297 lines
10 KiB
Plaintext
297 lines
10 KiB
Plaintext
---
|
|
title: "Cursor & Claude: AI-Powered Honcho Setup"
|
|
icon: "wand-magic-sparkles"
|
|
description: "Build Honcho-powered AI agents quickly using Cursor or Claude. Zero configuration required."
|
|
sidebarTitle: 'Vibecoding Setup'
|
|
---
|
|
|
|
Get Honcho up and running in minutes using AI coding assistants. These prompts are specifically optimized for Cursor and Claude to generate production-ready code with minimal effort.
|
|
|
|
## 🚀 Quick Start
|
|
|
|
Choose your path based on your use case:
|
|
|
|
### Personal AI Assistant
|
|
|
|
Build an AI assistant that remembers conversations and learns user preferences.
|
|
|
|
<Tip>
|
|
Copy this prompt into Cursor or Claude to get a complete implementation:
|
|
</Tip>
|
|
|
|
```
|
|
Create a personal AI assistant using Honcho that remembers user preferences and conversations. Requirements:
|
|
|
|
REFERENCE DOCUMENTATION:
|
|
- Honcho Docs: https://docs.honcho.dev
|
|
- Honcho GitHub: https://github.com/plastic-labs/honcho
|
|
- Python SDK: https://github.com/plastic-labs/honcho-python
|
|
- API Reference: https://docs.honcho.dev/v2/api-reference/introduction
|
|
|
|
WHAT TO BUILD:
|
|
- Personal assistant that learns about the user automatically
|
|
- Remembers preferences, habits, and conversation history
|
|
- Provides personalized responses based on past interactions
|
|
- Uses Honcho's demo server (no setup required)
|
|
|
|
TECHNICAL SETUP:
|
|
- Python with Honcho SDK and OpenAI
|
|
- Simple command-line interface for testing
|
|
- Environment: Use demo.honcho.dev (no API key needed)
|
|
- LLM: OpenAI GPT-4 (provide env var setup)
|
|
|
|
CODE REQUIREMENTS:
|
|
- Complete working example with extensive comments
|
|
- Error handling and user-friendly messages
|
|
- Demonstration of key Honcho concepts:
|
|
* Creating peers (user and assistant)
|
|
* Managing sessions and conversations
|
|
* Automatic learning from interactions
|
|
* Querying learned information
|
|
* Getting context for AI responses
|
|
|
|
EXAMPLE WORKFLOW:
|
|
1. User starts conversation with assistant
|
|
2. Assistant responds using any existing knowledge about user
|
|
3. System automatically learns facts from the conversation
|
|
4. System stores conversation in session
|
|
5. Future conversations reference past interactions
|
|
|
|
Include installation instructions, environment setup, and example conversations to test.
|
|
```
|
|
|
|
### Discord Bot with Memory
|
|
|
|
Create a Discord bot that learns about server members and provides personalized interactions.
|
|
|
|
```
|
|
Build a Discord bot using Honcho that learns about server members and provides personalized interactions.
|
|
|
|
REFERENCE DOCUMENTATION:
|
|
- Honcho Docs: https://docs.honcho.dev
|
|
- Honcho GitHub: https://github.com/plastic-labs/honcho
|
|
- Python SDK: https://github.com/plastic-labs/honcho-python
|
|
- Discord Guide: https://docs.honcho.dev/v2/guides/discord
|
|
- API Reference: https://docs.honcho.dev/v2/api-reference/introduction
|
|
|
|
STARTER TEMPLATE:
|
|
- Use the official discord-python-starter from Plastic Labs: https://github.com/plastic-labs/discord-python-starter
|
|
- This template already includes Honcho integration, py-cord, and fly.io deployment
|
|
- Modify the existing bot.py file to add enhanced memory features
|
|
|
|
WHAT TO BUILD:
|
|
- Discord bot with persistent memory using Honcho
|
|
- Learns about users through natural conversation
|
|
- Provides personalized responses based on user history
|
|
- Handles multi-user conversations with context awareness
|
|
- Extends the starter template with advanced memory features
|
|
|
|
TECHNICAL SETUP:
|
|
- Clone the discord-python-starter repository
|
|
- Python with py-cord, Honcho SDK, and OpenRouter LLM support
|
|
- Uses uv for package management (already configured)
|
|
- Environment variables template provided (.env.template)
|
|
- Docker and fly.io deployment ready
|
|
|
|
CORE FEATURES TO ADD:
|
|
- Enhanced per-user memory and personality modeling
|
|
- Channel-specific session management
|
|
- Theory-of-mind queries ("What does this user like?")
