Comparison
rushdb vs awesome
Verdict
Pick rushdb when tags unique to rushdb: ai, docker, ai-memory, cloud; pick awesome when tags unique to awesome: resources, awesome-list.
Markdown twin · rushdb alternatives · awesome alternatives
GraphCanon updated today
Trust & integrity
| Signal | rushdb | awesome |
|---|---|---|
| Maintenance | Very active (0d since push) As of today · github_public_v1 | Active (11d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · github_public_v1 | Not a fork · Personal account As of today · github_public_v1 |
| Security (OSV) | No MCP manifest As of today · mcp_manifest | No lockfile As of today · none |
Tagline
- 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
Stars
- rushdb
- 313
- awesome
- 484k
Forks
- rushdb
- 25
- awesome
- 36k
Open issues
- rushdb
- 18
- awesome
- 92
Language
- rushdb
- TypeScript
- awesome
- -
Adopt for
- rushdb
- -
- awesome
- -
Persona
- rushdb
- -
- awesome
- -
Runtime
- rushdb
- -
- awesome
- -
License
- rushdb
- -
- awesome
- CC0-1.0
Last pushed
- rushdb
- Jul 11, 2026
- awesome
- Jun 30, 2026
Categories
- rushdb
- LLM Frameworks, AI Agents, Vector Databases
- awesome
- LLM Frameworks
Trust and health
Maintenance
- rushdb
- Very active (96%)
- awesome
- Active (82%)
Days since push
- rushdb
- 0d
- awesome
- 11d
Open issues (now)
- rushdb
- 18
- awesome
- 92
Owner type
- rushdb
- Organization
- awesome
- User
Security scan
- rushdb
- No MCP manifest
- awesome
- No lockfile
Full report
- rushdb
- Trust report
- awesome
- Trust report
Choose rushdb if…
- Tags unique to rushdb: ai, docker, ai-memory, cloud.
- Also covers AI Agents, Vector Databases.
- More recently updated (last pushed Jul 11, 2026).
When NOT to use rushdb
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism.
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (rush-db/rushdb) · observed Jul 11, 2026
- GitHub forks (rush-db/rushdb) · observed Jul 11, 2026
- Last push (rush-db/rushdb) · observed Jul 11, 2026
- License file (unknown) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (sindresorhus/awesome) · observed Jul 11, 2026
- GitHub forks (sindresorhus/awesome) · observed Jul 11, 2026
- Last push (sindresorhus/awesome) · observed Jun 30, 2026
- License file (CC0-1.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: rushdb 313 · awesome 484k (synced Jul 11, 2026).
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, docker, ai-memory, cloud; 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: resources, awesome-list; More GitHub stars (484k vs 313) - visibility, not fit.
- When should I avoid rushdb?
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. 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 and awesome alternatives (rushdb markdown twin, awesome markdown twin), 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 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; awesome trust report.