Home/Compare/dust vs AutoGPT

Comparison

dust vs AutoGPT

Verdict

Pick dust when dust is primarily TypeScript; AutoGPT is Python; pick AutoGPT when autoGPT is primarily Python; dust is TypeScript.

Markdown twin · dust alternatives · AutoGPT alternatives

GraphCanon updated today

dust logo

dust

dust-tt/dust

1.4kpushed Jul 11, 2026
vs
AutoGPT logo

AutoGPT

Significant-Gravitas/AutoGPT

185kpushed Jul 11, 2026

Trust & integrity

SignaldustAutoGPT
Maintenance
Very active (0d since push)
As of today · github_public_v1
Very active (0d since push)
As of today · github_public_v1
Provenance
Not a fork · Organization account
As of today · github_public_v1
Not a fork · Organization account
As of today · github_public_v1
Security (OSV)
No lockfile
As of today · none
No lockfile
As of today · none

Tagline

dust
Custom AI agent platform to speed up your work.
AutoGPT
AutoGPT is the vision of accessible AI for everyone, to use and to build on.

Stars

dust
1.4k
AutoGPT
185k

Forks

dust
302
AutoGPT
46k

Open issues

dust
224
AutoGPT
494

Language

dust
TypeScript
AutoGPT
Python

Adopt for

dust
-
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.

Persona

dust
-
AutoGPT
-

Runtime

dust
-
AutoGPT
-

License

dust
MIT
AutoGPT
Other

Last pushed

dust
Jul 11, 2026
AutoGPT
Jul 11, 2026

Categories

dust
AI Agents, LLM Frameworks, Developer Tools
AutoGPT
AI Agents, LLM Frameworks

Trust and health

Open issues (now)

dust
224
AutoGPT
494

Full report

Choose dust if…

  • dust is primarily TypeScript; AutoGPT is Python.
  • License: dust is MIT, AutoGPT is Other.
  • Tags unique to dust: large-language-models, rust, typescript.
  • Also covers Developer Tools.
  • dust ships Docker support for self-hosted deployment.

When NOT to use dust

  • 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.
  • Developer Tools: A gateway is overkill when you're pinned to a single provider and model.

Choose AutoGPT if…

  • AutoGPT is primarily Python; dust is TypeScript.
  • License: AutoGPT is Other, dust is MIT.
  • Tags unique to AutoGPT: ai, 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.

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.

Explore

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: dust 1.4k · AutoGPT 185k (synced Jul 11, 2026).

Common questions

What is the difference between dust and AutoGPT?
dust: Custom AI agent platform to speed up your work.. 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 dust over AutoGPT?
Choose dust over AutoGPT when dust is primarily TypeScript; AutoGPT is Python; License: dust is MIT, AutoGPT is Other; Tags unique to dust: large-language-models, rust, typescript; Also covers Developer Tools; dust ships Docker support for self-hosted deployment.
When should I choose AutoGPT over dust?
Choose AutoGPT over dust when AutoGPT is primarily Python; dust is TypeScript; License: AutoGPT is Other, dust is MIT; Tags unique to AutoGPT: ai, 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.
When should I avoid dust?
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. Developer Tools: A gateway is overkill when you're pinned to a single provider and model.
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 dust or AutoGPT more popular on GitHub?
AutoGPT has more GitHub stars (185,464 vs 1,413). Stars measure visibility, not whether either tool fits your constraints.
Are dust and AutoGPT open source?
Yes - both are open-source projects on GitHub (dust: MIT, AutoGPT: Other).
Where can I find alternatives to dust or AutoGPT?
GraphCanon lists graph-backed alternatives at dust alternatives and AutoGPT alternatives (dust markdown twin, AutoGPT 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, dust or AutoGPT?
dust: Very active. 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 dust and AutoGPT?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: dust trust report; AutoGPT trust report.