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Basic Information

MindSearch is an open source project for building and deploying an LLM-driven multi-agent web search system that mimics human search behavior to produce deep, multi-query search results. The repository provides a backend API implemented with FastAPI, configurable model backends and search engines, and multiple frontend options so researchers and developers can run a local or deployed search assistant. It is intended to be cloned, configured via environment variables, and started with supplied command-line options to specify language, model format, search engine, and asynchronous agent behavior. The project was refactored to improve concurrency using an agent module based on Lagent and includes examples for interacting with the backend directly and debugging locally.

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App Details

Features
Includes a FastAPI backend for serving LLM agents and multiple frontend interfaces including React, Gradio, and Streamlit. Supports configurable model formats such as an InternLM server format and GPT-4, with the ability to add other models via a models configuration file. Offers pluggable web search adapters for DuckDuckGo, Bing, Brave, Google Serper, and Tencent with environment-driven API keys. Provides command-line options for language, model format, search engine, and asynchronous agent deployment. Contains developer utilities such as a backend example script, a terminal debug mode, and guidance for changing the searcher type in configuration. Distributed under the Apache 2.0 license and documented for replication and research citation.
Use Cases
MindSearch helps developers and researchers prototype and run advanced search-oriented LLM agents without building orchestration from scratch. It centralizes model and search engine configuration so teams can evaluate different LLM backends and web retrieval APIs quickly. The refactored agent module and asynchronous options aim to improve concurrency for simultaneous multi-query search workflows, enabling richer retrieval-augmented responses. Frontend choices let users demonstrate and test interactive search experiences, while backend examples and a debug terminal simplify local development and integration. The project is positioned for experimentation, deployment, and academic use, and includes citation information for research reuse.

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