Deep-Searcher is an agentic retrieval-augmented generation system that combines large language models with vector databases to perform search, evaluation, and reasoning over private data.
The tool addresses the need for enterprises to extract accurate answers and generate comprehensive reports from internal knowledge bases while maintaining data security. It works by integrating reasoning-capable LLMs with vector databases like Milvus to retrieve relevant private data, then uses the LLM's reasoning capabilities to evaluate and synthesize that information into answers. The system can optionally augment private data with online content when needed for improved accuracy.
Deep-Searcher suits organizations building enterprise knowledge management systems, intelligent question-answering platforms, or information retrieval applications that require reasoning over proprietary datasets. It is particularly valuable when you need to keep sensitive data private while still leveraging advanced LLM reasoning. The project supports multiple LLM providers including DeepSeek, OpenAI, Qwen, Claude, Grok, and Llama, as well as multiple vector database backends, giving teams flexibility in choosing their infrastructure. Document loading currently supports local files, with web crawling capabilities in development.
The project shows active development with regular updates to support newly released reasoning models and LLM providers. The codebase maintains clear separation between LLM and vector database configuration, allowing straightforward swapping of providers. Documentation includes quick-start examples and configuration details for multiple LLM services. The project accepts contributions through development mode installation and provides guidance for extending functionality.
