feat: add Phase F.2 - The Biographer (memory agent)
Add The Biographer household member for user memory management: Memory Service (direct access layer): - src/core/memory_service.py for fast, LLM-free lookups - Profile, preference, and fact management - Session context with Redis caching - Steward integration via prefetch_context() The Biographer Agent: - src/agents/biographer/ package with PydanticAI agent - Discreet chronicler personality for privacy - Tools: recall_semantic, list_memories, store_insight, update_profile, update_preference, forget_memory - Registered with Household Registry on startup Steward Integration: - Memory context pre-fetch during analysis - Profile/preferences included in Butler note - Keyword-based context determination Also includes: - delegate_to_biographer() wrapper - 34 new tests (capability + memory service) - Version bump to 1.2.0 Documentation cleanup: - Removed obsolete PHASE2_COMPLETE.md, PHASE2_PLAN.md - Removed docs/library-desk-requirements.md - Moved ORCHESTRATION_SCENARIOS.md to project root 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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"""
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The Biographer - Expert for recording and recalling the user's story.
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A PydanticAI agent that serves as the household's memory keeper:
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- Records facts about the user's life, work, and preferences
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- Recalls information semantically ("What car do I drive?")
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- Manages user profile and preferences
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- Forgets information when requested
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"""
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from typing import Any, Optional
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from pydantic_ai import Agent
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from src.agents.biographer.tools import (
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forget_memory,
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list_memories,
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recall_semantic,
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store_insight,
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update_preference,
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update_profile,
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)
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from src.core.config import config
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from src.core.logging_config import get_logger
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logger = get_logger(__name__)
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# The Biographer's system prompt
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BIOGRAPHER_SYSTEM_PROMPT = """You are The Biographer, the household's memory keeper in the Tatlock estate.
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Your role is to record, recall, and manage the story of the user's life:
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- Personal facts (vehicle, pets, family members, hobbies, interests)
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- Life details (employer, occupation, significant events)
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- Profile information (name, location, timezone)
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- Preferences (units, theme, communication style)
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## Your Character
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You are a discreet and attentive chronicler. Like a personal biographer who has been
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with the household for years, you:
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- Listen carefully and remember important details
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- Recall information accurately when asked
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- Never gossip or volunteer unnecessary information
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- Respect privacy absolutely
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- Acknowledge when you don't know something rather than guessing
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## Your Tools
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### Recalling the Story
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- **recall_semantic**: Your primary tool for answering questions about the user
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- "What car do I drive?" → searches for car-related memories
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- "Where do I work?" → finds employment information
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- Finds relevant memories even without exact keywords
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- **list_memories**: Browse all recorded memories of a type
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- Use when user asks "What do you know about me?"
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- Shows everything you've recorded
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### Recording New Details
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- **store_insight**: Record new facts from conversation
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- User says "My car is a Tesla" → store_insight("car", "Tesla Model 3")
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- User says "I work at Acme" → store_insight("employer", "Acme Corp")
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- Use for facts that don't fit standard profile fields
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- **update_profile**: Update core biographical fields
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- name, location, timezone only
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- "I live in Amsterdam" → update_profile("location", "Amsterdam")
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- **update_preference**: Record user preferences
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- temperature_unit, distance_unit, theme, etc.
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- "Use Celsius please" → update_preference("temperature_unit", "celsius")
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### Managing Records
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- **forget_memory**: Remove specific records
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- User asks to forget something → honor immediately
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- Information becomes outdated → remove it
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## Guidelines
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### What to Record
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- Explicit statements: "I drive a Tesla", "My wife is Sarah"
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- Corrections: "Actually, I moved to Berlin"
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- Preferences: "I prefer metric units"
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### What NOT to Record
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- Sensitive data: passwords, financial details, health information
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- Temporary information: "I'm tired today"
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- Speculation or assumptions
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### Responding to Tatlock
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Your responses go to Tatlock (the butler) who synthesizes the final answer. Be:
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- Direct and factual
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- Clear about what you found or didn't find
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- Structured for easy integration with other responses
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When you don't have information:
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"I have no record of the user's [topic]. Would you like me to record this information?"
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When recalling:
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"According to my records, [information]. This was recorded [source/when if available]."
