--- title: "Ori-Mnemos vs ragflow" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aayoawoyemi-ori-mnemos-vs-infiniflow-ragflow" tools: ["aayoawoyemi-ori-mnemos", "infiniflow-ragflow"] --- # Ori-Mnemos vs ragflow *GraphCanon updated Jul 11, 2026* ## Verdict Pick Ori-Mnemos if ori-Mnemos is a local-first, persistent agentic memory system leveraging SQLite and TypeScript. It incorporates Recursive Memory Harness (RMH) for AI agents; pick ragflow if 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. [Ori-Mnemos](https://orimnemos.com.) reports 314 GitHub stars, 28 forks, and 5 open issues, last pushed Jun 21, 2026. [ragflow](https://ragflow.io) has 85k stars, 9.9k forks, and 2.3k open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [Ori-Mnemos's repository](https://github.com/aayoawoyemi/Ori-Mnemos) and [ragflow's repository](https://github.com/infiniflow/ragflow). | | [Ori-Mnemos](/tools/aayoawoyemi-ori-mnemos.md) | [ragflow](/tools/infiniflow-ragflow.md) | | --- | --- | --- | | Tagline | Local-first persistent agentic memory powered by Recursive Memory Harness (RMH). | Retrieval-Augmented Generation engine with agent capabilities | | Stars | 314 | 84,818 | | Forks | 28 | 9,905 | | Open issues | 5 | 2,302 | | Language | TypeScript | Go | | Adopt for | Ori-Mnemos is a local-first, persistent agentic memory system leveraging SQLite and TypeScript. It incorporates Recursive Memory Harness (RMH) for AI agents. | 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 | Apache-2.0 License | | Categories | AI Agents, Data & Retrieval | AI Agents, Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Ori-Mnemos](/tools/aayoawoyemi-ori-mnemos.md) | [ragflow](/tools/infiniflow-ragflow.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 20d | 0d | | Open issues (now) | 5 | 2.3k | | Owner type | User | Organization | | Security scan | No MCP manifest | 4 low (4 low) | | Full report | [trust report](/tools/aayoawoyemi-ori-mnemos/trust.md) | [trust report](/tools/infiniflow-ragflow/trust.md) | ## Decision facts: Ori-Mnemos - **Adopt for:** Ori-Mnemos is a local-first, persistent agentic memory system leveraging SQLite and TypeScript. It incorporates Recursive Memory Harness (RMH) for AI agents. ## 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 Ori-Mnemos if… - Ori-Mnemos is primarily TypeScript; ragflow is Go. - Tags unique to Ori-Mnemos: agent-memory, ai-agents, knowledge-graph, llm. - Ori-Mnemos ships an MCP server manifest. - When you need a robust, local-first solution that prioritizes offline capabilities and security. ### Choose ragflow if… - ragflow is primarily Go; Ori-Mnemos is TypeScript. - 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 NOT to use Ori-Mnemos - When real-time synchronization across devices or cloud integration is a non-negotiable requirement for your application. - If you are looking for a memory system that leverages distributed databases for scalable access patterns; Ori-Mnemos focuses on local storage using SQLite. - In environments where complex, multi-node architectures and high availability requirements demand more than a single point of data persistence. ## 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. ## Common questions ### What is the difference between Ori-Mnemos and ragflow? Ori-Mnemos: Local-first persistent agentic memory powered by Recursive Memory Harness (RMH).. ragflow: Retrieval-Augmented Generation engine with agent capabilities. See the comparison table for live GitHub stats and shared categories. ### When should I choose Ori-Mnemos over ragflow? Choose Ori-Mnemos over ragflow when Ori-Mnemos is primarily TypeScript; ragflow is Go; Tags unique to Ori-Mnemos: agent-memory, ai-agents, knowledge-graph, llm; Ori-Mnemos ships an MCP server manifest; When you need a robust, local-first solution that prioritizes offline capabilities and security. ### When should I choose ragflow over Ori-Mnemos? Choose ragflow over Ori-Mnemos when ragflow is primarily Go; Ori-Mnemos is TypeScript; 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 avoid Ori-Mnemos? When real-time synchronization across devices or cloud integration is a non-negotiable requirement for your application. If you are looking for a memory system that leverages distributed databases for scalable access patterns; Ori-Mnemos focuses on local storage using SQLite. In environments where complex, multi-node architectures and high availability requirements demand more than a single point of data persistence. ### 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. ### Is Ori-Mnemos or ragflow more popular on GitHub? ragflow has more GitHub stars (84,818 vs 314). Stars measure visibility, not whether either tool fits your constraints. ### Are Ori-Mnemos and ragflow open source? Yes - both are open-source projects on GitHub (Ori-Mnemos: Apache-2.0, ragflow: Apache-2.0). ### Where can I find alternatives to Ori-Mnemos or ragflow? GraphCanon lists graph-backed alternatives at [Ori-Mnemos alternatives](/tools/aayoawoyemi-ori-mnemos/alternatives) and [ragflow alternatives](/tools/infiniflow-ragflow/alternatives) ([Ori-Mnemos markdown twin](/tools/aayoawoyemi-ori-mnemos/alternatives.md), [ragflow markdown twin](/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 [this comparison](/compare/aayoawoyemi-ori-mnemos-vs-infiniflow-ragflow.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Ori-Mnemos or ragflow? Ori-Mnemos: Active. 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 Ori-Mnemos and ragflow? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Ori-Mnemos trust report](/tools/aayoawoyemi-ori-mnemos/trust); [ragflow trust report](/tools/infiniflow-ragflow/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=aayoawoyemi-ori-mnemos`](/api/graphcanon/graph?tool=aayoawoyemi-ori-mnemos) - 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/_