# Research: Autogen, Dify, Lian Adapter for Robust Session Management
## Current Claude CLI Limitations
### Session Management Issues:
1. **--print mode**: No state persistence between calls
2. **Session IDs**: Available but may not work in --print mode
3. **--continue**: Works but requires recent conversation
4. **No built-in conversation history**: Each call is independent
### Needs:
- Robust state management across sessions
- Conversation history persistence
- Multi-agent coordination
- Tool usage limits and budgets
- Thinking/reasoning mode
- Sub-agent delegation
## Option 1: AutoGen
### What is AutoGen?
- Microsoft's framework for multi-agent conversations
- Supports multiple LLM agents working together
- Built-in conversation management
- Tool/function calling support
### Pros:
- ✅ Multi-agent support
- ✅ Conversation persistence
- ✅ Tool orchestration
- ✅ Agent delegation
- ✅ Open source
### Cons:
- ⚠️ Python-based (we're Node.js)
- ⚠️ More complex setup
- ⚠️ May need API wrapper
### Integration Approach:
```python
# AutoGen could wrap Claude API
from autogen import ConversableAgent
claude_agent = ConversableAgent(
name="claude",
system_message="You are Claude",
llm_config={
"model": "claude-3-5-sonnet",
"api_key": "...",
"api_type": "anthropic"
}
)
```
## Option 2: Dify
### What is Dify?
- LLM application development platform
- Workflow orchestration
- Built-in conversation management
- API-first design
- Supports Claude API
### Pros:
- ✅ Built-in session management
- ✅ Workflow orchestration
- ✅ API integration
- ✅ Conversation history
- ✅ Multi-agent support
- ✅ Tool/function calling
- ✅ Open source
### Cons:
- ⚠️ Requires Dify server setup
- ⚠️ Additional infrastructure
- ⚠️ Learning curve
### Integration Approach:
```bash
# Dify API endpoint
POST https://api.dify.ai/v1/chat-messages
{
"inputs": {},
"query": "user message",
"response_mode": "streaming",
"conversation_id": "session-id",
"user": "user-id"
}
```
## Option 3: Lian Adapter
### What is Lian?
- Need to research - may be a Claude API adapter
- Could provide session management wrapper
- Might handle API calls with state
### Research Needed:
- Is this a real project?
- What does it provide?
- How does it work?
## Option 4: Custom Session Manager
### Build Our Own:
- Use Claude API directly
- Implement session management
- Store conversation history
- Manage state ourselves
### Architecture:
```
┌─────────────┐
│ Browser │
└──────┬──────┘
│ WebSocket
┌──────▼──────────────────┐
│ Session Manager │
│ - Conversation History │
│ - State Management │
│ - Tool Tracking │
└──────┬──────────────────┘
│ API Calls
┌──────▼──────┐
│ Claude API │
└─────────────┘
```
### Implementation:
```javascript
class SessionManager {
constructor() {
this.sessions = new Map();
this.conversations = new Map();
}
async sendMessage(sessionId, message) {
const session = this.sessions.get(sessionId) || this.createSession(sessionId);
const conversation = this.conversations.get(sessionId) || [];
conversation.push({ role: 'user', content: message });
const response = await this.callClaude(conversation);
conversation.push({ role: 'assistant', content: response });
this.conversations.set(sessionId, conversation);
return response;
}
}
```
## Recommendation: Hybrid Approach
### Use Dify for Production:
1. **Setup Dify Server**: Deploy Dify instance
2. **Configure Claude API**: Add Claude as LLM provider
3. **Create Workflow**: Define conversation flow
4. **Use API**: Call Dify API from our server
5. **Get Benefits**: Session management, history, workflows
### Use Custom Manager for Development:
1. **Build Session Manager**: Simple state management
2. **Store History**: In-memory or database
3. **Manage Tools**: Track usage, limits
4. **Handle API**: Direct Claude API calls
## Implementation Plan
### Phase 1: Custom Session Manager (Quick)
- Build basic session management
- Store conversation history
- Track tool usage
- Implement limits
### Phase 2: Dify Integration (Robust)
- Setup Dify server
- Configure Claude API
- Create workflows
- Migrate to Dify API
### Phase 3: Advanced Features
- Multi-agent coordination
- Sub-agent delegation
- Thinking mode integration
- Advanced tool orchestration
## Next Steps
1. **Test Dify**: Setup and test Dify with Claude API
2. **Build Custom Manager**: Quick implementation for now
3. **Research Lian**: Find out what it is
4. **Evaluate AutoGen**: If Python integration is feasible
5. **Choose Solution**: Based on testing results
