Development Plan M1.1, as discussed.
- Schema v4: a work_items table (id, scope, title, status, outcome,
position, data) and an agent row whose active_item points at one
(NULL: idle). Canonical events and intents carry their work item, and
events their actor. Older stores are refused with a clear message:
there was no production data, so no migration (the mechanism stays).
- The log is reached through a Transcript bound to one work item and
one actor: store.transcript(item, actor) reads only that item and
writes with both. The planner, executor, delegation and expert desk
get one per call from the orchestrator instead of holding the store,
so a component can neither read another task's events (the planner
read every earlier task's, #78) nor write without attribution.
store.all_events() is kept for readers that span items (evaluation
capture, dumps). Intents are begun through the transcript or with
work_item=, so each belongs to its item.
- The orchestrator opens a work item on 'use', closes it with its
outcome (completed or abandoned), and carries what the operator said
while idle into the new item as copies with their origin, so a task
sent before 'use' is still planned from (D1). control.py keeps its
state machine through get_state()/transition(), now over the active
work item, until M2.6 retires it.
- nisse_dev.dump shows work items and each event's item and actor.
Tests: test_work_items.py (scoping, attribution, intent ownership,
restart, outcomes, carried messages, a second task never seeing the
first); the suites updated for the per-call transcript.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01PPQ14L3LikyefCMFBqEbKE
Architecture L9's role names, for the two roles with a direct
counterpart: the delegation role is now 'helper' and the ask_expert role
'expert', in RoleMap, configuration (roles.helper, roles.expert),
recorded events and tests. 'planner' and 'executor' stay distinct for
now; they become 'driver' when the new loop merges them (Development
Plan M2.3).
Configurations must rename their keys: nisse-dev.example.yaml and
tests/integration/nisse-test.yaml are updated here.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01PPQ14L3LikyefCMFBqEbKE
Development Plan M0.1. Every section reference in code, tests, specs/,
the example config, the tuning guide, and the CLAUDE.md and README files
now cites docs/Architecture.md (P, L, D, O, X, Q) instead of the old
spec's A-H, and docs/Architecture-Build-Spec.md is deleted (git keeps
it). The plan's old-to-new table goes with it.
Mostly mechanical, with a per-site choice where one old section split:
A4 became L2 where it meant stop/abandon/the loop and L3 where it meant
approvals; A10 became L6 in the context-budget code and L7 elsewhere; B2
became D4 for outbound Gitea and publishing and D5 for review intake;
'A3 step N' became the matching step of L8's boot sequence. Comments
describing behaviour the architecture drops (planning always escalating,
thresholds tuned at an old build step) now describe the current code and
point at the Development Plan.
docs/Architecture.md gains one line the rewrite missed: intake outages
delay but never fail work (old B2), now in L5, where the poller and
Matrix code cite it.
Comments and docs only; no behaviour change.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01PPQ14L3LikyefCMFBqEbKE
The test checks that a request reaches the executor model through
LiteLLM and text comes back. It also asserted the model obeyed "Reply
with exactly one word: pong", which Haiku once answered with "ping" and
failed CI on #66. Assert non-empty text instead.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01PPQ14L3LikyefCMFBqEbKE
It drove the orchestrator until EXECUTING and accepted EXECUTING as the
end state, which with no publisher configured was the stranding this
branch removes: the milestone now finishes in PR_OPEN. Return once
'start milestone 1' is sent, since the milestone runs inside it, and
expect PR_OPEN or AWAIT_INPUT.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01PPQ14L3LikyefCMFBqEbKE
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>
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>
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>
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>
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>
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>
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>
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>
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>
A frontier model receives a scoped sub-task, works autonomously with
full tool access, and hands back; the local loop reads the handback
note and the file state, never the reasoning.
