Comparison
AutoGPT vs awesome-hacking-lists
Verdict
Pick AutoGPT when tags unique to AutoGPT: llm, artificial-intelligence, agentic-ai, autonomous-agents; pick awesome-hacking-lists when tags unique to awesome-hacking-lists: aiagent, awesome-list, bounty-hunters, bug-bounty.
Markdown twin · AutoGPT alternatives · awesome-hacking-lists alternatives
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Trust & integrity
| Signal | AutoGPT | awesome-hacking-lists |
|---|---|---|
| Maintenance | Very active (0d since push) As of today · github_public_v1 | Slowing (219d 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 MCP manifest As of today · mcp_manifest |
Tagline
- AutoGPT
- AutoGPT is the vision of accessible AI for everyone, to use and to build on.
- awesome-hacking-lists
- A curated collection of top-tier penetration testing tools and productivity utilities across multiple domains. Join us to explore, contribute, and enhance your hacking toolkit!
Stars
- AutoGPT
- 185k
- awesome-hacking-lists
- 1.4k
Forks
- AutoGPT
- 46k
- awesome-hacking-lists
- 264
Open issues
- AutoGPT
- 494
- awesome-hacking-lists
- 2
Language
- AutoGPT
- Python
- awesome-hacking-lists
- -
Adopt for
- AutoGPT
- AutoGPT is a Python-based tool for creating accessible autonomous AI agents that can leverage various LLM APIs including OpenAI's GPT and Anthropic's Claude.
- awesome-hacking-lists
- -
Persona
- AutoGPT
- -
- awesome-hacking-lists
- -
Runtime
- AutoGPT
- -
- awesome-hacking-lists
- -
License
- AutoGPT
- Other
- awesome-hacking-lists
- -
Last pushed
- AutoGPT
- Jul 11, 2026
- awesome-hacking-lists
- Dec 4, 2025
Categories
- AutoGPT
- AI Agents, LLM Frameworks
- awesome-hacking-lists
- LLM Frameworks, AI Agents, Inference & Serving
Trust and health
Maintenance
- AutoGPT
- Very active (96%)
- awesome-hacking-lists
- Slowing (36%)
Days since push
- AutoGPT
- 0d
- awesome-hacking-lists
- 219d
Open issues (now)
- AutoGPT
- 494
- awesome-hacking-lists
- 2
Owner type
- AutoGPT
- Organization
- awesome-hacking-lists
- User
Security scan
- AutoGPT
- No lockfile
- awesome-hacking-lists
- No MCP manifest
Full report
- AutoGPT
- Trust report
- awesome-hacking-lists
- Trust report
Choose AutoGPT if…
- Tags unique to AutoGPT: llm, artificial-intelligence, agentic-ai, autonomous-agents.
- When you need to rapidly prototype or deploy an autonomous agent using existing language models without deep AI expertise.
- More GitHub stars (185k vs 1.4k) - visibility, not fit.
When NOT to use AutoGPT
- Avoid if you require absolute control over the underlying AI infrastructure and APIs used by your autonomous agents, as AutoGPT imposes its own framework.
- If your project demands proprietary or specialized models that aren't supported by AutoGPT's API ecosystem (e.g., custom TensorFlow or PyTorch models), consider other tools.
Choose awesome-hacking-lists if…
- Tags unique to awesome-hacking-lists: aiagent, awesome-list, bounty-hunters, bug-bounty.
- Also covers Inference & Serving.
- Leaner open-issue backlog (2).
When NOT to use awesome-hacking-lists
- Last GitHub push was 219 days ago (slowing maintenance, Dec 4, 2025). Validate activity before betting a new project on awesome-hacking-lists.
- 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.
- Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (Significant-Gravitas/AutoGPT) · observed Jul 11, 2026
- GitHub forks (Significant-Gravitas/AutoGPT) · observed Jul 11, 2026
- Last push (Significant-Gravitas/AutoGPT) · observed Jul 11, 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 (taielab/awesome-hacking-lists) · observed Jul 11, 2026
- GitHub forks (taielab/awesome-hacking-lists) · observed Jul 11, 2026
- Last push (taielab/awesome-hacking-lists) · observed Dec 4, 2025
- License file (unknown) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: AutoGPT 185k · awesome-hacking-lists 1.4k (synced Jul 11, 2026).
Common questions
- What is the difference between AutoGPT and awesome-hacking-lists?
- AutoGPT: AutoGPT is the vision of accessible AI for everyone, to use and to build on.. awesome-hacking-lists: A curated collection of top-tier penetration testing tools and productivity utilities across multiple domains. Join us to explore, contribute, and enhance your hacking toolkit!. See the comparison table for live GitHub stats and shared categories.
- When should I choose AutoGPT over awesome-hacking-lists?
- Choose AutoGPT over awesome-hacking-lists when Tags unique to AutoGPT: llm, artificial-intelligence, agentic-ai, autonomous-agents; When you need to rapidly prototype or deploy an autonomous agent using existing language models without deep AI expertise; More GitHub stars (185k vs 1.4k) - visibility, not fit.
- When should I choose awesome-hacking-lists over AutoGPT?
- Choose awesome-hacking-lists over AutoGPT when Tags unique to awesome-hacking-lists: aiagent, awesome-list, bounty-hunters, bug-bounty; Also covers Inference & Serving; Leaner open-issue backlog (2).
- When should I avoid AutoGPT?
- Avoid if you require absolute control over the underlying AI infrastructure and APIs used by your autonomous agents, as AutoGPT imposes its own framework. If your project demands proprietary or specialized models that aren't supported by AutoGPT's API ecosystem (e.g., custom TensorFlow or PyTorch models), consider other tools.
- When should I avoid awesome-hacking-lists?
- Last GitHub push was 219 days ago (slowing maintenance, Dec 4, 2025). Validate activity before betting a new project on awesome-hacking-lists. 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. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
- Is AutoGPT or awesome-hacking-lists more popular on GitHub?
- AutoGPT has more GitHub stars (185,464 vs 1,362). Stars measure visibility, not whether either tool fits your constraints.
- Are AutoGPT and awesome-hacking-lists open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to AutoGPT or awesome-hacking-lists?
- GraphCanon lists graph-backed alternatives at AutoGPT alternatives and awesome-hacking-lists alternatives (AutoGPT markdown twin, awesome-hacking-lists 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, AutoGPT or awesome-hacking-lists?
- AutoGPT: Very active. awesome-hacking-lists: Slowing. 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 AutoGPT and awesome-hacking-lists?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: AutoGPT trust report; awesome-hacking-lists trust report.