English | 中文 | 日本語 | 한국어

🔎 GPT Researcher

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• LLMs trained on outdated information can hallucinate, becoming irrelevant for current research tasks.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.

Tutorials

• How it Works
• How to Install
• Live Demo

Features

• 📝 Generate detailed research reports using web and local documents.
• 🖼️ Smart image scraping and filtering for reports.
• 🍌 AI-generated inline images using Google Gemini (Nano Banana) for visual illustrations.
• 📜 Generate detailed reports exceeding 2,000 words.
• 🌐 Aggregate over 20 sources for objective conclusions.
• 🖥️ Frontend available in lightweight (HTML/CSS/JS) and production-ready (NextJS + Tailwind) versions.
• 🔍 JavaScript-enabled web scraping.
• 📂 Maintains memory and context throughout research.
• 📄 Export reports to PDF, Word, and other formats.

📖 Documentation

See the Documentation for:

• Installation and setup guides
• Configuration and customization options
• How-To examples
• Full API references

Komodo Homelab Stack

This fork includes compose.komodo.yaml for a Komodo repo-backed stack. Komodo can clone or pull the GitHub repo during a stack deploy, run docker compose build, and redeploy the backend, Next.js UI, and MCP containers from the freshly built images.

Deep-research deployments can compare tree expansion and branch retrieval independently with DEEP_RESEARCH_TREE_POLICY (ranked or legacy_all) and DEEP_RESEARCH_BRANCH_MODE (focused or standard). Leaving both at auto preserves the existing DEEP_RESEARCH_FOCUSED_RETRIEVAL compatibility switch. When RESEARCH_TRAJECTORY_ENABLED=true, each MCP job writes an append-only JSONL trace beneath the configured persistent RESEARCH_TRAJECTORY_DIR; use evals/trajectory_compare.py to summarize multiple policy runs.

The homelab stack enables the generalized retrieval pipeline with GPTR_RETRIEVAL_PIPELINE_MODE=v2. It keeps preliminary result cards in a job-wide candidate ledger, renders compact entity-first queries, resolves canonical raw/API/PDF variants, and batches every hard-valid fetched source through an evidence judge before synthesis. Set the mode to legacy for an immediate retrieval rollback.

⚙️ Getting Started

Installation

1. Install Python 3.11 or later. Guide.

2. Clone the project and navigate to the directory:

   git clone https://github.com/assafelovic/gpt-researcher.git
   cd gpt-researcher

3. Set up API keys by exporting them or storing them in a .env file.

   export OPENAI_API_KEY={Your OpenAI API Key here}
   export TAVILY_API_KEY={Your Tavily API Key here}

   (Optional) For enhanced tracing and observability, you can also set:

   # export LANGCHAIN_TRACING_V2=true
   # export LANGCHAIN_API_KEY={Your LangChain API Key here}

   For custom OpenAI-compatible APIs (e.g., local models, other providers), you can also set:

   export OPENAI_BASE_URL={Your custom API base URL here}

4. Install dependencies and start the server:

   pip install -r requirements.txt
   python -m uvicorn main:app --reload

Visit http://localhost:8000 to start.

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:

1. Analyze your research context to identify visualization opportunities
2. Pre-generate 2-3 relevant images during the research phase
3. 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.

Learn more about Image Generation in our documentation.

✨ Deep Research

The MCP deep-research tool exposes a best-effort research duration instead of caller-selected breadth and depth:

deep_research(query, research_duration_seconds=60)

The accepted range is 15–600 seconds and excludes final report generation. The backend calculates bounded aspect coverage, selective repair, source-card and scrape limits, and focused deepening at job start. It never creates nested planner trees; individual PDF downloads are capped at eight seconds and 32 MiB. Report generation is measured separately and report length scales with the requested research duration.

• 🌳 Structured, original-query-anchored aspect planning
• 🛠️ Coverage repair before optional deepening
• 🛡️ Verified primary/reputable evidence and corroborated broader-web fallback
• ⚡️ Shared concurrency of four across focused branches
• 📈 Job trajectories with timing calibration and compression diagnostics

The v2 production switches are:

GPTR_RETRIEVAL_PIPELINE_MODE=v2
GPTR_SOURCE_EVIDENCE_JUDGE_MODE=all
GPTR_SOURCE_EVIDENCE_JUDGE_FALLBACK=hybrid
GPTR_CANONICAL_CONTENT_RESOLUTION=true

hybrid retries malformed evidence-judge output once, then accepts only strongly entity/relation-anchored primary evidence or independently corroborated practitioner evidence. Set the retrieval mode to legacy without changing the public MCP interface.

Production defaults to RESEARCH_DURATION_CONTROLLER_MODE=enabled. off restores the internal 2/3 ranked/focused policy; shadow calculates and records a policy while executing that rollback path.

Learn more about Deep Research in our documentation.

Run with Docker

Step 1 - Install Docker

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.

To enable tracing:

1. Set the following environment variables:

   export LANGCHAIN_TRACING_V2=true
   export LANGCHAIN_API_KEY=your_api_key
   export LANGCHAIN_PROJECT="gpt-researcher"

2. Run your research tasks as usual. All LangGraph-based agent interactions will be automatically traced and visualized in your LangSmith dashboard.

🖥️ 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:

1. A lightweight static frontend served by FastAPI
2. 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.

✉️ Support / Contact us

• Community Discord
• Author Email: assaf.elovic@gmail.com

🛡 Disclaimer

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:

1. 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.
2. 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.
3. 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.

---

⬆️ Back to Top