_value_counts_every_unit() -> None:\n items = [Item(\"bolt\", 0.25, 100), Item(\"nut\", 0.10, 40)]\n> assert total_value(items) == 29.0\nE AssertionError: assert 0.35 =", "prose_turns": 0, "provider": "openai", "success": false, "task": "fix-bug", "task_hash": "b78248aff00ddc39", "tokens_in": 1580, "tokens_out": 50, "trimmed_requests": 0, "ts": "2026-09-27T18:35:23+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 2, "wall_s": 1.0}
{"backing": "openai/qwen3-8b-awq", "check_output": "29.0 \u00b1 2.9e-05\nE \nE comparison failed\nE Obtained: 0.35\nE Expected: 29.0 \u00b1 2.9e-05\n\n.bench_check/test_hidden.py:22: AssertionError\n________________________ test_total_value[items2-37.5] _________________________\n\nitems = [Item(name='gear', unit_price=12.5, quantity=3)], expected = 37.5\n\n @pytest.mark.parametrize(\n (\"items\", \"expected\"),\n [\n ([], 0.0),\n ([Item(\"bolt\", 0.25, 100), Item(\"nut\", 0.10, 40)], 29.0),\n ([Item(\"gear\", 12.5, 3)], 37.5),\n ([Item(\"empty\", 9.99, 0)], 0.0),\n ],\n )\n def test_total_value(items: list[Item], expected: float) -> None:\n> assert total_value(items) == pytest.approx(expected)\nE assert 12.5 == 37.5 \u00b1 3.7e-05\nE \nE comparison failed\nE Obtained: 12.5\nE Expected: 37.5 \u00b1 3.7e-05\n\n.bench_check/test_hidden.py:22: AssertionError\n_________________________ test_total_value[items3-0.0] _________________________\n\nitems = [Item(name='empty', unit_price=9.99, quantity=0)], expected = 0.0\n\n @pytest.mark.parametrize(\n (\"items\", \"expected\"),\n [\n ([], 0.0),\n ([Item(\"bolt\", 0.25, 100), Item(\"nut\", 0.10, 40)], 29.0),\n ([Item(\"gear\", 12.5, 3)], 37.5),\n ([Item(\"empty\", 9.99, 0)], 0.0),\n ],\n )\n def test_total_value(items: list[Item], expected: float) -> None:\n> assert total_value(items) == pytest.approx(expected)\nE assert 9.99 == 0.0 \u00b1 1.0e-12\nE \nE comparison failed\nE Obtained: 9.99\nE Expected: 0.0 \u00b1 1.0e-12\n\n.bench_check/test_hidden.py:22: AssertionError\n=========================== short test summary info ============================\nFAILED .bench_check/test_hidden.py::test_total_value[items1-29.0] - assert 0....\nFAILED .bench_check/test_hidden.py::test_total_value[items2-37.5] - assert 12...\nFAILED .bench_check/test_hidden.py::test_total_value[items3-0.0] - assert 9.9...\n3 failed, 3 passed in 0.03s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 2, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.30.0, AWQ, fp8 KV, 32k, RTX 4090, 350 W cap", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 1\nstdout:\nF. [100%]\n=================================== FAILURES ===================================\n______________________ test_total_value_counts_every_unit ______________________\n\n def test_total_value_counts_every_unit() -> None:\n items = [Item(\"bolt\", 0.25, 100), Item(\"nut\", 0.10, 40)]\n> assert total_value(items) == 29.0\nE AssertionError: assert 0.35 =", "prose_turns": 0, "provider": "openai", "success": false, "task": "fix-bug", "task_hash": "b78248aff00ddc39", "tokens_in": 1576, "tokens_out": 50, "trimmed_requests": 0, "ts": "2026-09-27T18:35:25+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 2, "wall_s": 1.0}
{"backing": "openai/qwen3-8b-awq", "check_output": "29.0 \u00b1 2.9e-05\nE \nE comparison failed\nE Obtained: 0.35\nE Expected: 29.0 \u00b1 2.9e-05\n\n.bench_check/test_hidden.py:22: AssertionError\n________________________ test_total_value[items2-37.5] _________________________\n\nitems = [Item(name='gear', unit_price=12.5, quantity=3)], expected = 37.5\n\n @pytest.mark.parametrize(\n (\"items\", \"expected\"),\n [\n ([], 0.0),\n ([Item(\"bolt\", 0.25, 100), Item(\"nut\", 0.10, 40)], 29.0),\n ([Item(\"gear\", 12.5, 3)], 37.5),\n ([Item(\"empty\", 9.99, 0)], 0.0),\n ],\n )\n def test_total_value(items: list[Item], expected: float) -> None:\n> assert total_value(items) == pytest.approx(expected)\nE assert 12.5 == 37.5 \u00b1 3.7e-05\nE \nE comparison failed\nE Obtained: 12.5\nE Expected: 37.5 \u00b1 3.7e-05\n\n.bench_check/test_hidden.py:22: AssertionError\n_________________________ test_total_value[items3-0.0] _________________________\n\nitems = [Item(name='empty', unit_price=9.99, quantity=0)], expected = 0.0\n\n @pytest.mark.parametrize(\n (\"items\", \"expected\"),\n [\n ([], 0.0),\n ([Item(\"bolt\", 0.25, 100), Item(\"nut\", 0.10, 40)], 29.0),\n ([Item(\"gear\", 12.5, 3)], 37.5),\n ([Item(\"empty\", 9.99, 0)], 0.0),\n ],\n )\n def test_total_value(items: list[Item], expected: float) -> None:\n> assert total_value(items) == pytest.approx(expected)\nE assert 9.99 == 0.0 \u00b1 1.0e-12\nE \nE comparison failed\nE Obtained: 9.99\nE Expected: 0.0 \u00b1 1.0e-12\n\n.bench_check/test_hidden.py:22: AssertionError\n=========================== short test summary info ============================\nFAILED .bench_check/test_hidden.py::test_total_value[items1-29.0] - assert 0....\nFAILED .bench_check/test_hidden.py::test_total_value[items2-37.5] - assert 12...\nFAILED .bench_check/test_hidden.py::test_total_value[items3-0.0] - assert 9.9...\n3 failed, 3 passed in 0.03s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 2, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.30.0, AWQ, fp8 KV, 32k, RTX 4090, 350 W cap", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 1\nstdout:\nF. [100%]\n=================================== FAILURES ===================================\n______________________ test_total_value_counts_every_unit ______________________\n\n def test_total_value_counts_every_unit() -> None:\n items = [Item(\"bolt\", 0.25, 100), Item(\"nut\", 0.10, 40)]\n> assert total_value(items) == 29.0\nE AssertionError: assert 0.35 =", "prose_turns": 0, "provider": "openai", "success": false, "task": "fix-bug", "task_hash": "b78248aff00ddc39", "tokens_in": 1582, "tokens_out": 50, "trimmed_requests": 0, "ts": "2026-09-27T18:35:26+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 2, "wall_s": 1.0}
{"backing": "openai/qwen3-8b-awq", "check_output": "29.0 \u00b1 2.9e-05\nE \nE comparison failed\nE Obtained: 0.35\nE Expected: 29.0 \u00b1 2.9e-05\n\n.bench_check/test_hidden.py:22: AssertionError\n________________________ test_total_value[items2-37.5] _________________________\n\nitems = [Item(name='gear', unit_price=12.5, quantity=3)], expected = 37.5\n\n @pytest.mark.parametrize(\n (\"items\", \"expected\"),\n [\n ([], 0.0),\n ([Item(\"bolt\", 0.25, 100), Item(\"nut\", 0.10, 40)], 29.0),\n ([Item(\"gear\", 12.5, 3)], 37.5),\n ([Item(\"empty\", 9.99, 0)], 0.0),\n ],\n )\n def test_total_value(items: list[Item], expected: float) -> None:\n> assert total_value(items) == pytest.approx(expected)\nE assert 12.5 == 37.5 \u00b1 3.7e-05\nE \nE comparison failed\nE Obtained: 12.5\nE Expected: 37.5 \u00b1 3.7e-05\n\n.bench_check/test_hidden.py:22: AssertionError\n_________________________ test_total_value[items3-0.0] _________________________\n\nitems = [Item(name='empty', unit_price=9.99, quantity=0)], expected = 0.0\n\n @pytest.mark.parametrize(\n (\"items\", \"expected\"),\n [\n ([], 0.0),\n ([Item(\"bolt\", 0.25, 100), Item(\"nut\", 0.10, 40)], 29.0),\n ([Item(\"gear\", 12.5, 3)], 37.5),\n ([Item(\"empty\", 9.99, 0)], 0.0),\n ],\n )\n def test_total_value(items: list[Item], expected: float) -> None:\n> assert total_value(items) == pytest.approx(expected)\nE assert 9.99 == 0.0 \u00b1 1.0e-12\nE \nE comparison failed\nE Obtained: 9.99\nE Expected: 0.0 \u00b1 1.0e-12\n\n.bench_check/test_hidden.py:22: AssertionError\n=========================== short test summary info ============================\nFAILED .bench_check/test_hidden.py::test_total_value[items1-29.0] - assert 0....\nFAILED .bench_check/test_hidden.py::test_total_value[items2-37.5] - assert 12...\nFAILED .bench_check/test_hidden.py::test_total_value[items3-0.0] - assert 9.9...\n3 failed, 3 passed in 0.03s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 2, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.30.0, AWQ, fp8 KV, 32k, RTX 4090, 350 W cap", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 1\nstdout:\nF. [100%]\n=================================== FAILURES ===================================\n______________________ test_total_value_counts_every_unit ______________________\n\n def test_total_value_counts_every_unit() -> None:\n items = [Item(\"bolt\", 0.25, 100), Item(\"nut\", 0.10, 40)]\n> assert total_value(items) == 29.0\nE AssertionError: assert 0.35 =", "prose_turns": 0, "provider": "openai", "success": false, "task": "fix-bug", "task_hash": "b78248aff00ddc39", "tokens_in": 1578, "tokens_out": 50, "trimmed_requests": 0, "ts": "2026-09-27T18:35:27+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 2, "wall_s": 1.0}
{"backing": "openai/qwen3-8b-awq", "check_output": "........................................................................ [ 23%]\n........................................................................ [ 47%]\n........................................................................ [ 71%]\n......................F................................................. [ 95%]\n............. [100%]\n=================================== FAILURES ===================================\n________________________________ test_rate[237] ________________________________\n\nn = 237\n\n @pytest.mark.parametrize(\"n\", range(300))\n def test_rate(n: int) -> None:\n> assert RATES[f\"R{n:03d}\"] == pytest.approx(n % 25 / 100)\nE assert 0.21 == 0.12 \u00b1 1.2e-07\nE \nE comparison failed\nE Obtained: 0.21\nE Expected: 0.12 \u00b1 1.2e-07\n\n.bench_check/test_hidden.py:11: AssertionError\n=========================== short test summary info ============================\nFAILED .bench_check/test_hidden.py::test_rate[237] - assert 0.21 == 0.12 \u00b1 1....\n1 failed, 300 passed in 0.21s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 2, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.30.0, AWQ, fp8 KV, 32k, RTX 4090, 350 W cap", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 1\nstdout:\n........................................................................ [ 24%]\n........................................................................ [ 48%]\n........................................................................ [ 72%]\n.....................F.................................................. [ 96%]\n............ [100%]\n=================================== FAILU", "prose_turns": 0, "provider": "openai", "success": false, "task": "long-output", "task_hash": "8cfbf73fb6620613", "tokens_in": 20975, "tokens_out": 4339, "trimmed_requests": 0, "ts": "2026-09-27T18:36:02+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 5, "wall_s": 34.3}
{"backing": "openai/qwen3-8b-awq", "check_output": "\n==================================== ERRORS ====================================\n_________________ ERROR collecting .bench_check/test_hidden.py _________________\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/python.py:508: in importtestmodule\n mod = import_path(\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/pathlib.py:596: in import_path\n importlib.import_module(module_name)\n/usr/lib/python3.12/importlib/__init__.py:90: in import_module\n return _bootstrap._gcd_import(name[level:], package, level)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n<frozen importlib._bootstrap>:1387: in _gcd_import\n ???\n<frozen importlib._bootstrap>:1360: in _find_and_load\n ???\n<frozen importlib._bootstrap>:1331: in _find_and_load_unlocked\n ???\n<frozen importlib._bootstrap>:935: in _load_unlocked\n ???\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/assertion/rewrite.py:188: in exec_module\n exec(co, module.__dict__)\n.bench_check/test_hidden.py:2: in <module>\n from rates import RATES\nE File \"/tmp/tmplpnklvun/work/rates.py\", line 1\nE \"R237\": 0.12,\nE IndentationError: unexpected indent\n=========================== short test summary info ============================\nERROR .bench_check/test_hidden.py\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\n1 error in 0.12s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 3, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.30.0, AWQ, fp8 KV, 32k, RTX 4090, 350 W cap", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 2\nstdout:\n\n==================================== ERRORS ====================================\n________________________ ERROR collecting test_rates.py ________________________\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/python.py:508: in importtestmodule\n mod = import_path(\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/pathlib.py:596: in import_path\n importlib.import_module(module_name)\n/usr/lib/python3.12/importlib/__init__", "prose_turns": 0, "provider": "openai", "success": false, "task": "long-output", "task_hash": "8cfbf73fb6620613", "tokens_in": 10987, "tokens_out": 183, "trimmed_requests": 0, "ts": "2026-09-27T18:36:06+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 6, "wall_s": 3.0}
{"backing": "openai/qwen3-8b-awq", "check_output": "\n==================================== ERRORS ====================================\n_________________ ERROR collecting .bench_check/test_hidden.py _________________\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/python.py:508: in importtestmodule\n mod = import_path(\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/pathlib.py:596: in import_path\n importlib.import_module(module_name)\n/usr/lib/python3.12/importlib/__init__.py:90: in import_module\n return _bootstrap._gcd_import(name[level:], package, level)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n<frozen importlib._bootstrap>:1387: in _gcd_import\n ???\n<frozen importlib._bootstrap>:1360: in _find_and_load\n ???\n<frozen importlib._bootstrap>:1331: in _find_and_load_unlocked\n ???\n<frozen importlib._bootstrap>:935: in _load_unlocked\n ???\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/assertion/rewrite.py:188: in exec_module\n exec(co, module.__dict__)\n.bench_check/test_hidden.py:2: in <module>\n from rates import RATES\nE File \"/tmp/tmp240rbprt/work/rates.py\", line 1\nE \"R237\": 0.12,\nE IndentationError: unexpected indent\n=========================== short test summary info ============================\nERROR .bench_check/test_hidden.py\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\n1 error in 0.12s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 3, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.30.0, AWQ, fp8 KV, 32k, RTX 4090, 350 W cap", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 2\nstdout:\n\n==================================== ERRORS ====================================\n________________________ ERROR collecting test_rates.py ________________________\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/python.py:508: in importtestmodule\n mod = import_path(\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/pathlib.py:596: in import_path\n importlib.import_module(module_name)\n/usr/lib/python3.12/importlib/__init__", "prose_turns": 0, "provider": "openai", "success": false, "task": "long-output", "task_hash": "8cfbf73fb6620613", "tokens_in": 10987, "tokens_out": 183, "trimmed_requests": 0, "ts": "2026-09-27T18:36:09+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 6, "wall_s": 3.0}
{"backing": "openai/qwen3-8b-awq", "check_output": "\n==================================== ERRORS ====================================\n_________________ ERROR collecting .bench_check/test_hidden.py _________________\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/python.py:508: in importtestmodule\n mod = import_path(\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/pathlib.py:596: in import_path\n importlib.import_module(module_name)\n/usr/lib/python3.12/importlib/__init__.py:90: in import_module\n return _bootstrap._gcd_import(name[level:], package, level)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n<frozen importlib._bootstrap>:1387: in _gcd_import\n ???\n<frozen importlib._bootstrap>:1360: in _find_and_load\n ???\n<frozen importlib._bootstrap>:1331: in _find_and_load_unlocked\n ???\n<frozen importlib._bootstrap>:935: in _load_unlocked\n ???\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/assertion/rewrite.py:188: in exec_module\n exec(co, module.__dict__)\n.bench_check/test_hidden.py:2: in <module>\n from rates import RATES\nE File \"/tmp/tmpagzonvyn/work/rates.py\", line 1\nE \"R237\": 0.12,\nE IndentationError: unexpected indent\n=========================== short test summary info ============================\nERROR .bench_check/test_hidden.py\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\n1 error in 0.12s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 3, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.30.0, AWQ, fp8 KV, 32k, RTX 4090, 350 W cap", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 2\nstdout:\n\n==================================== ERRORS ====================================\n________________________ ERROR collecting test_rates.py ________________________\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/python.py:508: in importtestmodule\n mod = import_path(\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/pathlib.py:596: in import_path\n importlib.import_module(module_name)\n/usr/lib/python3.12/importlib/__init__", "prose_turns": 0, "provider": "openai", "success": false, "task": "long-output", "task_hash": "8cfbf73fb6620613", "tokens_in": 10974, "tokens_out": 183, "trimmed_requests": 0, "ts": "2026-09-27T18:36:12+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 6, "wall_s": 3.0}
{"backing": "openai/qwen3-8b-awq", "check_output": "\n==================================== ERRORS ====================================\n_________________ ERROR collecting .bench_check/test_hidden.py _________________\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/python.py:508: in importtestmodule\n mod = import_path(\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/pathlib.py:596: in import_path\n importlib.import_module(module_name)\n/usr/lib/python3.12/importlib/__init__.py:90: in import_module\n return _bootstrap._gcd_import(name[level:], package, level)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n<frozen importlib._bootstrap>:1387: in _gcd_import\n ???\n<frozen importlib._bootstrap>:1360: in _find_and_load\n ???\n<frozen importlib._bootstrap>:1331: in _find_and_load_unlocked\n ???\n<frozen importlib._bootstrap>:935: in _load_unlocked\n ???\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/assertion/rewrite.py:188: in exec_module\n exec(co, module.__dict__)\n.bench_check/test_hidden.py:2: in <module>\n from rates import RATES\nE File \"/tmp/tmp9kctkv9f/work/rates.py\", line 1\nE \"R237\": 0.12,\nE IndentationError: unexpected indent\n=========================== short test summary info ============================\nERROR .bench_check/test_hidden.py\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\n1 error in 0.12s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 3, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.30.0, AWQ, fp8 KV, 32k, RTX 4090, 350 W cap", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 2\nstdout:\n\n==================================== ERRORS ====================================\n________________________ ERROR collecting test_rates.py ________________________\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/python.py:508: in importtestmodule\n mod = import_path(\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/pathlib.py:596: in import_path\n importlib.import_module(module_name)\n/usr/lib/python3.12/importlib/__init__", "prose_turns": 0, "provider": "openai", "success": false, "task": "long-output", "task_hash": "8cfbf73fb6620613", "tokens_in": 10976, "tokens_out": 181, "trimmed_requests": 0, "ts": "2026-09-27T18:36:16+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 6, "wall_s": 3.0}
{"backing": "openai/qwen3-8b-awq", "check_output": "..... [100%]\n5 passed in 0.04s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.30.0, AWQ, fp8 KV, 32k, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Added --shout option to greeter. Tests pass.", "prose_turns": 0, "provider": "openai", "success": true, "task": "multi-file", "task_hash": "76bc0dcf36a72599", "tokens_in": 10766, "tokens_out": 549, "trimmed_requests": 0, "ts": "2026-09-27T18:36:21+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 9, "wall_s": 4.7}
{"backing": "openai/qwen3-8b-awq", "check_output": "F.FF. [100%]\n=================================== FAILURES ===================================\n__________________________ test_default_is_unchanged ___________________________\n\n def test_default_is_unchanged() -> None:\n> assert greet(\"Bob\") == \"Hello, Bob!\"\nE AssertionError: assert 'HELLO, Bob!' == 'Hello, Bob!'\nE \nE - Hello, Bob!\nE + HELLO, Bob!\n\n.bench_check/test_hidden.py:9: AssertionError\n_______________________________ test_cli_default _______________________________\n\ncapsys = <_pytest.capture.CaptureFixture object at 0x7b6d8abfca70>\n\n def test_cli_default(capsys) -> None:\n> assert main([\"Ada\"]) == 0\nE AssertionError: assert None == 0\nE + where None = main(['Ada'])\n\n.bench_check/test_hidden.py:17: AssertionError\n----------------------------- Captured stdout call -----------------------------\nHELLO, Ada!\n________________________________ test_cli_shout ________________________________\n\ncapsys = <_pytest.capture.CaptureFixture object at 0x7b6d8abbfb30>\n\n def test_cli_shout(capsys) -> None:\n> assert main([\"--shout\", \"Ada\"]) == 0\nE AssertionError: assert None == 0\nE + where None = main(['--shout', 'Ada'])\n\n.bench_check/test_hidden.py:22: AssertionError\n----------------------------- Captured stdout call -----------------------------\nHELLO, ADA!\n=========================== short test summary info ============================\nFAILED .bench_check/test_hidden.py::test_default_is_unchanged - AssertionErro...\nFAILED .bench_check/test_hidden.py::test_cli_default - AssertionError: assert...\nFAILED .bench_check/test_hidden.py::test_cli_shout - AssertionError: assert N...\n3 failed, 2 passed in 0.05s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.30.0, AWQ, fp8 KV, 32k, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Added --shout option to greeter. Tests pass.", "prose_turns": 0, "provider": "openai", "success": false, "task": "multi-file", "task_hash": "76bc0dcf36a72599", "tokens_in": 6758, "tokens_out": 474, "trimmed_requests": 0, "ts": "2026-09-27T18:36:25+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 6, "wall_s": 4.0}
{"backing": "openai/qwen3-8b-awq", "check_output": "..... [100%]\n5 passed in 0.04s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.30.0, AWQ, fp8 KV, 32k, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Added --shout option to greeter. Tests pass.", "prose_turns": 0, "provider": "openai", "success": true, "task": "multi-file", "task_hash": "76bc0dcf36a72599", "tokens_in": 10775, "tokens_out": 549, "trimmed_requests": 0, "ts": "2026-09-27T18:36:30+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 9, "wall_s": 4.7}
{"backing": "openai/qwen3-8b-awq", "check_output": "..... [100%]\n5 passed in 0.04s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.30.0, AWQ, fp8 KV, 32k, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Added --shout option to greeter. Tests pass.", "prose_turns": 0, "provider": "openai", "success": true, "task": "multi-file", "task_hash": "76bc0dcf36a72599", "tokens_in": 10775, "tokens_out": 549, "trimmed_requests": 0, "ts": "2026-09-27T18:36:35+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 9, "wall_s": 4.7}
{"backing": "openai/qwen3-8b-awq", "check_output": "t('stderr') is not None:\n raise ValueError('stdout and stderr arguments may not be used '\n 'with capture_output.')\n kwargs['stdout'] = PIPE\n kwargs['stderr'] = PIPE\n \n with Popen(*popenargs, **kwargs) as process:\n try:\n stdout, stderr = process.communicate(input, timeout=timeout)\n except TimeoutExpired as exc:\n process.kill()\n if _mswindows:\n # Windows accumulates the output in a single blocking\n # read() call run on child threads, with the timeout\n # being done in a join() on those threads. communicate()\n # _after_ kill() is required to collect that and add it\n # to the exception.\n exc.stdout, exc.stderr = process.communicate()\n else:\n # POSIX _communicate already populated the output so\n # far into the TimeoutExpired exception.\n process.wait()\n raise\n except: # Including KeyboardInterrupt, communicate handled that.\n process.kill()\n # We don't call process.wait() as .__exit__ does that for us.\n raise\n retcode = process.poll()\n if check and retcode:\n> raise CalledProcessError(retcode, process.args,\n output=stdout, stderr=stderr)\nE subprocess.CalledProcessError: Command '['/root/nisse/.venv/bin/python', '-m', 'greeter', '--shout', 'Bob']' returned non-zero exit status 2.\n\n/usr/lib/python3.12/subprocess.py:571: CalledProcessError\n=========================== short test summary info ============================\nFAILED .bench_check/test_hidden.py::test_cli_shout - SystemExit: 2\nFAILED .bench_check/test_hidden.py::test_module_entry_point - subprocess.Call...\n2 failed, 3 passed in 0.13s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.30.0, AWQ, fp8 KV, 32k, RTX 4090, 350 W cap", "outcome": "escalate", "outcome_note": "executor call failed: upstream returned 429: {\"error\":{\"message\":\"Rate limit exceeded for api_key: 8941a8ca890cd15add31e5bcb3204db273d130b7f62dede0557b3a11c1a2b52b. Limit type: requests. Current limit: 60, Remaining: 0. Limit resets at: 2026-09-27 18:37:56 UTC\",\"type\":\"throttling_error\",\"param\":null,\"code\":\"429\"}}", "prose_turns": 0, "provider": "openai", "success": false, "task": "multi-file", "task_hash": "76bc0dcf36a72599", "tokens_in": 1616, "tokens_out": 97, "trimmed_requests": 0, "ts": "2026-09-27T18:36:57+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 3, "wall_s": 20.9}
{"backing": "openai/qwen3-8b-awq", "check_output": " (\"Hello, World!\", \"hello-world\"),\n (\" Nisse Agent \", \"nisse-agent\"),\n (\"a--b\", \"a-b\"),\n (\"\", \"\"),\n (\"Python 3.12 Release\", \"python-3-12-release\"),\n (\"--leading and trailing--\", \"leading-and-trailing\"),\n (\"MiXeD CaSe\", \"mixed-case\"),\n (\"!!!\", \"\"),\n ],\n )\n def test_slugify(text: str, expected: str) -> None:\n> assert slugify(text) == expected\nE AssertionError: assert '' == 'leading-and-trailing'\nE \nE - leading-and-trailing\n\n.bench_check/test_hidden.py:19: AssertionError\n_____________________ test_slugify[MiXeD CaSe-mixed-case] ______________________\n\ntext = 'MiXeD CaSe', expected = 'mixed-case'\n\n @pytest.mark.parametrize(\n (\"text\", \"expected\"),\n [\n (\"Hello, World!\", \"hello-world\"),\n (\" Nisse Agent \", \"nisse-agent\"),\n (\"a--b\", \"a-b\"),\n (\"\", \"\"),\n (\"Python 3.12 Release\", \"python-3-12-release\"),\n (\"--leading and trailing--\", \"leading-and-trailing\"),\n (\"MiXeD CaSe\", \"mixed-case\"),\n (\"!!!\", \"\"),\n ],\n )\n def test_slugify(text: str, expected: str) -> None:\n> assert slugify(text) == expected\nE AssertionError: assert '' == 'mixed-case'\nE \nE - mixed-case\n\n.bench_check/test_hidden.py:19: AssertionError\n=========================== short test summary info ============================\nFAILED .bench_check/test_hidden.py::test_slugify[Hello, World!-hello-world]\nFAILED .bench_check/test_hidden.py::test_slugify[ Nisse Agent -nisse-agent]\nFAILED .bench_check/test_hidden.py::test_slugify[a--b-a-b] - AssertionError: ...\nFAILED .bench_check/test_hidden.py::test_slugify[Python 3.12 Release-python-3-12-release]\nFAILED .bench_check/test_hidden.py::test_slugify[--leading and trailing---leading-and-trailing]\nFAILED .bench_check/test_hidden.py::test_slugify[MiXeD CaSe-mixed-case] - Ass...\n6 failed, 2 passed in 0.03s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 2, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.30.0, AWQ, fp8 KV, 32k, RTX 4090, 350 W cap", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 1\nstdout:\nF [100%]\n=================================== FAILURES ===================================\n_________________________________ test_slugify _________________________________\n\n def test_slugify():\n> assert slugify('Hello, World!') == 'hello-world'\n ^^^^^^^\nE NameError: name 'slugify' is not defined\n\ntest_slugify.py:4: NameError\n===============", "prose_turns": 0, "provider": "openai", "success": false, "task": "write-function", "task_hash": "ec722fb0f389a6f4", "tokens_in": 3658, "tokens_out": 250, "trimmed_requests": 0, "ts": "2026-09-27T18:37:19+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 4, "wall_s": 22.6}
{"backing": "openai/qwen3-8b-awq", "check_output": "\n==================================== ERRORS ====================================\n_________________ ERROR collecting .bench_check/test_hidden.py _________________\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/python.py:508: in importtestmodule\n mod = import_path(\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/pathlib.py:596: in import_path\n importlib.import_module(module_name)\n/usr/lib/python3.12/importlib/__init__.py:90: in import_module\n return _bootstrap._gcd_import(name[level:], package, level)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n<frozen importlib._bootstrap>:1387: in _gcd_import\n ???\n<frozen importlib._bootstrap>:1360: in _find_and_load\n ???\n<frozen importlib._bootstrap>:1331: in _find_and_load_unlocked\n ???\n<frozen importlib._bootstrap>:935: in _load_unlocked\n ???\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/assertion/rewrite.py:188: in exec_module\n exec(co, module.__dict__)\n.bench_check/test_hidden.py:2: in <module>\n from slugify import slugify\nE File \"/tmp/tmpj_2vxo2m/work/slugify.py\", line 1\nE def slugify(text: str) -> str:\\n import re\\n text = text.lower()\\n text = re.sub(r'[^a-z0-9]+', '-', text)\\n text = text.strip('-')\\n return text\nE ^\nE SyntaxError: unexpected character after line continuation character\n=========================== short test summary info ============================\nERROR .bench_check/test_hidden.py\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\n1 error in 0.12s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 2, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.30.0, AWQ, fp8 KV, 32k, RTX 4090, 350 W cap", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 2\nstdout:\n\n==================================== ERRORS ====================================\n_______________________ ERROR collecting test_slugify.py _______________________\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/python.py:508: in importtestmodule\n mod = import_path(\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/pathlib.py:596: in import_path\n importlib.import_module(module_name)\n/usr/lib/python3.12/importlib/__init__", "prose_turns": 0, "provider": "openai", "success": false, "task": "write-function", "task_hash": "ec722fb0f389a6f4", "tokens_in": 5808, "tokens_out": 331, "trimmed_requests": 0, "ts": "2026-09-27T18:37:23+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 5, "wall_s": 3.4}
