{"repo":"assafelovic/gpt-researcher","free":true,"listed":false,"github":"https://github.com/assafelovic/gpt-researcher","clone":"git clone https://github.com/assafelovic/gpt-researcher.git","description":"An autonomous agent that conducts deep research on any data using any LLM providers","language":"Python","stars":29004,"topics":["agent","ai","automation","deepresearch","llms","mcp","mcp-server","python","research","search","webscraping"],"license":"Apache-2.0","category":"research_agent","readme_excerpt":"
\n\n\"Logo\"\n\n####\n\n[![Website](https://img.shields.io/badge/Official%20Website-gptr.dev-teal?style=for-the-badge&logo=world&logoColor=white&color=0891b2)](https://gptr.dev)\n[![Documentation](https://img.shields.io/badge/Documentation-DOCS-f472b6?logo=googledocs&logoColor=white&style=for-the-badge)](https://docs.gptr.dev)\n[![Discord](https://img.shields.io/discord/1127851779011391548?logo=discord&logoColor=white&label=Discord&color=34b76a&style=for-the-badge)](https://discord.gg/QgZXvJAccX)\n\n\n[![PyPI version](https://img.shields.io/pypi/v/gpt-researcher?logo=pypi&logoColor=white&style=flat)](https://badge.fury.io/py/gpt-researcher)\n![GitHub Release](https://img.shields.io/github/v/release/assafelovic/gpt-researcher?style=flat&logo=github)\n[![Open In Colab](https://img.shields.io/static/v1?message=Open%20in%20Colab&logo=googlecolab&labelColor=grey&color=yellow&label=%20&style=flat&logoSize=40)](https://colab.research.google.com/github/assafelovic/gpt-researcher/blob/master/docs/docs/examples/pip-run.ipynb)\n[![Docker Image Version](https://img.shields.io/docker/v/elestio/gpt-researcher/latest?arch=amd64&style=flat&logo=docker&logoColor=white&color=1D63ED)](https://hub.docker.com/r/gptresearcher/gpt-researcher)\n[![Skill](https://img.shields.io/badge/Claude%20Skill-skills.sh-blueviolet?style=flat&logo=anthropic&logoColor=white)](https://skills.sh/assafelovic/gpt-researcher/gpt-researcher)\n[![Twitter Follow](https://img.shields.io/twitter/follow/assaf_elovic?style=social)](https://twitter.com/assaf_elovic)\n\n[English](README.md) | [中文](README-zh_CN.md) | [日本語](README-ja_JP.md) | [한국어](README-ko_KR.md)\n\n
\n\n# 🔎 GPT Researcher\n\n**GPT Researcher the first open deep research agent designed for both web and local research on any given task.** \n\nThe agent produces detailed, factual, and unbiased research reports with citations. GPT Researcher provides a full suite of customization options to create tailor made and domain specific research agents. Inspired by the recent [Plan-and-Solve](https://arxiv.org/abs/2305.04091) and [RAG](https://arxiv.org/abs/2005.11401) papers, GPT Researcher addresses misinformation, speed, determinism, and reliability by offering stable performance and increased speed through parallelized agent work.\n\n**Our mission is to empower individuals and organizations with accurate, unbiased, and factual information through AI.**\n\n## Why GPT Researcher?\n\n- Objective conclusions for manual research can take weeks, requiring vast resources and time.\n- LLMs trained on outdated information can hallucinate, becoming irrelevant for current research tasks.\n- Current LLMs have token limitations, insufficient for generating long research reports.\n- Limited web sources in existing services lead to misinformation and shallow results.\n- Selective web sources can introduce bias into research tasks.\n\n## Demo\n\n \"Demo\n\n\n## Install as Claude Skill\n\nExtend Claude's deep research capabilities by installing GPT Researcher as a [Claude Skill](https://skills.sh/assafelovic/gpt-researcher/gpt-researcher):\n\n```bash\nnpx skills add assafelovic/gpt-researcher\n```\n\nOnce installed, Claude can leverage GPT Researcher's deep research capabilities directly within your conversations.\n\n## Architecture\n\nThe core idea is to utilize 'planner' and 'execution' agents. The planner generates research questions, while the execution agents gather relevant information. The publisher then aggregates all findings into a comprehensive report.\n\n
\n\n
\n\nSteps:\n* Create a task-specific agent based on a research query.\n* Generate questions that collectively form an objective opinion on the task.\n* Use a crawler agent for gathering information for each question.\n* Summarize and source-track each resource.\n* Filter and aggregate summaries into a final research report.\n\n## Tutorials\n - [How it Works](https://docs.gptr.dev/blog/building-gpt-researcher)\n - [How to Install](https://www.loom.com/share/04ebffb6ed2a4520a27c3e3addcdde20?sid=da1848e8-b1f1-42d1-93c3-5b0b9c3b24ea)\n - [Live Demo](https://www.loom.com/share/6a3385db4e8747a1913dd85a7834846f?sid=a740fd5b-2aa3-457e-8fb7-86976f59f9b8)\n\n## Features\n\n- 📝 Generate detailed research reports using web and local documents.\n- 🖼️ Smart image scraping and filtering for reports.\n- 🍌 **AI-generated inline images** using Google Gemini (Nano Banana) for visual illustrations.\n- 📜 Generate detailed reports exceeding 2,000 words.