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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).

August 14, 2025: See our free coursehere (and course repohere) 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 theDeep 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 ourblog post.

July 16, 2025: Read more in ourblog and watch ourvideo for a quick overview.

1. Clone the repository and activate a virtual environment:

git clone https://github.com/langchain-ai/open_deep_research.gitcd open_deep_researchuv venvsource .venv/bin/activate# On Windows: .venv\Scripts\activate

2. Install dependencies:

uv sync# oruv 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):

cp .env.example .env

4. Launch agent with the LangGraph server locally:

# Install dependencies and start the LangGraph serveruvx --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 themessages input field and clickSubmit. Select different configuration in the "Manage Assistants" tab.

Open Deep Research supports a wide range of LLM providers via theinit_chat_model() API. It uses LLMs for a few different tasks. See the below model fields in theconfiguration.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 supportstructured outputs andtool calling.

Note: For OpenRouter: Followthis guide and for local models via Ollama seesetup instructions.

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

See the fields in theconfiguration.py for various other settings to customize the behavior of Open Deep Research.

Open Deep Research is configured for evaluation withDeep Research Bench. This benchmark has 100 PhD-level research tasks (50 English, 50 Chinese), crafted by domain experts across 22 fields (e.g., Science & Tech, Business & Finance) to mirror real-world deep-research needs. It has 2 evaluation metrics, but the leaderboard is based on the RACE score. This uses LLM-as-a-judge (Gemini) to evaluate research reports against a golden set of reports compiled by experts across a set of metrics.

Warning: Running across the 100 examples can cost ~$20-$100 depending on the model selection.

The dataset is available onLangSmith via this link. To kick off evaluation, run the following command:

# Run comprehensive evaluation on LangSmith datasetspython tests/run_evaluate.py

This will provide a link to a LangSmith experiment, which will have a nameYOUR_EXPERIMENT_NAME. Once this is done, extract the results to a JSONL file that can be submitted to the Deep Research Bench.

python tests/extract_langsmith_data.py --project-name"YOUR_EXPERIMENT_NAME" --model-name"you-model-name" --dataset-name"deep_research_bench"

This createstests/expt_results/deep_research_bench_model-name.jsonl with the required format. Move the generated JSONL file to a local clone of the Deep Research Bench repository and follow theirQuick Start guide for evaluation submission.

Follow thequickstart to start LangGraph server locally and test the agent out on LangGraph Studio.

You can easily deploy toLangGraph Platform.

Open Agent Platform (OAP) is a UI from which non-technical users can build and configure their own agents. OAP is great for allowing users to configure the Deep Researcher with different MCP tools and search APIs that are best suited to their needs and the problems that they want to solve.

We've deployed Open Deep Research to our public demo instance of OAP. All you need to do is add your API Keys, and you can test out the Deep Researcher for yourself! Try it outhere

You can also deploy your own instance of OAP, and make your own custom agents (like Deep Researcher) available on it to your users.

1. Deploy Open Agent Platform
2. Add Deep Researcher to OAP

Thesrc/legacy/ folder contains two earlier implementations that provide alternative approaches to automated research. They are less performant than the current implementation, but provide alternative ideas understanding the different approaches to deep research.

• Plan-and-Execute: Structured workflow with human-in-the-loop planning
• Sequential Processing: Creates sections one by one with reflection
• Interactive Control: Allows feedback and approval of report plans
• Quality Focused: Emphasizes accuracy through iterative refinement

• Supervisor-Researcher Architecture: Coordinated multi-agent system
• Parallel Processing: Multiple researchers work simultaneously
• Speed Optimized: Faster report generation through concurrency
• MCP Support: Extensive Model Context Protocol integration