ИИ-агенты
deepagents-in-action
Chinese guide to building production-grade AI agents with LangChain and LangGraph.
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Смотреть руководство →deepagents — проект с открытым исходным кодом из категории «ИИ-агенты» от langchain-ai (langchain-ai/deepagents на GitHub). У него 30 015 звёзд на GitHub и 4 212 форков, он распространяется по лицензии MIT и последний раз обновлялся на GitHub 8 октября 2026.
The batteries-included agent harness from the LangChain team.
Know what you want the agent to accomplish before touching any code.
Clone the repo and read the README's requirements section.
Install what's needed and add API keys or configuration.
Run the quickstart example, then iterate from there.
Agents that plan before they act.
Delegate to specialized subagents.
Read and write files out of the box.
Handles work that takes a while.
Plays well with LangGraph and friends.
Pick the language that fits your stack.
Download the source and move into the project folder.
git clone https://github.com/langchain-ai/deepagents.git
cd deepagentsIsolate dependencies, then install requirements.
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txtLaunch using the entry point shown in the README.
python -m deepagents # check README for the exact commandIt is an open-source project at langchain-ai/deepagents with 4212 forks. Check the contributors tab and recent commits to judge how active maintenance is.
Yes — it's MIT licensed: free to use, modify, and ship commercially. Just keep the license notice in copies.
LangGraph is the underlying orchestration; DeepAgents is the batteries-included harness on top — less assembly required.
Both are supported — pick the one matching your stack.
Clone the repo and follow the README quickstart — the docs at https://docs.langchain.com/deepagents walk through setup.
Chinese guide to building production-grade AI agents with LangChain and LangGraph.
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