--- title: "OpenMemory vs embedbase" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/caviraoss-openmemory-vs-different-ai-embedbase" tools: ["caviraoss-openmemory", "different-ai-embedbase"] --- # OpenMemory vs embedbase *GraphCanon updated Aug 22, 2026* ## Verdict Pick OpenMemory if openMemory offers local storage tailored for AI agents like Claude Desktop and GitHub Copilot; pick embedbase if embedbase is a TypeScript-based API designed to facilitate the creation of Large Language Model (LLM) powered applications via integrations with embeddings and vector databases. [OpenMemory](https://openmemory.cavira.app) reports 4.5k GitHub stars, 501 forks, and 18 open issues, last pushed Aug 20, 2026. [embedbase](https://docs.embedbase.xyz) has 523 stars, 54 forks, and 35 open issues, last pushed Nov 27, 2024. Figures are from public GitHub metadata via [OpenMemory's repository](https://github.com/CaviraOSS/OpenMemory) and [embedbase's repository](https://github.com/different-ai/embedbase). | | [OpenMemory](/tools/caviraoss-openmemory.md) | [embedbase](/tools/different-ai-embedbase.md) | | --- | --- | --- | | Tagline | Local persistent memory store for LLM applications | A dead-simple API to build LLM-powered apps | | Stars | 4,457 | 523 | | Forks | 501 | 54 | | Open issues | 18 | 35 | | Language | TypeScript | TypeScript | | Adopt for | OpenMemory offers local storage tailored for AI agents like Claude Desktop and GitHub Copilot. | Embedbase is a TypeScript-based API designed to facilitate the creation of Large Language Model (LLM) powered applications via integrations with embeddings and vector databases. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Data & Retrieval, Vector Databases | Data & Retrieval, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [OpenMemory](/tools/caviraoss-openmemory.md) | [embedbase](/tools/different-ai-embedbase.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 632d | | Open issues (now) | 18 | 35 | | Stars delta | +100 (30d) | -1 (30d) | | Open issues delta | +5 (30d) | 0 (30d) | | Full report | [trust report](/tools/caviraoss-openmemory/trust.md) | [trust report](/tools/different-ai-embedbase/trust.md) | ## Decision facts: OpenMemory - **Adopt for:** OpenMemory offers local storage tailored for AI agents like Claude Desktop and GitHub Copilot. ## Decision facts: embedbase - **Adopt for:** Embedbase is a TypeScript-based API designed to facilitate the creation of Large Language Model (LLM) powered applications via integrations with embeddings and vector databases. ## Choose when ### Choose OpenMemory if… - License: OpenMemory is Apache-2.0, embedbase is MIT. - Tags unique to OpenMemory: ai-agents, long-term-memory, memory-engine. - OpenMemory ships Docker support for self-hosted deployment. - Need to integrate persistent memory support for specific LLMs like Claude. ### Choose embedbase if… - License: embedbase is MIT, OpenMemory is Apache-2.0. - Tags unique to embedbase: ai, artificial-intelligence, chatgpt, machine-learning. - * Use Embedbase if you require direct integration capabilities specifically designed for embeddings and vector databases, like pgvector or Supabase. ## When NOT to use OpenMemory - Require cloud-based memory storage services. - Your project must use languages other than TypeScript. - Seek a generic vector database without specific LLM integrations. ## When NOT to use embedbase - * Avoid using Embedbase if your application's technology stack cannot effectively integrate TypeScript, as its primary language support is in this framework and not others like Python. - * Do not use it when you need extensive customization options for the vector database configurations beyond what pgvector or Supabase offers. ## Common questions ### What is the difference between OpenMemory and embedbase? OpenMemory: Local persistent memory store for LLM applications. embedbase: A dead-simple API to build LLM-powered apps. See the comparison table for live GitHub stats and shared categories. ### When should I choose OpenMemory over embedbase? Choose OpenMemory over embedbase when License: OpenMemory is Apache-2.0, embedbase is MIT; Tags unique to OpenMemory: ai-agents, long-term-memory, memory-engine; OpenMemory ships Docker support for self-hosted deployment; Need to integrate persistent memory support for specific LLMs like Claude. ### When should I choose embedbase over OpenMemory? Choose embedbase over OpenMemory when License: embedbase is MIT, OpenMemory is Apache-2.0; Tags unique to embedbase: ai, artificial-intelligence, chatgpt, machine-learning; * Use Embedbase if you require direct integration capabilities specifically designed for embeddings and vector databases, like pgvector or Supabase. ### When should I avoid OpenMemory? Require cloud-based memory storage services. Your project must use languages other than TypeScript. Seek a generic vector database without specific LLM integrations. ### When should I avoid embedbase? * Avoid using Embedbase if your application's technology stack cannot effectively integrate TypeScript, as its primary language support is in this framework and not others like Python. * Do not use it when you need extensive customization options for the vector database configurations beyond what pgvector or Supabase offers. ### Is OpenMemory or embedbase more popular on GitHub? OpenMemory has more GitHub stars (4,457 vs 523). Stars measure visibility, not whether either tool fits your constraints. ### Are OpenMemory and embedbase open source? Yes - both are open-source projects on GitHub (OpenMemory: Apache-2.0, embedbase: MIT). ### Where can I find alternatives to OpenMemory or embedbase? GraphCanon lists graph-backed alternatives at [OpenMemory alternatives](/tools/caviraoss-openmemory/alternatives) and [embedbase alternatives](/tools/different-ai-embedbase/alternatives) ([OpenMemory markdown twin](/tools/caviraoss-openmemory/alternatives.md), [embedbase markdown twin](/tools/different-ai-embedbase/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/caviraoss-openmemory-vs-different-ai-embedbase.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, OpenMemory or embedbase? OpenMemory: Very active. embedbase: Dormant. 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 embedbase? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [OpenMemory trust report](/tools/caviraoss-openmemory/trust); [embedbase trust report](/tools/different-ai-embedbase/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/_