Comparison
GTA vs awesome
Verdict
Pick GTA when license: GTA is Apache-2.0, awesome is CC0-1.0; pick awesome when license: awesome is CC0-1.0, GTA is Apache-2.0.
Markdown twin · GTA alternatives · awesome alternatives
GraphCanon updated today
Trust & integrity
| Signal | GTA | awesome |
|---|---|---|
| Maintenance | Steady (82d 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 lockfile As of today · none | No lockfile As of today · none |
Tagline
- GTA
- [NeurIPS 2024 D&B] GTA: A Benchmark for General Tool Agents & [arXiv 2026] GTA-2
- awesome
- 😎 Curated list of awesome topics including hardware resources
Stars
- GTA
- 147
- awesome
- 484k
Forks
- GTA
- 10
- awesome
- 36k
Open issues
- GTA
- 1
- awesome
- 92
Language
- GTA
- Python
- awesome
- -
Adopt for
- GTA
- -
- awesome
- -
Persona
- GTA
- -
- awesome
- -
Runtime
- GTA
- -
- awesome
- -
License
- GTA
- Apache-2.0
- awesome
- CC0-1.0
Last pushed
- GTA
- Apr 20, 2026
- awesome
- Jun 30, 2026
Categories
- GTA
- LLM Frameworks, AI Agents, Vector Databases
- awesome
- LLM Frameworks
Trust and health
Maintenance
- GTA
- Steady (60%)
- awesome
- Active (82%)
Days since push
- GTA
- 82d
- awesome
- 11d
Open issues (now)
- GTA
- 1
- awesome
- 92
Owner type
- GTA
- Organization
- awesome
- User
Full report
- GTA
- Trust report
- awesome
- Trust report
Choose GTA if…
- License: GTA is Apache-2.0, awesome is CC0-1.0.
- Tags unique to GTA: python, llm-agent, llm evaluation.
- Also covers AI Agents, Vector Databases.
When NOT to use GTA
- 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.
Choose awesome if…
- License: awesome is CC0-1.0, GTA is Apache-2.0.
- Tags unique to awesome: resources, awesome-list.
- More GitHub stars (484k vs 147) - 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 (open-compass/GTA) · observed Jul 11, 2026
- GitHub forks (open-compass/GTA) · observed Jul 11, 2026
- Last push (open-compass/GTA) · observed Apr 20, 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: GTA 147 · awesome 484k (synced Jul 11, 2026).
Common questions
- What is the difference between GTA and awesome?
- GTA: [NeurIPS 2024 D&B] GTA: A Benchmark for General Tool Agents & [arXiv 2026] GTA-2. awesome: 😎 Curated list of awesome topics including hardware resources. See the comparison table for live GitHub stats and shared categories.
- When should I choose GTA over awesome?
- Choose GTA over awesome when License: GTA is Apache-2.0, awesome is CC0-1.0; Tags unique to GTA: python, llm-agent, llm evaluation; Also covers AI Agents, Vector Databases.
- When should I choose awesome over GTA?
- Choose awesome over GTA when License: awesome is CC0-1.0, GTA is Apache-2.0; Tags unique to awesome: resources, awesome-list; More GitHub stars (484k vs 147) - visibility, not fit.
- When should I avoid GTA?
- 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 GTA or awesome more popular on GitHub?
- awesome has more GitHub stars (484,026 vs 147). Stars measure visibility, not whether either tool fits your constraints.
- Are GTA and awesome open source?
- Yes - both are open-source projects on GitHub (GTA: Apache-2.0, awesome: CC0-1.0).
- Where can I find alternatives to GTA or awesome?
- GraphCanon lists graph-backed alternatives at GTA alternatives and awesome alternatives (GTA 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, GTA or awesome?
- GTA: Steady. 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 GTA and awesome?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: GTA trust report; awesome trust report.