--- title: "mengram vs memsearch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/alibaizhanov-mengram-vs-zilliztech-memsearch" tools: ["alibaizhanov-mengram", "zilliztech-memsearch"] --- # mengram vs memsearch *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 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. [mengram](https://mengram.io) reports 196 GitHub stars, 34 forks, and 25 open issues, last pushed Sep 16, 2026. [memsearch](https://zilliztech.github.io/memsearch/) has 2.6k stars, 253 forks, and 254 open issues, last pushed Sep 20, 2026. Figures are from public GitHub metadata via [mengram's repository](https://github.com/alibaizhanov/mengram) and [memsearch's repository](https://github.com/zilliztech/memsearch). | | [mengram](/tools/alibaizhanov-mengram.md) | [memsearch](/tools/zilliztech-memsearch.md) | | --- | --- | --- | | Tagline | Human-like memory for AI agents | A persistent, unified memory layer for all your AI agents backed by Markdown and Milvus. | | Stars | 196 | 2,627 | | Forks | 34 | 253 | | Open issues | 25 | 254 | | Language | Python | Python | | 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 | 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 | Apache-2.0 | MIT | | Categories | AI Agents, Data & Retrieval | AI Agents, Data & Retrieval, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [mengram](/tools/alibaizhanov-mengram.md) | [memsearch](/tools/zilliztech-memsearch.md) | | --- | --- | --- | | Days since push | 1d | 0d | | Open issues (now) | 25 | 254 | | Stars delta | +12 (30d) | +291 (30d) | | Open issues delta | -2 (30d) | +23 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/alibaizhanov-mengram/trust.md) | [trust report](/tools/zilliztech-memsearch/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: 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 mengram if… - License: mengram is Apache-2.0, memsearch is MIT. - Tags unique to mengram: ai-agents, ai-memory, claude-desktop, cognitive-architecture. - mengram ships Docker support for self-hosted deployment. - When you need an AI agent that can learn from its experiences and failures, leveraging semantic, episodic, and procedural memory capabilities. ### Choose memsearch if… - License: memsearch is MIT, mengram is Apache-2.0. - Tags unique to memsearch: long-term-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 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 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 mengram and memsearch? mengram: Human-like memory for AI agents. 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 mengram over memsearch? Choose mengram over memsearch when License: mengram is Apache-2.0, memsearch is MIT; Tags unique to mengram: ai-agents, ai-memory, claude-desktop, cognitive-architecture; mengram ships Docker support for self-hosted deployment; 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 memsearch over mengram? Choose memsearch over mengram when License: memsearch is MIT, mengram is Apache-2.0; Tags unique to memsearch: long-term-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 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 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 mengram or memsearch more popular on GitHub? memsearch has more GitHub stars (2,627 vs 196). Stars measure visibility, not whether either tool fits your constraints. ### Are mengram and memsearch open source? Yes - both are open-source projects on GitHub (mengram: Apache-2.0, memsearch: MIT). ### Where can I find alternatives to mengram or memsearch? GraphCanon lists graph-backed alternatives at [mengram alternatives](/tools/alibaizhanov-mengram/alternatives) and [memsearch alternatives](/tools/zilliztech-memsearch/alternatives) ([mengram markdown twin](/tools/alibaizhanov-mengram/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/alibaizhanov-mengram-vs-zilliztech-memsearch.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, mengram or memsearch? mengram: 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 mengram and memsearch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [mengram trust report](/tools/alibaizhanov-mengram/trust); [memsearch trust report](/tools/zilliztech-memsearch/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/_