--- title: "llama_index vs rags" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/run-llama-llama-index-vs-run-llama-rags" tools: ["run-llama-llama-index", "run-llama-rags"] --- # llama_index vs rags Neutral, constraint-first comparison with live GitHub stats. | | [llama_index](/tools/run-llama-llama-index.md) | [rags](/tools/run-llama-rags.md) | | --- | --- | --- | | Tagline | Document agent and OCR platform | Build ChatGPT over your data using natural language with RAGs | | Stars | 50,723 | 6,546 | | Forks | 7,711 | 660 | | Open issues | 494 | 38 | | Language | Python | Python | | Adopt for | LlamaIndex is an open-source framework that enables developers to build agentic applications, integrating with various LLMs, embeddings, and vector stores. | RAGs is a Python-based Streamlit app designed to build Retriever-Augmented Generation pipelines using natural language instructions and configurations. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | Vector Databases, AI Agents | AI Agents, Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llama_index](/tools/run-llama-llama-index.md) | [rags](/tools/run-llama-rags.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 5d | 824d | | Open issues (now) | 494 | 38 | | Security scan | No lockfile | 39 low (39 low) | | Full report | [trust report](/tools/run-llama-llama-index/trust.md) | [trust report](/tools/run-llama-rags/trust.md) | **Typed relationship:** llama_index _(successor)_ rags RAGs seems like a more specialized service for building ChatGPT-like applications over user-specific data, likely representing an evolution of LlamaIndex's capabilities. Coexists - While RAGs might be seen as an advanced or targeted implementation using concepts from LlamaIndex, both tools serve different audiences and use cases. ## Shared compatibility - **Python**: [llama_index](/tools/run-llama-llama-index.md) - Python runtime; [rags](/tools/run-llama-rags.md) - Python runtime ## Decision facts: llama_index - **Adopt for:** LlamaIndex is an open-source framework that enables developers to build agentic applications, integrating with various LLMs, embeddings, and vector stores. ## 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 llama_index if… - RAGs seems like a more specialized service for building ChatGPT-like applications over user-specific data, likely representing an evolution of LlamaIndex's capabilities. - Tags unique to llama_index: llamaindex, fine-tuning, agents, application. - Also covers Vector Databases. - When you require a flexible and extensive set of integrations for building agentic applications using different LLMs, embedding models, and vector storages. ### Choose rags if… - 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.. - RAGs seems like a more specialized service for building ChatGPT-like applications over user-specific data, likely representing an evolution of LlamaIndex's capabilities. - Tags unique to rags: streamlit, rag, chatbot, agent. - Also covers Data & Retrieval. - 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 llama_index - If your project does not require agentic application development or advanced document processing capabilities beyond basic OCR. - In scenarios where using an open-source framework with extensive integrations introduces unnecessary complexity, especially if you are already committed to a specific technology stack that does not co ## 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 llama_index and rags? llama_index: Document agent and OCR platform. 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 llama_index over rags? Choose llama_index over rags when RAGs seems like a more specialized service for building ChatGPT-like applications over user-specific data, likely representing an evolution of LlamaIndex's capabilities; Tags unique to llama_index: llamaindex, fine-tuning, agents, application; Also covers Vector Databases; When you require a flexible and extensive set of integrations for building agentic applications using different LLMs, embedding models, and vector storages. ### When should I choose rags over llama_index? Choose rags over llama_index when 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.; RAGs seems like a more specialized service for building ChatGPT-like applications over user-specific data, likely representing an evolution of LlamaIndex's capabilities; Tags unique to rags: streamlit, rag, chatbot, agent; Also covers Data & Retrieval; 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 llama_index? If your project does not require agentic application development or advanced document processing capabilities beyond basic OCR. In scenarios where using an open-source framework with extensive integrations introduces unnecessary complexity, especially if you are already committed to a specific technology stack that does not co ### 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 llama_index or rags more popular on GitHub? llama_index has more GitHub stars (50,723 vs 6,546). Stars measure visibility, not whether either tool fits your constraints. ### Are llama_index and rags open source? Yes - both are open-source projects on GitHub (llama_index: MIT, rags: MIT). ### Where can I find alternatives to llama_index or rags? GraphCanon lists graph-backed alternatives at /tools/run-llama-llama-index/alternatives and /tools/run-llama-rags/alternatives (/tools/run-llama-llama-index/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/run-llama-llama-index-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, llama_index or rags? llama_index: Very active. 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 llama_index and rags? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: llama_index: /tools/run-llama-llama-index/trust; rags: /tools/run-llama-rags/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=run-llama-llama-index`](/api/graphcanon/graph?tool=run-llama-llama-index) - 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/_