- Branch at delegation start, and abort discards it. That required
committing each turn's work onto the branch: without commits,
deleting a branch discards nothing, so "abort discards the work"
was a comforting phrase rather than a fact. The caller's own
in-flight changes are committed to their branch first, so an abort
cannot destroy work nobody delegated
- Hard caps on turns and wall clock; a breach is a defined failure
return, and its result is not usable, so the caller gates rather
than resuming on a half-edited tree
- Every event is checkpointed as it arrives, so a crash mid-delegation
leaves the branch at a known commit and a truthful transcript
- Handback is validated locally: blank fields or a non-boolean success
flag are refused and the delegation continues rather than accepting
a useless note
Two failure modes found by running it against a real model:
- A success claim with nothing changed was accepted. The handback plus
file state is the entire interface back, so a note describing work
that does not exist is worse than no note. Success now requires a
successful mutating tool call; reporting failure still does not,
since "this is impossible" is a legitimate finding
- The delegate invented the absolute path /workspace, wrote there, ran
its tests there, and handed back truthfully — with the repo empty
and git seeing nothing. Writes outside the workspace are now refused
with a message naming the real root, and both prompts state where
the workspace is instead of leaving the model to guess. This is not
sandboxing: work outside the repo is invisible to git, so the
milestone would publish an empty PR
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
The second renderer adapter, and the concrete case for A13's decision
to normalise in the renderer rather than on the wire. The two vendors
disagree structurally, not cosmetically: the system prompt is a
top-level field rather than a message, a tool call is a tool_use
content block rather than an array, a tool result is a block inside a
user message rather than a "tool" role, and tool schemas use
input_schema rather than parameters. A middlebox flattening those
would be lossy exactly where a long-running delegation lives.
- render_anthropic.py builds vendor-native /v1/messages bodies from
the canonical log, coalescing adjacent same-role blocks so a
tool_result lands directly after its tool_use
- Tool manifests are declared once in OpenAI shape and converted per
vendor, so adding a tool does not mean editing two places
- AnthropicPassthroughClient implements the same LLMClient protocol,
so callers pick a route rather than a different kind of client, and
maps provider tool ids to canonical ones at the boundary
- Dangling tool calls get a placeholder result here too; this API
rejects them more strictly than the OpenAI shape
- Vendor-opaque reasoning blocks are dropped for now with the reason
recorded: A10 wants them stored and never replayed across sessions,
and the canonical schema has no event for that yet
Live tests prove the body is accepted as sent, that a tool_result
turn round-trips, and — as A5 asks — that prompt cache accounting
survives the passthrough rather than being flattened by the proxy.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
- approvals.py: PlanBoard opens one issue per project and posts each
plan revision as a comment carrying its rev tag. The state store
keeps only a pointer, so gate state can be re-derived from Gitea
after a state-volume loss — external truth wins
- Gitea comments are a second approval source: revision binding, the
typed word approve, and first-approval-wins work exactly as over
transport, with a per-channel allowlist because a Gitea login is
not a Matrix MXID
- Outcomes are echoed to the other channel, so the two records cannot
silently disagree about what was approved
- A Gitea outage degrades durability of the record, not the ability
to approve: planning proceeds and Matrix remains a complete channel
- Live test now asserts the server refuses the agent's own merge and
its push to the protected branch. That property was previously
true only because our code did not attempt it; branch protection
makes it structural, and this test fails if the protection is ever
relaxed
- Fixed a race in the process-group test: killing a group is
asynchronous and the grandchild is an orphan, so its exit is not
synchronised with our return; the test now waits for death
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
The standalone half of the Gitea layer; orchestrator wiring follows
once the executor loop lands, to avoid colliding with it in
control.py.
- gitea.py: GiteaClient over httpx — open PRs, list and post issue
comments, close PRs. find_pull_request is the A3 external-truth
query that makes opening a PR idempotent across a crash, and it
checks closed PRs too, since a crash after opening must not be
answered with "nothing is open, so open another"
- pull_request_reconciler: an unreachable Gitea returns None rather
than a guess, so the agent gates to the human instead of risking a
duplicate side effect
- git.py: branch, commit, push for the workspace. The token is passed
per-invocation rather than stored as a remote, so it never lands in
.git/config, and anything raised is scrubbed of it
- commit_all returns None when there is nothing to commit: an empty
milestone is a fact to report, not an error
- Live test does the real round trip against the sandbox repo and
cleans up after itself
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Two invariants only a live homeserver can check, now enforced rather
than verified by hand:
- The configured NISSE_MATRIX_USER_ID must be the token's real owner.
MatrixTransport filters its own messages by comparing sender to that
value, so a mismatch would make the bot read its own posts as
operator input.
- The account must be a testing identity, so a stray live run can
never act as the production agent.
Both hold today; this keeps them holding when the production
nisse-dev account is created.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Found while chasing a flaky live test, but the flakiness was a real
bug: only the ask_human path recorded a pending item, so AWAIT_INPUT
reached any other way — planning failure, stopped milestone, boot
reconciliation — refused every operator reply with "no pending
question to answer" and the agent could never be recovered. The
operator had no way back short of wiping state.
