--- title: "AutoGPT vs blast" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/significant-gravitas-autogpt-vs-stanford-mast-blast" tools: ["significant-gravitas-autogpt", "stanford-mast-blast"] --- # AutoGPT vs blast *GraphCanon updated Jul 11, 2026* ## Verdict Pick AutoGPT when license: AutoGPT is Other, blast is MIT; pick blast when license: blast is MIT, AutoGPT is Other. [AutoGPT](https://agpt.co) reports 185k GitHub stars, 46k forks, and 494 open issues, last pushed Jul 11, 2026. [blast](http://blastproject.org/) has 776 stars, 50 forks, and 6 open issues, last pushed May 29, 2026. Figures are from public GitHub metadata via [AutoGPT's repository](https://github.com/Significant-Gravitas/AutoGPT) and [blast's repository](https://github.com/stanford-mast/blast). | | [AutoGPT](/tools/significant-gravitas-autogpt.md) | [blast](/tools/stanford-mast-blast.md) | | --- | --- | --- | | Tagline | AutoGPT is the vision of accessible AI for everyone, to use and to build on. | Open-source VMs-as-a-service | | Stars | 185,464 | 776 | | Forks | 46,111 | 50 | | Open issues | 494 | 6 | | 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 | MIT | | Categories | LLM Frameworks, AI Agents | AI Agents, LLM Frameworks, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [AutoGPT](/tools/significant-gravitas-autogpt.md) | [blast](/tools/stanford-mast-blast.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 0d | 42d | | Open issues (now) | 494 | 6 | | Full report | [trust report](/tools/significant-gravitas-autogpt/trust.md) | [trust report](/tools/stanford-mast-blast/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, blast is MIT. - Tags unique to AutoGPT: agents, llm, ai, artificial-intelligence. - When you need to rapidly prototype or deploy an autonomous agent using existing language models without deep AI expertise. ### Choose blast if… - License: blast is MIT, AutoGPT is Other. - Tags unique to blast: python, browser-automation, llm-inference, ai-agents. - Also covers Inference & Serving. ## 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 blast - 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. - 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 blast? AutoGPT: AutoGPT is the vision of accessible AI for everyone, to use and to build on.. blast: Open-source VMs-as-a-service. See the comparison table for live GitHub stats and shared categories. ### When should I choose AutoGPT over blast? Choose AutoGPT over blast when License: AutoGPT is Other, blast is MIT; Tags unique to AutoGPT: agents, llm, 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 blast over AutoGPT? Choose blast over AutoGPT when License: blast is MIT, AutoGPT is Other; Tags unique to blast: python, browser-automation, llm-inference, ai-agents; Also covers Inference & Serving. ### 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 blast? 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. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ### Is AutoGPT or blast more popular on GitHub? AutoGPT has more GitHub stars (185,464 vs 776). Stars measure visibility, not whether either tool fits your constraints. ### Are AutoGPT and blast open source? Yes - both are open-source projects on GitHub (AutoGPT: Other, blast: MIT). ### Where can I find alternatives to AutoGPT or blast? GraphCanon lists graph-backed alternatives at [AutoGPT alternatives](/tools/significant-gravitas-autogpt/alternatives) and [blast alternatives](/tools/stanford-mast-blast/alternatives) ([AutoGPT markdown twin](/tools/significant-gravitas-autogpt/alternatives.md), [blast markdown twin](/tools/stanford-mast-blast/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-stanford-mast-blast.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, AutoGPT or blast? AutoGPT: Very active. blast: Steady. 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 blast? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [AutoGPT trust report](/tools/significant-gravitas-autogpt/trust); [blast trust report](/tools/stanford-mast-blast/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/_