RAG & Knowledge
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Graph RAG conversational knowledge base built on TiDB Serverless vector storage
★ 3.0k starsApache-2.0
View guide →Local knowledge-base RAG and agent app built on LangChain with ChatGLM, Qwen and Llama models
Langchain-Chatchat is an open-source rag & knowledge project by chatchat-space (chatchat-space/Langchain-Chatchat on GitHub). It has 38,676 GitHub stars and 6,257 forks, is released under the Apache-2.0 license, and was last updated on GitHub on 10 November 2025.
Decide what you want from Langchain-Chatchat and check the README and requirements to confirm it fits your setup and Python environment.
Clone the repository from GitHub (github.com/chatchat-space/Langchain-Chatchat) or use the documented install channel if you do not need the source.
Follow the README quickstart to install dependencies and set any required configuration or credentials.
Work through the README's first example end to end, then adapt it to your own project or data.
Chat over your own documents using local models like ChatGLM, Qwen and Llama.
Combines retrieval-augmented generation with agent capabilities in one app.
Supports deployment frameworks including Ollama, Xinference and FastChat.
Works with FAISS and Milvus for embedding storage.
Ships a web UI for chatting with the knowledge base.
Can be deployed with pip or Docker, per the quick start.
Start by getting the source and entering the project directory.
git clone https://github.com/chatchat-space/Langchain-Chatchat.git
cd Langchain-ChatchatInstall the package from PyPI.
pip install langchain-chatchat -UFollow the quick start to initialize the knowledge base and launch the app.
chatchat init # see README for the exact command sequenceIt is maintained by chatchat-space and contributors on GitHub, where the repository github.com/chatchat-space/Langchain-Chatchat is the canonical home for code, issues and releases.
Yes — it is open source under the Apache-2.0 license. Read the license in the repository for what is permitted, including any conditions on redistribution or commercial use.
No paid key needed when using local models — you run ChatGLM, Qwen or Llama yourself.
Compared with PrivateGPT: Both do private local RAG; Chatchat is a full LangChain application with UI, while PrivateGPT focuses on an API layer.
Clone github.com/chatchat-space/Langchain-Chatchat, follow the README quickstart to install it, and run the documented first example before adapting it to your own work.
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