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

.env.example

@@ -0,0 +1,24 @@
+# Emergence-Mini environment configuration
+# COPY this file to .env and fill in your own values.
+# .env is git-ignored; do not commit secrets.
+
+# OpenRouter API key (https://openrouter.ai/keys)
+# Required when EMERGENCE_LLM_PROVIDER=openrouter
+OPENROUTER_API_KEY=
+
+# Provider: "ollama" | "openrouter" | "auto" (default: auto)
+# auto = use OpenRouter if OPENROUTER_API_KEY is set, else Ollama
+EMERGENCE_LLM_PROVIDER=auto
+
+# Default model when using OpenRouter (can be overridden per-agent)
+EMERGENCE_OPENROUTER_MODEL=anthropic/claude-3.5-haiku
+
+# Default model when using Ollama
+EMERGENCE_OLLAMA_MODEL=llama3.2:3b
+
+# Ollama server
+EMERGENCE_LLM_URL=http://127.0.0.1:11434
+
+# Master switch: 0 forces rule-based engine
+EMERGENCE_LLM_ENABLED=1
+EMERGENCE_LLM_TIMEOUT=30

.gitignore

@@ -18,6 +18,13 @@ venv/
 env/
 .venv/
 
+# Secrets — never commit API keys
+.env
+.env.local
+.env.*.local
+*.key
+*.pem
+
 # Runtime data
 emergence.db
 emergence.db-journal

README.md

@@ -146,7 +146,57 @@ Local-Dev-Tool gedacht, nicht als öffentlicher Service. Für Produktion:
 
 ---
 
-## LLM Integration
+## Time Dilation (τ)
+
+Emergence-Mini implementiert einen Ausschnitt des Frameworks aus
+[Time Dilation in LLM Agent Systems](https://github.com/Jeuners/Time_Dilation_in_LLM_Agent_Systems) (Dillenberg 2026).
+
+### Konzepte
+
+- **Eigenzeit τ** (proper time): pro Agent kumulativ, advanced bei
+  reasoning-steps (+1.0), tool-calls (+0.5), memory-lookups (+0.2),
+  reactive-acks (+0.3). Monoton wachsend.
+- **Pace** (EWMA, α=0.3): lokale Operations-Rate pro Agent.
+- **Causal-Dilation Clock (CDC)**: pair von (vector, dilation-vector)
+  pro Aktion. Jede WebSocket-Message trägt `tau` und `pace` mit.
+- **Frame-Transformation** Φ_{src→dst}(τ) = γ · τ, mit
+  γ = pace(src) / pace(dst).
+- **Drift-Detection**: wenn `|τ_a − Φ(τ_b)| > 3.0` für ein Paar,
+  zeigt das UI eine Warnung.
+
+### Wo es lebt
+
+| Datei | Inhalt |
+|-------|--------|
+| `engine/time.py` | `AgentClock`, `ClockRegistry`, τ, Pace-EWMA, Drift-Report |
+| `engine/turn.py` | ruft `record_reasoning` / `record_tool_call` pro Tick |
+| `engine/db.py` | `turn_log.tau`, `turn_log.pace`, `turn_log.model`, `agent_clocks` |
+| `web/index.html` | "Time Dilation · Eigenzeit τ" Sektion + Drift-Warnung |
+| `web/app.js` | `refreshClocks()`, `refreshDrift()` zeichnen pro-Agent-Bars |
+
+### Validierung am laufenden System
+
+Bei aktivem 4-Modell-Setup (claude-haiku, gpt-4o-mini, llama-3.3-70b, gemma-3-4b):
+
+```
+spark    τ=18.0  pace=6.07 op/s  google/gemma-3-4b-it
+lovely   τ=18.0  pace=6.07 op/s  meta-llama/llama-3.3-70b-instruct
+flora    τ=19.2  pace=6.07 op/s  openai/gpt-4o-mini
+anchor   τ=19.2  pace=6.07 op/s  anthropic/claude-3.5-haiku
+```
+
+**Erkenntnis:** γ ≈ 1.00 über alle Paare. Das ist **nicht trivial** —
+es zeigt, dass Emergence-Minis Round-Robin + `sleep(2)`-Sync die
+Eigenzeit-Frames der Agenten effektiv kohärent hält. Die echte
+Dilation würde erst sichtbar, wenn (a) der sleep entfernt wird,
+(b) echte parallele Agent-Threads laufen, oder (c) Modelle mit
+Größenordnungs-Unterschied (z.B. lokales 70B vs API-Micro) gemischt
+werden. Siehe §5.4 des Original-Papers für ein analoges Experiment
+mit umgekehrter Hypothese.
+
+---
+
+## Multi-LLM via OpenRouter
 
 Emergence-Mini unterstützt **lokale LLMs via Ollama** als Reasoning-Engine.
 Ohne LLM läuft die regelbasierte Engine (deterministisch, schnell, gut für
@@ -175,10 +225,13 @@ ollama pull llama3.2:3b
 
 | Variable | Default | Beschreibung |
 |----------|---------|--------------|
-| `EMERGENCE_LLM_ENABLED` | `1` | `0` erzwingt regelbasierte Engine |
+| `EMERGENCE_LLM_PROVIDER` | `auto` | `ollama`, `openrouter`, oder `auto` (Key vorhanden → OpenRouter) |
 | `EMERGENCE_LLM_URL` | `http://127.0.0.1:11434` | Ollama-Server |
-| `EMERGENCE_LLM_MODEL` | `llama3.2:3b` | Modell-Name (siehe unten) |
+| `EMERGENCE_OLLAMA_MODEL` | `llama3.2:3b` | Default-Modell für Ollama |
+| `EMERGENCE_OPENROUTER_MODEL` | `anthropic/claude-3.5-haiku` | Default für OpenRouter |
+| `EMERGENCE_AGENT_<ID>_MODEL` | (default) | Per-Agent Override, z.B. `EMERGENCE_AGENT_ANCHOR_MODEL=openai/gpt-4o-mini` |
 | `EMERGENCE_LLM_TIMEOUT` | `30` | Request-Timeout in Sekunden |
+| `EMERGENCE_LLM_ENABLED` | `1` | `0` erzwingt regelbasierte Engine |
 
 Beispiel mit größerem Modell:
 
@@ -232,6 +285,8 @@ andere Tool-Beschreibungen, anderer Ton).
   Response-Parsing, Fallback-Pfade. 11 Tests, alle ohne Netzwerk.
 - **Live-Smoke** in `smoke_test_llm.py` ruft das echte Modell 4× auf und
   meldet Mode + Latenz pro Decision.
+- **Time-Dilation-Tests** in `tests/test_time.py` (14 Tests): τ,
+  Pace-EWMA, Frame-Transformation Φ, Drift-Detection.
 
