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The Chat endpoint (peer.chat()) is the natural language interface to Honcho’s reasoning. Instead of manually retrieving conclusions, your LLM can ask questions and get synthesized answers based on all the reasoning Honcho has done about a peer. Think of it as agent-to-agent communication.

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Basic Usage

The simplest way to use the chat endpoint is to ask a question and get a text response: 
from honcho import Honcho

honcho = Honcho()
peer = honcho.peer("user-123")

# Ask Honcho about the peer
query = "What is the user's favorite way of completing the task?"
answer = peer.chat(query)

print(answer)
# "Based on observations, the user prefers using keyboard shortcuts..."
The chat endpoint searches through the peer’s representation—all the conclusions Honcho has reasoned about them—and synthesizes a natural language answer.

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Streaming Responses

For longer answers, use streaming to get incremental responses: 
query = "What do we know about the user?"
response_stream = peer.chat(query, stream=True)

for chunk in response_stream.iter_text():
    print(chunk, end="", flush=True)
Streaming is useful for displaying real-time responses in chat interfaces or when asking complex questions that require longer answers.

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Integration Patterns

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Dynamic Prompt Enhancement

Let your LLM decide what it needs to know, then inject that context into the next generation: 
# Your LLM generates a query based on the conversation
llm_query = "Does the user prefer formal or casual communication?"

# Get answer from Honcho
context = peer.chat(llm_query)

# Add to your next LLM prompt
enhanced_prompt = f"""
Context about the user: {context}

User message: {user_input}

Respond appropriately based on the context.
"""

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Conditional Logic

Use chat endpoint responses to drive application logic: 
# Check if user has completed onboarding
onboarding_status = peer.chat("Has the user completed the onboarding flow?")

if "yes" in onboarding_status.lower():
    # Show main interface
    pass
else:
    # Show onboarding
    pass

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Preference Extraction

Extract specific preferences for personalization: 
# Get multiple insights
tone = peer.chat("What tone does the user prefer in responses?")
expertise = peer.chat("What is the user's level of technical expertise?")
goals = peer.chat("What are the user's main goals or objectives?")

# Use these to configure your agent's behavior

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How Honcho Answers

When you call peer.chat(query):
  1. Honcho searches through the peer’s peer card and representation—conclusions drawn from reasoning over their messages
  2. Retrieves conclusions semantically relevant to your query
  3. Combines them with segments of source messages, if needed, to gather more context
  4. Synthesizes them into a coherent natural language response to your query
Honcho reasoning runs continuously in the background, processing new messages and updating representations. The chat endpoint always has access to Honcho’s latest conclusions about the peer.

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Best Practices

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Ask specific questions

Instead of “Tell me about the user”, ask “What communication style does the user prefer?” You’ll get more actionable answers.

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Let your LLM formulate queries

The chat endpoint shines when your LLM decides what it needs to know. This creates dynamic, context-aware personalization. An excellent way to achieve this, if building an agent, is to give access to the Honcho chat endpoint as just another tool.

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Use for runtime decisions

Don’t just use chat for LLM prompts - use it to drive application logic, routing, and feature flags based on user behavior.

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Combine with get_context()

Use get_context() for conversation context and peer.chat() for specific insights. They complement each other. For more ideas on using the chat endpoint, see our guides.