Comparison
every_eval_ever vs AutoGPT
Verdict
Pick every_eval_ever when license: every_eval_ever is MIT, AutoGPT is Other; pick AutoGPT when license: AutoGPT is Other, every_eval_ever is MIT.
Markdown twin · every_eval_ever alternatives · AutoGPT alternatives
GraphCanon updated today
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Trust & integrity
| Signal | every_eval_ever | AutoGPT |
|---|---|---|
| Maintenance | Active (10d since push) As of today · github_public_v1 | Very active (0d since push) As of 4d · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · github_public_v1 | Not a fork · Organization account As of 4d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of today · osv@v1 | No lockfile (source not queried) As of 4d · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- every_eval_ever
- Every Eval Ever is a shared schema and crowdsourced eval database. It defines a standardized metadata format for storing AI evaluation results, from leaderboard scrapes and research papers to local ev
- AutoGPT
- AutoGPT is the vision of accessible AI for everyone, to use and to build on.
Stars
- every_eval_ever
- 93
- AutoGPT
- 185k
Forks
- every_eval_ever
- 42
- AutoGPT
- 46k
Open issues
- every_eval_ever
- 48
- AutoGPT
- 494
Language
- every_eval_ever
- Python
- AutoGPT
- Python
Adopt for
- every_eval_ever
- -
- 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
- every_eval_ever
- -
- AutoGPT
- -
Runtime
- every_eval_ever
- -
- AutoGPT
- -
License
- every_eval_ever
- MIT
- AutoGPT
- Other
Last pushed
- every_eval_ever
- Jul 4, 2026
- AutoGPT
- Jul 11, 2026
Categories
- every_eval_ever
- AI Agents, Inference & Serving, LLM Frameworks
- AutoGPT
- AI Agents, LLM Frameworks
Trust and health
Maintenance
- every_eval_ever
- Active (82%)
- AutoGPT
- Very active (96%)
Days since push
- every_eval_ever
- 10d
- AutoGPT
- 0d
Open issues (now)
- every_eval_ever
- 48
- AutoGPT
- 494
Full report
- every_eval_ever
- Trust report
- AutoGPT
- Trust report
Choose every_eval_ever if…
- License: every_eval_ever is MIT, AutoGPT is Other.
- Tags unique to every_eval_ever: agent-evaluation, ai-evaluation, evaluations, infra.
- Also covers Inference & Serving.
When NOT to use every_eval_ever
- AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism.
- Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
Choose AutoGPT if…
- License: AutoGPT is Other, every_eval_ever is MIT.
- 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 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 (evaleval/every_eval_ever) · observed Jul 15, 2026
- GitHub forks (evaleval/every_eval_ever) · observed Jul 15, 2026
- Last push (evaleval/every_eval_ever) · observed Jul 4, 2026
- License file (MIT) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- GitHub stars (Significant-Gravitas/AutoGPT) · observed Jul 11, 2026
- GitHub forks (Significant-Gravitas/AutoGPT) · observed Jul 11, 2026
- Last push (Significant-Gravitas/AutoGPT) · observed Jul 11, 2026
- License file (Other) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: every_eval_ever 93 · AutoGPT 185k (synced Jul 15, 2026).
Common questions
- What is the difference between every_eval_ever and AutoGPT?
- every_eval_ever: Every Eval Ever is a shared schema and crowdsourced eval database. It defines a standardized metadata format for storing AI evaluation results, from leaderboard scrapes and research papers to local ev. 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 every_eval_ever over AutoGPT?
- Choose every_eval_ever over AutoGPT when License: every_eval_ever is MIT, AutoGPT is Other; Tags unique to every_eval_ever: agent-evaluation, ai-evaluation, evaluations, infra; Also covers Inference & Serving.
- When should I choose AutoGPT over every_eval_ever?
- Choose AutoGPT over every_eval_ever when License: AutoGPT is Other, every_eval_ever is MIT; 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 avoid every_eval_ever?
- AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. 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 every_eval_ever or AutoGPT more popular on GitHub?
- AutoGPT has more GitHub stars (185,464 vs 93). Stars measure visibility, not whether either tool fits your constraints.
- Are every_eval_ever and AutoGPT open source?
- Yes - both are open-source projects on GitHub (every_eval_ever: MIT, AutoGPT: Other).
- Where can I find alternatives to every_eval_ever or AutoGPT?
- GraphCanon lists graph-backed alternatives at every_eval_ever alternatives and AutoGPT alternatives (every_eval_ever 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, every_eval_ever or AutoGPT?
- every_eval_ever: 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 every_eval_ever and AutoGPT?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: every_eval_ever trust report; AutoGPT trust report.