--- title: "agent-learning-kit vs simple-evals" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/future-agi-agent-learning-kit-vs-openai-simple-evals" tools: ["future-agi-agent-learning-kit", "openai-simple-evals"] --- # agent-learning-kit vs simple-evals *GraphCanon updated Aug 7, 2026* ## 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. [agent-learning-kit](https://futureagi.com) reports 118 GitHub stars, 43 forks, and 6 open issues, last pushed Aug 1, 2026. [simple-evals](https://github.com/openai/simple-evals) has 4.6k stars, 501 forks, and 56 open issues, last pushed Apr 22, 2026. Figures are from public GitHub metadata via [agent-learning-kit's repository](https://github.com/future-agi/agent-learning-kit) and [simple-evals's repository](https://github.com/openai/simple-evals). | | [agent-learning-kit](/tools/future-agi-agent-learning-kit.md) | [simple-evals](/tools/openai-simple-evals.md) | | --- | --- | --- | | Tagline | Evaluation Framework for all your AI related Workflows | A lightweight library for evaluating language models. | | Stars | 118 | 4,595 | | Forks | 43 | 501 | | Open issues | 6 | 56 | | Language | Python | Python | | Adopt for | 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 provides lightweight tools for evaluating language models using reference implementations from HealthBench, BrowseComp, SimpleQA. Last updates July 2025. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT licensed Python library for transparent language model evaluations with specific benchmark support until July 2025. | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agent-learning-kit](/tools/future-agi-agent-learning-kit.md) | [simple-evals](/tools/openai-simple-evals.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 106d | | Open issues (now) | 6 | 56 | | Full report | [trust report](/tools/future-agi-agent-learning-kit/trust.md) | [trust report](/tools/openai-simple-evals/trust.md) | ## Decision facts: agent-learning-kit - **Adopt for:** 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. ## Decision facts: simple-evals - **Adopt for:** simple-evals provides lightweight tools for evaluating language models using reference implementations from HealthBench, BrowseComp, SimpleQA. Last updates July 2025. - **License detail:** MIT licensed Python library for transparent language model evaluations with specific benchmark support until July 2025. ## Choose when ### 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. ### 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 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 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 ## 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](/tools/future-agi-agent-learning-kit/alternatives) and [simple-evals alternatives](/tools/openai-simple-evals/alternatives) ([agent-learning-kit markdown twin](/tools/future-agi-agent-learning-kit/alternatives.md), [simple-evals markdown twin](/tools/openai-simple-evals/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/future-agi-agent-learning-kit-vs-openai-simple-evals.md) 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](/tools/future-agi-agent-learning-kit/trust); [simple-evals trust report](/tools/openai-simple-evals/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=future-agi-agent-learning-kit`](/api/graphcanon/graph?tool=future-agi-agent-learning-kit) - 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/_