--- title: "Langchain-Chatchat vs rags" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/chatchat-space-langchain-chatchat-vs-run-llama-rags" tools: ["chatchat-space-langchain-chatchat", "run-llama-rags"] --- # Langchain-Chatchat vs rags Neutral, constraint-first comparison with live GitHub stats. | | [Langchain-Chatchat](/tools/chatchat-space-langchain-chatchat.md) | [rags](/tools/run-llama-rags.md) | | --- | --- | --- | | Tagline | Local knowledge-based LLM RAG and Agent app | Build ChatGPT over your data using natural language with RAGs | | Stars | 38,268 | 6,546 | | Forks | 6,218 | 660 | | Open issues | 23 | 38 | | Language | Python | Python | | Adopt for | Langchain-Chatchat is an open-source, local application framework designed for RAG and AI agent applications using a variety of language models such as ChatGLM, Qwen, and Llama. It supports multiple deployment methods,离线 | RAGs is a Python-based Streamlit app designed to build Retriever-Augmented Generation pipelines using natural language instructions and configurations. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Evaluation & Observability, LLM Frameworks | Data & Retrieval, AI Agents | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Langchain-Chatchat](/tools/chatchat-space-langchain-chatchat.md) | [rags](/tools/run-llama-rags.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 239d | 824d | | Open issues (now) | 23 | 38 | | Security scan | No lockfile | Not scanned | | Full report | [trust report](/tools/chatchat-space-langchain-chatchat/trust.md) | [trust report](/tools/run-llama-rags/trust.md) | **Typed relationship:** Langchain-Chatchat _(alternative)_ rags Both RAGs and LangChain-Chatchat aim to provide local knowledge-based LLM RAG and agent app functionality, yet they approach the problem with different configurations and user experiences. ## Decision facts: Langchain-Chatchat - **Adopt for:** Langchain-Chatchat is an open-source, local application framework designed for RAG and AI agent applications using a variety of language models such as ChatGLM, Qwen, and Llama. It supports multiple deployment methods,离线 ## Decision facts: rags - **Pricing:** freemium - RAGs is open-source under MIT license. Costs arise from any third-party API usage such as OpenAI and are not covered by RAGs itself. - **Requirements:** Min 4 GB RAM; RAGs requires an internet connection to interact with external APIs like OpenAI.; Ensure you configure your environment with the necessary API keys and secrets as per the installation guide. - **Adopt for:** RAGs is a Python-based Streamlit app designed to build Retriever-Augmented Generation pipelines using natural language instructions and configurations. ## Choose when ### Choose Langchain-Chatchat if… - License: Langchain-Chatchat is Apache-2.0, rags is MIT. - Both RAGs and LangChain-Chatchat aim to provide local knowledge-based LLM RAG and agent app functionality, yet they approach the problem with different configurations and user experiences. - Tags unique to Langchain-Chatchat: chatchat, chatglm, embedding, gpt. - Also covers Evaluation & Observability, LLM Frameworks. - - Use Langchain-Chatchat when you need to deploy a knowledge-based chatbot that leverages local, open-source models. ### Choose rags if… - License: rags is MIT, Langchain-Chatchat is Apache-2.0. - Pricing: RAGs is open-source under MIT license. Costs arise from any third-party API usage such as OpenAI and are not covered by RAGs itself.. - Requirements: Min 4 GB RAM; RAGs requires an internet connection to interact with external APIs like OpenAI.; Ensure you configure your environment with the necessary API keys and secrets as per the installation guide.. - Both RAGs and LangChain-Chatchat aim to provide local knowledge-based LLM RAG and agent app functionality, yet they approach the problem with different configurations and user experiences. - Tags unique to rags: llm, streamlit, rag, agent. - Also covers Data & Retrieval, AI Agents. - Use RAGs if you want an interactive way to configure and query your data with simple textual instructions through an intuitive UI in a Streamlit app. ## When NOT to use Langchain-Chatchat - - Do not use Langchain-Chatchat when your application requires direct support for the latest commercial language models which are only accessible