--- title: "agent-learning-kit vs eval-view" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/future-agi-agent-learning-kit-vs-hidai25-eval-view" tools: ["future-agi-agent-learning-kit", "hidai25-eval-view"] --- # agent-learning-kit vs eval-view *GraphCanon updated Aug 2, 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 eval-view if regression testing for AI agents to detect behavioral changes and output quality regressions over time. [agent-learning-kit](https://futureagi.com) reports 118 GitHub stars, 43 forks, and 6 open issues, last pushed Aug 1, 2026. [eval-view](https://evalview.com) has 126 stars, 21 forks, and 3 open issues, last pushed Jul 26, 2026. Figures are from public GitHub metadata via [agent-learning-kit's repository](https://github.com/future-agi/agent-learning-kit) and [eval-view's repository](https://github.com/hidai25/eval-view). | | [agent-learning-kit](/tools/future-agi-agent-learning-kit.md) | [eval-view](/tools/hidai25-eval-view.md) | | --- | --- | --- | | Tagline | Evaluation Framework for all your AI related Workflows | Regression testing for AI agents | | Stars | 118 | 126 | | Forks | 43 | 21 | | Open issues | 6 | 3 | | 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. | Regression testing for AI agents to detect behavioral changes and output quality regressions over time. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | The software uses the Apache-2.0 license, offering permissive terms for use and distribution. | | Categories | Evaluation & Observability | AI Agents, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agent-learning-kit](/tools/future-agi-agent-learning-kit.md) | [eval-view](/tools/hidai25-eval-view.md) | | --- | --- | --- | | Days since push | 0d | 6d | | Open issues (now) | 6 | 3 | | Owner type | Organization | User | | Full report | [trust report](/tools/future-agi-agent-learning-kit/trust.md) | [trust report](/tools/hidai25-eval-view/trust.md) | ## Shared compatibility - **Python**: [agent-learning-kit](/tools/future-agi-agent-learning-kit.md) - Python runtime; [eval-view](/tools/hidai25-eval-view.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: eval-view - **Pricing:** freemium - Free to use under the terms of the Apache License, Version 2.0. - **Requirements:** Python environment is required for installation and usage.; Installation with pip: `pip install evalview`; Offline support means no live API keys necessary for the basic diff functionality. - **Adopt for:** Regression testing for AI agents to detect behavioral changes and output quality regressions over time. - **License detail:** The software uses the Apache-2.0 license, offering permissive terms for use and distribution. ## Choose when ### Choose agent-learning-kit if… - Tags unique to agent-learning-kit: ci-cd, evaluation, 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 eval-view if… - Pricing: Free to use under the terms of the Apache License, Version 2.0.. - Requirements: Python environment is required for installation and usage.; Installation with pip: `pip install evalview`; Offline support means no live API keys necessary for the basic diff functionality.. - Tags unique to eval-view: agent-benchmark, agent-evaluation, regression-testing. - Also covers AI Agents. - eval-view ships Docker support for self-hosted deployment. - When you need to track and assess the behavior consistency of your AI agent across versions without involving live API calls. ## 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 eval-view - If you do not need to monitor specific behavioral characteristics such as tool call sequences and parameter consistency over time. - When real-time output quality evaluation is critical, as eval-view's offline diffing does not provide immediate feedback on output changes without an LLM judge. ## Common questions ### What is the difference between agent-learning-kit and eval-view? agent-learning-kit: Evaluation Framework for all your AI related Workflows. eval-view: Regression testing for AI agents. See the comparison table for live GitHub stats and shared categories. ### When should I choose agent-learning-kit over eval-view? Choose agent-learning-kit over eval-view when Tags unique to agent-learning-kit: ci-cd, evaluation, 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 eval-view over agent-learning-kit? Choose eval-view over agent-learning-kit when Pricing: Free to use under the terms of the Apache License, Version 2.0.; Requirements: Python environment is required for installation and usage.; Installation with pip: `pip install evalview`; Offline support means no live API keys necessary for the basic diff functionality.; Tags unique to eval-view: agent-benchmark, agent-evaluation, regression-testing; Also covers AI Agents; eval-view ships Docker support for self-hosted deployment; When you need to track and assess the behavior consistency of your AI agent across versions without involving live API calls. ### 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 eval-view? If you do not need to monitor specific behavioral characteristics such as tool call sequences and parameter consistency over time. When real-time output quality evaluation is critical, as eval-view's offline diffing does not provide immediate feedback on output changes without an LLM judge. ### Is agent-learning-kit or eval-view more popular on GitHub? eval-view has more GitHub stars (126 vs 118). Stars measure visibility, not whether either tool fits your constraints. ### Are agent-learning-kit and eval-view open source? Yes - both are open-source projects on GitHub (agent-learning-kit: Apache-2.0, eval-view: Apache-2.0). ### Where can I find alternatives to agent-learning-kit or eval-view? GraphCanon lists graph-backed alternatives at [agent-learning-kit alternatives](/tools/future-agi-agent-learning-kit/alternatives) and [eval-view alternatives](/tools/hidai25-eval-view/alternatives) ([agent-learning-kit markdown twin](/tools/future-agi-agent-learning-kit/alternatives.md), [eval-view markdown twin](/tools/hidai25-eval-view/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-hidai25-eval-view.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 eval-view? agent-learning-kit: Very active. eval-view: 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 agent-learning-kit and eval-view? 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); [eval-view trust report](/tools/hidai25-eval-view/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/_