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 |
|---|---|
| 2026-08-04 | 803 |
| 2026-08-06 | 803 |
| 2026-08-08 | 804 |
| 2026-08-11 | 803 |
| 2026-08-12 | 803 |
| 2026-08-14 | 802 |
| 2026-08-15 | 803 |
| 2026-08-17 | 805 |
| 2026-08-18 | 805 |
| 2026-08-22 | 806 |
| 2026-08-25 | 809 |
| 2026-08-26 | 810 |
| 2026-08-27 | 811 |
| 2026-08-29 | 812 |
| 2026-08-30 | 813 |
| 2026-08-31 | 818 |
| 2026-09-01 | 831 |
| 2026-09-02 | 835 |
| 2026-09-03 | 836 |
| 2026-09-04 | 837 |
| 2026-09-06 | 836 |
| 2026-09-07 | 836 |
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| 2026-09-12 | 837 |
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| 2026-09-19 | 838 |
| 2026-09-20 | 839 |
| 2026-09-21 | 840 |
| 2026-09-22 | 841 |
| 2026-09-23 | 840 |
| 2026-09-24 | 841 |
| 2026-09-25 | 842 |
| 2026-09-26 | 843 |
| 2026-09-27 | 842 |
| 2026-09-29 | 842 |
| 2026-09-30 | 843 |
| 2026-10-02 | 844 |
| 2026-10-04 | 845 |
| 2026-10-06 | 846 |
| 2026-10-09 | 847 |
| 2026-10-12 | 846 |
| 2026-10-14 | 847 |
| 2026-10-16 | 848 |
| 2026-10-18 | 847 |
| 2026-10-19 | 847 |
| 2026-10-21 | 849 |
| 2026-10-23 | 850 |
| 2026-10-24 | 851 |
| 2026-10-29 | 853 |
| 2026-10-31 | 855 |
| 2026-11-01 | 855 |
| 2026-11-03 | 855 |
| 2026-11-04 | 855 |
Today
— stars today
This week
+4 stars this week
This month
+10 stars this month
Momentum
0.0
growth rate 0.47%/day
# 🧱 Deep Agents from Scratch <img width="720" height="289" alt="Screenshot 2025-08-12 at 2 13 54 PM" src="https://github.com/user-attachments/assets/90e5a7a3-7e88-4cbe-98f6-5b2581c94036" /> [Deep Research](https://academy.langchain.com/courses/deep-research-with-langgraph) broke out as one of the first major agent use-cases along with coding. Now, we've seeing an emergence of general purpose agents that can be used for a wide range of tasks. For example, [Manus](https://manus.im/blog/Context-Engineering-for-AI-Agents-Lessons-from-Building-Manus) has gained significant attention and popularity for long-horizon tasks; the average Manus task uses ~50 tool calls!. As a second example, Claude Code is being used generally for tasks beyond coding. Careful review of the [context engineering patterns](https://docs.google.com/presentation/d/16aaXLu40GugY-kOpqDU4e-S0hD1FmHcNyF0rRRnb1OU/edit?slide=id.p#slide=id.p) across these popular "deep" agents shows some common approaches: * **Task planning (e.g., TODO), often with recitation** * **Context offloading to file systems** * **Context isolation through sub-agent delegation** This course will show how to implement these patterns from scratch using LangGraph! ## 🚀 Quickstart ### Prerequisites - 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-agents-from-scratch.git cd deep-agents-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 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-agents-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 ``` ## 📚 Tutorial Overview This repository contains five progressive notebooks that teach you to build advanced AI agents: ### `0_create_agent.ipynb` - Learn how to use the create_agent component. This component, - implements a ReAct (Reason - Act) loop that forms the foundation for many agents. - is easy to use and quick to set up. - serves as the basis for the following lessons. ### `1_todo.ipynb` - Task Planning Foundations Learn to implement structured task planning using TODO lists. This notebook introduces: - Task tracking with status management (pending/in_progress/completed) - Progress monitoring and context management - The `write_todos()` tool for organizing complex multi-step workflows - Best practices for maintaining focus and preventing task drift ### `2_files.ipynb` - Virtual File Systems Implement a virtual file system stored in agent state for context offloading: - File operations: `ls()`, `read_file()`, `write_file()`, `edit_file()` - Context management through information persistence - Enabling agent "memory" across conversation turns - Reducing token usage by storing detailed information in files ### `3_subagents.ipynb` - Context Isolation Master sub-agent delegation for handling complex workflows: - Creating specialized sub-agents with focused tool sets - Context isolation to prevent confusion and task interference - The `task()` delegation tool and agent regi
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Lance Martin
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DaveB · United States
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Harrison Chase
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:1102c83f2a5223e6, llm:Repository name 'deep-agents-from-scratch' under langchain-ai suggests educational materials/tutorials for building deep learning agents from scratch. Likely focuses on reinforcement learning agents or agent architectures using deep learning. No topics/readme provided, language Jupyter Notebook indicates notebooks with tutorials/learning resources.
matched fp:1102c83f2a5223e6, llm:Repository name 'deep-agents-from-scratch' under langchain-ai suggests educational materials/tutorials for building deep learning agents from scratch. Likely focuses on reinforcement learning agents or agent architectures using deep learning. No topics/readme provided, language Jupyter Notebook indicates notebooks with tutorials/learning resources.
matched fp:1102c83f2a5223e6, llm:Repository name 'deep-agents-from-scratch' under langchain-ai suggests educational materials/tutorials for building deep learning agents from scratch. Likely focuses on reinforcement learning agents or agent architectures using deep learning. No topics/readme provided, language Jupyter Notebook indicates notebooks with tutorials/learning resources.
