Move configuration to a YAML file, and make the tuning knobs configurable

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
1 parent 4a9354ae3b
commit df449c7e34
@@ -1,5 +1,10 @@
 # Copy to .env (chmod 600). Real environment variables win over this file.
 # .env is gitignored; never commit real values.
 #
 # Settings can also live in a YAML file (nisse-dev.example.yaml), which keeps
 # structure and the tuning knobs; secrets stay here and are referenced from
 # the file as ${NISSE_LLM_TOKEN} and so on. Every variable below overrides the
 # matching key in that file.
 # --- LLM access (A5): the LiteLLM proxy is the single LLM destination ---
 NISSE_LLM_BASE_URL=http://litellm.example:4000
@@ -6,3 +6,4 @@ __pycache__/
 .pytest_cache/
 .ruff_cache/
 dist/
 nisse-dev.yaml
@@ -0,0 +1,289 @@
 # 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. It is sent with every request, for every
 role.
 - **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. This one knob covers
  every role for now; a per-model setting is coming with the context budget
  (see *Coming next*).
 - Environment override: `NISSE_MAX_OUTPUT_TOKENS`.
 ### `tuning.executor.max_turns` — default 24
 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.
 ### `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. Anything past this is cut, with a note saying how
 much was dropped.
 - **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.
 - **A known weakness:** truncation keeps the *start* of the output, but test
  runners put their failure summary at the *end*. A long, failing pytest run
  can lose exactly the lines the model needed. Keeping the head and the tail
  would fix this, and is a candidate for the context-budget work.
 ---
 ## 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:
       NISSE_CAPABILITY_MODELS=alias-a,alias-b \
         uv run pytest -m integration tests/integration/test_model_capability.py
   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.
 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. **`llm.max_output_tokens`**, so the prompt keeps most of that context.
 4. **Real milestones.** Run representative work and look at the evidence:
   turns used, escalations, delegation outcomes, 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
 The next changes in the 4090-readiness work add knobs, and this guide gains a
 section for each:
 - **A context budget.** Rendering a transcript that fits the model's context
  window, with what to drop or summarize when it doesn't, and per-model output
  ceilings.
 - **Enforced tool calls.** An explicit `finish` tool, so the server can be told
  a tool call is required and hold the model to the contract, where today it
  can fall back to prose.
 - **A benchmark script** that runs the capability matrix and representative
  milestones across candidate models and prints a comparison table.
@@ -0,0 +1,59 @@
 # 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) at startup.
 # - Environment variables win. Every NISSE_* variable in .env.example
 #   overrides the key noted beside it here.
 # - 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    # NISSE_STATE_DB
 workspace: ~/nisse-demo-workspace/project    # NISSE_WORKSPACE, the clone
 project: demo                                # NISSE_PROJECT
 llm:                                  # A5: LiteLLM is the only LLM destination
  base_url: http://litellm.example:4000      # NISSE_LLM_BASE_URL
  token: ${NISSE_LLM_TOKEN}
  # Ceiling on each response. Must fit inside a local model's context window
  # alongside the prompt; see docs/Tuning-Guide.md.
  max_output_tokens: 16384                   # NISSE_MAX_OUTPUT_TOKENS
 roles:                                # A11: role -> LiteLLM model alias
  executor: local-fast                       # NISSE_ROLE_EXECUTOR
  planner: claude-opus-5                     # NISSE_ROLE_PLANNER
  delegate: claude-opus-5                    # NISSE_ROLE_DELEGATE
  consultant: claude-opus-5                  # NISSE_ROLE_CONSULTANT
 matrix:                               # A1
  homeserver: https://matrix.example         # NISSE_MATRIX_HOMESERVER
  user_id: "@nisse-dev:example"              # NISSE_MATRIX_USER_ID
  token: ${NISSE_MATRIX_TOKEN}
  room_id: "!roomid:example"                 # NISSE_MATRIX_ROOM_ID
