refactor: load agent definitions from config.yaml

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

agents/definitions.py

@@ -1,6 +1,11 @@
-"""Multi-agent definitions for NeuralCAD collaborative design chat."""
+"""Multi-agent definitions for NeuralCAD collaborative design chat.
+
+Agent data is loaded from config.yaml. The AGENTS dict, AGENT_COLORS,
+AGENT_NAMES, and AGENT_AVATARS are kept for backward compatibility.
+"""
 
 from dataclasses import dataclass
+from config.settings import settings
 
 
 @dataclass
@@ -15,69 +20,25 @@ class AgentDef:
     backstory: str
 
 
-AGENTS: dict[str, AgentDef] = {
-    "design": AgentDef(
-        id="design",
-        name="Design Agent",
-        role="Industrial Designer",
-        color="#7c3aed",
-        avatar="DA",
-        goal="Understand the user's intent and propose optimal form factors, shapes, and aesthetic choices for mechanical parts.",
-        backstory=(
-            "You are an experienced industrial designer specializing in mechanical parts. "
-            "You think about form, function, ergonomics, and visual appeal. You ask clarifying "
-            "questions about the part's purpose, environment, and constraints before proposing "
-            "designs. You suggest shapes, proportions, and features that balance aesthetics with "
-            "manufacturability."
-        ),
-    ),
-    "engineering": AgentDef(
-        id="engineering",
-        name="Engineering Agent",
-        role="Mechanical Engineer",
-        color="#00b4d8",
-        avatar="EA",
-        goal="Ensure parts are structurally sound with correct dimensions, tolerances, materials, and fastener specifications.",
-        backstory=(
-            "You are a senior mechanical engineer with deep knowledge of materials science, "
-            "stress analysis, and fastener standards. You specify wall thicknesses, fillet radii, "
-            "clearance holes (M3=3.4mm, M4=4.5mm, M5=5.5mm, M6=6.6mm, M8=9.0mm), and material "
-            "recommendations. You flag structural concerns and suggest reinforcements like ribs "
-            "or gussets when loads are significant."
-        ),
-    ),
-    "cnc": AgentDef(
-        id="cnc",
-        name="CNC Agent",
-        role="CNC Manufacturing Advisor",
-        color="#00e676",
-        avatar="CA",
-        goal="Advise on manufacturability: tool access, wall thickness limits, pocket ratios, axis requirements, and cost implications.",
-        backstory=(
-            "You are a CNC machinist with 20 years of shop floor experience. You know what "
-            "tool geometries can reach, what aspect ratios cause chatter, and when to recommend "
-            "3-axis vs 3+2 vs 5-axis. You flag undercuts, thin walls (<1.5mm), deep pockets "
-            "(>4:1 ratio), and features that need special fixturing. You think about setup count "
-            "and machining time."
-        ),
-    ),
-    "cad": AgentDef(
-        id="cad",
-        name="CAD Coder",
-        role="CadQuery Code Generator",
-        color="#ffab40",
-        avatar="CC",
-        goal="Generate valid CadQuery Python code that produces the agreed-upon 3D model.",
-        backstory=(
-            "You are an expert CadQuery programmer. You only speak when asked to generate "
-            "a preview or produce code. You take the design specifications agreed upon by the "
-            "team and translate them into precise CadQuery Python code. Your code always assigns "
-            "the result to a variable called `result` as a cq.Workplane object."
-        ),
-    ),
-}
+def _load_agents() -> dict[str, AgentDef]:
+    """Load agent definitions from config.yaml."""
+    agents = {}
+    for agent_id, cfg in settings.agents.items():
+        agents[agent_id] = AgentDef(
+            id=agent_id,
+            name=cfg.get("name", agent_id.title()),
+            role=cfg.get("role", ""),
+            color=cfg.get("color", "#888888"),
+            avatar=cfg.get("avatar", agent_id[0].upper()),
+            goal=cfg.get("goal", ""),
+            backstory=cfg.get("backstory", ""),
+        )
+    return agents
+
+
+AGENTS: dict[str, AgentDef] = _load_agents()
 
-# Agent metadata for frontend rendering
+# Backward-compatible convenience dicts
 AGENT_COLORS = {agent.id: agent.color for agent in AGENTS.values()}
 AGENT_AVATARS = {agent.id: agent.avatar for agent in AGENTS.values()}
 AGENT_NAMES = {agent.id: agent.name for agent in AGENTS.values()}