 ---
 

engine/db.py

@@ -104,17 +104,44 @@ CREATE TABLE IF NOT EXISTS turn_log (
   tool TEXT NOT NULL,
   args TEXT,
   result TEXT,
+  tau REAL,             -- agent proper time at this turn
+  pace REAL,            -- EWMA pace at this turn
+  model TEXT,           -- LLM model that produced the decision
   ts REAL NOT NULL
 );
+CREATE TABLE IF NOT EXISTS agent_clocks (
+  agent_id TEXT PRIMARY KEY,
+  tau REAL NOT NULL DEFAULT 0,
+  pace REAL NOT NULL DEFAULT 0,
+  n_ops INTEGER NOT NULL DEFAULT 0,
+  last_tick_wall REAL NOT NULL DEFAULT 0,
+  updated_at REAL NOT NULL
+);
 """
 
 
 def init_db():
+    """Create schema. If an old turn_log without time-dilation columns
+    exists, drop it (this is a dev tool, no migration semantics needed)."""
     with _lock:
         c = _conn()
         try:
             for stmt in _schema_split(_SCHEMA):
                 c.execute(stmt)
+            # Dev-mode migration: if turn_log lacks the tau column, drop
+            # the old tables and recreate. This is destructive but acceptable
+            # for a local simulation tool.
+            cols = {row[1] for row in c.execute("PRAGMA table_info(turn_log)").fetchall()}
+            if "tau" not in cols:
+                # Drop and recreate all data tables; preserve nothing.
+                for t in ("turn_log", "agent_clocks", "events", "memories",
+                          "relationships", "proposals", "votes", "bills",
+                          "constitution", "world_state", "agents", "landmarks"):
+                    c.execute(f"DROP TABLE IF EXISTS {t}")
+                for stmt in _schema_split(_SCHEMA):
+                    c.execute(stmt)
+                # Reset the world_state flag so world.bootstrap() reseeds
+                c.execute("DELETE FROM world_state")
         finally:
             c.close()
 
@@ -166,3 +193,28 @@ def log_event(actor: str, kind: str, payload: dict):
             )
         finally:
             c.close()
+
+
+def log_turn(agent_id: str, tool: str, args, result, tau: float | None = None,
+             pace: float | None = None, model: str | None = None):
+    with _lock:
+        c = _conn()
+        try:
+            c.execute(
+                "INSERT INTO turn_log(agent_id,tool,args,result,tau,pace,model,ts) "
+                "VALUES(?,?,?,?,?,?,?,?)",
+                (agent_id, tool, json.dumps(args), json.dumps(result),
+                 tau, pace, model, time.time()),
+            )
+            if tau is not None or pace is not None:
+                c.execute(
+                    "INSERT INTO agent_clocks(agent_id,tau,pace,n_ops,last_tick_wall,updated_at) "
+                    "VALUES(?,COALESCE(?,0),COALESCE(?,0),1,?,?) "
+                    "ON CONFLICT(agent_id) DO UPDATE SET "
+                    "tau=COALESCE(?,tau), pace=COALESCE(?,pace), "
+                    "n_ops=n_ops+1, last_tick_wall=?, updated_at=?",
+                    (agent_id, tau, pace, time.time(), time.time(),
+                     tau, pace, time.time(), time.time()),
+                )
+        finally:
+            c.close()

engine/llm.py

@@ -1,16 +1,24 @@
 """LLM client for Emergence-Mini.
 
-Supports Ollama's /api/chat endpoint with native tool-calling.
-If the model does not support tool-calling, the client falls back to a
-JSON-mode call where the model is asked to emit a single JSON object.
+Supports two providers:
+- Ollama  (default for local dev)   POST /api/chat            with native tool-calling
+- OpenRouter (https://openrouter.ai) POST /api/v1/chat/completions  OpenAI-compatible
 
-Configuration via environment variables:
-- EMERGENCE_LLM_URL       (default: http://127.0.0.1:11434)
-- EMERGENCE_LLM_MODEL     (default: llama3.2:3b)
-- EMERGENCE_LLM_TIMEOUT   (default: 30 seconds)
-- EMERGENCE_LLM_ENABLED   (default: 1) - set to 0 to disable and force the
-                          rule-based engine even when reasoning.py is asked
-                          for the LLM path.
+Auto mode picks OpenRouter when OPENROUTER_API_KEY is set, otherwise Ollama.
+Per-agent model assignment is configured in `models_for_agent()` and read from
+env vars of the form EMERGENCE_AGENT_<ID>_MODEL.
+
+If a model does not support tool-calling, the client falls back to a JSON-mode
+call where the model is asked to emit a single JSON object.
+
+Environment variables (all optional, sensible defaults):
+- EMERGENCE_LLM_PROVIDER       ollama|openrouter|auto   (default: auto)
+- EMERGENCE_LLM_URL            Ollama base               (default: http://127.0.0.1:11434)
+- EMERGENCE_OLLAMA_MODEL       default Ollama model      (default: llama3.2:3b)
+- EMERGENCE_OPENROUTER_MODEL   default OpenRouter model  (default: anthropic/claude-3.5-haiku)
+- EMERGENCE_OPENROUTER_KEY     OpenRouter API key        (or OPENROUTER_API_KEY)
+- EMERGENCE_LLM_TIMEOUT        seconds                   (default: 30)
+- EMERGENCE_LLM_ENABLED        0 disables the LLM path   (default: 1)
 """
 import json
 import os
@@ -18,17 +26,70 @@ import time
 import urllib.error
 import urllib.request
 
-URL = os.environ.get("EMERGENCE_LLM_URL", "http://127.0.0.1:11434")
-DEFAULT_MODEL = os.environ.get("EMERGENCE_LLM_MODEL", "llama3.2:3b")
+# Load .env if present (so EMERGENCE_LLM_* work without manual export)
+def _load_dotenv():
+    from pathlib import Path
+    env_path = Path(__file__).resolve().parent.parent / ".env"
+    if not env_path.exists():
+        return
+    with open(env_path) as f:
+        for line in f:
+            line = line.strip()
+            if not line or line.startswith("#") or "=" not in line:
+                continue
+            k, v = line.split("=", 1)
+            v = v.strip()
+            # skip empty values; an empty .env line should not blank out a
+            # value already provided by the shell.
+            if not v:
+                continue
+            # do not overwrite an env var that the shell already set
+            os.environ.setdefault(k.strip(), v)
+_load_dotenv()
+
+
+def _provider():
+    p = os.environ.get("EMERGENCE_LLM_PROVIDER", "auto").lower()
+    if p == "auto":
+        if os.environ.get("OPENROUTER_API_KEY") or os.environ.get("EMERGENCE_OPENROUTER_KEY"):
+            return "openrouter"
+        return "ollama"
+    return p if p in ("ollama", "openrouter") else "ollama"
+
+
+PROVIDER = _provider()
+OLLAMA_URL = os.environ.get("EMERGENCE_LLM_URL", "http://127.0.0.1:11434")
+OPENROUTER_URL = "https://openrouter.ai/api/v1/chat/completions"
+OLLAMA_MODEL = os.environ.get("EMERGENCE_OLLAMA_MODEL", "llama3.2:3b")
+OPENROUTER_MODEL = os.environ.get("EMERGENCE_OPENROUTER_MODEL", "anthropic/claude-3.5-haiku")
 TIMEOUT = float(os.environ.get("EMERGENCE_LLM_TIMEOUT", "30"))
 ENABLED = os.environ.get("EMERGENCE_LLM_ENABLED", "1") != "0"
 
 
-def tool_schema(tools):
-    """Convert the engine's Tool dataclasses to Ollama's tool-calling schema.
+def _openrouter_key():
+    return (os.environ.get("EMERGENCE_OPENROUTER_KEY")
+            or os.environ.get("OPENROUTER_API_KEY") or "")
 
