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Honcho is an AI-native memory library for building agents with state-of-the-art long-term memory. Agents using Honcho have perfect recall with a wide variety of tools to traverse their history and get the exact context they need when they need it. It then goes beyond basic memory by reasoning about the stored history to expand the latent information available to your agent. Agents using Honcho will understand who they are, who they are interacting with, what happened, and when it happened — all without you having to think about it. Use it to build
  • Highly personalized experiences
  • Agents with social cognition
  • Agents with rich identity that evolve over time
  • Multi-agent systems with complex social dynamics
# Start simple by just adding messages
session.add_messages([alice.message("I learn best with examples")])

# Honcho will automatically reason about the message to generate insights about Alice

# Get insights by chatting with the agent
insight = peer.chat("How should I explain this concept?")
# > "This user learns best through concrete examples..."
Designed for developers and agents alike:
  • Natural Language Queries: Chat with Honcho in natural language via the Dialectic API to get insights about your users and agents
  • Automatic Context Management: Smart conversation summaries to have infinite chats
  • Native multi-agent support: Sessions can natively have as many participants as you need
  • Agent-first interfaces: MCP connections and APIs designed for agents to consume and use as tools
  • Provider Agnostic: Works with any LLM or Agent Framework

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How It Works

High Level Diagram

High Level Honcho Diagram
At a high level Honcho works very simply:
  1. Store messages sent by users and agents in Honcho
  2. Honcho reasons about the messages to generate insights about each entity in the system
  3. At runtime your agents can leverage insights from Honcho to get the exact context they need
There are several API endpoints to leverage the memory & insights in Honcho.

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Get Context

This is the easiest way to leverage Honcho. simply call get context and get the most relevant information for your conversation. This endpoint is highly customizable so you can specify parameters such as:
  • A number of tokens you want
  • An option to include summaries of the conversation
  • An option to get a profile of a specific user (Peer Card & Representation)
This endpoint lets you search across Honcho for relevant messages using a hybrid search strategy that combines full-text and semantic search. You can optionally scope the endpoint to a specific workspace, peer, or session.

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Working Representation

This endpoint gives you a snapshot of a user or what we call a Representation. Essentially, a list of explicit and deductive facts about the user that are relevant to the current conversation. Plug this into your prompt to get a quick overview of the user.

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Dialectic API

This endpoint lets you chat with Honcho about any entity in your system. Honcho will leverage what it has remembered and learned about the entity to provide in-context actionable insights. This is especially helpful when you want your agent to back-channel with Honcho to change its behavior at runtime. Example Queries:
  • “What’s the best way to explain technical concepts to this user?”
  • “Is this user more task-oriented or relationship-oriented?”
  • “What time of day is this user most engaged?”
  • “How does this user prefer to receive feedback?”
  • “What are this user’s core values based on our conversations?”

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Getting Started

Ready to integrate Honcho into your application?

Quickstart Guide

Get up and running with Honcho in minutes

Core Concepts

Understand Honcho’s fundamental concepts

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Community & Support