126 lines
5.4 KiB
Plaintext
126 lines
5.4 KiB
Plaintext
---
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title: "Honcho"
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description: "Go beyond memory to agents with actual social intelligence"
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icon: "brain"
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sidebarTitle: "Overview"
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---
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When building agents developers often run into the same walls:
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> "My agent forgets everything between chats"
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You need memory: session management, message storage, context handling. It's table stakes, but surprisingly complex to get right.
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> "My agent treats everyone exactly the same"
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You need personalization: user modeling, preference learning, behavioral adaptation. Now you're building a [social cognition](../core-concepts/glossary#social-cognition) engine.
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> "I'm writing infrastructure instead of features"
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You need Honcho
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<img src="/images/agent_hierarchy.png" alt="Honcho's Hiearchy of Agents" />
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Honcho delivers production-ready memory infrastructure from day one. Store
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conversations, manage sessions, get perfectly formatted context for any LLM.
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But here's the magic: while your agents are chatting, Honcho is learning. It
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builds Theory of Mind models automatically, transforming raw conversations into
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rich psychological understanding.
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```python
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# Start simple - just add messages
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session.add_messages([alice.message("I learn best with examples")])
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# Get powerful - query user psychology
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insight = peer.chat("How should I explain this concept?")
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# > "This user learns best through concrete examples..."
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```
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Your agents evolve from goldfish to counselor, on the same infrastructure. That's Honcho.
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Designed for developers and agents alike:
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- **Natural Language Queries**: Chat with Honcho in natural language via the [Dialectic API](../core-concepts/architecture#dialectic-api) and let agents backchannel
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- **Automatic Context Management**: Smart summarization that respects token limits
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- **Native multi-agent support**: Break out of User/Assistant Paradigms and build complex multi-agent systems
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- **Agent-first interfaces**: MCP connections and APIs designed for agents to consume and use as tools
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- **Provider Agnostic**: Works with any LLM or Agent Framework
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## How It Works
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### Storage
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Developers use Honcho to store information about their users and application via
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two integrated layers:
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<img src="/images/basic_honcho_flowchart.png" alt="Basic Honcho Flowchart" />
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**Memory Layer**: Captures all user interactions - messages, preferences, and
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behavioral patterns - in a peer-centric data model that scales from individual
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conversations to complex multi-agent scenarios. This also queues up messages for
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the reasoning layer to process.
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**Reasoning Layer**: Continuously analyzes stored interactions to build
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psychological profiles using [theory of mind](../core-concepts/glossary#theory-of-mind)
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inference, extracting patterns about communication style, decision-making
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preferences, and mental models.
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### Retrieval
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Once data is stored and generated within Honcho, the API exposes several
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different ways to retrieve and use those insights.
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**[Dialectic API](/v2/guides/dialectic-endpoint)**: This is the
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flagship endpoint that allows developers to send natural language queries to
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Honcho to chat with the representation of each user in your system to get
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dynamic, in-context actionable insights.
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Example Queries
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- "What's the best way to explain technical concepts to this user?"
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- "Is this user more task-oriented or relationship-oriented?"
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- "What time of day is this user most engaged?"
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- "How does this user prefer to receive feedback?"
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- "What are this user's core values based on our conversations?"
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**[Get Context](/v2/guides/get-context)**: This endpoint abstracts context window
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constraints and continuously retrieves the most relevant and recent data from a
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conversation. Provide a token budget and Honcho will return a combination of
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summaries and messages that provide session context. Use this for creating
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long-running conversations. We crafted our summaries to provide the most
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[coverage of a session possible](../core-concepts/summarizer).
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**[Search](/v2/guides/search)**: This endpoint allows you to search across Honcho
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for relevant messages either at the workspace, peer, or session level. This
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endpoint uses a hybrid search strategy that combines text search and cosine
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similarity.
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**[Working Representations](/v2/guides/working-rep)**: Get a cached, snapshot
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of a user in the context of a session. Instead of waiting for an LLM to
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synthesize an in-context response via the Dialectic endpoint, use this to get
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recent insights you can plug into your context window.
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## Ideal For
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**Personalized AI assistants** that need to understand individual psychology, not just remember conversations.
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**Customer-facing agents** that must adapt their approach based on user communication preferences and emotional context.
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**Multi-agent systems** where AI needs to understand human collaborators' working styles and decision-making patterns.
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**NPCs** where you want autonomous agents with a rich and deep personality that isn't the average sycophantic llm
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## Getting Started
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Ready to integrate Honcho into your application?
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<CardGroup cols={2}> <Card title="Quickstart Guide" icon="rocket"
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href="/v2/documentation/introduction/quickstart"> Get up and running with
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Honcho in minutes </Card> <Card title="Core Concepts" icon="brain"
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href="/v2/documentation/core-concepts/glossary"> Understand Honcho's
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fundamental concepts </Card> </CardGroup>
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## Community & Support
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- **GitHub**: [plastic-labs/honcho](https://github.com/plastic-labs/honcho)
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- **Discord**: [Join our community](http://discord.gg/plasticlabs)
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- **Issues**: Report bugs and request features on GitHub
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