DeepSearcher - AI RAG and Search Tool

Overview

DeepSearcher is an open-source framework for private data search, evaluation, and reasoning that combines multiple large language models with vector-database-powered retrieval. It is designed for enterprise knowledge management and intelligent Q&A systems, producing concise answers alongside evidence and evaluative reports. The project focuses on delivering accurate responses over private corpora by orchestrating retrieval, LLM-based reasoning/evaluation, and report generation into an auditable pipeline. According to the GitHub repository, DeepSearcher is published under the Apache-2.0 license and shows substantial community interest (7,299 stars, 696 forks). The implementation targets production-oriented workflows: private data ingestion, vector-indexed retrieval, multi-model comparison/evaluation, and structured output that can feed analytics or compliance processes. The project is maintained actively (last commit recorded on 2025-11-19) and positions itself as a building block for enterprises that need explainable, private-domain Q&A and knowledge evaluation rather than a closed commercial service.

GitHub Statistics

• Stars: 7,299
• Forks: 696
• Contributors: 32
• License: Apache-2.0
• Primary Language: Python
• Last Updated: 2025-11-19T06:03:20Z

The repository demonstrates strong community traction: 7,299 stars, 696 forks, and 32 contributors, which indicates active adoption and outside contribution. The project is Apache-2.0 licensed, reducing friction for enterprise use. A recent commit on 2025-11-19 suggests ongoing maintenance. The contributor count and fork activity imply a healthy open-source ecosystem, though specifics about issue resolution times or roadmap cadence should be checked directly in the repository's Issues and Discussions pages.

Installation

Install via docker:

git clone https://github.com/zilliztech/deep-searcher.git

cd deep-searcher

docker compose up -d --build

Key Features

• Orchestrates multiple large language models for comparative answering and evaluation
• Integrates retrieval via vector-database-backed search over private corpora
• Generates evidence-backed answers with provenance for enterprise compliance
• Built-in evaluation pipelines to score model outputs and produce reports
• Supports reasoning workflows that combine retrieval with LLM chain-of-thought
• Designed for enterprise knowledge management and private Q&A deployments
• Apache-2.0 open-source license for commercial and internal use

Community

The project has a sizable and active community on GitHub (7,299 stars, 696 forks, 32 contributors). Activity is recent (last commit 2025-11-19), and the Apache-2.0 license encourages enterprise adoption. For hands-on feedback, example deployments, and issue tracking, consult the repository’s Issues, Discussions, and Pull Requests pages.

Key Information

• Category: RAG and Search
• Type: AI RAG and Search Tool