{"backing": "openai/qwen3-8b-awq", "check_output": "\n==================================== ERRORS ====================================\n_________________ ERROR collecting .bench_check/test_hidden.py _________________\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/python.py:508: in importtestmodule\n mod = import_path(\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/pathlib.py:596: in import_path\n importlib.import_module(module_name)\n/usr/lib/python3.12/importlib/__init__.py:90: in import_module\n return _bootstrap._gcd_import(name[level:], package, level)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n<frozen importlib._bootstrap>:1387: in _gcd_import\n ???\n<frozen importlib._bootstrap>:1360: in _find_and_load\n ???\n<frozen importlib._bootstrap>:1331: in _find_and_load_unlocked\n ???\n<frozen importlib._bootstrap>:935: in _load_unlocked\n ???\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/assertion/rewrite.py:188: in exec_module\n exec(co, module.__dict__)\n.bench_check/test_hidden.py:2: in <module>\n from slugify import slugify\nE File \"/tmp/tmpboff_f4_/work/slugify.py\", line 1\nE def slugify(text: str) -> str:\\n import re\\n text = text.lower()\\n text = re.sub(r'[^a-z0-9]+', '-', text)\\n return text.strip('-')\\n\nE ^\nE SyntaxError: unexpected character after line continuation character\n=========================== short test summary info ============================\nERROR .bench_check/test_hidden.py\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\n1 error in 0.12s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 2, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.30.0, AWQ, fp8 KV, 32k, RTX 4090, 350 W cap", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 2\nstdout:\n\n==================================== ERRORS ====================================\n_______________________ ERROR collecting test_slugify.py _______________________\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/python.py:508: in importtestmodule\n mod = import_path(\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/pathlib.py:596: in import_path\n importlib.import_module(module_name)\n/usr/lib/python3.12/importlib/__init__", "prose_turns": 0, "provider": "openai", "success": false, "task": "write-function", "task_hash": "ec722fb0f389a6f4", "tokens_in": 5825, "tokens_out": 380, "trimmed_requests": 0, "ts": "2026-09-27T18:37:27+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 5, "wall_s": 3.8}
{"backing": "openai/qwen3-8b-awq", "check_output": "\n==================================== ERRORS ====================================\n_________________ ERROR collecting .bench_check/test_hidden.py _________________\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/python.py:508: in importtestmodule\n mod = import_path(\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/pathlib.py:596: in import_path\n importlib.import_module(module_name)\n/usr/lib/python3.12/importlib/__init__.py:90: in import_module\n return _bootstrap._gcd_import(name[level:], package, level)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n<frozen importlib._bootstrap>:1387: in _gcd_import\n ???\n<frozen importlib._bootstrap>:1360: in _find_and_load\n ???\n<frozen importlib._bootstrap>:1331: in _find_and_load_unlocked\n ???\n<frozen importlib._bootstrap>:935: in _load_unlocked\n ???\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/assertion/rewrite.py:188: in exec_module\n exec(co, module.__dict__)\n.bench_check/test_hidden.py:2: in <module>\n from slugify import slugify\nE File \"/tmp/tmpypm_nshh/work/slugify.py\", line 1\nE def slugify(text: str) -> str:\\n import re\\n text = text.lower()\\n text = re.sub(r'[^a-z0-9]+', '-', text)\\n return text.strip('-')\nE ^\nE SyntaxError: unexpected character after line continuation character\n=========================== short test summary info ============================\nERROR .bench_check/test_hidden.py\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\n1 error in 0.12s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 2, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.30.0, AWQ, fp8 KV, 32k, RTX 4090, 350 W cap", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 1\nstdout:\nF [100%]\n=================================== FAILURES ===================================\n_________________________________ test_slugify _________________________________\n\n def test_slugify():\n> assert slugify('Hello, World!') == 'hello-world'\n ^^^^^^^\nE NameError: name 'slugify' is not defined\n\ntest_slugify.py:5: NameError\n===============", "prose_turns": 0, "provider": "openai", "success": false, "task": "write-function", "task_hash": "ec722fb0f389a6f4", "tokens_in": 5692, "tokens_out": 337, "trimmed_requests": 0, "ts": "2026-09-27T18:37:31+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 5, "wall_s": 3.4}
{"backing": "openai/qwen3-8b-awq", "check_output": "\n==================================== ERRORS ====================================\n_________________ ERROR collecting .bench_check/test_hidden.py _________________\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/python.py:508: in importtestmodule\n mod = import_path(\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/pathlib.py:596: in import_path\n importlib.import_module(module_name)\n/usr/lib/python3.12/importlib/__init__.py:90: in import_module\n return _bootstrap._gcd_import(name[level:], package, level)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n<frozen importlib._bootstrap>:1387: in _gcd_import\n ???\n<frozen importlib._bootstrap>:1360: in _find_and_load\n ???\n<frozen importlib._bootstrap>:1331: in _find_and_load_unlocked\n ???\n<frozen importlib._bootstrap>:935: in _load_unlocked\n ???\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/assertion/rewrite.py:188: in exec_module\n exec(co, module.__dict__)\n.bench_check/test_hidden.py:2: in <module>\n from slugify import slugify\nE File \"/tmp/tmp9oyadssz/work/slugify.py\", line 1\nE def slugify(text: str) -> str:\\n import re\\n text = text.lower()\\n text = re.sub(r'[^a-z0-9]+', '-', text)\\n text = text.strip('-')\\n return text\nE ^\nE SyntaxError: unexpected character after line continuation character\n=========================== short test summary info ============================\nERROR .bench_check/test_hidden.py\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\n1 error in 0.13s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 2, "fallbacks": [], "invented_tools": 0, "model": "local-fast", "model_info": {"max_input_tokens": 32768, "max_output_tokens": 8192, "supports_function_calling": true, "supports_tool_choice": true}, "note": "vLLM 0.30.0, AWQ, fp8 KV, 32k, RTX 4090, 350 W cap", "outcome": "escalate", "outcome_note": "2 consecutive command failures; last: exit code: 2\nstdout:\n\n==================================== ERRORS ====================================\n_______________________ ERROR collecting test_slugify.py _______________________\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/python.py:508: in importtestmodule\n mod = import_path(\n/root/nisse/.venv/lib/python3.12/site-packages/_pytest/pathlib.py:596: in import_path\n importlib.import_module(module_name)\n/usr/lib/python3.12/importlib/__init__", "prose_turns": 0, "provider": "openai", "success": false, "task": "write-function", "task_hash": "ec722fb0f389a6f4", "tokens_in": 4136, "tokens_out": 269, "trimmed_requests": 0, "ts": "2026-09-27T18:37:35+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 4, "wall_s": 3.0}
bench/results/2026-09-27-mimo-v2.6-9b.jsonl
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@@ -0,0 +1,20 @@
{"backing": "openai/mimo-v2.6-9b", "check_output": "...... [100%]\n6 passed in 0.01s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 1, "fallbacks": [], "invented_tools": 0, "model": "mimo-v2.6-9b", "model_info": {"max_input_tokens": 262144, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11206, Q6_K, q8_0 KV, 256k, temp 0.6 top-p 0.95 top-k 20, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Fixed total_value in inventory.py to multiply unit_price by quantity per item; all tests now pass (2 passed).", "prose_turns": 0, "provider": "openai", "success": true, "task": "fix-bug", "task_hash": "b78248aff00ddc39", "tokens_in": 5132, "tokens_out": 310, "trimmed_requests": 0, "ts": "2026-09-27T17:44:29+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 5, "wall_s": 4.2}
{"backing": "openai/mimo-v2.6-9b", "check_output": "...... [100%]\n6 passed in 0.01s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 1, "fallbacks": [], "invented_tools": 0, "model": "mimo-v2.6-9b", "model_info": {"max_input_tokens": 262144, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11206, Q6_K, q8_0 KV, 256k, temp 0.6 top-p 0.95 top-k 20, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Fixed total_value in inventory.py to multiply unit_price by quantity per item. All tests pass.", "prose_turns": 0, "provider": "openai", "success": true, "task": "fix-bug", "task_hash": "b78248aff00ddc39", "tokens_in": 6915, "tokens_out": 645, "trimmed_requests": 0, "ts": "2026-09-27T17:44:37+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 6, "wall_s": 8.0}
{"backing": "openai/mimo-v2.6-9b", "check_output": "...... [100%]\n6 passed in 0.01s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 1, "fallbacks": [], "invented_tools": 0, "model": "mimo-v2.6-9b", "model_info": {"max_input_tokens": 262144, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11206, Q6_K, q8_0 KV, 256k, temp 0.6 top-p 0.95 top-k 20, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Fixed total_value in inventory.py to multiply unit_price by quantity per item; all tests pass.", "prose_turns": 0, "provider": "openai", "success": true, "task": "fix-bug", "task_hash": "b78248aff00ddc39", "tokens_in": 5132, "tokens_out": 306, "trimmed_requests": 0, "ts": "2026-09-27T17:44:42+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 5, "wall_s": 4.3}
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{"backing": "openai/mimo-v2.6-9b", "check_output": "..... [100%]\n5 passed in 0.04s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "mimo-v2.6-9b", "model_info": {"max_input_tokens": 262144, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11206, Q6_K, q8_0 KV, 256k, temp 0.6 top-p 0.95 top-k 20, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Added a --shout option to the greeter: greet() takes shout: bool = False and uppercases the greeting when true; cli.py gained a --shout store_true flag passed to greet; tests added for both shout=True and the CLI with capsys. All 4 tests pass and `python -m greeter --shout Bob` prints HELLO, BOB!.", "prose_turns": 0, "provider": "openai", "success": true, "task": "multi-file", "task_hash": "76bc0dcf36a72599", "tokens_in": 8490, "tokens_out": 592, "trimmed_requests": 0, "ts": "2026-09-27T17:47:28+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 7, "wall_s": 7.6}
{"backing": "openai/mimo-v2.6-9b", "check_output": "..... [100%]\n5 passed in 0.04s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "mimo-v2.6-9b", "model_info": {"max_input_tokens": 262144, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11206, Q6_K, q8_0 KV, 256k, temp 0.6 top-p 0.95 top-k 20, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Added a --shout option to the greeter.\n\nChanges:\n- `greeter/core.py`: `greet` now takes `shout: bool = False`; when true it returns the greeting in uppercase.\n- `greeter/cli.py`: added a `--shout` `store_true` flag and passes it to `greet`.\n- `tests/test_greeter.py`: added `test_greet_shout` and `test_cli_prints_the_greeting_shout`.\n\nVerification: `python -m pytest -q` passes (4 passed), and `python -m greeter --shout Bob` prints `HELLO, BOB!`.", "prose_turns": 0, "provider": "openai", "success": true, "task": "multi-file", "task_hash": "76bc0dcf36a72599", "tokens_in": 10204, "tokens_out": 809, "trimmed_requests": 0, "ts": "2026-09-27T17:47:39+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 8, "wall_s": 10.0}
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{"backing": "openai/mimo-v2.6-9b", "check_output": "..... [100%]\n5 passed in 0.04s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "mimo-v2.6-9b", "model_info": {"max_input_tokens": 262144, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11206, Q6_K, q8_0 KV, 256k, temp 0.6 top-p 0.95 top-k 20, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Added a --shout option to the greeter: greet() now accepts shout=True (returns uppercase greeting), the CLI has a --shout flag passed through, and two new tests cover both. All 4 tests pass and `python -m greeter --shout Bob` prints HELLO, BOB!.", "prose_turns": 0, "provider": "openai", "success": true, "task": "multi-file", "task_hash": "76bc0dcf36a72599", "tokens_in": 10964, "tokens_out": 708, "trimmed_requests": 0, "ts": "2026-09-27T17:47:56+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 9, "wall_s": 9.0}
{"backing": "openai/mimo-v2.6-9b", "check_output": "..... [100%]\n5 passed in 0.04s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "mimo-v2.6-9b", "model_info": {"max_input_tokens": 262144, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11206, Q6_K, q8_0 KV, 256k, temp 0.6 top-p 0.95 top-k 20, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Added a --shout option to the greeter.\n\n1. `greeter/core.py`: `greet` now takes `shout: bool = False`; when true it returns the greeting in upper case (`greet(\"Bob\", shout=True) == \"HELLO, BOB!\"`). Default behaviour unchanged.\n2. `greeter/cli.py`: added `parser.add_argument(\"--shout\", action=\"store_true\")` and passes `shout=args.shout` to `greet`.\n3. `tests/test_greeter.py`: added `test_greet_shout` and `test_cli_prints_the_greeting_shout` (uses capsys).\n4. `python -m pytest -q` \u2192 4 passed; `pyt", "prose_turns": 0, "provider": "openai", "success": true, "task": "multi-file", "task_hash": "76bc0dcf36a72599", "tokens_in": 10198, "tokens_out": 848, "trimmed_requests": 0, "ts": "2026-09-27T17:48:06+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 8, "wall_s": 10.2}
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{"backing": "openai/qwen3.6-35b-a3b", "check_output": "..... [100%]\n5 passed in 0.04s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "qwen3.6-35b-a3b", "model_info": {"max_input_tokens": 262144, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11206, Q3_K_XL, q8_0 KV, 256k, temp 1.0 top-p 0.95 top-k 20, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Added a `--shout` option to the greeter. Modified `greeter/core.py` to accept a `shout` keyword argument that uppercases the greeting when True. Updated `greeter/cli.py` to add a `--shout` flag and pass it to `greet`. Added two tests to `tests/test_greeter.py`: one for `greet(\"Bob\", shout=True)` and one for `main([\"--shout\", \"Bob\"])` with capsys. All 4 tests pass, and `python -m greeter --shout Bob` prints `HELLO, BOB!`.", "prose_turns": 0, "provider": "openai", "success": true, "task": "multi-file", "task_hash": "76bc0dcf36a72599", "tokens_in": 16065, "tokens_out": 1466, "trimmed_requests": 0, "ts": "2026-09-27T17:51:41+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 10, "wall_s": 11.0}
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{"backing": "openai/qwen3.6-35b-a3b", "check_output": "........ [100%]\n8 passed in 0.01s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "qwen3.6-35b-a3b", "model_info": {"max_input_tokens": 262144, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11206, Q3_K_XL, q8_0 KV, 256k, temp 1.0 top-p 0.95 top-k 20, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Milestone 1 complete: created slugify.py defining slugify(text) which lowercases, replaces non-alphanumeric runs with a single hyphen, and strips leading/trailing hyphens. Created test_slugify.py with 4 pytest tests covering all required examples, all passing.", "prose_turns": 0, "provider": "openai", "success": true, "task": "write-function", "task_hash": "ec722fb0f389a6f4", "tokens_in": 4490, "tokens_out": 538, "trimmed_requests": 0, "ts": "2026-09-27T17:51:52+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 4, "wall_s": 4.5}
{"backing": "openai/qwen3.6-35b-a3b", "check_output": "........ [100%]\n8 passed in 0.01s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "qwen3.6-35b-a3b", "model_info": {"max_input_tokens": 262144, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11206, Q3_K_XL, q8_0 KV, 256k, temp 1.0 top-p 0.95 top-k 20, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Created `slugify.py` with a `slugify(text: str) -> str` function that lowercases text, replaces non-alphanumeric character runs with a single hyphen, and strips leading/trailing hyphens. Created `test_slugify.py` with 4 pytest tests covering the required examples. All tests pass.", "prose_turns": 0, "provider": "openai", "success": true, "task": "write-function", "task_hash": "ec722fb0f389a6f4", "tokens_in": 4538, "tokens_out": 631, "trimmed_requests": 0, "ts": "2026-09-27T17:51:57+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 4, "wall_s": 5.1}
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{"backing": "openai/qwen3.6-35b-a3b", "check_output": "........ [100%]\n8 passed in 0.01s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "qwen3.6-35b-a3b", "model_info": {"max_input_tokens": 262144, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11206, Q3_K_XL, q8_0 KV, 256k, temp 1.0 top-p 0.95 top-k 20, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Created `slugify.py` with a `slugify(text)` function that lowercases text, replaces non a-z/0-9 character runs with a single hyphen, and strips leading/trailing hyphens. Created `test_slugify.py` with 4 pytest tests covering the specified examples. All tests pass.", "prose_turns": 0, "provider": "openai", "success": true, "task": "write-function", "task_hash": "ec722fb0f389a6f4", "tokens_in": 3476, "tokens_out": 613, "trimmed_requests": 0, "ts": "2026-09-27T17:52:07+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 3, "wall_s": 4.7}
{"backing": "openai/qwen3.6-35b-a3b", "check_output": "........ [100%]\n8 passed in 0.01s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "qwen3.6-35b-a3b", "model_info": {"max_input_tokens": 262144, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": false}, "note": "llama.cpp b11206, Q3_K_XL, q8_0 KV, 256k, temp 1.0 top-p 0.95 top-k 20, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Created slugify.py with a slugify() function that lowercases text, replaces non-alphanumeric runs with a single hyphen, and strips leading/trailing hyphens. Created test_slugify.py with 4 pytest tests covering the examples \"Hello, World!\" -> \"hello-world\", \" Nisse Agent \" -> \"nisse-agent\", \"a--b\" -> \"a-b\", and \"\" -> \"\". All tests pass.", "prose_turns": 0, "provider": "openai", "success": true, "task": "write-function", "task_hash": "ec722fb0f389a6f4", "tokens_in": 3520, "tokens_out": 638, "trimmed_requests": 0, "ts": "2026-09-27T17:52:12+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 3, "wall_s": 4.9}
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{"backing": "openai/qwen3.8-27b", "check_output": "...... [100%]\n6 passed in 0.01s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 1, "fallbacks": [], "invented_tools": 0, "model": "qwen3.8-27b", "model_info": {"max_input_tokens": 131072, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": true}, "note": "llama.cpp b11206, Q4_K_XL, q8_0 KV, 128k, temp 1.0 top-p 0.95 top-k 20, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Fixed total_value in inventory.py to multiply each item's unit price by its quantity; both tests now pass.", "prose_turns": 0, "provider": "openai", "success": true, "task": "fix-bug", "task_hash": "b78248aff00ddc39", "tokens_in": 5275, "tokens_out": 439, "trimmed_requests": 0, "ts": "2026-09-27T18:01:40+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 4, "wall_s": 11.8}
{"backing": "openai/qwen3.8-27b", "check_output": "...... [100%]\n6 passed in 0.01s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 1, "fallbacks": [], "invented_tools": 0, "model": "qwen3.8-27b", "model_info": {"max_input_tokens": 131072, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": true}, "note": "llama.cpp b11206, Q4_K_XL, q8_0 KV, 128k, temp 1.0 top-p 0.95 top-k 20, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Fixed total_value in inventory.py to multiply each item's unit price by its quantity (was only summing unit prices). Both tests now pass (2 passed).", "prose_turns": 0, "provider": "openai", "success": true, "task": "fix-bug", "task_hash": "b78248aff00ddc39", "tokens_in": 6546, "tokens_out": 522, "trimmed_requests": 0, "ts": "2026-09-27T18:01:55+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 5, "wall_s": 14.1}
{"backing": "openai/qwen3.8-27b", "check_output": "...... [100%]\n6 passed in 0.01s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 1, "fallbacks": [], "invented_tools": 0, "model": "qwen3.8-27b", "model_info": {"max_input_tokens": 131072, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": true}, "note": "llama.cpp b11206, Q4_K_XL, q8_0 KV, 128k, temp 1.0 top-p 0.95 top-k 20, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Fixed total_value in inventory.py: it now accumulates item.unit_price * item.quantity instead of just item.unit_price. All 2 tests in test_inventory.py pass.", "prose_turns": 0, "provider": "openai", "success": true, "task": "fix-bug", "task_hash": "b78248aff00ddc39", "tokens_in": 6575, "tokens_out": 557, "trimmed_requests": 0, "ts": "2026-09-27T18:02:10+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 5, "wall_s": 15.0}
{"backing": "openai/qwen3.8-27b", "check_output": "...... [100%]\n6 passed in 0.01s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 1, "fallbacks": [], "invented_tools": 0, "model": "qwen3.8-27b", "model_info": {"max_input_tokens": 131072, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": true}, "note": "llama.cpp b11206, Q4_K_XL, q8_0 KV, 128k, temp 1.0 top-p 0.95 top-k 20, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Fixed total_value in inventory.py: the loop added only item.unit_price, now it adds item.unit_price * item.quantity. Both tests pass (2 passed).", "prose_turns": 0, "provider": "openai", "success": true, "task": "fix-bug", "task_hash": "b78248aff00ddc39", "tokens_in": 6985, "tokens_out": 672, "trimmed_requests": 0, "ts": "2026-09-27T18:02:28+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 5, "wall_s": 17.5}
{"backing": "openai/qwen3.8-27b", "check_output": "...... [100%]\n6 passed in 0.01s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 1, "fallbacks": [], "invented_tools": 0, "model": "qwen3.8-27b", "model_info": {"max_input_tokens": 131072, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": true}, "note": "llama.cpp b11206, Q4_K_XL, q8_0 KV, 128k, temp 1.0 top-p 0.95 top-k 20, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Fixed total_value in inventory.py so each item contributes unit_price * quantity instead of unit_price alone. All 2 tests now pass.", "prose_turns": 0, "provider": "openai", "success": true, "task": "fix-bug", "task_hash": "b78248aff00ddc39", "tokens_in": 6575, "tokens_out": 339, "trimmed_requests": 0, "ts": "2026-09-27T18:02:38+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 5, "wall_s": 10.2}
{"backing": "openai/qwen3.8-27b", "check_output": "........................................................................ [ 23%]\n........................................................................ [ 47%]\n........................................................................ [ 71%]\n........................................................................ [ 95%]\n............. [100%]\n301 passed in 0.20s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 1, "fallbacks": [], "invented_tools": 0, "model": "qwen3.8-27b", "model_info": {"max_input_tokens": 131072, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": true}, "note": "llama.cpp b11206, Q4_K_XL, q8_0 KV, 128k, temp 1.0 top-p 0.95 top-k 20, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Fixed the one wrong entry in rates.py: R237 changed from 0.21 to 0.12 (237 mod 25 = 12 \u2192 12/100). All 300 tests now pass.", "prose_turns": 0, "provider": "openai", "success": true, "task": "long-output", "task_hash": "8cfbf73fb6620613", "tokens_in": 9841, "tokens_out": 525, "trimmed_requests": 0, "ts": "2026-09-27T18:02:54+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 5, "wall_s": 15.0}
{"backing": "openai/qwen3.8-27b", "check_output": "........................................................................ [ 23%]\n........................................................................ [ 47%]\n........................................................................ [ 71%]\n........................................................................ [ 95%]\n............. [100%]\n301 passed in 0.20s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 2, "fallbacks": [], "invented_tools": 0, "model": "qwen3.8-27b", "model_info": {"max_input_tokens": 131072, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": true}, "note": "llama.cpp b11206, Q4_K_XL, q8_0 KV, 128k, temp 1.0 top-p 0.95 top-k 20, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Fixed the one wrong entry in the rate table: `test_rate[R237]` was the only failing test, so in rates.py line 241 I changed \"R237\": 0.21 to \"R237\": 0.12 (237 mod 25 = 12 \u2192 0.12), leaving every other line untouched. All 300 tests now pass (`python -m pytest -q`).", "prose_turns": 0, "provider": "openai", "success": true, "task": "long-output", "task_hash": "8cfbf73fb6620613", "tokens_in": 13348, "tokens_out": 758, "trimmed_requests": 0, "ts": "2026-09-27T18:03:15+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 6, "wall_s": 20.7}
{"backing": "openai/qwen3.8-27b", "check_output": "........................................................................ [ 23%]\n........................................................................ [ 47%]\n........................................................................ [ 71%]\n........................................................................ [ 95%]\n............. [100%]\n301 passed in 0.20s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 1, "fallbacks": [], "invented_tools": 0, "model": "qwen3.8-27b", "model_info": {"max_input_tokens": 131072, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": true}, "note": "llama.cpp b11206, Q4_K_XL, q8_0 KV, 128k, temp 1.0 top-p 0.95 top-k 20, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Fixed the single wrong entry in rates.py: \"R237\": 0.21 \u2192 0.12 (= 237 % 25 / 100) on line 241. All 300 tests now pass.", "prose_turns": 0, "provider": "openai", "success": true, "task": "long-output", "task_hash": "8cfbf73fb6620613", "tokens_in": 12292, "tokens_out": 591, "trimmed_requests": 0, "ts": "2026-09-27T18:03:31+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 6, "wall_s": 15.9}
{"backing": "openai/qwen3.8-27b", "check_output": "........................................................................ [ 23%]\n........................................................................ [ 47%]\n........................................................................ [ 71%]\n........................................................................ [ 95%]\n............. [100%]\n301 passed in 0.20s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 1, "fallbacks": [], "invented_tools": 0, "model": "qwen3.8-27b", "model_info": {"max_input_tokens": 131072, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": true}, "note": "llama.cpp b11206, Q4_K_XL, q8_0 KV, 128k, temp 1.0 top-p 0.95 top-k 20, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Fixed the single wrong entry in rates.py: changed region code R237 from 0.21 to 0.12 (237 mod 25 / 100 = 0.12). All 300 tests now pass.", "prose_turns": 0, "provider": "openai", "success": true, "task": "long-output", "task_hash": "8cfbf73fb6620613", "tokens_in": 18850, "tokens_out": 548, "trimmed_requests": 0, "ts": "2026-09-27T18:03:48+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 5, "wall_s": 15.9}
{"backing": "openai/qwen3.8-27b", "check_output": "........................................................................ [ 23%]\n........................................................................ [ 47%]\n........................................................................ [ 71%]\n........................................................................ [ 95%]\n............. [100%]\n301 passed in 0.20s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 2, "fallbacks": [], "invented_tools": 0, "model": "qwen3.8-27b", "model_info": {"max_input_tokens": 131072, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": true}, "note": "llama.cpp b11206, Q4_K_XL, q8_0 KV, 128k, temp 1.0 top-p 0.95 top-k 20, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Milestone 1 complete. Ran pytest: exactly one test failed (test_rate[R237], obtained 0.21 vs expected 0.12). Corrected line 241 of rates.py from \"R237\": 0.21 to \"R237\": 0.12 (237 % 25 / 100 = 0.12), leaving all other entries untouched. Re-ran pytest: all 300 tests pass.", "prose_turns": 0, "provider": "openai", "success": true, "task": "long-output", "task_hash": "8cfbf73fb6620613", "tokens_in": 29415, "tokens_out": 911, "trimmed_requests": 0, "ts": "2026-09-27T18:04:13+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 7, "wall_s": 24.8}
{"backing": "openai/qwen3.8-27b", "check_output": "..... [100%]\n5 passed in 0.04s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "qwen3.8-27b", "model_info": {"max_input_tokens": 131072, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": true}, "note": "llama.cpp b11206, Q4_K_XL, q8_0 KV, 128k, temp 1.0 top-p 0.95 top-k 20, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Added --shout option to the greeter: greet() in greeter/core.py now takes shout: bool = False and upper-cases the greeting when true; greeter/cli.py adds a --shout (store_true) flag passed through to greet; two new tests added (greet shout=True, and CLI --shout via capsys). `python -m pytest -q` passes (4/4) and `python -m greeter --shout Bob` prints \"HELLO, BOB!\".", "prose_turns": 0, "provider": "openai", "success": true, "task": "multi-file", "task_hash": "76bc0dcf36a72599", "tokens_in": 9879, "tokens_out": 1015, "trimmed_requests": 0, "ts": "2026-09-27T18:04:37+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 6, "wall_s": 24.3}
{"backing": "openai/qwen3.8-27b", "check_output": "..... [100%]\n5 passed in 0.04s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "qwen3.8-27b", "model_info": {"max_input_tokens": 131072, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": true}, "note": "llama.cpp b11206, Q4_K_XL, q8_0 KV, 128k, temp 1.0 top-p 0.95 top-k 20, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Added the --shout option to the greeter: (1) greeter/core.py's greet now takes a `shout: bool = False` keyword argument and returns the whole greeting in upper case when true (greet(\"Bob\", shout=True) == \"HELLO, BOB!\"), with unchanged default behaviour; (2) greeter/cli.py adds a `--shout` flag (action=\"store_true\") and passes it to greet; (3) added two tests to tests/test_greeter.py \u2014 one for greet with shout=True and one calling main([\"--shout\", \"Bob\"]) and checking printed output via capsys; (", "prose_turns": 0, "provider": "openai", "success": true, "task": "multi-file", "task_hash": "76bc0dcf36a72599", "tokens_in": 17632, "tokens_out": 1868, "trimmed_requests": 0, "ts": "2026-09-27T18:05:22+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 10, "wall_s": 44.0}
