以为 OpenAI 那种高门槛的 Deep Research 离普通人很远？LangChain 官方直接把底裤都给扒出来了。

LangChain 开源的 open_deep_research，说白了就是完全开源复刻了顶尖 AI 机构的“深度研报生成引擎”。基于 LangGraph 构建，采用了 Supervisor + Sub-Agent 的多智能体协作架构（MIT 协议）。不管你是接 OpenAI、Claude 还是挂 DeepSeek，几分钟就能在本地搭出一个能自主拆解课题、多线程联网搜资料、自动交叉验证并生成长篇研报的 AI 代理系统。

需求主动澄清：课题太模糊它不上杠，先提问把你需求搞明白再动手

任务并行拆解：主智能体切分子课题，派多个子 Agent 独立去抓资料
智能体开发者社区 - CSDN

上下文防暴击：子 Agent 自动清洗垃圾网页，只向上汇报高密度信息

模型自由切换：想省钱直接用开源大模型，不用被单一厂商绑定

GitHub：https://github.com/langchain-ai/open_deep_research…

langchain-ai/open_deep_research

Source: https://github.com/langchain-ai/open_deep_research

🔬 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:

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:

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):

cp .env.example .env

4. 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 :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

🚀 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)

• 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