chatchat-space/Langchain-Chatchat
Langchain-Chatchat
Langchain-Chatchat（原Langchain-ChatGLM）基于 Langchain 与 ChatGLM, Qwen 与 Llama 等语言模型的 RAG 与 Agent 应用 | Langchain-Chatchat (formerly langchain-ChatGLM), local knowledge based LLM (like ChatGLM, Qwen and Llama) RAG and Agent app with langchain
Usage guide
Langchain-Chatchat is an open-source project around chatbot, chatchat, chatglm with 38,227 GitHub stars. This guide focuses on when to use it, how to install it, how to run the first example, and what to verify before adopting it.
Key features
- Langchain-Chatchat（原Langchain-ChatGLM）基于 Langchain 与 ChatGLM, Qwen 与 Llama 等语言模型的 RAG 与 Agent 应用 Langchain-Chatchat (formerly langchain-ChatGLM), local knowledge based LLM (like ChatGLM, Qwen and Llama) RAG and Agent app with langchain
- Implemented mainly in Python, useful for judging integration effort in a similar stack.
- GitHub detected the Apache-2.0 repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
- GitHub is the main evaluation surface; review the README, issues, and recent commits first.
Best for
- Evaluating Langchain-Chatchat for Python AI workflows.
- Comparing a GitHub project with 38,227 stars and current repository activity.
Pros
- Langchain-Chatchat has visible GitHub traction with 38,227 stars. Topics: chatbot, chatchat, chatglm.
- The GitHub repository is the primary evaluation surface.
Cons
- Production fit still depends on documentation depth, issue activity, and release cadence.
- License review should confirm the Apache-2.0 terms fit your use case.
Production readiness
Langchain-Chatchat should be validated with its README, release history, open issues, and integration requirements before production use.
License risk
Apache-2.0 is reported by GitHub; review the repository license before redistribution or commercial use.
Langchain-Chatchat architecture preview
Langchain-Chatchat's main path starts at the entry surface, runs through Agent orchestration runtime, combines OpenAI / Ollama / Llama / Qwen, Vector index / Milvus, GitHub, and returns Grounded answers / search results.
Entry
API / SDK entry
External applications call the project through API, SDK, or server entry points.
API / SDK
Runtime
Agent orchestration runtime
The orchestration layer plans tasks, calls tools, manages context, and decides the next action.
agent workflow
Model
OpenAI / Ollama / Llama / Qwen
Model calls are likely routed through OpenAI, Ollama, Llama, Qwen based on README and topic signals.
OpenAI, Ollama, Llama, Qwen
Context
Vector index / Milvus
Context comes from Vector index, Milvus, which constrains what the model or runtime can use.
Vector index, Milvus
Tools
GitHub
Tool adapters let the runtime act outside the model through GitHub.
GitHub
Output
Grounded answers / search results
The final result is an answer or ranked result grounded in retrieved context.
answer output
Featured video
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chatchat-space/Langchain-Chatchat - Gource visualisation
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Install tutorial
Before you install
- Python runtime and an isolated virtual environment
- A clean working directory for the first test run
Check the runtime environment
Langchain-Chatchat depends on a Python-style environment. Use venv, conda, or a container to keep dependencies isolated.
Get the project files
Start from the official repository or package so the first run matches the documented behavior.
$ git clone https://github.com/chatchat-space/Langchain-Chatchat.gitInstall or build dependencies
No extra setup command was detected. Check the README before adding custom configuration.
Adoption guidance and sources
Practical use cases
Knowledge-base assistant
Use it for document-grounded AI workflows where retrieval quality matters.
Langchain-Chatchat（原Langchain-ChatGLM）基于 Langchain 与 ChatGLM, Qwen 与 L
This is one of the documented reasons to evaluate Langchain-Chatchat before choosing a stack.
Focus area: chatbot
This is one of the documented reasons to evaluate Langchain-Chatchat before choosing a stack.
RAG project comparison
Compare Langchain-Chatchat with similar projects before committing to a stack.
Before adopting
- Complete one clean-environment verification using the official Langchain-Chatchat setup path.
- Review repository license, model weights, external services, and dependency terms for your use case.
- Check recent commits, release cadence, issue response, and documentation depth.
- Evaluate output quality, latency, resource usage, and recovery behavior with a small dataset.
Configuration notes
- Review README configuration notes before using production data.
Sources checked
These links are used to verify repository, documentation, or tutorial details. Review the source pages before adopting the project.
Troubleshooting
- If installation fails, first confirm the command is being run from the README-specified directory.
- If dependencies conflict, retry in a fresh virtual environment, container, or working directory.
- If output looks wrong, return to the smallest documented Langchain-Chatchat example before adding complex data.
- For keys, model files, or external services, verify environment variables, local paths, and permissions one by one.
- Before production use, review recent updates, open issues, license terms, and safety boundaries.
What is Langchain-Chatchat?
Langchain-Chatchat is an open-source rag project. Langchain-Chatchat（原Langchain-ChatGLM）基于 Langchain 与 ChatGLM, Qwen 与 Llama 等语言模型的 RAG 与 Agent 应用 | Langchain-Chatchat (formerly langchain-ChatGLM), local knowledge based LLM (like ChatGLM, Qwen and Llama) RAG and Agent app with langchain
How do I install Langchain-Chatchat?
Start with the official README. The first detected setup step is: git clone https://github.com/chatchat-space/Langchain-Chatchat.git.
Is Langchain-Chatchat beginner-friendly?
If you already know the Python ecosystem, start with the smallest example. Otherwise test it in an isolated environment first.
Can Langchain-Chatchat be used commercially?
GitHub detected the Apache-2.0 repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
Does Langchain-Chatchat need a GPU?
GPU requirements depend on the workload, model, and dataset size. Start with the smallest README example before scaling up.
How should I decide whether to adopt Langchain-Chatchat?
Evaluate setup cost, maintenance activity, issue health, license terms, and fit with your real workflow.