{"backing": "openai/qwen3.8-27b", "check_output": "..... [100%]\n5 passed in 0.04s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "qwen3.8-27b", "model_info": {"max_input_tokens": 131072, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": true}, "note": "llama.cpp b11206, Q4_K_XL, q8_0 KV, 128k, temp 1.0 top-p 0.95 top-k 20, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Added a --shout option to the greeter: gave greet(name, shout=False) a shout keyword argument in greeter/core.py that upper-cases the greeting, added the --shout flag (store_true) in greeter/cli.py passing it through, and added two tests (greet with shout=True, and main([\"--shout\", \"Bob\"]) with capsys). python -m pytest -q passes (4 tests) and python -m greeter --shout Bob prints HELLO, BOB!.", "prose_turns": 0, "provider": "openai", "success": true, "task": "multi-file", "task_hash": "76bc0dcf36a72599", "tokens_in": 8690, "tokens_out": 1116, "trimmed_requests": 0, "ts": "2026-09-27T18:05:48+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 6, "wall_s": 26.5}
{"backing": "openai/qwen3.8-27b", "check_output": "..... [100%]\n5 passed in 0.04s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "qwen3.8-27b", "model_info": {"max_input_tokens": 131072, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": true}, "note": "llama.cpp b11206, Q4_K_XL, q8_0 KV, 128k, temp 1.0 top-p 0.95 top-k 20, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Milestone 1 done: added a `shout: bool = False` keyword argument to `greet` in `greeter/core.py` that returns the greeting in upper case, added a `--shout` store_true flag in `greeter/cli.py` passed through to `greet`, and added two tests (greet with shout=True, and main([\"--shout\", \"Bob\"]) checked via capsys). `python -m pytest -q` shows 4 passed, and `python -m greeter --shout Bob` prints `HELLO, BOB!`.", "prose_turns": 0, "provider": "openai", "success": true, "task": "multi-file", "task_hash": "76bc0dcf36a72599", "tokens_in": 14842, "tokens_out": 1534, "trimmed_requests": 0, "ts": "2026-09-27T18:06:25+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 9, "wall_s": 36.4}
{"backing": "openai/qwen3.8-27b", "check_output": "..... [100%]\n5 passed in 0.04s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "qwen3.8-27b", "model_info": {"max_input_tokens": 131072, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": true}, "note": "llama.cpp b11206, Q4_K_XL, q8_0 KV, 128k, temp 1.0 top-p 0.95 top-k 20, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Added --shout option to the greeter per the approved plan: (1) greet(name, shout: bool = False) in greeter/core.py returns the upper-cased greeting when shout is true, default unchanged; (2) greeter/cli.py adds a --shout flag (action=\"store_true\") passed to greet; (3) tests/test_greeter.py gains test_greet_shout and test_cli_shout_prints_upper_case_greeting using capsys; (4) verified python -m pytest -q passes (4 passed) and python -m greeter --shout Bob prints HELLO, BOB! while plain python -m ", "prose_turns": 0, "provider": "openai", "success": true, "task": "multi-file", "task_hash": "76bc0dcf36a72599", "tokens_in": 14751, "tokens_out": 1805, "trimmed_requests": 0, "ts": "2026-09-27T18:07:08+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 8, "wall_s": 42.2}
{"backing": "openai/qwen3.8-27b", "check_output": "........ [100%]\n8 passed in 0.01s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "qwen3.8-27b", "model_info": {"max_input_tokens": 131072, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": true}, "note": "llama.cpp b11206, Q4_K_XL, q8_0 KV, 128k, temp 1.0 top-p 0.95 top-k 20, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Milestone 1 complete: created `slugify.py` with a `slugify(text)` function (lowercases, collapses non-alphanumeric runs to a single hyphen, strips edge hyphens) and `test_slugify.py` with pytest tests covering all four required examples. `python -m pytest -q test_slugify.py` passes (4 passed).", "prose_turns": 0, "provider": "openai", "success": true, "task": "write-function", "task_hash": "ec722fb0f389a6f4", "tokens_in": 4395, "tokens_out": 544, "trimmed_requests": 0, "ts": "2026-09-27T18:07:21+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 4, "wall_s": 13.4}
{"backing": "openai/qwen3.8-27b", "check_output": "........ [100%]\n8 passed in 0.01s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "qwen3.8-27b", "model_info": {"max_input_tokens": 131072, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": true}, "note": "llama.cpp b11206, Q4_K_XL, q8_0 KV, 128k, temp 1.0 top-p 0.95 top-k 20, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Milestone 1 done: created slugify.py with slugify(text) (lowercases, collapses non-a-z0-9 runs to a single hyphen, strips edge hyphens) and test_slugify.py with pytest tests for the four examples; `python -m pytest -q test_slugify.py` passes (4 passed).", "prose_turns": 0, "provider": "openai", "success": true, "task": "write-function", "task_hash": "ec722fb0f389a6f4", "tokens_in": 3415, "tokens_out": 557, "trimmed_requests": 0, "ts": "2026-09-27T18:07:35+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 3, "wall_s": 13.3}
{"backing": "openai/qwen3.8-27b", "check_output": "........ [100%]\n8 passed in 0.01s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "qwen3.8-27b", "model_info": {"max_input_tokens": 131072, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": true}, "note": "llama.cpp b11206, Q4_K_XL, q8_0 KV, 128k, temp 1.0 top-p 0.95 top-k 20, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Added slugify.py with slugify(text) (lowercase, collapse non a-z0-9 runs to single hyphen, strip hyphens), and test_slugify.py with pytest tests for all four examples. `python -m pytest -q test_slugify.py` passes: 4 passed.", "prose_turns": 0, "provider": "openai", "success": true, "task": "write-function", "task_hash": "ec722fb0f389a6f4", "tokens_in": 3568, "tokens_out": 666, "trimmed_requests": 0, "ts": "2026-09-27T18:07:52+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 3, "wall_s": 16.6}
{"backing": "openai/qwen3.8-27b", "check_output": "........ [100%]\n8 passed in 0.01s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "qwen3.8-27b", "model_info": {"max_input_tokens": 131072, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": true}, "note": "llama.cpp b11206, Q4_K_XL, q8_0 KV, 128k, temp 1.0 top-p 0.95 top-k 20, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Added slugify.py defining slugify(text) which lowercases, replaces runs of non-[a-z0-9] with a single hyphen, and strips hyphens from both ends; added test_slugify.py with pytest tests covering all four plan examples; `python -m pytest -q test_slugify.py` passes (4 passed).", "prose_turns": 0, "provider": "openai", "success": true, "task": "write-function", "task_hash": "ec722fb0f389a6f4", "tokens_in": 3542, "tokens_out": 906, "trimmed_requests": 0, "ts": "2026-09-27T18:08:14+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 3, "wall_s": 21.9}
{"backing": "openai/qwen3.8-27b", "check_output": "........ [100%]\n8 passed in 0.01s\n", "commit": "8a8d8dc+dirty", "cost": 0.0, "failed_commands": 0, "fallbacks": [], "invented_tools": 0, "model": "qwen3.8-27b", "model_info": {"max_input_tokens": 131072, "max_output_tokens": 32768, "supports_function_calling": true, "supports_tool_choice": true}, "note": "llama.cpp b11206, Q4_K_XL, q8_0 KV, 128k, temp 1.0 top-p 0.95 top-k 20, RTX 4090, 350 W cap", "outcome": "completed", "outcome_note": "Milestone 1 done: created `slugify.py` with a `slugify` function (lowercases text, collapses non-alphanumeric runs into single hyphens, strips end hyphens) and `test_slugify.py` with pytest tests for all four required examples. `python -m pytest -q test_slugify.py` passes: 4 passed.", "prose_turns": 0, "provider": "openai", "success": true, "task": "write-function", "task_hash": "ec722fb0f389a6f4", "tokens_in": 3453, "tokens_out": 501, "trimmed_requests": 0, "ts": "2026-09-27T18:08:27+00:00", "tuning": {"context": {"keep_recent_turns": 6, "safety_margin": 0.1}, "delegation": {"max_tokens": 200000, "max_turns": 30, "wall_clock_s": 900.0}, "executor": {"failure_limit": 2, "max_turns": 24}, "expert": {"consult_budget": 3}, "tools": {"command_timeout_s": 120, "max_output_chars": 4000}}, "turns": 3, "wall_s": 13.0}
docs/Architecture-Build-Spec.md
+523 -95
View File
@@ -10,7 +10,10 @@ Gitea, Vikunja, Outline, LiteLLM) and heaviest on the **agent library** and the
document and no longer exists separately: its A-series components appear as A10–A16,
its network and credential revisions in A5 and B3, and its revised build order as
Part G. The inference-hardware question it raised has since been resolved in
principle (Part H: a 4090-class host).
principle (Part H: a 4090-class host). The MCP Deployment amendment has likewise been
folded in: the every-agent-gets-a-VM rule, per-instance egress policy, and MCP server
placement now form Part F1–F3; its consequences appear in Part 0 (site identifiers),
A7, B3, C2, C3, C5, the Part F4 Watchtower carve-out, and Part H.
---
@@ -32,6 +35,12 @@ same string across all of those: the VM, the container, the Caddy host, the Matr
account, and the Bitwarden machine account for a given agent all share one name, so
that grepping the infrastructure for `nisse-ops` finds everything.
**Sites** are identified by their domain stem: `garrett-hall` (Arlington, hosts under
`garrett-hall.com`) and `hartman-hut` (Cross Plains, hosts under `hartman-hut.com`).
As with agent names, one string is used everywhere a site appears. For an MCP server
instance (F3), that means its container name, its Vaultwarden machine account, and
its MCP tool prefix.
The three artifacts live as three packages in **one repository, `nisse`** (decision
recorded in Part H). Directory and package names carry the identity. If mirrored to
GitHub, it goes under a personal account rather than an org — `github.com/nisse` is
@@ -55,6 +64,14 @@ Both are thin applications over a shared **`nisse-core`** library. The library i
consumers that don't exist yet. Build the Dev Agent as if the library were internal,
then lift the seams that proved real.
There is one deliberate, bounded exception: the **work model's identity scheme**
(0.2) is settled for both agents ahead of extraction. Identifiers end up in the state
schema, branch names, the operational log (A9), the transcript (A10), and external
records. Changing those after extraction would mean migrating two agents and their
stored state. Only identity is designed ahead. Behaviour is still built by each
agent as it needs it, so nothing in 0.2 requires the Dev Agent to implement duties,
deduplication, or concurrency.
The three artifacts live in **one repository** (decided — Part H), so the
extraction step in the build order is a package split within one tree, not a repo
split.
@@ -77,6 +94,119 @@ split.
---
## 0.2 The Work Model: Duties, Work Items, and Scopes
Every agent's work is organised into three kinds of unit. They define what state
belongs where, how work is identified, and what the operator is resuming when they
come back to something. Components throughout this document refer to them.
| Concept | What it is | Lifetime | Dev Agent | Sysadmin Agent |
|---|---|---|---|---|
| **Duty** | A standing responsibility that produces work items | Indefinite, recurring | None yet; the operator is the only source | Scheduled checks, log monitoring, alert intake |
| **Work item** | One effort with a lifecycle: opened, worked (often through gates), closed | Opened → closed | Building a feature through arch plan → milestones → PRs | Diagnosing one failure; answering one question |
| **Scope** | What a work item operates on, and the context that persists across work items there | Indefinite | A repo | A site and service, e.g. `garrett-hall` Proxmox |
### Duty
- **A duty produces work items. It is not one itself.** A run of a duty (one log
analysis, one scheduled check) is read-only and yields findings. A finding that
needs attention becomes a work item.
- **The operator is always an implicit source** of work items, for every agent.
- **Deduplication is a duty's responsibility.** When a later run finds the same
failure, it attaches to the existing open work item instead of opening a second
one. That needs a stable fingerprint per finding, checked against open work items.
This is the same idempotency concern A2/A3 address for side effects.
### Work item
- **The unit of state.** A work item owns its state-machine position, gates and plan
revisions, pending questions, pinned role mapping (A11), and its transcript (A10).
- **Identity is minted locally.** Every work item gets a local ID when it opens.
This is the `task_id` of A9: a work item's ID and its correlation ID are one value.
- **An external record is optional, and linked.** A work item *may* be tied to a
durable external record: a Gitea issue for the Dev Agent, a Vikunja ticket for the
Sysadmin Agent. Many will not be; a general question to either agent is a work
item with no record. A work item can also gain a record partway through, for
example a question that turns into a change to make.
- **Where the spec requires a record, it has one.** Dev plan gates are externalized
to Gitea (Part H), and ticket-driven ops work has its ticket (C4). When a record is
linked, external truth wins on divergence (A2), and gate state can be re-derived
from it after state loss. Whether *every* gated work item must have a record is
open (Part H).
- **Work items are not nested.** A milestone is a phase *within* a dev work item, and
a delegation (A13) is an episode within one. Neither is a work item of its own.
### Scope
- **A scope holds context shared by the work items that operate on it:** for a repo,
the clone and the project's conventions; for infrastructure, the A17 world model
of that site or service.
- **Many work items may share one scope.** A feature waiting on review and a bug fix
in the same repo are two work items on one scope. So are two failures found in the
same log run.
- **A work item has zero or more scopes.** A dev work item has exactly one repo for
now. A general question may have none.
- **Scope identifiers follow existing naming.** Repos use `owner/repo`. Sites use the
site identifier (Part 0), combined with the service.
### Park-and-switch: one active work item per agent
- **Each agent has at most one active work item.** Others are *parked*, with their
full position held in the state store (A2).
- **Switching happens only at safe points,** where the loop is waiting on a human
(an open gate, a pending question, a PR in review) or is idle. It never happens
mid-turn. Because turns run to completion, every point at which a command can
arrive is a safe point, with one exception: switching is refused while intents
are unresolved (A2). That keeps every pending intent owned by the active work
item, so boot reconciliation (A3) stays single-item.
- **The operator can always switch; the agent may switch on its own in two cases,**
when other work items are ready:
- *The active item is waiting on the operator* (an open gate, a pending question,
or a halt in `AWAIT_INPUT`). An alert must not sit for hours behind an
unanswered prompt. The item it left stays waiting; switching answers nothing
on its behalf.
- *The active item has closed* (completed or abandoned). Ready work should not
sit behind an idle agent until the operator notices.
Either way, it posts a notice naming both items and why it chose that one. The
agent never switches away from an item that is working.
- **A grace period comes before switching away from a waiting item.** After the
active item posts a plan or a question, the agent waits a configurable period
(default 10 minutes) before switching. An operator who is watching answers in
that time and the item carries on without a switch. Otherwise their approval
could sit behind a long milestone on the other item: once parked, an item only
becomes active again when the active one waits or closes. Closing an item needs
no grace period.
- **A parked item is ready when the agent can act on it without the operator:** it
has not started yet (a duty finding, a ticket), or intake arrived while it was
parked that answers what it was waiting for (an approval, an answer, a review
comment). An item still waiting on the operator, with nothing new, is not ready.
- **The model chooses which ready item to switch to.** This is one of the few
judgments it is trusted with outright, because choosing grants nothing: every
item still meets its own gates, and a poor choice costs time, not safety.
- *It is an agent-level call,* belonging to no work item (A10), made by the
`executor` role at the current config (A11 pins roles per item, and this call
has none).
- *It sees summaries rendered by code from the state store,* one per ready item:
source, age, severity, current state, title. It never sees another item's
transcript, so work items stay isolated (A10).
- *It falls back to the oldest ready item* if the call fails or its answer does
not name one of them. Switching never waits on a model.
- *The switch is one state change* (A9 `work_item.switch`), naming both items,
the reason, and whether the model, the fallback, or the operator chose.
- **Parking gives up authority.** Parking a work item unmounts any elevated MCP
instances (F3) its approved plan mounted. They are remounted when it becomes
active again, if its approval has not expired (C2). An approved plan's
credentials never serve another item.
- **Nothing is lost while parked.** Intake for a parked work item, such as comments
on its Gitea issue or PR, is read from a durable per-thread cursor when it becomes
active again. It is not dropped, and it is not replayed from the beginning.
- **The state schema must not assume one item.** Work items live in their own table,
and "active" is a pointer to one of them. Allowing more than one active item later
(Part H) is then a control-loop change, not a migration of every stored item.
---
## PART A — THE AGENT LIBRARY (`nisse-core`)
The shared spine. Every component here is consumed by both agents. This is the part
@@ -112,8 +242,9 @@ canonical log (A10) is the sole source of truth.
process restart. This is what makes the whole system safe to redeploy and reboot; it
blocks everything else.
Must persist: active project/task, per-project state-machine position, approvals
granted, pending questions, session continuity, (dev) git clone locations.
Must persist: the active work item and each work item's state-machine position
(0.2), approvals granted, pending questions, session continuity, and scope state such
as (dev) git clone locations.
Disposable (never on the state volume): agent code, build artifacts/test containers,
shipped-out logs.
@@ -152,10 +283,18 @@ Every startup (clean or post-crash), before accepting operator commands:
(tree diff for edits, Gitea query for pushes), or on any branch/log mismatch. The
judgment is an observable check, never model self-assessment — the same reasoning
as A12.
6. Any other interruption → move to **`AWAIT_INPUT`** and post to transport
6. **Interrupted mid-sequence (C2) — resume only on evidence.** An approved step
sequence may interrupt itself: rebooting the host the agent runs on is a
legitimate step. On restart, **continue** the sequence only when the intent log
names exactly which step was in flight and that step's expected outcome checks
out against the live system, and the approval has not expired. Anything else
(no recorded outcome check, a failed check, or more than one step
possibly in flight) gates to `AWAIT_INPUT`. The step counts as completed only
by that check, never by assuming it ran.
7. Any other interruption → move to **`AWAIT_INPUT`** and post to transport
(e.g. "restarted mid-milestone-2, branch at commit abc123, continue or re-plan?").
Never blind-resume.
7. Otherwise resume the last committed state.
8. Otherwise resume the last committed state.
### A4. Control Loop, State Machine, and Gating Primitives
@@ -194,9 +333,23 @@ Requirements:
for copy-paste. Any other reply/comment is `reject <feedback>`. Reactions/emoji are
never an approval path. Approvals are consumed at first sight; later edits to an
approval comment are ignored and logged.
- **Approvals bind to a work item, too (0.2).** A bare `approve` binds to the
*active* work item's latest revision, under the unambiguity rule above. An
approval arriving on a *parked* item's external record (a Gitea or Vikunja
comment) is delivered when that item next becomes active. So is a Matrix reply to
a parked item's plan or prompt: the reply relation binds it to that item. Neither
is ever applied to the active item.
- **Autonomy inside an approved unit of work:** all tool/command use auto-approved
(Claude Agent SDK `bypassPermissions` or an allow-all `canUseTool`). Escalation to
the human happens *only* via `ask_human`.
the human happens *only* via `ask_human`. For the Sysadmin Agent, "approved"
means the step list of C2. Only calls that match an approved step are
auto-approved.
- **Kill switch.** The operator command `stop` works in every state, for every
agent. It ends the current turn or delegation at the next tool boundary, unmounts
elevated MCP instances (F3), and moves to `AWAIT_INPUT`. The unfinished delegation
or step sequence is then handled like any other halt: continue, re-plan, or
abandon, on the operator's word. Unattended work needs a way to end it that does
not depend on the model cooperating.
- The loop must survive restart mid-work (relies on A2/A3).
### A5. LLM Access *(tiered)*
@@ -236,8 +389,10 @@ Requirements:
- **No vendor credentials on the agent VM.** LiteLLM holds every key, sourced from
Bitwarden machine accounts (A7); the agent holds one LiteLLM token. Adding a vendor
does not touch the agent VM, and a compromised agent leaks no provider keys.
- Config from env/`.env`, **nothing hardcoded**: base URL, auth token, and the role
mapping (A11). Changing any of it is an env change + restart, nothing more.
- Config from the agent's config file, **nothing hardcoded**: base URL, auth token,
and the role mapping (A11). Secrets in it are references to environment
variables, never values. Changing any of it is a config edit + restart,
nothing more.
- **Budget caps and spend alerts live on the LiteLLM virtual key**, not in agent code.
### A6. Browser Tool
@@ -261,9 +416,13 @@ Requirements:
Requirements:
- Client for **Bitwarden/Vaultwarden** via **machine account / Secrets Manager** (or
`bw` CLI + API key). Not the personal vault.
- Reads only from a **dedicated, minimally-scoped machine account** per agent.
- A single **bootstrap token** (the machine-account credential) is the one secret
that lives on the box: tight file perms, never in image or repo.
- Reads only from a **dedicated, minimally-scoped machine account** per agent, **plus
one per MCP server instance** (F3). Each (service, site, privilege) instance holds
its own upstream credential, so it gets its own machine account, and the agent's
account cannot read an instance's credential.
- Each machine account has a single **bootstrap token**. These are the only secrets
that live on the box: tight file perms, never in image or repo. Each token is
available only to its own container.
- (Deferred, not first cut: dynamic/short-lived secrets via Vault/OpenBao. Leave the
interface able to grow toward this; don't build it yet.)
@@ -298,9 +457,12 @@ by replaying the operational trail:
- `ts` — UTC ISO-8601 timestamp
- `agent` — `nisse-dev` | `nisse-ops`
- `event` — dotted type from the taxonomy below
- `task_id` — correlation ID for the enclosing unit of work, minted when the
unit starts (`use <project>`, milestone start, ticket pickup) and threaded
through every event it causes; `null` only outside any unit (e.g. boot).
- `task_id` — the enclosing work item's ID (0.2), minted when the work item
opens (`use <project>`, ticket pickup, an operator question, a duty's finding)
and threaded through every event it causes; `null` only outside any work item
(e.g. boot, a duty run before it yields a finding, or choosing which item to
switch to). A milestone is a phase
within a work item, so starting one does not mint a new ID.
- `cause` — what triggered the mutation: operator command, approval
(`rev-N` + channel), escalation trigger, reconciliation verdict, or crash
recovery.
@@ -311,6 +473,7 @@ by replaying the operational trail:
|---|---|
| `state.transition` | `from`, `to`, `project` |
| `intent.begin` / `intent.commit` / `intent.abort` | `intent_id`, `kind` |
| `work_item.switch` | `from` (parked or closed item, or `null`), `to`, `reason`, `chosen_by` (`model` / `fallback` / `operator`) |
| `gate.open` / `gate.approve` / `gate.reject` | `gate`, `rev`, `channel` |
| `boot.reconcile` | `action`, per-intent verdicts |
| `schema.migrate` | `from_version`, `to_version` |
@@ -338,6 +501,15 @@ The durable record is a **canonical event log** in SQLite, not a provider-shaped
| `knowledge_retrieved` | document text as read, document ID, revision, retrieved-at (A17) |
Rules:
- **The log is scoped to a work item (0.2).** Every event carries its work item's ID,
and a model call for one work item renders only that item's events. Evaluation
capture (A16) may read across items.
- **Some calls belong to no work item,** and their events go to an **agent-level
scope**, with no work item ID: a duty run until it yields a finding, and the
choice of which item to switch to (0.2). Each such call renders only its own
events plus what code gives it. Agent-level events are never rendered into a
work item's call, and a work item's events reach an agent-level call only as a
code-rendered summary.
- **Canonical tool-call IDs are minted locally.** Provider IDs are mapped at the
boundary and never used as a join key. Providers differ in both ID format and in
where tool calls sit structurally (content blocks vs. a separate array on the
@@ -496,7 +668,13 @@ subsystem.
### A15. Local Model Accommodations
- **Constrained decoding for tool-call JSON.**
- **Constrained decoding for tool-call JSON.** Requested with `tool_choice`
(`required`), and only of models whose LiteLLM entry declares
`supports_tool_choice`: enforcement is the model server's, and asking one that
can't honour it can be worse than not asking. Where it isn't available, the
loop enforces the contract itself: an explicit `finish` tool ends the work, a
prose reply is a violation, and a call to a tool not offered counts as a
failure (A12).
- **Reduced tool schema for the executor role:** fewer tools, flatter arguments, no
deeply nested optionals. Maintain **two tool manifests keyed on role**, rendered at
request time like everything else.
@@ -536,33 +714,24 @@ Part D describes the service; this defines the contract.
desynchronise from the project.
**Agent-owned knowledge is always a cache, never authority.** An agent may rely on
it to know *what exists and where to look*; it must confirm a specific fact
against the live system *before acting on it*. Hard requirement for anything in
C2's confirm-first tier; advisory for read-only and reversible actions.
it to know *what exists and where to look*. It must not rely on it for the facts an
action depends on. A prompt cannot enforce that: the model cannot be prevented from
*believing* a fact it has read. The enforcement is elsewhere, and it is structural:
- **Step preconditions (C2) carry the weight.** Every elevated write is an approved
step, and a step that depends on a fact states it as a precondition, checked
against the live system immediately before the step runs. A stale world model
can produce a wrong plan, but it cannot turn that plan into an action on a
system that no longer matches it.
- **Provenance at the human gate.** When a plan is drafted from recalled facts, the
approval message says so: "target recalled from the world model, observed
2026-08-02" versus "confirmed by `zfs list` two minutes ago". The operator is
the authority there; this makes the evidence quality visible.
- **Audit for the rest.** Retrievals are logged (A10), so a wrong fact can be traced
to the actions that followed it.
**Enforcement is structural, because a prompt is not enforcement.** "Treat this as
a cache" is exactly the advisory guarantee A4 and A12 reject, and the model cannot
be prevented from *believing* a fact it has read. The enforceable goal is
narrower: **an unverified belief must not become an irreversible action.**
1. **The store decides what it serves.** Facts carry provenance and an observed-at
timestamp; retrieval marks or withholds anything past a staleness bound for
high-consequence classes.
2. **Verification as a precondition, checked against the log.** For confirm-first
actions, refuse unless a verifying observation exists *in this task's canonical
log*, recorded after the task began, naming the action's target. Inspectable
without asking the model anything — the same move as A12's observable triggers.
Approximate by nature, so build it for the confirm-first tier only.
3. **Provenance at the human gate** — the primary reliance, because C2 already
stops these actions for confirmation. The prompt states evidence quality:
"target recalled from the world model, observed 2026-08-02, not verified this
session" versus "confirmed by `zfs list` two minutes ago". The operator is
already the authority there; this makes the risk legible rather than invisible.
4. **Audit, not prevention, for the cheap tiers.** Log which facts were retrieved
and which actions followed, so a wrong fact is traceable afterwards.
Residual risk, stated plainly: a model can retype a fact from context with no
retrieval event, and no mechanism here catches that. That is why the world model
stays out of the authority path entirely.
**Dropped:** withholding facts past a staleness bound at retrieval, and refusing
confirm-first actions without a verifying observation in the log. Both were
approximations of what step preconditions (C2) now check directly.
**Never an instruction channel, and never an approval channel.**
- **Task instructions never come from the knowledge store.** Work is driven by
@@ -593,15 +762,14 @@ a model rewriting a precise fact loses precision invisibly.
log** — the text, plus document id and revision for provenance — never referenced
by handle alone: A10 forbids a provider handle being the only reference to content,
and these documents are mutable. Snapshot once per task at first use, not per turn.
**A3 consequence:** a task resumed across a restart carries the old snapshot, so
boot reconciliation re-fetches snapshotted knowledge and flags divergence rather
than silently continuing on a stale copy.
A task resumed across a restart carries the old snapshot; step preconditions, not
a re-fetch, are what stop it acting on a stale copy.
**Retrieval is structural first, discretionary second** — the A12/A14 pattern.
- Procedures are **bound to C2 action classes**: the policy taxonomy and the
procedure index are the same list. Binding is deterministic and auditable
("which procedure governed this action?" has an exact answer), and costs nothing
extra because the taxonomy must exist anyway.
- Procedures are **bound to entries in the C2 policy file** (a tool, or a protected
target), and fetched when a plan that uses that entry is drafted. Binding is
deterministic and auditable ("which procedure governed this action?" has an
exact answer), and costs little because the policy file must exist anyway.
- A model-invoked **search** exists *in addition* for discovery, never as a
replacement for the bound retrieval — a procedure the model may skip does not
constrain it.
@@ -609,10 +777,10 @@ than silently continuing on a stale copy.
**Outage posture is asymmetric.** Unlike B2's poller, "never fails a task" does not
generalise here.
- **Write failure never blocks:** retry, or record locally and reconcile.
- **Read failure for a procedure bound to a confirm-first action gates.**
Proceeding is not a delayed reaction; it is acting unconstrained on precisely the
operation someone wrote a procedure for.
- Read failure for read-only or reversible actions proceeds, logging the gap.