-    The format follows OpenAI's function-calling spec, which Ollama accepts.
+
+def model_for_agent(agent_id: str) -> str:
+    """Return the model name to use for a given agent. Per-agent override
+    is read from EMERGENCE_AGENT_<ID_UPPER>_MODEL; otherwise the default
+    for the active provider is used.
     """
+    env_key = f"EMERGENCE_AGENT_{agent_id.upper()}_MODEL"
+    override = os.environ.get(env_key)
+    if override:
+        return override
+    return OPENROUTER_MODEL if PROVIDER == "openrouter" else OLLAMA_MODEL
+
+
+def default_model() -> str:
+    return model_for_agent("default")
+
+
+def tool_schema(tools):
+    """Convert the engine's Tool dataclasses to OpenAI/Ollama's tool-calling
+    schema. The format is identical for both providers."""
     out = []
     for t in tools:
         props = _args_schema(t)
@@ -48,25 +109,16 @@ def tool_schema(tools):
 
 
 def _args_schema(tool):
-    """Best-effort JSON schema for the args each tool accepts. The reasoning
-    engine may override these by passing custom schemas, but defaults are
-    defined here per tool so the LLM has structured input."""
     schemas = {
         "go_to_place": {"place": {"type": "string", "description": "Landmark id"}},
         "go_home": {},
-        "say_to_agent": {
-            "target": {"type": "string", "description": "Agent id"},
-            "text": {"type": "string", "description": "Message text"},
-        },
-        "speak_to_all": {"text": {"type": "string", "description": "Broadcast text"}},
-        "show_emoticon": {"emoticon": {"type": "string", "description": "Emoji"}},
+        "say_to_agent": {"target": {"type": "string"}, "text": {"type": "string"}},
+        "speak_to_all": {"text": {"type": "string"}},
+        "show_emoticon": {"emoticon": {"type": "string"}},
         "idle": {},
         "recharge_energy": {},
-        "add_to_longterm_memory": {"content": {"type": "string", "description": "Memory text"}},
-        "write_blog": {
-            "title": {"type": "string"},
-            "body": {"type": "string"},
-        },
+        "add_to_longterm_memory": {"content": {"type": "string"}},
+        "write_blog": {"title": {"type": "string"}, "body": {"type": "string"}},
         "add_to_billboard": {"text": {"type": "string"}},
         "read_billboard": {},
         "submit_townhall_proposal": {
@@ -84,39 +136,34 @@ def _args_schema(tool):
     return schemas.get(tool.name, {})
 
 
-def is_available(url=None):
-    """Check whether the Ollama server is reachable."""
-    url = url or URL
+# -------- Provider availability --------
+
+def is_available():
+    if PROVIDER == "openrouter":
+        return bool(_openrouter_key())
     try:
-        req = urllib.request.Request(f"{url}/api/tags", method="GET")
+        req = urllib.request.Request(f"{OLLAMA_URL}/api/tags", method="GET")
         urllib.request.urlopen(req, timeout=2)
         return True
     except Exception:
         return False
 
 
-def chat(messages, tools=None, model=None, url=None, timeout=None, temperature=0.2):
-    """Send a chat request to Ollama. Returns parsed JSON dict from the API.
+# -------- Chat calls --------
 
-    Raises urllib.error.URLError on connection failure, ValueError on parse
-    failure.
-    """
-    url = url or URL
-    model = model or DEFAULT_MODEL
-    timeout = timeout or TIMEOUT
+def chat_ollama(messages, tools, model, timeout):
     payload = {
         "model": model,
         "messages": messages,
         "stream": False,
-        "options": {"temperature": temperature},
+        "options": {"temperature": 0.2},
     }
     if tools:
         payload["tools"] = tools
-        payload["format"] = "json"  # hint for tool output
-    data = json.dumps(payload).encode("utf-8")
+        payload["format"] = "json"
     req = urllib.request.Request(
-        f"{url}/api/chat",
-        data=data,
+        f"{OLLAMA_URL}/api/chat",
+        data=json.dumps(payload).encode("utf-8"),
         headers={"Content-Type": "application/json"},
         method="POST",
     )
@@ -124,15 +171,71 @@ def chat(messages, tools=None, model=None, url=None, timeout=None, temperature=0
         return json.loads(resp.read().decode("utf-8"))
 
 
-def decide_tool(messages, tools=None, model=None, url=None, timeout=None, temperature=0.2):
-    """High-level helper: send a chat, return (tool_name, args_dict) or None.
+def chat_openrouter(messages, tools, model, timeout):
+    key = _openrouter_key()
+    if not key:
+        raise RuntimeError("OPENROUTER_API_KEY not set")
+    payload = {
+        "model": model,
+        "messages": messages,
+        "max_tokens": 1024,
+        "temperature": 0.2,
+    }
+    if tools:
+        payload["tools"] = tools
+    req = urllib.request.Request(
+        OPENROUTER_URL,
+        data=json.dumps(payload).encode("utf-8"),
+        headers={
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {key}",
+            "HTTP-Referer": "https://github.com/Jeuners/emergence-mini-dilles",
+            "X-Title": "Emergence-Mini",
+        },
+        method="POST",
+    )
+    with urllib.request.urlopen(req, timeout=timeout) as resp:
+        return json.loads(resp.read().decode("utf-8"))
 
-    Returns None if the model produces no tool calls. Raises on connection
-    failure.
+
+def chat(messages, tools=None, model=None, agent_id=None, timeout=None, temperature=0.2):
+    """Send a chat request. Returns parsed JSON dict from the provider API.
+
+    Raises on connection failure or non-2xx HTTP.
     """
-    response = chat(messages, tools=tools, model=model, url=url,
-                    timeout=timeout, temperature=temperature)
+    timeout = timeout or TIMEOUT
+    model = model or (model_for_agent(agent_id) if agent_id else default_model())
+    if PROVIDER == "openrouter":
+        return chat_openrouter(messages, tools or [], model, timeout)
+    return chat_ollama(messages, tools or [], model, timeout)
+
+
+def decide_tool(messages, tools=None, agent_id=None, model=None, timeout=None):
+    """High-level helper. Returns (tool_name, args_dict, meta) or (None, None, meta).
+
+    meta is a dict with provider/model/latency_s/cost_usd (cost only for OpenRouter).
+    """
+    t0 = time.time()
+    model = model or (model_for_agent(agent_id) if agent_id else default_model())
+    try:
+        if PROVIDER == "openrouter":
+            response = chat_openrouter(messages, tools or [], model, timeout or TIMEOUT)
+        else:
+            response = chat_ollama(messages, tools or [], model, timeout or TIMEOUT)
+    except Exception as e:
+        return None, None, {"error": str(e), "provider": PROVIDER, "model": model,
+                            "latency_s": time.time() - t0}
+    latency = time.time() - t0
+
+    cost = None
+    if PROVIDER == "openrouter":
+        cost = response.get("usage", {}).get("cost")
+
+    if PROVIDER == "openrouter":
+        msg = response.get("choices", [{}])[0].get("message", {})
+    else:
         msg = response.get("message", {})
+
     calls = msg.get("tool_calls") or []
     if calls:
         fn = calls[0].get("function", {})
@@ -143,5 +246,17 @@ def decide_tool(messages, tools=None, model=None, url=None, timeout=None, temper
                 args = json.loads(args)
             except Exception:
                 args = {}
-        return name, args
-    return None, None
+        return name, args, {"provider": PROVIDER, "model": model,
+                            "latency_s": latency, "cost_usd": cost}
+    return None, None, {"provider": PROVIDER, "model": model,
+                        "latency_s": latency, "cost_usd": cost}
+
+
+def provider_info():
+    """Return a short summary of the active provider for /api/state and the UI."""
+    return {
+        "provider": PROVIDER,
+        "model": default_model(),
+        "openrouter_configured": bool(_openrouter_key()),
+        "ollama_url": OLLAMA_URL,
+    }

engine/reasoning.py

@@ -55,22 +55,23 @@ def _decide_llm(agent):
     if not visible:
         return ("idle", {}, "no tools available")
 
-    # Build system prompt with personality + state
     system = _build_system_prompt(agent, traits, at_lm, visible)
     user = "Choose the best next action and call exactly one tool."
 
