Honcho Integration

Honcho Integration Guide

What is Honcho

Honcho is an open source memory library for building stateful agents. It works with any model, framework, or architecture. You send Honcho the messages from your conversations, and custom reasoning models process them in the background — extracting premises, drawing conclusions, and building rich representations of each participant over time. Your agent can then query those representations on-demand ("What does this user care about?", "How technical is this person?") and get grounded, reasoned answers.

The key mental model: Peers are any participant — human or AI. Both are represented the same way. Observation settings (observe_me, observe_others) control which peers Honcho reasons about. Typically you want Honcho to model your users (observe_me=True) but not your AI assistant (observe_me=False). Sessions scope conversations between peers. Messages are the raw data you feed in — Honcho reasons about them asynchronously and stores the results as the peer's representation. No messages means no reasoning means no memory.

Your agent accesses this memory through peer.chat(query) (ask a natural language question, get a reasoned answer), session.context() (get formatted conversation history + representations), or both.

Integration Workflow

Follow these phases in order:

Phase 1: Codebase Exploration

Before asking the user anything, explore the codebase to understand:

1. Language & Framework: Is this Python or TypeScript? What frameworks are used (FastAPI, Express, Next.js, etc.)?
2. Existing AI/LLM code: Search for existing LLM integrations (OpenAI, Anthropic, LangChain, etc.)
3. Entity structure: Identify users, agents, bots, or other entities that interact
4. Session/conversation handling: How does the app currently manage conversations?
5. Message flow: Where are messages sent/received? What's the request/response cycle?

Use Glob and Grep to find:

• **/*.py or **/*.ts files with "openai", "anthropic", "llm", "chat", "message"
• User/session models or types
• API routes handling chat or conversation endpoints

Bot framework detected? If the codebase is built around an agent loop, tool registry, session manager, and message bus (e.g., nanobot, openclaw, picoclaw), read {baseDir}/references/bot-frameworks.md for framework-specific integration guidance and check {baseDir}/references/bot-frameworks/<framework>/ for concrete reference implementations.

Phase 2: Interview (REQUIRED)

After exploring the codebase, use the AskUserQuestion tool to clarify integration requirements. Ask these questions (adapt based on what you learned in Phase 1):

Question Set 1 - Entities & Peers

Ask about which entities should be Honcho peers:

• header: "Peers"
• question: "Which entities should Honcho track and build representations for?"
• options based on what you found (e.g., "End users only", "Users + AI assistant", "Users + multiple AI agents", "All participants including third-party services")
• Include a follow-up if they have multiple AI agents: should any AI peers be observed?

Question Set 2 - Integration Pattern

Ask how they want to use Honcho context:

• header: "Pattern"
• question: "How should your AI access Honcho's user context?"
• options:
  • "Tool call (Recommended)" - "Agent queries Honcho on-demand via function calling"
  • "Pre-fetch" - "Fetch user context before each LLM call with predefined queries"
  • "context()" - "Include conversation history and representations in prompt"
  • "Multiple patterns" - "Combine approaches for different use cases"

Question Set 3 - Session Structure

Ask about conversation structure:

• header: "Sessions"
• question: "How should conversations map to Honcho sessions?"
• options based on their app (e.g., "One session per chat thread", "One session per user", "Multiple users per session (group chat)", "Custom session logic")

Question Set 4 - Specific Queries (if using pre-fetch pattern)

If they chose pre-fetch, ask what context matters:

• header: "Context"
• question: "What user context should be fetched for the AI?"
• multiSelect: true
• options: "Communication style", "Expertise level", "Goals/priorities", "Preferences", "Recent activity summary", "Custom queries"

Phase 3: Implementation

Based on interview responses, implement the integration:

1. Install the SDK
2. Create Honcho client initialization
3. Set up peer creation for identified entities
4. Implement the chosen integration pattern(s)
5. Add message storage after exchanges
6. Update any existing conversation handlers