- **Read failure for a bound procedure gates the plan.** Planning without it is not
a delayed reaction; it is acting unconstrained on precisely the operation someone
wrote a procedure for.
- Read failure for anything else proceeds, logging the gap.
**Accepted single point of failure.** Once the agent is the sole author and Outline
the only home, losing Outline loses the world model. No local mirror: infrastructure
@@ -620,7 +788,8 @@ facts are re-derivable by observation, which is the same premise the cache rule
rests on.
**Deferred:** the operator-writable procedures collection. The interface is defined
here; the content and the C2 binding list arrive with the policy taxonomy (Part H).
here; the content arrives when there are procedures worth writing, and binds to the
C2 policy file.
**Concrete mapping onto Outline.** Observed in the running instance: a **collection**
carries per-user permissions with three tiers — visible-to (read), read-write, and
@@ -658,9 +827,9 @@ manager. Permissions are collection-scoped, not per-document.
Outline can expose search, read, create and edit over MCP (remote, Streamable HTTP,
enabled workspace-wide). **Not adopted for the retrieval path**, because A17's
enforcement depends on this library sitting *between* the model and the store: staleness
marking, snapshotting into the canonical log, binding procedures to action classes, and
gating a failed confirm-first read all happen to a retrieval before the model sees it.
contract depends on this library sitting *between* the model and the store:
snapshotting into the canonical log, binding procedures to C2 policy entries, and
gating a failed bound read all happen to a retrieval before the model sees it.
MCP's purpose is the opposite — direct model access — and a procedure the model may
decline to fetch does not constrain it (A12).
@@ -672,12 +841,56 @@ membership, and that the workspace-level toggle does not force broader exposure
intended.
### A18. Prompt Injection
**Assume any model that reads untrusted content may be compromised.** There is no
robust way to tell a model "this is data, not instruction". Instructions and data
reach it as one token stream, and every marker meant to separate them is text that
the data can imitate or argue against. Role separation, delimiters and detection
classifiers lower the success rate of injection. None of them bring it to zero, and
an attacker gets to try repeatedly. So the design does not depend on the model
telling them apart. It limits what a compromised model can do.
Untrusted content, for these agents: alert payloads, log lines, ticket and comment
text, web pages the browser reads, repository contents, and downloaded packages.
Requirements:
- **Gates hold against a compromised model.** Nothing a model says can approve a
plan (A4), widen what a plan authorizes (C2), or reach a credential that was not
mounted (F3). Every one of those rules is enforced in code, outside the model.
- **Approval messages are rendered by code from what will actually run** (C2),
never from a model's description of it. Otherwise a compromised model can write
"restart Gitea" above a step that destroys a dataset.
- **Instruction channels are operator-scoped.** Only allowlisted identities can
drive work: operator commands (A1), PR review comments (B2), action-triggering
tickets (C4). Content from anyone else is data.
- **Parse intake into structured fields in code** wherever the source allows it,
e.g. alert name, severity, host, from Alertmanager's JSON. Less free text then
reaches the model at all, and what reaches it is labelled by where it came from.
- **Some injection is accepted because it can only reorder work.** An alert title
can tell the switch decision (0.2) to pick it first. The worst case is ordering,
and the switch notice makes it visible.
- **Check each agent against the lethal trifecta:** access to private data, exposure
to untrusted content, and a channel to send data out. Damage needs all three.
- `nisse-dev` reads untrusted content and holds little private data: its Gitea
token and the repos it works on. Its only ways out are Gitea and package
registries (B3). The registries are an accepted, narrow residual channel.
Routing them through a local mirror would close it, if that ever becomes worth
doing.
- `nisse-ops` has all three: it reads alerts and logs, holds infrastructure
credentials, and has broad egress. It relies on the gates above, not on
removing a leg.
---
## PART B — THE DEV AGENT / `nisse-dev` (build first)
A thin app over `nisse-core`. Plans and implements software projects; produces PRs.
In 0.2's terms, a **project** below is a dev work item on a repo scope. `use
<project>` opens or resumes a work item on a repo, and `project complete` closes the
work item. The repo, its clone, and any other work items on it continue.
### B1. Workflow (state machine)
```
@@ -700,10 +913,12 @@ PR_OPEN
### B2. Requirements
- **One active project at a time.** Operator selects it.
- **One active project at a time** (park-and-switch, 0.2). The operator selects it,
or the agent switches on its own under 0.2's rules; other projects stay parked at
their last position and resume where they left off.
- **Operator commands** (over transport): `use <project>`, `approve`,
`reject <feedback>`, `start milestone <n>`, `status`, plus free text during
planning (forwarded to the agent as answers).
`reject <feedback>`, `start milestone <n>`, `status`, `stop` (A4), plus free
text during planning (forwarded to the agent as answers).
- **Plan gating** per A4: architecture plan and each milestone plan require sign-off
before the next phase. Enforced structurally. Plans and approvals are externalized
to Gitea issue comments (Part H, decided); Matrix and Gitea are both approval
@@ -746,13 +961,16 @@ PR_OPEN
### B3. Deployment & Isolation
- **Dedicated VM** (Proxmox), not a bare container — the agent builds and runs Docker
containers, so it needs its own real Docker daemon. (Docker-in-a-container via
socket-mount ≈ host root; via dind = nested-storage pain. The VM boundary is the
correct trust boundary for unattended arbitrary command execution.)
- **Sealed to start:** egress allow-list (LiteLLM, Gitea, package registries), **no
ingress**. Egress denial must include **RFC1918 except the specific LiteLLM/Gitea
IPs** — sealed from the internet must also mean no reach to NAS/Proxmox/UDM.
- **Dedicated VM** (Proxmox), as for every agent (F1). The Dev Agent has an
additional reason: it builds and runs Docker containers, so it needs its own real
Docker daemon. (Docker-in-a-container via socket-mount ≈ host root; via dind =
nested-storage pain. The VM boundary is the correct trust boundary for unattended
arbitrary command execution.)
- **Sealed to start:** `nisse-dev`'s egress allowlist (F2) permits LiteLLM, Gitea,
and package registries, and there is **no ingress**. Egress denial must include
**RFC1918 except the specific LiteLLM/Gitea IPs**, and also the tailnet's
**`100.64.0.0/10`**, which is not RFC1918. Sealed from the internet must also mean
no reach to NAS/Proxmox/UDM.
- **The LAN-resident inference host is not a second exception.** It sits behind
LiteLLM (A5), so the allowlist keeps exactly one LLM destination regardless of how
many models or vendors are in play.
@@ -777,7 +995,7 @@ tickets; gated on consequences. Built after the Dev Agent works.
| Axis | Dev Agent | Sysadmin Agent |
|---|---|---|
| Rollback | revert snapshot / discard branch | none for `zfs destroy` or a firewall lockout |
| Trust model | allow-all in sandbox | tiered **allow / deny / confirm** policy |
| Trust model | allow-all in sandbox | **approved step sequences**, checked on every call |
| Work shape | long milestones → a PR | short reactive tasks + scheduled checks → live changes |
| Driver | Gitea projects | **Vikunja tickets** |
| Placement | sealed, own VLAN, no Tailscale | Tailscale, credentials, broad reach |
@@ -787,27 +1005,100 @@ deployments over the same library.
### C2. Permission Policy Engine *(the defining addition)*
Tiered policy, evaluated on every action:
- **Read-only telemetry** (metrics, logs, status) — free.
- **Reversible service actions** (restart a container) — allowed.
- **High-consequence actions** (storage, networking, auth) — **confirm-first** over
transport before executing.
**Every write requires an approved plan first.** Write credentials exist only in the
elevated MCP server instances (F3), and those are mounted only after plan approval.
Read-only telemetry (metrics, logs, status) goes through the read-only instances,
which are mounted by default, so it needs no plan.
This is the sysadmin analog of the dev agent's plan gate: the human boundary sits at
irreversible / high-blast-radius actions.
This is the sysadmin analog of the dev agent's plan gate. The dev agent has a second
gate after the work (the PR); the sysadmin agent does not, because a live change has
no review step before it takes effect. So its plan gate has to be exact.
Special cases fold into the tiers:
- **Browser actions against admin UIs default to confirm-first.** Changes to
**network gear (UniFi) are the most conservatively gated action in the system** — a
bad remote UI click can sever the path used to fix it.
**An ops plan is a list of literal steps, and approval covers exactly those steps.**
A write plan is submitted through `submit_plan` (A4) as structured data, not prose.
Each step names:
- the **tool and target**, e.g. the elevated Proxmox instance for `garrett-hall`,
node and VM ID;
- the **arguments**, in full. A config change carries the exact diff;
- an optional **precondition**, checked against the live system just before the
step runs;
- an optional **expected outcome**, checked just after, e.g. host reachable again,
config hash matches;
- a **retry allowance**, default one.
One approval can cover a sequence with several high-consequence steps: "reboot the
host, change the config, reboot again" is one plan and one `approve`. The plan may
also carry prose explaining it, which is context for the operator, never scope.
**The approval message is rendered by code, from the steps.** What the operator reads
is what will run; a model's description of a step is never the thing approved (see
A18). Steps that are irreversible, that touch a protected target, or that will take
down the agent's own host are flagged at the top of the message.
**Execution is checked against the approved steps.** Every tool call passes through a
matcher before it reaches an MCP instance:
- **A read-only call is always allowed.** The agent can look around between steps.
- **An elevated call runs only if it exactly matches the next unfinished approved
step.** Arguments are normalized against the tool's JSON Schema before comparison
(types, key order), so a model's formatting cannot cause a false mismatch.
- **Anything else halts:** a call not in the plan, different arguments, steps out of
order, or a step run again after it completed. The agent moves to `AWAIT_INPUT`,
and the operator decides whether to continue, re-plan, or abandon.
- **A failed step may be retried** up to its allowance. A failed precondition or
expected-outcome check halts, without retry: the world does not match the plan,
and improvising around that is exactly what the gate exists to prevent.
- **A step that may have interrupted the agent** (a reboot of its own host) resumes
under A3's sequence rule.
**Approvals expire.** An approved plan that has not started within the expiry window
must be approved again. An approval is a judgment about the world as it was when the
operator read the plan. The window is policy data (Part H).
**Classification is small, conservative, and data.** The policy is a declared,
versioned file, like the egress allowlist (F2). It holds:
- **Instance privilege.** Read-only or elevated, per MCP instance (F3). This alone
decides whether a call needs an approved step.
- **Protected targets.** The NAS, the gateways, the hosts the agents run on, and
anything else whose loss is expensive. Protection changes how a step is
presented, not whether it is allowed.
- **Each elevated tool's target argument** (`vmid`, `node`, a device MAC), so the
matcher and the renderer can tell which target a call touches. An elevated tool
with no mapping is treated as touching a protected target.
Defaults fail safe. Anything unclassified is treated as elevated. MCP tool annotations
(`readOnlyHint`, `destructiveHint`) are declared by the server itself, so they may
make a tool stricter, never looser. A model may propose entries for a new server's
tools offline, as a PR the operator reviews. Nothing classifies at run time.
**Deferred, with the reason:**
- **Bounded matching** for reversible tools: allowing any call within an approved
target and action, not only the exact arguments. Added one tool at a time, where
exact matching causes real friction. It needs a per-tool reversibility
classification, which is the up-front cost exact matching avoids.
- **Standing approvals:** a plan template with a trigger, a rate limit and an
expiry, e.g. "restart Gitea on sustained 502, at most twice an hour, notify
after". The same matcher, run without a fresh `approve`. Added when a routine fix
has been approved by hand often enough to show its shape.
Special cases:
- **Browser actions against admin UIs cannot be matched as steps,** since a click is
not a stable literal. Each one is confirm-first over transport, even within an
approved plan. Changes to **network gear (UniFi) are the most conservatively
gated action in the system**: a bad remote UI click can sever the path used to fix
it.
- **Action-triggering tickets are operator-scoped** (C4). Auto-generated tickets
(e.g. from a monitoring alert) route through confirm-first, never drive unattended.
(e.g. from a monitoring alert) can only lead to a plan, which the operator
approves like any other. They never drive unattended.
### C3. Interfaces
- **MCP servers (F3):** Proxmox and UniFi, at both sites, through per-site,
per-privilege server instances on the agent's own Docker host. This is the primary
path to that infrastructure.
- **Browser (A6):** into admin UIs. **Prefer real APIs where they exist** (Proxmox,
TrueNAS good; UniFi partial); use the browser only for genuinely UI-only gaps —
it's the least stable interface.
TrueNAS good; UniFi now reachable over MCP); use the browser only for genuinely
UI-only gaps. It's the least stable interface. For network gear, it is a fallback
for API gaps, not the primary path.
- **SSO (A8):** logs into admin UIs as `svc-nisse-ops`. This account is a high-value
credential; its blast radius equals its Authelia scope, so scope it minimally.
- **Secrets (A7):** UI/SSO logins and service API tokens from Bitwarden.
@@ -818,8 +1109,12 @@ Special cases fold into the tiers:
### C4. Ticket-Driven Control (Vikunja)
The sysadmin analog of "dev agent driven by Gitea."
- **Vikunja ticket = task of record** (durable, has status/queue/history). **Matrix =
live conversation about it** (questions, confirm-first prompts, progress).
- **Vikunja ticket = task of record** (durable, has status/queue/history): the
external record of a work item (0.2). **Matrix = live conversation about it**
(questions, halts, progress).
- **Scheduled checks and alert intake are duties (0.2).** Tickets they generate are
duty findings, deduplicated against open tickets, and like any auto-generated
ticket they can only lead to a plan the operator approves (C2).
- **Flow:** ticket filed → agent picks up → discuss/confirm over Matrix → update
status as it progresses → close with result.
- **Mechanics:** Vikunja REST API + webhooks (same two-direction, JSON-only pattern
@@ -827,16 +1122,19 @@ The sysadmin analog of "dev agent driven by Gitea."
installed version's webhooks fire on comment creation; if not, poll comments as
B2 polls Gitea.
- **Approvals:** Vikunja is an `ApprovalSource` (A4) for plan gates on its own
tickets, via `approve rev-N` comments. Live **confirm-first** prompts (C2) stay
Matrix-primary — polling latency makes a ticket comment a poor interactive
channel — with the outcome written back to the ticket as the record.
tickets, via `approve rev-N` comments. Live prompts (C2's halts and browser
confirmations) stay Matrix-primary — polling latency makes a ticket comment a poor
interactive channel — with the outcome written back to the ticket as the record.
- **Trust:** a driving ticket is an instruction channel into an infrastructure-
changing agent → action-triggering ticket creation is operator-scoped (C2).
### C5. Deployment & Isolation
- Longer-lived, **stateful VM** (not disposable).
- Longer-lived, **stateful VM** (not disposable), dedicated to this agent (F1).
- Has **Tailscale** + scoped credentials to reach infrastructure across both sites.
- **Its MCP servers (F3) run on its own Docker host.** Its egress allowlist (F2)
therefore includes the Proxmox and UniFi endpoints at both sites, even though the
agent process never opens those connections itself.
- Updated **in place** via the container pipeline, **restart gated on an idle flag**.
---
@@ -899,7 +1197,89 @@ each agent is built — not standing up the service itself.
---
## PART F — DEPLOYMENT MODEL (both agents)
## PART F — DEPLOYMENT MODEL (all agents)
These rules apply to every agent, current and future. Parts B3 and C5 record how each
existing agent instantiates them.
### F1. Agent hosts: every agent gets its own VM
- **Every agent gets its own VM** (Proxmox). No agent runs as a container on a shared
host, and no two agents share a VM.
- **Why, in general:**
- *Isolation boundary between agents.* The VM is the trust boundary (Part H). A
compromised or misbehaving agent must not reach another agent's state,
credentials, or processes.
- *Per-agent credential attribution.* Everything an agent holds lives on its own
host and is issued to its own identity, so every upstream action traces back to
exactly one agent.
- **Agent-specific reasons add to this; they are not its foundation.** The Dev Agent
also needs a real Docker daemon, because it builds and runs containers (B3). An
agent that builds no code still gets a VM, for the two reasons above.
### F2. Egress policy: per agent instance
- **Every agent instance has its own default-deny allowlist.** Requirements differ by
agent type. No agent inherits another agent's policy.
- **The allowlist is a declared artifact,** versioned alongside the agent's
definition. Each entry is justified by the component that needs it.
- **The declaration point is fixed. The enforcement point is not.** Policy may be
enforced at the VM, at the Docker network, at the UniFi firewall, or at several of
these at once. Layering is deliberate: enforcement at the network layer survives
compromise of the agent's own host.
- **The allowlist is the union** of what the agent reaches directly and what its MCP
servers (F3) reach on its behalf. `nisse-ops` must permit Proxmox and UniFi
endpoints at both sites, even though the agent never opens those connections
itself.
- **The tailnet is `100.64.0.0/10`** (CGNAT shared address space), not RFC1918. A
rule written around RFC1918 denial does not cover it. Deny it explicitly.
### F3. MCP servers
**One instance per agent VM.** MCP servers run as containers on the owning agent's
Docker host. Images are shared through the OCI registry. Processes are not shared
between agents.
- *A shared process means a shared credential.* The upstream API's own audit log
then cannot attribute a call to a specific agent. Separate instances with separate
tokens give attribution at the API layer, instead of depending on our own
transcripts. This matters most for a read-only auditing agent, such as a future
`nisse-security`.
- *An agent should not host the tooling used to inspect it.*
- *Duplication is nearly free.* These servers wrap remote HTTP APIs and hold no
state. (Stateful servers are the exception that could justify relocation. That
item is parked in Part H.)
**One instance per (service, site, privilege level).**
- **Site belongs in the server's identity,** using the site identifier (Part 0), not
in a `site` tool argument. A wrong-house action should require a tool that isn't
mounted, not just a wrong parameter value.
- **Read-only and elevated access are separate instances** with separate upstream
credentials, each from its own machine account (A7). The read-only instance is
mounted by default. The elevated instance is mounted only after plan approval (C2).
This puts the capability gate at the credential layer, instead of a flag inside a
third-party server.
- **Per-task tool profiles** mount only the servers that a given task's scope
requires.
#### Rejected alternative: routed meta-tools / lazy tool registration
Several community Proxmox and UniFi MCP servers use search-then-dispatch over a large
generated tool catalog. This is **rejected in favor of per-task tool profiles**:
- *Two extra round-trips per call,* which is worse with a local model driving the
control loop.
- *The model can no longer discover capabilities it wasn't already looking for.*
That is backwards for exploratory diagnostic work.
- *Arguments arrive as an opaque blob,* instead of being constrained by per-tool JSON
Schema.
- *A dynamic tool list defeats prompt prefix caching,* and caching is much of what
makes a large static list affordable.
Acceptable only when consuming a third-party server whose surface we can't trim.
### F4. Containers, images, and restarts
- **Containers + persistent volumes.** Stateless image; named volumes carry state
across redeploys (and allow optional persistence between deployments).
@@ -907,6 +1287,9 @@ each agent is built — not standing up the service itself.
- **Gate the restart, not the pull:** an idle/"safe-to-restart" flag; Watchtower must
never restart mid-work. *Unplanned* interruptions (reboot/crash) are handled by the
durability layers (A2/A3), not by restart timing.
- **Carve-out: third-party MCP server images** (F3) are excluded from Watchtower.
They hold root-equivalent credentials to Proxmox and UniFi, so they are
digest-pinned and updated deliberately after review.
- **Survive reboots:** VM + Docker service set to start on boot; `restart:
unless-stopped`; ZFS snapshots of the state dataset (scheduled + on each approval).
- **Terraform + cloud-init:** deferred. When adopted, `bpg/proxmox` + cloud-init,
@@ -936,7 +1319,7 @@ each agent is built — not standing up the service itself.
10. **Extract `nisse-core`**; build the **Sysadmin Agent** (`nisse-ops`): policy
engine (C2), SSO + scoped browser (A8/A6), secrets (A7), Vikunja control (C4),
knowledge store (A17/D) — world model first, operator procedures when the
C2 taxonomy they bind to exists — scheduled checks, alert intake.
C2 policy file they bind to exists — scheduled checks, alert intake.
- With the monorepo layout (Part H, decided), extraction is a **package split**
within one tree: move code behind a package boundary, no release machinery
required.
@@ -976,10 +1359,24 @@ The rest resolve when reached, not now.
are still moving. One repo can still build both agent images via path-filtered
CI. Splitting later stays easy if a real need appears; merging later would not
have been.
- **Sysadmin browser access to network gear:** standing vs. per-task hand-off. The
lock-yourself-out risk argues for per-task.
- **Policy action taxonomy (C2):** the concrete list of which operations are
allow vs. confirm. Best decided with the agent half-built in front of you.
- **Sysadmin fallback browser access to network gear:** standing vs. per-task
hand-off. With UniFi reachable over MCP (F3), the browser is no longer the primary
path to network gear. It is a fallback for operations the UniFi API does not
expose. The open question is whether that *fallback* access is standing or handed
off per task. The lock-yourself-out risk still argues for per-task.
- **Ops gating model (C2) — decided: approved step sequences, exact matching.** A
per-tool allow/confirm taxonomy was the original plan. It would have to be
written in advance for a tool catalog that will grow large, and every mistake in
it is either friction or a hole. Approving literal step sequences makes most of
it unnecessary: every elevated write has to be an approved step anyway, and
classification is left to decide only how an approval is *presented* (protected
targets, irreversible steps). What remains open is data, best filled in with the
agent half-built in front of you: the protected-target list, each elevated
tool's target argument, and the approval expiry window.
- **Parked: bounded matching and standing approvals (C2).** Both loosen exact
matching and reuse the same matcher. Add bounded matching one tool at a time,
where exact matching causes real friction. Add standing approvals when a routine
fix has been approved by hand often enough to show its shape.
- **Approved-plan externalization — decided: Gitea-backed from the start.** Plans are
posted as issue comments on the project repo; the approval comment (author,
timestamp, revision tag) is the durable off-box record, authenticated by Gitea's
@@ -987,6 +1384,36 @@ The rest resolve when reached, not now.
so boot-time reconciliation (A3) can re-derive gate state from Gitea even after
state-volume loss — external truth wins (A2). Approval mechanics and revision
binding are specified in A4.
- **Parked: relocating stateful MCP servers off the owning agent's VM.** The trigger
is **statefulness** (a browser pool, a vector index, a warm cache), not sharing:
duplicating a stateful server duplicates its state. Relocating has a cost. A shared
server can't hold per-agent tokens, so per-agent identity has to move into the
request, through caller auth plus per-caller upstream credentials. That loses the
attribution property that makes instance-per-agent-VM (F3) clean.
Instance-per-agent-VM is the starting position. Migrate one server at a time, when
a specific server justifies it.
- **Parked: more than one active work item (0.2).** Park-and-switch is the starting
position. The pressure to relax it will come first from the Sysadmin Agent: an
alert arriving mid-diagnosis, or a duty's scheduled run coming due while a work
item is active, has to wait for a safe point. The agent's own switch (0.2) takes
the edge off: an alert waits for an item that is working, not for an operator
who is away. Allowing concurrent items would cost:
- *Serialization in the state store and control loop.* Transitions for different
items interleave, and the single orchestrator lock becomes per-item.
- *A scheduler.* Something has to decide which item's turn runs, especially with
one local inference host serving all of them.
- *Approval binding.* Two plans or halts open at once make a bare `approve`
ambiguous everywhere, so per-item rooms or strict reply threading become
mandatory rather than convenient.
- *Shared-scope conflicts.* Two active work items on one repo need separate
working trees. Two on one infrastructure scope could make changes that
interfere with each other.
- *Intent ownership.* Boot reconciliation has to sort pending intents by item, and
refusing to switch no longer keeps them single-item.
- **Open: when a work item must have an external record (0.2).** Plan-gated dev work
and ticket-driven ops work already require one. Whether every gated work item
needs one (for instance, an ops question that turns into a change to make)
is best settled once C2's step sequences are in use.
---
@@ -1011,9 +1438,10 @@ nisse-dev/ # app: sealed VM (Part B)
browser → internal test network only
nisse-ops/ # app: separate host, Tailscale (Part C)
policy engine, tiered confirmation, scheduled checks
policy engine (approved step sequences, C2), scheduled checks
Vikunja ticket intake, Outline runbooks/records
browser → admin UIs (API-first, UI fallback), confirm-first gating
MCP → Proxmox/UniFi per site and privilege (F3); runs on its own VM
browser → admin UIs (API-first, UI fallback), confirm-first per action
infra/ # (future) Terraform + cloud-init (Part F)
nisse-dev.tf | nisse-ops.tf | templates/
docs/Tuning-Guide.md
+515
View File
@@ -0,0 +1,515 @@
# Tuning Guide
How to fit a local model onto the inference host, and how to set the agent's
tuning knobs. Part 1 covers the ideas you need to reason about the hardware.
Part 2 covers each knob in `nisse-dev.yaml`. Part 3 gives an order to tune
in.
The knobs live under `tuning:` in the config file (see
`nisse-dev.example.yaml`). They are read at startup, so a change takes effect
on the next restart. Unknown or misspelt keys are startup errors.
---
## Part 1 — How a local model spends VRAM
A model server holding a model in GPU memory spends it on three things:
1. **Weights**, which are fixed for a given model and quantization.
2. **The KV cache**, which grows with context length.
3. **Working memory** for activations and runtime buffers. Budget 1–2 GB of
headroom.
On a 24 GB card, the whole game is trading (1) against (2).
### Weights and quantization
A model's weights take roughly *parameters × bits per weight ÷ 8* bytes.
| | 16-bit (full) | 8-bit | ~4.5-bit (Q4_K_M, AWQ) |
|---|---|---|---|
| 24B | 48 GB | 24 GB | ~14 GB |
| 32B | 64 GB | 32 GB | ~19 GB |
Quantization stores each weight in fewer bits. Common 4-bit formats come out
nearer 4.5–5 bits per weight once scaling factors are counted, so check the
actual file size. The quality cost is small at 5–6 bits, usually acceptable at
4, and it climbs quickly below that. For agent work the cost shows up first as
**worse tool-call discipline**, not as worse prose. The capability matrix
(Part 3) measures exactly that.
**Mixture-of-experts** models such as Qwen3-30B-A3B are different. Memory is
set by *total* parameters (30B), but speed is set by the parameters *active*
for each token (3B). An MoE model of a given size therefore generates much
faster than a dense model of the same size. It's often weaker than a dense
model of the same total size, but not always for tool use.
### The KV cache, and why context costs memory
As the model reads the prompt, every layer stores a key and a value vector for
every token, so that later tokens can attend to them without recomputing.
That store is the KV cache. Its size per token is:
2 (key and value) × layers × KV heads × head size × bytes per value
The numbers come from the model's `config.json` (`num_hidden_layers`,
`num_key_value_heads`, `head_dim`). Some worked examples at 16-bit:
| Model | Layers × KV heads × head size | Per token | 32k tokens |
|---|---|---|---|
| Qwen3-32B | 64 × 8 × 128 | 256 KB | ~8 GB |
| Mistral/Devstral Small 24B | 40 × 8 × 128 | 160 KB | ~5 GB |
| Qwen3-30B-A3B (MoE) | 48 × 4 × 128 | 96 KB | ~3 GB |
(Taken from the published configs; recheck against the model card before
relying on them.)
Two consequences:
- **Context length is a memory decision, not a free setting.** The spec's
estimate (Part H) was about 5 GB left for KV cache beside a 4-bit 32B model.
At 16-bit, that's only about 20k tokens of context for Qwen3-32B.
- **KV-cache quantization** stores keys and values in 8 bits (or 4). At 8
bits it halves the cache and is close to lossless. At 4 bits it is
noticeably worse. Quantizing the cache to 8-bit is usually the first lever
to pull, since it buys twice the context for almost nothing.
The server allocates the cache for the context length you configure. It
doesn't matter how much of it a request uses. So set the context length on the
server deliberately: it is the ceiling for *prompt plus response* on every
request.
### Prompt processing vs generation
A request has two phases, and they run at very different speeds:
- **Prefill** reads the prompt. It is parallel and fast: thousands of tokens
per second on a 4090.
- **Decode** writes the response one token at a time. Roughly 30–40 tokens per
second for a dense 32B at 4-bit, and several times that for a 3B-active MoE.
Treat these numbers as ballpark; measure your own.
An agent loop re-sends the whole transcript on every turn, which makes the
prompt long and the responses short (a tool call is often under 200 tokens).
So prefill matters more than the headline tokens-per-second suggests.
**Prefix caching** is the fix. If the start of a prompt matches the previous
request, the server reuses the KV cache for it and prefills only the new part.
llama.cpp does this per slot, and vLLM does it automatically. For this
workload it is often the single largest speed win. It works here because the
rendered transcript only grows at the end, so consecutive turns share
everything but their newest events.
### Tokens and characters
For English and code, a token is roughly **4 characters**. That's useful for
converting the character-based knobs below into context cost.
---
## Part 2 — The knobs
For each knob: what it controls, what goes wrong at either extreme, and what
to look at when deciding.
### `llm.max_output_tokens` — default 16384
The ceiling on a single response, for every role. The ceiling actually sent
is the smaller of this and the model's own `max_output_tokens` in LiteLLM, so
a local model can be held to less without affecting the frontier roles.
