Is Langchain-Chatchat free to use?

Yes, it is free and open-source under the Apache-2.0 license. The only costs are your own infrastructure, such as GPUs for self-hosting, or optional cloud LLM calls if you choose to wire them up.

Which local LLMs does it support?

It supports multi-model setups including GLM-4, Qwen2, and Llama 3. It connects to these models via Xinference, Ollama, LocalAI, FastChat, or One API, and uses BGE-class embedding models for vectorization.

Is it suitable for non-technical users?

No, it is aimed at developers and infrastructure teams. You must manage Python, CUDA dependencies, and your own vector store. It is an integration framework, not a polished, turnkey consumer product.

Can it run completely offline?

Yes, it is designed to run fully offline against local LLMs. This allows teams to stand up private knowledge-base chatbots without piping documents through third-party services, making it ideal for air-gapped environments.

What agent capabilities does it offer?

Version 0.3.x added an agent layer with tools for SQL chat, arXiv lookup, Wolfram, and text-to-image. It functions as a scaffold for building local LLM agents alongside standard RAG retrieval.

What are the main drawbacks?

Documentation is Chinese-first, and release cadence has slowed. It requires reading code and managing dependencies. It is best treated as a strong scaffolding starter rather than a turnkey enterprise RAG appliance.

Langchain-Chatchat

Self-hostable RAG and agent framework that wires LangChain to any local open-source LLM and a knowledge base.

Free· Apache-2.0 open source; self-hosted, infra costs onlyRAGMulti-model (GLM-4, Qwen2, Llama 3, etc. via Xinference/Ollama/LocalAI/FastChat)7.4 / 10

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In short

Langchain-Chatchat is a free, open-source RAG framework for running local LLMs like Llama 3 or Qwen2 against private knowledge bases.

Best for

Pick Langchain-Chatchat if you need an open-source, on-prem RAG and agent scaffold that can drive local Qwen, GLM or Llama models against a private knowledge base.

Skip if

Skip it if you want a hosted, turnkey RAG product or a polished consumer chatbot without managing Python, GPUs and a vector store yourself.

Langchain-Chatchat is an open-source RAG and agent application platform built on top of LangChain, designed to run fully offline against local LLMs. It bundles document ingestion, vectorization, retrieval, a FastAPI service and a Streamlit web UI so a team can stand up a private knowledge-base chatbot without piping documents through a third party. Out of the box it speaks to Xinference, Ollama, LocalAI, FastChat and One API, and works with GLM-4, Qwen2, Llama 3 and other open-weight models, plus BGE-class embedding models.

It is squarely aimed at developers and infrastructure teams who want a Chinese-and-English RAG stack they can air-gap on their own GPUs, not at end users buying a hosted SaaS. The project is Apache-2.0 and free; the only cost is your own compute (and any optional cloud LLM calls if you wire those up). With ~38k GitHub stars it is one of the most popular Chinese-language LangChain wrappers, and v0.3.x added a meaningful agent layer with tools for SQL chat, arXiv lookup, Wolfram, and text-to-image.

Caveats: this is an integration framework rather than a polished product, so expect to read code, manage Python and CUDA dependencies, and pick your own vector DB (FAISS, Milvus and others are supported). Documentation skews Chinese-first, and release cadence has slowed compared to the project's peak, so treat it as a strong scaffolding starter rather than a turnkey enterprise RAG appliance.

Editor's take

A pragmatic LangChain wrapper that solved the 'private GPT over my own docs' problem early and still holds up as a reference architecture. We would use it as a starting template rather than a finished product, and we would budget time for the Python and CUDA plumbing before any of the agent magic appears.

— The AI Tool Bible editorial team

Pros

✅ Fully offline, self-hosted RAG stack with Apache-2.0 license
✅ Framework-agnostic: plugs into Xinference, Ollama, LocalAI, FastChat, One API
✅ Ships both Streamlit UI and FastAPI service with OpenAI-compatible endpoints
✅ Built-in agent tools (SQL chat, arXiv, Wolfram, text-to-image)
✅ Large community (~38k stars) and broad model coverage

Cons

⚠️ Dependency and GPU setup is non-trivial; not a one-click install
⚠️ Documentation is Chinese-first; English coverage lags
⚠️ Release cadence has slowed since the v0.3 peak
⚠️ You still pick and operate your own vector DB and model server

Use cases

private-knowledge-baseoffline-ragdocument-qalocal-llm-agentsenterprise-chatbot

Frequently asked

Is Langchain-Chatchat free to use?: Yes, it is free and open-source under the Apache-2.0 license. The only costs are your own infrastructure, such as GPUs for self-hosting, or optional cloud LLM calls if you choose to wire them up.
Which local LLMs does it support?: It supports multi-model setups including GLM-4, Qwen2, and Llama 3. It connects to these models via Xinference, Ollama, LocalAI, FastChat, or One API, and uses BGE-class embedding models for vectorization.
Is it suitable for non-technical users?: No, it is aimed at developers and infrastructure teams. You must manage Python, CUDA dependencies, and your own vector store. It is an integration framework, not a polished, turnkey consumer product.
Can it run completely offline?: Yes, it is designed to run fully offline against local LLMs. This allows teams to stand up private knowledge-base chatbots without piping documents through third-party services, making it ideal for air-gapped environments.
What agent capabilities does it offer?: Version 0.3.x added an agent layer with tools for SQL chat, arXiv lookup, Wolfram, and text-to-image. It functions as a scaffold for building local LLM agents alongside standard RAG retrieval.
What are the main drawbacks?: Documentation is Chinese-first, and release cadence has slowed. It requires reading code and managing dependencies. It is best treated as a strong scaffolding starter rather than a turnkey enterprise RAG appliance.