--- title: "last_layer vs AutoGPT" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/arekusandr-last-layer-vs-significant-gravitas-autogpt" tools: ["arekusandr-last-layer", "significant-gravitas-autogpt"] --- # last_layer vs AutoGPT *GraphCanon updated Jul 15, 2026* ## Verdict Pick last_layer when license: last_layer is MIT, AutoGPT is Other; pick AutoGPT when license: AutoGPT is Other, last_layer is MIT. [last_layer](https://vibe-eval.com) reports 129 GitHub stars, 4 forks, and 13 open issues, last pushed Jul 26, 2024. [AutoGPT](https://agpt.co) has 185k stars, 46k forks, and 494 open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [last_layer's repository](https://github.com/arekusandr/last_layer) and [AutoGPT's repository](https://github.com/Significant-Gravitas/AutoGPT). | | [last_layer](/tools/arekusandr-last-layer.md) | [AutoGPT](/tools/significant-gravitas-autogpt.md) | | --- | --- | --- | | Tagline | Ultra-fast, low latency LLM prompt injection/jailbreak detection ⛓️ | AutoGPT is the vision of accessible AI for everyone, to use and to build on. | | Stars | 129 | 185,464 | | Forks | 4 | 46,111 | | Open issues | 13 | 494 | | Language | Python | 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 | MIT | Other | | Categories | Computer Vision, LLM Frameworks | AI Agents, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [last_layer](/tools/arekusandr-last-layer.md) | [AutoGPT](/tools/significant-gravitas-autogpt.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 719d | 0d | | Open issues (now) | 13 | 494 | | Owner type | User | Organization | | Full report | [trust report](/tools/arekusandr-last-layer/trust.md) | [trust report](/tools/significant-gravitas-autogpt/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 last_layer if… - License: last_layer is MIT, AutoGPT is Other. - Tags unique to last_layer: chatgpt-prompts, jailbreak, large-language-models, llm-guard. - Also covers Computer Vision. ### Choose AutoGPT if… - License: AutoGPT is Other, last_layer is MIT. - Tags unique to AutoGPT: agentic-ai, agents, ai, artificial-intelligence. - Also covers AI Agents. - When you need to rapidly prototype or deploy an autonomous agent using existing language models without deep AI expertise. ## When NOT to use last_layer - Last GitHub push was 719 days ago (dormant maintenance, Jul 26, 2024). Validate activity before betting a new project on last_layer. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## 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. ## Common questions ### What is the difference between last_layer and AutoGPT? last_layer: Ultra-fast, low latency LLM prompt injection/jailbreak detection ⛓️. AutoGPT: AutoGPT is the vision of accessible AI for everyone, to use and to build on.. See the comparison table for live GitHub stats and shared categories. ### When should I choose last_layer over AutoGPT? Choose last_layer over AutoGPT when License: last_layer is MIT, AutoGPT is Other; Tags unique to last_layer: chatgpt-prompts, jailbreak, large-language-models, llm-guard; Also covers Computer Vision. ### When should I choose AutoGPT over last_layer? Choose AutoGPT over last_layer when License: AutoGPT is Other, last_layer is MIT; Tags unique to AutoGPT: agentic-ai, agents, ai, artificial-intelligence; Also covers AI Agents; When you need to rapidly prototype or deploy an autonomous agent using existing language models without deep AI expertise. ### When should I avoid last_layer? Last GitHub push was 719 days ago (dormant maintenance, Jul 26, 2024). Validate activity before betting a new project on last_layer. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### 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. ### Is last_layer or AutoGPT more popular on GitHub? AutoGPT has more GitHub stars (185,464 vs 129). Stars measure visibility, not whether either tool fits your constraints. ### Are last_layer and AutoGPT open source? Yes - both are open-source projects on GitHub (last_layer: MIT, AutoGPT: Other). ### Where can I find alternatives to last_layer or AutoGPT? GraphCanon lists graph-backed alternatives at [last_layer alternatives](/tools/arekusandr-last-layer/alternatives) and [AutoGPT alternatives](/tools/significant-gravitas-autogpt/alternatives) ([last_layer markdown twin](/tools/arekusandr-last-layer/alternatives.md), [AutoGPT markdown twin](/tools/significant-gravitas-autogpt/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/arekusandr-last-layer-vs-significant-gravitas-autogpt.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, last_layer or AutoGPT? last_layer: Dormant. AutoGPT: 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 last_layer and AutoGPT? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [last_layer trust report](/tools/arekusandr-last-layer/trust); [AutoGPT trust report](/tools/significant-gravitas-autogpt/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=arekusandr-last-layer`](/api/graphcanon/graph?tool=arekusandr-last-layer) - 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/_