  operators: ["@you:example"]                # NISSE_MATRIX_OPERATORS (comma-separated)
 gitea:                                # B2
  url: https://gitea.example                 # NISSE_GITEA_URL
  token: ${NISSE_GITEA_TOKEN}
  repo: owner/demo-repo                      # NISSE_GITEA_REPO
  operators: [your-gitea-login]              # NISSE_GITEA_OPERATORS (comma-separated)
  base_branch: main                          # NISSE_BASE_BRANCH
 # 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: 24            # 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; the rest is cut
@@ -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"
@@ -16,7 +16,7 @@ from nisse_dev.cancel import Cancel
 from nisse_dev.catalog import ModelCatalog, select_client
 from nisse_dev.config import AgentConfig
 from nisse_dev.control import Orchestrator
-from nisse_dev.delegation import Delegation
+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
@@ -149,18 +149,29 @@ def build_agent(
    account = gitea.whoami()
    git_repo = prepare_workspace(config, account)
-    workspace = Workspace(config.workspace)
+    tuning = config.tuning
    workspace = Workspace(
        config.workspace,
        timeout_s=tuning.tools.command_timeout_s,
        max_output_chars=tuning.tools.max_output_chars,
    )
-    normalized = LiteLLMClient(config.llm_base_url, config.llm_token)
+    normalized = LiteLLMClient(
        config.llm_base_url, config.llm_token, max_tokens=config.max_output_tokens
    )
    # 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 = AnthropicPassthroughClient(config.llm_base_url, config.llm_token)
+    passthrough = AnthropicPassthroughClient(
        config.llm_base_url, config.llm_token, max_tokens=config.max_output_tokens
    )
    transport = MatrixTransport(config.matrix)
    announce = notify or transport.send_message
-    expert = ExpertDesk(normalized, store, config.roles)
+    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),
@@ -169,6 +180,11 @@ def build_agent(
        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(
@@ -180,6 +196,8 @@ def build_agent(
        delegate=delegation,
        notify=announce,
        cancel=cancel,
        max_turns=tuning.executor.max_turns,
        failure_limit=tuning.executor.failure_limit,
    )
    publisher = MilestonePublisher(
        store,
@@ -1,49 +1,234 @@
-"""Agent configuration (spec A5: from env/.env, nothing hardcoded).
+"""Agent configuration (spec A5: nothing hardcoded).
 Configuration comes from a YAML file plus the environment:
 - **The file carries structure.** Roles, connections, and the tuning knobs
  (see docs/Tuning-Guide.md). It holds no secrets: a credential is written as
  a reference, ``token: ${NISSE_LLM_TOKEN}``, and resolved from the
  environment, so the file can be reviewed or shared without leaking.
 - **The environment wins.** Every setting that existed as an environment
  variable still does, and overrides the file. The file is optional, so a
  deployment or CI run configured purely from the environment works as before.
 Every value is collected and validated **before** anything is built, and a
-single error names every missing variable at once. The alternative — failing
+single error names every problem at once. The alternative — failing at first
-at first use — surfaces a missing token twenty minutes into a milestone, on
+use — surfaces a missing token twenty minutes into a milestone, on the call
-the call that mattered.
+that mattered. Unknown keys are errors too: a misspelt tuning knob that was
 silently ignored would look exactly like a knob that has no effect.
 """
 import os
 import re
 from collections.abc import Mapping
-from dataclasses import dataclass
+from dataclasses import dataclass, field, fields
 from pathlib import Path
 from typing import Any
-from nisse_dev.llm import RoleMap
+import yaml
 from nisse_dev.llm import DEFAULT_MAX_OUTPUT_TOKENS, RoleMap
 from nisse_dev.matrix import MatrixConfig
-REQUIRED = (
+DEFAULT_CONFIG_FILE = "nisse-dev.yaml"
-    "NISSE_STATE_DB",
+
-    "NISSE_WORKSPACE",
+# Each environment variable and the key it overrides. The names are the ones
-    "NISSE_PROJECT",
+# the agent has always used, so existing .env files and CI keep working.