Every path into AWAIT_INPUT now records what a reply should resume,
and answering dispatches on it: planning failures and questions
resume planning, a stopped milestone returns to its still-approved
gate, and the fallback says what commands work instead of
dead-ending. Two regression tests cover the recovery paths.
The live test also scripted a fixed approve/approve/start sequence,
which a real model breaks by legitimately calling ask_human at a
gate. It now plays the operator adaptively, responding to whatever
the agent actually did. Five consecutive live runs pass.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
- tools.py: four flat tools (read_file, write_file, list_dir,
run_command) per A15's reduced manifest. Deliberately unsandboxed:
the VM is the trust boundary (B3) and tool use is auto-approved
inside an approved unit of work (A4), so path confinement would add
failure modes without adding safety. What is enforced is
operational — timeout with process-group kill so a backgrounded
server cannot outlive its command, output truncation so a build log
cannot eat the context window, closed stdin so prompts fail fast,
and a scrubbed environment so project code never sees the agent's
credentials
- executor.py: renders the log, calls the executor role with the
reduced manifest, dispatches tool calls, and checkpoints every
result (A13). Escalation is observable, never self-assessed (A12):
consecutive command failures and a hard turn cap. Failed reads do
not count — looking around is not failing
- Until delegation exists (step 7), escalation gates to the human
with the failures attached
- control.py: start milestone now runs the executor; a stopped
milestone gates to AWAIT_INPUT
Two bugs found by running it live against real models:
- A turn-ending gate tool (submit_plan) left a tool_call with no
tool_result, which providers reject outright, so every planning
turn after the first failed with a 400. The planner now records the
gate as the call's outcome, which is also the truthful transcript
- The renderer now emits a placeholder for any tool_call lacking a
result, so a crash between call and result (A3) cannot poison the
transcript permanently
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
- planner.py: LLMPlanningAgent offers exactly two tools, submit_plan
and ask_human, so gating stays structural at the tool layer; no
proceed tool exists. System prompt requires explicit, low-inference
plans because a weaker model executes them (A13)
- Model output is appended to the canonical log as it arrives, before
being interpreted, so a crash mid-planning leaves a truthful
transcript
- A bad turn (prose instead of a tool call, malformed arguments, or a
failed call) retries once, then raises PlanningError; the
orchestrator gates to AWAIT_INPUT rather than inventing a plan
- Orchestrator: appends every operator message, plan_submitted, and
gate_opened to the transcript, so re-planning sees rejection
feedback as conversation rather than out-of-band parameters; pins
the A11 role mapping at task start
- llm.py: non-JSON or non-object tool arguments now raise LLMError
instead of escaping as JSONDecodeError — observed from llama3.2,
which emits truncated argument strings
- Live planning test is opt-in on NISSE_LIVE_PLANNER_MODEL: planning
escalates unconditionally (A12) and the local model does not hold
the tool-call contract, so it awaits a frontier route
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Skipping now keys solely on NISSE_LLM_BASE_URL and
NISSE_MATRIX_HOMESERVER: if the sentinel is set, the live path is
expected to work and any other missing variable is an assertion
failure naming it, not a silent skip. A misnamed or misplaced CI
secret previously produced a green run with no live coverage.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
- matrix.py: Transport implementation over the Matrix client-server
HTTP API directly (token auth, /sync long-poll, /send) using the
existing httpx dependency — no Matrix SDK; four endpoints total.
Deviation from the presented plan (matrix-nio) flagged in the PR
- First sync learns position and skips backlog, so room history is
never delivered as context (A1); own messages and non-message
events filtered; sender MXID passed through for the orchestrator's
operator allowlist
- Sync loop backs off (capped at 60s) and retries indefinitely on
homeserver errors, mirroring B2's polling posture
- Encrypted rooms unsupported: bot rooms are created unencrypted
until E2E lands (operator decision; room recreated accordingly)
- NISSE_MATRIX_TEST_ROOM_ID added: the live smoke test binds only
to the dedicated test room, never the production room
- 8 unit tests over a mocked HTTP layer; live integration test
verified against the real homeserver
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
PR 2 of the steps-3/4 plan.
- llm.py: LLMClient protocol — complete(events, tools, model) ->
canonical events — present from the first real LLM call.
