feat: integrate AirLLM as optional high-performance backend
Adds the `bigbrain` optional dependency group (airllm>=2.9.0) and a
complete second inference path that runs 8B / 70B / 405B Llama models
locally via layer-by-layer loading — no GPU required, no cloud, fully
sovereign.
Key changes:
- src/timmy/backends.py — TimmyAirLLMAgent (same print_response interface
as Agno Agent); auto-selects AirLLMMLX on Apple
Silicon, AutoModel (PyTorch) everywhere else
- src/timmy/agent.py — _resolve_backend() routing with explicit override,
env-config, and 'auto' Apple-Silicon detection
- src/timmy/cli.py — --backend / --model-size flags on all commands
- src/config.py — timmy_model_backend + airllm_model_size settings
- src/timmy/prompts.py — mentions AirLLM "even bigger brains, still fully
sovereign"
- pyproject.toml — bigbrain optional dep; wheel includes updated
- .env.example — TIMMY_MODEL_BACKEND + AIRLLM_MODEL_SIZE docs
- tests/conftest.py — stubs 'airllm' module so tests run without GPU
- tests/test_backends.py — 13 new tests covering helpers + TimmyAirLLMAgent
- tests/test_agent.py — 7 new tests for backend routing
- README.md — Big Brain section with one-line install
- activate_self_tdd.sh — bootstrap script (venv + install + tests +
watchdog + dashboard); --big-brain flag
All 61 tests pass. Self-TDD watchdog unaffected.
https://claude.ai/code/session_01DMjQ5qMZ8iHeyix1j3GS7c
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.env.example
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.env.example
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# Enable FastAPI interactive docs at /docs and /redoc (default: false)
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# DEBUG=true
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# ── AirLLM / big-brain backend ───────────────────────────────────────────────
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# Inference backend: "ollama" (default) | "airllm" | "auto"
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# "auto" → uses AirLLM on Apple Silicon if installed, otherwise Ollama.
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# Requires: pip install ".[bigbrain]"
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# TIMMY_MODEL_BACKEND=ollama
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# AirLLM model size (default: 70b).
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# 8b ~16 GB RAM | 70b ~140 GB RAM | 405b ~810 GB RAM
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# AIRLLM_MODEL_SIZE=70b
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