🔬 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

Clone the repository and activate a virtual environment:

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

Install dependencies:

uv sync
# or
uv pip install -r pyproject.toml

Set up your .env file to customize the environment variables (for model selection, search tools, and other configuration settings):
```
cp .env.example .env
```
Launch agent with the LangGraph server locally:

# 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 🔍

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.

Other

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

📊 Evaluation

Open Deep Research is configured for evaluation with Deep 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.

Usage

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

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

# Run comprehensive evaluation on LangSmith datasets
python tests/run_evaluate.py

This will provide a link to a LangSmith experiment, which will have a name YOUR_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 creates tests/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 their Quick Start guide for evaluation submission.

Results

Name	Commit	Summarization	Research	Compression	Total Cost	Total Tokens	RACE Score	Experiment
GPT-5	ca3951d	openai:gpt-4.1-mini	openai:gpt-5	openai:gpt-4.1		204,640,896	0.4943	Link
Defaults	6532a41	openai:gpt-4.1-mini	openai:gpt-4.1	openai:gpt-4.1	$45.98	58,015,332	0.4309	Link
Claude Sonnet 4	f877ea9	openai:gpt-4.1-mini	anthropic:claude-sonnet-4-20250514	openai:gpt-4.1	$187.09	138,917,050	0.4401	Link
Deep Research Bench Submission	c0a160b	openai:gpt-4.1-nano	openai:gpt-4.1	openai:gpt-4.1	$87.83	207,005,549	0.4344	Link

🚀 Deployments and Usage

LangGraph Studio

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

Hosted deployment

You can easily deploy to LangGraph Platform.

Open Agent 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 out here

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

Legacy Implementations 🏛️

The src/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.

1. Workflow Implementation (`legacy/graph.py`)

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

2. Multi-Agent Implementation (`legacy/multi_agent.py`)

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