--- title: "memory-os vs Memori" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/claudiodrews-memory-os-vs-memorilabs-memori" tools: ["claudiodrews-memory-os", "memorilabs-memori"] --- # memory-os vs Memori Neutral, constraint-first comparison with live GitHub stats. | | [memory-os](/tools/claudiodrews-memory-os.md) | [Memori](/tools/memorilabs-memori.md) | | --- | --- | --- | | Tagline | A 7-layer memory operating system for Hermes Agent | Memory infrastructure for AI agents that captures actions and conversations into a structured, persistent state. | | Stars | 1,233 | 15,549 | | Forks | 117 | 2,784 | | Open issues | 7 | 21 | | Language | Python | Python | | Adopt for | Provides a comprehensive, local-first memory operating system for Hermes Agent with seven layers of memory infrastructure including Qdrant and structured facts. | Memori is designed for enterprise users seeking seamless memory infrastructure that integrates with existing data architectures across multiple deployment environments. | | Persona | - | - | | Runtime | - | - | | License | MIT | Memori is licensed under the Apache License 2.0. | | Categories | AI Agents, Vector Databases | Model Training, AI Agents | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [memory-os](/tools/claudiodrews-memory-os.md) | [Memori](/tools/memorilabs-memori.md) | | --- | --- | --- | | Days since push | 27d | 22d | | Open issues (now) | 7 | 21 | | Owner type | User | Organization | | Security scan | 56 low (56 low) | No lockfile | | Full report | [trust report](/tools/claudiodrews-memory-os/trust.md) | [trust report](/tools/memorilabs-memori/trust.md) | **Typed relationship:** memory-os _(alternative)_ Memori Memori and Memory OS both serve as memory infrastructure for AI agents, providing persistent, structured state. However, they approach this with different designs and features. ## Shared compatibility - **Python**: [memory-os](/tools/claudiodrews-memory-os.md) - Python runtime; [Memori](/tools/memorilabs-memori.md) - Python runtime ## Decision facts: memory-os - **Requirements:** Min 8 GB RAM; Requires Docker; Hermes Agent + Docker (Qdrant + Redis + ARQ Worker) are required.; Python 3.11+ is needed. - **Adopt for:** Provides a comprehensive, local-first memory operating system for Hermes Agent with seven layers of memory infrastructure including Qdrant and structured facts. ## 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. ## Choose when ### Choose memory-os if… - License: memory-os is MIT, Memori is Other. - Requirements: Min 8 GB RAM; Requires Docker; Hermes Agent + Docker (Qdrant + Redis + ARQ Worker) are required.; Python 3.11+ is needed.. - Memori and Memory OS both serve as memory infrastructure for AI agents, providing persistent, structured state. However, they approach this with different designs and features. - Tags unique to memory-os: context-injection, persistent-memory, ground-truth, docker. - Also covers Vector Databases. - When you need persistent and structured memory that doesn't rely on cloud services. ### Choose Memori if… - License: Memori is Other, memory-os is MIT. - 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 Memory OS both serve as memory infrastructure for AI agents, providing persistent, structured state. However, they approach this with different designs and features. - Tags unique to Memori: stateful, memory-management, llm-agnostic, agent. - Also covers Model Training. - Memori ships Docker support for self-hosted deployment. - When you need a system to turn agent execution and conversation into structured, persistent state without disrupting your current IT environment. ## When NOT to use memory-os - When you prefer a tool that is fully cloud-based or requires minimal local setup. - If your environment does not support local memory infrastructure such as Docker, Qdrant, Redis, and ARQ Worker. - For scenarios requiring lightweight memory solutions without the complexity of multiple layers and self-hosted services. ## 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 ## Common questions ### What is the difference between memory-os and Memori? memory-os: A 7-layer memory operating system for Hermes Agent. Memori: Memory infrastructure for AI agents that captures actions and conversations into a structured, persistent state.. See the comparison table for live GitHub stats and shared categories. ### When should I choose memory-os over Memori? Choose memory-os over Memori when License: memory-os is MIT, Memori is Other; Requirements: Min 8 GB RAM; Requires Docker; Hermes Agent + Docker (Qdrant + Redis + ARQ Worker) are required.; Python 3.11+ is needed.; Memori and Memory OS both serve as memory infrastructure for AI agents, providing persistent, structured state. However, they approach this with different designs and features; Tags unique to memory-os: context-injection, persistent-memory, ground-truth, docker; Also covers Vector Databases; When you need persistent and structured memory that doesn't rely on cloud services. ### When should I choose Memori over memory-os? Choose Memori over memory-os when License: Memori is Other, memory-os is MIT; 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 Memory OS both serve as memory infrastructure for AI agents, providing persistent, structured state. However, they approach this with different designs and features; Tags unique to Memori: stateful, memory-management, llm-agnostic, agent; Also covers Model Training; Memori ships Docker support for self-hosted deployment; When you need a system to turn agent execution and conversation into structured, persistent state without disrupting your current IT environment. ### When should I avoid memory-os? When you prefer a tool that is fully cloud-based or requires minimal local setup. If your environment does not support local memory infrastructure such as Docker, Qdrant, Redis, and ARQ Worker. For scenarios requiring lightweight memory solutions without the complexity of multiple layers and self-hosted services. ### 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 ### Is memory-os or Memori more popular on GitHub? Memori has more GitHub stars (15,549 vs 1,233). Stars measure visibility, not whether either tool fits your constraints. ### Are memory-os and Memori open source? Yes - both are open-source projects on GitHub (memory-os: MIT, Memori: Other). ### Where can I find alternatives to memory-os or Memori? GraphCanon lists graph-backed alternatives at /tools/claudiodrews-memory-os/alternatives and /tools/memorilabs-memori/alternatives (/tools/claudiodrews-memory-os/alternatives.md, /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 /compare/claudiodrews-memory-os-vs-memorilabs-memori.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, memory-os or Memori? memory-os: Active. Memori: 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 memory-os and Memori? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: memory-os: /tools/claudiodrews-memory-os/trust; Memori: /tools/memorilabs-memori/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=claudiodrews-memory-os`](/api/graphcanon/graph?tool=claudiodrews-memory-os) - 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/_