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4 commits

Author SHA1 Message Date
Jeuners
eb41d4b196 Rewrite README: highlight real LLM support, time dilation, token savings
Major restructure of the README:

- Removed the misleading 'Keine echten LLMs' line from the
  'Was es bewusst NICHT kann' section (we now have full Ollama +
  OpenRouter support with per-agent models).
- Added a Highlights table at the top with status badges.
- Reorganised Quickstart into 3 paths: rule-based, Ollama,
  OpenRouter (was a single Ollama path with optional LLM).
- New 'Was fehlt gegenüber dem Original' section: clear comparison
  table mapping each original feature to the Mini equivalent and
  explaining why we skipped it.
- New 'Token-Spar-Design' section: token budgets, model cost
  examples, explicit 0-cost path via Ollama.
- 'Tests' section updated: real test counts per file (was a
  generic '50+' stat), 99 total, breakdown by file.
- 'Time Dilation' section reorganised and made the live-validated
  observation the headline.
- LLM provider section split into Ollama (default) and OpenRouter
  (opt-in), with a free-model tool-use table and a per-day cost
  example.
- Architecture tree includes engine/time.py, .env.example,
  tests/ and removes nothing.
- Security section moved up and split from 'Tests' cleanly.
- All anchors updated and TOC added at the top.
2026-06-15 02:39:50 +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
Jeuners
ddf9598518 Emergence-Mini: minimaler Klon von Emergence-World
4 Agenten, 14 Landmarks, 15 Tools, 240x240 Grid, SQLite-Persistenz.
Round-Robin Turn-Manager mit Reactive Triggern, Town-Hall-Voting
(70%-Threshold) mit Live-Constitution-Amendment.

- engine/: db, world, agents, needs, tools, reasoning, governance, turn
- web/: Canvas-basierte Live-View mit WebSocket-Stream
- server.py: FastAPI + WebSocket auf 127.0.0.1:8080
- tests/: 70 Unit + Integration Tests (pytest), alle gruen
- smoke_test.py: 50+ End-to-End-Checks
- README: Quickstart, Architektur, Security, Tests, Lizenz
- .gitignore: DB, Cache, Logs

Basiert auf https://github.com/EmergenceAI/Emergence-World
(Lizenz: CC-BY-NC-4.0, Research-only)
2026-06-15 01:07:38 +02:00