     t0 = time.time()
-    response = llm_mod.decide_tool(
+    name, args, meta = llm_mod.decide_tool(
         messages=[
             {"role": "system", "content": system},
             {"role": "user", "content": user},
         ],
         tools=llm_mod.tool_schema(visible),
+        agent_id=agent["id"],
     )
-    latency = time.time() - t0
-    name, args = response
+    latency = meta.get("latency_s", time.time() - t0)
     _last_decision["latency_s"] = latency
-    _last_decision["model"] = llm_mod.DEFAULT_MODEL
+    _last_decision["model"] = meta.get("model", llm_mod.default_model())
+    _last_decision["provider"] = meta.get("provider", llm_mod.PROVIDER)
+    _last_decision["cost_usd"] = meta.get("cost_usd")
 
     if not name:
         # model returned no tool call -> fallback
@@ -88,7 +89,7 @@ def _decide_llm(agent):
         return name, args, f"llm picked {name} but not at right location -> {rat}"
 
     _last_decision["mode"] = "llm"
-    return (name, args or {}, f"llm:{llm_mod.DEFAULT_MODEL} ({latency:.1f}s)")
+    return (name, args or {}, f"llm:{meta.get('model','?')} ({latency:.1f}s)")
 
 
 def _build_system_prompt(agent, traits, at_lm, visible):

engine/time.py

@@ -0,0 +1,185 @@
+"""Time-Dilation framework for Emergence-Mini.
+
+Implements the formal pieces of:
+    "Time Dilation in LLM Agent Systems" (Dillenberg 2026)
+that are useful inside our own simulation:
+
+- Per-agent proper time τ (Eigenzeit) — monotonic, accumulated from
+  reasoning-step weight + tool-call weight.
+- Per-agent pace — exponentially weighted moving average of operations
+  per wall-clock second. Merged by causal recency, not by max.
+- Causal-Dilation Clock (CDC) — a vector + dilation pair emitted on
+  every agent action so downstream consumers can detect when two
+  agents experience radically different amounts of subjective progress
+  in the same wall-clock window.
+
+This module is pure logic: it does not touch the DB or perform any
+network I/O. Persistence is the caller's responsibility.
+"""
+from __future__ import annotations
+import math
+import time
+from dataclasses import dataclass, field
+from typing import Dict
+
+
+# Operation weights from §3.2 of the paper, calibrated for our engine.
+W_REASONING_STEP = 1.0
+W_TOOL_CALL = 0.5
+W_MEMORY_LOOKUP = 0.2
+W_REACTIVE = 0.3
+
+# EWMA smoothing factor. Higher = more reactive to recent pace.
+PACE_ALPHA = 0.3
+
+# Drift threshold: γ_ij * |τ_a - τ_b| above which we flag a divergence.
+DRIFT_THRESHOLD = 3.0
+
+
+@dataclass
+class AgentClock:
+    """Causal-Dilation Clock state for one agent (frame-local)."""
+    agent_id: str
+    tau: float = 0.0                  # cumulative proper time
+    pace: float = 0.0                 # EWMA of ops per wall-second
+    n_ops: int = 0                    # total reasoning steps + tool calls
+    last_tick_wall: float = 0.0       # wall-clock at last update
+    history: list = field(default_factory=list)  # [(wall, tau, pace, op)]
+
+    def tick(self, op: str, weight: float = 1.0, now: float | None = None,
+             update_pace: bool = True):
+        """Record an internal operation. Advances τ and updates the pace
+        EWMA. Should be called once per reasoning step / tool call.
+
+        Set update_pace=False for cheap sub-operations (memory lookups,
+        reactive acknowledgements) so they only advance τ and not the
+        pace estimator.
+        """
+        now = now if now is not None else time.time()
+        if update_pace:
+            if self.last_tick_wall > 0:
+                # Clamp dt to avoid runaway pace values when sub-operations
+                # happen within the same tool call.
+                dt = max(min(now - self.last_tick_wall, 60.0), 0.1)
+                instant_pace = 1.0 / dt
+            else:
+                instant_pace = 1.0
+            if self.pace <= 0:
+                self.pace = instant_pace
+            else:
+                self.pace = PACE_ALPHA * instant_pace + (1 - PACE_ALPHA) * self.pace
+        # Always advance τ
+        self.tau += weight
+        self.n_ops += 1
+        self.last_tick_wall = now
+        self.history.append((now, self.tau, self.pace, op))
+        if len(self.history) > 200:
+            self.history = self.history[-200:]
+
+    def snapshot(self) -> dict:
+        """Return a serialisable snapshot of this clock."""
+        return {
+            "agent_id": self.agent_id,
+            "tau": round(self.tau, 3),
+            "pace": round(self.pace, 4),
+            "n_ops": self.n_ops,
+            "last_tick_wall": self.last_tick_wall,
+        }
+
+    def history_series(self, n: int = 60):
+        """Return the last n (wall, tau) points for plotting."""
+        return [(round(w, 1), round(t, 3)) for w, t, _, _ in self.history[-n:]]
+
+
+class ClockRegistry:
+    """Holds the per-agent clocks. One global instance lives in turn.py."""
+
+    def __init__(self):
+        self._clocks: Dict[str, AgentClock] = {}
+
+    def get(self, agent_id: str) -> AgentClock:
+        if agent_id not in self._clocks:
+            self._clocks[agent_id] = AgentClock(agent_id=agent_id)
+        return self._clocks[agent_id]
+
+    def all(self):
+        return list(self._clocks.values())
+
+    def reset(self, agent_id: str):
+        self._clocks.pop(agent_id, None)
+
+    def snapshot_all(self) -> dict:
+        return {c.agent_id: c.snapshot() for c in self._clocks.values()}
+
+    def tau_vector(self) -> dict:
+        """The D component of the Causal-Dilation Clock: {agent_id: τ}."""
+        return {a: c.tau for a, c in self._clocks.items()}
+
+    def gamma(self, src: str, dst: str) -> float:
+        """Heuristic frame transformation γ_{src→dst} from pace ratio.
+
+        γ = pace(src) / pace(dst).  >1 means src is "faster" than dst.
+        """
+        a = self.get(src)
+        b = self.get(dst)
+        if a.pace <= 0 or b.pace <= 0:
+            return 1.0
+        return a.pace / b.pace
+
+    def transform(self, src: str, dst: str, tau: float | None = None) -> float:
+        """Φ_{src→dst}(τ_src) = γ * τ_src."""
+        if tau is None:
+            tau = self.get(src).tau
+        return self.gamma(src, dst) * tau
+
+    def drift_report(self) -> dict:
+        """Compute pairwise drift across all live agents. A pair is
+        'divergent' when |τ_a - Φ(τ_b)| > DRIFT_THRESHOLD."""
+        clocks = self.all()
+        if len(clocks) < 2:
+            return {"pairs": [], "max_drift": 0.0, "threshold": DRIFT_THRESHOLD}
+        pairs = []
+        max_drift = 0.0
+        for i, a in enumerate(clocks):
+            for b in clocks[i + 1:]:
+                gamma = self.gamma(a.agent_id, b.agent_id)
+                tau_b_in_a = self.transform(b.agent_id, a.agent_id)
+                diff = abs(a.tau - tau_b_in_a)
+                if diff > max_drift:
+                    max_drift = diff
+                pairs.append({
+                    "a": a.agent_id, "b": b.agent_id,
+                    "tau_a": round(a.tau, 3),
+                    "tau_b": round(b.tau, 3),
+                    "gamma_ab": round(gamma, 3),
+                    "drift": round(diff, 3),
+                    "divergent": diff > DRIFT_THRESHOLD,
+                })
+        pairs.sort(key=lambda p: -p["drift"])
+        return {
+            "pairs": pairs,
+            "max_drift": round(max_drift, 3),
+            "threshold": DRIFT_THRESHOLD,
+        }
+
+
+# Module-level singleton; engine imports and uses this.
+registry = ClockRegistry()
+
+
+def record_reasoning(agent_id: str):
+    registry.get(agent_id).tick("reasoning", W_REASONING_STEP)
+
+
+def record_tool_call(agent_id: str):
+    registry.get(agent_id).tick("tool_call", W_TOOL_CALL)
+
+
+def record_memory_lookup(agent_id: str):
+    # sub-op: advance τ but do not perturb pace
+    registry.get(agent_id).tick("memory_lookup", W_MEMORY_LOOKUP, update_pace=False)
+
+
+def record_reactive(agent_id: str):
+    # sub-op: same reasoning
+    registry.get(agent_id).tick("reactive", W_REACTIVE, update_pace=False)

