GPT Researcher the first open deep research agent designed for both web and local research on any given task.
The 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 and RAG papers, GPT Researcher addresses misinformation, speed, determinism, and reliability by offering stable performance and increased speed through parallelized agent work.
Our mission is to empower individuals and organizations with accurate, unbiased, and factual information through AI.
Why GPT Researcher?
Objective conclusions for manual research can take weeks, requiring vast resources and time.
LLMs trained on outdated information can hallucinate, becoming irrelevant for current research tasks.
Current LLMs have token limitations, insufficient for generating long research reports.
Limited web sources in existing services lead to misinformation and shallow results.
Selective web sources can introduce bias into research tasks.
Demo
Install as Claude Skill
Extend Claude's deep research capabilities by installing GPT Researcher as a Claude Skill:
npx skills add assafelovic/gpt-researcher
Once installed, Claude can leverage GPT Researcher's deep research capabilities directly within your conversations.
Architecture
The 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.
Steps:
Create a task-specific agent based on a research query.
Generate questions that collectively form an objective opinion on the task.
Use a crawler agent for gathering information for each question.
Summarize and source-track each resource.
Filter and aggregate summaries into a final research report.
For other setups (e.g., Poetry or virtual environments), check the Getting Started page.
Run as PIP package
pip install gpt-researcher
Example Usage:
...
from gpt_researcher import GPTResearcher
query = "why is Nvidia stock going up?"
researcher = GPTResearcher(query=query)
# Conduct research on the given query
research_result = await researcher.conduct_research()
# Write the report
report = await researcher.write_report()
...
For more examples and configurations, please refer to the PIP documentation page.
🔧 MCP Client
GPT 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.
export RETRIEVER=tavily,mcp # Enable hybrid web + MCP research
from gpt_researcher import GPTResearcher
import asyncio
import os
async def mcp_research_example():
# Enable MCP with web search
os.environ["RETRIEVER"] = "tavily,mcp"
researcher = GPTResearcher(
query="What are the top open source web research agents?",
mcp_configs=[
{
"name": "github",
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-github"],
"env": {"GITHUB_TOKEN": os.getenv("GITHUB_TOKEN")}
}
]
)
research_result = await researcher.conduct_research()
report = await researcher.write_report()
return report
For comprehensive MCP documentation and advanced examples, visit the MCP Integration Guide.
🍌 Inline Image Generation
GPT Researcher can automatically generate and embed AI-created illustrations in your research reports using Google's Gemini models (Nano Banana).
# Enable in your .env file
IMAGE_GENERATION_ENABLED=true
GOOGLE_API_KEY=your_google_api_key
IMAGE_GENERATION_MODEL=models/gemini-2.5-flash-image
When enabled, the system will:
Analyze your research context to identify visualization opportunities
Pre-generate 2-3 relevant images during the research phase
Embed them inline as the report is written
Images are generated with dark-mode styling that matches the GPT Researcher UI, featuring professional infographic aesthetics with teal accents.
GPT 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.
🌳 Tree-like exploration with configurable depth and breadth
⚡️ Concurrent processing for faster results
🤝 Smart context management across research branches
⏱️ Takes ~5 minutes per deep research
💰 Costs ~$0.4 per research (using o3-mini on "high" reasoning effort)
Step 2 - Clone the '.env.example' file, add your API Keys to the cloned file and save the file as '.env'
Step 3 - Within the docker-compose file comment out services that you don't want to run with Docker.
docker-compose up --build
If that doesn't work, try running it without the dash:
docker compose up --build
Step 4 - By default, if you haven't uncommented anything in your docker-compose file, this flow will start 2 processes:
the Python server running on localhost:8000
the React app running on localhost:3000
Visit localhost:3000 on any browser and enjoy researching!
📄 Research on Local Documents
You can instruct the GPT Researcher to run research tasks based on your local documents. Currently supported file formats are: PDF, plain text, CSV, Excel, Markdown, PowerPoint, and Word documents.
Step 1: Add the env variable DOC_PATH pointing to the folder where your documents are located.
export DOC_PATH="./my-docs"
Step 2:
If you're running the frontend app on localhost:8000, simply select "My Documents" from the "Report Source" Dropdown Options.
If you're running GPT Researcher with the PIP package, pass the report_source argument as "local" when you instantiate the GPTResearcher class code sample here.
🤖 MCP Server
We've moved our MCP server to a dedicated repository: gptr-mcp.
The GPT Researcher MCP Server enables AI applications like Claude to conduct deep research. While LLM apps can access web search tools with MCP, GPT Researcher MCP delivers deeper, more reliable research results.
Features:
Deep research capabilities for AI assistants
Higher quality information with optimized context usage
Comprehensive results with better reasoning for LLMs
Claude Desktop integration
For detailed installation and usage instructions, please visit the official repository.
👪 Multi-Agent Assistant
As AI evolves from prompt engineering and RAG to multi-agent systems, we're excited to introduce multi-agent assistants built with LangGraph and AG2.
By using multi-agent frameworks, the research process can be significantly improved in depth and quality by leveraging multiple agents with specialized skills. Inspired by the recent STORM paper, this project showcases how a team of AI agents can work together to conduct research on a given topic, from planning to publication.
An average run generates a 5-6 page research report in multiple formats such as PDF, Docx and Markdown.
Check it out here or head over to our documentation for LangGraph and AG2 for more information.
🔍 Observability
GPT Researcher supports LangSmith for enhanced tracing and observability, making it easier to debug and optimize complex multi-agent workflows.
