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gpt-researcher

assafelovic/gpt-researcher

An autonomous agent that conducts deep research using LLM providers

GraphCanon updated 1w · GitHub synced 1w · 41 views this month

29k stars3.9k forksLast push 1mo Python Apache-2.0

Decision brief

gpt-researcher is an autonomous agent that uses Language Model Providers to conduct deep research automatically, supporting various installation methods including Docker and deployment as a Claude Skill.

Good fit when

  • - When you require automated in-depth research capabilities across diverse LLM providers like OpenAI and Tavily.
  • - For environments where Python 3.11+ is available and flexible API key configuration supports multiple data sources or custom APIs, enhancing adaptability.

Avoid when

  • - If your setup strictly adheres to a specific tool that does not support the extension of capabilities through skills like Claude Skills.
  • - In scenarios with stringent network restrictions where running an autonomous agent on top of various LLM providers is prohibited or poses security risks.
Pricing:
freemium - The core functionality of gpt-researcher under Apache-2.0 license is free to use, however, users need API keys from external Language Model Providers like OpenAI and Tavily, which are subject to their

Observed Jul 11, 2026 · Source: enrich:decision_facts

Verify the decision

Maintenance and security

Full trust report
Maintenance
Active (21d since push)
As of 1w
Provenance
Not a fork · Personal account
As of 1w
Security (OSV)
63 low (63 low)
As of 1mo

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

pip install gpt-researcher
PyPI

How it fits your stack(19)

Typed graph edges - alternatives, integrations, successors, and dependencies. Ranked by relationship type, not raw GitHub stars.

Alternative

Integrates

Relationship graph

Optional deeper exploration of typed edges and category neighbours.

Similar tools

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Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

Assafelovic/gpt-researcher is a Python-based project that provides an automated research tool leveraging Language Model Providers for in-depth analysis and data retrieval.

Capability facts

Deploy
Self-host

Source: dockerfile:Dockerfile · Aug 8, 2026

Docker
Dockerfile present

Source: dockerfile:Dockerfile · Aug 8, 2026

Languages
python

Source: github.language+pyproject.toml · Aug 8, 2026

Categories

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

LangChain integrationLangChain

Source: README excerpt (regex_v1, Aug 8, 2026)

# export LANGCHAIN_API_KEY={Your LangChain API Key here}
Source link
Node.js runtimeNode.js

Source: README excerpt (regex_v1, Aug 8, 2026)

npx skills add assafelovic/gpt-researcher
Source link
OpenAI APIOpenAI API

Source: README excerpt (regex_v1, Aug 8, 2026)

export OPENAI_API_KEY={Your OpenAI API Key here}
Source link

Tags

README

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.


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 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!

For agents

This page has a .md twin and JSON over the API.

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