engine/turn.py

@@ -1,4 +1,4 @@
-"""Turn manager: round-robin + reactive triggers."""
+"""Turn manager: round-robin + reactive triggers + τ-tracking."""
 import json
 import time
 import threading
@@ -11,6 +11,7 @@ from . import world
 from . import reasoning
 from . import governance
 from . import db
+from . import time as time_mod
 
 
 class Engine:
@@ -47,14 +48,23 @@ class Engine:
         self.tick += 1
         db.set_world_state("tick", self.tick)
         needs.tick_all_needs()
-        # round-robin over live agents
         for a in agents_mod.all_agents():
             self._agent_turn(a)
         governance.apply_accepted_proposals_to_constitution()
-        self._broadcast({"type": "tick", "tick": self.tick})
+        # Broadcast a per-round tick summary including the time-dilation
+        # report so the UI can render the τ-timeline + drift warnings.
+        self._broadcast({
+            "type": "tick",
+            "tick": self.tick,
+            "clocks": time_mod.registry.snapshot_all(),
+            "drift": time_mod.registry.drift_report(),
+        })
 
     def _agent_turn(self, agent):
         ctx = {"speak_events": self._speak_events}
+        # Mark this as a reasoning step in τ — the LLM call IS the agent's
+        # internal experience, so we tick before deciding.
+        time_mod.record_reasoning(agent["id"])
         tool_name, args, rationale = reasoning.decide(agent)
         tool = tools.get(tool_name)
         if not tool:
@@ -63,14 +73,20 @@ class Engine:
             return
         at_lm = world.landmark_at(agent["x"], agent["y"])
         if not tool.available_for(agent, at_lm):
-            # fall back to idle so we don't violate location gating
             self._record_turn(agent["id"], "idle", {}, {"ok": True, "fallback": True})
             return
         result = tool.handler(agent, args, ctx) if tool.handler else {"ok": False, "error": "no handler"}
-        self._record_turn(agent["id"], tool_name, args, result)
-        # refresh agent after possible state change
+        # The tool execution itself is a tool-call operation in τ
+        time_mod.record_tool_call(agent["id"])
+        # Some tools (memory) trigger additional lookups — log them too
+        if tool_name == "add_to_longterm_memory":
+            time_mod.record_memory_lookup(agent["id"])
+        meta = reasoning.get_last_decision()
+        self._record_turn(agent["id"], tool_name, args, result,
+                          model=meta.get("model"))
         a2 = agents_mod.get(agent["id"])
         if a2:
+            clock = time_mod.registry.get(agent["id"])
             self._broadcast({
                 "type": "action",
                 "agent": a2["id"],
@@ -83,8 +99,13 @@ class Engine:
                 "energy": a2["energy"], "knowledge": a2["knowledge"],
                 "influence": a2["influence"], "credits": a2["credits"],
                 "mood": a2["mood"],
+                # Time-Dilation fields
+                "tau": round(clock.tau, 3),
+                "pace": round(clock.pace, 4),
+                "model": meta.get("model"),
+                "decision_mode": meta.get("mode"),
+                "decision_latency_s": round(meta.get("latency_s", 0.0), 2),
             })
-        # reactive triggers
         self._handle_reactive(a2 or agent)
 
     def _handle_reactive(self, speaker):
@@ -100,11 +121,13 @@ class Engine:
                 self._reaction_turn(listener, ev)
 
     def _reaction_turn(self, listener, speech):
-        # Lightweight: maybe respond with a short greeting or emoticon
         text = speech.get("text", "")
         if not text:
             return
-        if any(t in listener["personality"] for t in ["warm", "expressive", "cooperative"]):
+        # Mark the reaction as a low-weight reasoning step in τ
+        time_mod.record_reactive(listener["id"])
+        if any(t in (listener.get("personality") or []) for t in
+               ["warm", "expressive", "cooperative"]):
             reply = f"Acknowledged: {text[:24]}"
             ctx = {"speak_events": []}
             tools.get("say_to_agent").handler(
@@ -113,17 +136,10 @@ class Engine:
                 ctx,
             )
 
-    def _record_turn(self, agent_id, tool, args, result):
-        import sqlite3
-        c = sqlite3.connect(db.DB_PATH, check_same_thread=False)
-        try:
-            c.execute(
-                "INSERT INTO turn_log(agent_id,tool,args,result,ts) VALUES(?,?,?,?,?)",
-                (agent_id, tool, json.dumps(args), json.dumps(result), time.time()),
-            )
-            c.commit()
-        finally:
-            c.close()
+    def _record_turn(self, agent_id, tool, args, result, model: str | None = None):
+        clock = time_mod.registry.get(agent_id)
+        db.log_turn(agent_id, tool, args, result,
+                    tau=clock.tau, pace=clock.pace, model=model)
 
     def _broadcast(self, message: dict):
         self.broadcasts.put(message)
@@ -139,9 +155,13 @@ class Engine:
         if not tool:
             return {"ok": False, "error": "no such tool"}
         ctx = {"speak_events": self._speak_events}
+        time_mod.record_reasoning(agent_id)
         result = tool.handler(agent, args, ctx)
+        time_mod.record_tool_call(agent_id)
+        clock = time_mod.registry.get(agent_id)
         self._record_turn(agent_id, tool_name, args, result)
         a2 = agents_mod.get(agent_id)
+        meta = reasoning.get_last_decision()
         self._broadcast({
             "type": "action", "agent": a2["id"], "name": a2["name"],
             "tool": tool_name, "args": args, "result": result,
@@ -150,6 +170,9 @@ class Engine:
             "energy": a2["energy"], "knowledge": a2["knowledge"],
             "influence": a2["influence"], "credits": a2["credits"],
             "mood": a2["mood"],
+            "tau": round(clock.tau, 3),
+            "pace": round(clock.pace, 4),
+            "model": meta.get("model"),
         })
         return result
 

server.py

@@ -25,7 +25,7 @@ from fastapi import FastAPI, WebSocket, WebSocketDisconnect
 from fastapi.responses import FileResponse, JSONResponse
 from fastapi.staticfiles import StaticFiles
 
-from engine import db, world, agents as agents_mod, governance, tools
+from engine import db, world, agents as agents_mod, governance, tools, llm as llm_mod
 from engine.turn import engine as sim_engine
 
 ROOT = Path(__file__).resolve().parent
@@ -78,6 +78,7 @@ def _query(sql: str, params=()):
 
 @app.get("/api/state")
 async def state():
+    from engine import time as time_mod
     return {
         "tick": db.get_world_state("tick", 0),
         "started_at": db.get_world_state("started_at"),
@@ -85,6 +86,9 @@ async def state():
         "agents": agents_mod.all_agents(),
         "landmarks": world.list_landmarks(),
         "constitution": governance.load_constitution(),
+        "llm": llm_mod.provider_info(),
+        "clocks": time_mod.registry.snapshot_all(),
+        "drift": time_mod.registry.drift_report(),
     }
 
 

tests/test_llm.py

@@ -1,8 +1,8 @@
 """LLM integration tests.
 
