Comparison
pentest-ai vs awesome
Verdict
Pick pentest-ai when license: pentest-ai is MIT, awesome is CC0-1.0; pick awesome when license: awesome is CC0-1.0, pentest-ai is MIT.
Markdown twin · pentest-ai alternatives · awesome alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | pentest-ai | awesome |
|---|---|---|
| Maintenance | Very active (6d 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
- pentest-ai
- Offensive-security MCP server with 205 wrapped tools, 17 specialist agents, and 60 SPA-aware probes for OWASP Top 10. CLI + MCP, BYO LLM. No API key needed on MCP path.
- awesome
- 😎 Curated list of awesome topics including hardware resources
Stars
- pentest-ai
- 1.3k
- awesome
- 484k
Forks
- pentest-ai
- 249
- awesome
- 36k
Open issues
- pentest-ai
- 2
- awesome
- 92
Language
- pentest-ai
- Python
- awesome
- -
Adopt for
- pentest-ai
- -
- awesome
- -
Persona
- pentest-ai
- -
- awesome
- -
Runtime
- pentest-ai
- -
- awesome
- -
License
- pentest-ai
- MIT
- awesome
- CC0-1.0
Last pushed
- pentest-ai
- Jul 5, 2026
- awesome
- Jun 30, 2026
Categories
- pentest-ai
- AI Agents, Vector Databases, LLM Frameworks
- awesome
- LLM Frameworks
Trust and health
Maintenance
- pentest-ai
- Very active (96%)
- awesome
- Active (82%)
Days since push
- pentest-ai
- 6d
- awesome
- 11d
Open issues (now)
- pentest-ai
- 2
- awesome
- 92
Security scan
- pentest-ai
- No MCP manifest
- awesome
- No lockfile
Full report
- pentest-ai
- Trust report
- awesome
- Trust report
Choose pentest-ai if…
- License: pentest-ai is MIT, awesome is CC0-1.0.
- Tags unique to pentest-ai: cybersecurity, exploit-chaining, ctf, hacking-tools.
- Also covers AI Agents, Vector Databases.
When NOT to use pentest-ai
- 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.
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
Choose awesome if…
- License: awesome is CC0-1.0, pentest-ai is MIT.
- Tags unique to awesome: resources, awesome-list.
- More GitHub stars (484k vs 1.3k) - 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 (0xSteph/pentest-ai) · observed Jul 11, 2026
- GitHub forks (0xSteph/pentest-ai) · observed Jul 11, 2026
- Last push (0xSteph/pentest-ai) · observed Jul 5, 2026
- License file (MIT) · 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: pentest-ai 1.3k · awesome 484k (synced Jul 11, 2026).
Common questions
- What is the difference between pentest-ai and awesome?
- pentest-ai: Offensive-security MCP server with 205 wrapped tools, 17 specialist agents, and 60 SPA-aware probes for OWASP Top 10. CLI + MCP, BYO LLM. No API key needed on MCP path.. awesome: 😎 Curated list of awesome topics including hardware resources. See the comparison table for live GitHub stats and shared categories.
- When should I choose pentest-ai over awesome?
- Choose pentest-ai over awesome when License: pentest-ai is MIT, awesome is CC0-1.0; Tags unique to pentest-ai: cybersecurity, exploit-chaining, ctf, hacking-tools; Also covers AI Agents, Vector Databases.
- When should I choose awesome over pentest-ai?
- Choose awesome over pentest-ai when License: awesome is CC0-1.0, pentest-ai is MIT; Tags unique to awesome: resources, awesome-list; More GitHub stars (484k vs 1.3k) - visibility, not fit.
- When should I avoid pentest-ai?
- 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. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- 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 pentest-ai or awesome more popular on GitHub?
- awesome has more GitHub stars (484,026 vs 1,269). Stars measure visibility, not whether either tool fits your constraints.
- Are pentest-ai and awesome open source?
- Yes - both are open-source projects on GitHub (pentest-ai: MIT, awesome: CC0-1.0).
- Where can I find alternatives to pentest-ai or awesome?
- GraphCanon lists graph-backed alternatives at pentest-ai alternatives and awesome alternatives (pentest-ai 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, pentest-ai or awesome?
- pentest-ai: 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 pentest-ai and awesome?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: pentest-ai trust report; awesome trust report.