--- title: "eval-view vs agentic-vbench" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hidai25-eval-view-vs-philolabs-agentic-vbench" tools: ["hidai25-eval-view", "philolabs-agentic-vbench"] --- # eval-view vs agentic-vbench *GraphCanon updated Sep 20, 2026* ## Verdict 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; pick agentic-vbench if agenticVBench evaluates AI agents' real-world post-production capabilities with specific task prompts for activities like audio restoration. [eval-view](https://evalview.com) reports 134 GitHub stars, 24 forks, and 2 open issues, last pushed Sep 5, 2026. [agentic-vbench](https://agenticvbench.com/) has 96 stars, 27 forks, and 37 open issues, last pushed Sep 2, 2026. Figures are from public GitHub metadata via [eval-view's repository](https://github.com/hidai25/eval-view) and [agentic-vbench's repository](https://github.com/PhiloLabs/agentic-vbench). | | [eval-view](/tools/hidai25-eval-view.md) | [agentic-vbench](/tools/philolabs-agentic-vbench.md) | | --- | --- | --- | | Tagline | Regression testing for AI agents, snapshots behavior, diffs tool calls, catches regressions in CI | A benchmark for evaluating AI agents in performing real-world post-production tasks like audio and video editing. | | Stars | 134 | 96 | | Forks | 24 | 27 | | Open issues | 2 | 37 | | Language | Python | Python | | 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 | AgenticVBench evaluates AI agents' real-world post-production capabilities with specific task prompts for activities like audio restoration. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | AI Agents, Evaluation & Observability | AI Agents, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [eval-view](/tools/hidai25-eval-view.md) | [agentic-vbench](/tools/philolabs-agentic-vbench.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 13d | 6d | | Open issues (now) | 2 | 37 | | Stars delta | +8 (30d) | +14 (30d) | | Open issues delta | -1 (30d) | -20 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/hidai25-eval-view/trust.md) | [trust report](/tools/philolabs-agentic-vbench/trust.md) | ## Shared compatibility - **Python**: [eval-view](/tools/hidai25-eval-view.md) - Python runtime; [agentic-vbench](/tools/philolabs-agentic-vbench.md) - Python runtime ## 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 ## Decision facts: agentic-vbench - **Requirements:** Requires Docker; Install via scripts provided in the repository.; Python virtual environment setup for reproducibility. - **Adopt for:** AgenticVBench evaluates AI agents' real-world post-production capabilities with specific task prompts for activities like audio restoration. ## Choose when ### Choose eval-view if… - Tags unique to eval-view: agent-benchmark, agent-evaluation, agentic-ai, anthropic. - 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. ### Choose agentic-vbench if… - Requirements: Requires Docker; Install via scripts provided in the repository.; Python virtual environment setup for reproducibility.. - Tags unique to agentic-vbench: benchmark, harbor, llm-evaluation, video-editing. - When you need to benchmark the performance of AI agents in handling specialized tasks such as audio and video editing that require precise restorative actions. ## 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. ## When NOT to use agentic-vbench - When the focus is on generic performance evaluations rather than on real-world, task-specific benchmarks that assess handling complex post-production scenarios. - If your budget or timeline cannot accommodate a per-task wall clock time of ~10 minutes and cost ranging from $0.10 to $2 based on agent token usage. ## Common questions ### What is the difference between eval-view and agentic-vbench? eval-view: Regression testing for AI agents, snapshots behavior, diffs tool calls, catches regressions in CI. agentic-vbench: A benchmark for evaluating AI agents in performing real-world post-production tasks like audio and video editing.. See the comparison table for live GitHub stats and shared categories. ### When should I choose eval-view over agentic-vbench? Choose eval-view over agentic-vbench when Tags unique to eval-view: agent-benchmark, agent-evaluation, agentic-ai, anthropic; 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 choose agentic-vbench over eval-view? Choose agentic-vbench over eval-view when Requirements: Requires Docker; Install via scripts provided in the repository.; Python virtual environment setup for reproducibility.; Tags unique to agentic-vbench: benchmark, harbor, llm-evaluation, video-editing; When you need to benchmark the performance of AI agents in handling specialized tasks such as audio and video editing that require precise restorative actions. ### 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. ### When should I avoid agentic-vbench? When the focus is on generic performance evaluations rather than on real-world, task-specific benchmarks that assess handling complex post-production scenarios. If your budget or timeline cannot accommodate a per-task wall clock time of ~10 minutes and cost ranging from $0.10 to $2 based on agent token usage. ### Is eval-view or agentic-vbench more popular on GitHub? eval-view has more GitHub stars (134 vs 96). Stars measure visibility, not whether either tool fits your constraints. ### Are eval-view and agentic-vbench open source? Yes - both are open-source projects on GitHub (eval-view: Apache-2.0, agentic-vbench: Apache-2.0). ### Where can I find alternatives to eval-view or agentic-vbench? GraphCanon lists graph-backed alternatives at [eval-view alternatives](/tools/hidai25-eval-view/alternatives) and [agentic-vbench alternatives](/tools/philolabs-agentic-vbench/alternatives) ([eval-view markdown twin](/tools/hidai25-eval-view/alternatives.md), [agentic-vbench markdown twin](/tools/philolabs-agentic-vbench/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/hidai25-eval-view-vs-philolabs-agentic-vbench.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, eval-view or agentic-vbench? eval-view: Active. agentic-vbench: 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 eval-view and agentic-vbench? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [eval-view trust report](/tools/hidai25-eval-view/trust); [agentic-vbench trust report](/tools/philolabs-agentic-vbench/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=hidai25-eval-view`](/api/graphcanon/graph?tool=hidai25-eval-view) - 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/_