- **Why it exists:** a response that hits the ceiling is truncated
mid-sentence, or worse, mid-tool-call, where it can still parse. The agent
detects truncation and treats it as an error, but a ceiling that's too
tight still costs a failed turn. Planning with a frontier model was observed
using over 4,000 output tokens.
- **Too high, on a local model:** the server reserves room for the whole
response inside the context window. With a 32k context and a 16k ceiling,
only 16k is left for the prompt. Some servers reject any request where
prompt plus ceiling exceeds the context.
- **Too low:** truncated plans and tool calls.
- **Deciding:** executor turns are short, so 2–4k is plenty for a local
executor. Frontier planning wants the large default. Leave this high, and
set a local model's smaller ceiling as its `max_output_tokens` in LiteLLM.
### `llm.request_timeout_s` — default 600
How long one model request may take before it is abandoned. It applies to
every request, on both routes.
- **Too low:** a slow model's long answer is cut off, and the executor
escalates work that was going well. At 120 s, the old fixed value, a dense
27B model on the RTX 4090 timed out writing one module in one turn.
- **Too high:** a hung connection holds the milestone for that long before
anything notices.
- **Deciding:** this is a safety net, not a budget. `max_output_tokens` and
`max_turns` bound the work; set this above the slowest single answer you
expect, which is the model's output ceiling divided by its generation
speed.
### `tuning.executor.max_turns` — default 40
How many model calls one milestone may make before the executor stops with
`capped` and asks you what to do.
- **Too low:** milestones that were making progress stop short. You'll see
`capped` outcomes on work that was going well.
- **Too high:** a confused model keeps going. Every turn also adds its tool
calls and results to the transcript, so a long run spends context as well
as time.
- **Deciding:** look at how many turns *successful* milestones take, and set
the cap comfortably above the high end, around 1.5–2× the turns of your
longer successful runs. A stronger model usually needs fewer turns, not
more.
- **Measured:** the default was 24 until the harder benchmark tasks arrived.
Haiku, used as the ceiling in calibration, hit 24 on both runs of
`refactor-rename` (editing ten files one at a time) and of
`long-milestone`, one of which had in fact finished the work. It is a guard
against a model trying forever, not a budget, so it was raised to 40.
### `tuning.executor.failure_limit` — default 2
How many consecutive failed `run_command` calls trigger escalation: the
sub-task is handed to a frontier model as a delegation (spec A12). A
successful command resets the count. Failed file reads don't count, since
reading is looking around, not doing the work.
- **Why it's a count and not a judgment:** small models are poorly calibrated
about their own competence. Asked "are you stuck?", they say no until far
too late, or yes constantly. A count of observable failures is a signal
that doesn't depend on the model's opinion of itself.
- **Too low:** normal iteration escalates. Test-first work *expects* a failing
test run before the fix, so a limit of 1 hands off routine work and spends
frontier tokens on it.
- **Too high:** a weak model circles, re-running the same failing command
with small variations, while the transcript fills up.
- **Deciding:** raise it as local models get stronger. Watch two things
together. First, how often delegations fix what the local model couldn't
(escalation is doing its job). Second, how often the local model recovers
on its own after its second failure (the limit may be too low).
### `tuning.expert.consult_budget` — default 3
How many `ask_expert` calls one milestone may make. `ask_expert` is a single
stateless question to a stronger model, with no access to the repository
(spec A14), for things like "is this API deprecated?".
- **Too low:** the executor can't get unstuck on a knowledge question it
can't answer from the code.
- **Too high:** a confused executor asks variations of the same question
instead of doing the work or failing. That's the circling A12 warns about.
- **Deciding:** count consults per milestone, and check whether the answers
changed what the executor did next. If it regularly exhausts the budget
without progress, the budget is fine and the model or the plan is the
problem.
### `tuning.delegation` — hard caps on one delegation
A delegation is a frontier model working a sub-task with full tool access
(spec A13). When a cap is hit, the delegation **fails**, and its branch and
work are discarded, because resuming on a half-edited tree isn't safe.
| Knob | Default | Caps |
|---|---|---|
| `max_turns` | 30 | model calls |
| `max_tokens` | 200000 | input + output tokens, as reported by the provider |
| `wall_clock_s` | 900 | elapsed time |
- **`max_tokens` is a scope limit, not the money limit.** Spending limits live
on the LiteLLM key (spec A5). This cap stops one delegation from growing
without bound. An unbounded one could still exhaust a daily budget and take
every frontier model away until it resets.
- **Too low:** delegations fail just short of finishing and their work is
thrown away. That is the most expensive way to be wrong, because you paid
for the work.
- **Too high:** a stuck delegation burns tokens and time before failing.
- **Deciding:** every delegation records the tokens it used and how long it
ran (the `delegation_completed` event, captured for evaluation per spec
A16). Set each cap above what successful delegations actually use, with
room to spare.
### `tuning.tools.command_timeout_s` — default 120
How long one `run_command` may run before its whole process group is killed.
- **Too low:** slow test suites, dependency installs and Docker builds get
killed and reported as failures. That also counts toward `failure_limit`.
- **Too high:** a command that never exits blocks the turn for the whole
timeout, such as a dev server started in the foreground or a test waiting on
input. `stop` waits for a running command to finish too, so this is also the
worst-case delay before a stop takes effect.
- **Deciding:** set it just above the slowest legitimate command in the
projects the agent works on.
### `tuning.tools.max_output_chars` — default 4000
How much of a tool's output (command output, file contents, directory
listings) the model sees. Longer output keeps its first quarter and last three
quarters, with a note in between saying how much was left out. The end gets
the larger share because test runners and compilers print their summary of
what failed last; the start keeps the exit code.
- **This is a context knob.** Every tool result stays in the transcript and is
re-sent every turn. 4,000 characters is about 1,000 tokens, so 20 turns of
maximal output is about 20k tokens, which is already most of a local
model's context.
- **Too low:** the model doesn't see the part of the output that matters.
- **Too high:** the transcript outgrows the context window sooner. The
context budget copes (see below), but by cutting older output harder.
### `tuning.context` — fitting each request into the window
Before any request is sent, the agent checks it against the model's context
window and makes it fit. A server doesn't always refuse a prompt that's too
long. Ollama silently drops its start, which is where the system prompt and
the plan are, so an unfitted request can fail in a way nobody sees.
The window is each model's `max_input_tokens` in LiteLLM (see *Declaring a
local model's limits* below). A model in use without one stops the agent at
startup. From the window come the output ceiling and a safety margin, and
the rest is for the prompt. When the prompt is too big, three steps apply in
order, stopping at the first that fits:
1. **Older tool results are cut** to about 600 characters, head and tail.
The most recent turns keep their output whole.
2. **The oldest turns are dropped,** tool calls together with their results.
A note at the start tells the model how many events were left out. The
log keeps everything; only the request is shortened.
3. **If the system prompt and the recent turns still don't fit,** the
request is not sent. The milestone stops and asks you.
Tokens are estimated from characters (pessimistically, at 3 per token) and
the estimate is corrected from the prompt sizes the server reports. The
correction only ever makes the estimate larger.
The executor also sends less in the first place: the approved milestone plan
goes in its system prompt, which is never cut, and it sees only what happened
since that plan. The planning conversation and earlier milestones stay in the
log.
| Knob | Default | |
|---|---|---|
| `keep_recent_turns` | 6 | model turns that are never cut or dropped |
| `safety_margin` | 0.1 | fraction of the window held back for estimation error |
- **`keep_recent_turns` too low:** the model loses sight of what it just did
and repeats itself. **Too high:** less room to cut before step 3 gives up;
on a small window, overflows come sooner.
- **`safety_margin` too low:** an underestimate reaches the server, and a
silently cutting server drops the start of the prompt. **Too high:** wasted
context. Lower it only once the correction from reported usage has settled,
which takes a few calls per model.
- **Deciding:** if milestones stop with "cannot fit", look first at
`tools.max_output_chars` and the model's window, not at these.
### Enforced tool calls — `supports_tool_choice`, declared in LiteLLM
The agent's loops depend on every turn being a well-formed call to a real
tool. A model only *writes text*: a "tool call" is text in a format the model
was trained to produce, which the server recognises and turns into a
structured call. By default (`tool_choice: auto`) the server just parses
whatever comes out, so prose, broken JSON and calls to invented tools are all
possible.
With `tool_choice: required`, a server that supports it **constrains
decoding**: at every step it rules out any token that couldn't lead to a
valid call to one of the offered tools, with arguments matching its schema.
The model still chooses the tool and the arguments; it can't produce anything
else. That makes it a property of the **model server** (and sometimes of the
model), not something a prompt can buy.
So the agent asks for it only where it is known to work: models whose LiteLLM
entry declares `supports_tool_choice: true`. Measured on the 4070:
| Alias | Server | `required` on a chat prompt | Declare |
|---|---|---|---|
| `local-fast` (Qwen3-8B-AWQ) | vLLM 0.29 | calls `finish` every time | `true` |
| `local-coder` (Qwen3-14B) | llama.cpp | can't stop; writes until the output ceiling (a known Qwen3-14B bug) | `false` |
| `claude-haiku-4-5` | Anthropic | calls `finish` every time | `true` (LiteLLM already says so) |
Asking a model that can't honour it can be worse than not asking: the
Qwen3-14B turn above costs a full output ceiling of generation, where a plain
prose reply would have cost a few hundred tokens.
Where it isn't enforced, the executor holds the contract itself:
- **`finish` is the only way to end a milestone.** It is refused once if
nothing has been changed yet.
- **A prose reply is a violation.** The model is told once; a second escalates.
- **A call to a tool that wasn't offered counts as a failure** toward
`failure_limit`, like a failed command, so an inventing model escalates in a
couple of turns instead of using up `max_turns`.
The capability matrix checks the declaration: a model declared `true` that
answers a required call in prose fails
`test_supports_tool_choice_is_declared_truthfully`.
### Declaring a local model's limits
LiteLLM knows commercial models' limits. For a local model, declare them in
LiteLLM's config as the model's `model_info`:
```yaml
model_list:
- model_name: local-fast
litellm_params: {model: openai/qwen3-8b-awq, api_base: http://inference-host:8080/v1}
model_info:
max_input_tokens: 32768 # the server's per-request context
max_output_tokens: 4096 # the most one response may use
supports_function_calling: true
supports_tool_choice: true # only once measured; see above
```
`max_input_tokens` is the *whole* window, prompt plus response. The agent
takes the output ceiling out of it. That's how the commercial numbers work
too: Claude's prompt and response together must fit in its 200k.
The number to use is what the **server** allocates per request, not the
model's advertised maximum:
- **The model's ceiling:** `max_position_embeddings` in `config.json`, or
`<arch>.context_length` in a GGUF file. Some models reach further with RoPE
scaling; the model card says whether that needs switching on.
- **llama.cpp / llama-swap:** the `-c` / `--ctx-size` flag, shown as `n_ctx`
in the load log and in `GET /props`. With `--parallel N` each request gets
`n_ctx / N`, so run the agent's models with `--parallel 1`.
- **vLLM:** `--max-model-len`, reported by `GET /v1/models`.
- **Ollama:** 4096 unless `OLLAMA_CONTEXT_LENGTH` or a Modelfile's `num_ctx`
says otherwise. `GET /api/ps` shows the loaded model's `context_length`;
`ollama show` shows only the trained maximum.
Then check the declaration against the server:
uv run python scripts/probe_context.py local-fast
It sends one prompt half again as long as the declared window and reports
whether the server refused it, cut it (and where), or took it all. A cut
below the declaration is the case to fix: prompts between the two would be
silently cut. Measured on the Jetson's `llama3.2`: cut at 4096, matching its
declaration.
---
## Benchmarking models
The capability matrix asks whether a model *can* hold the tool contract. The
benchmark asks whether it *finishes real work*: eight canned milestones, each an
approved plan over a small seed project, judged by a hidden check the model
never sees. Success means the check passes, not that the model said it was
done.
| Task | What it exercises |
|---|---|
| `write-function` | write a small function and its tests |
| `fix-bug` | find and fix the bug a failing test exposes, without touching the test |
| `multi-file` | thread a new option through several files |
| `long-output` | find one failure in test output far longer than `max_output_chars` |
| `trace-bug` | follow a wrong total to its cause in a shared helper, and fix it there rather than at the symptom |
| `refactor-rename` | rename a function across a ten-module package, sparing two similar names a blind replace would break |
| `spec-parser` | implement a parser from an eight-rule written spec when the plan gives only three examples, with tests |
| `long-milestone` | carry out eight dependent steps (model, storage, sorting, CLI) over 25+ turns |
The first four were the original set. By September 2026 all seven newer local
models passed all of them, so the last four were added to separate models that
can hold the tool contract from models that can do a milestone's real work.
Their plans stay explicit, as the planner is told to write them; what makes
them harder is the work itself: tracing a cause, searching a codebase,
reading a spec closely, and holding a plan over many turns.
It runs the executor exactly as the agent does (the same client wiring,
context budget and `tool_choice` handling, and your config's tuning knobs),
with **delegation and `ask_expert` turned off**. An escalation is a failure of
the model under test, not a frontier model's success.
**Evaluating a new model** is one command, once its LiteLLM entry declares its
limits:
scripts/evaluate_model.sh qwen3-32b-q4 "vLLM 0.29, AWQ, fp8 KV" 5
It runs the context probe, then the capability matrix, then five runs of
every task, and stops at the first step that fails. Or run the steps
yourself:
uv run python scripts/benchmark.py run --models A,B --runs 5 --note "how it's served"
uv run python scripts/benchmark.py report [--models A,B] [--tasks T]
**Results are kept.** Every run is one line in `bench/results/`, committed
with the repo, recording:
- the model's backing name and declared facts;
- your note on how it was served;
- the task's content hash;
- the agent's commit and tuning;
- the outcome, turns, failures, tokens, time and cost.
The report rebuilds the table from every stored run. Benchmarking a new model
means running only that model and reading it next to everything before it.
Runs against an older version of a task are marked `(old)`. Runs of one model
served differently (different note) are kept apart.
**Reading it:**
- **Compare the intervals, not the rates.** 3 of 5 (23–88%) and 5 of 5
(57–100%) overlap: five runs don't separate them. Add runs until the
intervals stop overlapping, or accept that the models are close.
- **Look at why runs failed**, not just how many (`outcome_note` and the
counts in each result line). An early shakedown on `local-fast` failed
mostly by escalation: the model was fixing its own mistakes and hit
`failure_limit: 2` mid-recovery. That says as much about the knob as about
the model. Tuning a knob and re-running is a fair comparison, because the
tuning is recorded with every run.
- **Cost** is per run, straight from LiteLLM, so a frontier model as executor
can be compared on success rate *and* dollars.
---
## Part 3 — An order to tune in
Upstream choices change what's right downstream, so tune in this order and
change one thing at a time.
1. **Model and quantization.** Start with whether a candidate holds the
tool-call contract at all, and whether its server enforces `tool_choice`:
uv run pytest -m integration tests/integration/test_model_capability.py \
--capability-models=alias-a,alias-b
A model that falls back to prose instead of calling tools is disqualified,
however good its benchmarks look. Before comparing aliases, check what
each one actually points at in LiteLLM (`/model/info`, the
`litellm_params.model` field). Two aliases believed to be the same model
once weren't, and that cost a lot of debugging.
**Make sure LiteLLM has no fallback for a model you are measuring.** A
fallback answers a failed request with another model, under that model's
name, in a response that otherwise looks normal. One made a local model
that couldn't call tools at all look like it "held the tool contract 20 of
20": every answer was Claude's. The capability matrix and the probe now
check which model answered and fail on a mismatch, but the fallback also
quietly turns local work into frontier spending in production.
2. **Context length and KV-cache precision** on the server. Take the largest
context that fits beside the weights with 1–2 GB of headroom. Try an 8-bit
KV cache before giving up context.
3. **Declare the window** in LiteLLM and check it with the probe, then set
the model's `max_output_tokens` there so the prompt keeps most of the
window.
4. **The benchmark.** Run it and look at the evidence: success rate, turns,
escalations, and time taken.
5. **The loop knobs**: `failure_limit`, `max_turns`, `consult_budget`, the
delegation caps, then the tool limits, one at a time.
**Expect noise.** Local model behaviour varies a lot from run to run. One
early comparison measured 12 of 20 runs holding the tool contract against 20
of 20, a real difference (p ≈ 0.003). Three runs each could easily have
pointed the other way. Before changing a knob because of a result, repeat the
run enough times that the result would survive a coin flip.
---
## Coming next
Later work that will add knobs, each with its own section here:
- **Compaction.** A model-written summary replacing old turns, instead of
dropping them. The log already has an event for it; it needs a summarizing
model and a policy for what is safe to summarize.
- **Benchmarking the other roles:** planner and delegate, with delegation
turned back on for comparisons across frontier tiers.
nisse-dev.example.yaml
+63
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@@ -0,0 +1,63 @@
# Example configuration for nisse-dev. Copy to nisse-dev.yaml (or point
# NISSE_CONFIG at your copy) and edit.
#
# - Structure lives here. Secrets do not: write them as ${VARIABLE} references,
# resolved from the environment (or .env, see .env.example) at startup.
# - Keep this file outside the workspace clone. Startup refuses otherwise,
# because the agent commits and pushes everything in the clone.
# - Unknown keys are errors, so a misspelt knob fails at startup instead of
# silently doing nothing.
state_db: ~/nisse-demo-workspace/state.db
workspace: ~/nisse-demo-workspace/project # the clone
project: demo
llm: # A5: LiteLLM is the only LLM destination
base_url: http://litellm.example:4000
token: ${NISSE_LLM_TOKEN}
# Ceiling on each response. The model's own max_output_tokens in LiteLLM
# caps it further, which is where to hold a local model to less.
max_output_tokens: 16384
# How long one request may take before it is abandoned. A safety net for a
# hung connection; slow local models can take minutes over a long answer.
request_timeout_s: 600
roles: # A11: role -> LiteLLM model alias
executor: local-fast # the local tier; the benchmark picks the model
planner: claude-opus-5-5 # high-end judgement: plans a weaker executor follows
delegate: claude-opus-5-5 # takes over what the executor got stuck on
consultant: claude-haiku-4-5 # one question at a time, no tools: cheap is enough
matrix: # A1
homeserver: https://matrix.example
user_id: "@nisse-dev:example"
token: ${NISSE_MATRIX_TOKEN}
room_id: "!roomid:example"
operators: ["@you:example"]
gitea: # B2
url: https://gitea.example
token: ${NISSE_GITEA_TOKEN}
repo: owner/demo-repo
operators: [your-gitea-login]
base_branch: main
# What each knob does, and how to choose a value: docs/Tuning-Guide.md.
# These are the defaults; the section can be left out entirely. The file is
# read at startup, so a change takes effect on the next restart.
tuning:
executor:
max_turns: 40 # model calls per milestone before it stops as capped
failure_limit: 2 # consecutive failed commands before escalating (A12)
expert:
consult_budget: 3 # ask_expert calls per milestone (A14)
delegation: # hard caps on one delegation (A13)
max_turns: 30
max_tokens: 200000
wall_clock_s: 900
tools:
command_timeout_s: 120 # one run_command before it is killed
max_output_chars: 4000 # tool output kept for the model, head and tail
context: # fitting each request into the model's window
keep_recent_turns: 6 # turns never cut or dropped
safety_margin: 0.1 # fraction of the window held back
nisse-dev/pyproject.toml
+5 -1
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@@ -3,7 +3,11 @@ name = "nisse-dev"
version = "0.1.0"
description = "Dev agent: plans and implements software projects, produces Gitea PRs"
requires-python = ">=3.12"
dependencies = ["nisse-core", "httpx>=0.27"]
dependencies = [
"nisse-core",
"httpx>=0.27",
"pyyaml>=6",
]
[project.scripts]
nisse-dev = "nisse_dev.main:main"
nisse-dev/src/nisse_dev/agent.py
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@@ -0,0 +1,297 @@
"""Composition root: the one place that knows how the whole agent fits together.
Every component below has been unit-tested in isolation for some time; nothing
had ever assembled them. That gap hid two live bugs — the delegation harness
and the Gitea comment poller were both fully built and reachable from nothing
but their own tests — which is the argument for having a single root rather
than constructing pieces ad hoc.
"""
import threading
from collections.abc import Callable
from dataclasses import dataclass
from nisse_dev.approvals import PlanBoard
from nisse_dev.cancel import Cancel
from nisse_dev.catalog import ModelCatalog, select_client
from nisse_dev.config import AgentConfig
from nisse_dev.context import BudgetedClient
from nisse_dev.control import Orchestrator
from nisse_dev.delegation import Caps, Delegation
from nisse_dev.executor import LLMExecutor
from nisse_dev.expert import ExpertDesk
from nisse_dev.git import GitRepo, authenticated_url
from nisse_dev.gitea import Comment, GiteaClient
from nisse_dev.llm import AnthropicPassthroughClient, LiteLLMClient
from nisse_dev.matrix import MatrixTransport
from nisse_dev.oplog import OpLog
from nisse_dev.planner import LLMPlanningAgent
from nisse_dev.poller import CommentPoller
from nisse_dev.publish import MilestonePublisher
from nisse_dev.state import StateStore
from nisse_dev.tools import Workspace
from nisse_dev.worker import SerialWorker
# How long shutdown waits for running work before leaving the store to the
# durability layer (A2/A3). A milestone can run far longer than a service
# manager allows for a stop, and a hard exit mid-work is already safe.
SHUTDOWN_WAIT_S = 10.0
TOOL_DRIVING_ROLES = ("planner", "executor", "delegate")
def discover_models(config: AgentConfig, oplog: OpLog) -> ModelCatalog:
"""Resolve every configured role against LiteLLM before anything runs.
Refusing to boot is the whole point: a role pointed at a model this key
cannot see is a misconfiguration, and the cheapest place to learn that is
here rather than ten minutes into a milestone. An unreachable proxy is
reported separately -- it is an outage, not a mistake, and telling them
apart is what lets an operator know whether to edit config or wait.
"""
catalog = ModelCatalog.discover(config.llm_base_url, config.llm_token)
catalog.validate(config.roles.pinned())
# Every role's context window must be known before a request is built
# against it (A10): a guess that is too large reaches a server that may
# silently cut the prompt.
catalog.require_limits(config.roles.pinned())
for role in TOOL_DRIVING_ROLES:
facts = catalog.facts(config.roles.resolve(role))
if facts.supports_tools is False:
# A warning, not a refusal: models LiteLLM declares tool-less are
# observed driving tool loops anyway, and A15 expects weak local
# models to be accommodated rather than excluded.
oplog.emit(
"model_tools_unsupported",
cause="startup",
role=role,
model=facts.alias,
backing=facts.backing,
)
return catalog
@dataclass
class Agent:
config: AgentConfig
store: StateStore
transport: MatrixTransport
orchestrator: Orchestrator
gitea: GiteaClient
account: str
worker: SerialWorker
_stopped: threading.Event
def start(self) -> None:
"""Boot in the order A3 requires: reconcile against external truth
before accepting a single operator command."""
self.worker.call(self.orchestrator.start)
self.transport.start()
state = self.worker.call(self.store.get_state)
where = state.state if state else "IDLE"
self.transport.send_message(
f"nisse-dev up as {self.account} — project {self.config.project}, "
f"state {where}, planner {self.config.roles.planner}, "
f"executor {self.config.roles.executor}"
)
def wait(self) -> None:
self._stopped.wait()
def stop(self) -> None:
"""Shut down outermost-first: stop accepting work, then stop talking,
then close the state store last so nothing writes to a closed file.
The store belongs to the worker, so the worker closes it."""
self.orchestrator.stop_watching()
self.transport.close()
self.gitea.close()
try:
self.worker.call(self.store.close, timeout=SHUTDOWN_WAIT_S)
except TimeoutError:
pass # still working; exiting mid-work is what A2/A3 are for
self.worker.stop(timeout=SHUTDOWN_WAIT_S)
self._stopped.set()
def make_llm_clients(
config: AgentConfig, catalog: ModelCatalog
) -> tuple[BudgetedClient, BudgetedClient]:
"""The two LLM routes, as every model call in the agent uses them.
Shared with the benchmark, so that what it measures is exactly what the
agent would do: the same context budget, the same declared limits, and
tool_choice asked only of models declared to enforce it.
"""
tuning = config.tuning
def limits_for(model: str) -> tuple[int, int | None]:
facts = catalog.facts(model)
assert facts.max_input_tokens is not None # require_limits checked
return facts.max_input_tokens, facts.max_output_tokens
def budgeted(client: object) -> BudgetedClient:
# Every request, on either route, is fitted to its model's window.
return BudgetedClient(
client,
limits_for,
max_output_tokens=config.max_output_tokens,
keep_recent_turns=tuning.context.keep_recent_turns,
safety_margin=tuning.context.safety_margin,
enforces_tool_choice=lambda m: (
catalog.facts(m).supports_tool_choice is True
),
)
normalized = budgeted(
LiteLLMClient(
config.llm_base_url,
config.llm_token,
max_tokens=config.max_output_tokens,
timeout_s=config.request_timeout_s,
)
)
# Delegations prefer the vendor-native route; fidelity is cheap
# elsewhere (A5). Which route a given model gets is decided by
# select_client, not by where the caller happens to sit.
passthrough = budgeted(
AnthropicPassthroughClient(
config.llm_base_url,
config.llm_token,
max_tokens=config.max_output_tokens,
timeout_s=config.request_timeout_s,
)
)
return normalized, passthrough
def prepare_workspace(config: AgentConfig, account: str) -> GitRepo:
"""Ensure the workspace is a clone of the project repo.
Cloning here rather than on `use <project>` is the single-project
shortcut: resolving a project name to a repo is issue #27's work.
"""
repo = GitRepo(config.workspace)
remote = authenticated_url(
config.gitea_url, config.gitea_repo, account, config.gitea_token
)
if not (config.workspace / ".git").exists():
config.workspace.parent.mkdir(parents=True, exist_ok=True)
GitRepo(config.workspace.parent).clone(remote, config.workspace)
repo.configure_identity(account, f"{account}@users.noreply.local")
return repo
def build_agent(
config: AgentConfig, *, notify: Callable[[str], None] | None = None
) -> Agent:
oplog = OpLog("nisse-dev")
worker = SerialWorker(
on_error=lambda exc: oplog.emit(
"worker.job_failed", cause="unhandled exception", error=repr(exc)
)
)
worker.start()
# Opened on the worker, which is the only thread allowed to use it.
store = worker.call(lambda: StateStore(config.state_db, oplog=oplog))
cancel = Cancel()
# Before the clone, before the token checks: a typo'd role name should
# cost a second, not a repository.
catalog = discover_models(config, oplog)
gitea = GiteaClient(config.gitea_url, config.gitea_token)
# Whoami rather than another config variable: it names the identity every
# Gitea permission is evaluated against, and proves the token works before
# anything depends on it.
account = gitea.whoami()
git_repo = prepare_workspace(config, account)
tuning = config.tuning
workspace = Workspace(
config.workspace,
timeout_s=tuning.tools.command_timeout_s,
max_output_chars=tuning.tools.max_output_chars,
)
normalized, passthrough = make_llm_clients(config, catalog)
transport = MatrixTransport(config.matrix)
announce = notify or transport.send_message
expert = ExpertDesk(
normalized, store, config.roles, budget=tuning.expert.consult_budget
)
delegate_model = config.roles.resolve("delegate")
delegation = Delegation(
select_client(delegate_model, normalized, passthrough, catalog),
store,
workspace,
git_repo,
model=delegate_model,
backing_model=catalog.facts(delegate_model).backing,
caps=Caps(
turns=tuning.delegation.max_turns,
tokens=tuning.delegation.max_tokens,
wall_clock_s=tuning.delegation.wall_clock_s,
),
cancel=cancel,
)
executor = LLMExecutor(
normalized,
store,
config.roles,
workspace,
expert=expert,
delegate=delegation,
notify=announce,
cancel=cancel,
max_turns=tuning.executor.max_turns,
failure_limit=tuning.executor.failure_limit,
)
publisher = MilestonePublisher(
store,
gitea,
config.gitea_repo,
git_repo,
base_url=config.gitea_url,
account=account,
token=config.gitea_token,
base_branch=config.base_branch,
)
def poller_factory(on_comment: Callable[[Comment], None]) -> CommentPoller:
return CommentPoller(
gitea,
config.gitea_repo,
on_comment,
oplog=oplog,
# Its own comments are not new input; without this the agent
# answers itself.
ignore_authors={account},
)
orchestrator = Orchestrator(
store,
transport,
LLMPlanningAgent(normalized, store, config.roles, cancel=cancel),
operators=config.operators,
roles=config.roles,
executor=executor,
publisher=publisher,
gitea_operators=config.gitea_operators,
board=PlanBoard(gitea, config.gitea_repo),
delegate=delegation,
poller_factory=poller_factory,
catalog=catalog,
worker=worker,
cancel=cancel,
)
return Agent(
config=config,
store=store,
transport=transport,
orchestrator=orchestrator,
gitea=gitea,
account=account,
worker=worker,
_stopped=threading.Event(),
)
nisse-dev/src/nisse_dev/bench/__init__.py
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"""The executor benchmark: canned milestones, run repeatedly per model.
