--- title: "mengram vs MemOS" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/alibaizhanov-mengram-vs-memtensor-memos" tools: ["alibaizhanov-mengram", "memtensor-memos"] --- # mengram vs MemOS *GraphCanon updated Sep 20, 2026* ## Verdict Pick mengram if mengram provides human-like memory capabilities for AI agents, including semantic, episodic, and procedural memory, enabling learning from experiences and failures. It offers free API access, Python and JS SDKs, and is a; pick MemOS if memOS is a self-evolving operating system tailored for LSTM systems and AI agents, offering ultra-persistent memory, hybrid retrieval technologies, and skill reuse. [mengram](https://mengram.io) reports 196 GitHub stars, 34 forks, and 25 open issues, last pushed Sep 16, 2026. [MemOS](https://memos.openmem.net) has 11k stars, 994 forks, and 84 open issues, last pushed Aug 18, 2026. Figures are from public GitHub metadata via [mengram's repository](https://github.com/alibaizhanov/mengram) and [MemOS's repository](https://github.com/MemTensor/MemOS). | | [mengram](/tools/alibaizhanov-mengram.md) | [MemOS](/tools/memtensor-memos.md) | | --- | --- | --- | | Tagline | Human-like memory for AI agents | Self-evolving memory OS for LLM & AI Agents: ultra-persistent memory, hybrid-retrieval, and cross-task skill reuse | | Stars | 196 | 10,758 | | Forks | 34 | 994 | | Open issues | 25 | 84 | | Language | Python | TypeScript | | Adopt for | mengram provides human-like memory capabilities for AI agents, including semantic, episodic, and procedural memory, enabling learning from experiences and failures. It offers free API access, Python and JS SDKs, and is a | MemOS is a self-evolving operating system tailored for LSTM systems and AI agents, offering ultra-persistent memory, hybrid retrieval technologies, and skill reuse across tasks. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MemOS is available under the Apache-2.0 license, allowing you to use it freely with certain conditions. | | Categories | AI Agents, Data & Retrieval | AI Agents, Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [mengram](/tools/alibaizhanov-mengram.md) | [MemOS](/tools/memtensor-memos.md) | | --- | --- | --- | | Days since push | 1d | 0d | | Open issues (now) | 25 | 84 | | Stars delta | +12 (30d) | +497 (30d) | | Open issues delta | -2 (30d) | +4 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/alibaizhanov-mengram/trust.md) | [trust report](/tools/memtensor-memos/trust.md) | ## Decision facts: mengram - **Adopt for:** mengram provides human-like memory capabilities for AI agents, including semantic, episodic, and procedural memory, enabling learning from experiences and failures. It offers free API access, Python and JS SDKs, and is a ## Decision facts: MemOS - **Pricing:** unknown - Pricing details are not clearly specified in the repository data, but the self-hosted option provides flexibility at the expense of managing infrastructure costs. - **Requirements:** Requires Docker; Self-hosting MemOS typically requires setting up Neo4j and Qdrant. The cloud API or OpenClaw Cloud Plugin options do not require these components. - **Adopt for:** MemOS is a self-evolving operating system tailored for LSTM systems and AI agents, offering ultra-persistent memory, hybrid retrieval technologies, and skill reuse across tasks. - **License detail:** MemOS is available under the Apache-2.0 license, allowing you to use it freely with certain conditions. ## Choose when ### Choose mengram if… - mengram is primarily Python; MemOS is TypeScript. - Tags unique to mengram: agent-memory, ai-agents, ai-memory, claude-desktop. - When you need an AI agent that can learn from its experiences and failures, leveraging semantic, episodic, and procedural memory capabilities. ### Choose MemOS if… - MemOS is primarily TypeScript; mengram is Python. - Pricing: Pricing details are not clearly specified in the repository data, but the self-hosted option provides flexibility at the expense of managing infrastructure costs.. - Requirements: Requires Docker; Self-hosting MemOS typically requires setting up Neo4j and Qdrant. The cloud API or OpenClaw Cloud Plugin options do not require these components.. - Tags unique to MemOS: agent, agentic-ai, llm, long-term-memory. - If you require ultra-persistent memory management within your application for long-term storage. ## When NOT to use mengram - If your project does not require the specific memory capabilities of semantic, episodic, and procedural memory, mengram might