--- title: "Ori-Mnemos vs Memori" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aayoawoyemi-ori-mnemos-vs-memorilabs-memori" tools: ["aayoawoyemi-ori-mnemos", "memorilabs-memori"] --- # Ori-Mnemos vs Memori *GraphCanon updated Aug 23, 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 Memori if memori is an agent-native memory infrastructure layer that converts agent execution and conversation into a structured, persistent state for use in production systems. It supports various deployment environments such as云. [Ori-Mnemos](https://orimnemos.com.) reports 319 GitHub stars, 27 forks, and 1 open issues, last pushed Jul 30, 2026. [Memori](https://memorilabs.ai) has 16k stars, 3.2k forks, and 33 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 [Memori's repository](https://github.com/MemoriLabs/Memori). | | [Ori-Mnemos](/tools/aayoawoyemi-ori-mnemos.md) | [Memori](/tools/memorilabs-memori.md) | | --- | --- | --- | | Tagline | Local-first persistent agentic memory powered by Recursive Memory Harness (RMH). | Agent-native memory infrastructure for LLM systems | | Stars | 319 | 16,130 | | Forks | 27 | 3,188 | | Open issues | 1 | 33 | | Language | TypeScript | Python | | 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. | Memori is an agent-native memory infrastructure layer that converts agent execution and conversation into a structured, persistent state for use in production systems. It supports various deployment environments such as云 | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Other | | 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) | [Memori](/tools/memorilabs-memori.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 23d | 0d | | Open issues (now) | 1 | 33 | | Stars delta | +5 (30d) | +515 (30d) | | Open issues delta | +1 (30d) | +7 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/aayoawoyemi-ori-mnemos/trust.md) | [trust report](/tools/memorilabs-memori/trust.md) | ## Shared compatibility - **Node.js**: [Ori-Mnemos](/tools/aayoawoyemi-ori-mnemos.md) - Node.js runtime; [Memori](/tools/memorilabs-memori.md) - Node.js runtime - **Python**: [Ori-Mnemos](/tools/aayoawoyemi-ori-mnemos.md) - Python runtime; [Memori](/tools/memorilabs-memori.md) - Python 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: Memori - **Adopt for:** Memori is an agent-native memory infrastructure layer that converts agent execution and conversation into a structured, persistent state for use in production systems. It supports various deployment environments such as云 ## Choose when ### Choose Ori-Mnemos if… - Ori-Mnemos is primarily TypeScript; Memori is Python. - License: Ori-Mnemos is Apache-2.0, Memori is Other. - Tags unique to Ori-Mnemos: ai-agents, knowledge-graph, local-first, markdown. - Ori-Mnemos ships an MCP server manifest. - When you need a robust, local-first solution that prioritizes offline capabilities and security. ### Choose Memori if… - Memori is primarily Python; Ori-Mnemos is TypeScript. - License: Memori is Other, Ori-Mnemos is Apache-2.0. - Tags unique to Memori: enterprise, memory-management, python, rag. - Memori ships Docker support for self-hosted deployment. - 您需要一个可以在多种部署环境中工作的内存基础设施，包括云端和本地环境时。 ## 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 Memori - （TypeScriptPython），MemoriSDK。 ## Common questions ### What is the difference between Ori-Mnemos and Memori? Ori-Mnemos: Local-first persistent agentic memory powered by Recursive Memory Harness (RMH).. Memori: Agent-native memory infrastructure for LLM systems. See the comparison table for live GitHub stats and shared categories. ### When should I choose Ori-Mnemos over Memori? Choose Ori-Mnemos over Memori when Ori-Mnemos is primarily TypeScript; Memori is Python; License: Ori-Mnemos is Apache-2.0, Memori is Other; Tags unique to Ori-Mnemos: ai-agents, knowledge-graph, local-first, markdown; 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 Memori over Ori-Mnemos? Choose Memori over Ori-Mnemos when Memori is primarily Python; Ori-Mnemos is TypeScript; License: Memori is Other, Ori-Mnemos is Apache-2.0; Tags unique to Memori: enterprise, memory-management, python, rag; Memori ships Docker support for self-hosted deployment; 您需要一个可以在多种部署环境中工作的内存基础设施，包括云端和本地环境时。. ### 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 Memori? （TypeScriptPython），MemoriSDK。 ### Is Ori-Mnemos or Memori more popular on GitHub? Memori has more GitHub stars (16,130 vs 319). Stars measure visibility, not whether either tool fits your constraints. ### Are Ori-Mnemos and Memori open source? Yes - both are open-source projects on GitHub (Ori-Mnemos: Apache-2.0, Memori: Other). ### Where can I find alternatives to Ori-Mnemos or Memori? GraphCanon lists graph-backed alternatives at [Ori-Mnemos alternatives](/tools/aayoawoyemi-ori-mnemos/alternatives) and [Memori alternatives](/tools/memorilabs-memori/alternatives) ([Ori-Mnemos markdown twin](/tools/aayoawoyemi-ori-mnemos/alternatives.md), [Memori markdown twin](/tools/memorilabs-memori/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-memorilabs-memori.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Ori-Mnemos or Memori? Ori-Mnemos: Active. Memori: 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 Memori? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Ori-Mnemos trust report](/tools/aayoawoyemi-ori-mnemos/trust); [Memori trust report](/tools/memorilabs-memori/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/_