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Comparison

core vs AutoGPT

Verdict

Pick core when license: core is GPL-3.0, AutoGPT is Other; pick AutoGPT when license: AutoGPT is Other, core is GPL-3.0.

Markdown twin · core alternatives · AutoGPT alternatives

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core logo

core

cheshire-cat-ai/core

★ 3.1kpushed Jul 8, 2026
vs
AutoGPT logo

AutoGPT

Significant-Gravitas/AutoGPT

★ 185kpushed Jul 11, 2026

Trust & integrity

SignalcoreAutoGPT
Maintenance
Very active (2d 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)
2 low (2 low)
As of today · mcp_manifest@v1
No lockfile
As of today · none
core trust reportAutoGPT trust reportTrust methodology

Tagline

core
AI agent microservice
AutoGPT
AutoGPT is the vision of accessible AI for everyone, to use and to build on.

Stars

core
3.1k
AutoGPT
185k

Forks

core
410
AutoGPT
46k

Open issues

core
4
AutoGPT
494

Language

core
Python
AutoGPT
Python

Adopt for

core
-
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

core
-
AutoGPT
-

Runtime

core
-
AutoGPT
-

License

core
GPL-3.0
AutoGPT
Other

Last pushed

core
Jul 8, 2026
AutoGPT
Jul 11, 2026

Categories

core
AI Agents, Vector Databases, LLM Frameworks
AutoGPT
AI Agents, LLM Frameworks

Trust and health

Days since push

core
2d
AutoGPT
0d

Open issues (now)

core
4
AutoGPT
494

Security scan

core
2 low (2 low)
AutoGPT
No lockfile

Full report

core
Trust report
AutoGPT
Trust report

Choose core if…

  • License: core is GPL-3.0, AutoGPT is Other.
  • Tags unique to core: assistant, ag-ui-protocol, docker, conversational.
  • Also covers Vector Databases.

When NOT to use core

  • AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism.
  • Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
  • LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
GitHubDetails

Choose AutoGPT if…

  • License: AutoGPT is Other, core is GPL-3.0.
  • Tags unique to AutoGPT: agents, llm, artificial-intelligence, agentic-ai.
  • 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.
GitHubDetails

Explore

core trust report →AutoGPT trust report →AI Agents category →Vector Databases category →LLM Frameworks category →All comparisonsStack workflowsTrending tools

Sources

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

GitHub stars on cards: core 3.1k · AutoGPT 185k (synced Jul 11, 2026).

Common questions

What is the difference between core and AutoGPT?
core: AI agent microservice. 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 core over AutoGPT?
Choose core over AutoGPT when License: core is GPL-3.0, AutoGPT is Other; Tags unique to core: assistant, ag-ui-protocol, docker, conversational; Also covers Vector Databases.
When should I choose AutoGPT over core?
Choose AutoGPT over core when License: AutoGPT is Other, core is GPL-3.0; Tags unique to AutoGPT: agents, llm, artificial-intelligence, agentic-ai; When you need to rapidly prototype or deploy an autonomous agent using existing language models without deep AI expertise.
When should I avoid core?
AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. 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 core or AutoGPT more popular on GitHub?
AutoGPT has more GitHub stars (185,464 vs 3,072). Stars measure visibility, not whether either tool fits your constraints.
Are core and AutoGPT open source?
Yes - both are open-source projects on GitHub (core: GPL-3.0, AutoGPT: Other).
Where can I find alternatives to core or AutoGPT?
GraphCanon lists graph-backed alternatives at core alternatives and AutoGPT alternatives (core 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, core or AutoGPT?
core: 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 core and AutoGPT?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: core trust report; AutoGPT trust report.