AIエージェント
deep-research
Deep Research with any LLM: in-depth reports in minutes, with an SSE API and MCP server built in
★ 4.7k スターMIT
ガイドを見る →gpt-researcherは、assafelovicが公開しているオープンソースのAIエージェントプロジェクトです（GitHub: assafelovic/gpt-researcher）。GitHubスターは29,955、フォークは4,095で、Apache-2.0ライセンスの下で公開されています。GitHubでの最終更新は2026年10月1日です。
An autonomous research agent that plans, searches and writes cited reports from any data source
Decide what you want to achieve with gpt-researcher and check the README on GitHub for requirements before installing.
Clone the repository from GitHub (or take the packaged install route in step 3 if the README offers one).
Follow the project's documented install commands — the Run section below lists the exact ones — and add any required API keys or models.
Work through the README's quickstart example end to end, then adapt it to your own project or data.
The agent breaks a question into sub-queries, searches them in parallel and synthesizes a report.
Output reports aggregate multiple sources with citations rather than a single model's memory.
A dedicated deep-research flow spends more time exploring for complex questions.
Point it at your own PDFs/documents to research private material, not just the web.
Works across LLM providers — configure the models you already have keys for.
Can run as an MCP server for agent hosts, and installs as a Claude Skill via npx skills.
Get the GPT Researcher source and examples.
git clone https://github.com/assafelovic/gpt-researcher.git
cd gpt-researcherInstall the requirements (or simply pip install gpt-researcher for library use), then set your LLM and search API keys in .env.
pip install -r requirements.txt
# library alternative: pip install gpt-researcherStart the app (or the Python example from the README), give it a research question and read the generated report.
# see the README 'Example Usage' for the exact Python snippetgpt-researcher is maintained by the assafelovic organization/account on GitHub, together with open-source contributors, in the public repository assafelovic/gpt-researcher.
Yes — the code is open source under the Apache-2.0 license. You can use, modify and redistribute it under that license's terms.
Yes — you need your own LLM API key and a web-search API key for the search provider you configure (Tavily is the common choice); the software itself is free and Apache-2.0 licensed.
Hosted deep-research tools are convenient but closed; GPT Researcher is self-hostable, provider-agnostic and scriptable, at the cost of your own keys and setup.
Clone the repository from GitHub, follow the install commands in the Run section (taken from the project's README), and complete the documented quickstart example before adapting it to your own use case.
Deep Research with any LLM: in-depth reports in minutes, with an SSE API and MCP server built in
Microsoft's R&D-Agent: LLM agents that automate data-driven research, Kaggle modelling and LLM fine-tuning.
Turn any website into clean, agent-ready data with one API.
Parallel multi-agent research that compiles wikis from any topic.
Autonomous research loops, from topic to experiments.
OpenScience — the open-source AI workbench for scientific research: give it a goal, it reads the literature, runs the experiments and writes up the findings
LibreAIカタログに掲載されたAIエージェントプロジェクトの中で最もスターの多いもの — gpt-researcherが合わなかった場合に検討したい、現実的な候補リストです。
A personal AI that really does things — on any OS, with your data staying yours.
An agentic skills framework & software development methodology that works.
Agent harness optimization system with skills, memory and security for Claude Code, Codex and more
The agent that grows with you — a Python framework for adaptive AI agents.
DeepSeek's plugin-based agent harness where everything is a plugin.
カタログの中でgpt-researcherとスター数が近いAIエージェントプロジェクト — 前後の棚もそのまま見て回れます。
The batteries-included agent harness from the LangChain team.
smolagents — Hugging Face's barebones agent library where agents think in code: a CodeAgent that writes its actions as Python, model-agnostic, Apache-2.0
A Python simulation platform for robotics and embodied AI — fast physics, rendering and learning
A lightweight, powerful framework for multi-agent workflows
Over a thousand tool integrations so agents can turn intent into action.
Open-source search infrastructure built for AI workloads.