-    "NISSE_LLM_BASE_URL",
+ENV_KEYS: dict[str, str] = {
-    "NISSE_LLM_TOKEN",
+    "NISSE_STATE_DB": "state_db",
-    "NISSE_ROLE_EXECUTOR",
+    "NISSE_WORKSPACE": "workspace",
-    "NISSE_ROLE_PLANNER",
+    "NISSE_PROJECT": "project",
-    "NISSE_ROLE_DELEGATE",
+    "NISSE_LLM_BASE_URL": "llm.base_url",
-    "NISSE_ROLE_CONSULTANT",
+    "NISSE_LLM_TOKEN": "llm.token",
-    "NISSE_MATRIX_HOMESERVER",
+    "NISSE_MAX_OUTPUT_TOKENS": "llm.max_output_tokens",
-    "NISSE_MATRIX_USER_ID",
+    "NISSE_ROLE_EXECUTOR": "roles.executor",
-    "NISSE_MATRIX_TOKEN",
+    "NISSE_ROLE_PLANNER": "roles.planner",
-    "NISSE_MATRIX_ROOM_ID",
+    "NISSE_ROLE_DELEGATE": "roles.delegate",
-    "NISSE_MATRIX_OPERATORS",
+    "NISSE_ROLE_CONSULTANT": "roles.consultant",
-    "NISSE_GITEA_URL",
+    "NISSE_MATRIX_HOMESERVER": "matrix.homeserver",
-    "NISSE_GITEA_TOKEN",
+    "NISSE_MATRIX_USER_ID": "matrix.user_id",
-    "NISSE_GITEA_REPO",
+    "NISSE_MATRIX_TOKEN": "matrix.token",
-    "NISSE_GITEA_OPERATORS",
+    "NISSE_MATRIX_ROOM_ID": "matrix.room_id",
    "NISSE_MATRIX_OPERATORS": "matrix.operators",
    "NISSE_GITEA_URL": "gitea.url",
    "NISSE_GITEA_TOKEN": "gitea.token",
    "NISSE_GITEA_REPO": "gitea.repo",
    "NISSE_GITEA_OPERATORS": "gitea.operators",
    "NISSE_BASE_BRANCH": "gitea.base_branch",
 }
 KEY_ENV = {key: env for env, key in ENV_KEYS.items()}
 REQUIRED_KEYS = (
    "state_db",
    "workspace",
    "project",
    "llm.base_url",
    "llm.token",
    "roles.executor",
    "roles.planner",
    "roles.delegate",
    "roles.consultant",
    "matrix.homeserver",
    "matrix.user_id",
    "matrix.token",
    "matrix.room_id",
    "matrix.operators",
    "gitea.url",
    "gitea.token",
    "gitea.repo",
    "gitea.operators",
 )
 OPTIONAL_KEYS = ("llm.max_output_tokens", "gitea.base_branch")
 # Kept for callers that list what a pure-environment deployment must set.
 REQUIRED = tuple(KEY_ENV[key] for key in REQUIRED_KEYS)
 _REFERENCE = re.compile(r"\$\{([A-Za-z_][A-Za-z0-9_]*)\}")
 class ConfigError(RuntimeError):
    pass
-def _people(raw: str) -> frozenset[str]:
+# --- tuning ----------------------------------------------------------------
-    """Operator allowlists are comma-separated; blanks are dropped so a
+# The defaults are the values the agent ran with before they were
-    trailing comma cannot admit an empty identity."""
+# configurable, so an absent `tuning:` section changes nothing.
-    return frozenset(part.strip() for part in raw.split(",") if part.strip())
+
@dataclass(frozen=True)
 class ExecutorTuning:
    max_turns: int = 24
    failure_limit: int = 2
@dataclass(frozen=True)
 class ExpertTuning:
    consult_budget: int = 3
@dataclass(frozen=True)
 class DelegationTuning:
    max_turns: int = 30
    max_tokens: int = 200_000
    wall_clock_s: float = 900.0
@dataclass(frozen=True)
 class ToolsTuning:
    command_timeout_s: int = 120
    max_output_chars: int = 4000
@dataclass(frozen=True)
 class Tuning:
    executor: ExecutorTuning = field(default_factory=ExecutorTuning)
    expert: ExpertTuning = field(default_factory=ExpertTuning)
    delegation: DelegationTuning = field(default_factory=DelegationTuning)
    tools: ToolsTuning = field(default_factory=ToolsTuning)
 _TUNING_SECTIONS: dict[str, type] = {
    f.name: f.default_factory  # type: ignore[misc]
    for f in fields(Tuning)
 }
 def _parse_tuning(raw: Any, errors: list[str]) -> Tuning:
    if raw is None:
        return Tuning()
    if not isinstance(raw, dict):
        errors.append("tuning: must be a mapping")
        return Tuning()
    sections: dict[str, Any] = {}
    for name, section in raw.items():
        cls = _TUNING_SECTIONS.get(name)
        if cls is None:
            errors.append(f"tuning.{name}: unknown section")
            continue
        if not isinstance(section, dict):
            errors.append(f"tuning.{name}: must be a mapping")
            continue
        known = {f.name: f for f in fields(cls)}
        values: dict[str, Any] = {}
        for key, value in section.items():
            spec = known.get(key)
            if spec is None:
                errors.append(f"tuning.{name}.{key}: unknown setting")
                continue
            number = _positive(value, float if spec.type in (float, "float") else int)
            if number is None:
                errors.append(f"tuning.{name}.{key}: must be a positive number")
                continue
            values[key] = number
        sections[name] = cls(**values)
    return Tuning(**sections)
 def _positive(value: Any, kind: type) -> int | float | None:
    # bool is an int in Python; `true` for a turn limit is a mistake.
    if isinstance(value, bool) or not isinstance(value, int | float):
        return None
    if kind is int and not float(value).is_integer():
        return None
    if value <= 0:
        return None
    return kind(value)
 # --- loading ---------------------------------------------------------------
 def _people(raw: Any) -> frozenset[str]:
    """Operator allowlists: a YAML list, or comma-separated in the
    environment. Blanks are dropped so a trailing comma cannot admit an empty
    identity."""
