--- title: "OpenMemory vs memvid" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/caviraoss-openmemory-vs-memvid-memvid" tools: ["caviraoss-openmemory", "memvid-memvid"] --- # OpenMemory vs memvid Neutral, constraint-first comparison with live GitHub stats. | | [OpenMemory](/tools/caviraoss-openmemory.md) | [memvid](/tools/memvid-memvid.md) | | --- | --- | --- | | Tagline | Local persistent memory store for LLM applications | Memory layer for AI Agents | | Stars | 4,315 | 15,736 | | Forks | 489 | 1,360 | | Open issues | 13 | 21 | | Language | TypeScript | Rust | | Adopt for | Decision-critical facts for OpenMemory: Local persistent memory store for LLM applications. | Memvid is a Rust-based single-file memory layer for AI agents that offers high accuracy, low latency, and portable long-term memory without the need for complex infrastructure. | | Persona | - | - | | Runtime | - | - | | License | OpenMemory is distributed under the Apache-2.0 license. | Memvid is licensed under Apache-2.0, which allows free usage in both open-source and commercial projects with attribution. | | Categories | AI Agents, Evaluation & Observability, Data & Retrieval, Model Training | AI Agents | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [OpenMemory](/tools/caviraoss-openmemory.md) | [memvid](/tools/memvid-memvid.md) | | --- | --- | --- | | Maintenance | Active (82%) | Steady (60%) | | Days since push | 11d | 41d | | Open issues (now) | 13 | 21 | | Full report | [trust report](/tools/caviraoss-openmemory/trust.md) | [trust report](/tools/memvid-memvid/trust.md) | **Typed relationship:** OpenMemory _(alternative)_ memvid Memvid and OpenMemory both aim to provide a memory layer for AI Agents, focusing on state persistence over sessions. ## Decision facts: OpenMemory - **Requirements:** Requires Python or Node.js for SDK usage; Local SQLite support by default with optional configuration for external DB like Postgres; Dependencies include TypeScript environment for setup and use - **Adopt for:** Decision-critical facts for OpenMemory: Local persistent memory store for LLM applications. - **License detail:** OpenMemory is distributed under the Apache-2.0 license. ## Decision facts: memvid - **Requirements:** Operates independently of databases or server infrastructure. - **Adopt for:** Memvid is a Rust-based single-file memory layer for AI agents that offers high accuracy, low latency, and portable long-term memory without the need for complex infrastructure. - **License detail:** Memvid is licensed under Apache-2.0, which allows free usage in both open-source and commercial projects with attribution. ## Choose when ### Choose OpenMemory if… - OpenMemory is primarily TypeScript; memvid is Rust. - Requirements: Requires Python or Node.js for SDK usage; Local SQLite support by default with optional configuration for external DB like Postgres; Dependencies include TypeScript environment for setup and use. - Memvid and OpenMemory both aim to provide a memory layer for AI Agents, focusing on state persistence over sessions. - Tags unique to OpenMemory: self-hosted, llm, ai-infrastructure, python. - Also covers Evaluation & Observability, Data & Retrieval, Model Training. - OpenMemory ships Docker support for self-hosted deployment. - - You require real long-term memory capabilities that go beyond simple embeddings in a table ### Choose memvid if… - memvid is primarily Rust; OpenMemory is TypeScript. - Requirements: Operates independently of databases or server infrastructure.. - Memvid and OpenMemory both aim to provide a memory layer for AI Agents, focusing on state persistence over sessions. - Tags unique to memvid: memory, retrieval-augmented-generation. - - Your use case requires ultra-low latency retrieval where every millisecond counts. ## When NOT to use OpenMemory - - When a fully managed cloud service with no local setup and self-hosted capabilities is preferred - - If the project specifically requires integration only with vector databases or RAG (Retrieval-Augmented Generation) systems - - Projects that are not compatible with TypeScript for backend or JavaScript for application use, as OpenMemory's SDKs are built for these environments - - Situations where the project cannot benefit from local-first data storage and prefers cloud-based solutions ## When NOT to use memvid - - Your application demands real-time updates to memory contents across multiple agents without manual intervention. - - The use case involves large-scale data that necessitates a distributed database for handling the scale. ## Common questions ### What is the difference between OpenMemory and memvid? OpenMemory: Local persistent memory store for LLM applications. memvid: Memory layer for AI Agents. See the comparison table for live GitHub stats and shared categories. ### When should I choose OpenMemory over memvid? Choose OpenMemory over memvid when OpenMemory is primarily TypeScript; memvid is Rust; Requirements: Requires Python or Node.js for SDK usage; Local SQLite support by default with optional configuration for external DB like Postgres; Dependencies include TypeScript environment for setup and use; Memvid and OpenMemory both aim to provide a memory layer for AI Agents, focusing on state persistence over sessions; Tags unique to OpenMemory: self-hosted, llm, ai-infrastructure, python; Also covers Evaluation & Observability, Data & Retrieval, Model Training; OpenMemory ships Docker support for self-hosted deployment; - You require real long-term memory capabilities that go beyond simple embeddings in a table. ### When should I choose memvid over OpenMemory? Choose memvid over OpenMemory when memvid is primarily Rust; OpenMemory is TypeScript; Requirements: Operates independently of databases or server infrastructure.; Memvid and OpenMemory both aim to provide a memory layer for AI Agents, focusing on state persistence over sessions; Tags unique to memvid: memory, retrieval-augmented-generation; - Your use case requires ultra-low latency retrieval where every millisecond counts. ### When should I avoid OpenMemory? - When a fully managed cloud service with no local setup and self-hosted capabilities is preferred - If the project specifically requires integration only with vector databases or RAG (Retrieval-Augmented Generation) systems - Projects that are not compatible with TypeScript for backend or JavaScript for application use, as OpenMemory's SDKs are built for these environments - Situations where the project cannot benefit from local-first data storage and prefers cloud-based solutions ### When should I avoid memvid? - Your application demands real-time updates to memory contents across multiple agents without manual intervention. - The use case involves large-scale data that necessitates a distributed database for handling the scale. ### Is OpenMemory or memvid more popular on GitHub? memvid has more GitHub stars (15,736 vs 4,315). Stars measure visibility, not whether either tool fits your constraints. ### Are OpenMemory and memvid open source? Yes - both are open-source projects on GitHub (OpenMemory: Apache-2.0, memvid: Apache-2.0). ### Where can I find alternatives to OpenMemory or memvid? GraphCanon lists graph-backed alternatives at /tools/caviraoss-openmemory/alternatives and /tools/memvid-memvid/alternatives (/tools/caviraoss-openmemory/alternatives.md, /tools/memvid-memvid/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 /compare/caviraoss-openmemory-vs-memvid-memvid.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, OpenMemory or memvid? OpenMemory: Active. memvid: Steady. 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 OpenMemory and memvid? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: OpenMemory: /tools/caviraoss-openmemory/trust; memvid: /tools/memvid-memvid/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=caviraoss-openmemory`](/api/graphcanon/graph?tool=caviraoss-openmemory) - 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/_