open_deep_research README

The project's README from github.com/langchain-ai/open_deep_research. As one markdown file: README.md.

🔬 Open Deep Research

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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).

Screenshot 2025-07-13 at 11 21 12 PM

🔥 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:
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
  1. Install dependencies:
uv sync
# or
uv pip install -r pyproject.toml
  1. Set up your .env file to customize the environment variables (for model selection, search tools, and other configuration settings):
cp .env.example .env
  1. 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.

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.

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.

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

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)

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

open_deep_research (langchain-ai/open_deep_research)

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12.7K ·GitHub
Open_deep_research
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What is this?Open Deep Research is a simple, configurable, fully open-source deep research agent that can research a topic across the web and MCP servers and write a report, working with many different LLM providers and search tools.
Is this a ready-made app or an agent framework I run myself?It is a LangGraph-based agent you run yourself: locally via the LangGraph server with the LangGraph Studio UI, or deployed to LangGraph Platform; a no-code UI called Open Agent Platform also lets non-technical users configure it.
How do I get it running?Clone the repo, create and activate a virtual environment, install dependencies, set up a .env file with your model/search keys, then launch the LangGraph server locally to open LangGraph Studio in your browser.
Which LLM providers and search tools does it need?It uses init_chat_model() to support many LLM providers (defaults are openai:gpt-4.1 / gpt-4.1-mini), and by default uses the Tavily search API, with full MCP compatibility and native web search for Anthropic and OpenAI.
Is this project still actively maintained?No — the repository is archived on GitHub, so while the code and legacy implementations remain usable, no further active development should be expected.
Who is this built for?Developers who want to build or customize a deep-research agent, and via Open Agent Platform, non-technical users who just want to configure and use one.
How good is it compared to other deep research agents?It ranked #6 on the Deep Research Bench Leaderboard with a RACE score of 0.4344, and with GPT-5 for the research step it scores 0.4943.
How much does running the benchmark cost?Running the full 100-example Deep Research Bench evaluation can cost roughly $20-$100 depending on which models are selected.
What are the legacy implementations in the repo?src/legacy/ contains two earlier approaches: a plan-and-execute workflow with human-in-the-loop section review, and a supervisor-researcher multi-agent system optimized for parallel speed.
Can I deploy it beyond my own machine?Yes — besides running it locally with LangGraph Studio, you can deploy it to LangGraph Platform or make it available through a self-hosted Open Agent Platform instance.
What are similar projects?ruflo, Raven, open-multi-agent, turnstone, rrsi, PocketFlow, ChatDev, trueforge
Which topics is it in?deep-research, web-search, llm-agents, langchain, agent-framework, autonomous-agents, evaluation, orchestration, model-context-protocol, agentic-ai, anthropic, multi-agent
Creator
langchain-ai
Size
6.6 MB
Stars
12.7K
Updated
CommitAug 10
Age
1y
Commits · 90 Days