Choosing an executor model, and later tuning its knobs, should rest on
measured success rates, not on one run or on reputation. Each task is a fixed
approved plan over a seed project, with a hidden check the model never sees;
success means the check passes, not that the model said it finished. Runs use
the agent's own client wiring (context budget, declared limits, tool_choice
only where declared), so what is measured is what the agent would do.
Results are stored, one JSON line per run, with everything needed to compare
them later: the model's identity and declared facts, a note on how it was
served, the task's content hash, the agent's commit, and its tuning. A new
model then needs only its own runs to be compared with everything measured
before it.
See scripts/benchmark.py, and docs/Tuning-Guide.md for how to read results.
"""
nisse-dev/src/nisse_dev/bench/results.py
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"""Stored benchmark results, and the report that compares them.
Every run is one JSON line. The report is rebuilt from all of them each
time, so a model benchmarked today is read alongside every model measured
before it, as long as the task is still the same version.
"""
import json
import math
import statistics
from collections import defaultdict
from collections.abc import Iterable
from dataclasses import asdict, dataclass
from datetime import UTC, datetime
from pathlib import Path
from typing import Any
from nisse_dev.bench.runner import RunRecord
from nisse_dev.config import Tuning
def results_file(directory: Path, model: str, when: datetime | None = None) -> Path:
day = (when or datetime.now(UTC)).strftime("%Y-%m-%d")
return directory / f"{day}-{model}.jsonl"
def append(path: Path, record: RunRecord) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
with path.open("a") as out:
out.write(json.dumps(record.to_json(), sort_keys=True) + "\n")
def load(directory: Path) -> list[dict[str, Any]]:
records = []
for path in sorted(directory.glob("*.jsonl")):
for line in path.read_text().splitlines():
if line.strip():
records.append(json.loads(line))
return records
def wilson(successes: int, runs: int, z: float = 1.96) -> tuple[float, float]:
"""A 95% confidence interval for a success rate, sound at small counts.
Three successes in five runs and five in five overlap here, which is the
point: small samples of a noisy model do not separate much."""
if runs == 0:
return 0.0, 1.0
p = successes / runs
denominator = 1 + z * z / runs
centre = (p + z * z / (2 * runs)) / denominator
half = z * math.sqrt(p * (1 - p) / runs + z * z / (4 * runs * runs)) / denominator
return max(0.0, centre - half), min(1.0, centre + half)
@dataclass
class Row:
model: str
note: str
task: str
runs: int
successes: int
median_turns: float
mean_wall_s: float # working time: rate-limit waits are left out
cost_per_run: float
stale: bool
rate_limited: int = 0 # runs that waited out the proxy's rate limit
tuning: str = "" # how the runs' tuning differs from today's defaults
@property
def interval(self) -> tuple[float, float]:
return wilson(self.successes, self.runs)
def tuning_label(tuning: dict[str, Any]) -> str:
"""The settings in a run's tuning that differ from today's defaults, as
"executor.max_turns=24"; empty when there are none. Relative to today's
defaults so that rows at the current settings stay unlabelled."""
defaults = asdict(Tuning())
return ", ".join(
f"{section}.{key}={value}"
for section, values in sorted(tuning.items())
for key, value in sorted((values or {}).items())
if defaults.get(section, {}).get(key) != value
)
def summarise(
records: Iterable[dict[str, Any]], current_hashes: dict[str, str]
) -> list[Row]:
"""One row per (model, how it was served, task, task version, tuning).
Tuning is part of the key because a knob changes the result: runs capped
at 24 turns and runs allowed 40 are not the same measurement."""
groups: dict[tuple[str, str, str, str, str], list[dict[str, Any]]] = defaultdict(
list
)
for r in records:
key = (
r["backing"],
r.get("note", ""),
r["task"],
r["task_hash"],
tuning_label(r.get("tuning", {})),
)
groups[key].append(r)
rows = []
for (backing, note, task, task_hash, tuning), runs in sorted(groups.items()):
rows.append(
Row(
model=backing,
note=note,
task=task,
runs=len(runs),
successes=sum(1 for r in runs if r["success"]),
median_turns=statistics.median(r["turns"] for r in runs),
mean_wall_s=statistics.fmean(
r["wall_s"] - r.get("rate_limit_wait_s", 0.0) for r in runs
),
cost_per_run=statistics.fmean(r["cost"] for r in runs),
stale=current_hashes.get(task) != task_hash,
rate_limited=sum(1 for r in runs if r.get("rate_limit_wait_s")),
tuning=tuning,
)
)
return rows
def render(rows: list[Row]) -> str:
# A tuning column only when some rows need one, so a report at today's
# settings looks as it always has.
tuned = any(row.tuning for row in rows)
header = (
f"{'model':<34} {'note':<22} "
+ (f"{'tuning':<24} " if tuned else "")
+ f"{'task':<15} {'success':>8} {'95% CI':>11} "
f"{'turns':>6} {'time':>7} {'$/run':>8} {'429':>4}"
)
lines = [header, "-" * len(header)]
for row in rows:
low, high = row.interval
task = row.task + (" (old)" if row.stale else "")
tuning = f"{row.tuning[:24]:<24} " if tuned else ""
lines.append(
f"{row.model[:34]:<34} {row.note[:22]:<22} {tuning}{task[:15]:<15} "
f"{row.successes:>3}/{row.runs:<4} {low:>4.0%}-{high:<5.0%} "
f"{row.median_turns:>6.0f} {row.mean_wall_s:>6.0f}s {row.cost_per_run:>8.4f} "
f"{row.rate_limited or '':>4}"
)
if any(row.rate_limited for row in rows):
lines.append("")
lines.append(
"429 = runs that waited out the proxy's rate limit; time leaves the "
"waits out."
)
if tuned:
lines.append("")
lines.append("tuning = settings that differ from today's defaults.")
if any(row.stale for row in rows):
lines.append("")
lines.append(
"(old) = measured against an earlier version of the task; "
"not comparable with current runs."
)
return "\n".join(lines)
nisse-dev/src/nisse_dev/bench/runner.py
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"""Running one task once with one executor model, and recording it."""
import io
import subprocess
import tempfile
import time
from dataclasses import asdict, dataclass, field
from datetime import UTC, datetime
from pathlib import Path
from typing import Any
from nisse_dev.bench.tasks import Task
from nisse_dev.canonical import (
CanonicalEvent,
PlanSubmitted,
StoredEvent,
ToolCall,
ToolResult,
UserMessage,
)
from nisse_dev.catalog import ModelFacts
from nisse_dev.config import Tuning
from nisse_dev.executor import LLMExecutor
from nisse_dev.llm import RoleMap
from nisse_dev.oplog import OpLog
from nisse_dev.state import StateStore
from nisse_dev.tools import Workspace
class MeteringClient:
"""Counts what a run spends, and notices a fallback: an answer from a
model other than the one asked for is not a measurement of it."""
def __init__(self, inner: Any, facts: ModelFacts) -> None:
self._inner = inner
self._facts = facts
self.calls = 0
self.prose_turns = 0
self.tokens_in = 0
self.tokens_out = 0
self.cost = 0.0
self.fallbacks: list[str] = []
self.rate_limit_wait_s = 0.0
def complete(
self,
events: list[StoredEvent],
tools: list[dict[str, Any]] | None,
model: str,
system: str | None = None,
) -> list[CanonicalEvent]:
self.calls += 1
try:
produced = self._inner.complete(events, tools, model, system=system)
finally:
# Counted even when the call fails: a rate limit that never
# cleared still cost the run its wait.
self.rate_limit_wait_s += float(
getattr(self._inner, "last_rate_limit_wait_s", 0.0) or 0.0
)
usage = getattr(self._inner, "last_usage", {}) or {}
self.tokens_in += int(usage.get("input", 0))
self.tokens_out += int(usage.get("output", 0))
self.cost += float(getattr(self._inner, "last_cost", 0.0) or 0.0)
answered = getattr(self._inner, "last_model", None)
if not self._facts.answered(answered):
self.fallbacks.append(str(answered))
if not any(isinstance(e, ToolCall) for e in produced):
self.prose_turns += 1
return produced
@dataclass
class RunRecord:
"""One run, with everything needed to compare it with runs made later
against other models, other servers, or other tuning."""
ts: str
model: str
backing: str
provider: str
model_info: dict[str, Any]
note: str
task: str
task_hash: str
commit: str
tuning: dict[str, Any]
success: bool
outcome: str
outcome_note: str
turns: int
prose_turns: int
failed_commands: int
invented_tools: int
trimmed_requests: int
tokens_in: int
tokens_out: int
cost: float
wall_s: float
fallbacks: list[str] = field(default_factory=list)
# Part of wall_s spent waiting out the proxy's rate limit, not working.
rate_limit_wait_s: float = 0.0
check_output: str = ""
def to_json(self) -> dict[str, Any]:
return asdict(self)
def agent_commit(repo: Path | None = None) -> str:
"""The agent's commit, marked when the tree had uncommitted changes."""
where = repo or Path(__file__).resolve().parent
try:
sha = subprocess.run(
["git", "rev-parse", "--short", "HEAD"],
cwd=where,
capture_output=True,
text=True,
check=True,
).stdout.strip()
dirty = subprocess.run(
["git", "status", "--porcelain"],
cwd=where,
capture_output=True,
text=True,
check=True,
).stdout.strip()
except (OSError, subprocess.CalledProcessError):
return "unknown"
return f"{sha}+dirty" if dirty else sha
def _count(events: list[StoredEvent]) -> tuple[int, int]:
tools = {
e.event.call_id: e.event.tool for e in events if isinstance(e.event, ToolCall)
}
failed = invented = 0
for stored in events:
event = stored.event
if isinstance(event, ToolResult) and not event.success:
if event.result.startswith("no such tool"):
invented += 1
elif tools.get(event.call_id) == "run_command":
failed += 1
return failed, invented
def run_task(
task: Task,
facts: ModelFacts,
client: Any,
tuning: Tuning,
*,
note: str = "",
commit: str = "",
workdir: Path | None = None,
) -> RunRecord:
"""Run one task once: seed a workspace, hand the executor the approved
plan, then judge the result with the hidden check.
Delegation and ask_expert are off: an escalation is a failure of the
model under test, not a frontier model's success."""
with tempfile.TemporaryDirectory(dir=workdir) as tmp:
root = Path(tmp)
workspace = Workspace(
root / "work",
timeout_s=tuning.tools.command_timeout_s,
max_output_chars=tuning.tools.max_output_chars,
)
task.seed_into(workspace.root)
store = StateStore(root / "state.db", oplog=OpLog("nisse-bench", io.StringIO()))
store.append_event(PlanSubmitted(plan=task.plan, model="benchmark"))
store.append_event(UserMessage(text="start milestone 1", source="benchmark"))
meter = MeteringClient(client, facts)
trims_before = getattr(client, "trimmed_requests", 0)
model = facts.alias
roles = RoleMap(executor=model, planner=model, delegate=model, consultant=model)
executor = LLMExecutor(
meter,
store,
roles,
workspace,
max_turns=tuning.executor.max_turns,
failure_limit=tuning.executor.failure_limit,
)
started = time.monotonic()
try:
outcome = executor.run(1)
status, detail = outcome.status, outcome.note
except Exception as exc: # noqa: BLE001 -- recorded, the benchmark goes on
status, detail = "error", repr(exc)
wall = time.monotonic() - started
success, check_output = task.run_check(workspace.root)
failed, invented = _count(store.read_events())
store.close()
return RunRecord(
ts=datetime.now(UTC).isoformat(timespec="seconds"),
model=model,
backing=facts.backing,
provider=facts.provider,
model_info={
"max_input_tokens": facts.max_input_tokens,
"max_output_tokens": facts.max_output_tokens,
"supports_tool_choice": facts.supports_tool_choice,
"supports_function_calling": facts.supports_tools,
},
note=note,
task=task.name,
task_hash=task.content_hash,
commit=commit,
tuning=asdict(tuning),
success=success and not meter.fallbacks,
outcome=status,
outcome_note=detail[:500],
turns=meter.calls,
prose_turns=meter.prose_turns,
failed_commands=failed,
invented_tools=invented,
trimmed_requests=getattr(client, "trimmed_requests", 0) - trims_before,
tokens_in=meter.tokens_in,
tokens_out=meter.tokens_out,
cost=round(meter.cost, 6),
wall_s=round(wall, 1),
fallbacks=meter.fallbacks,
rate_limit_wait_s=meter.rate_limit_wait_s,
check_output=check_output,
)
nisse-dev/src/nisse_dev/bench/tasks.py
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"""Benchmark tasks: a seed project, an approved plan, and a hidden check.
Each task is a directory under ``tasks/``:
- ``task.yaml``: ``title``, ``plan`` (the approved milestone plan, written the
way the planner is asked to write them), and ``check`` (a shell command run
in the workspace afterwards).
- ``seed/``: the project the executor starts from.
- ``hidden/``: files the check needs, copied in only after the run, into
``.bench_check/``. The executor never sees them.
- ``solution/``: a reference solution, overlaid on the seed by the tests to
prove each task can be solved and that its check tells the difference.
"""
import hashlib
import shutil
import subprocess
from dataclasses import dataclass
from pathlib import Path
import yaml
TASKS_DIR = Path(__file__).parent / "tasks"
CHECK_DIR = ".bench_check"
@dataclass(frozen=True)
class Task:
name: str
title: str
plan: str
check: str
path: Path
@property
def content_hash(self) -> str:
"""What the task is, so a changed task is never compared with runs of
its earlier version. Covers everything the model or the check sees;
not the reference solution."""
digest = hashlib.sha256()
for part in ("task.yaml", "seed", "hidden"):
root = self.path / part
files = [root] if root.is_file() else sorted(root.rglob("*"))
for file in files:
if file.is_file() and "__pycache__" not in file.parts:
digest.update(str(file.relative_to(self.path)).encode())
digest.update(file.read_bytes())
return digest.hexdigest()[:16]
def seed_into(self, workspace: Path) -> None:
shutil.copytree(self.path / "seed", workspace, dirs_exist_ok=True)
def apply_solution(self, workspace: Path) -> None:
shutil.copytree(self.path / "solution", workspace, dirs_exist_ok=True)
def run_check(self, workspace: Path, timeout_s: int = 300) -> tuple[bool, str]:
"""Copy the hidden files in and run the check. Returns whether it
passed, and its output for the record."""
target = workspace / CHECK_DIR
if target.exists():
shutil.rmtree(target) # never trust a directory the model made
shutil.copytree(self.path / "hidden", target)
try:
result = subprocess.run(
self.check,
shell=True,
cwd=workspace,
capture_output=True,
text=True,
timeout=timeout_s,
check=False,
)
except subprocess.TimeoutExpired:
return False, f"check timed out after {timeout_s}s"
output = (result.stdout + result.stderr)[-2000:]
return result.returncode == 0, output
def load_task(path: Path) -> Task:
spec = yaml.safe_load((path / "task.yaml").read_text())
return Task(
name=path.name,
title=str(spec["title"]),
plan=str(spec["plan"]).strip(),
check=str(spec["check"]),
path=path,
)
def load_tasks(names: list[str] | None = None, root: Path = TASKS_DIR) -> list[Task]:
available = {
p.name: p for p in sorted(root.iterdir()) if (p / "task.yaml").exists()
}
if names:
unknown = [n for n in names if n not in available]
if unknown:
raise ValueError(
f"unknown task(s): {', '.join(unknown)}; "
f"available: {', '.join(available)}"
)
return [load_task(available[n]) for n in names]
return [load_task(p) for p in available.values()]
nisse-dev/src/nisse_dev/bench/tasks/fix-bug/hidden/original_test_inventory.py.txt
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from inventory import Item, low_stock, total_value
def test_total_value_counts_every_unit() -> None:
items = [Item("bolt", 0.25, 100), Item("nut", 0.10, 40)]
assert total_value(items) == 29.0
def test_low_stock_is_alphabetical() -> None:
items = [Item("washer", 0.05, 2), Item("bolt", 0.25, 100), Item("anchor", 3.0, 1)]
assert low_stock(items) == ["anchor", "washer"]
nisse-dev/src/nisse_dev/bench/tasks/fix-bug/hidden/test_hidden.py
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from pathlib import Path
import pytest
from inventory import Item, low_stock, total_value
def test_the_visible_tests_were_not_changed() -> None:
original = (Path(__file__).parent / "original_test_inventory.py.txt").read_text()
assert Path("test_inventory.py").read_text() == original
@pytest.mark.parametrize(
("items", "expected"),
[
([], 0.0),
([Item("bolt", 0.25, 100), Item("nut", 0.10, 40)], 29.0),
([Item("gear", 12.5, 3)], 37.5),
([Item("empty", 9.99, 0)], 0.0),
],
)
def test_total_value(items: list[Item], expected: float) -> None:
assert total_value(items) == pytest.approx(expected)
def test_low_stock_still_works() -> None:
items = [Item("washer", 0.05, 2), Item("bolt", 0.25, 100)]
assert low_stock(items) == ["washer"]
nisse-dev/src/nisse_dev/bench/tasks/fix-bug/seed/inventory.py
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"""A tiny inventory."""
from dataclasses import dataclass
@dataclass
class Item:
name: str
unit_price: float
quantity: int
def total_value(items: list[Item]) -> float:
"""The value of all stock: each item's unit price times its quantity."""
total = 0.0
for item in items:
total += item.unit_price
return total
def low_stock(items: list[Item], threshold: int = 5) -> list[str]:
"""Names of items with fewer than `threshold` units, alphabetically."""
return sorted(item.name for item in items if item.quantity < threshold)
nisse-dev/src/nisse_dev/bench/tasks/fix-bug/seed/test_inventory.py
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from inventory import Item, low_stock, total_value
def test_total_value_counts_every_unit() -> None:
items = [Item("bolt", 0.25, 100), Item("nut", 0.10, 40)]
assert total_value(items) == 29.0
def test_low_stock_is_alphabetical() -> None:
items = [Item("washer", 0.05, 2), Item("bolt", 0.25, 100), Item("anchor", 3.0, 1)]
assert low_stock(items) == ["anchor", "washer"]
nisse-dev/src/nisse_dev/bench/tasks/fix-bug/solution/inventory.py
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"""A tiny inventory."""
from dataclasses import dataclass
@dataclass
class Item:
name: str
unit_price: float
quantity: int
def total_value(items: list[Item]) -> float:
"""The value of all stock: each item's unit price times its quantity."""
total = 0.0
for item in items:
total += item.unit_price * item.quantity
return total
def low_stock(items: list[Item], threshold: int = 5) -> list[str]:
"""Names of items with fewer than `threshold` units, alphabetically."""
return sorted(item.name for item in items if item.quantity < threshold)
nisse-dev/src/nisse_dev/bench/tasks/fix-bug/task.yaml
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title: Fix a bug a failing test exposes
check: python -m pytest -q .bench_check
plan: |
Milestone 1: fix the failing inventory test.
1. Run `python -m pytest -q` and read the failure.
2. The failing test is correct; the bug is in `inventory.py`. Do not change
any test file.
3. Fix `total_value` so that each item contributes its unit price times its
quantity.
4. Run `python -m pytest -q` until every test passes.
5. Call finish.
nisse-dev/src/nisse_dev/bench/tasks/long-milestone/hidden/test_hidden.py
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import json
import os
import subprocess
import sys
from datetime import date
from pathlib import Path
import pytest
from tracker import storage
from tracker.core import add_task, complete, overdue, sort_tasks
from tracker.formatting import format_task
from tracker.models import Task
TODAY = date(2026, 10, 5)
# step 1
def test_task_has_an_optional_due_date() -> None:
assert Task(1, "a").due is None
assert Task(1, "a", False, date(2026, 1, 2)).due == date(2026, 1, 2)
# step 2
def test_save_writes_version_2_with_iso_dates(tmp_path: Path) -> None:
path = tmp_path / "t.json"
storage.save(path, [Task(1, "a", due=date(2026, 10, 1)), Task(2, "b")])
data = json.loads(path.read_text())
assert data["version"] == 2
assert [t["due"] for t in data["tasks"]] == ["2026-10-01", None]
def test_round_trip_keeps_dates(tmp_path: Path) -> None:
path = tmp_path / "t.json"
tasks = [Task(1, "a", due=date(2026, 10, 1)), Task(2, "b", done=True)]
storage.save(path, tasks)
assert storage.load(path) == tasks
def test_version_1_files_still_load(tmp_path: Path) -> None:
path = tmp_path / "t.json"
path.write_text(
json.dumps({"version": 1, "tasks": [{"id": 4, "title": "old", "done": True}]})
)
assert storage.load(path) == [Task(4, "old", done=True, due=None)]
@pytest.mark.parametrize("version", [0, 3, 99])
def test_other_versions_are_refused(tmp_path: Path, version: int) -> None:
path = tmp_path / "t.json"
path.write_text(json.dumps({"version": version, "tasks": []}))
with pytest.raises(ValueError, match=f"unsupported tasks file version: {version}"):
storage.load(path)
# step 3
def test_add_task_takes_a_due_date() -> None:
tasks: list[Task] = []
assert add_task(tasks, "a", due=date(2026, 1, 1)).due == date(2026, 1, 1)
assert add_task(tasks, "b").due is None
# step 4
def test_sort_order() -> None:
tasks = [
Task(1, "undated"),
Task(2, "late", due=date(2026, 12, 1)),
Task(3, "early", due=date(2026, 9, 1)),
Task(4, "done dated", done=True, due=date(2026, 1, 1)),
Task(5, "done undated", done=True),
Task(6, "early twin", due=date(2026, 9, 1)),
Task(7, "undated twin"),
]
assert [t.id for t in sort_tasks(tasks)] == [3, 6, 2, 1, 7, 4, 5]
# step 5
def test_overdue() -> None:
tasks = [
Task(1, "due today", due=TODAY),
Task(2, "yesterday", due=date(2026, 10, 4)),
Task(3, "done late", done=True, due=date(2026, 1, 1)),
Task(4, "undated"),
Task(5, "long ago", due=date(2026, 1, 1)),
]
assert [t.id for t in overdue(tasks, TODAY)] == [5, 2]
# step 6
@pytest.mark.parametrize(
("task", "line"),
[
(Task(3, "Buy milk"), "[ ] 3 Buy milk"),
(
Task(4, "Call Bob", done=True, due=date(2026, 10, 1)),
"[x] 4 Call Bob (due 2026-10-01)",
),
(Task(5, "Pay rent", due=date(2026, 9, 1)), "[!] 5 Pay rent (due 2026-09-01)"),
(Task(6, "Soon", due=date(2026, 10, 9)), "[ ] 6 Soon (due 2026-10-09)"),
(Task(7, "Today", due=TODAY), "[ ] 7 Today (due 2026-10-05)"),
],
)
def test_format(task: Task, line: str) -> None:
assert format_task(task, TODAY) == line
# step 7, end to end through the real command line
def run(tmp_path: Path, *args: str) -> subprocess.CompletedProcess[str]:
env = {
**os.environ,
"TRACKER_FILE": str(tmp_path / "t.json"),
"TRACKER_TODAY": "2026-10-05",
}
return subprocess.run(
[sys.executable, "-m", "tracker", *args],
capture_output=True,
text=True,
env=env,
cwd=Path.cwd(),
check=False,
)
def test_command_line_flow(tmp_path: Path) -> None:
assert run(tmp_path, "add", "Buy milk").returncode == 0
assert run(tmp_path, "add", "Pay rent", "--due", "2026-10-01").returncode == 0
assert run(tmp_path, "add", "Dentist", "--due", "2026-11-20").returncode == 0
assert run(tmp_path, "done", "1").returncode == 0
listing = run(tmp_path, "list")
assert listing.stdout.splitlines() == [
"[!] 2 Pay rent (due 2026-10-01)",
"[ ] 3 Dentist (due 2026-11-20)",
"[x] 1 Buy milk",
], listing.stderr
assert run(tmp_path, "list", "--overdue").stdout.splitlines() == [
"[!] 2 Pay rent (due 2026-10-01)"
]
def test_a_bad_due_date_is_a_usage_error(tmp_path: Path) -> None:
result = run(tmp_path, "add", "x", "--due", "2026-13-40")
assert result.returncode == 2
assert "invalid --due date: 2026-13-40" in result.stderr
def test_today_defaults_to_the_real_date(tmp_path: Path) -> None:
env = {**os.environ, "TRACKER_FILE": str(tmp_path / "t.json")}
env.pop("TRACKER_TODAY", None)
subprocess.run(
[sys.executable, "-m", "tracker", "add", "Ancient", "--due", "2000-01-01"],
env=env,
cwd=Path.cwd(),
check=True,
capture_output=True,
)
listing = subprocess.run(
[sys.executable, "-m", "tracker", "list", "--overdue"],
env=env,
cwd=Path.cwd(),
capture_output=True,
text=True,
check=False,
)
assert listing.stdout.splitlines() == ["[!] 1 Ancient (due 2000-01-01)"]
def test_existing_behaviour_is_kept() -> None:
tasks: list[Task] = []
add_task(tasks, "a")
add_task(tasks, "b")
complete(tasks, 1)
assert [t.id for t in sort_tasks(tasks)] == [2, 1]
# step 8
def test_the_projects_own_tests_pass_and_cover_due_dates() -> None:
result = subprocess.run(
[sys.executable, "-m", "pytest", "-q", "tests"],
capture_output=True,
text=True,
check=False,
cwd=Path.cwd(),
)
assert result.returncode == 0, result.stdout[-1500:]
written = "\n".join(p.read_text() for p in Path("tests").glob("test_*.py"))
for name in ("due", "overdue", "version"):
assert name in written, f"no test mentions {name!r}"
nisse-dev/src/nisse_dev/bench/tasks/long-milestone/seed/tests/test_core.py
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import pytest
from tracker.core import add_task, complete, sort_tasks
from tracker.models import Task
def test_ids_count_up() -> None:
tasks: list[Task] = []
assert [add_task(tasks, t).id for t in ("a", "b", "c")] == [1, 2, 3]
def test_a_title_is_required() -> None:
with pytest.raises(ValueError):
add_task([], " ")
def test_complete_and_sort() -> None:
tasks: list[Task] = []
for title in ("a", "b", "c"):
add_task(tasks, title)
complete(tasks, 1)
assert [t.id for t in sort_tasks(tasks)] == [2, 3, 1]
def test_completing_an_unknown_task() -> None:
with pytest.raises(KeyError):
complete([], 7)
nisse-dev/src/nisse_dev/bench/tasks/long-milestone/seed/tests/test_storage_and_cli.py
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from pathlib import Path
from tracker import storage
from tracker.cli import main
from tracker.models import Task
def test_round_trip(tmp_path: Path) -> None:
path = tmp_path / "t.json"
storage.save(path, [Task(1, "a"), Task(2, "b", done=True)])
assert storage.load(path) == [Task(1, "a"), Task(2, "b", done=True)]
def test_no_file_is_no_tasks(tmp_path: Path) -> None:
assert storage.load(tmp_path / "missing.json") == []
def test_cli_add_done_list(tmp_path: Path, capsys) -> None:
path = str(tmp_path / "t.json")
main(["--file", path, "add", "Buy milk"])
main(["--file", path, "add", "Call Bob"])
main(["--file", path, "done", "1"])
capsys.readouterr()
main(["--file", path, "list"])
assert capsys.readouterr().out.splitlines() == ["[ ] 2 Call Bob", "[x] 1 Buy milk"]
nisse-dev/src/nisse_dev/bench/tasks/long-milestone/seed/tracker/__init__.py
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"""A small command-line task tracker."""
nisse-dev/src/nisse_dev/bench/tasks/long-milestone/seed/tracker/__main__.py
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from tracker.cli import main
raise SystemExit(main())
nisse-dev/src/nisse_dev/bench/tasks/long-milestone/seed/tracker/cli.py
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"""python -m tracker [--file PATH] {add,list,done} ...
The file defaults to $TRACKER_FILE, or tasks.json in the current directory.
