Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
| Date | Stars |
|---|---|
| 2026-07-31 | 12473 |
| 2026-08-01 | 12478 |
| 2026-08-02 | 12481 |
| 2026-08-03 | 12492 |
| 2026-08-04 | 12503 |
| 2026-08-05 | 12514 |
| 2026-08-06 | 12517 |
Today
+3 stars today
This week
— stars this week
This month
— stars this month
Momentum
12.0
growth rate 0.00%/day
# 🔬 Open Deep Research <img width="1388" height="298" alt="full_diagram" src="https://github.com/user-attachments/assets/12a2371b-8be2-4219-9b48-90503eb43c69" /> 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](https://huggingface.co/spaces/Ayanami0730/DeepResearch-Leaderboard)). <img width="817" height="666" alt="Screenshot 2025-07-13 at 11 21 12 PM" src="https://github.com/user-attachments/assets/052f2ed3-c664-4a4f-8ec2-074349dcaa3f" /> ### 🔥 Recent Updates **August 14, 2025**: See our free course [here](https://academy.langchain.com/courses/deep-research-with-langgraph) (and course repo [here](https://github.com/langchain-ai/deep_research_from_scratch)) 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](https://huggingface.co/spaces/Ayanami0730/DeepResearch-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](https://rlancemartin.github.io/2025/07/30/bitter_lesson/). **July 16, 2025**: Read more in our [blog](https://blog.langchain.com/open-deep-research/) and watch our [video](https://www.youtube.com/watch?v=agGiWUpxkhg) 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](https://python.langchain.com/docs/how_to/chat_models_universal_init/). It uses LLMs for a few different tasks. See the below model fields in the [configuration.py](https://github.com/langchain-ai/open_deep_research/blob/main/src/open_deep_research/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](https://python.langchain.com/docs/integrations/chat/) and [tool calling](https://python.langchain.com/docs/how_to/tool_calling/). > Note: For OpenRouter: Follow [this guide](https://github.com/langchain-ai/open_deep_research/issues/75#issuecomment-2811472408) and for local models via Ollama see [setup instructions](https://github.com/langchain-ai/open_deep_research/issues/65#issuecomment-2743586318). #### Search API :mag: Open Deep Research s
Excerpt of 10,233 characters
Read on GitHub
Lance Martin
132
24
17
9
Angel Bartolli
5
5
Vadym Barda · United States
3
2
ccurme
2
John Kennedy · @langchain-ai
2
2
Ykoh · 42 SEOUL · South Korea
2
2
Denis Charrier · France
2
1
1
1
clayz
1
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f0c12117e21d7fd9, llm:Repository name 'open_deep_research' under org 'langchain-ai'; no topics, description, or README provided. Name suggests tooling or resources around deep learning research, likely related to LangChain ecosystem for AI research.
matched fp:f0c12117e21d7fd9, llm:Repository name 'open_deep_research' under org 'langchain-ai'; no topics, description, or README provided. Name suggests tooling or resources around deep learning research, likely related to LangChain ecosystem for AI research.
matched fp:f0c12117e21d7fd9, llm:Repository name 'open_deep_research' under org 'langchain-ai'; no topics, description, or README provided. Name suggests tooling or resources around deep learning research, likely related to LangChain ecosystem for AI research.
