--- title: "Memori vs MemOS" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/memorilabs-memori-vs-memtensor-memos" tools: ["memorilabs-memori", "memtensor-memos"] --- # Memori vs MemOS Neutral, constraint-first comparison with live GitHub stats. | | [Memori](/tools/memorilabs-memori.md) | [MemOS](/tools/memtensor-memos.md) | | --- | --- | --- | | Tagline | Memory infrastructure for AI agents that captures actions and conversations into a structured, persistent state. | Self-evolving memory OS for LLM & AI Agents | | Stars | 15,549 | 10,135 | | Forks | 2,784 | 920 | | Open issues | 21 | 158 | | Language | Python | TypeScript | | Adopt for | Memori is designed for enterprise users seeking seamless memory infrastructure that integrates with existing data architectures across multiple deployment environments. | 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 | Memori is licensed under the Apache License 2.0. | Apache-2.0 | | Categories | Model Training, AI Agents | AI Agents, Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Memori](/tools/memorilabs-memori.md) | [MemOS](/tools/memtensor-memos.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 22d | 0d | | Open issues (now) | 21 | 158 | | Security scan | No lockfile | 2 low (2 low) | | Full report | [trust report](/tools/memorilabs-memori/trust.md) | [trust report](/tools/memtensor-memos/trust.md) | **Typed relationship:** Memori _(alternative)_ MemOS MemORI and MemOS both address the need for persistent memory infrastructure in AI agents, though MemOS emphasizes self-evolution and token efficiency. ## Decision facts: Memori - **Pricing:** unknown - Pricing details are not explicitly stated in the provided repository content. - **Requirements:** The tool requires set up of an API key for Memori and your LLM - **Adopt for:** Memori is designed for enterprise users seeking seamless memory infrastructure that integrates with existing data architectures across multiple deployment environments. - **License detail:** Memori is licensed under the Apache License 2.0. ## 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 Memori if… - Memori is primarily Python; MemOS is TypeScript. - License: Memori is Other, MemOS is Apache-2.0. - Pricing: Pricing details are not explicitly stated in the provided repository content.. - Requirements: The tool requires set up of an API key for Memori and your LLM. - MemORI and MemOS both address the need for persistent memory infrastructure in AI agents, though MemOS emphasizes self-evolution and token efficiency. - Tags unique to Memori: stateful, ai-memory, llm-agnostic, enterprise. - Also covers Model Training. - When you need a system to turn agent execution and conversation into structured, persistent state without disrupting your current IT environment. ### Choose MemOS if… - MemOS is primarily TypeScript; Memori is Python. - License: MemOS is Apache-2.0, Memori is Other. - MemORI and MemOS both address the need for persistent memory infrastructure in AI agents, though MemOS emphasizes self-evolution and token efficiency. - Tags unique to MemOS: self-evolving, agentic-ai, long-term-memory. - Also covers Data & Retrieval. - When you require significant token savings (up to 72%) in the context of OpenClaw or Hermes agents. ## When NOT to use Memori - Avoid if you need a tool that natively extends beyond memory management to include features like autonomous agent navigation or extensive model training utilities, as Memori focuses specifically on AI ## 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 Memori and MemOS? Memori: Memory infrastructure for AI agents that captures actions and conversations into a structured, persistent state.. MemOS: Self-evolving memory OS for LLM & AI Agents. See the comparison table for live GitHub stats and shared categories. ### When should I choose Memori over MemOS? Choose Memori over MemOS when Memori is primarily Python; MemOS is TypeScript; License: Memori is Other, MemOS is Apache-2.0; Pricing: Pricing details are not explicitly stated in the provided repository content.; Requirements: The tool requires set up of an API key for Memori and your LLM; MemORI and MemOS both address the need for persistent memory infrastructure in AI agents, though MemOS emphasizes self-evolution and token efficiency; Tags unique to Memori: stateful, ai-memory, llm-agnostic, enterprise; Also covers Model Training; When you need a system to turn agent execution and conversation into structured, persistent state without disrupting your current IT environment. ### When should I choose MemOS over Memori? Choose MemOS over Memori when MemOS is primarily TypeScript; Memori is Python; License: MemOS is Apache-2.0, Memori is Other; MemORI and MemOS both address the need for persistent memory infrastructure in AI agents, though MemOS emphasizes self-evolution and token efficiency; Tags unique to MemOS: self-evolving, agentic-ai, long-term-memory; Also covers Data & Retrieval; When you require significant token savings (up to 72%) in the context of OpenClaw or Hermes agents. ### When should I avoid Memori? Avoid if you need a tool that natively extends beyond memory management to include features like autonomous agent navigation or extensive model training utilities, as Memori focuses specifically on AI ### 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 Memori or MemOS more popular on GitHub? Memori has more GitHub stars (15,549 vs 10,135). Stars measure visibility, not whether either tool fits your constraints. ### Are Memori and MemOS open source? Yes - both are open-source projects on GitHub (Memori: Other, MemOS: Apache-2.0). ### Where can I find alternatives to Memori or MemOS? GraphCanon lists graph-backed alternatives at /tools/memorilabs-memori/alternatives and /tools/memtensor-memos/alternatives (/tools/memorilabs-memori/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/memorilabs-memori-vs-memtensor-memos.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Memori or MemOS? Memori: 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 Memori and MemOS? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Memori: /tools/memorilabs-memori/trust; MemOS: /tools/memtensor-memos/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=memorilabs-memori`](/api/graphcanon/graph?tool=memorilabs-memori) - 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/_