|
|
- Advanced fact extraction from conversations
|
|
- Multi-participant conversation handling
|
|
- Slash commands for memory management
|
|
|
|
IMPLEMENTATION REQUIREMENTS:
|
|
- Extend the existing on_message function with memory features
|
|
- Add new slash commands for memory testing and management
|
|
- Integrate Honcho's dialectic API for personalized responses
|
|
- Add session management for different channels
|
|
- Implement background fact learning and storage
|
|
- Error handling and comprehensive logging
|
|
|
|
DEPLOYMENT:
|
|
- Use the included fly.toml for deployment
|
|
- Environment variable management with fly secrets
|
|
- Docker containerization (Dockerfile provided)
|
|
|
|
Include examples of enhanced bot interactions and memory demonstrations.
|
|
```
|
|
|
|
## 🎯 Using Cursor
|
|
|
|
### Setup Workflow
|
|
|
|
<Tabs>
|
|
<Tab title="New Project">
|
|
```bash
|
|
# Create project
|
|
mkdir my-honcho-agent
|
|
cd my-honcho-agent
|
|
|
|
# Open in Cursor
|
|
cursor .
|
|
|
|
# Use Cmd+L to open AI chat
|
|
# Paste one of the prompts above
|
|
```
|
|
</Tab>
|
|
<Tab title="Discord Starter">
|
|
```bash
|
|
# Clone the starter template
|
|
git clone https://github.com/plastic-labs/discord-python-starter.git
|
|
cd discord-python-starter
|
|
|
|
# Install dependencies
|
|
uv sync
|
|
|
|
# Configure environment
|
|
cp .env.template .env
|
|
# Add your Discord token and API keys
|
|
```
|
|
</Tab>
|
|
</Tabs>
|
|
|
|
### Cursor Tips
|
|
|
|
<CardGroup cols={2}>
|
|
<Card title="Reference Codebase" icon="code">
|
|
Use `@codebase` to ask questions about your entire project
|
|
</Card>
|
|
<Card title="Reference Docs" icon="book">
|
|
Use `@docs https://docs.honcho.dev` for documentation context
|
|
</Card>
|
|
<Card title="Generate Tests" icon="vial">
|
|
Ask Cursor to write comprehensive tests for your Honcho integration
|
|
</Card>
|
|
<Card title="Iterate Quickly" icon="arrows-rotate">
|
|
Request specific improvements: "Add better error handling"
|
|
</Card>
|
|
</CardGroup>
|
|
|
|
## 🤖 Claude Workflows
|
|
|
|
### Rapid Development
|
|
|
|
```
|
|
I want to quickly prototype an AI agent with Honcho. Help me build:
|
|
|
|
REFERENCE DOCUMENTATION:
|
|
- Honcho Docs: https://docs.honcho.dev
|
|
- Honcho GitHub: https://github.com/plastic-labs/honcho
|
|
- Quickstart Guide: https://docs.honcho.dev/v2/documentation/introduction/quickstart
|
|
- SDK Documentation: https://docs.honcho.dev/v2/documentation/platform/sdk
|
|
|
|
1. SETUP: Complete development environment with Honcho demo server
|
|
2. CORE: Basic peer/session/message workflow with memory
|
|
3. INTEGRATION: OpenAI LLM integration with context management
|
|
4. TESTING: Simple test cases to verify memory functionality
|
|
5. ITERATION: Framework for adding features incrementally
|
|
|
|
Focus on:
|
|
- Working code over perfect architecture
|
|
- Clear comments explaining Honcho concepts
|
|
- Easy-to-modify structure for experimentation
|
|
- Immediate feedback and testing capabilities
|
|
|
|
Start with the most minimal viable example and show me how to extend it.