via API without open source. - - Refrain from using this tool if you need continuous real-time data updates or depend on cloud-based services that require internet connectivity. ## When NOT to use rags - Avoid RAGs if you need full customization of the backend logic and don't want the constraints imposed by the Streamlit interface. - Not recommended for environments with strict security policies that forbid the use of external APIs like OpenAI, unless you have the capability to replace those services. ## Common questions ### What is the difference between Langchain-Chatchat and rags? Langchain-Chatchat: Local knowledge-based LLM RAG and Agent app. rags: Build ChatGPT over your data using natural language with RAGs. See the comparison table for live GitHub stats and shared categories. ### When should I choose Langchain-Chatchat over rags? Choose Langchain-Chatchat over rags when License: Langchain-Chatchat is Apache-2.0, rags is MIT; Both RAGs and LangChain-Chatchat aim to provide local knowledge-based LLM RAG and agent app functionality, yet they approach the problem with different configurations and user experiences; Tags unique to Langchain-Chatchat: chatchat, chatglm, embedding, gpt; Also covers Evaluation & Observability, LLM Frameworks; - Use Langchain-Chatchat when you need to deploy a knowledge-based chatbot that leverages local, open-source models. ### When should I choose rags over Langchain-Chatchat? Choose rags over Langchain-Chatchat when License: rags is MIT, Langchain-Chatchat is Apache-2.0; Pricing: RAGs is open-source under MIT license. Costs arise from any third-party API usage such as OpenAI and are not covered by RAGs itself.; Requirements: Min 4 GB RAM; RAGs requires an internet connection to interact with external APIs like OpenAI.; Ensure you configure your environment with the necessary API keys and secrets as per the installation guide.; Both RAGs and LangChain-Chatchat aim to provide local knowledge-based LLM RAG and agent app functionality, yet they approach the problem with different configurations and user experiences; Tags unique to rags: llm, streamlit, rag, agent; Also covers Data & Retrieval, AI Agents; Use RAGs if you want an interactive way to configure and query your data with simple textual instructions through an intuitive UI in a Streamlit app. ### When should I avoid Langchain-Chatchat? - Do not use Langchain-Chatchat when your application requires direct support for the latest commercial language models which are only accessible via API without open source. - Refrain from using this tool if you need continuous real-time data updates or depend on cloud-based services that require internet connectivity. ### When should I avoid rags? Avoid RAGs if you need full customization of the backend logic and don't want the constraints imposed by the Streamlit interface. Not recommended for environments with strict security policies that forbid the use of external APIs like OpenAI, unless you have the capability to replace those services. ### Is Langchain-Chatchat or rags more popular on GitHub? Langchain-Chatchat has more GitHub stars (38,268 vs 6,546). Stars measure visibility, not whether either tool fits your constraints. ### Are Langchain-Chatchat and rags open source? Yes - both are open-source projects on GitHub (Langchain-Chatchat: Apache-2.0, rags: MIT). ### Where can I find alternatives to Langchain-Chatchat or rags? GraphCanon lists graph-backed alternatives at /tools/chatchat-space-langchain-chatchat/alternatives and /tools/run-llama-rags/alternatives (/tools/chatchat-space-langchain-chatchat/alternatives.md, /tools/run-llama-rags/alternatives.md), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at /compare/chatchat-space-langchain-chatchat-vs-run-llama-rags.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Langchain-Chatchat or rags? Langchain-Chatchat: Slowing. rags: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for Langchain-Chatchat and rags? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Langchain-Chatchat: /tools/chatchat-space-langchain-chatchat/trust; rags: /tools/run-llama-rags/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=chatchat-space-langchain-chatchat`](/api/graphcanon/graph?tool=chatchat-space-langchain-chatchat) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_