    parts = raw if isinstance(raw, list) else str(raw).split(",")
    return frozenset(str(part).strip() for part in parts if str(part).strip())
 def _flatten(tree: Mapping[str, Any], prefix: str = "") -> dict[str, Any]:
    flat: dict[str, Any] = {}
    for key, value in tree.items():
        dotted = f"{prefix}{key}"
        if isinstance(value, dict) and dotted != "tuning":
            flat.update(_flatten(value, f"{dotted}."))
        else:
            flat[dotted] = value
    return flat
 def _resolve(value: Any, key: str, env: Mapping[str, str], errors: list[str]) -> Any:
    """Replace ${VAR} references with the environment's value. A reference to
    an unset variable is an error, never an empty string: an empty token would
    pass for a value and fail at the first request."""
    if not isinstance(value, str):
        return value
    def lookup(match: re.Match[str]) -> str:
        name = match.group(1)
        found = (env.get(name) or "").strip()
        if not found:
            errors.append(f"{key}: references ${{{name}}}, which is unset or empty")
        return found
    return _REFERENCE.sub(lookup, value)
 def _read_file(path: Path, errors: list[str]) -> dict[str, Any]:
    try:
        loaded = yaml.safe_load(path.read_text())
    except yaml.YAMLError as exc:
        errors.append(f"{path}: not valid YAML ({exc})")
        return {}
    if loaded is None:
        return {}
    if not isinstance(loaded, dict):
        errors.append(f"{path}: must be a mapping at the top level")
        return {}
    return loaded
 def _describe(key: str) -> str:
    env = KEY_ENV.get(key)
    return f"{key} ({env})" if env else key
@dataclass(frozen=True)
@@ -61,30 +246,102 @@ class AgentConfig:
    gitea_repo: str
    gitea_operators: frozenset[str]
    base_branch: str = "main"
    max_output_tokens: int = DEFAULT_MAX_OUTPUT_TOKENS
    tuning: Tuning = field(default_factory=Tuning)
    config_file: Path | None = None
    @classmethod
    def load(cls, env: Mapping[str, str] = os.environ) -> "AgentConfig":
        """The file named by NISSE_CONFIG, or ./nisse-dev.yaml if present,
        overlaid by the environment. Naming a file that does not exist is an
        error; the default file is simply optional."""
        named = env.get("NISSE_CONFIG")
        path = Path(named or DEFAULT_CONFIG_FILE).expanduser()
        if named and not path.is_file():
            raise ConfigError(
                f"cannot start: NISSE_CONFIG names {path}, which is missing"
            )
        return cls.from_sources(env, path if path.is_file() else None)
    @classmethod
    def from_env(cls, env: Mapping[str, str] = os.environ) -> "AgentConfig":
        """Environment only, with no file."""
        return cls.from_sources(env, None)
    @classmethod
    def from_sources(
        cls, env: Mapping[str, str], config_file: Path | None
    ) -> "AgentConfig":
        errors: list[str] = []
        tree = _read_file(config_file, errors) if config_file else {}
        tuning = _parse_tuning(tree.get("tuning"), errors)
        flat = _flatten(tree)
        flat.pop("tuning", None)
        known = set(REQUIRED_KEYS) | set(OPTIONAL_KEYS)
        for key in sorted(set(flat) - known):
            errors.append(f"{key}: unknown setting")
        overrides = {key: env[var] for var, key in ENV_KEYS.items() if var in env}
        # A key the environment overrides is not resolved from the file, so a
        # reference there to an unset variable is not an error.
        values = {
            key: _resolve(value, key, env, errors)
            for key, value in flat.items()
            if key in known and key not in overrides
        }
        values.update(overrides)