Phase 4: Verification

• If the Honcho CLI is available, run honcho doctor to confirm connectivity before testing the integration code
• Use honcho peer list and honcho peer chat to verify peers exist and the dialectic endpoint works independently of the integration
• Ensure all message exchanges are stored to Honcho
• Verify AI peers have observe_me=False (unless user specifically wants AI observation)
• Check that the workspace ID is consistent across the codebase
• Confirm environment variable for API key is documented

Before You Start

1. Check the latest SDK versions at https://docs.honcho.dev/changelog/introduction

   • Python SDK: honcho-ai
   • TypeScript SDK: @honcho-ai/sdk

2. Get an API key ask the user to get a Honcho API key from https://app.honcho.dev and add it to the environment.

3. Verify with the CLI (optional but recommended). If the user has the Honcho CLI installed (pip install honcho-cli), they can validate their setup before writing any integration code:

   honcho init          # persist API key + URL to ~/.honcho/config.json
   honcho doctor        # verify connectivity, config, workspace health
   honcho peer chat     # test the dialectic endpoint interactively
   

   This is the fastest way to confirm the API key and URL are correct before debugging SDK code.

Installation

Python (use uv)

uv add honcho-ai

TypeScript (use bun)

bun add @honcho-ai/sdk

Sync vs Async

TypeScript — The SDK is async by default. All methods return promises. No separate sync API.

Python — The SDK provides both sync and async interfaces:

• Sync (default): from honcho import Honcho — use in sync frameworks (Flask, Django, CLI scripts)
• Async: from honcho import Honcho with .aio namespace — use in async frameworks (FastAPI, Starlette, async workers)

# Sync usage (Flask, Django, scripts)
from honcho import Honcho
honcho = Honcho(workspace_id="my-app", api_key=os.environ["HONCHO_API_KEY"])
peer = honcho.peer("user-123")
response = peer.chat("What does this user prefer?")

# Async usage (FastAPI, Starlette)
from honcho import Honcho
honcho = Honcho(workspace_id="my-app", api_key=os.environ["HONCHO_API_KEY"])
peer = await honcho.aio.peer("user-123")
response = await peer.aio.chat("What does this user prefer?")

Match the client to the framework — check whether the codebase uses async def handlers or sync def handlers and choose accordingly. The rest of this skill shows sync Python examples; swap to .aio equivalents for async codebases.

Core Integration Patterns

1. Initialize with a Single Workspace

Use ONE workspace for your entire application. The workspace name should reflect your app/product.

Python:

from honcho import Honcho
import os

# Sync client (Flask, Django, scripts)
honcho = Honcho(
    workspace_id="your-app-name",
    api_key=os.environ["HONCHO_API_KEY"],
    environment="production"
)

# Async client (FastAPI, Starlette) — use honcho.aio for all operations
# honcho.aio.peer(), honcho.aio.session(), etc.

TypeScript:

import { Honcho } from '@honcho-ai/sdk';

// All methods are async by default
const honcho = new Honcho({
    workspaceId: "your-app-name",
    apiKey: process.env.HONCHO_API_KEY,
    environment: "production"
});

2. Create Peers for ALL Entities

Create peers for every entity in your business logic - users AND AI assistants.

Python:

from honcho.api_types import PeerConfig

# Human users
user = honcho.peer("user-123")

# AI assistants - set observe_me=False so Honcho doesn't model the AI
assistant = honcho.peer("assistant", configuration=PeerConfig(observe_me=False))
support_bot = honcho.peer("support-bot", configuration=PeerConfig(observe_me=False))

TypeScript:

// Human users
const user = await honcho.peer("user-123");

// AI assistants - set observeMe=false so Honcho doesn't model the AI
const assistant = await honcho.peer("assistant", { configuration: { observeMe: false } });
const supportBot = await honcho.peer("support-bot", { configuration: { observeMe: false } });

3. Multi-Peer Sessions

Sessions can have multiple participants. Configure observation settings per-peer.

