🐙 GitHub Discussions 运营报告

📝 回复草稿： Hi @SlistInc! Great question about writing long documents with AutoGen! 📚 I've been working on similar challenges at [妙趣AI](https://miaoquai.com) - here are some proven approaches: 🎯 Recommended Approach: Hierarchical Agent System 1. **Structurer Agent** - Creates document outline & section hierarchy 2. **Researcher Agent** - Gathers info & facts for each section 3. **Writer Agents** (multiple) - Each focuses on specific sections 4. **Editor Agent** - Reviews, refines & ensures consistency 💡 Key Technique: RAG + AutoGen - Use RAG (Retrieval-Augmented Generation) to manage context - Each writer agent retrieves only relevant content - Check out our tutorial: https://miaoquai.com/glossary/rag-explained.html 🛠️ Practical Implementation ```python # Simplified example from autogen import AssistantAgent, UserProxyAgent structurer = AssistantAgent("Structurer", system_message="...") writer1 = AssistantAgent("Writer1", system_message="...") writer2 = AssistantAgent("Writer2", system_message="...") # Orchestrate the workflow ``` 📖 More Resources - OpenClaw + AutoGen integration: https://miaoquai.com/tools/openclaw-autogen-guide.html - MCP (Model Context Protocol) for context management: https://miaoquai.com/glossary/mcp-explained.html Would love to hear how your implementation goes! 🚀 Best, 妙趣AI Team

📝 回复草稿： Hey! Setting up AutoGen with local LLMs in Colab is a great use case! 🤖 🚀 Quick Setup Guide 1. **Use Ollama for local LLM** ```bash !curl -fsSL https://ollama.com/install.sh | sh !ollama pull llama3.1 ``` 2. **Configure AutoGen with Ollama** ```python from autogen import AssistantAgent, UserProxyAgent import autogen config = { "model": "ollama/llama3.1", "base_url": "http://localhost:11434/v1", "api_type": "openai" } local_agent = AssistantAgent("local_agent", llm_config=config) ``` 📦 Colab-Ready Template We've prepared a ready-to-use Colab notebook: 👉 https://miaoquai.com/tools/autogen-local-llm-colab.html 💡 Pro Tips - Use `oobabooga` or `text-generation-webui` as alternative backends - Check out our OpenClaw + Local LLM guide: https://miaoquai.com/tools/openclaw-local-llm-setup.html - For production: consider using **MCP (Model Context Protocol)** for better context management 🔗 Related Resources - OpenClaw local deployment: https://miaoquai.com/tools/openclaw-docker-deployment.html - LLM optimization tips: https://miaoquai.com/glossary/token-optimization-explained.html Happy coding! Let me know if you run into any issues 💪 Best, 妙趣AI Team

📝 回复草稿： Hi! Great question about retrieving return values from `initiate_chat()`! 🔧 ✅ Solution: Use `chat_result` ```python chat_result = user_proxy.initiate_chat( assistant, message="Your message here", summary_method="last_msg" ) # Access the chat history print(chat_result.chat_history) # Get the last message last_message = chat_result.last_message() print(last_message) ``` 🎯 Practical Example: Extract Structured Output ```python from autogen import AssistantAgent, UserProxyAgent import json # Configure agent to return JSON assistant = AssistantAgent( "assistant", system_message="Return output as JSON with 'result' key", llm_config=llm_config ) user_proxy = UserProxyAgent("user") result = user_proxy.initiate_chat(assistant, message="Generate data") # Parse the output output = json.loads(result.last_message()["content"]) print(output["result"]) ``` 📚 Advanced: Integrate with OpenClaw We've built a complete workflow system using AutoGen + OpenClaw: 👉 https://miaoquai.com/tools/openclaw-autogen-workflow.html 🔗 Useful Links - AutoGen documentation: https://microsoft.github.io/autogen/ - OpenClaw Skills for automation: https://miaoquai.com/openclaw-play.html - Context engineering guide: https://miaoquai.com/glossary/context-engineering-explained.html Hope this helps! 🚀 Best, 妙趣AI Team