--- title: "ragflow vs llama_index" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/infiniflow-ragflow-vs-run-llama-llama-index" tools: ["infiniflow-ragflow", "run-llama-llama-index"] --- # ragflow vs llama_index Neutral, constraint-first comparison with live GitHub stats. | | [ragflow](/tools/infiniflow-ragflow.md) | [llama_index](/tools/run-llama-llama-index.md) | | --- | --- | --- | | Tagline | Retrieval-Augmented Generation (RAG) engine fusing Agent capabilities with LLM context management | Document agent and OCR platform | | Stars | 84,561 | 50,723 | | Forks | 9,862 | 7,711 | | Open issues | 2,325 | 494 | | Language | Go | Python | | Adopt for | Decide whether to use RAGFlow based on its unique integration of retrieval and AI agent capabilities for generating enhanced context layers with LLMs, while considering its language choice (Go) and Apache-2.0 license. | LlamaIndex is an open-source framework that enables developers to build agentic applications, integrating with various LLMs, embeddings, and vector stores. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | AI Agents, Data & Retrieval | AI Agents, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ragflow](/tools/infiniflow-ragflow.md) | [llama_index](/tools/run-llama-llama-index.md) | | --- | --- | --- | | Days since push | 0d | 5d | | Open issues (now) | 2.3k | 494 | | Security scan | 4 low (4 low) | No lockfile | | Full report | [trust report](/tools/infiniflow-ragflow/trust.md) | [trust report](/tools/run-llama-llama-index/trust.md) | **Typed relationship:** ragflow _(alternative)_ llama_index In both cases, they are tools for Retrieval-Augmented Generation with agent capabilities. However, RagFlow integrates directly into existing workflows, making it an alternative approach. ## Decision facts: ragflow - **Pricing:** freemium - RAGFlow is offered under an Apache-2.0 license, making the core functionality free and open-source. However, there may be additional costs associated with hosting, infrastructure maintenance, and any云 - **Adopt for:** Decide whether to use RAGFlow based on its unique integration of retrieval and AI agent capabilities for generating enhanced context layers with LLMs, while considering its language choice (Go) and Apache-2.0 license. ## 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. ## Choose when ### Choose ragflow if… - ragflow is primarily Go; llama_index is Python. - License: ragflow is Apache-2.0, llama_index is MIT. - Pricing: RAGFlow is offered under an Apache-2.0 license, making the core functionality free and open-source. However, there may be additional costs associated with hosting, infrastructure maintenance, and any云. - In both cases, they are tools for Retrieval-Augmented Generation with agent capabilities. However, RagFlow integrates directly into existing workflows, making it an alternative approach. - Tags unique to ragflow: context-management, llm-context-layer, rag, agentic-ai. - Also covers Data & Retrieval. - ragflow ships Docker support for self-hosted deployment. - When you need a tool that integrates both retrieval-augmented generation and AI agent functionalities to enhance the contextual layer for any use case involving large language models. ### Choose llama_index if… - llama_index is primarily Python; ragflow is Go. - License: llama_index is MIT, ragflow is Apache-2.0. - In both cases, they are tools for Retrieval-Augmented Generation with agent capabilities. However, RagFlow integrates directly into existing workflows, making it an alternative approach. - Tags unique to llama_index: llamaindex, fine-tuning, agents, llm. - 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 NOT to use ragflow - If your project strictly requires a Python environment as RAGFlow is written in Go, transitioning or integrating might pose technical challenges. - In situations where you need real-time processing capabilities superior to what's currently offered by RAGFlow’s architecture without significant customization efforts. - When looking for specialized RAG platforms that offer more mature features like extensive pre-trained models or advanced data handling specific to niche industries. ## 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 ## Common questions ### What is the difference between ragflow and llama_index? ragflow: Retrieval-Augmented Generation (RAG) engine fusing Agent capabilities with LLM context management. llama_index: Document agent and OCR platform. See the comparison table for live GitHub stats and shared categories. ### When should I choose ragflow over llama_index? Choose ragflow over llama_index when ragflow is primarily Go; llama_index is Python; License: ragflow is Apache-2.0, llama_index is MIT; Pricing: RAGFlow is offered under an Apache-2.0 license, making the core functionality free and open-source. However, there may be additional costs associated with hosting, infrastructure maintenance, and any云; In both cases, they are tools for Retrieval-Augmented Generation with agent capabilities. However, RagFlow integrates directly into existing workflows, making it an alternative approach; Tags unique to ragflow: context-management, llm-context-layer, rag, agentic-ai; Also covers Data & Retrieval; ragflow ships Docker support for self-hosted deployment; When you need a tool that integrates both retrieval-augmented generation and AI agent functionalities to enhance the contextual layer for any use case involving large language models. ### When should I choose llama_index over ragflow? Choose llama_index over ragflow when llama_index is primarily Python; ragflow is Go; License: llama_index is MIT, ragflow is Apache-2.0; In both cases, they are tools for Retrieval-Augmented Generation with agent capabilities. However, RagFlow integrates directly into existing workflows, making it an alternative approach; Tags unique to llama_index: llamaindex, fine-tuning, agents, llm; 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 avoid ragflow? If your project strictly requires a Python environment as RAGFlow is written in Go, transitioning or integrating might pose technical challenges. In situations where you need real-time processing capabilities superior to what's currently offered by RAGFlow’s architecture without significant customization efforts. When looking for specialized RAG platforms that offer more mature features like extensive pre-trained models or advanced data handling specific to niche industries. ### 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 ### Is ragflow or llama_index more popular on GitHub? ragflow has more GitHub stars (84,561 vs 50,723). Stars measure visibility, not whether either tool fits your constraints. ### Are ragflow and llama_index open source? Yes - both are open-source projects on GitHub (ragflow: Apache-2.0, llama_index: MIT). ### Where can I find alternatives to ragflow or llama_index? GraphCanon lists graph-backed alternatives at /tools/infiniflow-ragflow/alternatives and /tools/run-llama-llama-index/alternatives (/tools/infiniflow-ragflow/alternatives.md, /tools/run-llama-llama-index/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/infiniflow-ragflow-vs-run-llama-llama-index.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ragflow or llama_index? ragflow: Very active. llama_index: Very active. 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 ragflow and llama_index? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: ragflow: /tools/infiniflow-ragflow/trust; llama_index: /tools/run-llama-llama-index/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=infiniflow-ragflow`](/api/graphcanon/graph?tool=infiniflow-ragflow) - 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/_