        # Whitespace-only counts as missing: a token of spaces would pass a
        # truthiness check and fail at the first request instead.
-        missing = [name for name in REQUIRED if not (env.get(name) or "").strip()]
+        missing = [
            _describe(key)
            for key in REQUIRED_KEYS
            if values.get(key) is None
            or (isinstance(values[key], str) and not values[key].strip())
            or (isinstance(values[key], list) and not _people(values[key]))
        ]
        if missing:
-            raise ConfigError(
+            errors.append("unset or empty — " + ", ".join(missing))
-                "cannot start: these are unset or empty — " + ", ".join(missing)
+
        max_output = values.get("llm.max_output_tokens", DEFAULT_MAX_OUTPUT_TOKENS)
        try:
            max_output = int(max_output)
            if max_output <= 0:
                raise ValueError
        except (TypeError, ValueError):
            errors.append(
                f"{_describe('llm.max_output_tokens')}: must be a positive integer"
            )
        if errors:
            raise ConfigError("cannot start: " + "; ".join(errors))
        config = cls(
-            state_db=Path(env["NISSE_STATE_DB"]).expanduser(),
+            state_db=Path(values["state_db"]).expanduser(),
-            workspace=Path(env["NISSE_WORKSPACE"]).expanduser(),
+            workspace=Path(values["workspace"]).expanduser(),
-            project=env["NISSE_PROJECT"],
+            project=str(values["project"]),
-            llm_base_url=env["NISSE_LLM_BASE_URL"],
+            llm_base_url=str(values["llm.base_url"]),
-            llm_token=env["NISSE_LLM_TOKEN"],
+            llm_token=str(values["llm.token"]),
-            roles=RoleMap.from_env(env),
+            roles=RoleMap(
-            matrix=MatrixConfig.from_env(env),
+                executor=str(values["roles.executor"]),
-            operators=_people(env["NISSE_MATRIX_OPERATORS"]),
+                planner=str(values["roles.planner"]),
-            gitea_url=env["NISSE_GITEA_URL"],
+                delegate=str(values["roles.delegate"]),
-            gitea_token=env["NISSE_GITEA_TOKEN"],
+                consultant=str(values["roles.consultant"]),
-            gitea_repo=env["NISSE_GITEA_REPO"],
+            ),
-            gitea_operators=_people(env["NISSE_GITEA_OPERATORS"]),
+            matrix=MatrixConfig(
-            base_branch=env.get("NISSE_BASE_BRANCH", "main"),
+                homeserver=str(values["matrix.homeserver"]).rstrip("/"),
                user_id=str(values["matrix.user_id"]),
                token=str(values["matrix.token"]),
                room_id=str(values["matrix.room_id"]),
            ),
            operators=_people(values["matrix.operators"]),
            gitea_url=str(values["gitea.url"]),
            gitea_token=str(values["gitea.token"]),
            gitea_repo=str(values["gitea.repo"]),
            gitea_operators=_people(values["gitea.operators"]),
            base_branch=str(values.get("gitea.base_branch") or "main"),
            max_output_tokens=max_output,
            tuning=tuning,
            config_file=config_file,
        )
        config._refuse_secrets_inside_the_clone(env)
        return config
@@ -93,14 +350,20 @@ class AgentConfig:
        """The workspace is a git clone the agent commits with ``git add -A``
        and pushes. Anything inside it reaches the project repository, so a
        state store or an env file placed there would publish credentials as a
-        side effect of the agent working correctly — silently, and once.
+        side effect of the agent working correctly — silently, and once. The
        config file holds no secrets itself, but it names where they come
        from and how the agent is deployed, which is not the project's
        business either.
        """
        workspace = self.workspace.expanduser().resolve()
-        offenders = []
+        candidates = [
        for label, path in (
            ("NISSE_STATE_DB", self.state_db),
            ("NISSE_ENV", Path(env.get("NISSE_ENV", ".env"))),
-        ):
+        ]
        if self.config_file is not None:
            candidates.append(("NISSE_CONFIG", self.config_file))
        offenders = []
        for label, path in candidates:
            resolved = path.expanduser().resolve()
            if resolved == workspace or workspace in resolved.parents:
                offenders.append(f"{label} ({resolved})")
@@ -20,7 +20,7 @@ from nisse_dev.envfile import load_env_file
 def main() -> int:
    load_env_file(Path(os.environ.get("NISSE_ENV", ".env")).expanduser())
    try:
-        config = AgentConfig.from_env()
+        config = AgentConfig.load()
    except ConfigError as exc:
        # Fail here rather than twenty minutes into a milestone on the one
        # call that needed the missing value.
@@ -86,13 +86,6 @@ EXECUTOR_TOOLS: list[dict[str, Any]] = [
 ]
 def _truncate(text: str) -> str:
    if len(text) <= MAX_OUTPUT_CHARS:
        return text
    dropped = len(text) - MAX_OUTPUT_CHARS
    return f"{text[:MAX_OUTPUT_CHARS]}\n[... {dropped} characters truncated]"
 def _child_env() -> dict[str, str]:
    """The project's build and test code is arbitrary; it has no business
    reading the agent's LiteLLM, Matrix, or Gitea credentials."""
