What’s your actual agentic web research stack? (fully local, no cloud APIs)
📰 Reddit r/LocalLLaMA
Learn how to build a fully local web research pipeline for AI agents without relying on cloud APIs, and explore the tools and techniques used in this setup
Action Steps
- Set up a self-hosted SearXNG instance for search functionality
- Implement a persistent cache/index layer using Hister to store fetched web pages
- Configure the cache layer to integrate with the SearXNG search instance
- Test the pipeline with a sample web research task to ensure functionality
- Optimize the pipeline for performance and reliability by fine-tuning the cache and search settings
Who Needs to Know This
AI engineers and researchers working on local AI agent setups can benefit from this pipeline to improve their agents' web research capabilities, and DevOps teams can learn from the self-hosted and caching solutions implemented
Key Insight
💡 A layered pipeline approach with self-hosted search and caching can enable efficient and reliable local web research for AI agents
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🤖 Build a fully local web research pipeline for your AI agent with SearXNG and Hister! 🚀 No cloud APIs needed!
Key Takeaways
Learn how to build a fully local web research pipeline for AI agents without relying on cloud APIs, and explore the tools and techniques used in this setup
Full Article
Been running a fully local web research pipeline for my AI agent setup for a while now and realized I haven't seen much discussion about how others are handling this part. The inference side gets all the attention, but getting an agent to actually browse the real web without everything falling apart is its own problem. My stack ended up as a layered pipeline: self-hosted SearXNG for search, a persistent cache/index layer (Hister) that stores every fetched
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