初始化：记忆系统源代码上传（已脱敏）

- 排除 .env / *.bak / 内部运维文档（README_INTERNAL.html）
- config.py 默认密码已替换为占位符 CHANGE_ME_*
- init_db.sql 移除生产数据库用户 GRANT 段
- README.html 数据库用户名已脱敏
- 保留：源码 + 公网 API 文档 + 建表 SQL（无授权语句）

lifecycle/fact_extractor.py

@@ -0,0 +1,101 @@
+"""Fact Extractor - Extract structured atomic facts from working memory using LLM."""
+import json
+import logging
+from typing import Optional, List
+
+from lifecycle.llm_parse import parse_llm_json_array
+
+logger = logging.getLogger(__name__)
+
+FACT_SYSTEM_PROMPT = """你是JSON生成器。输入工作记忆，直接提取结构化事实。
+
+严格规则：
+1. 禁止输出分析、推理、思考、解释
+2. 第一个字符必须是 [，最后一个字符必须是 ]
+3. 每条事实：content（50-200字）、importance（0.0-1.0）、category（分类）
+4. 最多20条，无值得提取的事实返回 []
+
+输出格式（只输出JSON，不输出其他任何文字）：
+[{"content":"事实","importance":0.8,"category":"分类"}]"""
+
+
+class FactExtractor:
+    """Extract atomic facts from working memory using LLM."""
+
+    def __init__(self, mysql_store, redis_cache, llm_client, embedding_service):
+        self.mysql = mysql_store
+        self.redis = redis_cache
+        self.llm = llm_client
+        self.embedder = embedding_service
+
+    def extract(self, agent_id: str, team_id: str,
+                max_memories: int = 20,
+                delete_after: bool = False) -> dict:
+        """Extract structured facts from working memory."""
+        if not self.llm.available:
+            return {"error": "LLM not configured. Set LLM_API_URL, LLM_API_KEY, LLM_MODEL environment variables."}
+
+        items = self.redis.get_working_memories(agent_id, limit=max_memories)
+        if not items:
+            return {"facts": [], "count": 0}
+
+        memory_lines = []
+        for i, item in enumerate(items):
+            if not isinstance(item, dict):
+                logger.warning('Skipping non-dict working memory item: %r', item)
+                continue
+            content = item.get("content", "")[:300]
+            memory_lines.append(f"[{i+1}] {content}")
+        user_prompt = "以下是需要提取事实的工作记忆：\n\n" + "\n".join(memory_lines)
+
+        try:
+            facts = parse_llm_json_array(
+                self.llm, FACT_SYSTEM_PROMPT, user_prompt,
+                temperature=0.2, context="extract-facts",
+            )
+        except ValueError as e:
+            return {"error": str(e)}
+
+        stored = []
+        for fact in facts:
+            if not isinstance(fact, dict):
+                logger.warning('Skipping non-dict fact from LLM: %r', fact)
+                continue
+            content = fact.get("content", "").strip()
+            if not content or len(content) < 10:
+                continue
+
+            importance = fact.get("importance", 0.6)
+            if not isinstance(importance, (int, float)) or importance < 0 or importance > 1:
+                importance = 0.6
+
+            category = fact.get("category", "")
+
+            embedding = self.embedder.embed(content)
+            from storage.vector_search import embedding_to_bytes
+            emb_bytes = embedding_to_bytes(embedding)
+
+            memory_id = self.mysql.add_personal_memory(
+                agent_id=agent_id,
+                team_id=team_id,
+                content=content,
+                embedding=emb_bytes,
+                importance=float(importance),
+                metadata={
+                    "source": "fact_extraction",
+                    "category": category,
+                    "extracted_from": "working_memory",
+                },
+            )
+            stored.append({
+                "id": memory_id,
+                "content": content,
+                "importance": importance,
+                "category": category,
+            })
+
+        if delete_after and stored:
+            self.redis.clear_working_memory(agent_id)
+            logger.info(f"Deleted working memories for {agent_id} after extracting {len(stored)} facts")
+
+        return {"facts": stored, "count": len(stored)}