--- title: "awesome-evals vs eval-view" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/benchflow-ai-awesome-evals-vs-hidai25-eval-view" tools: ["benchflow-ai-awesome-evals", "hidai25-eval-view"] --- # awesome-evals vs eval-view *GraphCanon updated Aug 2, 2026* ## Verdict Pick awesome-evals if curated resources for AI agent evaluation with BenchFlow backing its maintenance; pick eval-view if regression testing for AI agents to detect behavioral changes and output quality regressions over time. [awesome-evals](https://github.com/benchflow-ai/awesome-evals) reports 761 GitHub stars, 71 forks, and 21 open issues, last pushed Jul 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 [awesome-evals's repository](https://github.com/benchflow-ai/awesome-evals) and [eval-view's repository](https://github.com/hidai25/eval-view). | | [awesome-evals](/tools/benchflow-ai-awesome-evals.md) | [eval-view](/tools/hidai25-eval-view.md) | | --- | --- | --- | | Tagline | A curated library of resources for building and evaluating AI agents | Regression testing for AI agents | | Stars | 761 | 126 | | Forks | 71 | 21 | | Open issues | 21 | 3 | | Language | - | Python | | Adopt for | Curated resources for AI agent evaluation with BenchFlow backing its maintenance | Regression testing for AI agents to detect behavioral changes and output quality regressions over time. | | Persona | - | - | | Runtime | - | - | | License | Other | The software uses the Apache-2.0 license, offering permissive terms for use and distribution. | | Categories | AI Agents, Evaluation & Observability | AI Agents, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-evals](/tools/benchflow-ai-awesome-evals.md) | [eval-view](/tools/hidai25-eval-view.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 26d | 6d | | Open issues (now) | 21 | 3 | | Owner type | Organization | User | | Full report | [trust report](/tools/benchflow-ai-awesome-evals/trust.md) | [trust report](/tools/hidai25-eval-view/trust.md) | ## Decision facts: awesome-evals - **Adopt for:** Curated resources for AI agent evaluation with BenchFlow backing its maintenance ## 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 awesome-evals if… - License: awesome-evals is Other, eval-view is Apache-2.0. - Tags unique to awesome-evals: awesome-list, benchmarks, llm-evaluation, rl-environments. - Need diverse resources encompassing papers, blogs, talks, tools, and benchmarks specifically curated for AI agent evaluation ### Choose eval-view if… - License: eval-view is Apache-2.0, awesome-evals is Other. - 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, regression-testing. - 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 awesome-evals - Require real-time interactive support or direct tool integrations not covered by a static resource list - Seeking proprietary tools from specific vendors rather than open resources and community content ## 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 awesome-evals and eval-view? awesome-evals: A curated library of resources for building and evaluating AI agents. eval-view: Regression testing for AI agents. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-evals over eval-view? Choose awesome-evals over eval-view when License: awesome-evals is Other, eval-view is Apache-2.0; Tags unique to awesome-evals: awesome-list, benchmarks, llm-evaluation, rl-environments; Need diverse resources encompassing papers, blogs, talks, tools, and benchmarks specifically curated for AI agent evaluation. ### When should I choose eval-view over awesome-evals? Choose eval-view over awesome-evals when License: eval-view is Apache-2.0, awesome-evals is Other; 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, regression-testing; 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 awesome-evals? Require real-time interactive support or direct tool integrations not covered by a static resource list Seeking proprietary tools from specific vendors rather than open resources and community content ### 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 awesome-evals or eval-view more popular on GitHub? awesome-evals has more GitHub stars (761 vs 126). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-evals and eval-view open source? Yes - both are open-source projects on GitHub (awesome-evals: Other, eval-view: Apache-2.0). ### Where can I find alternatives to awesome-evals or eval-view? GraphCanon lists graph-backed alternatives at [awesome-evals alternatives](/tools/benchflow-ai-awesome-evals/alternatives) and [eval-view alternatives](/tools/hidai25-eval-view/alternatives) ([awesome-evals markdown twin](/tools/benchflow-ai-awesome-evals/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/benchflow-ai-awesome-evals-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, awesome-evals or eval-view? awesome-evals: 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 awesome-evals and eval-view? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-evals trust report](/tools/benchflow-ai-awesome-evals/trust); [eval-view trust report](/tools/hidai25-eval-view/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=benchflow-ai-awesome-evals`](/api/graphcanon/graph?tool=benchflow-ai-awesome-evals) - 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/_