LiteLLMClient renders requests via the A10 renderer, targets the
proxy's OpenAI-compatible route, and maps provider tool-call IDs
to locally-minted canonical IDs at the boundary
- RoleMap (A11): executor/planner/delegate/consultant resolved from
env; pinned() returns the mapping to record at task start
- Config strictly from env: NISSE_LLM_BASE_URL, NISSE_LLM_TOKEN;
envfile.py is a minimal .env loader where real env wins; .env now
gitignored with a committed .env.example documenting all variables
(Matrix ones included for the upcoming transport PR)
- Live smoke test against the proxy is integration-marked and skips
without credentials — CI stays credential-free per operator
decision; the test runs locally once .env exists
- httpx added as nisse-dev's HTTP client
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Build-order step 4, sequenced before the LLM client (step 3) because
the client interface consumes the canonical log.
- canonical.py: typed events for the full A10 table (user_message
through compaction); canonical tool-call IDs minted locally,
provider IDs never a join key; strict payload validation on load
- StateStore: canonical_events table via schema v2 — the first real
migration, exercised in a test against a hand-built v1 database —
with append_event/read_events on the same fsync-per-commit path
- render.py: request bodies rendered from the log at call time;
first adapter targets the OpenAI-compatible shape of LiteLLM's
normalized route; compaction events replace their id range at
render time while staying in the durable log
- Crash integration test now also verifies transcript appends
survive process death
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Step-2 PR A: the approval loop end to end with a stub agent, no LLM
and no network.
- transport.py: Transport interface per A1 (send_message,
send_approval_request, ask_question, on_message) with an
InMemoryTransport; no platform SDK outside an implementation
- gates.py: PlanRevision with monotonic rev-N tags; Gate.evaluate
enforces revision binding (stale tags refused with a notice),
operator allowlisting, and approve-word-only semantics; any other
reply is rejection feedback. Channel seam ready for Gitea/Vikunja
approval sources
- control.py: B1 state machine subset with structural gating — the
agent's only actions are SubmitPlan (ends its turn) and AskHuman;
no proceed tool exists. Gate state, revision counters, task_id,
and pending questions checkpoint through StateStore, and boot
resumes an open gate or gates to AWAIT_INPUT, never blind-resumes
- gate.open/approve/reject A9 events complete the log taxonomy
- CLAUDE.md: record the plan-first working convention
- Tests: 15 unit (gates + orchestrator flows), 2 integration
(mid-gate restart with revisions intact; unresolved intents gate
the whole boot)
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Retrofit for the conventions merged in #4 (issue #3):
- OpLog: JSON-lines emitter with the fixed envelope (ts, agent,
event, task_id, cause); task_id set by the orchestrator at
unit-of-work start
- StateStore owns emission: transition, intent begin/commit/abort,
and schema migrations each emit their event right after the
durable write commits; callers pass the cause
- reconcile_on_boot emits boot.reconcile with action and verdicts,
and resolves intents with reconciliation-verdict causes
- Failed resolutions emit nothing (a line exists iff the write
committed)
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Build-order step 1: the critical path everything else sits on.
- StateStore: SQLite in WAL mode with synchronous=FULL, so every
state transition and intent-log write is committed and fsynced
before returning; a power cut cannot tear state
- Durable intent log: begin_intent records a unit of work before it
starts; commit/abort resolve it; pending intents survive restart
- schema_version stamped from the first commit, with a sequential
migration hook; a newer state file than the code supports is
refused rather than misread
- reconcile_on_boot (A3 first cut): pending intents are checked
against external truth by registered reconcilers (observable
checks, never model self-assessment); verified effects are
committed, verified no-ops aborted, anything unverifiable gates
to AWAIT_INPUT — never blind-resume
- Lives in nisse_dev per spec 0.1: the library is extracted after
the dev agent works, not designed up front
- Integration test kills a subprocess mid-work with os._exit and
verifies state integrity and intent visibility on reopen
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
- uv workspace tying nisse-core, nisse-dev, nisse-ops together
(src layouts, workspace-sourced nisse-core dependency)
- Placeholder hello-world entry points for both agents, calling
through nisse-core to exercise the package boundary
- pytest unit tests per package; integration tests (marker-gated)
running the installed console scripts as subprocesses
- ruff lint + format, nisse_* declared first-party
- Gitea Actions workflow running lint, format check, unit and
integration tests; scripts/check.sh mirrors it locally
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
dongarandClaude Opus 5.5
dev-agent