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📄 回复草稿 (已准备)

Hi @OP! 👋

Great to see someone pushing AutoGen to its limits with long-form content! You're right that token limits are the main bottleneck. Here are a few solutions:

### 🎯 Solution: RAG + Agent Chain
Break the document into sections, use RAG to retrieve context, and chain agents.

### 📚 Related Resources
I've written a practical tutorial on **RAG + Agent patterns**:
🔗 https://miaoquai.com/tools/rag-explained.html
- Code examples with AutoGen
- Token optimization strategies

Also check **OpenClaw Play** for Agent chaining examples:
🔗 https://miaoquai.com/openclaw-play.html

Best,
MiaoQu AI Team

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📄 回复草稿 (已准备)

Hi @OP! 👋

Running local LLMs in Colab is tricky due to inline limitations. Here's a working solution:

### 📝 Colab Setup
Use daemon threads to run llmlite server in background.

### 🔧 Complete Template
I've prepared a **ready-to-use Colab notebook**:
🔗 https://miaoquai.com/tools/autogen-colab-template.html

Also check our **Local LLM Deployment Guide**:
🔗 https://miaoquai.com/tools/local-llm-guide.html

Hope this helps! Let me know if you hit any issues.

Best,
MiaoQu AI Team

📄 回复草稿 (已准备)

Hi @OP! 👋

Great question! Migrating from LangChain to AutoGen while keeping existing tools is common. Here's how:

### 🔧 Approach: Wrap LangChain Tools
LangChain tools = AutoGen functions. Wrap them and register.

### 📚 Migration Guide
I've written a **step-by-step migration tutorial**:
🔗 https://miaoquai.com/tools/langchain-to-autogen-migration.html

Also check **OpenClaw Agent Patterns**:
🔗 https://miaoquai.com/openclaw-play.html

The key: **LangChain tools = AutoGen functions**.

Best,
MiaoQu AI Team