README

🔬 Open Deep Research



Deep research has broken out as one of the most popular agent applications. This is a simple, configurable, fully open source deep research agent that works across many model providers, search tools, and MCP servers. It's performance is on par with many popular deep research agents (see Deep Research Bench leaderboard).



 🔥 Recent Updates

August 14, 2025: See our free course here (and course repo here) on building open deep research.

August 7, 2025: Added GPT-5 and updated the Deep Research Bench evaluation w/ GPT-5 results.

August 2, 2025: Achieved 6 ranking on the Deep Research Bench Leaderboard with an overall score of 0.4344. 

July 30, 2025: Read about the evolution from our original implementations to the current version in our blog post.

July 16, 2025: Read more in our blog and watch our video for a quick overview.

 🚀 Quickstart

1. Clone the repository and activate a virtual environment:
bash
git clone https://github.com/langchain-ai/open_deep_research.git
cd open_deep_research
uv venv
source .venv/bin/activate   On Windows: .venv\Scripts\activate


2. Install dependencies:
bash
uv sync
 or
uv pip install -r pyproject.toml


3. Set up your .env file to customize the environment variables (for model selection, search tools, and other configuration settings):
bash
cp .env.example .env


4. Launch agent with the LangGraph server locally:

bash
 Install dependencies and start the LangGraph server
uvx --refresh --from "langgraph-cli[inmem]" --with-editable . --python 3.11 langgraph dev --allow-blocking


This will open the LangGraph Studio UI in your browser.


- 🚀 API: http://127.0.0.1:2024
- 🎨 Studio UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024
- 📚 API Docs: http://127.0.0.1:2024/docs


Ask a question in the messages input field and click Submit. Select different configuration in the "Manage Assistants" tab.

 ⚙️ Configurations

 LLM :brain:

Open Deep Research supports a wide range of LLM providers via the init_chat_model() API. It uses LLMs for a few different tasks. See the below model fields in the configuration.py file for more details. This can be accessed via the LangGraph Studio UI. 

- Summarization (default: openai:gpt-4.1-mini): Summarizes search API results
- Research (default: openai:gpt-4.1): Power the search agent
- Compression (default: openai:gpt-4.1): Compresses research findings
- Final Report Model (default: openai:gpt-4.1): Write the final report

 Note: the selected model will need to support structured outputs and tool calling.

 Note: For OpenRouter: Follow this guide and for local models via Ollama  see setup instructions.

 Search API :mag:

Open Deep Research supports a wide range of search tools. By default it uses the Tavily search API. Has full MCP compatibility and work native web search for Anthropic and OpenAI. See the search_api and mcp_config fields in the configuration.py file for more details. This can be accessed via the LangGraph Studio UI. 

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