3f8886cbd2
RED->GREEN->REFACTOR cycle: - UI node has state store (key-value), action bindings (op/var), and local action handlers (inc/dec/set/toggle — no LLM round-trip) - Thinker self-model: knows its environment, that ACTIONS create real buttons, that UI handles state locally. Emits var/op payload for stateful actions. - Thinker's context includes UI state so it can report current values - /api/clear resets UI state, bindings, and controls - Test runner: action_match for fuzzy action names, persistent actions across steps, _stream_text restored - Counter test: 16/16 passed (create, read, inc, inc, dec, verify) - Pub test: 20/20 passed (conversation, language switch, tool use, mood) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
105 lines
4.2 KiB
Python
105 lines
4.2 KiB
Python
"""Memorizer Node: S2 — shared state / coordination."""
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import json
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import logging
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from .base import Node
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from ..llm import llm_call
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log = logging.getLogger("runtime")
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class MemorizerNode(Node):
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name = "memorizer"
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model = "google/gemini-2.0-flash-001"
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max_context_tokens = 3000
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DISTILL_SYSTEM = """You are the Memorizer node of a cognitive agent runtime.
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After each exchange you update the shared state that Input and Output nodes read.
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Given the conversation so far, output a JSON object with these fields:
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- user_name: string — how the user identifies themselves (null if unknown)
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- user_mood: string — current emotional tone (neutral, happy, frustrated, playful, etc.)
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- topic: string — what the conversation is about right now
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- topic_history: list of strings — previous topics in this session
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- situation: string — social/physical context if mentioned (e.g. "at a pub with tina", "private dev session")
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- language: string — primary language being used (en, de, mixed)
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- style_hint: string — how Output should talk (casual, formal, technical, poetic, etc.)
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- facts: list of strings — important facts learned about the user
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Output ONLY valid JSON. No explanation, no markdown fences."""
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def __init__(self, send_hud):
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super().__init__(send_hud)
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self.state: dict = {
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"user_name": None,
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"user_mood": "neutral",
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"topic": None,
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"topic_history": [],
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"situation": "localhost test runtime, private dev session",
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"language": "en",
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"style_hint": "casual, technical",
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"facts": [],
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}
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def get_context_block(self, sensor_lines: list[str] = None, ui_state: dict = None) -> str:
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lines = sensor_lines or ["Sensors: (none)"]
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lines.append("")
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lines.append("Shared memory (from Memorizer):")
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for k, v in self.state.items():
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if v:
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lines.append(f"- {k}: {v}")
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if ui_state:
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lines.append("")
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lines.append("UI state (visible to user in workspace):")
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for k, v in ui_state.items():
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lines.append(f"- {k} = {v}")
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return "\n".join(lines)
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async def update(self, history: list[dict]):
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if len(history) < 2:
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await self.hud("updated", state=self.state)
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return
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await self.hud("thinking", detail="updating shared state")
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messages = [
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{"role": "system", "content": self.DISTILL_SYSTEM},
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{"role": "system", "content": f"Current state: {json.dumps(self.state)}"},
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]
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for msg in history[-10:]:
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messages.append(msg)
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messages.append({"role": "user", "content": "Update the shared state based on this conversation. Output JSON only."})
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messages = self.trim_context(messages)
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await self.hud("context", messages=messages, tokens=self.last_context_tokens,
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max_tokens=self.max_context_tokens, fill_pct=self.context_fill_pct)
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raw = await llm_call(self.model, messages)
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log.info(f"[memorizer] raw: {raw[:200]}")
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text = raw.strip()
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if text.startswith("```"):
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text = text.split("\n", 1)[1] if "\n" in text else text[3:]
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if text.endswith("```"):
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text = text[:-3]
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text = text.strip()
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try:
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new_state = json.loads(text)
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old_facts = set(self.state.get("facts", []))
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new_facts = set(new_state.get("facts", []))
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new_state["facts"] = list(old_facts | new_facts)[-20:]
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if self.state.get("topic") and self.state["topic"] != new_state.get("topic"):
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hist = new_state.get("topic_history", [])
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if self.state["topic"] not in hist:
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hist.append(self.state["topic"])
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new_state["topic_history"] = hist[-5:]
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self.state = new_state
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log.info(f"[memorizer] updated state: {self.state}")
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await self.hud("updated", state=self.state)
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except (json.JSONDecodeError, Exception) as e:
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log.error(f"[memorizer] update error: {e}, raw: {text[:200]}")
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await self.hud("error", detail=f"Update failed: {e}")
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await self.hud("updated", state=self.state)
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