71 lines
3.6 KiB
Plaintext
71 lines
3.6 KiB
Plaintext
---
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title: "Overview"
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description: "Honcho solves context for AI agents by going beyond memory."
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icon: "brain"
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---
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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.
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Honcho gives agents [social cognition](../core-concepts/glossary#cognitive-science-terms) - the ability to understand users as individuals, not just conversation histories.
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**Context that scales:** Auto-summarized conversations and context-window management so agents never lose track, regardless of conversation length.
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**User understanding:** Rich psychological profiles built through [theory of mind](../core-concepts/glossary#cognitive-science-terms) "How should I deliver feedback to this user?" or "What's their communication style?"
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**Adaptive interactions:** Instead of generic responses, agents dynamically adjust based on individual user models, creating truly personalized experiences.
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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.
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This is the [dialectic endpoint](glossary#dialectic-endpoint) in action - agents that don't just have context, but have insight.
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## How It Works
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Honcho operates through two integrated layers:
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**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.
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**Insights Layer**: Continuously analyzes stored interactions to build psychological profiles using [theory of mind](glossary#theory-of-mind) inference, extracting patterns about communication style, decision-making preferences, and mental models.
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Agents access this understanding through the [dialectic endpoint](glossary#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
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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
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Smart conversation summarization that respects context windows while preserving psychological continuity.
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### 🏗️ Agent-First Design
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Built for AI consumption through MCP connectors and interfaces designed for autonomous agent workflows.
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### 🧠 Theory of Mind Inference
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Goes beyond pattern matching to understand user psychology, motivations, and mental models.
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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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## Getting Started
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Ready to integrate Honcho into your application?
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<CardGroup cols={2}>
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<Card title="Quick Start Guide" icon="rocket" href="/v2/documentation/introduction/quickstart">
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Get up and running with Honcho in minutes
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</Card>
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<Card title="Core Concepts" icon="brain" href="/v2/documentation/core-concepts/glossary">
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Understand Honcho's fundamental concepts
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</Card>
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</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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