# MindSearch — Multi-Agent AI Search Engine

> An open-source AI search engine that mimics human research behavior using a multi-agent architecture to decompose complex queries, search the web in parallel, and synthesize comprehensive answers.

## Install

Save as a script file and run:

# MindSearch — Multi-Agent AI Search Engine

## Quick Use
```bash
git clone https://github.com/InternLM/MindSearch.git
cd MindSearch
pip install -r requirements.txt
# Set your search API key and LLM API key
export SEARXNG_URL="http://localhost:8080"
export LLM_API_KEY="your-key"
python -m mindsearch.app --lang en
```

## Introduction
MindSearch is an open-source AI-powered search engine developed by the InternLM team at Shanghai AI Lab. It uses a multi-agent system to break down complex questions into sub-queries, search the web in parallel, and produce well-sourced, comprehensive answers similar to how a human researcher would work.

## What MindSearch Does
- Decomposes complex user questions into a directed acyclic graph (DAG) of sub-queries
- Dispatches multiple search agents in parallel to gather information for each sub-query
- Aggregates and synthesizes results from dozens of web pages into a coherent answer
- Provides source citations and a visual graph of the reasoning process
- Supports multiple LLM backends and search engines including SearXNG and Bing

## Architecture Overview
MindSearch consists of two core agent roles: a Planner and multiple Searchers. The Planner receives the user query and generates a DAG of atomic sub-questions with dependency edges. Independent sub-questions are dispatched to Searcher agents in parallel. Each Searcher calls a web search API, reads the returned pages, and extracts relevant information. Once all sub-tasks complete, the Planner synthesizes the individual findings into a final answer with citations. The system is built on the InternLM agent framework and uses a React-based frontend for the visual interface.

## Self-Hosting & Configuration
- Requires Python 3.9+ and a running SearXNG instance or Bing API key for web search
- Configure the LLM backend via environment variables (supports InternLM, OpenAI, and compatible APIs)
- Deploy SearXNG locally with Docker for a fully self-hosted, privacy-respecting setup
- Adjust the maximum number of parallel searcher agents and search depth in the config
- The frontend can be run separately with Node.js or served through the included Gradio/Streamlit interface

## Key Features
- Graph-based query decomposition that handles multi-hop, comparative, and aggregation questions
- Parallel search execution dramatically reduces latency compared to sequential approaches
- Visual reasoning graph showing how the answer was assembled from sub-queries
- Model-agnostic design works with both commercial and open-weight LLMs
- Self-hostable with local search via SearXNG for complete data privacy

## Comparison with Similar Tools
- **Perplexica** — single-agent search-and-answer; MindSearch uses multi-agent parallel decomposition for complex queries
- **SearchGPT** — closed commercial product; MindSearch is fully open source and self-hostable
- **SearXNG** — privacy metasearch engine without AI synthesis; MindSearch adds LLM-based reasoning on top
- **Tavily** — search API for AI agents; MindSearch is a full search application, not just an API
- **Stanford STORM** — research report generation; MindSearch focuses on real-time interactive question answering

## FAQ
**Q: Can I use MindSearch without an internet connection?**
A: No. MindSearch requires live web search to retrieve current information. However, the search and LLM components can all be self-hosted on your network.

**Q: Which search backends are supported?**
A: SearXNG (recommended for self-hosting), Bing Web Search API, and DuckDuckGo.

**Q: How many web pages does it process per query?**
A: Depending on query complexity, MindSearch typically reads and synthesizes content from 20 to 60 web pages per question.

**Q: Can I use GPT-4 or Claude as the reasoning model?**
A: Yes. Any OpenAI-compatible API can be configured as the LLM backend.

## Sources
- https://github.com/InternLM/MindSearch
- https://github.com/InternLM/MindSearch/blob/main/README.md

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Author: Script Depot