|
|
```
|
|
|
|
### Production Deployment
|
|
|
|
```
|
|
Help me deploy my Honcho application to production:
|
|
|
|
REFERENCE DOCUMENTATION:
|
|
- Self-Hosting Guide: https://docs.honcho.dev/v2/contributing/self-hosting
|
|
- Configuration Guide: https://docs.honcho.dev/v2/contributing/configuration-guide
|
|
- Platform Overview: https://docs.honcho.dev/v2/documentation/platform/overview
|
|
|
|
REQUIREMENTS:
|
|
- Environment configuration and secrets management
|
|
- Database setup and migrations
|
|
- API authentication and rate limiting
|
|
- Monitoring and logging setup
|
|
- Deployment automation
|
|
|
|
Provide step-by-step deployment instructions for [Fly.io/Vercel/Railway/Heroku].
|
|
```
|
|
|
|
## 💡 Common Patterns
|
|
|
|
### Basic Conversation Flow
|
|
|
|
1. **Initialize**: Create peers and start a session
|
|
2. **Converse**: Exchange messages between user and assistant
|
|
3. **Learn**: Honcho automatically extracts facts from conversations
|
|
4. **Remember**: Future conversations use accumulated context
|
|
5. **Personalize**: Responses adapt based on learned information
|
|
|
|
### Advanced Features
|
|
|
|
<Steps>
|
|
<Step title="Multi-User Sessions">
|
|
Implement separate memory contexts for different users or channels
|
|
</Step>
|
|
<Step title="Context Windows">
|
|
Manage conversation history to stay within LLM token limits
|
|
</Step>
|
|
<Step title="Fact Management">
|
|
Query and update the knowledge graph programmatically
|
|
</Step>
|
|
<Step title="Theory of Mind">
|
|
Use dialectic API to reason about user preferences and mental states
|
|
</Step>
|
|
</Steps>
|
|
|
|
## 🛠️ Troubleshooting
|
|
|
|
<CodeGroup>
|
|
```text Connection Issues
|
|
"My Honcho connection is failing with [ERROR]. Here's my environment setup: [paste code]. What's wrong and how do I fix it?"
|
|
```
|
|
|
|
```text Memory Not Persisting
|
|
"The agent isn't remembering conversations between sessions. Here's my session management code: [paste code]. Help me debug this."
|
|
```
|
|
|
|
```text Performance Optimization
|
|
"My Honcho queries are slow. Here's my implementation: [paste code]. How can I optimize this?"
|
|
```
|
|
|
|
```text Integration Problems
|
|
"I'm trying to integrate Honcho with [SYSTEM] but getting [ERROR]. Here's my approach: [paste code]. What's the correct way to do this?"
|
|
```
|
|
</CodeGroup>
|
|
|
|
## 🚀 Next Steps
|
|
|
|
After your initial setup:
|
|
|
|
1. **Add Features**: Extend with voice input, web UI, or API endpoints
|
|
2. **Improve Memory**: Implement custom fact extraction and retrieval
|
|
3. **Scale Up**: Add caching, background processing, and optimization
|
|
4. **Deploy**: Move from demo server to production environment
|
|
5. **Monitor**: Add logging, metrics, and error tracking
|
|
|
|
## 📚 Resources
|
|
|
|
### Documentation
|
|
- **Main Docs**: [docs.honcho.dev](https://docs.honcho.dev)
|
|
- **API Reference**: [docs.honcho.dev/v2/api-reference](https://docs.honcho.dev/v2/api-reference/introduction)
|
|
|
|
### Code & Examples
|
|
- **Honcho Core**: [github.com/plastic-labs/honcho](https://github.com/plastic-labs/honcho)
|
|
- **Python SDK**: [github.com/plastic-labs/honcho-python](https://github.com/plastic-labs/honcho-python)
|
|
- **TypeScript SDK**: [github.com/plastic-labs/honcho-node](https://github.com/plastic-labs/honcho-node)
|
|
- **Discord Starter**: [github.com/plastic-labs/discord-python-starter](https://github.com/plastic-labs/discord-python-starter)
|
|
|
|
### Key Concepts for AI Prompts
|
|
When working with AI assistants, mention these concepts:
|
|
- **Core**: "peers, sessions, messages, facts"
|
|
- **Advanced**: "dialectic API, theory of mind, context management"
|
|
- **Integration**: "LLM context injection, session persistence, multi-user handling"
|
|
|
|
<Note>
|
|
**Pro Tip**: Be specific about your requirements and constraints when prompting AI. The more context you provide, the better the generated code will match your needs.
|
|
</Note>
|