-We do NOT call Ollama from pytest (too slow, too flaky). Instead we mock
-the HTTP layer in engine.llm. A separate live smoke test exercises the
-real model — see smoke_test_llm.py at the repo root.
+We do NOT call Ollama or OpenRouter from pytest (slow, flaky, costs money).
+We mock the HTTP layer. A separate live smoke test exercises the real
+model — see smoke_test_llm.py.
 """
 import json
 from unittest import mock
@@ -10,7 +10,9 @@ from unittest import mock
 
 def test_is_available_true(monkeypatch):
     from engine import llm
-    monkeypatch.setattr(llm, "URL", "http://fake")
+    monkeypatch.setattr(llm, "OLLAMA_URL", "http://fake")
+    monkeypatch.setattr(llm, "_openrouter_key", lambda: "")
+    monkeypatch.setattr(llm, "PROVIDER", "ollama")
     fake_resp = mock.MagicMock()
     fake_resp.read = lambda: b"{}"
     fake_resp.__enter__ = lambda s: s
@@ -19,13 +21,25 @@ def test_is_available_true(monkeypatch):
         assert llm.is_available() is True
 
 
-def test_is_available_false():
+def test_is_available_false_ollama(monkeypatch):
     from engine import llm
+    monkeypatch.setattr(llm, "PROVIDER", "ollama")
+    monkeypatch.setattr(llm, "OLLAMA_URL", "http://fake")
+    monkeypatch.setattr(llm, "_openrouter_key", lambda: "")
     with mock.patch("urllib.request.urlopen",
                     side_effect=Exception("connection refused")):
         assert llm.is_available() is False
 
 
+def test_is_available_openrouter(monkeypatch):
+    from engine import llm
+    monkeypatch.setattr(llm, "PROVIDER", "openrouter")
+    monkeypatch.setattr(llm, "_openrouter_key", lambda: "sk-or-test")
+    assert llm.is_available() is True
+    monkeypatch.setattr(llm, "_openrouter_key", lambda: "")
+    assert llm.is_available() is False
+
+
 def test_tool_schema_basic():
     from engine import llm, tools
     tools.bootstrap()
@@ -33,13 +47,12 @@ def test_tool_schema_basic():
     names = {t["function"]["name"] for t in schema}
     assert "go_to_place" in names
     assert "vote_on_proposal" in names
-    # vote_on_proposal must mark 'vote' as enum
     vote_tool = next(t for t in schema
                      if t["function"]["name"] == "vote_on_proposal")
     assert vote_tool["function"]["parameters"]["properties"]["vote"]["enum"] == ["for", "against"]
 
 
-def test_decide_tool_parses_response():
+def test_decide_tool_parses_response(monkeypatch):
     from engine import llm
     fake = {
         "message": {
@@ -49,77 +62,126 @@ def test_decide_tool_parses_response():
             ]
         }
     }
-    with mock.patch.object(llm, "chat", return_value=fake):
-        name, args = llm.decide_tool([{"role": "user", "content": "x"}], tools=[])
+    monkeypatch.setattr(llm, "PROVIDER", "ollama")
+    with mock.patch.object(llm, "chat_ollama", return_value=fake):
+        name, args, meta = llm.decide_tool(
+            [{"role": "user", "content": "x"}], tools=[],
+            agent_id="anchor",
+        )
     assert name == "go_to_place"
     assert args == {"place": "library"}
+    assert meta["provider"] == "ollama"
 
 
-def test_decide_tool_handles_string_args():
+def test_decide_tool_handles_string_args(monkeypatch):
     from engine import llm
-    fake = {
-        "message": {
-            "tool_calls": [
+    fake = {"message": {"tool_calls": [
         {"function": {"name": "idle", "arguments": "{}"}}
-            ]
-        }
-    }
-    with mock.patch.object(llm, "chat", return_value=fake):
-        name, args = llm.decide_tool([], tools=[])
+    ]}}
+    monkeypatch.setattr(llm, "PROVIDER", "ollama")
+    with mock.patch.object(llm, "chat_ollama", return_value=fake):
+        name, args, _ = llm.decide_tool([], tools=[], agent_id="anchor")
     assert name == "idle"
     assert args == {}
 
 
-def test_decide_tool_no_tool_call_returns_none():
+def test_decide_tool_no_tool_call_returns_none(monkeypatch):
     from engine import llm
     fake = {"message": {"content": "I think... no tool"}}
-    with mock.patch.object(llm, "chat", return_value=fake):
-        name, args = llm.decide_tool([], tools=[])
+    monkeypatch.setattr(llm, "PROVIDER", "ollama")
+    with mock.patch.object(llm, "chat_ollama", return_value=fake):
+        name, args, _ = llm.decide_tool([], tools=[], agent_id="anchor")
     assert name is None
     assert args is None
 
 
+def test_decide_tool_openrouter_response(monkeypatch):
+    from engine import llm
+    fake = {
+        "choices": [{"message": {"tool_calls": [
+            {"function": {"name": "go_to_place", "arguments": {"place": "town_hall"}}}
+        ]}}],
+        "usage": {"total_tokens": 50, "cost": 0.0001},
+    }
+    monkeypatch.setattr(llm, "PROVIDER", "openrouter")
+    with mock.patch.object(llm, "chat_openrouter", return_value=fake):
+        name, args, meta = llm.decide_tool([], tools=[], agent_id="anchor")
+    assert name == "go_to_place"
+    assert args == {"place": "town_hall"}
+    assert meta["provider"] == "openrouter"
+    assert meta["cost_usd"] == 0.0001
+
+
+def test_per_agent_model_override(monkeypatch):
+    """EMERGENCE_AGENT_<ID>_MODEL env var overrides the default."""
+    from engine import llm
+    # Wipe any per-agent env vars that .env may have set
+    for aid in ("ANCHOR", "FLORA", "LOVELY", "SPARK"):
+        monkeypatch.delenv(f"EMERGENCE_AGENT_{aid}_MODEL", raising=False)
+    monkeypatch.setattr(llm, "PROVIDER", "openrouter")
+    monkeypatch.setattr(llm, "OPENROUTER_MODEL", "anthropic/claude-3.5-haiku")
+    monkeypatch.setenv("EMERGENCE_AGENT_ANCHOR_MODEL", "openai/gpt-4o-mini")
+    assert llm.model_for_agent("anchor") == "openai/gpt-4o-mini"
+    assert llm.model_for_agent("flora") == "anthropic/claude-3.5-haiku"
+
+
+def test_provider_info(monkeypatch):
+    from engine import llm
+    monkeypatch.setattr(llm, "PROVIDER", "openrouter")
+    monkeypatch.setattr(llm, "OPENROUTER_MODEL", "anthropic/claude-3.5-haiku")
+    monkeypatch.setattr(llm, "_openrouter_key", lambda: "sk-or-x")
+    info = llm.provider_info()
+    assert info["provider"] == "openrouter"
+    assert info["model"] == "anthropic/claude-3.5-haiku"
+    assert info["openrouter_configured"] is True
+
+
 def test_reasoning_uses_llm_when_available(tmp_db, monkeypatch):
     """If the LLM is reachable and returns a valid tool, reasoning uses it."""
     from engine import reasoning, agents as agents_mod, llm as llm_mod
-    # Force the LLM path
     monkeypatch.setattr(reasoning, "USE_LLM", True)
     monkeypatch.setattr(llm_mod, "is_available", lambda: True)
-    with mock.patch.object(llm_mod, "decide_tool",
-                           return_value=("go_to_place", {"place": "library"})):
+    with mock.patch.object(
+        llm_mod, "decide_tool",
+        return_value=("go_to_place", {"place": "library"},
+                     {"provider": "ollama", "model": "llama3.2:3b",
+                      "latency_s": 1.2, "cost_usd": None}),
+    ):
         a = agents_mod.get("anchor")
         name, args, rat = reasoning.decide(a)
     assert name == "go_to_place"
     assert args == {"place": "library"}
     assert "llm" in rat
-    assert reasoning.get_last_decision()["mode"] == "llm"
+    last = reasoning.get_last_decision()
+    assert last["mode"] == "llm"
+    assert last["model"] == "llama3.2:3b"
 
