Comparison
awesome vs stackql
Verdict
Pick awesome when license: awesome is CC0-1.0, stackql is MIT; pick stackql when license: stackql is MIT, awesome is CC0-1.0.
Markdown twin · awesome alternatives · stackql alternatives
GraphCanon updated today
Trust & integrity
| Signal | awesome | stackql |
|---|---|---|
| Maintenance | Active (11d since push) As of today · github_public_v1 | Active (7d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Personal account As of today · github_public_v1 | Not a fork · Organization account As of today · github_public_v1 |
| Security (OSV) | No lockfile As of today · none | No MCP manifest As of today · mcp_manifest |
Tagline
- awesome
- 😎 Curated list of awesome topics including hardware resources
- stackql
- Query, provision and operate Cloud, SaaS, API and Model Context Protocol (MCP) resources through a unified SQL-based framework for humans and AI agents.
Stars
- awesome
- 484k
- stackql
- 861
Forks
- awesome
- 36k
- stackql
- 80
Open issues
- awesome
- 92
- stackql
- 103
Language
- awesome
- -
- stackql
- Go
Adopt for
- awesome
- -
- stackql
- -
Persona
- awesome
- -
- stackql
- -
Runtime
- awesome
- -
- stackql
- -
License
- awesome
- CC0-1.0
- stackql
- MIT
Last pushed
- awesome
- Jun 30, 2026
- stackql
- Jul 3, 2026
Categories
- awesome
- LLM Frameworks
- stackql
- AI Agents, Computer Vision, LLM Frameworks
Trust and health
Days since push
- awesome
- 11d
- stackql
- 7d
Open issues (now)
- awesome
- 92
- stackql
- 103
Owner type
- awesome
- User
- stackql
- Organization
Security scan
- awesome
- No lockfile
- stackql
- No MCP manifest
Full report
- awesome
- Trust report
- stackql
- Trust report
Choose awesome if…
- License: awesome is CC0-1.0, stackql is MIT.
- Tags unique to awesome: awesome-list, resources.
- More GitHub stars (484k vs 861) - 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.
Choose stackql if…
- License: stackql is MIT, awesome is CC0-1.0.
- Tags unique to stackql: ai-agents, asset-management, cloud, cloud-automation.
- Also covers AI Agents, Computer Vision.
When NOT to use stackql
- 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- 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 (stackql/stackql) · observed Jul 11, 2026
- GitHub forks (stackql/stackql) · observed Jul 11, 2026
- Last push (stackql/stackql) · observed Jul 3, 2026
- License file (MIT) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome 484k · stackql 861 (synced Jul 11, 2026).
Common questions
- What is the difference between awesome and stackql?
- awesome: 😎 Curated list of awesome topics including hardware resources. stackql: Query, provision and operate Cloud, SaaS, API and Model Context Protocol (MCP) resources through a unified SQL-based framework for humans and AI agents.. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome over stackql?
- Choose awesome over stackql when License: awesome is CC0-1.0, stackql is MIT; Tags unique to awesome: awesome-list, resources; More GitHub stars (484k vs 861) - visibility, not fit.
- When should I choose stackql over awesome?
- Choose stackql over awesome when License: stackql is MIT, awesome is CC0-1.0; Tags unique to stackql: ai-agents, asset-management, cloud, cloud-automation; Also covers AI Agents, Computer Vision.
- 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.
- When should I avoid stackql?
- 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.
- Is awesome or stackql more popular on GitHub?
- awesome has more GitHub stars (484,026 vs 861). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome and stackql open source?
- Yes - both are open-source projects on GitHub (awesome: CC0-1.0, stackql: MIT).
- Where can I find alternatives to awesome or stackql?
- GraphCanon lists graph-backed alternatives at awesome alternatives and stackql alternatives (awesome markdown twin, stackql 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, awesome or stackql?
- awesome: Active. stackql: 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 awesome and stackql?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome trust report; stackql trust report.