"""
import argparse
import os
from pathlib import Path
from tracker import storage
from tracker.core import add_task, complete, sort_tasks
from tracker.formatting import format_task
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser(prog="tracker")
parser.add_argument("--file", default=os.environ.get("TRACKER_FILE", "tasks.json"))
commands = parser.add_subparsers(dest="command", required=True)
add = commands.add_parser("add", help="add a task")
add.add_argument("title")
commands.add_parser("list", help="list tasks")
done = commands.add_parser("done", help="mark a task done")
done.add_argument("id", type=int)
args = parser.parse_args(argv)
path = Path(args.file)
tasks = storage.load(path)
if args.command == "add":
task = add_task(tasks, args.title)
storage.save(path, tasks)
print(f"added {task.id}")
elif args.command == "done":
try:
complete(tasks, args.id)
except KeyError as exc:
parser.error(str(exc.args[0]))
storage.save(path, tasks)
else:
for task in sort_tasks(tasks):
print(format_task(task))
return 0
nisse-dev/src/nisse_dev/bench/tasks/long-milestone/seed/tracker/core.py
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"""Operations on a list of tasks."""
from tracker.models import Task
def add_task(tasks: list[Task], title: str) -> Task:
title = title.strip()
if not title:
raise ValueError("a task needs a title")
task = Task(id=max((t.id for t in tasks), default=0) + 1, title=title)
tasks.append(task)
return task
def complete(tasks: list[Task], task_id: int) -> Task:
for task in tasks:
if task.id == task_id:
task.done = True
return task
raise KeyError(f"no task {task_id}")
def sort_tasks(tasks: list[Task]) -> list[Task]:
"""Tasks not done first, then by id."""
return sorted(tasks, key=lambda t: (t.done, t.id))
nisse-dev/src/nisse_dev/bench/tasks/long-milestone/seed/tracker/formatting.py
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"""One line per task."""
from tracker.models import Task
def format_task(task: Task) -> str:
mark = "x" if task.done else " "
return f"[{mark}] {task.id} {task.title}"
nisse-dev/src/nisse_dev/bench/tasks/long-milestone/seed/tracker/models.py
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"""The task record."""
from dataclasses import dataclass
@dataclass
class Task:
id: int
title: str
done: bool = False
nisse-dev/src/nisse_dev/bench/tasks/long-milestone/seed/tracker/storage.py
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"""Tasks are kept in one JSON file."""
import json
from pathlib import Path
from tracker.models import Task
VERSION = 1
def load(path: Path) -> list[Task]:
"""Every task in the file; an empty list when there is no file yet."""
if not path.exists():
return []
data = json.loads(path.read_text())
return [Task(id=t["id"], title=t["title"], done=t["done"]) for t in data["tasks"]]
def save(path: Path, tasks: list[Task]) -> None:
data = {
"version": VERSION,
"tasks": [{"id": t.id, "title": t.title, "done": t.done} for t in tasks],
}
path.write_text(json.dumps(data, indent=2))
nisse-dev/src/nisse_dev/bench/tasks/long-milestone/solution/tests/test_storage_and_cli.py
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import json
from datetime import date
from pathlib import Path
import pytest
from tracker import storage
from tracker.cli import main
from tracker.core import add_task, overdue, sort_tasks
from tracker.formatting import format_task
from tracker.models import Task
def test_round_trip(tmp_path: Path) -> None:
path = tmp_path / "t.json"
tasks = [Task(1, "a"), Task(2, "b", done=True, due=date(2026, 10, 1))]
storage.save(path, tasks)
assert storage.load(path) == tasks
assert json.loads(path.read_text())["version"] == 2
def test_no_file_is_no_tasks(tmp_path: Path) -> None:
assert storage.load(tmp_path / "missing.json") == []
def test_version_1_files_load(tmp_path: Path) -> None:
path = tmp_path / "t.json"
path.write_text(
json.dumps({"version": 1, "tasks": [{"id": 1, "title": "a", "done": False}]})
)
assert storage.load(path) == [Task(1, "a")]
def test_unknown_versions_are_refused(tmp_path: Path) -> None:
path = tmp_path / "t.json"
path.write_text(json.dumps({"version": 9, "tasks": []}))
with pytest.raises(ValueError, match="unsupported tasks file version: 9"):
storage.load(path)
def test_due_dates_sort_and_overdue() -> None:
tasks: list[Task] = []
add_task(tasks, "undated")
add_task(tasks, "later", due=date(2026, 12, 1))
add_task(tasks, "sooner", due=date(2026, 9, 1))
assert [t.title for t in sort_tasks(tasks)] == ["sooner", "later", "undated"]
assert [t.title for t in overdue(tasks, date(2026, 10, 1))] == ["sooner"]
def test_format_marks_overdue() -> None:
today = date(2026, 10, 5)
assert format_task(Task(5, "Pay rent", due=date(2026, 9, 1)), today) == (
"[!] 5 Pay rent (due 2026-09-01)"
)
assert format_task(Task(3, "Buy milk"), today) == "[ ] 3 Buy milk"
def test_cli_add_done_list(tmp_path: Path, capsys, monkeypatch) -> None:
monkeypatch.setenv("TRACKER_TODAY", "2026-10-05")
path = str(tmp_path / "t.json")
main(["--file", path, "add", "Buy milk"])
main(["--file", path, "add", "Pay rent", "--due", "2026-10-01"])
main(["--file", path, "done", "1"])
capsys.readouterr()
main(["--file", path, "list"])
assert capsys.readouterr().out.splitlines() == [
"[!] 2 Pay rent (due 2026-10-01)",
"[x] 1 Buy milk",
]
main(["--file", path, "list", "--overdue"])
assert capsys.readouterr().out.splitlines() == ["[!] 2 Pay rent (due 2026-10-01)"]
def test_cli_rejects_a_bad_date(tmp_path: Path) -> None:
with pytest.raises(SystemExit) as caught:
main(["--file", str(tmp_path / "t.json"), "add", "x", "--due", "soon"])
assert caught.value.code == 2
nisse-dev/src/nisse_dev/bench/tasks/long-milestone/solution/tracker/cli.py
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"""python -m tracker [--file PATH] {add,list,done} ...
The file defaults to $TRACKER_FILE, or tasks.json in the current directory.
"""
import argparse
import os
from datetime import date
from pathlib import Path
from tracker import storage
from tracker.core import add_task, complete, overdue, sort_tasks
from tracker.formatting import format_task
def today() -> date:
value = os.environ.get("TRACKER_TODAY")
return date.fromisoformat(value) if value else date.today() # noqa: DTZ011 -- the plan asks for the local date
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser(prog="tracker")
parser.add_argument("--file", default=os.environ.get("TRACKER_FILE", "tasks.json"))
commands = parser.add_subparsers(dest="command", required=True)
add = commands.add_parser("add", help="add a task")
add.add_argument("title")
add.add_argument("--due", help="due date, YYYY-MM-DD")
listing = commands.add_parser("list", help="list tasks")
listing.add_argument("--overdue", action="store_true", help="only overdue tasks")
done = commands.add_parser("done", help="mark a task done")
done.add_argument("id", type=int)
args = parser.parse_args(argv)
path = Path(args.file)
tasks = storage.load(path)
now = today()
if args.command == "add":
due = None
if args.due is not None:
try:
due = date.fromisoformat(args.due)
except ValueError:
parser.error(f"invalid --due date: {args.due}")
task = add_task(tasks, args.title, due=due)
storage.save(path, tasks)
print(f"added {task.id}")
elif args.command == "done":
try:
complete(tasks, args.id)
except KeyError as exc:
parser.error(str(exc.args[0]))
storage.save(path, tasks)
else:
shown = overdue(tasks, now) if args.overdue else sort_tasks(tasks)
for task in shown:
print(format_task(task, now))
return 0
nisse-dev/src/nisse_dev/bench/tasks/long-milestone/solution/tracker/core.py
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"""Operations on a list of tasks."""
from datetime import date
from tracker.models import Task
def add_task(tasks: list[Task], title: str, *, due: date | None = None) -> Task:
title = title.strip()
if not title:
raise ValueError("a task needs a title")
task = Task(id=max((t.id for t in tasks), default=0) + 1, title=title, due=due)
tasks.append(task)
return task
def complete(tasks: list[Task], task_id: int) -> Task:
for task in tasks:
if task.id == task_id:
task.done = True
return task
raise KeyError(f"no task {task_id}")
def sort_tasks(tasks: list[Task]) -> list[Task]:
"""Not done first; then dated tasks, earliest first; then undated; then id."""
return sorted(
tasks,
key=lambda t: (t.done, t.due is None, t.due or date.max, t.id),
)
def overdue(tasks: list[Task], today: date) -> list[Task]:
return [t for t in sort_tasks(tasks) if not t.done and t.due and t.due < today]
nisse-dev/src/nisse_dev/bench/tasks/long-milestone/solution/tracker/formatting.py
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"""One line per task."""
from datetime import date
from tracker.models import Task
def format_task(task: Task, today: date) -> str:
if task.done:
mark = "x"
elif task.due and task.due < today:
mark = "!"
else:
mark = " "
due = f" (due {task.due.isoformat()})" if task.due else ""
return f"[{mark}] {task.id} {task.title}{due}"
nisse-dev/src/nisse_dev/bench/tasks/long-milestone/solution/tracker/models.py
+12
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@@ -0,0 +1,12 @@
"""The task record."""
from dataclasses import dataclass
from datetime import date
@dataclass
class Task:
id: int
title: str
done: bool = False
due: date | None = None
nisse-dev/src/nisse_dev/bench/tasks/long-milestone/solution/tracker/storage.py
+44
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@@ -0,0 +1,44 @@
"""Tasks are kept in one JSON file."""
import json
from datetime import date
from pathlib import Path
from tracker.models import Task
VERSION = 2
def load(path: Path) -> list[Task]:
"""Every task in the file; an empty list when there is no file yet."""
if not path.exists():
return []
data = json.loads(path.read_text())
version = data.get("version")
if version not in (1, 2):
raise ValueError(f"unsupported tasks file version: {version}")
return [
Task(
id=t["id"],
title=t["title"],
done=t["done"],
due=date.fromisoformat(t["due"]) if t.get("due") else None,
)
for t in data["tasks"]
]
def save(path: Path, tasks: list[Task]) -> None:
data = {
"version": VERSION,
"tasks": [
{
"id": t.id,
"title": t.title,
"done": t.done,
"due": t.due.isoformat() if t.due else None,
}
for t in tasks
],
}
path.write_text(json.dumps(data, indent=2))
nisse-dev/src/nisse_dev/bench/tasks/long-milestone/task.yaml
+40
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@@ -0,0 +1,40 @@
title: Carry out a long milestone whose steps build on each other
check: python -m pytest -q .bench_check
plan: |
Milestone 1: add due dates to the task tracker.
Run `python -m pytest -q` after each step; every test must still pass
before you start the next one. Use `datetime.date` for dates throughout.
1. `tracker/models.py`: add a field `due: date | None = None` to `Task`,
after `done`.
2. `tracker/storage.py`: `save` writes `"version": 2`, and each task's
`"due"` as an ISO string ("2026-10-01") or null. `load` reads version 1
files (their tasks have no "due"; use None) and version 2 files. Any
other version raises `ValueError("unsupported tasks file version: N")`,
with N the version found.
3. `tracker/core.py`: `add_task` takes a keyword argument
`due: date | None = None` and stores it on the new task.
4. `tracker/core.py`: change `sort_tasks` so that tasks not done come
before tasks done; within each of those two groups, tasks with a due
date come first, earliest due date first, then tasks without one; ties
are broken by id.
5. `tracker/core.py`: add `overdue(tasks: list[Task], today: date) -> list[Task]`:
tasks not done whose due date is before `today`, in `sort_tasks` order.
6. `tracker/formatting.py`: `format_task` takes a second argument
`today: date`. A task with a due date gets " (due 2026-10-01)" after its
title. An overdue task (as in step 5) shows `[!]` instead of `[ ]`.
Examples: "[ ] 3 Buy milk", "[x] 4 Call Bob (due 2026-10-01)",
"[!] 5 Pay rent (due 2026-09-01)".
7. `tracker/cli.py`: pass today's date to `format_task`. Today is the
`TRACKER_TODAY` environment variable (YYYY-MM-DD) when it is set, else
`date.today()`. `add` gains an option `--due YYYY-MM-DD`; an invalid date
must call `parser.error("invalid --due date: VALUE")`, which exits with
status 2. `list` gains a flag `--overdue` that lists only overdue tasks.
8. Add tests under `tests/` for each of steps 1-7, run `python -m pytest -q`
until everything passes, then check the whole flow by running:
`TRACKER_FILE=/tmp/tracker-check.json TRACKER_TODAY=2026-10-05 python -m tracker add "Pay rent" --due 2026-10-01`
then the same with `list --overdue`, which must print
`[!] 1 Pay rent (due 2026-10-01)`. Delete /tmp/tracker-check.json
before and after.
9. Call finish.
nisse-dev/src/nisse_dev/bench/tasks/long-output/hidden/test_hidden.py
+11
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@@ -0,0 +1,11 @@
import pytest
from rates import RATES
def test_every_region_is_present_once() -> None:
assert sorted(RATES) == [f"R{n:03d}" for n in range(300)]
@pytest.mark.parametrize("n", range(300))
def test_rate(n: int) -> None:
assert RATES[f"R{n:03d}"] == pytest.approx(n % 25 / 100)
nisse-dev/src/nisse_dev/bench/tasks/long-output/seed/rates.py
+304
View File
@@ -0,0 +1,304 @@
"""Tax rate per region code."""
RATES = {
"R000": 0.00,
"R001": 0.01,
"R002": 0.02,
"R003": 0.03,
"R004": 0.04,
"R005": 0.05,
"R006": 0.06,
"R007": 0.07,
"R008": 0.08,
"R009": 0.09,
"R010": 0.10,
"R011": 0.11,
"R012": 0.12,
"R013": 0.13,
"R014": 0.14,
"R015": 0.15,
"R016": 0.16,
"R017": 0.17,
"R018": 0.18,
"R019": 0.19,
"R020": 0.20,
"R021": 0.21,
"R022": 0.22,
"R023": 0.23,
"R024": 0.24,
"R025": 0.00,
"R026": 0.01,
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}
nisse-dev/src/nisse_dev/bench/tasks/long-output/seed/test_rates.py
+7
View File
@@ -0,0 +1,7 @@
import pytest
from rates import RATES
@pytest.mark.parametrize("code", [f"R{n:03d}" for n in range(300)])
def test_rate(code: str) -> None:
assert RATES[code] == pytest.approx(int(code[1:]) % 25 / 100)
nisse-dev/src/nisse_dev/bench/tasks/long-output/solution/rates.py
+304
View File
@@ -0,0 +1,304 @@
"""Tax rate per region code."""
RATES = {
"R000": 0.00,
"R001": 0.01,
"R002": 0.02,
"R003": 0.03,
"R004": 0.04,
"R005": 0.05,
"R006": 0.06,
"R007": 0.07,
"R008": 0.08,
"R009": 0.09,
"R010": 0.10,
"R011": 0.11,
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"R013": 0.13,
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}
nisse-dev/src/nisse_dev/bench/tasks/long-output/task.yaml
+12
View File
@@ -0,0 +1,12 @@
title: Find one failure in long test output
check: python -m pytest -q .bench_check
plan: |
Milestone 1: fix the one wrong entry in the rate table.
1. Run `python -m pytest -v`. Exactly one test fails, and its output names a
region code.
2. The tests are correct. In `rates.py`, the rate for region code RNNN must be
(NNN mod 25) / 100. Correct only the failing region's entry; leave every
other line as it is.
3. Run `python -m pytest -q` until every test passes.
4. Call finish.
nisse-dev/src/nisse_dev/bench/tasks/multi-file/hidden/test_hidden.py
+33
View File
@@ -0,0 +1,33 @@
import subprocess
import sys
from greeter.cli import main
from greeter.core import greet
def test_default_is_unchanged() -> None:
assert greet("Bob") == "Hello, Bob!"
def test_shout() -> None:
assert greet("Bob", shout=True) == "HELLO, BOB!"
def test_cli_default(capsys) -> None:
assert main(["Ada"]) == 0
assert capsys.readouterr().out == "Hello, Ada!\n"
def test_cli_shout(capsys) -> None:
assert main(["--shout", "Ada"]) == 0
assert capsys.readouterr().out == "HELLO, ADA!\n"
def test_module_entry_point() -> None:
result = subprocess.run(
[sys.executable, "-m", "greeter", "--shout", "Bob"],
capture_output=True,
text=True,
check=True,
)
assert result.stdout == "HELLO, BOB!\n"
nisse-dev/src/nisse_dev/bench/tasks/multi-file/seed/greeter/__init__.py
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Whitespace-only changes.
nisse-dev/src/nisse_dev/bench/tasks/multi-file/seed/greeter/__main__.py
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from greeter.cli import main
raise SystemExit(main())
nisse-dev/src/nisse_dev/bench/tasks/multi-file/seed/greeter/cli.py
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import argparse
from greeter.core import greet
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser(prog="greeter")
parser.add_argument("name")
args = parser.parse_args(argv)
print(greet(args.name))
return 0
nisse-dev/src/nisse_dev/bench/tasks/multi-file/seed/greeter/core.py
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def greet(name: str) -> str:
return f"Hello, {name}!"
nisse-dev/src/nisse_dev/bench/tasks/multi-file/seed/tests/test_greeter.py
+11
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from greeter.cli import main
from greeter.core import greet
def test_greet() -> None:
assert greet("Bob") == "Hello, Bob!"
def test_cli_prints_the_greeting(capsys) -> None:
assert main(["Bob"]) == 0
assert capsys.readouterr().out == "Hello, Bob!\n"
nisse-dev/src/nisse_dev/bench/tasks/multi-file/solution/greeter/cli.py
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import argparse
from greeter.core import greet
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser(prog="greeter")
parser.add_argument("name")
parser.add_argument("--shout", action="store_true")
args = parser.parse_args(argv)
print(greet(args.name, shout=args.shout))
return 0
nisse-dev/src/nisse_dev/bench/tasks/multi-file/solution/greeter/core.py
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def greet(name: str, shout: bool = False) -> str:
greeting = f"Hello, {name}!"
return greeting.upper() if shout else greeting
nisse-dev/src/nisse_dev/bench/tasks/multi-file/task.yaml
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title: Thread a new option through several files
check: python -m pytest -q .bench_check
plan: |
Milestone 1: add a --shout option to the greeter.
1. In `greeter/core.py`, give `greet` a keyword argument `shout: bool = False`.
When it is true, return the whole greeting in upper case:
greet("Bob", shout=True) == "HELLO, BOB!". The default behaviour is unchanged.
2. In `greeter/cli.py`, add a `--shout` flag (action="store_true") and pass it
to `greet`.
3. Add two tests to `tests/test_greeter.py`: one for greet with shout=True, and
one that calls `main(["--shout", "Bob"])` and checks the printed output
using pytest's capsys fixture.
4. Run `python -m pytest -q` until every test passes, and check that
`python -m greeter --shout Bob` prints `HELLO, BOB!`.
5. Call finish.
nisse-dev/src/nisse_dev/bench/tasks/refactor-rename/hidden/test_hidden.py
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import re
import subprocess
import sys
from pathlib import Path
import pytest
ROOT = Path.cwd()
OLD = re.compile(r"\bload_config\b")
def project_files() -> list[Path]:
return [
p
for p in [*ROOT.rglob("*.py"), *ROOT.rglob("*.md")]
if ".bench_check" not in p.parts and "__pycache__" not in p.parts
]
def test_the_old_name_is_gone_everywhere() -> None:
left = [
f"{p.relative_to(ROOT)}:{n}: {line.strip()}"
for p in project_files()
for n, line in enumerate(p.read_text().splitlines(), 1)
if OLD.search(line)
]
assert not left, "load_config still appears:\n" + "\n".join(left)
def test_the_new_name_is_exported() -> None:
import shopkeeper
from shopkeeper.config import read_settings
assert shopkeeper.read_settings is read_settings
assert "read_settings" in shopkeeper.__all__
assert not hasattr(shopkeeper, "load_config")
def test_the_similar_names_were_left_alone() -> None:
from shopkeeper import config
assert callable(config.load_config_defaults)
assert callable(config.reload_config)
assert not hasattr(config, "read_settings_defaults")
assert not hasattr(config, "reread_settings")
readme = (ROOT / "README.md").read_text()
assert "load_config_defaults()" in readme
assert "reload_config(settings)" in readme
def test_the_new_name_appears_in_every_file_that_used_the_old_one() -> None:
for name in [
"catalog",
"pricing",
"inventory",
"notifications",
"orders",
"reports",
"storage",
"cli",
]:
source = (ROOT / "shopkeeper" / f"{name}.py").read_text()
assert "read_settings" in source, f"shopkeeper/{name}.py"
assert "read_settings" in (ROOT / "README.md").read_text()
def test_behaviour_is_unchanged(
tmp_path: Path, monkeypatch: pytest.MonkeyPatch
) -> None:
from shopkeeper.config import read_settings, reload_config
(tmp_path / "catalog.csv").write_text("sku,name,unit_price\nSKU-1,bolt,0.25\n")
ini = tmp_path / "shop.ini"
ini.write_text(
f"[shop]\ncurrency = EUR\ntax_percent = 20\ncatalog_file = {tmp_path / 'catalog.csv'}\n"
)
monkeypatch.delenv("SHOPKEEPER_CONFIG", raising=False)
settings = read_settings(ini)
assert (settings.currency, settings.tax_percent, settings.low_stock_threshold) == (
"EUR",
20.0,
5,
)
ini.write_text(ini.read_text().replace("EUR", "GBP"))
assert reload_config(settings).currency == "GBP"
assert read_settings().currency == "USD"
def test_the_projects_own_tests_pass() -> None:
run = subprocess.run(
[sys.executable, "-m", "pytest", "-q", "tests"],
cwd=ROOT,
capture_output=True,
text=True,
check=False,
)
assert run.returncode == 0, run.stdout[-2000:]
def test_the_command_line_still_works(tmp_path: Path) -> None:
(tmp_path / "catalog.csv").write_text("sku,name,unit_price\nSKU-2,hammer,12.50\n")
ini = tmp_path / "shop.ini"
ini.write_text(
f"[shop]\ncurrency = EUR\ntax_percent = 20\ncatalog_file = {tmp_path / 'catalog.csv'}\n"
)
run = subprocess.run(
[
sys.executable,
"-m",
"shopkeeper",
"--config",
str(ini),
"price",
"SKU-2",
"2",
],
cwd=ROOT,
capture_output=True,
text=True,
check=False,
)
assert run.stdout.strip() == "30.00 EUR", run.stderr[-1000:]
nisse-dev/src/nisse_dev/bench/tasks/refactor-rename/seed/README.md
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# shopkeeper
A small shop back office: catalogue, pricing, stock, orders and reports.
## Settings
Settings come from an INI file. Load them once and pass them around:
```python
from shopkeeper import load_config
settings = load_config("shop.ini")
```
`load_config()` with no path uses `SHOPKEEPER_CONFIG`, or the built-in
defaults from `load_config_defaults()` when that is unset. After editing the
file, `reload_config(settings)` reads it again.
## Command line
python -m shopkeeper --config shop.ini price SKU-1 3
python -m shopkeeper --config shop.ini stock
nisse-dev/src/nisse_dev/bench/tasks/refactor-rename/seed/shopkeeper/__init__.py
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"""A small shop back office."""
from shopkeeper.config import Settings, load_config, load_config_defaults, reload_config
__all__ = ["Settings", "load_config", "load_config_defaults", "reload_config"]
nisse-dev/src/nisse_dev/bench/tasks/refactor-rename/seed/shopkeeper/__main__.py
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from shopkeeper.cli import main
raise SystemExit(main())
nisse-dev/src/nisse_dev/bench/tasks/refactor-rename/seed/shopkeeper/catalog.py
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"""The product catalogue: SKU, name and unit price, from a CSV file."""
import csv
from dataclasses import dataclass
from pathlib import Path
from shopkeeper.config import Settings, load_config
@dataclass(frozen=True)
class Product:
sku: str
name: str
unit_price: float
def read_catalog(settings: Settings | None = None) -> dict[str, Product]:
"""Every product, keyed by SKU. Uses load_config() when no settings are given."""
settings = settings or load_config()
path = Path(settings.catalog_file)
with path.open(newline="") as handle:
return {
row["sku"]: Product(row["sku"], row["name"], float(row["unit_price"]))
for row in csv.DictReader(handle)
}
nisse-dev/src/nisse_dev/bench/tasks/refactor-rename/seed/shopkeeper/cli.py
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"""Command line: python -m shopkeeper --config FILE COMMAND ..."""
import argparse
from pathlib import Path
from shopkeeper import storage
from shopkeeper.catalog import read_catalog
from shopkeeper.config import load_config
from shopkeeper.pricing import format_price, with_tax
from shopkeeper.reports import stock_report
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser(prog="shopkeeper")
parser.add_argument("--config", help="settings file (see load_config)")
parser.add_argument("--stock-file", default="stock.json")
commands = parser.add_subparsers(dest="command", required=True)
price = commands.add_parser(
"price", help="price of a quantity of one SKU, with tax"
)
price.add_argument("sku")
price.add_argument("quantity", type=int)
commands.add_parser("stock", help="stock report")
args = parser.parse_args(argv)
settings = load_config(args.config)
catalog = read_catalog(settings)
if args.command == "price":
amount = with_tax(catalog[args.sku].unit_price * args.quantity, settings)
print(format_price(amount, settings))
else:
print(
stock_report(
catalog, storage.load(Path(args.stock_file), settings), settings
)
)
return 0
nisse-dev/src/nisse_dev/bench/tasks/refactor-rename/seed/shopkeeper/config.py
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"""Settings, read from an INI file."""
import configparser
import os
from dataclasses import dataclass
from pathlib import Path
from typing import Any
ENV_VAR = "SHOPKEEPER_CONFIG"
@dataclass(frozen=True)
class Settings:
currency: str
tax_percent: float
low_stock_threshold: int
catalog_file: str
notify_email: str
source: str | None = None # the file these came from, if any
def load_config_defaults() -> dict[str, Any]:
"""The built-in settings, used for anything the file leaves out."""
return {
"currency": "USD",
"tax_percent": 0.0,
"low_stock_threshold": 5,
"catalog_file": "catalog.csv",
"notify_email": "",
}
def load_config(path: str | Path | None = None) -> Settings:
"""Settings from `path`, or from $SHOPKEEPER_CONFIG, or the defaults.
Values missing from the file fall back to load_config_defaults().
"""
values = load_config_defaults()
chosen = path or os.environ.get(ENV_VAR)
if chosen:
parser = configparser.ConfigParser()
if not parser.read(chosen):
raise FileNotFoundError(f"no settings file at {chosen}")
section = parser["shop"] if parser.has_section("shop") else {}
for key, default in values.items():
if key in section:
values[key] = type(default)(section[key])
return Settings(**values, source=str(chosen) if chosen else None)
def reload_config(settings: Settings) -> Settings:
"""Read the settings again from the file they came from."""
return load_config(settings.source)
nisse-dev/src/nisse_dev/bench/tasks/refactor-rename/seed/shopkeeper/inventory.py
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"""Stock levels."""
from shopkeeper.config import Settings, load_config
class Inventory:
def __init__(self, settings: Settings | None = None) -> None:
self.settings = settings or load_config()
self._stock: dict[str, int] = {}
def add(self, sku: str, quantity: int) -> None:
if quantity <= 0:
raise ValueError("quantity must be positive")
self._stock[sku] = self._stock.get(sku, 0) + quantity
def take(self, sku: str, quantity: int) -> None:
if self._stock.get(sku, 0) < quantity:
raise ValueError(f"not enough {sku} in stock")
self._stock[sku] -= quantity
def level(self, sku: str) -> int:
return self._stock.get(sku, 0)
def low_stock(self) -> list[str]:
"""SKUs below the threshold from load_config(), alphabetically."""
limit = self.settings.low_stock_threshold
return sorted(sku for sku, count in self._stock.items() if count < limit)
nisse-dev/src/nisse_dev/bench/tasks/refactor-rename/seed/shopkeeper/notifications.py
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"""Messages to the shop owner. Collected in memory; sending is elsewhere."""
import logging
from shopkeeper.config import Settings, load_config
log = logging.getLogger(__name__)
class Notifier:
def __init__(self, settings: Settings | None = None) -> None:
self.settings = settings or load_config()
self.outbox: list[tuple[str, str]] = []
def notify(self, message: str) -> bool:
if not self.settings.notify_email:
log.info("no notify_email in load_config settings; dropped: %s", message)
return False
self.outbox.append((self.settings.notify_email, message))
return True
nisse-dev/src/nisse_dev/bench/tasks/refactor-rename/seed/shopkeeper/orders.py
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"""Orders: take stock, price the lines, warn when stock runs low."""
from dataclasses import dataclass, field
from shopkeeper.catalog import Product
from shopkeeper.config import Settings, load_config
from shopkeeper.inventory import Inventory
from shopkeeper.notifications import Notifier
from shopkeeper.pricing import with_tax
@dataclass
class Order:
lines: list[tuple[Product, int]] = field(default_factory=list)
def add(self, product: Product, quantity: int) -> None:
self.lines.append((product, quantity))
def net(self) -> float:
return round(sum(p.unit_price * q for p, q in self.lines), 2)
def place(
order: Order,
inventory: Inventory,
notifier: Notifier,
settings: Settings | None = None,
) -> float:
"""Take the order's stock and return its price with tax."""
settings = settings or load_config()
for product, quantity in order.lines:
inventory.take(product.sku, quantity)
for sku in inventory.low_stock():
notifier.notify(f"low stock: {sku}")
return with_tax(order.net(), settings)
nisse-dev/src/nisse_dev/bench/tasks/refactor-rename/seed/shopkeeper/pricing.py
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"""Prices with tax, in the shop's currency."""
from shopkeeper.config import Settings, load_config
def with_tax(amount: float, settings: Settings | None = None) -> float:
settings = settings or load_config()
return round(amount * (1 + settings.tax_percent / 100), 2)
def format_price(amount: float, settings: Settings | None = None) -> str:
# The currency comes from load_config, never from the catalogue.
settings = settings or load_config()
return f"{amount:.2f} {settings.currency}"
nisse-dev/src/nisse_dev/bench/tasks/refactor-rename/seed/shopkeeper/reports.py
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"""Plain-text reports."""
from shopkeeper.catalog import Product
from shopkeeper.config import Settings, load_config
from shopkeeper.inventory import Inventory
from shopkeeper.pricing import format_price
def stock_report(
catalog: dict[str, Product], inventory: Inventory, settings: Settings | None = None
) -> str:
settings = settings or load_config()
lines = []
for sku in sorted(catalog):
product = catalog[sku]
value = product.unit_price * inventory.level(sku)
lines.append(
f"{sku:<8} {product.name:<16} {inventory.level(sku):>5} "
f"{format_price(value, settings):>14}"
)
return "\n".join(lines)
nisse-dev/src/nisse_dev/bench/tasks/refactor-rename/seed/shopkeeper/storage.py
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"""Saving and loading stock levels as JSON."""
import json
from pathlib import Path
from shopkeeper.config import Settings, load_config
from shopkeeper.inventory import Inventory
def save(inventory: Inventory, path: Path) -> None:
levels = {sku: inventory.level(sku) for sku in inventory._stock}
path.write_text(json.dumps(levels, indent=2, sort_keys=True))
def load(path: Path, settings: Settings | None = None) -> Inventory:
inventory = Inventory(settings or load_config())
for sku, count in json.loads(path.read_text()).items():
if count:
inventory.add(sku, count)
return inventory
nisse-dev/src/nisse_dev/bench/tasks/refactor-rename/seed/tests/conftest.py
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from pathlib import Path
import pytest
@pytest.fixture
def shop(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> Path:
"""A shop directory with a settings file and a catalogue."""
