--- title: "memfree vs automem" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/memfreeme-memfree-vs-verygoodplugins-automem" tools: ["memfreeme-memfree", "verygoodplugins-automem"] --- # memfree vs automem *GraphCanon updated Aug 21, 2026* ## Verdict Pick memfree if memfree; pick automem if autoMem leverages both graph and vector database technologies to provide AI assistants with durable relational memory. [memfree](https://www.memfree.me?ref=github.com) reports 1.5k GitHub stars, 209 forks, and 17 open issues, last pushed Jul 6, 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 [memfree's repository](https://github.com/memfreeme/memfree) and [automem's repository](https://github.com/verygoodplugins/automem). | | [memfree](/tools/memfreeme-memfree.md) | [automem](/tools/verygoodplugins-automem.md) | | --- | --- | --- | | Tagline | Hybrid AI Search Engine & AI Page Generator | Graph-vector memory service for durable, relational AI assistant memory | | Stars | 1,508 | 802 | | Forks | 209 | 102 | | Open issues | 17 | 15 | | Language | TypeScript | Python | | Adopt for | memfree | AutoMem leverages both graph and vector database technologies to provide AI assistants with durable relational memory. | | Persona | - | - | | Runtime | - | - | | License | MIT | 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 | Data & Retrieval, Vector Databases | AI Agents, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [memfree](/tools/memfreeme-memfree.md) | [automem](/tools/verygoodplugins-automem.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Active (82%) | | Days since push | 32d | 7d | | Open issues (now) | 17 | 15 | | Stars delta | Unknown | +9 (30d) | | Open issues delta | Unknown | +4 (30d) | | Full report | [trust report](/tools/memfreeme-memfree/trust.md) | [trust report](/tools/verygoodplugins-automem/trust.md) | ## Decision facts: memfree - **Pricing:** freemium - Open-Source under MIT License, but commercial support might come with additional costs - **Requirements:** Min 4 GB RAM; Requires Docker; Requires TypeScript for development or customization.; Best-suited for environments that can leverage serverless capabilities and React-based UI generation. - **Adopt for:** memfree ## 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 memfree if… - memfree is primarily TypeScript; automem is Python. - Pricing: Open-Source under MIT License, but commercial support might come with additional costs. - Requirements: Min 4 GB RAM; Requires Docker; Requires TypeScript for development or customization.; Best-suited for environments that can leverage serverless capabilities and React-based UI generation.. - Tags unique to memfree: ai-search, hybrid-ai-search, page-generator, serverless-vector. - Also covers Data & Retrieval. - When you need a hybrid approach combining traditional indexing with vector-based searches for more efficient AI-powered querying. ### Choose automem if… - automem is primarily Python; memfree is TypeScript. - 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 AI Agents. - 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 memfree - When your requirements strictly demand pure vector database solutions without the aid of traditional indexing methods. - If you do not need an integrated page generator as part of your AI search capabilities. ## 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 memfree and automem? memfree: Hybrid AI Search Engine & AI Page Generator. 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 memfree over automem? Choose memfree over automem when memfree is primarily TypeScript; automem is Python; Pricing: Open-Source under MIT License, but commercial support might come with additional costs; Requirements: Min 4 GB RAM; Requires Docker; Requires TypeScript for development or customization.; Best-suited for environments that can leverage serverless capabilities and React-based UI generation.; Tags unique to memfree: ai-search, hybrid-ai-search, page-generator, serverless-vector; Also covers Data & Retrieval; When you need a hybrid approach combining traditional indexing with vector-based searches for more efficient AI-powered querying. ### When should I choose automem over memfree? Choose automem over memfree when automem is primarily Python; memfree is TypeScript; 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 AI Agents; 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 memfree? When your requirements strictly demand pure vector database solutions without the aid of traditional indexing methods. If you do not need an integrated page generator as part of your AI search capabilities. ### 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 memfree or automem more popular on GitHub? memfree has more GitHub stars (1,508 vs 802). Stars measure visibility, not whether either tool fits your constraints. ### Are memfree and automem open source? Yes - both are open-source projects on GitHub (memfree: MIT, automem: MIT). ### Where can I find alternatives to memfree or automem? GraphCanon lists graph-backed alternatives at [memfree alternatives](/tools/memfreeme-memfree/alternatives) and [automem alternatives](/tools/verygoodplugins-automem/alternatives) ([memfree markdown twin](/tools/memfreeme-memfree/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/memfreeme-memfree-vs-verygoodplugins-automem.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, memfree or automem? memfree: Steady. 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 memfree and automem? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [memfree trust report](/tools/memfreeme-memfree/trust); [automem trust report](/tools/verygoodplugins-automem/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=memfreeme-memfree`](/api/graphcanon/graph?tool=memfreeme-memfree) - 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/_