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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 | 747 |
| 2026-08-01 | 748 |
| 2026-08-02 | 749 |
| 2026-08-03 | 750 |
| 2026-08-04 | 751 |
| 2026-08-05 | 752 |
| 2026-08-06 | 754 |
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# 🧱 Deep Research From Scratch Deep research has broken out as one of the most popular agent applications. [OpenAI](https://openai.com/index/introducing-deep-research/), [Anthropic](https://www.anthropic.com/engineering/built-multi-agent-research-system), [Perplexity](https://www.perplexity.ai/hub/blog/introducing-perplexity-deep-research), and [Google](https://gemini.google/overview/deep-research/?hl=en) all have deep research products that produce comprehensive reports using [various sources](https://www.anthropic.com/news/research) of context. There are also many [open](https://huggingface.co/blog/open-deep-research) [source](https://github.com/google-gemini/gemini-fullstack-langgraph-quickstart) implementations. We built an [open deep researcher](https://github.com/langchain-ai/open_deep_research) that is simple and configurable, allowing users to bring their own models, search tools, and MCP servers. In this repo, we'll build a deep researcher from scratch! Here is a map of the major pieces that we will build:  ## 🚀 Quickstart ### Prerequisites - **Node.js and npx** (required for MCP server in notebook 3): ```bash # Install Node.js (includes npx) # On macOS with Homebrew: brew install node # On Ubuntu/Debian: curl -fsSL https://deb.nodesource.com/setup_lts.x | sudo -E bash - sudo apt-get install -y nodejs # Verify installation: node --version npx --version ``` - Ensure you're using Python 3.11 or later. - This version is required for optimal compatibility with LangGraph. ```bash python3 --version ``` - [uv](https://docs.astral.sh/uv/) package manager ```bash curl -LsSf https://astral.sh/uv/install.sh | sh # Update PATH to use the new uv version export PATH="/Users/$USER/.local/bin:$PATH" ``` ### Installation 1. Clone the repository: ```bash git clone https://github.com/langchain-ai/deep_research_from_scratch cd deep_research_from_scratch ``` 2. Install the package and dependencies (this automatically creates and manages the virtual environment): ```bash uv sync ``` 3. Create a `.env` file in the project root with your API keys: ```bash # Create .env file touch .env ``` Add your API keys to the `.env` file: ```env # Required for research agents with external search TAVILY_API_KEY=your_tavily_api_key_here # Required for model usage OPENAI_API_KEY=your_openai_api_key_here ANTHROPIC_API_KEY=your_anthropic_api_key_here # Optional: For evaluation and tracing LANGSMITH_API_KEY=your_langsmith_api_key_here LANGSMITH_TRACING=true LANGSMITH_PROJECT=deep_research_from_scratch ``` 4. Run notebooks or code using uv: ```bash # Run Jupyter notebooks directly uv run jupyter notebook # Or activate the virtual environment if preferred source .venv/bin/activate # On Windows: .venv\Scripts\activate jupyter notebook ``` ## Background Research is an open‑ended task; the best strategy to answer a user request can’t be easily known in advance. Requests can require different research strategies and varying levels of search depth. Consider this request. [Agents](https://langchain-ai.github.io/langgraph/tutorials/workflows/#agent) are well suited to research because they can flexibly apply different strategies, using intermediate results to guide their exploration. Open deep research uses an agent to conduct research as part of a three step process: 1. **Scope** – clarify research scope 2. **Research** – perform research 3. **Write** – produce the final report ## 📝 Organization This repo contains 5 tutorial notebooks that build a deep research system from scratch: ### 📚 Tutorial Notebooks #### 1. User Clarification and Brief Generation (`notebooks/1_scoping.ipynb`) **Purpose**: Clarify research scope and transform user input into structured research briefs **Key Concepts**: - **User Clarification**: Determines if additional context is needed from the user using structured output - **Brief Generation**: Transforms conversati
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Lance Martin
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DaveB · United States
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:2290ee5cec3c141a, llm:Repository name 'deep_research_from_scratch' (langchain-ai organization). No README or topics provided; likely educational resource on deep learning research from scratch.
