--- title: "Ori-Mnemos vs EverOS" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aayoawoyemi-ori-mnemos-vs-evermind-ai-everos" tools: ["aayoawoyemi-ori-mnemos", "evermind-ai-everos"] --- # Ori-Mnemos vs EverOS *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 EverOS if everOS is designed as a portable and user-owned memory management solution that supports Markdown format, facilitating easy cross-app memory transfer and evolution. [Ori-Mnemos](https://orimnemos.com.) reports 314 GitHub stars, 28 forks, and 0 open issues, last pushed Jul 22, 2026. [EverOS](https://evermind.ai/everos) has 12k stars, 891 forks, and 72 open issues, last pushed Aug 17, 2026. Figures are from public GitHub metadata via [Ori-Mnemos's repository](https://github.com/aayoawoyemi/Ori-Mnemos) and [EverOS's repository](https://github.com/EverMind-AI/EverOS). | | [Ori-Mnemos](/tools/aayoawoyemi-ori-mnemos.md) | [EverOS](/tools/evermind-ai-everos.md) | | --- | --- | --- | | Tagline | Local-first persistent agentic memory powered by Recursive Memory Harness (RMH). | One portable memory layer for every AI agent | | Stars | 314 | 12,114 | | Forks | 28 | 891 | | Open issues | 0 | 72 | | 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. | EverOS is designed as a portable and user-owned memory management solution that supports Markdown format, facilitating easy cross-app memory transfer and evolution. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | 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) | [EverOS](/tools/evermind-ai-everos.md) | | --- | --- | --- | | Open issues (now) | 0 | 72 | | Stars delta | Unknown | +849 (30d) | | Open issues delta | Unknown | +16 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/aayoawoyemi-ori-mnemos/trust.md) | [trust report](/tools/evermind-ai-everos/trust.md) | ## Shared compatibility - **Python**: [Ori-Mnemos](/tools/aayoawoyemi-ori-mnemos.md) - Python runtime; [EverOS](/tools/evermind-ai-everos.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: EverOS - **Adopt for:** EverOS is designed as a portable and user-owned memory management solution that supports Markdown format, facilitating easy cross-app memory transfer and evolution. ## Choose when ### Choose Ori-Mnemos if… - Ori-Mnemos is primarily TypeScript; EverOS is Python. - Tags unique to Ori-Mnemos: ai-agents, knowledge-graph, llm, markdown. - Ori-Mnemos ships an MCP server manifest. - When you need a robust, local-first solution that prioritizes offline capabilities and security. ### Choose EverOS if… - EverOS is primarily Python; Ori-Mnemos is TypeScript. - Tags unique to EverOS: long-term-memory, markdown-native, memory-management, python3. - When you require a user-owned memory layer that allows for seamless cross-app integration and evolution using Markdown notes. ## 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 EverOS - If your project requires a non-markdown-based memory layer, since EverOS is specifically designed for Markdown-format memories and may not integrate well with other formats. - When the requirement is strictly cloud-centric without local-first capabilities; EverOS focuses on being local-first before operating across applications. ## Common questions ### What is the difference between Ori-Mnemos and EverOS? Ori-Mnemos: Local-first persistent agentic memory powered by Recursive Memory Harness (RMH).. EverOS: One portable memory layer for every AI agent. See the comparison table for live GitHub stats and shared categories. ### When should I choose Ori-Mnemos over EverOS? Choose Ori-Mnemos over EverOS when Ori-Mnemos is primarily TypeScript; EverOS is Python; Tags unique to Ori-Mnemos: ai-agents, knowledge-graph, llm, 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 EverOS over Ori-Mnemos? Choose EverOS over Ori-Mnemos when EverOS is primarily Python; Ori-Mnemos is TypeScript; Tags unique to EverOS: long-term-memory, markdown-native, memory-management, python3; When you require a user-owned memory layer that allows for seamless cross-app integration and evolution using Markdown notes. ### 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 EverOS? If your project requires a non-markdown-based memory layer, since EverOS is specifically designed for Markdown-format memories and may not integrate well with other formats. When the requirement is strictly cloud-centric without local-first capabilities; EverOS focuses on being local-first before operating across applications. ### Is Ori-Mnemos or EverOS more popular on GitHub? EverOS has more GitHub stars (12,114 vs 314). Stars measure visibility, not whether either tool fits your constraints. ### Are Ori-Mnemos and EverOS open source? Yes - both are open-source projects on GitHub (Ori-Mnemos: Apache-2.0, EverOS: Apache-2.0). ### Where can I find alternatives to Ori-Mnemos or EverOS? GraphCanon lists graph-backed alternatives at [Ori-Mnemos alternatives](/tools/aayoawoyemi-ori-mnemos/alternatives) and [EverOS alternatives](/tools/evermind-ai-everos/alternatives) ([Ori-Mnemos markdown twin](/tools/aayoawoyemi-ori-mnemos/alternatives.md), [EverOS markdown twin](/tools/evermind-ai-everos/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-evermind-ai-everos.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Ori-Mnemos or EverOS? Ori-Mnemos: Very active. EverOS: 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 EverOS? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Ori-Mnemos trust report](/tools/aayoawoyemi-ori-mnemos/trust); [EverOS trust report](/tools/evermind-ai-everos/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/_