--- title: "agent-learning-kit vs evals" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/future-agi-agent-learning-kit-vs-openai-evals" tools: ["future-agi-agent-learning-kit", "openai-evals"] --- # agent-learning-kit vs 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 evals if evals is an evaluation framework from OpenAI for assessing large language models and systems built with them. It includes an open-source registry of benchmarks and tools to create custom evaluations. [agent-learning-kit](https://futureagi.com) reports 118 GitHub stars, 43 forks, and 6 open issues, last pushed Aug 1, 2026. [evals](https://github.com/openai/evals) has 19k stars, 3.0k forks, and 213 open issues, last pushed Apr 14, 2026. Figures are from public GitHub metadata via [agent-learning-kit's repository](https://github.com/future-agi/agent-learning-kit) and [evals's repository](https://github.com/openai/evals). | | [agent-learning-kit](/tools/future-agi-agent-learning-kit.md) | [evals](/tools/openai-evals.md) | | --- | --- | --- | | Tagline | Evaluation Framework for all your AI related Workflows | Framework for evaluating LLMs and LLM systems with an open-source registry of benchmarks. | | Stars | 118 | 19,127 | | Forks | 43 | 3,050 | | Open issues | 6 | 213 | | 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. | Evals is an evaluation framework from OpenAI for assessing large language models and systems built with them. It includes an open-source registry of benchmarks and tools to create custom evaluations. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Other | | 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) | [evals](/tools/openai-evals.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 115d | | Open issues (now) | 6 | 213 | | Full report | [trust report](/tools/future-agi-agent-learning-kit/trust.md) | [trust report](/tools/openai-evals/trust.md) | ## Shared compatibility - **Python**: [agent-learning-kit](/tools/future-agi-agent-learning-kit.md) - Python runtime; [evals](/tools/openai-evals.md) - Python runtime ## 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: evals - **Adopt for:** Evals is an evaluation framework from OpenAI for assessing large language models and systems built with them. It includes an open-source registry of benchmarks and tools to create custom evaluations. ## Choose when ### Choose agent-learning-kit if… - License: agent-learning-kit is Apache-2.0, evals is Other. - Tags unique to agent-learning-kit: ai-agents, ci-cd, evaluation, ml. - When you need comprehensive evaluation of your AI models including faithfulness checks using DeBERTa NLI model installed. ### Choose evals if… - License: evals is Other, agent-learning-kit is Apache-2.0. - Tags unique to evals: benchmarking, custom eval creation, evaluation-framework, large language models. - * When you need a comprehensive set of pre-existing evals and the ability to create your own tailored tests using specific use cases, especially within the OpenAI model ecosystem. ## 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 evals - * When evaluating models or systems that do not benefit from being integrated with the OpenAI API, as some features like direct evals configuration in the OpenAI Dashboard require an OpenAI key. - * If you are looking for an evaluation framework that doesn’t involve external dependencies such as Git Large File Storage (LFS) and specific Python version requirements (Python 3.9 minimum), or if a ## Common questions ### What is the difference between agent-learning-kit and evals? agent-learning-kit: Evaluation Framework for all your AI related Workflows. evals: Framework for evaluating LLMs and LLM systems with an open-source registry of benchmarks.. See the comparison table for live GitHub stats and shared categories. ### When should I choose agent-learning-kit over evals? Choose agent-learning-kit over evals when License: agent-learning-kit is Apache-2.0, evals is Other; Tags unique to agent-learning-kit: ai-agents, ci-cd, evaluation, ml; When you need comprehensive evaluation of your AI models including faithfulness checks using DeBERTa NLI model installed. ### When should I choose evals over agent-learning-kit? Choose evals over agent-learning-kit when License: evals is Other, agent-learning-kit is Apache-2.0; Tags unique to evals: benchmarking, custom eval creation, evaluation-framework, large language models; * When you need a comprehensive set of pre-existing evals and the ability to create your own tailored tests using specific use cases, especially within the OpenAI model ecosystem. ### 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 evals? * When evaluating models or systems that do not benefit from being integrated with the OpenAI API, as some features like direct evals configuration in the OpenAI Dashboard require an OpenAI key. * If you are looking for an evaluation framework that doesn’t involve external dependencies such as Git Large File Storage (LFS) and specific Python version requirements (Python 3.9 minimum), or if a ### Is agent-learning-kit or evals more popular on GitHub? evals has more GitHub stars (19,127 vs 118). Stars measure visibility, not whether either tool fits your constraints. ### Are agent-learning-kit and evals open source? Yes - both are open-source projects on GitHub (agent-learning-kit: Apache-2.0, evals: Other). ### Where can I find alternatives to agent-learning-kit or evals? GraphCanon lists graph-backed alternatives at [agent-learning-kit alternatives](/tools/future-agi-agent-learning-kit/alternatives) and [evals alternatives](/tools/openai-evals/alternatives) ([agent-learning-kit markdown twin](/tools/future-agi-agent-learning-kit/alternatives.md), [evals markdown twin](/tools/openai-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-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 evals? agent-learning-kit: Very active. 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 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); [evals trust report](/tools/openai-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/_