--- title: "athina-evals vs eval-view" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/athina-ai-athina-evals-vs-hidai25-eval-view" tools: ["athina-ai-athina-evals", "hidai25-eval-view"] --- # athina-evals vs eval-view *GraphCanon updated Sep 20, 2026* ## Verdict Pick athina-evals if athina-evals is a Python SDK developed for facilitating the evaluation of outputs from large language models through predefined metrics and frameworks; pick eval-view if eval-view is a Python-based tool for regression testing of AI agents, supporting multiple platforms like LangGraph, CrewAI, OpenAI, and Anthropic. It snapshots AI behavior and detects regressions through diffing tool and. [athina-evals](https://docs.athina.ai) reports 301 GitHub stars, 22 forks, and 4 open issues, last pushed Jun 6, 2025. [eval-view](https://evalview.com) has 134 stars, 24 forks, and 2 open issues, last pushed Sep 5, 2026. Figures are from public GitHub metadata via [athina-evals's repository](https://github.com/athina-ai/athina-evals) and [eval-view's repository](https://github.com/hidai25/eval-view). | | [athina-evals](/tools/athina-ai-athina-evals.md) | [eval-view](/tools/hidai25-eval-view.md) | | --- | --- | --- | | Tagline | Python SDK for evaluating LLM generated responses | Regression testing for AI agents, snapshots behavior, diffs tool calls, catches regressions in CI | | Stars | 301 | 134 | | Forks | 22 | 24 | | Open issues | 4 | 2 | | Language | Python | Python | | Adopt for | athina-evals is a Python SDK developed for facilitating the evaluation of outputs from large language models through predefined metrics and frameworks. | Eval-view is a Python-based tool for regression testing of AI agents, supporting multiple platforms like LangGraph, CrewAI, OpenAI, and Anthropic. It snapshots AI behavior and detects regressions through diffing tool and | | Persona | - | - | | Runtime | - | - | | License | - | Apache-2.0 | | Categories | Evaluation & Observability | AI Agents, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [athina-evals](/tools/athina-ai-athina-evals.md) | [eval-view](/tools/hidai25-eval-view.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 470d | 13d | | Open issues (now) | 4 | 2 | | Stars delta | 0 (30d) | +8 (30d) | | Open issues delta | +1 (30d) | -1 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/athina-ai-athina-evals/trust.md) | [trust report](/tools/hidai25-eval-view/trust.md) | ## Decision facts: athina-evals - **Adopt for:** athina-evals is a Python SDK developed for facilitating the evaluation of outputs from large language models through predefined metrics and frameworks. ## Decision facts: eval-view - **Adopt for:** Eval-view is a Python-based tool for regression testing of AI agents, supporting multiple platforms like LangGraph, CrewAI, OpenAI, and Anthropic. It snapshots AI behavior and detects regressions through diffing tool and ## Choose when ### Choose athina-evals if… - Tags unique to athina-evals: evaluation, evaluation-framework, evaluation-metrics, llm-eval. - When comprehensive evaluation of LLM responses is required, leveraging athina's specific tools and metrics - More GitHub stars (301 vs 134) - visibility, not fit. ### Choose eval-view if… - Tags unique to eval-view: agent-benchmark, agent-evaluation, agentic-ai, ai-agents. - Also covers AI Agents. - eval-view ships Docker support for self-hosted deployment. - When you need to snapshot and diff the behavior of AI agents across multiple platforms, including LangGraph, CrewAI, OpenAI, and Anthropic. ## When NOT to use athina-evals - If open-source alternatives with transparent customization options are preferred over athina-evals' approach - In scenarios where API access requirements limit the ability to perform evaluations offline or in private environments ## When NOT to use eval-view - If you are working exclusively with AI platforms not supported by eval-view, such as those not listed among LangGraph, CrewAI, OpenAI, and Anthropic. - When you do not require regression testing or behavior snapshotting for your AI agents, as eval-view is specifically designed for these purposes. - If you are looking for a tool that does not involve backend API charges for executing your agent, as eval-view does not skip these charges even with the --no-judge flag. - If you need a tool that automatically handles the migration from the OpenAI Assistants API to the Responses API without manual intervention, as eval-view requires following a migration guide for this. ## Common questions ### What is the difference between athina-evals and eval-view? athina-evals: Python SDK for evaluating LLM generated responses. eval-view: Regression testing for AI agents, snapshots behavior, diffs tool calls, catches regressions in CI. See the comparison table for live GitHub stats and shared categories. ### When should I choose athina-evals over eval-view? Choose athina-evals over eval-view when Tags unique to athina-evals: evaluation, evaluation-framework, evaluation-metrics, llm-eval; When comprehensive evaluation of LLM responses is required, leveraging athina's specific tools and metrics; More GitHub stars (301 vs 134) - visibility, not fit. ### When should I choose eval-view over athina-evals? Choose eval-view over athina-evals when Tags unique to eval-view: agent-benchmark, agent-evaluation, agentic-ai, ai-agents; Also covers AI Agents; eval-view ships Docker support for self-hosted deployment; When you need to snapshot and diff the behavior of AI agents across multiple platforms, including LangGraph, CrewAI, OpenAI, and Anthropic. ### When should I avoid athina-evals? If open-source alternatives with transparent customization options are preferred over athina-evals' approach In scenarios where API access requirements limit the ability to perform evaluations offline or in private environments ### When should I avoid eval-view? If you are working exclusively with AI platforms not supported by eval-view, such as those not listed among LangGraph, CrewAI, OpenAI, and Anthropic. When you do not require regression testing or behavior snapshotting for your AI agents, as eval-view is specifically designed for these purposes. If you are looking for a tool that does not involve backend API charges for executing your agent, as eval-view does not skip these charges even with the --no-judge flag. If you need a tool that automatically handles the migration from the OpenAI Assistants API to the Responses API without manual intervention, as eval-view requires following a migration guide for this. ### Is athina-evals or eval-view more popular on GitHub? athina-evals has more GitHub stars (301 vs 134). Stars measure visibility, not whether either tool fits your constraints. ### Are athina-evals and eval-view open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to athina-evals or eval-view? GraphCanon lists graph-backed alternatives at [athina-evals alternatives](/tools/athina-ai-athina-evals/alternatives) and [eval-view alternatives](/tools/hidai25-eval-view/alternatives) ([athina-evals markdown twin](/tools/athina-ai-athina-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/athina-ai-athina-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, athina-evals or eval-view? athina-evals: Dormant. eval-view: 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 athina-evals and eval-view? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [athina-evals trust report](/tools/athina-ai-athina-evals/trust); [eval-view trust report](/tools/hidai25-eval-view/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=athina-ai-athina-evals`](/api/graphcanon/graph?tool=athina-ai-athina-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/_