deep-searcher

Why we included this project

If your team's questions need answers grounded in your own documents rather than the open web, DeepSearcher is a practical fit. You load local files into a vector database, and the tool runs an iterative retrieve-and-reason loop: a reasoning model pulls the relevant passages from your data, evaluates what it finds, and composes a structured report in answer to the question. It works with many model providers, so you can switch between OpenAI, DeepSeek, Grok, Claude, or a local Ollama instance without restructuring your pipeline. The two-phase design, offline ingestion followed by online querying, keeps private content inside your own infrastructure while still allowing web pages to fill gaps when needed. That makes it a solid starting point for enterprise knowledge management and internal Q&A where you want deep-research-style answers backed by verifiable sources.