I want to start building with Honcho - a memory and personalization platform for AI applications.
## Honcho Resources
**Documentation:**
- Main docs: https://honcho.dev/docs
- API Reference: https://honcho.dev/docs/v2/api-reference/introduction
- Quickstart: https://honcho.dev/docs/v2/documentation/introduction/quickstart
- Architecture: https://honcho.dev/docs/v2/documentation/reference/architecture
**Code & Examples:**
- Core repo: https://github.com/plastic-labs/honcho
- Python SDK: https://github.com/plastic-labs/honcho-python
- TypeScript SDK: https://github.com/plastic-labs/honcho-node
- Discord bot starter: https://github.com/plastic-labs/discord-python-starter
- Telegram bot example: https://github.com/plastic-labs/telegram-python-starter
**What Honcho Does:**
Honcho provides persistent memory and personalization for AI apps. It automatically:
- Stores conversation history across sessions
- Learns facts about users from conversations
- Builds user representations for personalized responses
- Manages multi-user sessions with theory of mind
- Provides context injection for any LLM
**Architecture Overview:**
- Core primitives: Workspaces contain Peers (users/agents) and Sessions (conversations)
- Peers can observe other peers in sessions (configurable with observe_me_observe_others)
- Background deriver processes messages to extract facts and update representations
- Dialectic API provides personalized responses based on learned context
- Supports any LLM (OpenAI, Anthropic, open source)
- Can use demo server or self-host
Please assess the resources above and ask me relevant questions to help build a well-structured application using Honcho. Consider asking about:
- What I'm trying to build
- My technical preferences and stack
- Whether I want to use the demo server or self-host
- My experience level with the technologies involved
- Specific features I need (multi-user, voice, web UI, etc.)
Once you understand my needs, help me create a working implementation with proper memory persistence.