@@ -106,10 +99,24 @@ class ToolOutcome:
 class Workspace:
-    def __init__(self, root: Path, *, timeout_s: int = DEFAULT_TIMEOUT_S) -> None:
+    def __init__(
        self,
        root: Path,
        *,
        timeout_s: int = DEFAULT_TIMEOUT_S,
        max_output_chars: int = MAX_OUTPUT_CHARS,
    ) -> None:
        self.root = Path(root)
        self.root.mkdir(parents=True, exist_ok=True)
        self._timeout_s = timeout_s
        self._max_output_chars = max_output_chars
    def _truncate(self, text: str) -> str:
        limit = self._max_output_chars
        if len(text) <= limit:
            return text
        dropped = len(text) - limit
        return f"{text[:limit]}\n[... {dropped} characters truncated]"
    def _resolve(self, path: str) -> Path:
        candidate = Path(path)
@@ -142,7 +149,7 @@ class Workspace:
    def read_file(self, path: str) -> ToolOutcome:
        target = self._resolve(path)
        try:
-            return ToolOutcome(_truncate(target.read_text()), success=True)
+            return ToolOutcome(self._truncate(target.read_text()), success=True)
        except (OSError, UnicodeDecodeError) as exc:
            return ToolOutcome(f"could not read {path}: {exc}", success=False)
@@ -175,7 +182,9 @@ class Workspace:
            )
        except OSError as exc:
            return ToolOutcome(f"could not list {path}: {exc}", success=False)
-        return ToolOutcome(_truncate("\n".join(entries) or "(empty)"), success=True)
+        return ToolOutcome(
            self._truncate("\n".join(entries) or "(empty)"), success=True
        )
    def describe(self) -> str:
        """Told to the model, so it does not have to guess where it is."""
@@ -212,7 +221,7 @@ class Workspace:
                f"command timed out after {self._timeout_s}s: {command}",
                success=False,
            )
-        body = _truncate(
+        body = self._truncate(
            f"exit code: {process.returncode}\nstdout:\n{stdout}\nstderr:\n{stderr}"
        )
        return ToolOutcome(body, success=process.returncode == 0)
@@ -286,3 +286,57 @@ def test_delegation_records_what_the_alias_resolved_to(
    agent = build_offline(tmp_path, origin, monkeypatch)
    assert agent.orchestrator._delegate._backing_model == "anthropic/claude-opus-5"
    agent.stop()
 def test_tuning_reaches_every_component(
    tmp_path: Path, origin: Path, monkeypatch
 ) -> None:
    from dataclasses import replace
    from nisse_dev.config import (
        DelegationTuning,
        ExecutorTuning,
        ExpertTuning,
        ToolsTuning,
        Tuning,
    )
    config = replace(
        config_for(tmp_path),
        max_output_tokens=8192,
        tuning=Tuning(
            executor=ExecutorTuning(max_turns=40, failure_limit=3),
            expert=ExpertTuning(consult_budget=5),
            delegation=DelegationTuning(
                max_turns=12, max_tokens=50_000, wall_clock_s=60
            ),
            tools=ToolsTuning(command_timeout_s=300, max_output_chars=2000),
        ),
    )
    GitRepo(tmp_path).clone(str(origin), config.workspace)
    monkeypatch.setattr(
        "nisse_dev.agent.GiteaClient.whoami", lambda self: "nisse-dev-testing"
    )
    monkeypatch.setattr(
        "nisse_dev.agent.ModelCatalog.discover",
        classmethod(lambda cls, *a, **kw: TEST_CATALOG),
    )
    monkeypatch.setattr("nisse_dev.agent.MatrixTransport", lambda cfg: FakeTransport())
    monkeypatch.setattr(
        "nisse_dev.agent.authenticated_url",
        lambda base, repo, user, token: str(origin),
    )
    agent = build_agent(config)
    executor = agent.orchestrator._executor
    delegation = agent.orchestrator._delegate
    assert (executor._max_turns, executor._failure_limit) == (40, 3)
