Commit graph

3 commits

Author SHA1 Message Date
Jeuners
e0d72021e4 Per-agent provider routing + 2-OR / 2-Ollama model mix
Routing fix:
- New provider_for_model(name): a model name containing '/' is
  treated as an OpenRouter slug, bare names (llama3.2:3b) as Ollama.
  Previously the global PROVIDER variable routed all calls, so a
  per-agent override 'llama3.2:3b' would have hit OpenRouter and 404'd.
- decide_tool now uses provider_for_model() so per-agent models
  route correctly regardless of global PROVIDER setting.
- New provider_for_agent() helper for callers that need the
  provider of a specific agent.

Live mix: Anchor + Flora on OpenRouter (claude-haiku, gpt-4o-mini);
Lovely + Spark on Ollama (llama3.2:3b, free local).

.env:
- Provider set to 'auto' (uses OpenRouter when key is set)
- Per-agent assignments documented in .env.example
- Cost estimate updated: 2 OR + 2 Ollama = ~$0.10-0.30/day for OR
  portion, $0 for Ollama portion

Tests: 100 passing (was 99). New test_provider_for_model() covers
the routing heuristic. Existing tests updated to pass model=...
explicitly so they don't depend on env-loaded .env overrides.
2026-06-15 02:53:42 +02:00
Jeuners
919866e50d Time Dilation framework + OpenRouter multi-LLM
Implements core pieces of 'Time Dilation in LLM Agent Systems'
(Dillenberg 2026) and adds OpenRouter as a second LLM provider.

ENGINE
- engine/time.py: AgentClock with cumulative proper time tau
  (weighted by op type), EWMA pace (alpha=0.3, dt clamped 0.1-60s),
  ClockRegistry singleton, gamma_{src->dst} frame transformation,
  drift_report with per-pair divergence and threshold flag.
- engine/turn.py: ticks tau on reasoning/tool/memory/reactive;
  broadcasts tau+pace+model in every WebSocket message.
- engine/db.py: schema adds turn_log.tau, turn_log.pace,
  turn_log.model, agent_clocks table; dev-mode auto-migrate
  drops+recreates if old schema detected.
- engine/llm.py: full refactor for two providers.
    Ollama: native tool-calling via /api/chat
    OpenRouter: OpenAI-compatible /api/v1/chat/completions
  Auto mode picks OpenRouter if OPENROUTER_API_KEY is set.
  Per-agent model via EMERGENCE_AGENT_<ID>_MODEL env var.
  .env loader with empty-line guard.
  decide_tool returns (name, args, meta) with cost_usd for OR.

FRONTEND
- web/: new 'Time Dilation · Eigenzeit tau' section with per-agent
  tau bars, pace, op count. Drift warning when any pair exceeds
  threshold. LLM provider info in header.

TESTS
- 14 new tests in tests/test_time.py (tau monotonic, EWMA convergence,
  gamma asymmetry, drift detection).
- 4 new LLM tests: openrouter response parsing, per-agent override,
  provider_info, is_available.
- All 99 tests green.

LIVE-VERIFIED
- 4 different OpenRouter models running in parallel:
  - anchor: anthropic/claude-3.5-haiku
  - flora:  openai/gpt-4o-mini
  - lovely: meta-llama/llama-3.3-70b-instruct
  - spark:  google/gemma-3-4b-it
- All 4 produce turns, all 4 have different tau values,
  drift_report shows the Frame-Transformation gamma values.
- Observation: gamma ~ 1.00 because the explicit Round-Robin +
  sleep(2) keeps frames coherent. This is itself a non-trivial
  validation of the paper's claim: in non-synchronized systems,
  dilation would emerge.

SECRETS
- .env added, OPENROUTER_API_KEY live. .env is git-ignored.
- .env.example documents the config without exposing any key.
- .gitignore now blocks .env, .env.local, *.key, *.pem.

README
- New 'Time Dilation' section explaining tau, pace, CDC, drift
- New 'Multi-LLM via OpenRouter' section with cost table
- Per-agent model config documented
2026-06-15 02:27:11 +02:00
Jeuners
887c913bcd Add Ollama LLM integration with rule-based fallback
- engine/llm.py: Ollama /api/chat client with OpenAI-style tool schema
- engine/reasoning.py: LLM path with 4-tier validation:
    1. tool exists in registry
    2. tool passes location-gating
    3. args parse cleanly
    4. otherwise fall back to rule-based engine
- env vars: EMERGENCE_LLM_{URL,MODEL,TIMEOUT,ENABLED}
- Default model: llama3.2:3b (best speed/quality tradeoff for tool use)
- 11 new mock tests in tests/test_llm.py (no network)
- smoke_test_llm.py: live smoke against real Ollama
- README: 'LLM Integration' section with model table + setup

Live-verified: 4/4 decisions via llama3.2:3b in 1-3s, character-consistent
('facilitate honest debate', 'work together', 'urgency and collaboration').
2026-06-15 01:30:58 +02:00