Run your research tasks as usual. All LangGraph-based agent interactions will be automatically traced and visualized in your LangSmith dashboard.
Monocle Tracing
GPT Researcher also supports Monocle, an OpenTelemetry-based tracer for agentic applications. It records each run end-to-end: LLM calls, agent steps, and tool invocations, with their inputs, outputs, timings, and token counts.
Monocle is an opt-in extra and is off by default. Install it, then add the following to your .env file:
pip install "gpt-researcher[monocle]"
MONOCLE_TRACING=true
MONOCLE_EXPORTERS=file # file, console, okahu, s3, blob, gcs (default: file)
OKAHU_API_KEY=okh_xxxxxxxx # required only for the `okahu` exporter
Each run writes one trace file to .monocle/; open it in the Monocle VS Code extension. Connect to Okahu to analyze traces across runs (via the okahu exporter).
🖥️ Frontend Applications
GPT-Researcher now features an enhanced frontend to improve the user experience and streamline the research process. The frontend offers:
An intuitive interface for inputting research queries
Real-time progress tracking of research tasks
Interactive display of research findings
Customizable settings for tailored research experiences
Two deployment options are available:
A lightweight static frontend served by FastAPI
A feature-rich NextJS application for advanced functionality
For detailed setup instructions and more information about the frontend features, please visit our documentation page.
🚀 Contributing
We highly welcome contributions! Please check out contributing if you're interested.
Please check out our roadmap page and reach out to us via our Discord community if you're interested in joining our mission.
This project, GPT Researcher, is an experimental application and is provided "as-is" without any warranty, express or implied. We are sharing codes for academic purposes under the Apache 2 license. Nothing herein is academic advice, and NOT a recommendation to use in academic or research papers.
Our view on unbiased research claims:
The main goal of GPT Researcher is to reduce incorrect and biased facts. How? We assume that the more sites we scrape the less chances of incorrect data. By scraping multiple sites per research, and choosing the most frequent information, the chances that they are all wrong is extremely low.
We do not aim to eliminate biases; we aim to reduce it as much as possible. We are here as a community to figure out the most effective human/llm interactions.
In research, people also tend towards biases as most have already opinions on the topics they research about. This tool scrapes many opinions and will evenly explain diverse views that a biased person would never have read.
Что это такое?Автономный агент с открытым исходным кодом, который проводит глубокое исследование любой задачи по интернету и вашим локальным документам и выдаёт подробные, основанные на фактах отчёты со ссылками на источники.
Это Python-библиотека, веб-приложение или что-то, что я запускаю сам?И то, и другое, и третье: pip-пакет, который можно вызывать в коде, самостоятельный веб-сервер — с лёгким фронтендом на FastAPI или полноценным интерфейсом на NextJS — и набор для Docker Compose. Также устанавливается как Claude Skill.
Как его запустить?Установите Python 3.11 или новее, склонируйте репозиторий, положите API-ключи в файл .env, затем установите зависимости и запустите сервер — или просто поднимите его через docker-compose и откройте localhost:3000 в браузере.
Нужен ли ключ OpenAI API, или можно подключить другие модели?По умолчанию нужен Python 3.11 или новее и ключ OpenAI API, но поддерживаются и совместимые с OpenAI API-сервисы, и локальные модели — они задаются через переменные окружения.
Что стоит знать, прежде чем доверять её отчётам?Проект открыто позиционируется как экспериментальный и поставляется «как есть», без каких-либо гарантий. Авторы также подчёркивают, что инструмент снижает предвзятость, но не устраняет её полностью, поэтому относитесь к его результатам как к отправной точке, а не как к окончательной истине.
Лицензия — это чистая Apache 2.0 или есть подвох?Apache 2.0, но с оговоркой: код распространяется в академических целях, и авторы заявляют, что это не академическая консультация и не рекомендация ссылаться на него в научных или исследовательских работах.
Может ли он исследовать мои собственные документы вместо интернета?Да — задайте переменную окружения DOCPATH, указывающую на папку с файлами PDF, обычного текста, CSV, Excel, Markdown, PowerPoint или Word, затем выберите «My Documents» в интерфейсе или передайте report_source='local' в коде.
Что добавляет режим Deep Research?Продвинутый рекурсивный процесс: тема разбирается как дерево, с погружением во вложенные подтемы настраиваемой глубины и ширины охвата, причём ветки обрабатываются параллельно. Один запуск занимает около 5 минут, стоит примерно $0.4 на o3-mini с высоким уровнем рассуждений и выдаёт отчёты объёмом свыше 2 000 слов.
Как он избегает однобоких и поверхностных результатов?Для каждого отчёта он собирает более 20 источников и доверяет информации, которая встречается чаще всего, исходя из того, что множество независимых сайтов вряд ли ошибаются одновременно. Заявленная цель — снизить предвзятость, а не устранить её полностью.
Могут ли ИИ-ассистенты вроде Claude использовать его напрямую?Да — установите его как Claude Skill, чтобы Claude мог запускать глубокое исследование прямо в разговоре, или подключите приложения вроде Claude Desktop к отдельному MCP-серверу (gptr-mcp) для более глубоких и надёжных результатов, чем у обычных инструментов веб-поиска.
Автономный агент с открытым исходным кодом, который проводит глубокое исследование любой задачи по интернету и вашим локальным документам и выдаёт подробные, основанные на фактах отчёты со ссылками на источники.