\n- 🌐 Aggregate over 20 sources for objective conclusions.\n- 🖥️ Frontend available in lightweight (HTML/CSS/JS) and production-ready (NextJS + Tailwind) versions.\n- 🔍 JavaScript-enabled web scraping.\n- 📂 Maintains memory and context throughout research.\n- 📄 Export reports to PDF, Word, and other formats.\n\n## 📖 Documentation\n\nSee the [Documentation](https://docs.gptr.dev/docs/gpt-researcher/getting-started) for:\n- Installation and setup guides\n- Configuration and customization options\n- How-To examples\n- Full API references\n\n## ⚙️ Getting Started\n\n### Installation\n\n1. Install Python 3.11 or later. [Guide](https://www.tutorialsteacher.com/python/install-python).\n2. Clone the project and navigate to the directory:\n\n ```bash\n git clone https://github.com/assafelovic/gpt-researcher.git\n cd gpt-researcher\n ```\n\n3. Set up API keys by exporting them or storing them in a `.env` file.\n\n ```bash\n export OPENAI_API_KEY={Your OpenAI API Key here}\n export TAVILY_API_KEY={Your Tavily API Key here}\n ```\n\n (Optional) For enhanced tracing and observability, you can also set:\n \n ```bash\n # export LANGCHAIN_TRACING_V2=true\n # export LANGCHAIN_API_KEY={Your LangChain API Key here}\n ```\n\n For custom OpenAI-compatible APIs (e.g., local models, other providers), you can also set:\n \n ```bash\n export OPENAI_BASE_URL={Your custom API base URL here}\n ```\n\n4. Install dependencies and start the server:\n\n ```bash\n pip install -r requirements.txt\n python -m uvicorn main:app --reload\n ```\n\nVisit [http://localhost:8000](http://localhost:8000) to start.\n\nFor other setups (e.g., Poetry or virtual environments), check the [Getting Started page](https://docs.gptr.dev/docs/gpt-researcher/getting-started).\n\n## Run as PIP package\n```bash\npip install gpt-researcher\n\n```\n### Example Usage:\n```python\n...\nfrom gpt_researcher import GPTResearcher\n\nquery = \"why is Nvidia stock going up?\"\nresearcher = GPTResearcher(query=query)\n# Conduct research on the given query\nresearch_result = await researcher.conduct_research()\n# Write the report\nreport = await researcher.write_report()\n...\n```\n\n**For more examples and configurations, please refer to the [PIP documentation](https://docs.gptr.dev/docs/gpt-researcher/gptr/pip-package) page.**\n\n### 🔧 MCP Client\nGPT Researcher supports MCP integration to connect with specialized data sources like GitHub repositories, databases, and custom APIs. This enables research from data sources alongside web search.\n\n```bash\nexport RETRIEVER=tavily,mcp # Enable hybrid web + MCP research\n```\n\n```python\nfrom gpt_researcher import GPTResearcher\nimport asyncio\nimport os\n\nasync def mcp_research_example():\n # Enable MCP with web search\n os.environ[\"RETRIEVER\"] = \"tavily,mcp\"\n \n researcher = GPTResearcher(\n query=\"What are the top open source web research agents?\",\n mcp_configs=[\n {\n \"name\": \"github\",\n \"command\": \"npx\",\n \"args\": [\"-y\", \"@modelcontextprotocol/server-github\"],\n \"env\": {\"GITHUB_TOKEN\": os.getenv(\"GITHUB_TOKEN\")}\n }\n ]\n )\n \n research_result = await researcher.conduct_research()\n report = await researcher.write_report()\n return report\n```\n\n> For comprehensive MCP documentation and advanced examples, visit the [MCP Integration Guide](https://docs.gptr.dev/docs/gpt-researcher/retrievers/mcp-configs).\n\n## 🍌 Inline Image Generation\n\nGPT Researcher can automatically generate and embed AI-created illustrations in your research reports using Google's Gemini models (Nano Banana).\n\n```bash\n# Enable in your .env file\nIMAGE_GENERATION_ENABLED=true\nGOOGLE_API_KEY=your_google_api_key\nIMAGE_GENERATION_MODEL=models/gemini-2.5-flash-image\n```\n\nWhen enabled, the system will:\n1. Analyze your research context to identify visualization opportunities\n2. Pre-generate 2-3 relevant images during the research phase\n3. Embed them inline as the report is written\n\nImages are generated with dark-mode styling that matches the GPT Researcher UI, featuring professional infographic aesthetics with teal accents.\n\n[Learn more about Image Generation](https://docs.gptr.dev/docs/gpt-researcher/gptr/image_generation) in our documentation.\n\n## ✨ Deep Research\n\nGPT Researcher now includes Deep Research - an advanced recursive research workflow that explores topics with agentic depth and breadth. This feature employs a tree-like exploration pattern, diving deeper into subtopics while maintaining a comprehensive view of the research subject.\n\n- 🌳 Tree-like exploration with configurable depth and breadth\n- ⚡️ Concurrent processing for faster results\n- 🤝 Smart context management across research branches\n- ⏱️ Takes ~5 minutes per deep research\n- 💰 Costs ~$0.4 per research (using `o3-mini` on \"high\" reasoning effort)\n\n[Learn more about Deep Research](https://docs.gptr.dev/docs/gpt-researcher/gptr/deep_research) in our documentation.\n\n## Run with Docker\n\n> **Step 1** - [Install 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