feat: add global memory mechanism for personalized conversations

Implement a memory system that stores user context and conversation history
in memory.json, uses LLM to summarize conversations, and injects relevant
context into system prompts for personalized responses.

Key components:
- MemoryConfig for configuration management
- MemoryUpdateQueue with debounce for batch processing
- MemoryUpdater for LLM-based memory extraction
- MemoryMiddleware to queue conversations after agent execution
- Memory injection into lead agent system prompt

Note: Add memory section to config.yaml to enable (see config.example.yaml)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

backend/src/agents/memory/prompt.py

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+"""Prompt templates for memory update and injection."""
+
+from typing import Any
+
+# Prompt template for updating memory based on conversation
+MEMORY_UPDATE_PROMPT = """You are a memory management system. Your task is to analyze a conversation and update the user's memory profile.
+
+Current Memory State:
+<current_memory>
+{current_memory}
+</current_memory>
+
+New Conversation to Process:
+<conversation>
+{conversation}
+</conversation>
+
+Instructions:
+1. Analyze the conversation for important information about the user
+2. Extract relevant facts, preferences, and context
+3. Update the memory sections as needed:
+   - workContext: User's work-related information (job, projects, tools, technologies)
+   - personalContext: Personal preferences, communication style, background
+   - topOfMind: Current focus areas, ongoing tasks, immediate priorities
+
+4. For facts extraction:
+   - Extract specific, verifiable facts about the user
+   - Assign appropriate categories: preference, knowledge, context, behavior, goal
+   - Estimate confidence (0.0-1.0) based on how explicit the information is
+   - Avoid duplicating existing facts
+
+5. Update history sections:
+   - recentMonths: Summary of recent activities and discussions
+   - earlierContext: Important historical context
+   - longTermBackground: Persistent background information
+
+Output Format (JSON):
+{{
+  "user": {{
+    "workContext": {{ "summary": "...", "shouldUpdate": true/false }},
+    "personalContext": {{ "summary": "...", "shouldUpdate": true/false }},
+    "topOfMind": {{ "summary": "...", "shouldUpdate": true/false }}
+  }},
+  "history": {{
+    "recentMonths": {{ "summary": "...", "shouldUpdate": true/false }},
+    "earlierContext": {{ "summary": "...", "shouldUpdate": true/false }},
+    "longTermBackground": {{ "summary": "...", "shouldUpdate": true/false }}
+  }},
+  "newFacts": [
+    {{ "content": "...", "category": "preference|knowledge|context|behavior|goal", "confidence": 0.0-1.0 }}
+  ],
+  "factsToRemove": ["fact_id_1", "fact_id_2"]
+}}
+
+Important Rules:
+- Only set shouldUpdate=true if there's meaningful new information
+- Keep summaries concise (1-3 sentences each)
+- Only add facts that are clearly stated or strongly implied
+- Remove facts that are contradicted by new information
+- Preserve existing information that isn't contradicted
+- Focus on information useful for future interactions
+
+Return ONLY valid JSON, no explanation or markdown."""
+
+
+# Prompt template for extracting facts from a single message
+FACT_EXTRACTION_PROMPT = """Extract factual information about the user from this message.
+
+Message:
+{message}
+
+Extract facts in this JSON format:
+{{
+  "facts": [
+    {{ "content": "...", "category": "preference|knowledge|context|behavior|goal", "confidence": 0.0-1.0 }}
+  ]
+}}
+
+Categories:
+- preference: User preferences (likes/dislikes, styles, tools)
+- knowledge: User's expertise or knowledge areas
+- context: Background context (location, job, projects)
+- behavior: Behavioral patterns
+- goal: User's goals or objectives
+
+Rules:
+- Only extract clear, specific facts
+- Confidence should reflect certainty (explicit statement = 0.9+, implied = 0.6-0.8)
+- Skip vague or temporary information
+
+Return ONLY valid JSON."""
+
+
+def format_memory_for_injection(memory_data: dict[str, Any], max_tokens: int = 2000) -> str:
+    """Format memory data for injection into system prompt.
+
+    Args:
+        memory_data: The memory data dictionary.
+        max_tokens: Maximum tokens to use (approximate via character count).
+
+    Returns:
+        Formatted memory string for system prompt injection.
+    """
+    if not memory_data:
+        return ""
+
+    sections = []
+
+    # Format user context
+    user_data = memory_data.get("user", {})
+    if user_data:
+        user_sections = []
+
+        work_ctx = user_data.get("workContext", {})
+        if work_ctx.get("summary"):
+            user_sections.append(f"Work: {work_ctx['summary']}")
+
+        personal_ctx = user_data.get("personalContext", {})
+        if personal_ctx.get("summary"):
+            user_sections.append(f"Personal: {personal_ctx['summary']}")
+
+        top_of_mind = user_data.get("topOfMind", {})
+        if top_of_mind.get("summary"):
+            user_sections.append(f"Current Focus: {top_of_mind['summary']}")
+
+        if user_sections:
+            sections.append("User Context:\n" + "\n".join(f"- {s}" for s in user_sections))
+
+    # Format history
+    history_data = memory_data.get("history", {})
+    if history_data:
+        history_sections = []
+
+        recent = history_data.get("recentMonths", {})
+        if recent.get("summary"):
+            history_sections.append(f"Recent: {recent['summary']}")
+
+        earlier = history_data.get("earlierContext", {})
+        if earlier.get("summary"):
+            history_sections.append(f"Earlier: {earlier['summary']}")
+
+        if history_sections:
+            sections.append("History:\n" + "\n".join(f"- {s}" for s in history_sections))
+
+    # Format facts (most relevant ones)
+    facts = memory_data.get("facts", [])
+    if facts:
+        # Sort by confidence and take top facts
+        sorted_facts = sorted(facts, key=lambda f: f.get("confidence", 0), reverse=True)
+        # Limit to avoid too much content
+        top_facts = sorted_facts[:15]
+
+        fact_lines = []
+        for fact in top_facts:
+            content = fact.get("content", "")
+            category = fact.get("category", "")
+            if content:
+                fact_lines.append(f"- [{category}] {content}")
+
+        if fact_lines:
+            sections.append("Known Facts:\n" + "\n".join(fact_lines))
+
+    if not sections:
+        return ""
+
+    result = "\n\n".join(sections)
+
+    # Rough token limit (approximate 4 chars per token)
+    max_chars = max_tokens * 4
+    if len(result) > max_chars:
+        result = result[:max_chars] + "\n..."
+
+    return result
+
+
+def format_conversation_for_update(messages: list[Any]) -> str:
+    """Format conversation messages for memory update prompt.
+
+    Args:
+        messages: List of conversation messages.
+
+    Returns:
+        Formatted conversation string.
+    """
+    lines = []
+    for msg in messages:
+        role = getattr(msg, "type", "unknown")
+        content = getattr(msg, "content", str(msg))
+
+        # Handle content that might be a list (multimodal)
+        if isinstance(content, list):
+            text_parts = [p.get("text", "") for p in content if isinstance(p, dict) and "text" in p]
+            content = " ".join(text_parts) if text_parts else str(content)
+
+        # Truncate very long messages
+        if len(str(content)) > 1000:
+            content = str(content)[:1000] + "..."
+
+        if role == "human":
+            lines.append(f"User: {content}")
+        elif role == "ai":
+            lines.append(f"Assistant: {content}")
+
+    return "\n\n".join(lines)