    assert executor._expert._budget == 5
    assert delegation._caps.turns == 12
    assert delegation._caps.tokens == 50_000
    assert delegation._caps.wall_clock_s == 60
    assert executor._workspace._timeout_s == 300
    assert executor._workspace._max_output_chars == 2000
    assert executor._client._max_tokens == 8192
    assert delegation._client._max_tokens == 8192
    agent.stop()
@@ -130,3 +130,180 @@ def test_sibling_paths_are_fine(tmp_path: Path) -> None:
        }
    )
    assert config.workspace.name == "project"
 # --- the YAML file (#32) ---------------------------------------------------
 FILE = """
 state_db: ~/demo/state.db
 workspace: ~/demo/work
 project: greet
 llm:
  base_url: https://litellm.test
  token: ${TEST_LLM_TOKEN}
 roles:
  executor: local-fast
  planner: claude-opus-5
  delegate: claude-opus-5
  consultant: claude-haiku-4-5
 matrix:
  homeserver: https://matrix.test/
  user_id: "@nisse-dev-testing:test"
  token: ${TEST_MATRIX_TOKEN}
  room_id: "!room:test"
  operators: ["@don:test", " "]
 gitea:
  url: https://gitea.test
  token: ${TEST_GITEA_TOKEN}
  repo: don/demo
  operators: [don]
 """
 SECRETS = {
    "TEST_LLM_TOKEN": "sk-x",
    "TEST_MATRIX_TOKEN": "syt_x",
    "TEST_GITEA_TOKEN": "gt",
 }
 def write(tmp_path: Path, text: str) -> Path:
    path = tmp_path / "nisse-dev.yaml"
    path.write_text(text)
    return path
 def test_a_file_configures_everything_with_secrets_by_reference(
    tmp_path: Path,
 ) -> None:
    config = AgentConfig.from_sources(SECRETS, write(tmp_path, FILE))
    assert config.llm_token == "sk-x"
    assert config.gitea_token == "gt"
    assert config.roles.executor == "local-fast"
    assert config.operators == {"@don:test"}  # a blank entry admits no one
    assert config.matrix.homeserver == "https://matrix.test"
    assert config.tuning.executor.max_turns == 24  # absent section: defaults
 def test_the_environment_wins_over_the_file(tmp_path: Path) -> None:
    config = AgentConfig.from_sources(
        {**SECRETS, "NISSE_ROLE_EXECUTOR": "claude-haiku-4-5"}, write(tmp_path, FILE)
    )
    assert config.roles.executor == "claude-haiku-4-5"
 def test_an_overridden_key_needs_no_resolvable_reference(tmp_path: Path) -> None:
    env = {k: v for k, v in SECRETS.items() if k != "TEST_LLM_TOKEN"}
    config = AgentConfig.from_sources(
        {**env, "NISSE_LLM_TOKEN": "sk-env"}, write(tmp_path, FILE)
    )
    assert config.llm_token == "sk-env"
 def test_a_reference_to_an_unset_variable_is_an_error(tmp_path: Path) -> None:
    env = {k: v for k, v in SECRETS.items() if k != "TEST_GITEA_TOKEN"}
    with pytest.raises(ConfigError, match=r"gitea.token: references \$\{TEST_GITEA"):
        AgentConfig.from_sources(env, write(tmp_path, FILE))
 def test_tuning_is_read_from_the_file(tmp_path: Path) -> None:
    text = (
        FILE
        + """
 tuning:
  executor: {max_turns: 40, failure_limit: 3}
  expert: {consult_budget: 5}
  delegation: {max_turns: 20, max_tokens: 100000, wall_clock_s: 600}
  tools: {command_timeout_s: 300, max_output_chars: 2000}
 """
    )
    tuning = AgentConfig.from_sources(SECRETS, write(tmp_path, text)).tuning
    assert (tuning.executor.max_turns, tuning.executor.failure_limit) == (40, 3)
    assert tuning.expert.consult_budget == 5
    assert tuning.delegation.max_tokens == 100_000
    assert tuning.delegation.wall_clock_s == 600.0
    assert tuning.tools.max_output_chars == 2000
    # A section that is only partly given keeps defaults for the rest.
    partial = FILE + "\ntuning:\n  executor: {max_turns: 40}\n"
    tuning = AgentConfig.from_sources(SECRETS, write(tmp_path, partial)).tuning
    assert tuning.executor.failure_limit == 2
 def test_every_problem_in_a_file_is_reported_at_once(tmp_path: Path) -> None:
