Introducing DeepSearcher: A Local Open Source Deep Research

DeepSearcher is an open-source Python library and command-line tool that uses a local search engine to build reports on given topics or questions. The agent follows four steps: define/refine the question, research, analyze, synthesize. DeepSearcher demonstrates additional concepts like query routing, conditional execution flow, and web crawling as a tool. It can input multiple source documents and set the embedding model and vector database used via a configuration file. The system uses Milvus for similarity search and has agentic reflection capabilities to refine the question based on prior outputs. DeepSearcher is built upon the idea of using local inference with a small 4-bit quantized reasoning model, but recently switched to an online inference service for the massive DeepSeek-R1 model, qualitatively improving its output report. The system works with most inference services like OpenAI and Gemini, and has been used to generate reports on various topics, including The Simpsons.

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