--- title: "Ori-Mnemos vs MemOS" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aayoawoyemi-ori-mnemos-vs-memtensor-memos" tools: ["aayoawoyemi-ori-mnemos", "memtensor-memos"] --- # Ori-Mnemos vs MemOS *GraphCanon updated Aug 18, 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 MemOS if memOS is a self-evolving operating system tailored for LSTM systems and AI agents, offering ultra-persistent memory, hybrid retrieval technologies, and skill reuse across tasks. [Ori-Mnemos](https://orimnemos.com.) reports 314 GitHub stars, 28 forks, and 0 open issues, last pushed Jul 22, 2026. [MemOS](https://memos.openmem.net) has 11k stars, 994 forks, and 84 open issues, last pushed Aug 18, 2026. Figures are from public GitHub metadata via [Ori-Mnemos's repository](https://github.com/aayoawoyemi/Ori-Mnemos) and [MemOS's repository](https://github.com/MemTensor/MemOS). | | [Ori-Mnemos](/tools/aayoawoyemi-ori-mnemos.md) | [MemOS](/tools/memtensor-memos.md) | | --- | --- | --- | | Tagline | Local-first persistent agentic memory powered by Recursive Memory Harness (RMH). | Self-evolving memory OS for LLM & AI Agents: ultra-persistent memory, hybrid-retrieval, and cross-task skill reuse | | Stars | 314 | 10,758 | | Forks | 28 | 994 | | Open issues | 0 | 84 | | Language | TypeScript | TypeScript | | 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. | MemOS is a self-evolving operating system tailored for LSTM systems and AI agents, offering ultra-persistent memory, hybrid retrieval technologies, and skill reuse across tasks. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MemOS is available under the Apache-2.0 license, allowing you to use it freely with certain conditions. | | 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) | [MemOS](/tools/memtensor-memos.md) | | --- | --- | --- | | Days since push | 1d | 0d | | Open issues (now) | 0 | 84 | | Stars delta | Unknown | +497 (30d) | | Open issues delta | Unknown | +4 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/aayoawoyemi-ori-mnemos/trust.md) | [trust report](/tools/memtensor-memos/trust.md) | ## Shared compatibility - **Node.js**: [Ori-Mnemos](/tools/aayoawoyemi-ori-mnemos.md) - Node.js runtime; [MemOS](/tools/memtensor-memos.md) - Node.js runtime ## 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: MemOS - **Pricing:** unknown - Pricing details are not clearly specified in the repository data, but the self-hosted option provides flexibility at the expense of managing infrastructure costs. - **Requirements:** Requires Docker; Self-hosting MemOS typically requires setting up Neo4j and Qdrant. The cloud API or OpenClaw Cloud Plugin options do not require these components. - **Adopt for:** MemOS is a self-evolving operating system tailored for LSTM systems and AI agents, offering ultra-persistent memory, hybrid retrieval technologies, and skill reuse across tasks. - **License detail:** MemOS is available under the Apache-2.0 license, allowing you to use it freely with certain conditions. ## Choose when ### Choose Ori-Mnemos if… - Tags unique to Ori-Mnemos: agent-memory, ai-agents, knowledge-graph, local-first. - Ori-Mnemos ships an MCP server manifest. - When you need a robust, local-first solution that prioritizes offline capabilities and security. ### Choose MemOS if… - Pricing: Pricing details are not clearly specified in the repository data, but the self-hosted option provides flexibility at the expense of managing infrastructure costs.. - Requirements: Requires Docker; Self-hosting MemOS typically requires setting up Neo4j and Qdrant. The cloud API or OpenClaw Cloud Plugin options do not require these components.. - Tags unique to MemOS: agent, agentic-ai, long-term-memory, memory-management. - MemOS ships Docker support for self-hosted deployment. - If you require ultra-persistent memory management within your application for long-term storage. ## 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 MemOS - If you prefer fully managed solutions and have no preference over where your data is stored (hosted in MemOS Cloud). - When minimal setup overhead is a critical requirement since self-hosting MemOS necessitates Neo4j and Qdrant for optimal performance. - In scenarios preferring simpler on-device solutions as MemOS could be more complex to set up compared to other lightweight plugins or services. ## Common questions ### What is the difference between Ori-Mnemos and MemOS? Ori-Mnemos: Local-first persistent agentic memory powered by Recursive Memory Harness (RMH).. MemOS: Self-evolving memory OS for LLM & AI Agents: ultra-persistent memory, hybrid-retrieval, and cross-task skill reuse. See the comparison table for live GitHub stats and shared categories. ### When should I choose Ori-Mnemos over MemOS? Choose Ori-Mnemos over MemOS when Tags unique to Ori-Mnemos: agent-memory, ai-agents, knowledge-graph, local-first; 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 MemOS over Ori-Mnemos? Choose MemOS over Ori-Mnemos when Pricing: Pricing details are not clearly specified in the repository data, but the self-hosted option provides flexibility at the expense of managing infrastructure costs.; Requirements: Requires Docker; Self-hosting MemOS typically requires setting up Neo4j and Qdrant. The cloud API or OpenClaw Cloud Plugin options do not require these components.; Tags unique to MemOS: agent, agentic-ai, long-term-memory, memory-management; MemOS ships Docker support for self-hosted deployment; If you require ultra-persistent memory management within your application for long-term storage. ### 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 MemOS? If you prefer fully managed solutions and have no preference over where your data is stored (hosted in MemOS Cloud). When minimal setup overhead is a critical requirement since self-hosting MemOS necessitates Neo4j and Qdrant for optimal performance. In scenarios preferring simpler on-device solutions as MemOS could be more complex to set up compared to other lightweight plugins or services. ### Is Ori-Mnemos or MemOS more popular on GitHub? MemOS has more GitHub stars (10,758 vs 314). Stars measure visibility, not whether either tool fits your constraints. ### Are Ori-Mnemos and MemOS open source? Yes - both are open-source projects on GitHub (Ori-Mnemos: Apache-2.0, MemOS: Apache-2.0). ### Where can I find alternatives to Ori-Mnemos or MemOS? GraphCanon lists graph-backed alternatives at [Ori-Mnemos alternatives](/tools/aayoawoyemi-ori-mnemos/alternatives) and [MemOS alternatives](/tools/memtensor-memos/alternatives) ([Ori-Mnemos markdown twin](/tools/aayoawoyemi-ori-mnemos/alternatives.md), [MemOS markdown twin](/tools/memtensor-memos/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-memtensor-memos.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Ori-Mnemos or MemOS? Ori-Mnemos: Very active. MemOS: 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 MemOS? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Ori-Mnemos trust report](/tools/aayoawoyemi-ori-mnemos/trust); [MemOS trust report](/tools/memtensor-memos/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/_