(tmp_path / "catalog.csv").write_text(
"sku,name,unit_price\nSKU-1,bolt,0.25\nSKU-2,hammer,12.50\n"
)
ini = tmp_path / "shop.ini"
ini.write_text(
"[shop]\ncurrency = EUR\ntax_percent = 20\nlow_stock_threshold = 3\n"
f"catalog_file = {tmp_path / 'catalog.csv'}\nnotify_email = owner@example.com\n"
)
monkeypatch.delenv("SHOPKEEPER_CONFIG", raising=False)
monkeypatch.chdir(tmp_path)
return ini
nisse-dev/src/nisse_dev/bench/tasks/refactor-rename/seed/tests/test_config.py
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import pytest
from shopkeeper import load_config, load_config_defaults, reload_config
def test_defaults_without_a_file() -> None:
settings = load_config()
assert settings.currency == load_config_defaults()["currency"]
assert settings.source is None
def test_values_from_the_file(shop) -> None:
settings = load_config(shop)
assert (settings.currency, settings.tax_percent, settings.low_stock_threshold) == (
"EUR",
20.0,
3,
)
def test_environment_variable(shop, monkeypatch: pytest.MonkeyPatch) -> None:
monkeypatch.setenv("SHOPKEEPER_CONFIG", str(shop))
assert load_config().currency == "EUR"
def test_reload_reads_the_file_again(shop) -> None:
settings = load_config(shop)
shop.write_text(shop.read_text().replace("EUR", "GBP"))
assert reload_config(settings).currency == "GBP"
def test_a_missing_file_is_an_error(tmp_path) -> None:
with pytest.raises(FileNotFoundError):
load_config(tmp_path / "nope.ini")
nisse-dev/src/nisse_dev/bench/tasks/refactor-rename/seed/tests/test_shop.py
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from shopkeeper.catalog import read_catalog
from shopkeeper.cli import main
from shopkeeper.config import load_config
from shopkeeper.inventory import Inventory
from shopkeeper.notifications import Notifier
from shopkeeper.orders import Order, place
from shopkeeper.pricing import format_price, with_tax
def test_tax_and_format(shop) -> None:
settings = load_config(shop)
assert with_tax(10.0, settings) == 12.0
assert format_price(12.0, settings) == "12.00 EUR"
def test_placing_an_order_takes_stock_and_warns(shop) -> None:
settings = load_config(shop)
catalog = read_catalog(settings)
inventory = Inventory(settings)
inventory.add("SKU-1", 10)
notifier = Notifier(settings)
order = Order()
order.add(catalog["SKU-1"], 8)
assert place(order, inventory, notifier, settings) == 2.4
assert inventory.level("SKU-1") == 2
assert notifier.outbox == [("owner@example.com", "low stock: SKU-1")]
def test_cli_price(shop, capsys) -> None:
assert main(["--config", str(shop), "price", "SKU-2", "2"]) == 0
assert capsys.readouterr().out.strip() == "30.00 EUR"
nisse-dev/src/nisse_dev/bench/tasks/refactor-rename/solution/README.md
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# shopkeeper
A small shop back office: catalogue, pricing, stock, orders and reports.
## Settings
Settings come from an INI file. Load them once and pass them around:
```python
from shopkeeper import read_settings
settings = read_settings("shop.ini")
```
`read_settings()` with no path uses `SHOPKEEPER_CONFIG`, or the built-in
defaults from `load_config_defaults()` when that is unset. After editing the
file, `reload_config(settings)` reads it again.
## Command line
python -m shopkeeper --config shop.ini price SKU-1 3
python -m shopkeeper --config shop.ini stock
nisse-dev/src/nisse_dev/bench/tasks/refactor-rename/solution/shopkeeper/__init__.py
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"""A small shop back office."""
from shopkeeper.config import (
Settings,
load_config_defaults,
read_settings,
reload_config,
)
__all__ = ["Settings", "load_config_defaults", "read_settings", "reload_config"]
nisse-dev/src/nisse_dev/bench/tasks/refactor-rename/solution/shopkeeper/catalog.py
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"""The product catalogue: SKU, name and unit price, from a CSV file."""
import csv
from dataclasses import dataclass
from pathlib import Path
from shopkeeper.config import Settings, read_settings
@dataclass(frozen=True)
class Product:
sku: str
name: str
unit_price: float
def read_catalog(settings: Settings | None = None) -> dict[str, Product]:
"""Every product, keyed by SKU. Uses read_settings() when no settings are given."""
settings = settings or read_settings()
path = Path(settings.catalog_file)
with path.open(newline="") as handle:
return {
row["sku"]: Product(row["sku"], row["name"], float(row["unit_price"]))
for row in csv.DictReader(handle)
}
nisse-dev/src/nisse_dev/bench/tasks/refactor-rename/solution/shopkeeper/cli.py
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"""Command line: python -m shopkeeper --config FILE COMMAND ..."""
import argparse
from pathlib import Path
from shopkeeper import storage
from shopkeeper.catalog import read_catalog
from shopkeeper.config import read_settings
from shopkeeper.pricing import format_price, with_tax
from shopkeeper.reports import stock_report
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser(prog="shopkeeper")
parser.add_argument("--config", help="settings file (see read_settings)")
parser.add_argument("--stock-file", default="stock.json")
commands = parser.add_subparsers(dest="command", required=True)
price = commands.add_parser(
"price", help="price of a quantity of one SKU, with tax"
)
price.add_argument("sku")
price.add_argument("quantity", type=int)
commands.add_parser("stock", help="stock report")
args = parser.parse_args(argv)
settings = read_settings(args.config)
catalog = read_catalog(settings)
if args.command == "price":
amount = with_tax(catalog[args.sku].unit_price * args.quantity, settings)
print(format_price(amount, settings))
else:
print(
stock_report(
catalog, storage.load(Path(args.stock_file), settings), settings
)
)
return 0
nisse-dev/src/nisse_dev/bench/tasks/refactor-rename/solution/shopkeeper/config.py
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"""Settings, read from an INI file."""
import configparser
import os
from dataclasses import dataclass
from pathlib import Path
from typing import Any
ENV_VAR = "SHOPKEEPER_CONFIG"
@dataclass(frozen=True)
class Settings:
currency: str
tax_percent: float
low_stock_threshold: int
catalog_file: str
notify_email: str
source: str | None = None # the file these came from, if any
def load_config_defaults() -> dict[str, Any]:
"""The built-in settings, used for anything the file leaves out."""
return {
"currency": "USD",
"tax_percent": 0.0,
"low_stock_threshold": 5,
"catalog_file": "catalog.csv",
"notify_email": "",
}
def read_settings(path: str | Path | None = None) -> Settings:
"""Settings from `path`, or from $SHOPKEEPER_CONFIG, or the defaults.
Values missing from the file fall back to load_config_defaults().
"""
values = load_config_defaults()
chosen = path or os.environ.get(ENV_VAR)
if chosen:
parser = configparser.ConfigParser()
if not parser.read(chosen):
raise FileNotFoundError(f"no settings file at {chosen}")
section = parser["shop"] if parser.has_section("shop") else {}
for key, default in values.items():
if key in section:
values[key] = type(default)(section[key])
return Settings(**values, source=str(chosen) if chosen else None)
def reload_config(settings: Settings) -> Settings:
"""Read the settings again from the file they came from."""
return read_settings(settings.source)
nisse-dev/src/nisse_dev/bench/tasks/refactor-rename/solution/shopkeeper/inventory.py
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"""Stock levels."""
from shopkeeper.config import Settings, read_settings
class Inventory:
def __init__(self, settings: Settings | None = None) -> None:
self.settings = settings or read_settings()
self._stock: dict[str, int] = {}
def add(self, sku: str, quantity: int) -> None:
if quantity <= 0:
raise ValueError("quantity must be positive")
self._stock[sku] = self._stock.get(sku, 0) + quantity
def take(self, sku: str, quantity: int) -> None:
if self._stock.get(sku, 0) < quantity:
raise ValueError(f"not enough {sku} in stock")
self._stock[sku] -= quantity
def level(self, sku: str) -> int:
return self._stock.get(sku, 0)
def low_stock(self) -> list[str]:
"""SKUs below the threshold from read_settings(), alphabetically."""
limit = self.settings.low_stock_threshold
return sorted(sku for sku, count in self._stock.items() if count < limit)
nisse-dev/src/nisse_dev/bench/tasks/refactor-rename/solution/shopkeeper/notifications.py
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"""Messages to the shop owner. Collected in memory; sending is elsewhere."""
import logging
from shopkeeper.config import Settings, read_settings
log = logging.getLogger(__name__)
class Notifier:
def __init__(self, settings: Settings | None = None) -> None:
self.settings = settings or read_settings()
self.outbox: list[tuple[str, str]] = []
def notify(self, message: str) -> bool:
if not self.settings.notify_email:
log.info("no notify_email in read_settings settings; dropped: %s", message)
return False
self.outbox.append((self.settings.notify_email, message))
return True
nisse-dev/src/nisse_dev/bench/tasks/refactor-rename/solution/shopkeeper/orders.py
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"""Orders: take stock, price the lines, warn when stock runs low."""
from dataclasses import dataclass, field
from shopkeeper.catalog import Product
from shopkeeper.config import Settings, read_settings
from shopkeeper.inventory import Inventory
from shopkeeper.notifications import Notifier
from shopkeeper.pricing import with_tax
@dataclass
class Order:
lines: list[tuple[Product, int]] = field(default_factory=list)
def add(self, product: Product, quantity: int) -> None:
self.lines.append((product, quantity))
def net(self) -> float:
return round(sum(p.unit_price * q for p, q in self.lines), 2)
def place(
order: Order,
inventory: Inventory,
notifier: Notifier,
settings: Settings | None = None,
) -> float:
"""Take the order's stock and return its price with tax."""
settings = settings or read_settings()
for product, quantity in order.lines:
inventory.take(product.sku, quantity)
for sku in inventory.low_stock():
notifier.notify(f"low stock: {sku}")
return with_tax(order.net(), settings)
nisse-dev/src/nisse_dev/bench/tasks/refactor-rename/solution/shopkeeper/pricing.py
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"""Prices with tax, in the shop's currency."""
from shopkeeper.config import Settings, read_settings
def with_tax(amount: float, settings: Settings | None = None) -> float:
settings = settings or read_settings()
return round(amount * (1 + settings.tax_percent / 100), 2)
def format_price(amount: float, settings: Settings | None = None) -> str:
# The currency comes from read_settings, never from the catalogue.
settings = settings or read_settings()
return f"{amount:.2f} {settings.currency}"
nisse-dev/src/nisse_dev/bench/tasks/refactor-rename/solution/shopkeeper/reports.py
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"""Plain-text reports."""
from shopkeeper.catalog import Product
from shopkeeper.config import Settings, read_settings
from shopkeeper.inventory import Inventory
from shopkeeper.pricing import format_price
def stock_report(
catalog: dict[str, Product], inventory: Inventory, settings: Settings | None = None
) -> str:
settings = settings or read_settings()
lines = []
for sku in sorted(catalog):
product = catalog[sku]
value = product.unit_price * inventory.level(sku)
lines.append(
f"{sku:<8} {product.name:<16} {inventory.level(sku):>5} "
f"{format_price(value, settings):>14}"
)
return "\n".join(lines)
nisse-dev/src/nisse_dev/bench/tasks/refactor-rename/solution/shopkeeper/storage.py
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"""Saving and loading stock levels as JSON."""
import json
from pathlib import Path
from shopkeeper.config import Settings, read_settings
from shopkeeper.inventory import Inventory
def save(inventory: Inventory, path: Path) -> None:
levels = {sku: inventory.level(sku) for sku in inventory._stock}
path.write_text(json.dumps(levels, indent=2, sort_keys=True))
def load(path: Path, settings: Settings | None = None) -> Inventory:
inventory = Inventory(settings or read_settings())
for sku, count in json.loads(path.read_text()).items():
if count:
inventory.add(sku, count)
return inventory
nisse-dev/src/nisse_dev/bench/tasks/refactor-rename/solution/tests/test_config.py
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import pytest
from shopkeeper import load_config_defaults, read_settings, reload_config
def test_defaults_without_a_file() -> None:
settings = read_settings()
assert settings.currency == load_config_defaults()["currency"]
assert settings.source is None
def test_values_from_the_file(shop) -> None:
settings = read_settings(shop)
assert (settings.currency, settings.tax_percent, settings.low_stock_threshold) == (
"EUR",
20.0,
3,
)
def test_environment_variable(shop, monkeypatch: pytest.MonkeyPatch) -> None:
monkeypatch.setenv("SHOPKEEPER_CONFIG", str(shop))
assert read_settings().currency == "EUR"
def test_reload_reads_the_file_again(shop) -> None:
settings = read_settings(shop)
shop.write_text(shop.read_text().replace("EUR", "GBP"))
assert reload_config(settings).currency == "GBP"
def test_a_missing_file_is_an_error(tmp_path) -> None:
with pytest.raises(FileNotFoundError):
read_settings(tmp_path / "nope.ini")
nisse-dev/src/nisse_dev/bench/tasks/refactor-rename/solution/tests/test_shop.py
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from shopkeeper.catalog import read_catalog
from shopkeeper.cli import main
from shopkeeper.config import read_settings
from shopkeeper.inventory import Inventory
from shopkeeper.notifications import Notifier
from shopkeeper.orders import Order, place
from shopkeeper.pricing import format_price, with_tax
def test_tax_and_format(shop) -> None:
settings = read_settings(shop)
assert with_tax(10.0, settings) == 12.0
assert format_price(12.0, settings) == "12.00 EUR"
def test_placing_an_order_takes_stock_and_warns(shop) -> None:
settings = read_settings(shop)
catalog = read_catalog(settings)
inventory = Inventory(settings)
inventory.add("SKU-1", 10)
notifier = Notifier(settings)
order = Order()
order.add(catalog["SKU-1"], 8)
assert place(order, inventory, notifier, settings) == 2.4
assert inventory.level("SKU-1") == 2
assert notifier.outbox == [("owner@example.com", "low stock: SKU-1")]
def test_cli_price(shop, capsys) -> None:
assert main(["--config", str(shop), "price", "SKU-2", "2"]) == 0
assert capsys.readouterr().out.strip() == "30.00 EUR"
nisse-dev/src/nisse_dev/bench/tasks/refactor-rename/task.yaml
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title: Rename a function used across a package
check: python -m pytest -q .bench_check
plan: |
Milestone 1: rename `load_config` to `read_settings` across the project.
1. Find every use of `load_config`: run
`grep -rnw load_config --include=*.py --include=*.md .`
It is defined in `shopkeeper/config.py` and used in many other modules,
in tests, in docstrings, in comments, in a log message and in
`README.md`.
2. Rename the function to `read_settings` everywhere: its definition,
imports, calls, `__all__`, docstrings, comments, strings and
`README.md`. Afterwards the grep in step 1 must print nothing.
3. Rename only that exact name. `load_config_defaults` and `reload_config`
are different functions and keep their names. Change no behaviour.
4. Update the tests under `tests/` to use the new name, and run
`python -m pytest -q` until every test passes.
5. Call finish.
nisse-dev/src/nisse_dev/bench/tasks/spec-parser/hidden/test_hidden.py
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import pytest
from durations import parse_duration
@pytest.mark.parametrize(
("text", "seconds"),
[
("2h", 7200), # rule 1
("1h30m", 5400),
("1d2h3m4s", 93784),
("45s", 45),
("1d", 86400),
("1H30M", 5400), # rule 2
("2D", 172800),
(" 1h 30m ", 5400), # rule 4
("1d 2h 3m 4s", 93784),
("90", 90), # rule 5
(" 7 ", 7),
("1.5h", 5400), # rule 6
("1.5m", 90),
("0.25h", 900),
("1:30", 90), # rule 7
("1:02:03", 3723),
("90:00", 5400),
("0:05", 5),
(" 1:30 ", 90),
("0", 0), # rule 8
("0s", 0),
("0:00", 0),
],
)
def test_valid(text: str, seconds: int) -> None:
assert parse_duration(text) == seconds
@pytest.mark.parametrize(
"text",
[
"1x", # rule 1: not a unit
"h",
".5h", # not a number
"1.h",
"+2h",
"30m1h", # rule 3
"1h2h",
"1s1m",
"1 h", # rule 4
"1h 30", # rule 5
"30 1h",
"1h1.5m", # rule 6
"0.5s",
"0.01m",
"1.5",
"1:60", # rule 7
"1:2",
"1:02:60",
"1:02:03:04",
"1.5:30",
"1: 30",
"", # rule 8
" ",
"-5",
"-1h",
],
)
def test_invalid(text: str) -> None:
with pytest.raises(ValueError) as caught:
parse_duration(text)
assert repr(text) in str(caught.value)
def test_the_result_is_an_int() -> None:
assert type(parse_duration("1.5h")) is int
def test_the_plan_asked_for_tests_and_they_pass() -> None:
# At least one valid and one invalid input for each of the eight rules.
import subprocess
import sys
run = subprocess.run(
[sys.executable, "-m", "pytest", "-q", "test_durations.py"],
capture_output=True,
text=True,
check=False,
)
assert run.returncode == 0, run.stdout[-1500:]
summary = run.stdout.strip().splitlines()[-1]
assert int(summary.split()[0]) >= 16, summary
nisse-dev/src/nisse_dev/bench/tasks/spec-parser/seed/SPEC.md
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# parse_duration
`parse_duration(text: str) -> int` turns a human-written duration into a whole
number of seconds.
A *number* below is one or more digits, optionally followed by a dot and one
or more digits: `2`, `1.5`, `0.25`. Nothing else is a number: not `.5`, `1.`,
`+2` or `-2`.
## Rules
1. **Unit form.** One or more components, each a number immediately followed
by a unit: `d` (86400 seconds), `h` (3600), `m` (60), `s` (1).
`"2h"` is 7200, `"1h30m"` is 5400, `"1d2h3m4s"` is 93784.
2. **Case.** Units are case-insensitive: `"1H30M"` is 5400.
3. **Order.** Components appear in the order d, h, m, s, and each unit at
most once. `"30m1h"` and `"1h2h"` are invalid.
4. **Spaces.** Spaces are allowed between components and around the whole
text: `" 1h 30m "` is 5400. A space between a number and its unit is not
allowed: `"1 h"` is invalid.
5. **Bare number.** A number with no unit, and nothing else, is seconds:
`"90"` is 90. A bare number mixed with components is invalid: `"1h 30"`.
6. **Decimals.** Only the first component may have a decimal part:
`"1.5h"` is 5400, but `"1h1.5m"` is invalid. The result must be a whole
number of seconds: `"1.5m"` is 90, but `"0.5s"` and `"0.01m"` are invalid.
7. **Clock form.** `M:SS` or `H:MM:SS`: whole numbers separated by colons.
`"1:30"` is 90, `"1:02:03"` is 3723, `"90:00"` is 5400. Every field after
the first is exactly two digits and below 60: `"1:60"`, `"1:2"` and
`"1:02:60"` are invalid. More than three fields, decimals, and spaces inside
the clock (`"1: 30"`) are invalid; spaces around it are allowed.
8. **Zero and errors.** Zero is allowed: `"0"`, `"0s"` and `"0:00"` are all 0.
An empty string, only spaces, or a negative duration is invalid.
Every invalid input raises `ValueError`, and its message includes the input
exactly as given, in `repr` form: `invalid duration: '1h2h'`.
nisse-dev/src/nisse_dev/bench/tasks/spec-parser/seed/durations.py
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"""Human-written durations. See SPEC.md."""
def parse_duration(text: str) -> int:
"""A duration in whole seconds, as SPEC.md specifies."""
raise NotImplementedError
nisse-dev/src/nisse_dev/bench/tasks/spec-parser/solution/durations.py
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"""Human-written durations. See SPEC.md."""
import re
from decimal import Decimal
_NUMBER = r"\d+(?:\.\d+)?"
_UNIT_FORM = re.compile(
rf"(?:(?P<d>{_NUMBER})d)?\s*(?:(?P<h>{_NUMBER})h)?\s*"
rf"(?:(?P<m>{_NUMBER})m)?\s*(?:(?P<s>{_NUMBER})s)?",
re.IGNORECASE,
)
_CLOCK = re.compile(r"\d+(?::[0-5]\d){1,2}")
_SECONDS = {"d": 86400, "h": 3600, "m": 60, "s": 1}
def parse_duration(text: str) -> int:
"""A duration in whole seconds, as SPEC.md specifies."""
def invalid() -> ValueError:
return ValueError(f"invalid duration: {text!r}")
stripped = text.strip()
if not stripped:
raise invalid()
if _CLOCK.fullmatch(stripped):
total = 0
for field in stripped.split(":"):
total = total * 60 + int(field)
return total
if re.fullmatch(_NUMBER, stripped):
seconds = Decimal(stripped)
else:
match = _UNIT_FORM.fullmatch(stripped)
if not match or not any(match.groupdict().values()):
raise invalid()
present = [(unit, value) for unit, value in match.groupdict().items() if value]
if any("." in value for _unit, value in present[1:]):
raise invalid()
seconds = sum(Decimal(value) * _SECONDS[unit] for unit, value in present)
if seconds != seconds.to_integral_value():
raise invalid()
return int(seconds)
nisse-dev/src/nisse_dev/bench/tasks/spec-parser/solution/test_durations.py
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import pytest
from durations import parse_duration
@pytest.mark.parametrize(
("text", "seconds"),
[
("1h30m", 5400),
("1H30M", 5400),
(" 1h 30m ", 5400),
("90", 90),
("1.5h", 5400),
("1:02:03", 3723),
("0s", 0),
],
)
def test_valid(text: str, seconds: int) -> None:
assert parse_duration(text) == seconds
@pytest.mark.parametrize(
"text", ["1x", "30m1h", "1 h", "1h 30", "1h1.5m", "0.5s", "1:60", "", "-5"]
)
def test_invalid(text: str) -> None:
with pytest.raises(ValueError, match="invalid duration"):
parse_duration(text)
nisse-dev/src/nisse_dev/bench/tasks/spec-parser/task.yaml
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title: Implement a parser from a written specification
check: python -m pytest -q .bench_check
plan: |
Milestone 1: implement `parse_duration` as `SPEC.md` specifies.
1. Read all of `SPEC.md`. It has eight numbered rules; every one applies.
2. In `durations.py`, replace the body of `parse_duration(text: str) -> int`
so that it follows every rule in `SPEC.md`, including the inputs each
rule says are invalid and the error message for them. Examples:
"1h30m" -> 5400; "90" -> 90; "1:02:03" -> 3723.
3. Create `test_durations.py` with pytest tests: at least one valid and one
invalid input for each of the eight rules.
4. Run `python -m pytest -q test_durations.py`, and fix `durations.py`
until every test passes.
5. Call finish.
nisse-dev/src/nisse_dev/bench/tasks/trace-bug/hidden/original_test_invoice.py.txt
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from decimal import Decimal
from invoicing.invoice import Invoice
from invoicing.lines import LineItem
from invoicing.report import render
def d(value: str) -> Decimal:
return Decimal(value)
def test_single_currency_total() -> None:
invoice = Invoice(
"USD", [LineItem("widget", d("19.99"), 3), LineItem("gadget", d("5.00"), 2)]
)
assert invoice.total() == d("69.97")
def test_twenty_percent_tax() -> None:
invoice = Invoice("USD", [LineItem("service", d("100.00"), 1)], tax_percent=d("20"))
assert invoice.total() == d("120.00")
def test_multi_currency_total() -> None:
# 100 USD is 91.95 EUR at the reference rates (1 EUR = 1.0875 USD).
invoice = Invoice(
"EUR",
[
LineItem("hosting", d("100.00"), 1, "USD"),
LineItem("support", d("50.00"), 1, "EUR"),
],
)
assert invoice.total() == d("141.95")
def test_report_shows_the_total() -> None:
invoice = Invoice("USD", [LineItem("widget", d("10.00"), 2)])
assert render(invoice).splitlines()[-1].split() == ["Total", "20.00", "USD"]
nisse-dev/src/nisse_dev/bench/tasks/trace-bug/hidden/test_hidden.py
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from decimal import Decimal
from pathlib import Path
import pytest
from invoicing.currency import convert
from invoicing.discounts import Discount
from invoicing.invoice import Invoice
from invoicing.lines import LineItem
def d(value: str) -> Decimal:
return Decimal(value)
def test_the_visible_tests_were_not_changed() -> None:
original = (Path(__file__).parent / "original_test_invoice.py.txt").read_text()
assert Path("tests/test_invoice.py").read_text() == original
@pytest.mark.parametrize(
("amount", "source", "target", "expected"),
[
("100.00", "USD", "EUR", "91.95"),
("100.00", "EUR", "USD", "108.75"),
("250.00", "GBP", "EUR", "290.44"),
("10000", "JPY", "USD", "67.12"),
("42.00", "USD", "USD", "42.00"),
],
)
def test_conversion(amount: str, source: str, target: str, expected: str) -> None:
assert convert(d(amount), source, target) == d(expected)
def test_multi_currency_total() -> None:
invoice = Invoice(
"EUR",
[
LineItem("hosting", d("100.00"), 1, "USD"),
LineItem("support", d("50.00"), 1, "EUR"),
],
)
assert invoice.total() == d("141.95")
def test_fractional_discount() -> None:
# Same shared helper, no currency involved: 12.5% of 1000.00 is 125.00.
invoice = Invoice(
"USD", [LineItem("licence", d("1000.00"), 1)], discount=Discount(d("12.5"))
)
assert invoice.discount_total() == d("125.00")
assert invoice.total() == d("875.00")
def test_fractional_tax() -> None:
# 8.25% of 1000.00 is 82.50.
invoice = Invoice(
"USD", [LineItem("licence", d("1000.00"), 1)], tax_percent=d("8.25")
)
assert invoice.tax() == d("82.50")
def test_everything_together() -> None:
invoice = Invoice(
"GBP",
[
LineItem("seats", d("40.00"), 10, "EUR"),
LineItem("setup", d("99.00"), 1, "GBP"),
],
discount=Discount(d("7.5")),
tax_percent=d("17.5"),
)
# 400 EUR = 344.31 GBP; subtotal 443.31; discount 33.25; tax 71.76.
assert invoice.subtotal() == d("443.31")
assert invoice.discount_total() == d("33.25")
assert invoice.tax() == d("71.76")
assert invoice.total() == d("481.82")
nisse-dev/src/nisse_dev/bench/tasks/trace-bug/seed/invoicing/__init__.py
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"""Invoices with lines in several currencies, discounts and tax."""
nisse-dev/src/nisse_dev/bench/tasks/trace-bug/seed/invoicing/currency.py
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"""Currency conversion at fixed reference rates."""
from decimal import Decimal
from invoicing.money import apply_rate, money, to_cents
# Value of one unit of each currency in US dollars.
RATES_TO_USD: dict[str, Decimal] = {
"USD": money("1"),
"EUR": money("1.0875"),
"GBP": money("1.2634"),
"JPY": money("0.006712"),
}
def rate(from_currency: str, to_currency: str) -> Decimal:
"""How many units of `to_currency` one unit of `from_currency` buys."""
try:
return RATES_TO_USD[from_currency] / RATES_TO_USD[to_currency]
except KeyError as exc:
raise ValueError(f"unknown currency: {exc.args[0]}") from None
def convert(amount: Decimal, from_currency: str, to_currency: str) -> Decimal:
if from_currency == to_currency:
return to_cents(amount)
return apply_rate(amount, rate(from_currency, to_currency))
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