    # A misspelt knob that was silently ignored would look exactly like a knob
    # with no effect, so unknown keys are errors.
    text = (
        FILE.replace("project: greet\n", "")
        + """
 llm_extra: 1
 tuning:
  executor: {max_turn: 40, failure_limit: 0}
  expert: {consult_budget: true}
  bogus: {x: 1}
 """
    )
    with pytest.raises(ConfigError) as excinfo:
        AgentConfig.from_sources(SECRETS, write(tmp_path, text))
    message = str(excinfo.value)
    for expected in (
        "project (NISSE_PROJECT)",
        "llm_extra: unknown setting",
        "tuning.executor.max_turn: unknown setting",
        "tuning.executor.failure_limit: must be a positive number",
        "tuning.expert.consult_budget: must be a positive number",
        "tuning.bogus: unknown section",
    ):
        assert expected in message
 def test_a_whole_number_is_required_where_one_is_meant(tmp_path: Path) -> None:
    text = FILE + "\ntuning:\n  executor: {max_turns: 2.5}\n"
    with pytest.raises(ConfigError, match="tuning.executor.max_turns"):
        AgentConfig.from_sources(SECRETS, write(tmp_path, text))
 def test_max_output_tokens_comes_from_the_file_or_the_environment(
    tmp_path: Path,
 ) -> None:
    text = FILE.replace(
        "  base_url: https://litellm.test\n",
        "  base_url: https://litellm.test\n  max_output_tokens: 8192\n",
    )
    path = write(tmp_path, text)
    assert AgentConfig.from_sources(SECRETS, path).max_output_tokens == 8192
    env = {**SECRETS, "NISSE_MAX_OUTPUT_TOKENS": "4096"}
    assert AgentConfig.from_sources(env, path).max_output_tokens == 4096
 def test_load_finds_the_named_file_and_refuses_a_missing_one(tmp_path: Path) -> None:
    path = write(tmp_path, FILE)
    assert AgentConfig.load({**SECRETS, "NISSE_CONFIG": str(path)}).project == "greet"
    with pytest.raises(ConfigError, match="missing"):
        AgentConfig.load({**SECRETS, "NISSE_CONFIG": str(tmp_path / "absent.yaml")})
 def test_load_without_a_file_uses_the_environment(
    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
 ) -> None:
    monkeypatch.chdir(tmp_path)  # no ./nisse-dev.yaml here
    assert AgentConfig.load(COMPLETE).config_file is None
 def test_a_config_file_inside_the_clone_is_refused(tmp_path: Path) -> None:
    work = tmp_path / "project"
    work.mkdir()
    text = FILE.replace("workspace: ~/demo/work", f"workspace: {work}")
    path = work / "nisse-dev.yaml"
    path.write_text(text)
    with pytest.raises(ConfigError, match="NISSE_CONFIG"):
        AgentConfig.from_sources(SECRETS, path)
 def test_the_example_file_is_valid(tmp_path: Path) -> None:
    # The example documents every knob; it must not drift out of loading.
    example = Path(__file__).resolve().parents[2] / "nisse-dev.example.yaml"
    env = {
        "NISSE_LLM_TOKEN": "sk-x",
        "NISSE_MATRIX_TOKEN": "syt_x",
        "NISSE_GITEA_TOKEN": "gt",
    }
    config = AgentConfig.from_sources(env, example)
    assert config.tuning.executor.max_turns == 24
@@ -113,12 +113,14 @@ source = { editable = "nisse-dev" }
 dependencies = [
    { name = "httpx" },
    { name = "nisse-core" },
    { name = "pyyaml" },
 ]
 [package.metadata]
 requires-dist = [
    { name = "httpx", specifier = ">=0.27" },
    { name = "nisse-core", editable = "nisse-core" },
    { name = "pyyaml", specifier = ">=6" },
 ]
 [[package]]
@@ -175,6 +177,52 @@ wheels = [
    { url = "https://files.pythonhosted.org/packages/24/25/1de2678b631f5a49215c6c96fff41ba892b0a34df68d6d80292b1b48aa7f/pytest-9.1.1-py3-none-any.whl", hash = "sha256:37a86b45efb9a47a61a36449063e8e18d0cab3161329fc099eb21783169c4f0c", size = 386536, upload-time = "2026-06-19T10:58:31.347Z" },
 ]
 [[package]]
 name = "pyyaml"
 version = "6.0.3"
 source = { registry = "https://pypi.org/simple" }
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