Implement config-driven models (Phase 1): graph MODELS dict, instantiate applies, per-request overrides
- Graph definitions (v3, v4) now declare MODELS mapping role → model string - engine.py extracts MODELS and applies to nodes during instantiation - frame_engine.process_message() accepts model_overrides for per-request swaps (restored via try/finally after processing) - 11/11 engine tests green Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Nico
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@ -63,6 +63,7 @@ def _graph_from_module(mod) -> dict:
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"conditions": getattr(mod, "CONDITIONS", {}),
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"audit": getattr(mod, "AUDIT", {}),
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"engine": getattr(mod, "ENGINE", "imperative"),
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"models": getattr(mod, "MODELS", {}),
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}
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@ -79,7 +80,13 @@ def instantiate_nodes(graph: dict, send_hud, process_manager: ProcessManager = N
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nodes[role] = cls(send_hud=send_hud, process_manager=process_manager)
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else:
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nodes[role] = cls(send_hud=send_hud)
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log.info(f"[engine] {role} = {impl_name} ({cls.__name__})")
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# Apply model from graph config (overrides class default)
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model = graph.get("models", {}).get(role)
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if model and hasattr(nodes[role], "model"):
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nodes[role].model = model
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log.info(f"[engine] {role} = {impl_name} ({cls.__name__}) model={model}")
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else:
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log.info(f"[engine] {role} = {impl_name} ({cls.__name__})")
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return nodes
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@ -173,57 +173,76 @@ class FrameEngine:
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# --- Main entry point ---
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async def process_message(self, text: str, dashboard: list = None) -> dict:
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async def process_message(self, text: str, dashboard: list = None,
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model_overrides: dict = None) -> dict:
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"""Process a message through the frame pipeline.
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Returns {response, controls, memorizer, frames, trace}."""
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Returns {response, controls, memorizer, frames, trace}.
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self._begin_trace(text)
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model_overrides: optional {role: model} to override node models for this request only.
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"""
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# Apply per-request model overrides (restored after processing)
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saved_models = {}
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if model_overrides:
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for role, model in model_overrides.items():
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node = self.nodes.get(role)
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if node and hasattr(node, "model"):
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saved_models[role] = node.model
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node.model = model
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# Handle ACTION: prefix
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if text.startswith("ACTION:"):
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return await self._handle_action(text, dashboard)
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try:
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self._begin_trace(text)
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# Setup
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envelope = Envelope(
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text=text, user_id=self.identity,
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session_id="test", timestamp=time.strftime("%Y-%m-%d %H:%M:%S"),
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)
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self.sensor.note_user_activity()
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if dashboard is not None:
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self.sensor.update_browser_dashboard(dashboard)
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self.history.append({"role": "user", "content": text})
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# Handle ACTION: prefix
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if text.startswith("ACTION:"):
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return await self._handle_action(text, dashboard)
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# --- Frame 1: Input ---
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mem_ctx = self._build_context(dashboard)
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rec = self._begin_frame(1, "input", input_summary=text[:100])
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# Setup
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envelope = Envelope(
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text=text, user_id=self.identity,
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session_id="test", timestamp=time.strftime("%Y-%m-%d %H:%M:%S"),
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)
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self.sensor.note_user_activity()
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if dashboard is not None:
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self.sensor.update_browser_dashboard(dashboard)
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self.history.append({"role": "user", "content": text})
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command = await self.nodes["input"].process(
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envelope, self.history, memory_context=mem_ctx,
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identity=self.identity, channel=self.channel)
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# --- Frame 1: Input ---
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mem_ctx = self._build_context(dashboard)
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rec = self._begin_frame(1, "input", input_summary=text[:100])
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a = command.analysis
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cmd_summary = f"intent={a.intent} language={a.language} tone={a.tone} complexity={a.complexity}"
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command = await self.nodes["input"].process(
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envelope, self.history, memory_context=mem_ctx,
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identity=self.identity, channel=self.channel)
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# Check reflex condition
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is_reflex = self._check_condition("reflex", command=command)
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if is_reflex:
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self._end_frame(rec, output_summary=cmd_summary,
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route="output (reflex)", condition="reflex=True")
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await self._send_hud({"node": "runtime", "event": "reflex_path",
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"detail": f"{a.intent}/{a.complexity}"})
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return await self._run_reflex(command, mem_ctx)
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else:
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next_node = "pa" if self.has_pa else ("director" if self.has_director else "thinker")
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self._end_frame(rec, output_summary=cmd_summary,
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route=next_node, condition=f"reflex=False")
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a = command.analysis
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cmd_summary = f"intent={a.intent} language={a.language} tone={a.tone} complexity={a.complexity}"
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# --- Frame 2+: Pipeline ---
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if self.has_pa:
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return await self._run_expert_pipeline(command, mem_ctx, dashboard)
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elif self.has_director:
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return await self._run_director_pipeline(command, mem_ctx, dashboard)
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else:
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return await self._run_thinker_pipeline(command, mem_ctx, dashboard)
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# Check reflex condition
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is_reflex = self._check_condition("reflex", command=command)
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if is_reflex:
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self._end_frame(rec, output_summary=cmd_summary,
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route="output (reflex)", condition="reflex=True")
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await self._send_hud({"node": "runtime", "event": "reflex_path",
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"detail": f"{a.intent}/{a.complexity}"})
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return await self._run_reflex(command, mem_ctx)
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else:
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next_node = "pa" if self.has_pa else ("director" if self.has_director else "thinker")
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self._end_frame(rec, output_summary=cmd_summary,
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route=next_node, condition=f"reflex=False")
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# --- Frame 2+: Pipeline ---
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if self.has_pa:
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return await self._run_expert_pipeline(command, mem_ctx, dashboard)
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elif self.has_director:
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return await self._run_director_pipeline(command, mem_ctx, dashboard)
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else:
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return await self._run_thinker_pipeline(command, mem_ctx, dashboard)
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finally:
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# Restore original models after per-request overrides
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for role, original_model in saved_models.items():
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node = self.nodes.get(role)
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if node:
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node.model = original_model
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# --- Pipeline variants ---
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@ -62,4 +62,13 @@ CONDITIONS = {
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"has_tool_output": "thinker.tool_used is not empty",
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}
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MODELS = {
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"input": "google/gemini-2.0-flash-001",
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"director": "anthropic/claude-haiku-4.5",
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"thinker": "google/gemini-2.0-flash-001",
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"interpreter": "google/gemini-2.0-flash-001",
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"output": "google/gemini-2.0-flash-001",
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"memorizer": "google/gemini-2.0-flash-001",
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}
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AUDIT = {}
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@ -68,4 +68,13 @@ CONDITIONS = {
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"has_tool_output": "expert.tool_used is not empty",
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}
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MODELS = {
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"input": "google/gemini-2.0-flash-001",
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"pa": "anthropic/claude-haiku-4.5",
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"expert_eras": "google/gemini-2.0-flash-001",
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"interpreter": "google/gemini-2.0-flash-001",
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"output": "google/gemini-2.0-flash-001",
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"memorizer": "google/gemini-2.0-flash-001",
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}
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AUDIT = {}
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