--- title: "lancedb vs rushdb" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/lancedb-lancedb-vs-rush-db-rushdb" tools: ["lancedb-lancedb", "rush-db-rushdb"] --- # lancedb vs rushdb *GraphCanon updated Aug 23, 2026* ## Verdict Pick lancedb if lanceDB is a developer-friendly, open-source embedded retrieval library for multimodal AI applications. It supports various SDKs and REST APIs, offering efficient functionalities including approximate nearest neighbor (k; pick rushdb if rushDB is a graph plus vector database for AI agents running code in TypeScript, needing no schema or migrations but leveraging Neo4j underneath. [lancedb](https://lancedb.com/docs) reports 11k GitHub stars, 972 forks, and 630 open issues, last pushed Jul 28, 2026. [rushdb](https://rushdb.com) has 321 stars, 25 forks, and 19 open issues, last pushed Aug 23, 2026. Figures are from public GitHub metadata via [lancedb's repository](https://github.com/lancedb/lancedb) and [rushdb's repository](https://github.com/rush-db/rushdb). | | [lancedb](/tools/lancedb-lancedb.md) | [rushdb](/tools/rush-db-rushdb.md) | | --- | --- | --- | | Tagline | Developer-friendly OSS embedded retrieval library for multimodal AI. | Graph and vector database for AI agents with zero schema required | | Stars | 11,014 | 321 | | Forks | 972 | 25 | | Open issues | 630 | 19 | | Language | Rust | TypeScript | | Adopt for | LanceDB is a developer-friendly, open-source embedded retrieval library for multimodal AI applications. It supports various SDKs and REST APIs, offering efficient functionalities including approximate nearest neighbor (k | RushDB is a graph plus vector database for AI agents running code in TypeScript, needing no schema or migrations but leveraging Neo4j underneath. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | - | | Categories | Data & Retrieval, Vector Databases | AI Agents, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [lancedb](/tools/lancedb-lancedb.md) | [rushdb](/tools/rush-db-rushdb.md) | | --- | --- | --- | | Open issues (now) | 630 | 19 | | Stars delta | Unknown | +2 (30d) | | Open issues delta | Unknown | +1 (30d) | | Full report | [trust report](/tools/lancedb-lancedb/trust.md) | [trust report](/tools/rush-db-rushdb/trust.md) | ## Decision facts: lancedb - **Pricing:** freemium - Open-source under Apache-2.0 license, ensuring free use and modification for everyone. - **Adopt for:** LanceDB is a developer-friendly, open-source embedded retrieval library for multimodal AI applications. It supports various SDKs and REST APIs, offering efficient functionalities including approximate nearest neighbor (k ## Decision facts: rushdb - **Adopt for:** RushDB is a graph plus vector database for AI agents running code in TypeScript, needing no schema or migrations but leveraging Neo4j underneath. ## Choose when ### Choose lancedb if… - lancedb is primarily Rust; rushdb is TypeScript. - Pricing: Open-source under Apache-2.0 license, ensuring free use and modification for everyone.. - Tags unique to lancedb: approximate-nearest-neighbor-search, image-search, nearest-neighbor-search, recommender-system. - Also covers Data & Retrieval. - lancedb ships Docker support for self-hosted deployment. - - When you need an easy-to-use, fully featured search functionality, encompassing approximate nearest neighbor searches, image searches, semantic searches, etc., specifically tailored for multimodal A ### Choose rushdb if… - rushdb is primarily TypeScript; lancedb is Rust. - Tags unique to rushdb: ai-agents, embeddings, graph-database, neo4j. - Also covers AI Agents. - You require a memory layer for your AI agent that integrates seamlessly with TypeScript-based applications ## When NOT to use lancedb - - Avoid LanceDB if your project requires real-time search latency below millisecond levels because the overhead for embedding storage and retrieval might affect performance. - - Not suitable when you are working with large-scale real-time applications that require extensive horizontal scalability beyond what its embedded design can offer. ## When NOT to use rushdb - If you need full control over database schema migrations, as RushDB intentionally does not support this - For environments where the use of Neo4j underlying technology is restricted or not preferred ## Common questions ### What is the difference between lancedb and rushdb? lancedb: Developer-friendly OSS embedded retrieval library for multimodal AI.. rushdb: Graph and vector database for AI agents with zero schema required. See the comparison table for live GitHub stats and shared categories. ### When should I choose lancedb over rushdb? Choose lancedb over rushdb when lancedb is primarily Rust; rushdb is TypeScript; Pricing: Open-source under Apache-2.0 license, ensuring free use and modification for everyone.; Tags unique to lancedb: approximate-nearest-neighbor-search, image-search, nearest-neighbor-search, recommender-system; Also covers Data & Retrieval; lancedb ships Docker support for self-hosted deployment; - When you need an easy-to-use, fully featured search functionality, encompassing approximate nearest neighbor searches, image searches, semantic searches, etc., specifically tailored for multimodal A. ### When should I choose rushdb over lancedb? Choose rushdb over lancedb when rushdb is primarily TypeScript; lancedb is Rust; Tags unique to rushdb: ai-agents, embeddings, graph-database, neo4j; Also covers AI Agents; You require a memory layer for your AI agent that integrates seamlessly with TypeScript-based applications. ### When should I avoid lancedb? - Avoid LanceDB if your project requires real-time search latency below millisecond levels because the overhead for embedding storage and retrieval might affect performance. - Not suitable when you are working with large-scale real-time applications that require extensive horizontal scalability beyond what its embedded design can offer. ### When should I avoid rushdb? If you need full control over database schema migrations, as RushDB intentionally does not support this For environments where the use of Neo4j underlying technology is restricted or not preferred ### Is lancedb or rushdb more popular on GitHub? lancedb has more GitHub stars (11,014 vs 321). Stars measure visibility, not whether either tool fits your constraints. ### Are lancedb and rushdb open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to lancedb or rushdb? GraphCanon lists graph-backed alternatives at [lancedb alternatives](/tools/lancedb-lancedb/alternatives) and [rushdb alternatives](/tools/rush-db-rushdb/alternatives) ([lancedb markdown twin](/tools/lancedb-lancedb/alternatives.md), [rushdb markdown twin](/tools/rush-db-rushdb/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/lancedb-lancedb-vs-rush-db-rushdb.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, lancedb or rushdb? lancedb: Very active. rushdb: Very 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 lancedb and rushdb? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [lancedb trust report](/tools/lancedb-lancedb/trust); [rushdb trust report](/tools/rush-db-rushdb/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=lancedb-lancedb`](/api/graphcanon/graph?tool=lancedb-lancedb) - 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/_