Building a multi-source autonomous research agent with LangGraph, ThreadPoolExecutor and Ollama
📰 Dev.to AI
Building a multi-source autonomous research agent using LangGraph, ThreadPoolExecutor, and Ollama
Action Steps
- Design the architecture of the research agent to handle multiple sources
- Implement parallel execution using ThreadPoolExecutor to speed up research tasks
- Develop a self-correction loop to improve the accuracy of research results
- Integrate LangGraph and Ollama to enable advanced natural language processing and knowledge retrieval
- Test and refine the agent using a live demo
Who Needs to Know This
This project benefits AI engineers and researchers who need to automate research tasks across multiple sources, and can be integrated into larger systems by software engineers and DevOps teams
Key Insight
💡 Combining multiple sources and parallel execution can significantly improve the depth and accuracy of research results
Share This
🤖 Built a multi-source research agent using LangGraph, ThreadPoolExecutor & Ollama! 🚀
Key Takeaways
Building a multi-source autonomous research agent using LangGraph, ThreadPoolExecutor, and Ollama
Full Article
I wanted a tool that could research any topic deeply — not just one web search, but Wikipedia, arXiv, Semantic Scholar, GitHub, Hacker News, Stack Overflow, Reddit, YouTube and local documents, all at once. So I built it. This post covers the architecture decisions, the parallel execution model, the self-correction loop, and a few things that didn't work before I got it right. Live demo: <a href="https://huggingface.co/spaces/ecerocg/research-agent" rel="noopene
DeepCamp AI