gpt-researcher
An autonomous agent that conducts deep research using LLM providers
GraphCanon updated 1w · GitHub synced 1w · 41 views this month
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 PyPIHow it fits your stack(19)
Typed graph edges - alternatives, integrations, successors, and dependencies. Ranked by relationship type, not raw GitHub stars.
Alternative
Integrates
Related
Relationship graph
Optional deeper exploration of typed edges and category neighbours.
Similar tools
Same-category neighbours not already linked as typed edges.
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.
Source: README excerpt (regex_v1, Aug 8, 2026)
# export LANGCHAIN_API_KEY={Your LangChain API Key here}Source link
Source: README excerpt (regex_v1, Aug 8, 2026)
npx skills add assafelovic/gpt-researcherSource link
Source: README excerpt (regex_v1, Aug 8, 2026)
export OPENAI_API_KEY={Your OpenAI API Key here}Source link
Source: README excerpt (regex_v1, Aug 8, 2026)
1. Install Python 3.11 or later. [Guide](https://www.tutorialsteacher.com/python/install-python).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
-
Install Python 3.11 or later. Guide.
-
Clone the project and navigate to the directory:
git clone https://github.com/assafelovic/gpt-researcher.git cd gpt-researcher -
Set up API keys by exporting them or storing them in a
.envfile.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} -
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.