--- title: "agent-learning-kit vs ragas" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/future-agi-agent-learning-kit-vs-vibrantlabsai-ragas" tools: ["future-agi-agent-learning-kit", "vibrantlabsai-ragas"] --- # agent-learning-kit vs ragas *GraphCanon updated Aug 20, 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 ragas if ragas is a Python-based tool designed to enhance the evaluation process of Large Language Model (LLM) applications through specialized workflows and performance insights. [agent-learning-kit](https://futureagi.com) reports 118 GitHub stars, 43 forks, and 6 open issues, last pushed Aug 1, 2026. [ragas](https://docs.ragas.io) has 15k stars, 1.6k forks, and 562 open issues, last pushed Feb 24, 2026. Figures are from public GitHub metadata via [agent-learning-kit's repository](https://github.com/future-agi/agent-learning-kit) and [ragas's repository](https://github.com/vibrantlabsai/ragas). | | [agent-learning-kit](/tools/future-agi-agent-learning-kit.md) | [ragas](/tools/vibrantlabsai-ragas.md) | | --- | --- | --- | | Tagline | Evaluation Framework for all your AI related Workflows | Supercharge Your LLM Application Evaluations 🚀 | | Stars | 118 | 15,388 | | Forks | 43 | 1,637 | | Open issues | 6 | 562 | | 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. | Ragas is a Python-based tool designed to enhance the evaluation process of Large Language Model (LLM) applications through specialized workflows and performance insights. | | Persona | - | developer harness | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | 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) | [ragas](/tools/vibrantlabsai-ragas.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 176d | | Open issues (now) | 6 | 562 | | Stars delta | Unknown | +470 (30d) | | Open issues delta | Unknown | +45 (30d) | | Full report | [trust report](/tools/future-agi-agent-learning-kit/trust.md) | [trust report](/tools/vibrantlabsai-ragas/trust.md) | ## Shared compatibility - **Python**: [agent-learning-kit](/tools/future-agi-agent-learning-kit.md) - Python runtime; [ragas](/tools/vibrantlabsai-ragas.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: ragas - **Requirements:** Min 4 GB RAM - **Adopt for:** Ragas is a Python-based tool designed to enhance the evaluation process of Large Language Model (LLM) applications through specialized workflows and performance insights. - **Persona:** developer harness ## Choose when ### Choose agent-learning-kit if… - 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. - More recently updated (last pushed Aug 1, 2026). ### Choose ragas if… - Requirements: Min 4 GB RAM. - Tags unique to ragas: llm, llmops. - When you need advanced tools tailored for evaluating LLM applications, as RAGAS offers specific optimizations not found in generic testing frameworks. ## 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 ragas - If your application does not involve Large Language Models or if the evaluation needs are basic; RAGAS is optimized for LLM-specific evaluations which may be overkill for simpler systems. - For projects that require real-time monitoring or continuous testing of live models where more dynamic observability tools might offer better support. ## Common questions ### What is the difference between agent-learning-kit and ragas? agent-learning-kit: Evaluation Framework for all your AI related Workflows. ragas: Supercharge Your LLM Application Evaluations 🚀. See the comparison table for live GitHub stats and shared categories. ### When should I choose agent-learning-kit over ragas? Choose agent-learning-kit over ragas when 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; More recently updated (last pushed Aug 1, 2026). ### When should I choose ragas over agent-learning-kit? Choose ragas over agent-learning-kit when Requirements: Min 4 GB RAM; Tags unique to ragas: llm, llmops; When you need advanced tools tailored for evaluating LLM applications, as RAGAS offers specific optimizations not found in generic testing frameworks. ### 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 ragas? If your application does not involve Large Language Models or if the evaluation needs are basic; RAGAS is optimized for LLM-specific evaluations which may be overkill for simpler systems. For projects that require real-time monitoring or continuous testing of live models where more dynamic observability tools might offer better support. ### Is agent-learning-kit or ragas more popular on GitHub? ragas has more GitHub stars (15,388 vs 118). Stars measure visibility, not whether either tool fits your constraints. ### Are agent-learning-kit and ragas open source? Yes - both are open-source projects on GitHub (agent-learning-kit: Apache-2.0, ragas: Apache-2.0). ### Where can I find alternatives to agent-learning-kit or ragas? GraphCanon lists graph-backed alternatives at [agent-learning-kit alternatives](/tools/future-agi-agent-learning-kit/alternatives) and [ragas alternatives](/tools/vibrantlabsai-ragas/alternatives) ([agent-learning-kit markdown twin](/tools/future-agi-agent-learning-kit/alternatives.md), [ragas markdown twin](/tools/vibrantlabsai-ragas/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-vibrantlabsai-ragas.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 ragas? agent-learning-kit: Very active. ragas: 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 ragas? 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); [ragas trust report](/tools/vibrantlabsai-ragas/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/_