--- title: "OpenMemory vs MemOS" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/caviraoss-openmemory-vs-memtensor-memos" tools: ["caviraoss-openmemory", "memtensor-memos"] --- # OpenMemory vs MemOS Neutral, constraint-first comparison with live GitHub stats. | | [OpenMemory](/tools/caviraoss-openmemory.md) | [MemOS](/tools/memtensor-memos.md) | | --- | --- | --- | | Tagline | Local persistent memory store for LLM applications | Self-evolving memory OS for LLM & AI Agents | | Stars | 4,315 | 10,135 | | Forks | 489 | 920 | | Open issues | 13 | 158 | | Language | TypeScript | TypeScript | | Adopt for | Decision-critical facts for OpenMemory: Local persistent memory store for LLM applications. | MemOS is a self-evolving memory operating system designed to enhance both Large Language Models (LLM) and AI agents. It offers ultra-persistent memory, hybrid-retrieval capabilities, and efficient cross-task skill reuse, | | Persona | - | - | | Runtime | - | - | | License | OpenMemory is distributed under the Apache-2.0 license. | Apache-2.0 | | Categories | AI Agents, Evaluation & Observability, Data & Retrieval, Model Training | AI Agents, Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [OpenMemory](/tools/caviraoss-openmemory.md) | [MemOS](/tools/memtensor-memos.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 11d | 0d | | Open issues (now) | 13 | 158 | | Security scan | No lockfile | 2 low (2 low) | | Full report | [trust report](/tools/caviraoss-openmemory/trust.md) | [trust report](/tools/memtensor-memos/trust.md) | **Typed relationship:** OpenMemory _(alternative)_ MemOS Both OpenMemory and MemOS provide local persistent memory solutions for LLM applications, with MemOS adding self-evolving capabilities and token savings. ## Decision facts: OpenMemory - **Requirements:** Requires Python or Node.js for SDK usage; Local SQLite support by default with optional configuration for external DB like Postgres; Dependencies include TypeScript environment for setup and use - **Adopt for:** Decision-critical facts for OpenMemory: Local persistent memory store for LLM applications. - **License detail:** OpenMemory is distributed under the Apache-2.0 license. ## Decision facts: MemOS - **Adopt for:** MemOS is a self-evolving memory operating system designed to enhance both Large Language Models (LLM) and AI agents. It offers ultra-persistent memory, hybrid-retrieval capabilities, and efficient cross-task skill reuse, ## Choose when ### Choose OpenMemory if… - Requirements: Requires Python or Node.js for SDK usage; Local SQLite support by default with optional configuration for external DB like Postgres; Dependencies include TypeScript environment for setup and use. - Both OpenMemory and MemOS provide local persistent memory solutions for LLM applications, with MemOS adding self-evolving capabilities and token savings. - Tags unique to OpenMemory: self-hosted, vector-database, llm, ai-infrastructure. - Also covers Evaluation & Observability, Model Training. - - You require real long-term memory capabilities that go beyond simple embeddings in a table ### Choose MemOS if… - Both OpenMemory and MemOS provide local persistent memory solutions for LLM applications, with MemOS adding self-evolving capabilities and token savings. - Tags unique to MemOS: self-evolving, memory-management, agentic-ai, agent. - When you require significant token savings (up to 72%) in the context of OpenClaw or Hermes agents. ## When NOT to use OpenMemory - - When a fully managed cloud service with no local setup and self-hosted capabilities is preferred - - If the project specifically requires integration only with vector databases or RAG (Retrieval-Augmented Generation) systems - - Projects that are not compatible with TypeScript for backend or JavaScript for application use, as OpenMemory's SDKs are built for these environments - - Situations where the project cannot benefit from local-first data storage and prefers cloud-based solutions ## When NOT to use MemOS - If your application does not leverage LLMs or AI agents that are compatible with MemOS, such as Hermes or OpenClaw. - In scenarios where token savings are not a priority, since MemOS's core benefit is its ability to significantly reduce token usage. ## Common questions ### What is the difference between OpenMemory and MemOS? OpenMemory: Local persistent memory store for LLM applications. MemOS: Self-evolving memory OS for LLM & AI Agents. See the comparison table for live GitHub stats and shared categories. ### When should I choose OpenMemory over MemOS? Choose OpenMemory over MemOS when Requirements: Requires Python or Node.js for SDK usage; Local SQLite support by default with optional configuration for external DB like Postgres; Dependencies include TypeScript environment for setup and use; Both OpenMemory and MemOS provide local persistent memory solutions for LLM applications, with MemOS adding self-evolving capabilities and token savings; Tags unique to OpenMemory: self-hosted, vector-database, llm, ai-infrastructure; Also covers Evaluation & Observability, Model Training; - You require real long-term memory capabilities that go beyond simple embeddings in a table. ### When should I choose MemOS over OpenMemory? Choose MemOS over OpenMemory when Both OpenMemory and MemOS provide local persistent memory solutions for LLM applications, with MemOS adding self-evolving capabilities and token savings; Tags unique to MemOS: self-evolving, memory-management, agentic-ai, agent; When you require significant token savings (up to 72%) in the context of OpenClaw or Hermes agents. ### When should I avoid OpenMemory? - When a fully managed cloud service with no local setup and self-hosted capabilities is preferred - If the project specifically requires integration only with vector databases or RAG (Retrieval-Augmented Generation) systems - Projects that are not compatible with TypeScript for backend or JavaScript for application use, as OpenMemory's SDKs are built for these environments - Situations where the project cannot benefit from local-first data storage and prefers cloud-based solutions ### When should I avoid MemOS? If your application does not leverage LLMs or AI agents that are compatible with MemOS, such as Hermes or OpenClaw. In scenarios where token savings are not a priority, since MemOS's core benefit is its ability to significantly reduce token usage. ### Is OpenMemory or MemOS more popular on GitHub? MemOS has more GitHub stars (10,135 vs 4,315). Stars measure visibility, not whether either tool fits your constraints. ### Are OpenMemory and MemOS open source? Yes - both are open-source projects on GitHub (OpenMemory: Apache-2.0, MemOS: Apache-2.0). ### Where can I find alternatives to OpenMemory or MemOS? GraphCanon lists graph-backed alternatives at /tools/caviraoss-openmemory/alternatives and /tools/memtensor-memos/alternatives (/tools/caviraoss-openmemory/alternatives.md, /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 /compare/caviraoss-openmemory-vs-memtensor-memos.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, OpenMemory or MemOS? OpenMemory: 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 OpenMemory and MemOS? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: OpenMemory: /tools/caviraoss-openmemory/trust; MemOS: /tools/memtensor-memos/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=caviraoss-openmemory`](/api/graphcanon/graph?tool=caviraoss-openmemory) - 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/_