Python:

from honcho.api_types import SessionPeerConfig

session = honcho.session("conversation-123")

# User is observed (Honcho builds a model of them)
user_config = SessionPeerConfig(observe_me=True, observe_others=True)

# AI is NOT observed (no model built of the AI)
ai_config = SessionPeerConfig(observe_me=False, observe_others=True)

session.add_peers([
    (user, user_config),
    (assistant, ai_config)
])

TypeScript:

const session = await honcho.session("conversation-123");

await session.addPeers([
    [user, { observeMe: true, observeOthers: true }],
    [assistant, { observeMe: false, observeOthers: true }]
]);

4. Add Messages to Sessions

Python:

session.add_messages([
    user.message("I'm having trouble with my account"),
    assistant.message("I'd be happy to help. What seems to be the issue?"),
    user.message("I can't reset my password")
])

TypeScript:

await session.addMessages([
    user.message("I'm having trouble with my account"),
    assistant.message("I'd be happy to help. What seems to be the issue?"),
    user.message("I can't reset my password")
]);

Using Honcho for AI Agents

Pattern A: Dialectic Chat as a Tool Call (Recommended for Agents)

Make Honcho's chat endpoint available as a tool for your AI agent. This lets the agent query user context on-demand.

Python (OpenAI function calling):

import openai
from honcho import Honcho

honcho = Honcho(workspace_id="my-app", api_key=os.environ["HONCHO_API_KEY"])

# Define the tool for your agent
honcho_tool = {
    "type": "function",
    "function": {
        "name": "query_user_context",
        "description": "Query Honcho to retrieve relevant context about the user based on their history and preferences. Use this when you need to understand the user's background, preferences, past interactions, or goals.",
        "parameters": {
            "type": "object",
            "properties": {
                "query": {
                    "type": "string",
                    "description": "A natural language question about the user, e.g. 'What are this user's main goals?' or 'What communication style does this user prefer?'"
                }
            },
            "required": ["query"]
        }
    }
}

def handle_honcho_tool_call(user_id: str, query: str) -> str:
    """Execute the Honcho chat tool call."""
    peer = honcho.peer(user_id)
    return peer.chat(query)

# Use in your agent loop
def run_agent(user_id: str, user_message: str):
    messages = [{"role": "user", "content": user_message}]

    response = openai.chat.completions.create(
        model="gpt-4",
        messages=messages,
        tools=[honcho_tool]
    )

    # Handle tool calls
    if response.choices[0].message.tool_calls:
        for tool_call in response.choices[0].message.tool_calls:
            if tool_call.function.name == "query_user_context":
                import json
                args = json.loads(tool_call.function.arguments)
                result = handle_honcho_tool_call(user_id, args["query"])
                # Continue conversation with tool result...

TypeScript (OpenAI function calling):

import OpenAI from 'openai';
import { Honcho } from '@honcho-ai/sdk';

const honcho = new Honcho({
    workspaceId: "my-app",
    apiKey: process.env.HONCHO_API_KEY
});

const honchoTool: OpenAI.ChatCompletionTool = {
    type: "function",
    function: {
        name: "query_user_context",
        description: "Query Honcho to retrieve relevant context about the user based on their history and preferences.",
        parameters: {
            type: "object",
            properties: {
                query: {
                    type: "string",
                    description: "A natural language question about the user"
                }
            },
            required: ["query"]
        }
    }
};

async function handleHonchoToolCall(userId: string, query: string): Promise<string> {
    const peer = await honcho.peer(userId);
    return await peer.chat(query);
}

Pattern B: Pre-fetch Context with Targeted Queries

For simpler integrations, fetch user context before the LLM call using pre-defined queries.

Python:

def get_user_context_for_prompt(user_id: str) -> dict:
    """Fetch key user attributes via targeted Honcho queries."""
    peer = honcho.peer(user_id)

    return {
        "communication_style": peer.chat("What communication style does this user prefer? Be concise."),
        "expertise_level": peer.chat("What is this user's technical expertise level? Be concise."),
        "current_goals": peer.chat("What are this user's current goals or priorities? Be concise."),
        "preferences": peer.chat("What key preferences should I know about this user? Be concise.")
    }

def build_system_prompt(user_context: dict) -> str:
    return f"""You are a helpful assistant. Here's what you know about this user:

Communication style: {user_context['communication_style']}
Expertise level: {user_context['expertise_level']}
Current goals: {user_context['current_goals']}
Key preferences: {user_context['preferences']}

Tailor your responses accordingly."""

