Comparison
Wax vs awesome
Verdict
Pick Wax when license: Wax is Apache-2.0, awesome is CC0-1.0; pick awesome when license: awesome is CC0-1.0, Wax is Apache-2.0.
Markdown twin · Wax alternatives · awesome alternatives
GraphCanon updated today
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Trust & integrity
| Signal | Wax | awesome |
|---|---|---|
| Maintenance | Very active (4d since push) As of today · github_public_v1 | Active (11d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Personal 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
- Wax
- Single-file memory layer for AI agents, sub mili-second RAG on Apple Silicon. Metal Optimized On-Device. No Server. No API. One File. Pure Swift
- awesome
- 😎 Curated list of awesome topics including hardware resources
Stars
- Wax
- 773
- awesome
- 484k
Forks
- Wax
- 46
- awesome
- 36k
Open issues
- Wax
- 0
- awesome
- 92
Language
- Wax
- Swift
- awesome
- -
Adopt for
- Wax
- -
- awesome
- -
Persona
- Wax
- -
- awesome
- -
Runtime
- Wax
- -
- awesome
- -
License
- Wax
- Apache-2.0
- awesome
- CC0-1.0
Last pushed
- Wax
- Jul 6, 2026
- awesome
- Jun 30, 2026
Categories
- Wax
- AI Agents, LLM Frameworks, Vector Databases
- awesome
- LLM Frameworks
Trust and health
Maintenance
- Wax
- Very active (96%)
- awesome
- Active (82%)
Days since push
- Wax
- 4d
- awesome
- 11d
Open issues (now)
- Wax
- 0
- awesome
- 92
Security scan
- Wax
- No MCP manifest
- awesome
- No lockfile
Full report
- Wax
- Trust report
- awesome
- Trust report
Choose Wax if…
- License: Wax is Apache-2.0, awesome is CC0-1.0.
- Tags unique to Wax: data-science, coreml-framework, mcp-server, machine-learning.
- Also covers AI Agents, Vector Databases.
When NOT to use Wax
- 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.
Choose awesome if…
- License: awesome is CC0-1.0, Wax is Apache-2.0.
- Tags unique to awesome: resources, awesome-list.
- More GitHub stars (484k vs 773) - visibility, not fit.
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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (christopherkarani/Wax) · observed Jul 11, 2026
- GitHub forks (christopherkarani/Wax) · observed Jul 11, 2026
- Last push (christopherkarani/Wax) · observed Jul 6, 2026
- License file (Apache-2.0) · 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: Wax 773 · awesome 484k (synced Jul 11, 2026).
Common questions
- What is the difference between Wax and awesome?
- Wax: Single-file memory layer for AI agents, sub mili-second RAG on Apple Silicon. Metal Optimized On-Device. No Server. No API. One File. Pure Swift. awesome: 😎 Curated list of awesome topics including hardware resources. See the comparison table for live GitHub stats and shared categories.
- When should I choose Wax over awesome?
- Choose Wax over awesome when License: Wax is Apache-2.0, awesome is CC0-1.0; Tags unique to Wax: data-science, coreml-framework, mcp-server, machine-learning; Also covers AI Agents, Vector Databases.
- When should I choose awesome over Wax?
- Choose awesome over Wax when License: awesome is CC0-1.0, Wax is Apache-2.0; Tags unique to awesome: resources, awesome-list; More GitHub stars (484k vs 773) - visibility, not fit.
- When should I avoid Wax?
- 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 Wax or awesome more popular on GitHub?
- awesome has more GitHub stars (484,026 vs 773). Stars measure visibility, not whether either tool fits your constraints.
- Are Wax and awesome open source?
- Yes - both are open-source projects on GitHub (Wax: Apache-2.0, awesome: CC0-1.0).
- Where can I find alternatives to Wax or awesome?
- GraphCanon lists graph-backed alternatives at Wax alternatives and awesome alternatives (Wax 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, Wax or awesome?
- Wax: 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 Wax and awesome?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Wax trust report; awesome trust report.