Comparison
pentest-ai vs hello-agents
Verdict
Pick pentest-ai when license: pentest-ai is MIT, hello-agents is Other; pick hello-agents when license: hello-agents is Other, pentest-ai is MIT.
Markdown twin · pentest-ai alternatives · hello-agents alternatives
GraphCanon updated today
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Trust & integrity
| Signal | pentest-ai | hello-agents |
|---|---|---|
| Maintenance | Very active (6d since push) As of today · github_public_v1 | Very active (0d 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 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.
- hello-agents
- Course on building intelligent agents from scratch
Stars
- pentest-ai
- 1.3k
- hello-agents
- 65k
Forks
- pentest-ai
- 249
- hello-agents
- 8.1k
Open issues
- pentest-ai
- 2
- hello-agents
- 144
Language
- pentest-ai
- Python
- hello-agents
- Python
Adopt for
- pentest-ai
- -
- hello-agents
- hello-agents is a comprehensive guide and hands-on tutorial for developing AI agents using LLMs (Large Language Models) and RAG methods.
Persona
- pentest-ai
- -
- hello-agents
- -
Runtime
- pentest-ai
- -
- hello-agents
- -
License
- pentest-ai
- MIT
- hello-agents
- hello-agents is covered under an unconventional license which may require further review before usage.
Last pushed
- pentest-ai
- Jul 5, 2026
- hello-agents
- Jul 10, 2026
Categories
- pentest-ai
- Vector Databases, LLM Frameworks, AI Agents
- hello-agents
- AI Agents, LLM Frameworks
Trust and health
Days since push
- pentest-ai
- 6d
- hello-agents
- 0d
Open issues (now)
- pentest-ai
- 2
- hello-agents
- 144
Owner type
- pentest-ai
- User
- hello-agents
- Organization
Security scan
- pentest-ai
- No MCP manifest
- hello-agents
- No lockfile
Full report
- pentest-ai
- Trust report
- hello-agents
- Trust report
Choose pentest-ai if…
- License: pentest-ai is MIT, hello-agents is Other.
- Tags unique to pentest-ai: cybersecurity, exploit-chaining, ctf, hacking-tools.
- Also covers Vector Databases.
When NOT to use pentest-ai
- 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.
- AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism.
Choose hello-agents if…
- License: hello-agents is Other, pentest-ai is MIT.
- Requirements: Min 4 GB RAM; Python knowledge assumed.
- Tags unique to hello-agents: llm, rag, tutorial, agent.
- You should use hello-agents if you are interested in practical, step-by-step instructions on building intelligent agents from the ground up.
When NOT to use hello-agents
- Avoid using hello-agents if you are looking for a quick, superficial introduction to AI agents; this tool focuses heavily on in-depth learning and practical application.
- Do not opt for hello-agents if you want a more general AI development resource; unlike some competitors, it has a narrower focus specifically on agent creation with advanced methods like LLMs and RAG.
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 (datawhalechina/hello-agents) · observed Jul 11, 2026
- GitHub forks (datawhalechina/hello-agents) · observed Jul 11, 2026
- Last push (datawhalechina/hello-agents) · observed Jul 10, 2026
- License file (Other) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: pentest-ai 1.3k · hello-agents 65k (synced Jul 11, 2026).
Common questions
- What is the difference between pentest-ai and hello-agents?
- 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.. hello-agents: Course on building intelligent agents from scratch. See the comparison table for live GitHub stats and shared categories.
- When should I choose pentest-ai over hello-agents?
- Choose pentest-ai over hello-agents when License: pentest-ai is MIT, hello-agents is Other; Tags unique to pentest-ai: cybersecurity, exploit-chaining, ctf, hacking-tools; Also covers Vector Databases.
- When should I choose hello-agents over pentest-ai?
- Choose hello-agents over pentest-ai when License: hello-agents is Other, pentest-ai is MIT; Requirements: Min 4 GB RAM; Python knowledge assumed; Tags unique to hello-agents: llm, rag, tutorial, agent; You should use hello-agents if you are interested in practical, step-by-step instructions on building intelligent agents from the ground up.
- When should I avoid pentest-ai?
- 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. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism.
- When should I avoid hello-agents?
- Avoid using hello-agents if you are looking for a quick, superficial introduction to AI agents; this tool focuses heavily on in-depth learning and practical application. Do not opt for hello-agents if you want a more general AI development resource; unlike some competitors, it has a narrower focus specifically on agent creation with advanced methods like LLMs and RAG.
- Is pentest-ai or hello-agents more popular on GitHub?
- hello-agents has more GitHub stars (65,432 vs 1,269). Stars measure visibility, not whether either tool fits your constraints.
- Are pentest-ai and hello-agents open source?
- Yes - both are open-source projects on GitHub (pentest-ai: MIT, hello-agents: Other).
- Where can I find alternatives to pentest-ai or hello-agents?
- GraphCanon lists graph-backed alternatives at pentest-ai alternatives and hello-agents alternatives (pentest-ai markdown twin, hello-agents 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 hello-agents?
- pentest-ai: Very active. hello-agents: Very 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 hello-agents?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: pentest-ai trust report; hello-agents trust report.