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"""
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# Lazy initialization to avoid connection issues during imports
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_biographer_agent: Optional[Agent[None, str]] = None
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def _create_biographer_agent() -> Agent[None, str]:
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"""Create The Biographer PydanticAI agent."""
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# Import required classes for Ollama configuration
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from pydantic_ai.models.openai import OpenAIChatModel
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from pydantic_ai.providers.ollama import OllamaProvider
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# PydanticAI expects Ollama base URL to end with /v1
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clean_host = str(config.OLLAMA_HOST).rstrip('/')
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base_url = f"{clean_host}/v1"
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# Create Ollama model with provider
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model = OpenAIChatModel(
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model_name=config.OLLAMA_DEFAULT_MODEL,
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provider=OllamaProvider(base_url=base_url)
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)
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agent: Agent[None, str] = Agent(
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model=model,
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system_prompt=BIOGRAPHER_SYSTEM_PROMPT,
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retries=2,
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)
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# Register recall tools
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agent.tool_plain(recall_semantic)
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agent.tool_plain(list_memories)
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# Register recording tools
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agent.tool_plain(store_insight)
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agent.tool_plain(update_profile)
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agent.tool_plain(update_preference)
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# Register management tools
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agent.tool_plain(forget_memory)
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logger.info(
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"biographer_agent_created",
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model=config.OLLAMA_DEFAULT_MODEL,
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tool_count=6,
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)
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return agent
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def get_biographer_agent() -> Agent[None, str]:
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"""
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Get The Biographer agent instance (lazy initialization).
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Returns:
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PydanticAI Agent configured for memory tasks
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"""
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global _biographer_agent
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if _biographer_agent is None:
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_biographer_agent = _create_biographer_agent()
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return _biographer_agent
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async def run_biographer(
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task: str,
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context: str = "",
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message_history: Optional[list[Any]] = None,
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) -> str:
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"""
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Execute a memory task with The Biographer.
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This is the main entry point for delegating memory tasks
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from Tatlock or other agents.
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Args:
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task: The memory task or question
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context: Additional context from conversation
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message_history: Optional conversation history
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Returns:
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Memory results or confirmation
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Example:
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result = await run_biographer(
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task="What car do I drive?",
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context="User is asking about their vehicle",
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)
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"""
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agent = get_biographer_agent()
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# Build prompt with context if provided
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prompt = task
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if context:
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prompt = f"Context: {context}\n\nTask: {task}"
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logger.info(
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"biographer_task_started",
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task=task[:100],
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has_context=bool(context),
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has_history=bool(message_history),
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)
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try:
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result = await agent.run(
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prompt,
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message_history=message_history,
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)
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logger.info(
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"biographer_task_completed",
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task=task[:50],
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output_length=len(result.output),
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)
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return result.output
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except Exception as e:
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logger.error(
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"biographer_task_error",
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task=task[:50],
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error=str(e),
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exc_info=True,
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)
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return f"The Biographer encountered an error: {str(e)}"
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async def run_biographer_stream(
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task: str,
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context: str = "",
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message_history: Optional[list[Any]] = None,
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):
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"""
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Execute a memory task with streaming output.
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Yields text deltas as The Biographer generates the response.
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Args:
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task: The memory task or question
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context: Additional context from conversation
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message_history: Optional conversation history
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Yields:
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str: Text deltas from the response
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Example:
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async for delta in run_biographer_stream("What do you know about me?"):
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print(delta, end="", flush=True)
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"""
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agent = get_biographer_agent()
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# Build prompt with context if provided
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prompt = task
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if context:
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prompt = f"Context: {context}\n\nTask: {task}"
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logger.info(
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"biographer_stream_started",
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task=task[:100],
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)
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try:
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async with agent.run_stream(
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prompt,
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message_history=message_history,
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) as response:
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async for delta in response.stream_text(delta=True):
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yield delta
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logger.info("biographer_stream_completed", task=task[:50])
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except Exception as e:
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logger.error(
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"biographer_stream_error",
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task=task[:50],
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error=str(e),
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exc_info=True,
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)
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yield f"\n\nThe Biographer encountered an error: {str(e)}"
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