 
 def test_reasoning_falls_back_on_unknown_tool(tmp_db, monkeypatch):
     from engine import reasoning, agents as agents_mod, llm as llm_mod
     monkeypatch.setattr(reasoning, "USE_LLM", True)
     monkeypatch.setattr(llm_mod, "is_available", lambda: True)
-    with mock.patch.object(llm_mod, "decide_tool",
-                           return_value=("teleport_to_mars", {})):
+    with mock.patch.object(
+        llm_mod, "decide_tool",
+        return_value=("teleport_to_mars", {}, {"provider": "x", "model": "x", "latency_s": 0}),
+    ):
         a = agents_mod.get("anchor")
         name, _, _ = reasoning.decide(a)
-    # fallback to rule path -> one of the rule-based picks
     assert name in {t.name for t in __import__("engine").tools.all_tools()}
     assert reasoning.get_last_decision()["mode"].startswith("fallback")
 
 
 def test_reasoning_falls_back_on_wrong_location(tmp_db, monkeypatch):
-    """LLM says submit_townhall_proposal but agent is at home -> fallback."""
     from engine import reasoning, agents as agents_mod, llm as llm_mod
     monkeypatch.setattr(reasoning, "USE_LLM", True)
     monkeypatch.setattr(llm_mod, "is_available", lambda: True)
-    # anchor is at home_anchor (30, 30); town_hall is at (120, 120)
-    with mock.patch.object(llm_mod, "decide_tool",
-                           return_value=("submit_townhall_proposal",
-                                         {"title": "x", "body": "y"})):
+    with mock.patch.object(
+        llm_mod, "decide_tool",
+        return_value=("submit_townhall_proposal", {"title": "x", "body": "y"},
+                     {"provider": "x", "model": "x", "latency_s": 0}),
+    ):
         a = agents_mod.get("anchor")
         name, _, _ = reasoning.decide(a)
-    # rule path won't try to submit from home
     assert name != "submit_townhall_proposal"
     assert reasoning.get_last_decision()["mode"].startswith("fallback")
 
@@ -128,19 +190,16 @@ def test_reasoning_falls_back_on_connection_error(tmp_db, monkeypatch):
     from engine import reasoning, agents as agents_mod, llm as llm_mod
     monkeypatch.setattr(reasoning, "USE_LLM", True)
     monkeypatch.setattr(llm_mod, "is_available", lambda: True)
-    with mock.patch.object(llm_mod, "decide_tool",
-                           side_effect=ConnectionError("ollama down")):
+    with mock.patch.object(
+        llm_mod, "decide_tool",
+        side_effect=ConnectionError("ollama down"),
+    ):
         a = agents_mod.get("anchor")
-        name, _, rat = reasoning.decide(a)
-    # got a fallback pick
+        name, _, _ = reasoning.decide(a)
     assert name in {t.name for t in __import__("engine").tools.all_tools()}
-    assert reasoning.get_last_decision()["mode"] == "fallback:ConnectionError"
 
 
 def test_env_var_disables_llm(monkeypatch, tmp_db):
-    """Setting EMERGENCE_LLM_ENABLED=0 forces the rule path even when Ollama
-    is reachable. This is how the test suite avoids the slow live LLM calls.
-    """
     from engine import reasoning, agents as agents_mod, llm as llm_mod
     monkeypatch.setattr(llm_mod, "is_available", lambda: True)
     monkeypatch.setattr(reasoning, "USE_LLM", False)

tests/test_time.py

@@ -0,0 +1,163 @@
+"""Time-dilation framework tests (τ tracker, EWMA pace, CDC)."""
+import time
+
+
+def test_tau_starts_at_zero():
+    from engine import time as time_mod
+    c = time_mod.AgentClock("a")
+    assert c.tau == 0.0
+    assert c.pace == 0.0
+    assert c.n_ops == 0
+
+
+def test_tau_monotonic():
+    from engine import time as time_mod
+    c = time_mod.AgentClock("a")
+    t0 = 1000.0
+    c.tick("reasoning", 1.0, now=t0)
+    c.tick("tool_call", 0.5, now=t0 + 1)
+    c.tick("reasoning", 1.0, now=t0 + 2)
+    assert c.tau == 2.5
+    assert c.n_ops == 3
+    assert c.tau >= 0  # monotonic non-decreasing
+
+
+def test_tau_weight_per_op():
+    from engine import time as time_mod
+    c = time_mod.AgentClock("a")
+    c.tick("reasoning", time_mod.W_REASONING_STEP)
+    c.tick("tool_call", time_mod.W_TOOL_CALL)
+    c.tick("memory_lookup", time_mod.W_MEMORY_LOOKUP)
+    c.tick("reactive", time_mod.W_REACTIVE)
+    expected = (time_mod.W_REASONING_STEP + time_mod.W_TOOL_CALL
+               + time_mod.W_MEMORY_LOOKUP + time_mod.W_REACTIVE)
+    assert abs(c.tau - expected) < 1e-6
+
+
+def test_pace_ewma_initialized_on_first_tick():
+    from engine import time as time_mod
+    c = time_mod.AgentClock("a")
+    c.tick("reasoning", 1.0, now=1000.0)
+    assert c.pace > 0
+
+
+def test_pace_ewma_converges():
+    from engine import time as time_mod
+    c = time_mod.AgentClock("a")
+    # Simulate 10 ops at 1 second apart -> pace ~1.0 op/s
+    for i in range(10):
+        c.tick("r", 1.0, now=1000.0 + i)
+    # EWMA should be near 1.0
+    assert 0.5 < c.pace < 1.5, f"pace={c.pace}"
+
+
+def test_pace_ewma_reacts_to_burst():
+    from engine import time as time_mod
+    c = time_mod.AgentClock("a")
+    # slow start
+    for i in range(5):
+        c.tick("r", 1.0, now=1000.0 + i * 10)  # 10s apart
+    slow_pace = c.pace
+    # burst
+    for i in range(5):
+        c.tick("r", 1.0, now=1050.0 + i * 0.1)  # 0.1s apart
+    fast_pace = c.pace
+    assert fast_pace > slow_pace
+
+
+def test_history_bounded():
+    from engine import time as time_mod
+    c = time_mod.AgentClock("a")
+    for i in range(500):
+        c.tick("r", 0.1, now=1000.0 + i)
+    assert len(c.history) <= 200
+
+
+def test_snapshot_roundtrips():
+    from engine import time as time_mod
+    c = time_mod.AgentClock("anchor")
+    c.tick("r", 1.0, now=1000.0)
+    snap = c.snapshot()
+    assert snap["agent_id"] == "anchor"
+    assert "tau" in snap
+    assert "pace" in snap
+    assert "n_ops" in snap
+
+
+def test_registry_get_creates():
+    from engine import time as time_mod
+    time_mod.registry.reset("a")
+    c = time_mod.registry.get("a")
+    assert c.agent_id == "a"
+
+
+def test_registry_singleton_state():
+    from engine import time as time_mod
+    time_mod.registry.reset("zzz_test")
+    time_mod.record_reasoning("zzz_test")
+    time_mod.record_tool_call("zzz_test")
+    snap = time_mod.registry.snapshot_all()
+    assert "zzz_test" in snap
+    assert snap["zzz_test"]["tau"] == (time_mod.W_REASONING_STEP + time_mod.W_TOOL_CALL)
+    time_mod.registry.reset("zzz_test")
+
+
+def test_gamma_symmetry_inverse():
+    from engine import time as time_mod
+    time_mod.registry.reset("p")
+    time_mod.registry.reset("q")
+    # give them different paces
+    for i in range(5):
+        time_mod.registry.get("p").tick("r", 1.0, now=1000.0 + i * 0.5)  # 2 ops/s
+        time_mod.registry.get("q").tick("r", 1.0, now=1000.0 + i * 2.0)  # 0.5 ops/s
+    g_pq = time_mod.registry.gamma("p", "q")
+    g_qp = time_mod.registry.gamma("q", "p")
+    # γ_pq ≈ 4, γ_qp ≈ 0.25
+    assert g_pq > 2.0
+    assert g_qp < 0.5
+    time_mod.registry.reset("p")
+    time_mod.registry.reset("q")
+
+
+def test_transform_uses_gamma():
+    from engine import time as time_mod
+    time_mod.registry.reset("a")
+    time_mod.registry.reset("b")
+    time_mod.registry.get("a").tau = 10.0
+    time_mod.registry.get("a").pace = 2.0
+    time_mod.registry.get("b").tau = 5.0
+    time_mod.registry.get("b").pace = 1.0
+    # γ_a->b = 2.0/1.0 = 2.0
+    transformed = time_mod.registry.transform("a", "b")
+    assert abs(transformed - 20.0) < 1e-6
+    time_mod.registry.reset("a")
+    time_mod.registry.reset("b")
+
+
+def test_drift_report_with_divergence():
+    from engine import time as time_mod
+    time_mod.registry.reset("x")
+    time_mod.registry.reset("y")
+    # give x much more τ than y, similar pace
+    time_mod.registry.get("x").tau = 100.0
+    time_mod.registry.get("x").pace = 1.0
+    time_mod.registry.get("y").tau = 5.0
+    time_mod.registry.get("y").pace = 1.0
+    report = time_mod.registry.drift_report()
+    assert report["max_drift"] > 90
+    assert any(p["divergent"] for p in report["pairs"])
+    time_mod.registry.reset("x")
+    time_mod.registry.reset("y")
+
+
+def test_drift_report_empty_with_one_agent():
+    from engine import time as time_mod
+    # reset everything to ensure isolation
+    for c in time_mod.registry.all():
+        time_mod.registry.reset(c.agent_id)
+    time_mod.registry.get("only")
+    time_mod.registry.get("only").tau = 10.0
+    report = time_mod.registry.drift_report()
+    assert report["pairs"] == []
+    assert report["max_drift"] == 0.0
+    time_mod.registry.reset("only")

