--- title: "memU vs automem" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/nevamind-ai-memu-vs-verygoodplugins-automem" tools: ["nevamind-ai-memu", "verygoodplugins-automem"] --- # memU vs automem *GraphCanon updated Aug 26, 2026* ## Verdict Pick memU if memU offers fast memory retrieval and self-evolving skills for AI agents at lower operational costs; pick automem if autoMem leverages both graph and vector database technologies to provide AI assistants with durable relational memory. [memU](https://memu.pro) reports 14k GitHub stars, 1.1k forks, and 116 open issues, last pushed Aug 21, 2026. [automem](https://automem.ai/) has 802 stars, 102 forks, and 15 open issues, last pushed Aug 14, 2026. Figures are from public GitHub metadata via [memU's repository](https://github.com/NevaMind-AI/memU) and [automem's repository](https://github.com/verygoodplugins/automem). | | [memU](/tools/nevamind-ai-memu.md) | [automem](/tools/verygoodplugins-automem.md) | | --- | --- | --- | | Tagline | Personal memory for agents with fast retrieval and self-evolving skills | Graph-vector memory service for durable, relational AI assistant memory | | Stars | 14,347 | 802 | | Forks | 1,062 | 102 | | Open issues | 116 | 15 | | Language | Python | Python | | Adopt for | memU offers fast memory retrieval and self-evolving skills for AI agents at lower operational costs. | AutoMem leverages both graph and vector database technologies to provide AI assistants with durable relational memory. | | Persona | - | - | | Runtime | - | - | | License | Other | AutoMem is licensed under the MIT License, which means it is free to use, modify, and distribute as long as license terms are met. | | Categories | AI Agents | AI Agents, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [memU](/tools/nevamind-ai-memu.md) | [automem](/tools/verygoodplugins-automem.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 4d | 7d | | Open issues (now) | 116 | 15 | | Stars delta | +285 (30d) | +9 (30d) | | Open issues delta | +22 (30d) | +4 (30d) | | Full report | [trust report](/tools/nevamind-ai-memu/trust.md) | [trust report](/tools/verygoodplugins-automem/trust.md) | ## Decision facts: memU - **Adopt for:** memU offers fast memory retrieval and self-evolving skills for AI agents at lower operational costs. ## Decision facts: automem - **Pricing:** freemium - Free for open-source use, with no explicit commercial licensing information provided. - **Adopt for:** AutoMem leverages both graph and vector database technologies to provide AI assistants with durable relational memory. - **License detail:** AutoMem is licensed under the MIT License, which means it is free to use, modify, and distribute as long as license terms are met. ## Choose when ### Choose memU if… - License: memU is Other, automem is MIT. - Tags unique to memU: agent-memory, claude-skills, harness, loop-engineering. - Use memU when your project requires fast access to stored agent actions and memories which can adapt over time without needing human intervention. ### Choose automem if… - License: automem is MIT, memU is Other. - Pricing: Free for open-source use, with no explicit commercial licensing information provided.. - Tags unique to automem: ai-memory, anthropic, falkordb, graph-database. - Also covers Vector Databases. - automem ships Docker support for self-hosted deployment. - Use AutoMem when you need an AI assistant capable of maintaining rich, relational memories over time. ## When NOT to use memU - Avoid using memU if your application mandates proprietary memory systems that are tightly integrated with specific agent architectures not supported by memU. - memU is unsuitable for projects where the customizability and control over skill evolution are limited or require extensive manual adjustments, as this tool emphasizes self-evolving features. ## When NOT to use automem - Avoid using AutoMem if your application does not benefit from persistent memory or relational context, as it might add unnecessary overhead. - If you require a simpler key-value storage system for less complex or non-relational data, AutoMem's graph and vector capabilities may be overkill. ## Common questions ### What is the difference between memU and automem? memU: Personal memory for agents with fast retrieval and self-evolving skills. automem: Graph-vector memory service for durable, relational AI assistant memory. See the comparison table for live GitHub stats and shared categories. ### When should I choose memU over automem? Choose memU over automem when License: memU is Other, automem is MIT; Tags unique to memU: agent-memory, claude-skills, harness, loop-engineering; Use memU when your project requires fast access to stored agent actions and memories which can adapt over time without needing human intervention. ### When should I choose automem over memU? Choose automem over memU when License: automem is MIT, memU is Other; Pricing: Free for open-source use, with no explicit commercial licensing information provided.; Tags unique to automem: ai-memory, anthropic, falkordb, graph-database; Also covers Vector Databases; automem ships Docker support for self-hosted deployment; Use AutoMem when you need an AI assistant capable of maintaining rich, relational memories over time. ### When should I avoid memU? Avoid using memU if your application mandates proprietary memory systems that are tightly integrated with specific agent architectures not supported by memU. memU is unsuitable for projects where the customizability and control over skill evolution are limited or require extensive manual adjustments, as this tool emphasizes self-evolving features. ### When should I avoid automem? Avoid using AutoMem if your application does not benefit from persistent memory or relational context, as it might add unnecessary overhead. If you require a simpler key-value storage system for less complex or non-relational data, AutoMem's graph and vector capabilities may be overkill. ### Is memU or automem more popular on GitHub? memU has more GitHub stars (14,347 vs 802). Stars measure visibility, not whether either tool fits your constraints. ### Are memU and automem open source? Yes - both are open-source projects on GitHub (memU: Other, automem: MIT). ### Where can I find alternatives to memU or automem? GraphCanon lists graph-backed alternatives at [memU alternatives](/tools/nevamind-ai-memu/alternatives) and [automem alternatives](/tools/verygoodplugins-automem/alternatives) ([memU markdown twin](/tools/nevamind-ai-memu/alternatives.md), [automem markdown twin](/tools/verygoodplugins-automem/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/nevamind-ai-memu-vs-verygoodplugins-automem.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, memU or automem? memU: Very active. automem: 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 memU and automem? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [memU trust report](/tools/nevamind-ai-memu/trust); [automem trust report](/tools/verygoodplugins-automem/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=nevamind-ai-memu`](/api/graphcanon/graph?tool=nevamind-ai-memu) - 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/_