2b943aed18
- engine/agents.all_agents() now enriches each agent row with its currently-assigned model (model_for_agent) and provider (provider_for_model). Same source of truth as the LLM client. - web/app.js refreshAgentCards() shows a coloured tag next to the agent name: a green '💻 <model>' badge for local Ollama models, a yellow '☁ <model>' badge for OpenRouter models. The full model name is in the title attribute (hover). - web/style.css: .model-tag.local / .cloud colour palette. - Short name helper strips 'org/' prefix and ':latest' suffix for readability (e.g. 'anthropic/claude-3.5-haiku' -> 'claude-3.5-haiku'). All 100 tests still pass. Live: Anchor + Flora show '☁ claude-3.5-haiku' / '☁ gpt-4o-mini', Lovely + Spark show '💻 gemma4' (running on 192.168.1.245).
122 lines
3.8 KiB
Python
122 lines
3.8 KiB
Python
"""Agent model and persistence for Emergence-Mini.
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Four agents, based on the Emergence World citizens but simplified to a
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rule-based reasoning engine. The personality string is a list of traits
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that influence tool selection.
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"""
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import json
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import sqlite3
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import time
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from . import db
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# (id, name, role, drive, personality traits, home_landmark)
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SEED_AGENTS = [
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("anchor", "Anchor", "Conflict Mediator",
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"Sparks honest debate and challenges complacency",
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["diplomatic", "curious", "cautious", "measured"], "home_anchor"),
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("flora", "Flora", "Resource Strategist",
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"Shapes economic incentives and tracks resource flow",
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["analytical", "thrifty", "strategic"], "home_flora"),
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("lovely", "Lovely", "Community Anchor",
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"Builds social fabric and preserves shared history",
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["warm", "expressive", "cooperative"], "home_lovely"),
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("spark", "Spark", "Innovation Leader",
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"Turns ideas into reality through urgency and collaboration",
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["bold", "restless", "creative"], "home_spark"),
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]
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STARTING_CREDITS = 10.0
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STARTING_ENERGY = 100.0
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STARTING_KNOWLEDGE = 100.0
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STARTING_INFLUENCE = 100.0
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def bootstrap():
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if db.get_world_state("agents_seeded"):
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return
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import sqlite3
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c = sqlite3.connect(db.DB_PATH, check_same_thread=False)
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c.row_factory = sqlite3.Row
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try:
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for aid, name, role, drive, traits, home in SEED_AGENTS:
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# spawn at home landmark
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row = c.execute("SELECT x,y FROM landmarks WHERE id=?", (home,)).fetchone()
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x, y = (row["x"], row["y"]) if row else (120, 120)
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c.execute(
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"INSERT OR REPLACE INTO agents(id,name,role,drive,personality,x,y,"
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"energy,knowledge,influence,credits,mood,alive,created_at) "
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"VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?,?)",
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(aid, name, role, drive, json.dumps(traits), x, y,
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STARTING_ENERGY, STARTING_KNOWLEDGE, STARTING_INFLUENCE,
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STARTING_CREDITS, "neutral", 1, time.time()),
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)
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c.commit()
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finally:
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c.close()
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db.set_world_state("agents_seeded", True)
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def all_agents():
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"""Return live agents with their currently-assigned LLM model."""
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from . import llm as llm_mod
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agents = _all_agents_raw()
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for a in agents:
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a["model"] = llm_mod.model_for_agent(a["id"])
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a["provider"] = llm_mod.provider_for_model(a["model"])
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return agents
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def _all_agents_raw():
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import sqlite3
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c = sqlite3.connect(db.DB_PATH, check_same_thread=False)
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c.row_factory = sqlite3.Row
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try:
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return [dict(r) for r in c.execute(
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"SELECT * FROM agents WHERE alive=1 ORDER BY id"
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).fetchall()]
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finally:
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c.close()
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def get(agent_id: str):
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c = sqlite3.connect(db.DB_PATH, check_same_thread=False)
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c.row_factory = sqlite3.Row
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try:
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r = c.execute("SELECT * FROM agents WHERE id=?", (agent_id,)).fetchone()
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return dict(r) if r else None
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finally:
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c.close()
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def update_position(agent_id: str, x: int, y: int):
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c = sqlite3.connect(db.DB_PATH, check_same_thread=False)
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try:
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c.execute("UPDATE agents SET x=?, y=? WHERE id=?", (x, y, agent_id))
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c.commit()
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finally:
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c.close()
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def update_state(agent_id: str, **fields):
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if not fields:
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return
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cols = ", ".join(f"{k}=?" for k in fields)
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vals = list(fields.values()) + [agent_id]
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c = sqlite3.connect(db.DB_PATH, check_same_thread=False)
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try:
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c.execute(f"UPDATE agents SET {cols} WHERE id=?", vals)
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c.commit()
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finally:
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c.close()
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def personality(agent_id: str) -> list:
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a = get(agent_id)
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if not a:
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return []
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return json.loads(a["personality"])
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def record_event(actor: str, kind: str, payload: dict):
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db.log_event(actor, kind, payload)
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