--- title: "MemOS vs memsearch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/memtensor-memos-vs-zilliztech-memsearch" tools: ["memtensor-memos", "zilliztech-memsearch"] --- # MemOS vs memsearch *GraphCanon updated Aug 18, 2026* ## Verdict 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 across tasks; pick memsearch if memsearch is a hybrid memory management solution for AI agents with Markdown and Milvus backing, ideal for rich semantic search and long-term data storage. [MemOS](https://memos.openmem.net) reports 11k GitHub stars, 994 forks, and 84 open issues, last pushed Aug 18, 2026. [memsearch](https://zilliztech.github.io/memsearch/) has 2.3k stars, 205 forks, and 231 open issues, last pushed Jul 22, 2026. Figures are from public GitHub metadata via [MemOS's repository](https://github.com/MemTensor/MemOS) and [memsearch's repository](https://github.com/zilliztech/memsearch). | | [MemOS](/tools/memtensor-memos.md) | [memsearch](/tools/zilliztech-memsearch.md) | | --- | --- | --- | | Tagline | Self-evolving memory OS for LLM & AI Agents: ultra-persistent memory, hybrid-retrieval, and cross-task skill reuse | A persistent, unified memory layer for all your AI agents backed by Markdown and Milvus. | | Stars | 10,758 | 2,336 | | Forks | 994 | 205 | | Open issues | 84 | 231 | | Language | TypeScript | Python | | 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. | memsearch is a hybrid memory management solution for AI agents with Markdown and Milvus backing, ideal for rich semantic search and long-term data storage. | | Persona | - | - | | Runtime | - | - | | License | MemOS is available under the Apache-2.0 license, allowing you to use it freely with certain conditions. | MIT | | Categories | AI Agents, Data & Retrieval | AI Agents, Data & Retrieval, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [MemOS](/tools/memtensor-memos.md) | [memsearch](/tools/zilliztech-memsearch.md) | | --- | --- | --- | | Open issues (now) | 84 | 231 | | Stars delta | +497 (30d) | Unknown | | Open issues delta | +4 (30d) | Unknown | | Full report | [trust report](/tools/memtensor-memos/trust.md) | [trust report](/tools/zilliztech-memsearch/trust.md) | ## 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. ## Decision facts: memsearch - **Adopt for:** memsearch is a hybrid memory management solution for AI agents with Markdown and Milvus backing, ideal for rich semantic search and long-term data storage. ## Choose when ### Choose MemOS if… - MemOS is primarily TypeScript; memsearch is Python. - License: MemOS is Apache-2.0, memsearch is MIT. - 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, memory-management. - MemOS ships Docker support for self-hosted deployment. - If you require ultra-persistent memory management within your application for long-term storage. ### Choose memsearch if… - memsearch is primarily Python; MemOS is TypeScript. - License: memsearch is MIT, MemOS is Apache-2.0. - Tags unique to memsearch: agent-memory, milvus, semantic-search. - Also covers Vector Databases. - When you need robust integration with AI agents like Claude Code or Codex ## 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. ## When NOT to use memsearch - If your application doesn't require integration with specific AI agents like Claude Code - In cases where only simple text data storage without semantic search is needed ## Common questions ### What is the difference between MemOS and memsearch? MemOS: Self-evolving memory OS for LLM & AI Agents: ultra-persistent memory, hybrid-retrieval, and cross-task skill reuse. memsearch: A persistent, unified memory layer for all your AI agents backed by Markdown and Milvus.. See the comparison table for live GitHub stats and shared categories. ### When should I choose MemOS over memsearch? Choose MemOS over memsearch when MemOS is primarily TypeScript; memsearch is Python; License: MemOS is Apache-2.0, memsearch is MIT; 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, memory-management; MemOS ships Docker support for self-hosted deployment; If you require ultra-persistent memory management within your application for long-term storage. ### When should I choose memsearch over MemOS? Choose memsearch over MemOS when memsearch is primarily Python; MemOS is TypeScript; License: memsearch is MIT, MemOS is Apache-2.0; Tags unique to memsearch: agent-memory, milvus, semantic-search; Also covers Vector Databases; When you need robust integration with AI agents like Claude Code or Codex. ### 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. ### When should I avoid memsearch? If your application doesn't require integration with specific AI agents like Claude Code In cases where only simple text data storage without semantic search is needed ### Is MemOS or memsearch more popular on GitHub? MemOS has more GitHub stars (10,758 vs 2,336). Stars measure visibility, not whether either tool fits your constraints. ### Are MemOS and memsearch open source? Yes - both are open-source projects on GitHub (MemOS: Apache-2.0, memsearch: MIT). ### Where can I find alternatives to MemOS or memsearch? GraphCanon lists graph-backed alternatives at [MemOS alternatives](/tools/memtensor-memos/alternatives) and [memsearch alternatives](/tools/zilliztech-memsearch/alternatives) ([MemOS markdown twin](/tools/memtensor-memos/alternatives.md), [memsearch markdown twin](/tools/zilliztech-memsearch/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/memtensor-memos-vs-zilliztech-memsearch.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, MemOS or memsearch? MemOS: Very active. memsearch: 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 MemOS and memsearch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [MemOS trust report](/tools/memtensor-memos/trust); [memsearch trust report](/tools/zilliztech-memsearch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=memtensor-memos`](/api/graphcanon/graph?tool=memtensor-memos) - 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/_