TypeScript:

async function getUserContextForPrompt(userId: string): Promise<Record<string, string>> {
    const peer = await honcho.peer(userId);

    const [style, expertise, goals, preferences] = await Promise.all([
        peer.chat("What communication style does this user prefer? Be concise."),
        peer.chat("What is this user's technical expertise level? Be concise."),
        peer.chat("What are this user's current goals or priorities? Be concise."),
        peer.chat("What key preferences should I know about this user? Be concise.")
    ]);

    return {
        communicationStyle: style,
        expertiseLevel: expertise,
        currentGoals: goals,
        preferences: preferences
    };
}

Pattern C: Get Context for LLM Integration

Use context() for conversation history with built-in LLM formatting.

Python:

import openai

session = honcho.session("conversation-123")
user = honcho.peer("user-123")
assistant = honcho.peer("assistant", configuration=PeerConfig(observe_me=False))

# Get context formatted for your LLM
context = session.context(
    tokens=2000,
    peer_target=user.id,  # Include representation of this user
    summary=True           # Include conversation summaries
)

# Convert to OpenAI format
messages = context.to_openai(assistant=assistant)

# Or Anthropic format
# messages = context.to_anthropic(assistant=assistant)

# Add the new user message
messages.append({"role": "user", "content": "What should I focus on today?"})

response = openai.chat.completions.create(
    model="gpt-4",
    messages=messages
)

# Store the exchange
session.add_messages([
    user.message("What should I focus on today?"),
    assistant.message(response.choices[0].message.content)
])

TypeScript:

import OpenAI from 'openai';

const session = await honcho.session("conversation-123");
const user = await honcho.peer("user-123");
const assistant = await honcho.peer("assistant", { configuration: { observeMe: false } });

// Get context formatted for your LLM
const context = await session.context({
    tokens: 2000,
    peerTarget: user.id,  // Include representation of this user
    summary: true          // Include conversation summaries
});

// Convert to OpenAI format
const messages = context.toOpenAI(assistant);

// Or Anthropic format
// const messages = context.toAnthropic(assistant);

// Add the new user message
messages.push({ role: "user", content: "What should I focus on today?" });

const openai = new OpenAI();
const response = await openai.chat.completions.create({
    model: "gpt-4",
    messages
});

// Store the exchange
await session.addMessages([
    user.message("What should I focus on today?"),
    assistant.message(response.choices[0].message.content!)
]);

Streaming Responses

Python:

stream = peer.chat_stream("What do we know about this user?")

for chunk in stream:
    print(chunk, end="", flush=True)

TypeScript:

const stream = await peer.chatStream("What do we know about this user?");

for await (const chunk of stream) {
    process.stdout.write(chunk);
}

Integration Checklist

When integrating Honcho into an existing codebase:

• Install SDK with uv add honcho-ai (Python) or bun add @honcho-ai/sdk (TypeScript)
• Set up HONCHO_API_KEY environment variable
• Initialize Honcho client with a single workspace ID
• Create peers for all entities (users AND AI assistants)
• Set observe_me=False for AI peers
• Configure sessions with appropriate peer observation settings
• Choose integration pattern:
  • Tool call pattern for agentic systems
  • Pre-fetch pattern for simpler integrations
  • context() for conversation history
• Store messages after each exchange to build user models
• (Optional) Run honcho doctor to verify connectivity before testing integration code
• (Optional) Use honcho peer chat to test dialectic queries independently

Common Mistakes to Avoid

1. Multiple workspaces: Use ONE workspace per application
2. Forgetting AI peers: Create peers for AI assistants, not just users
3. Observing AI peers: Set observe_me=False for AI peers unless you specifically want Honcho to model your AI's behavior
4. Not storing messages: Always call add_messages() to feed Honcho's reasoning engine
5. Blocking on processing: Messages are processed asynchronously — don't poll or wait for reasoning to complete before continuing

Resources

• Documentation: https://docs.honcho.dev
• Latest SDK versions: https://docs.honcho.dev/changelog/introduction
• API Reference: https://docs.honcho.dev/v3/api-reference/introduction