--- title: "embedbase vs chromem-go" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/different-ai-embedbase-vs-philippgille-chromem-go" tools: ["different-ai-embedbase", "philippgille-chromem-go"] --- # embedbase vs chromem-go *GraphCanon updated Aug 21, 2026* ## Verdict 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; pick chromem-go if chromem-go is an embeddable vector database for Go that provides a Chroma-like interface with no third-party dependencies, suitable for applications needing in-memory persistence and cosine similarity search capabilities. [embedbase](https://docs.embedbase.xyz) reports 524 GitHub stars, 55 forks, and 35 open issues, last pushed Nov 27, 2024. [chromem-go](https://github.com/philippgille/chromem-go) has 1.0k stars, 75 forks, and 18 open issues, last pushed May 17, 2026. Figures are from public GitHub metadata via [embedbase's repository](https://github.com/different-ai/embedbase) and [chromem-go's repository](https://github.com/philippgille/chromem-go). | | [embedbase](/tools/different-ai-embedbase.md) | [chromem-go](/tools/philippgille-chromem-go.md) | | --- | --- | --- | | Tagline | A dead-simple API to build LLM-powered apps | Embeddable vector database for Go with Chroma-like interface. | | Stars | 524 | 1,047 | | Forks | 55 | 75 | | Open issues | 35 | 18 | | Language | TypeScript | Go | | 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. | Chromem-go is an embeddable vector database for Go that provides a Chroma-like interface with no third-party dependencies, suitable for applications needing in-memory persistence and cosine similarity search capabilities | | Persona | - | - | | Runtime | - | - | | License | MIT | MPL-2.0 | | Categories | Data & Retrieval, Vector Databases | Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [embedbase](/tools/different-ai-embedbase.md) | [chromem-go](/tools/philippgille-chromem-go.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 601d | 96d | | Open issues (now) | 35 | 18 | | Stars delta | Unknown | +14 (30d) | | Open issues delta | Unknown | +1 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/different-ai-embedbase/trust.md) | [trust report](/tools/philippgille-chromem-go/trust.md) | ## 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. ## Decision facts: chromem-go - **Requirements:** Min 0.5 GB RAM - **Adopt for:** Chromem-go is an embeddable vector database for Go that provides a Chroma-like interface with no third-party dependencies, suitable for applications needing in-memory persistence and cosine similarity search capabilities ## Choose when ### Choose embedbase if… - embedbase is primarily TypeScript; chromem-go is Go. - License: embedbase is MIT, chromem-go is MPL-2.0. - Tags unique to embedbase: ai, artificial-intelligence, chatgpt, machine-learning. - Also covers Data & Retrieval. - * Use Embedbase if you require direct integration capabilities specifically designed for embeddings and vector databases, like pgvector or Supabase. ### Choose chromem-go if… - chromem-go is primarily Go; embedbase is TypeScript. - License: chromem-go is MPL-2.0, embedbase is MIT. - Requirements: Min 0.5 GB RAM. - Tags unique to chromem-go: chroma, cosine-similarity, in-memory, llms. - If you are building applications in Go and require an in-memory vector database without additional third-party libraries. ## 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. ## When NOT to use chromem-go - Avoid Chromem-go if you seek a traditional, disk-based persistence model as it primarily supports in-memory operations with optional persistence options. - Chromem-go is not the best choice if your application requires heavy concurrent load and large-scale data handling which might surpass the in-memory capability limits of this library. ## Common questions ### What is the difference between embedbase and chromem-go? embedbase: A dead-simple API to build LLM-powered apps. chromem-go: Embeddable vector database for Go with Chroma-like interface.. See the comparison table for live GitHub stats and shared categories. ### When should I choose embedbase over chromem-go? Choose embedbase over chromem-go when embedbase is primarily TypeScript; chromem-go is Go; License: embedbase is MIT, chromem-go is MPL-2.0; Tags unique to embedbase: ai, artificial-intelligence, chatgpt, machine-learning; Also covers Data & Retrieval; * Use Embedbase if you require direct integration capabilities specifically designed for embeddings and vector databases, like pgvector or Supabase. ### When should I choose chromem-go over embedbase? Choose chromem-go over embedbase when chromem-go is primarily Go; embedbase is TypeScript; License: chromem-go is MPL-2.0, embedbase is MIT; Requirements: Min 0.5 GB RAM; Tags unique to chromem-go: chroma, cosine-similarity, in-memory, llms; If you are building applications in Go and require an in-memory vector database without additional third-party libraries. ### 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. ### When should I avoid chromem-go? Avoid Chromem-go if you seek a traditional, disk-based persistence model as it primarily supports in-memory operations with optional persistence options. Chromem-go is not the best choice if your application requires heavy concurrent load and large-scale data handling which might surpass the in-memory capability limits of this library. ### Is embedbase or chromem-go more popular on GitHub? chromem-go has more GitHub stars (1,047 vs 524). Stars measure visibility, not whether either tool fits your constraints. ### Are embedbase and chromem-go open source? Yes - both are open-source projects on GitHub (embedbase: MIT, chromem-go: MPL-2.0). ### Where can I find alternatives to embedbase or chromem-go? GraphCanon lists graph-backed alternatives at [embedbase alternatives](/tools/different-ai-embedbase/alternatives) and [chromem-go alternatives](/tools/philippgille-chromem-go/alternatives) ([embedbase markdown twin](/tools/different-ai-embedbase/alternatives.md), [chromem-go markdown twin](/tools/philippgille-chromem-go/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/different-ai-embedbase-vs-philippgille-chromem-go.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, embedbase or chromem-go? embedbase: Dormant. chromem-go: Slowing. 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 embedbase and chromem-go? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [embedbase trust report](/tools/different-ai-embedbase/trust); [chromem-go trust report](/tools/philippgille-chromem-go/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=different-ai-embedbase`](/api/graphcanon/graph?tool=different-ai-embedbase) - 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/_