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Comparison

agent-learning-kit vs simple-evals

Verdict

Pick agent-learning-kit if agent-learning-kit is a Python framework for evaluating AI-related workflows with modules for faithfulness assessment, embedding similarity analysis, and feedback loop integration via ChromaDB; pick simple-evals if simple-evals provides lightweight tools for evaluating language models using reference implementations from HealthBench, BrowseComp, SimpleQA. Last updates July 2025.

Markdown twin · agent-learning-kit alternatives · simple-evals alternatives

GraphCanon updated 2w

agent-learning-kit logo

agent-learning-kit

future-agi/agent-learning-kit

★ 118pushed Aug 1, 2026
vs
simple-evals logo

simple-evals

openai/simple-evals

★ 4.6kpushed Apr 22, 2026

Trust & integrity

Signalagent-learning-kitsimple-evals
Maintenance
Very active (0d since push)
As of 3w · github_public_v1
Slowing (106d since push)
As of 2w · github_public_v1
Provenance
Not a fork · Organization account
As of 3w · github_public_v1
Not a fork · Organization account
As of 2w · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of 1mo · osv@v1
No lockfile (source not queried)
As of 1mo · 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
agent-learning-kit trust reportsimple-evals trust reportTrust methodology

Tagline

agent-learning-kit
Evaluation Framework for all your AI related Workflows
simple-evals
A lightweight library for evaluating language models.

Stars

agent-learning-kit
118
simple-evals
4.6k

Forks

agent-learning-kit
43
simple-evals
501

Open issues

agent-learning-kit
6
simple-evals
56

Language

agent-learning-kit
Python
simple-evals
Python

Adopt for

agent-learning-kit
Agent-learning-kit is a Python framework for evaluating AI-related workflows with modules for faithfulness assessment, embedding similarity analysis, and feedback loop integration via ChromaDB.
simple-evals
simple-evals provides lightweight tools for evaluating language models using reference implementations from HealthBench, BrowseComp, SimpleQA. Last updates July 2025.

Persona

agent-learning-kit
-
simple-evals
-

Runtime

agent-learning-kit
-
simple-evals
-

License

agent-learning-kit
Apache-2.0
simple-evals
MIT licensed Python library for transparent language model evaluations with specific benchmark support until July 2025.

Last pushed

agent-learning-kit
Aug 1, 2026
simple-evals
Apr 22, 2026

Categories

agent-learning-kit
Evaluation & Observability
simple-evals
Evaluation & Observability

Trust and health

Maintenance

agent-learning-kit
Very active (96%)
simple-evals
Slowing (36%)

Days since push

agent-learning-kit
0d
simple-evals
106d

Open issues (now)

agent-learning-kit
6
simple-evals
56

Full report

agent-learning-kit
Trust report
simple-evals
Trust report

Choose agent-learning-kit if…

  • License: agent-learning-kit is Apache-2.0, simple-evals is MIT.
  • Tags unique to agent-learning-kit: ai-agents, ci-cd, ml.
  • When you need comprehensive evaluation of your AI models including faithfulness checks using DeBERTa NLI model installed.

When NOT to use agent-learning-kit

  • If your workflow does not align with the specific evaluation criteria and methods supported by agent-learning-kit.
  • When you seek a framework that integrates with backend systems other than those provided as optional extras, such as MongoDB or DynamoDB instead of ChromaDB.
GitHubDetails

Choose simple-evals if…

  • License: simple-evals is MIT, agent-learning-kit is Apache-2.0.
  • Tags unique to simple-evals: benchmark, depreciation notice, language-models.
  • When you need a stable baseline to evaluate model performance with specific benchmarks like MMLU, HumanEval, and DROP that won't change after July 2025

When NOT to use simple-evals

  • For evaluating models released or significantly updated after July 2025, as this tool does not include future benchmarks
  • When you need a tool that will adapt and expand its benchmark set with emerging model releases and evaluation tasks beyond 2025
GitHubDetails

Explore

agent-learning-kit trust report →simple-evals trust report →Evaluation & Observability 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: agent-learning-kit 118 · simple-evals 4.6k (synced Aug 1, 2026).

Common questions

What is the difference between agent-learning-kit and simple-evals?
agent-learning-kit: Evaluation Framework for all your AI related Workflows. simple-evals: A lightweight library for evaluating language models.. See the comparison table for live GitHub stats and shared categories.
When should I choose agent-learning-kit over simple-evals?
Choose agent-learning-kit over simple-evals when License: agent-learning-kit is Apache-2.0, simple-evals is MIT; Tags unique to agent-learning-kit: ai-agents, ci-cd, ml; When you need comprehensive evaluation of your AI models including faithfulness checks using DeBERTa NLI model installed.
When should I choose simple-evals over agent-learning-kit?
Choose simple-evals over agent-learning-kit when License: simple-evals is MIT, agent-learning-kit is Apache-2.0; Tags unique to simple-evals: benchmark, depreciation notice, language-models; When you need a stable baseline to evaluate model performance with specific benchmarks like MMLU, HumanEval, and DROP that won't change after July 2025.
When should I avoid agent-learning-kit?
If your workflow does not align with the specific evaluation criteria and methods supported by agent-learning-kit. When you seek a framework that integrates with backend systems other than those provided as optional extras, such as MongoDB or DynamoDB instead of ChromaDB.
When should I avoid simple-evals?
For evaluating models released or significantly updated after July 2025, as this tool does not include future benchmarks When you need a tool that will adapt and expand its benchmark set with emerging model releases and evaluation tasks beyond 2025
Is agent-learning-kit or simple-evals more popular on GitHub?
simple-evals has more GitHub stars (4,595 vs 118). Stars measure visibility, not whether either tool fits your constraints.
Are agent-learning-kit and simple-evals open source?
Yes - both are open-source projects on GitHub (agent-learning-kit: Apache-2.0, simple-evals: MIT).
Where can I find alternatives to agent-learning-kit or simple-evals?
GraphCanon lists graph-backed alternatives at agent-learning-kit alternatives and simple-evals alternatives (agent-learning-kit markdown twin, simple-evals 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, agent-learning-kit or simple-evals?
agent-learning-kit: Very active. simple-evals: Slowing. 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 agent-learning-kit and simple-evals?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: agent-learning-kit trust report; simple-evals trust report.

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