# Overview

When building agents developers often run into the same walls:

> “My agent forgets everything between chats”

You need memory: session management, message storage, context handling. It’s table stakes, but surprisingly complex to get right.

> “My agent treats everyone exactly the same”

You need personalization: user modeling, preference learning, behavioral adaptation. Now you’re building a social cognition engine.

> “I’m writing infrastructure instead of features”

You need Honcho

<figure><img src="https://mintcdn.com/plasticlabs/lVyHfvNDd8wveJyM/images/agent_hierarchy.png?w=840&#x26;fit=max&#x26;auto=format&#x26;n=lVyHfvNDd8wveJyM&#x26;q=85&#x26;s=1defa8a8fd0ed3a09602dedb24e1a5e3" alt=""><figcaption></figcaption></figure>

Honcho delivers production-ready memory infrastructure from day one. Store conversations, manage sessions, get perfectly formatted context for any LLM. But here’s the magic: while your agents are chatting, Honcho is learning. It builds Theory of Mind models automatically, transforming raw conversations into rich psychological understanding.

```
# Start simple - just add messages
session.add_messages([alice.message("I learn best with examples")])

# Get powerful - query user psychology
insight = peer.chat("How should I explain this concept?")
# > "This user learns best through concrete examples..."
```

Your agents evolve from goldfish to counselor, on the same infrastructure. That’s Honcho.Designed for developers and agents alike:

* **Natural Language Queries**: Chat with Honcho in natural language via the [Dialectic API](https://docs.honcho.dev/v2/documentation/core-concepts/architecture#dialectic-api) and let agents backchannel
* **Automatic Context Management**: Smart summarization that respects token limits
* **Native multi-agent support**: Break out of User/Assistant Paradigms and build complex multi-agent systems
* **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

### [​](https://docs.honcho.dev/v2/documentation/introduction/overview#how-it-works)How It Works <a href="#how-it-works" id="how-it-works"></a>

#### [​](https://docs.honcho.dev/v2/documentation/introduction/overview#storage)Storage <a href="#storage" id="storage"></a>

Developers use Honcho to store information about their users and application via two integrated layers:

<figure><img src="https://mintcdn.com/plasticlabs/lVyHfvNDd8wveJyM/images/basic_honcho_flowchart.png?w=840&#x26;fit=max&#x26;auto=format&#x26;n=lVyHfvNDd8wveJyM&#x26;q=85&#x26;s=703e70273e60a9f4c174c9bd3bd28037" alt=""><figcaption></figcaption></figure>

**Memory Layer**: Captures all user interactions - messages, preferences, and behavioral patterns - in a peer-centric data model that scales from individual conversations to complex multi-agent scenarios. This also queues up messages for the reasoning layer to process.**Reasoning Layer**: Continuously analyzes stored interactions to build psychological profiles using theory of mind inference, extracting patterns about communication style, decision-making preferences, and mental models.

#### [​](https://docs.honcho.dev/v2/documentation/introduction/overview#retrieval)Retrieval <a href="#retrieval" id="retrieval"></a>

Once data is stored and generated within Honcho, the API exposes several different ways to retrieve and use those insights.&#x20;

**Dialectic API**: This is the flagship endpoint that allows developers to send natural language queries to Honcho to chat with the representation of each user in your system to get dynamic, in-context actionable insights.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?”

**Get Context**: This endpoint abstracts context window constraints and continuously retrieves the most relevant and recent data from a conversation. Provide a token budget and Honcho will return a combination of summaries and messages that provide session context. Use this for creating long-running conversations. We crafted our summaries to provide the most coverage of a session possible.&#x20;

**Search**: This endpoint allows you to search across Honcho for relevant messages either at the workspace, peer, or session level. This endpoint uses a hybrid search strategy that combines text search and cosine similarity.&#x20;

**Working Representations**: Get a cached, snapshot of a user in the context of a session. Instead of waiting for an LLM to synthesize an in-context response via the Dialectic endpoint, use this to get recent insights you can plug into your context window.

### [​](https://docs.honcho.dev/v2/documentation/introduction/overview#ideal-for)Ideal For <a href="#ideal-for" id="ideal-for"></a>

**Personalized AI assistants** that need to understand individual psychology, not just remember conversations.

**Customer-facing agents** that must adapt their approach based on user communication preferences and emotional context.

**Multi-agent systems** where AI needs to understand human collaborators’ working styles and decision-making patterns.

**NPCs** where you want autonomous agents with a rich and deep personality that isn’t the average sycophantic llm