--- title: "rushdb vs awesome" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/rush-db-rushdb-vs-sindresorhus-awesome" tools: ["rush-db-rushdb", "sindresorhus-awesome"] --- # rushdb vs awesome *GraphCanon updated Jul 12, 2026* ## Verdict Pick rushdb when tags unique to rushdb: ai, ai-agents, ai-memory, ai-tools; pick awesome when tags unique to awesome: awesome-list, resources. [rushdb](https://rushdb.com) reports 313 GitHub stars, 25 forks, and 18 open issues, last pushed Jul 11, 2026. [awesome](https://github.com/sindresorhus/awesome) has 484k stars, 36k forks, and 92 open issues, last pushed Jun 30, 2026. Figures are from public GitHub metadata via [rushdb's repository](https://github.com/rush-db/rushdb) and [awesome's repository](https://github.com/sindresorhus/awesome). | | [rushdb](/tools/rush-db-rushdb.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Tagline | RushDB is a graph + vector database and memory layer for AI agents. Push any JSON, get typed, searchable, relationship-aware records back — no schema, no migrations. Built on Neo4j. | 😎 Curated list of awesome topics including hardware resources | | Stars | 313 | 484,026 | | Forks | 25 | 35,799 | | Open issues | 18 | 92 | | Language | TypeScript | - | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | - | CC0-1.0 | | Categories | AI Agents, LLM Frameworks, Vector Databases | LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [rushdb](/tools/rush-db-rushdb.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 0d | 11d | | Open issues (now) | 18 | 92 | | Owner type | Organization | User | | Security scan | No MCP manifest | No lockfile | | Full report | [trust report](/tools/rush-db-rushdb/trust.md) | [trust report](/tools/sindresorhus-awesome/trust.md) | ## Choose when ### Choose rushdb if… - Tags unique to rushdb: ai, ai-agents, ai-memory, ai-tools. - Also covers AI Agents, Vector Databases. - More recently updated (last pushed Jul 11, 2026). ### Choose awesome if… - Tags unique to awesome: awesome-list, resources. - More GitHub stars (484k vs 313) - visibility, not fit. ## When NOT to use rushdb - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## When NOT to use awesome - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## Common questions ### What is the difference between rushdb and awesome? rushdb: RushDB is a graph + vector database and memory layer for AI agents. Push any JSON, get typed, searchable, relationship-aware records back — no schema, no migrations. Built on Neo4j.. awesome: 😎 Curated list of awesome topics including hardware resources. See the comparison table for live GitHub stats and shared categories. ### When should I choose rushdb over awesome? Choose rushdb over awesome when Tags unique to rushdb: ai, ai-agents, ai-memory, ai-tools; Also covers AI Agents, Vector Databases; More recently updated (last pushed Jul 11, 2026). ### When should I choose awesome over rushdb? Choose awesome over rushdb when Tags unique to awesome: awesome-list, resources; More GitHub stars (484k vs 313) - visibility, not fit. ### When should I avoid rushdb? AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### When should I avoid awesome? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### Is rushdb or awesome more popular on GitHub? awesome has more GitHub stars (484,026 vs 313). Stars measure visibility, not whether either tool fits your constraints. ### Are rushdb and awesome open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to rushdb or awesome? GraphCanon lists graph-backed alternatives at [rushdb alternatives](/tools/rush-db-rushdb/alternatives) and [awesome alternatives](/tools/sindresorhus-awesome/alternatives) ([rushdb markdown twin](/tools/rush-db-rushdb/alternatives.md), [awesome markdown twin](/tools/sindresorhus-awesome/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/rush-db-rushdb-vs-sindresorhus-awesome.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, rushdb or awesome? rushdb: Very active. awesome: 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 rushdb and awesome? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [rushdb trust report](/tools/rush-db-rushdb/trust); [awesome trust report](/tools/sindresorhus-awesome/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=rush-db-rushdb`](/api/graphcanon/graph?tool=rush-db-rushdb) - 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/_