offer more features than necessary. - If you are looking for a tool that does not require the setup of an MCP server, mengram might not be the best fit as it involves configuring this server. - For projects that do not need integration with LangChain, CrewAI, or OpenClaw, the specific integrations provided by mengram may not be beneficial. - If your AI agent development does not benefit from the use of pgvector, mengram's recent debugging efforts in this area may not be relevant to your needs. ## When NOT to use MemOS - If you prefer fully managed solutions and have no preference over where your data is stored (hosted in MemOS Cloud). - When minimal setup overhead is a critical requirement since self-hosting MemOS necessitates Neo4j and Qdrant for optimal performance. - In scenarios preferring simpler on-device solutions as MemOS could be more complex to set up compared to other lightweight plugins or services. ## Common questions ### What is the difference between mengram and MemOS? mengram: Human-like memory for AI agents. MemOS: Self-evolving memory OS for LLM & AI Agents: ultra-persistent memory, hybrid-retrieval, and cross-task skill reuse. See the comparison table for live GitHub stats and shared categories. ### When should I choose mengram over MemOS? Choose mengram over MemOS when mengram is primarily Python; MemOS is TypeScript; Tags unique to mengram: agent-memory, ai-agents, ai-memory, claude-desktop; When you need an AI agent that can learn from its experiences and failures, leveraging semantic, episodic, and procedural memory capabilities. ### When should I choose MemOS over mengram? Choose MemOS over mengram when MemOS is primarily TypeScript; mengram is Python; Pricing: Pricing details are not clearly specified in the repository data, but the self-hosted option provides flexibility at the expense of managing infrastructure costs.; Requirements: Requires Docker; Self-hosting MemOS typically requires setting up Neo4j and Qdrant. The cloud API or OpenClaw Cloud Plugin options do not require these components.; Tags unique to MemOS: agent, agentic-ai, llm, long-term-memory; If you require ultra-persistent memory management within your application for long-term storage. ### When should I avoid mengram? If your project does not require the specific memory capabilities of semantic, episodic, and procedural memory, mengram might offer more features than necessary. If you are looking for a tool that does not require the setup of an MCP server, mengram might not be the best fit as it involves configuring this server. For projects that do not need integration with LangChain, CrewAI, or OpenClaw, the specific integrations provided by mengram may not be beneficial. If your AI agent development does not benefit from the use of pgvector, mengram's recent debugging efforts in this area may not be relevant to your needs. ### When should I avoid MemOS? If you prefer fully managed solutions and have no preference over where your data is stored (hosted in MemOS Cloud). When minimal setup overhead is a critical requirement since self-hosting MemOS necessitates Neo4j and Qdrant for optimal performance. In scenarios preferring simpler on-device solutions as MemOS could be more complex to set up compared to other lightweight plugins or services. ### Is mengram or MemOS more popular on GitHub? MemOS has more GitHub stars (10,758 vs 196). Stars measure visibility, not whether either tool fits your constraints. ### Are mengram and MemOS open source? Yes - both are open-source projects on GitHub (mengram: Apache-2.0, MemOS: Apache-2.0). ### Where can I find alternatives to mengram or MemOS? GraphCanon lists graph-backed alternatives at [mengram alternatives](/tools/alibaizhanov-mengram/alternatives) and [MemOS alternatives](/tools/memtensor-memos/alternatives) ([mengram markdown twin](/tools/alibaizhanov-mengram/alternatives.md), [MemOS markdown twin](/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 [this comparison](/compare/alibaizhanov-mengram-vs-memtensor-memos.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, mengram or MemOS? mengram: Very 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 mengram and MemOS? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [mengram trust report](/tools/alibaizhanov-mengram/trust); [MemOS trust report](/tools/memtensor-memos/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=alibaizhanov-mengram`](/api/graphcanon/graph?tool=alibaizhanov-mengram) - 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/_