Overview

Most agents today are stateless - they forget everything between conversations or hit token limits mid-conversation. But even agents with perfect memory still treat every user the same, missing individual psychology and preferences.

Honcho gives agents social cognition - the ability to understand users as individuals, not just conversation histories.

Context that scales: Auto-summarized conversations and context-window management so agents never lose track, regardless of conversation length.

User understanding: Rich psychological profiles built through theory of mind “How should I deliver feedback to this user?” or “What’s their communication style?”

Adaptive interactions: Instead of generic responses, agents dynamically adjust based on individual user models, creating truly personalized experiences.

Your agents move from remembering what users said to understanding how they think. The result: AI that feels less like a chatbot, more like someone who actually gets you.

This is the dialectic endpoint in action - agents that don’t just have context, but have insight.

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How It Works

Honcho operates through two integrated layers:

Storage Layer: Captures all user interactions - messages, preferences, and behavioral patterns - in a user-centric data model that scales from individual conversations to complex multi-agent scenarios.

Insights 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.

Agents access this understanding through the dialectic endpoint - a natural language API where they can ask specific questions about users and receive actionable insights.

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Key Capabilities

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🗣️ Natural Language User Queries

Ask Honcho about users in plain English: “How should I approach this topic?” or “What’s their preferred communication style?”

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🔄 Automatic Context Optimization

Smart conversation summarization that respects context windows while preserving psychological continuity.

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🏗️ Agent-First Design

Built for AI consumption through MCP connectors and interfaces designed for autonomous agent workflows.

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🧠 Theory of Mind Inference

Goes beyond pattern matching to understand user psychology, motivations, and mental models.

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Ideal For

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.

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Getting Started

Ready to integrate Honcho into your application?

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Community & Support

• GitHub: plastic-labs/honcho
• Discord: Join our community
• Issues: Report bugs and request features on GitHub