--- title: "ragflow vs deepfabric" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/infiniflow-ragflow-vs-nolabs-ai-deepfabric" tools: ["infiniflow-ragflow", "nolabs-ai-deepfabric"] --- # ragflow vs deepfabric *GraphCanon updated Jul 12, 2026* ## Verdict Pick ragflow when ragflow is primarily Go; deepfabric is Python; pick deepfabric when deepfabric is primarily Python; ragflow is Go. [ragflow](https://ragflow.io) reports 85k GitHub stars, 9.9k forks, and 2.3k open issues, last pushed Jul 11, 2026. [deepfabric](http://docs.deepfabric.dev) has 877 stars, 83 forks, and 23 open issues, last pushed Jul 3, 2026. Figures are from public GitHub metadata via [ragflow's repository](https://github.com/infiniflow/ragflow) and [deepfabric's repository](https://github.com/nolabs-ai/deepfabric). | | [ragflow](/tools/infiniflow-ragflow.md) | [deepfabric](/tools/nolabs-ai-deepfabric.md) | | --- | --- | --- | | Tagline | Retrieval-Augmented Generation engine with agent capabilities | Generate High-Quality Synthetics, Train, Measure, and Evaluate in a Single Pipeline | | Stars | 84,818 | 877 | | Forks | 9,905 | 83 | | Open issues | 2,302 | 23 | | Language | Go | Python | | Adopt for | RAGFlow is a Retrieval-Augmented Generation (RAG) engine that integrates AI agents for enhanced context management in LLM applications, built using Go language and released under the Apache-2.0 license. | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 License | Apache-2.0 | | Categories | AI Agents, Data & Retrieval | AI Agents, Data & Retrieval, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ragflow](/tools/infiniflow-ragflow.md) | [deepfabric](/tools/nolabs-ai-deepfabric.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 0d | 7d | | Open issues (now) | 2.3k | 23 | | Security scan | 4 low (4 low) | No lockfile | | Full report | [trust report](/tools/infiniflow-ragflow/trust.md) | [trust report](/tools/nolabs-ai-deepfabric/trust.md) | ## Decision facts: ragflow - **Requirements:** Requires Docker; Docker image size is approximately 2 GB; build process requires access to external LLM and embedding services. - **Adopt for:** RAGFlow is a Retrieval-Augmented Generation (RAG) engine that integrates AI agents for enhanced context management in LLM applications, built using Go language and released under the Apache-2.0 license. - **License detail:** Apache-2.0 License ## Choose when ### Choose ragflow if… - ragflow is primarily Go; deepfabric is Python. - Requirements: Requires Docker; Docker image size is approximately 2 GB; build process requires access to external LLM and embedding services.. - Tags unique to ragflow: agentic-ai, context-management, rag, retrieval-augmented-generation. - ragflow ships Docker support for self-hosted deployment. - - You need an integrated RAG system with AI agent capabilities for better context management in your applications. ### Choose deepfabric if… - deepfabric is primarily Python; ragflow is Go. - Tags unique to deepfabric: agents, ai, data-science, dataset. - Also covers Model Training. ## When NOT to use ragflow - - If you specifically require a non-Golang developed RAG engine, as RAGFlow is built entirely in Go. - - Your setup does not support or need Docker (RAGFlow requires building a Docker image that is approximately 2 GB). - - You cannot use external LLM services and embedding services, as RAGFlow relies on them to function. ## When NOT to use deepfabric - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## Common questions ### What is the difference between ragflow and deepfabric? ragflow: Retrieval-Augmented Generation engine with agent capabilities. deepfabric: Generate High-Quality Synthetics, Train, Measure, and Evaluate in a Single Pipeline. See the comparison table for live GitHub stats and shared categories. ### When should I choose ragflow over deepfabric? Choose ragflow over deepfabric when ragflow is primarily Go; deepfabric is Python; Requirements: Requires Docker; Docker image size is approximately 2 GB; build process requires access to external LLM and embedding services.; Tags unique to ragflow: agentic-ai, context-management, rag, retrieval-augmented-generation; ragflow ships Docker support for self-hosted deployment; - You need an integrated RAG system with AI agent capabilities for better context management in your applications. ### When should I choose deepfabric over ragflow? Choose deepfabric over ragflow when deepfabric is primarily Python; ragflow is Go; Tags unique to deepfabric: agents, ai, data-science, dataset; Also covers Model Training. ### When should I avoid ragflow? - If you specifically require a non-Golang developed RAG engine, as RAGFlow is built entirely in Go. - Your setup does not support or need Docker (RAGFlow requires building a Docker image that is approximately 2 GB). - You cannot use external LLM services and embedding services, as RAGFlow relies on them to function. ### When should I avoid deepfabric? AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### Is ragflow or deepfabric more popular on GitHub? ragflow has more GitHub stars (84,818 vs 877). Stars measure visibility, not whether either tool fits your constraints. ### Are ragflow and deepfabric open source? Yes - both are open-source projects on GitHub (ragflow: Apache-2.0, deepfabric: Apache-2.0). ### Where can I find alternatives to ragflow or deepfabric? GraphCanon lists graph-backed alternatives at [ragflow alternatives](/tools/infiniflow-ragflow/alternatives) and [deepfabric alternatives](/tools/nolabs-ai-deepfabric/alternatives) ([ragflow markdown twin](/tools/infiniflow-ragflow/alternatives.md), [deepfabric markdown twin](/tools/nolabs-ai-deepfabric/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 [this comparison](/compare/infiniflow-ragflow-vs-nolabs-ai-deepfabric.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ragflow or deepfabric? ragflow: Very active. deepfabric: 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 deepfabric? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ragflow trust report](/tools/infiniflow-ragflow/trust); [deepfabric trust report](/tools/nolabs-ai-deepfabric/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/_