3 Commits

Include renames

Author	SHA1	Message	Date
dongarandClaude Opus 5.5	df449c7e34	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
dongarandClaude Fable 5	0c2021fced	Add LLM client interface with LiteLLM adapter (A5/A11) ... 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>	2026-09-06 20:57:51 +00:00
dongarandClaude Fable 5	c1041a5ba1	Scaffold hello-world apps, test frameworks, and Gitea CI ... - 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>	2026-09-05 18:48:51 +00:00