--- title: "AutoGPT vs agent-kernel" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/significant-gravitas-autogpt-vs-yaalalabs-agent-kernel" tools: ["significant-gravitas-autogpt", "yaalalabs-agent-kernel"] --- # AutoGPT vs agent-kernel *GraphCanon updated Jul 15, 2026* ## Verdict Pick AutoGPT when license: AutoGPT is Other, agent-kernel is Apache-2.0; pick agent-kernel when license: agent-kernel is Apache-2.0, AutoGPT is Other. [AutoGPT](https://agpt.co) reports 185k GitHub stars, 46k forks, and 494 open issues, last pushed Jul 11, 2026. [agent-kernel](https://kernel.yaala.ai/) has 92 stars, 46 forks, and 23 open issues, last pushed Jul 15, 2026. Figures are from public GitHub metadata via [AutoGPT's repository](https://github.com/Significant-Gravitas/AutoGPT) and [agent-kernel's repository](https://github.com/yaalalabs/agent-kernel). | | [AutoGPT](/tools/significant-gravitas-autogpt.md) | [agent-kernel](/tools/yaalalabs-agent-kernel.md) | | --- | --- | --- | | Tagline | AutoGPT is the vision of accessible AI for everyone, to use and to build on. | The Operating System for Scalable Enterprise AI Agents - Run, orchestrate, and deploy Compliant Enterprise AI Agents at scale across frameworks, without lock-in, rewrites or fragile glue code. Native | | Stars | 185,464 | 92 | | Forks | 46,111 | 46 | | Open issues | 494 | 23 | | 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 | Other | Apache-2.0 | | Categories | AI Agents, LLM Frameworks | AI Agents, LLM Frameworks, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [AutoGPT](/tools/significant-gravitas-autogpt.md) | [agent-kernel](/tools/yaalalabs-agent-kernel.md) | | --- | --- | --- | | Open issues (now) | 494 | 23 | | Full report | [trust report](/tools/significant-gravitas-autogpt/trust.md) | [trust report](/tools/yaalalabs-agent-kernel/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… - License: AutoGPT is Other, agent-kernel is Apache-2.0. - Tags unique to AutoGPT: agentic-ai, agents, ai, artificial-intelligence. - When you need to rapidly prototype or deploy an autonomous agent using existing language models without deep AI expertise. ### Choose agent-kernel if… - License: agent-kernel is Apache-2.0, AutoGPT is Other. - Tags unique to agent-kernel: a2a, adk, ai-agents, aws. - Also covers Vector Databases. ## 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 agent-kernel - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## Common questions ### What is the difference between AutoGPT and agent-kernel? AutoGPT: AutoGPT is the vision of accessible AI for everyone, to use and to build on.. agent-kernel: The Operating System for Scalable Enterprise AI Agents - Run, orchestrate, and deploy Compliant Enterprise AI Agents at scale across frameworks, without lock-in, rewrites or fragile glue code. Native . See the comparison table for live GitHub stats and shared categories. ### When should I choose AutoGPT over agent-kernel? Choose AutoGPT over agent-kernel when License: AutoGPT is Other, agent-kernel is Apache-2.0; Tags unique to AutoGPT: agentic-ai, agents, ai, artificial-intelligence; When you need to rapidly prototype or deploy an autonomous agent using existing language models without deep AI expertise. ### When should I choose agent-kernel over AutoGPT? Choose agent-kernel over AutoGPT when License: agent-kernel is Apache-2.0, AutoGPT is Other; Tags unique to agent-kernel: a2a, adk, ai-agents, aws; Also covers Vector Databases. ### 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 agent-kernel? AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### Is AutoGPT or agent-kernel more popular on GitHub? AutoGPT has more GitHub stars (185,464 vs 92). Stars measure visibility, not whether either tool fits your constraints. ### Are AutoGPT and agent-kernel open source? Yes - both are open-source projects on GitHub (AutoGPT: Other, agent-kernel: Apache-2.0). ### Where can I find alternatives to AutoGPT or agent-kernel? GraphCanon lists graph-backed alternatives at [AutoGPT alternatives](/tools/significant-gravitas-autogpt/alternatives) and [agent-kernel alternatives](/tools/yaalalabs-agent-kernel/alternatives) ([AutoGPT markdown twin](/tools/significant-gravitas-autogpt/alternatives.md), [agent-kernel markdown twin](/tools/yaalalabs-agent-kernel/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-yaalalabs-agent-kernel.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, AutoGPT or agent-kernel? AutoGPT: Very active. agent-kernel: 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 AutoGPT and agent-kernel? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [AutoGPT trust report](/tools/significant-gravitas-autogpt/trust); [agent-kernel trust report](/tools/yaalalabs-agent-kernel/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/_