--- title: "airweave vs ragflow" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/airweave-ai-airweave-vs-infiniflow-ragflow" tools: ["airweave-ai-airweave", "infiniflow-ragflow"] --- # airweave vs ragflow Neutral, constraint-first comparison with live GitHub stats. | | [airweave](/tools/airweave-ai-airweave.md) | [ragflow](/tools/infiniflow-ragflow.md) | | --- | --- | --- | | Tagline | Open-source context retrieval layer for AI agents and RAG systems. | Retrieval-Augmented Generation (RAG) engine fusing Agent capabilities with LLM context management | | Stars | 6,468 | 84,561 | | Forks | 813 | 9,862 | | Open issues | 132 | 2,325 | | Language | Python | Go | | Adopt for | Airweave is an open-source context retrieval layer that supports AI agents and RAG systems. It provides solid data connectivity and information retrieval capabilities, making it a versatile solution for projects needing渊 | 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. | | Persona | - | - | | Runtime | - | - | | License | MIT License, allowing for free usage and modification provided the copyright notice and permission notice are included. | Apache-2.0 | | Categories | AI Agents, Data & Retrieval | AI Agents, Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [airweave](/tools/airweave-ai-airweave.md) | [ragflow](/tools/infiniflow-ragflow.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 32d | 0d | | Open issues (now) | 132 | 2.3k | | Security scan | Not scanned | 4 low (4 low) | | Full report | [trust report](/tools/airweave-ai-airweave/trust.md) | [trust report](/tools/infiniflow-ragflow/trust.md) | **Typed relationship:** airweave _(alternative)_ ragflow Both Airweave and RAGFlow focus on retrieval-augmented generation (RAG) for context management in AI agents, but they offer different solutions and implementations. ## Decision facts: airweave - **Requirements:** Requires Docker; Airweave requires Docker to be installed and running on your system. Follow specific setup steps for local deployment as outlined in the README, which includes:; - Verifying Docker installation and version with commands like `docker --version` and `docker info`.; - Using a script (`.start.sh`) that automates setup tasks including `.env` file creation, secret generation, and service startup. - **Adopt for:** Airweave is an open-source context retrieval layer that supports AI agents and RAG systems. It provides solid data connectivity and information retrieval capabilities, making it a versatile solution for projects needing渊 - **License detail:** MIT License, allowing for free usage and modification provided the copyright notice and permission notice are included. ## 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. ## Choose when ### Choose airweave if… - airweave is primarily Python; ragflow is Go. - License: airweave is MIT, ragflow is Apache-2.0. - Requirements: Requires Docker; Airweave requires Docker to be installed and running on your system. Follow specific setup steps for local deployment as outlined in the README, which includes:; - Verifying Docker installation and version with commands like `docker --version` and `docker info`.; - Using a script (`.start.sh`) that automates setup tasks including `.env` file creation, secret generation, and service startup.. - Both Airweave and RAGFlow focus on retrieval-augmented generation (RAG) for context management in AI agents, but they offer different solutions and implementations. - Tags unique to airweave: context-retrieval, data-connectors, retrieval, ai-agents. - You are building or enhancing AI agents that require robust context retrieval and integration with existing datasets ### Choose ragflow if… - ragflow is primarily Go; airweave is Python. - License: ragflow is Apache-2.0, airweave 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. - Both Airweave and RAGFlow focus on retrieval-augmented generation (RAG) for context management in AI agents, but they offer different solutions and implementations. - Tags unique to ragflow: context-management, llm-context-layer, agentic-ai. - 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 NOT to use airweave - If your project strictly requires proprietary solutions for data retrieval, as Airweave operates under the open-source MIT license and may not offer certain levels of customization or support compared - You are working in an environment with strict security policies that do not permit the use of non-proprietary software, particularly when handling sensitive information ## 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. ## Common questions ### What is the difference between airweave and ragflow? airweave: Open-source context retrieval layer for AI agents and RAG systems.. ragflow: Retrieval-Augmented Generation (RAG) engine fusing Agent capabilities with LLM context management. See the comparison table for live GitHub stats and shared categories. ### When should I choose airweave over ragflow? Choose airweave over ragflow when airweave is primarily Python; ragflow is Go; License: airweave is MIT, ragflow is Apache-2.0; Requirements: Requires Docker; Airweave requires Docker to be installed and running on your system. Follow specific setup steps for local deployment as outlined in the README, which includes:; - Verifying Docker installation and version with commands like `docker --version` and `docker info`.; - Using a script (`.start.sh`) that automates setup tasks including `.env` file creation, secret generation, and service startup.; Both Airweave and RAGFlow focus on retrieval-augmented generation (RAG) for context management in AI agents, but they offer different solutions and implementations; Tags unique to airweave: context-retrieval, data-connectors, retrieval, ai-agents; You are building or enhancing AI agents that require robust context retrieval and integration with existing datasets. ### When should I choose ragflow over airweave? Choose ragflow over airweave when ragflow is primarily Go; airweave is Python; License: ragflow is Apache-2.0, airweave 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; Both Airweave and RAGFlow focus on retrieval-augmented generation (RAG) for context management in AI agents, but they offer different solutions and implementations; Tags unique to ragflow: context-management, llm-context-layer, agentic-ai; 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 avoid airweave? If your project strictly requires proprietary solutions for data retrieval, as Airweave operates under the open-source MIT license and may not offer certain levels of customization or support compared You are working in an environment with strict security policies that do not permit the use of non-proprietary software, particularly when handling sensitive information ### 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. ### Is airweave or ragflow more popular on GitHub? ragflow has more GitHub stars (84,561 vs 6,468). Stars measure visibility, not whether either tool fits your constraints. ### Are airweave and ragflow open source? Yes - both are open-source projects on GitHub (airweave: MIT, ragflow: Apache-2.0). ### Where can I find alternatives to airweave or ragflow? GraphCanon lists graph-backed alternatives at /tools/airweave-ai-airweave/alternatives and /tools/infiniflow-ragflow/alternatives (/tools/airweave-ai-airweave/alternatives.md, /tools/infiniflow-ragflow/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/airweave-ai-airweave-vs-infiniflow-ragflow.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, airweave or ragflow? airweave: Steady. ragflow: 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 airweave and ragflow? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: airweave: /tools/airweave-ai-airweave/trust; ragflow: /tools/infiniflow-ragflow/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=airweave-ai-airweave`](/api/graphcanon/graph?tool=airweave-ai-airweave) - 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/_