--- title: "AutoGPT vs awesome-hacking-lists" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/significant-gravitas-autogpt-vs-taielab-awesome-hacking-lists" tools: ["significant-gravitas-autogpt", "taielab-awesome-hacking-lists"] --- # AutoGPT vs awesome-hacking-lists *GraphCanon updated Jul 11, 2026* ## 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. [AutoGPT](https://agpt.co) reports 185k GitHub stars, 46k forks, and 494 open issues, last pushed Jul 11, 2026. [awesome-hacking-lists](https://github.com/taielab/awesome-hacking-lists) has 1.4k stars, 264 forks, and 2 open issues, last pushed Dec 4, 2025. Figures are from public GitHub metadata via [AutoGPT's repository](https://github.com/Significant-Gravitas/AutoGPT) and [awesome-hacking-lists's repository](https://github.com/taielab/awesome-hacking-lists). | | [AutoGPT](/tools/significant-gravitas-autogpt.md) | [awesome-hacking-lists](/tools/taielab-awesome-hacking-lists.md) | | --- | --- | --- | | Tagline | AutoGPT is the vision of accessible AI for everyone, to use and to build on. | 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 | 185,464 | 1,362 | | Forks | 46,111 | 264 | | Open issues | 494 | 2 | | Language | Python | - | | Adopt for | 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. | - | | Persona | - | - | | Runtime | - | - | | License | Other | - | | Categories | LLM Frameworks, AI Agents | LLM Frameworks, AI Agents, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [AutoGPT](/tools/significant-gravitas-autogpt.md) | [awesome-hacking-lists](/tools/taielab-awesome-hacking-lists.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 219d | | Open issues (now) | 494 | 2 | | Owner type | Organization | User | | Security scan | No lockfile | No MCP manifest | | Full report | [trust report](/tools/significant-gravitas-autogpt/trust.md) | [trust report](/tools/taielab-awesome-hacking-lists/trust.md) | ## Decision facts: AutoGPT - **Adopt for:** 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. ## Choose when ### 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. ### 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 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 NOT to use awesome-hacking-lists - Last GitHub push was 220 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. ## 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 220 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](/tools/significant-gravitas-autogpt/alternatives) and [awesome-hacking-lists alternatives](/tools/taielab-awesome-hacking-lists/alternatives) ([AutoGPT markdown twin](/tools/significant-gravitas-autogpt/alternatives.md), [awesome-hacking-lists markdown twin](/tools/taielab-awesome-hacking-lists/alternatives.md)), 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](/compare/significant-gravitas-autogpt-vs-taielab-awesome-hacking-lists.md) 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](/tools/significant-gravitas-autogpt/trust); [awesome-hacking-lists trust report](/tools/taielab-awesome-hacking-lists/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=significant-gravitas-autogpt`](/api/graphcanon/graph?tool=significant-gravitas-autogpt) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_