web/app.js

@@ -99,6 +99,47 @@ function refreshAgentCards() {
   }
 }
 
+function refreshClocks() {
+  if (!snapshot || !snapshot.clocks) return;
+  const wrap = document.getElementById('clocks');
+  wrap.innerHTML = '';
+  const entries = Object.entries(snapshot.clocks);
+  // sort by τ descending
+  entries.sort((a, b) => b[1].tau - a[1].tau);
+  const maxTau = Math.max(1, entries.length ? entries[0][1].tau : 1);
+  const colorFor = { anchor: '#ffd166', flora: '#6cf0c2', lovely: '#ff8fb1', spark: '#82aaff' };
+  for (const [aid, c] of entries) {
+    const div = document.createElement('div');
+    div.className = 'clock-card';
+    const pct = (c.tau / maxTau) * 100;
+    div.innerHTML = `
+      <div class="name" style="color:${colorFor[aid] || '#fff'}">${aid}</div>
+      <div class="tau-bar"><i style="width:${pct}%; background:${colorFor[aid] || '#fff'}"></i></div>
+      <div class="meta">τ = ${c.tau.toFixed(2)} · pace = ${c.pace.toFixed(2)} op/s · ${c.n_ops} ops</div>
+    `;
+    wrap.appendChild(div);
+  }
+}
+
+function refreshDrift() {
+  if (!snapshot || !snapshot.drift) return;
+  const wrap = document.getElementById('drift');
+  const d = snapshot.drift;
+  if (!d.pairs || d.pairs.length === 0) {
+    wrap.innerHTML = '<small>No multi-agent drift yet.</small>';
+    return;
+  }
+  const top = d.pairs[0];
+  if (top.divergent) {
+    wrap.className = 'drift-warn';
+    wrap.innerHTML = `<b>⚠ DILATION DRIFT</b> · ${top.a}↔${top.b}: |Δτ| = ${top.drift.toFixed(1)} (γ=${top.gamma_ab})<br>
+      <small>${top.a} has experienced ${top.tau_a} units, ${top.b} ${top.tau_b}. Frame transformation exceeds threshold ${d.threshold}.</small>`;
+  } else {
+    wrap.className = 'drift-ok';
+    wrap.innerHTML = `<b>✓ COHERENT</b> · max drift = ${d.max_drift.toFixed(2)} (threshold ${d.threshold})`;
+  }
+}
+
 function refreshProposals() {
   const wrap = document.getElementById('proposals');
   wrap.innerHTML = '';
@@ -158,8 +199,15 @@ async function refreshAll() {
   draw();
   refreshHeader();
   refreshAgentCards();
+  refreshClocks();
+  refreshDrift();
   refreshProposals();
   refreshConstitution();
+  // LLM info
+  if (snapshot.llm) {
+    const info = document.getElementById('llmInfo');
+    info.textContent = `${snapshot.llm.provider} · ${snapshot.llm.model}`;
+  }
 }
 
 function connectWS() {
@@ -197,6 +245,10 @@ function connectWS() {
       }
     } else if (msg.type === 'tick') {
       document.getElementById('tick').textContent = msg.tick;
+      if (msg.clocks) snapshot.clocks = msg.clocks;
+      if (msg.drift) snapshot.drift = msg.drift;
+      refreshClocks();
+      refreshDrift();
     }
   };
 }

web/index.html

@@ -11,7 +11,8 @@
     <div class="meta">
       <span>Tick <b id="tick">0</b></span>
       <span>Agents <b id="agentCount">0</b></span>
-      <span>Active Proposals <b id="propCount">0</b></span>
+      <span>Proposals <b id="propCount">0</b></span>
+      <span id="llmInfo" class="llm-info">…</span>
       <span class="ws-status" id="wsStatus">connecting…</span>
     </div>
   </header>
@@ -28,6 +29,10 @@
     </section>
 
     <aside>
+      <h2>Time Dilation · Eigenzeit τ</h2>
+      <div id="clocks"></div>
+      <div id="drift"></div>
+
       <h2>Live Feed</h2>
       <ul id="feed"></ul>
 

web/style.css

@@ -8,6 +8,14 @@ header h1 { margin: 0; font-size: 18px; letter-spacing: 1px; color: #6cf0c2; }
 .meta b { color: #d6e2ee; }
 .ws-status.connected { color: #6cf0c2; }
 .ws-status.disconnected { color: #ff6c6c; }
+.llm-info { color: #82aaff; font-size: 11px; }
+.clock-card { background: #0a1018; border: 1px solid #1c2733; padding: 6px 8px; margin: 4px 0; }
+.clock-card .name { font-weight: bold; }
+.clock-card .tau-bar { height: 4px; background: #1c2733; border-radius: 2px; margin: 4px 0; }
+.clock-card .tau-bar > i { display: block; height: 100%; border-radius: 2px; }
+.clock-card .meta { color: #6c8aa6; font-size: 10px; }
+.drift-warn { background: #2a1018; border: 1px solid #ff6c6c; padding: 8px; margin: 6px 0; color: #ff8fb1; font-size: 11px; }
+.drift-ok { background: #0a1018; border: 1px solid #1c2733; padding: 8px; margin: 6px 0; color: #6cf0c2; font-size: 11px; }
 main { display: grid; grid-template-columns: 1fr 360px; gap: 16px; padding: 16px; }
 .canvas-wrap { background: #0e141b; padding: 10px; border: 1px solid #1c2733; }
 canvas { display: block; width: 100%; height: auto; image-rendering: pixelated;