--- title: "eval-view vs promptguard" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hidai25-eval-view-vs-mturac-promptguard" tools: ["hidai25-eval-view", "mturac-promptguard"] --- # eval-view vs promptguard *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 promptguard if promptGuard audits prompts for AI agents to ensure safe code generation. [eval-view](https://evalview.com) reports 134 GitHub stars, 24 forks, and 2 open issues, last pushed Sep 5, 2026. [promptguard](https://github.com/mturac/promptguard) has 86 stars, 1 forks, and 0 open issues, last pushed Jul 22, 2026. Figures are from public GitHub metadata via [eval-view's repository](https://github.com/hidai25/eval-view) and [promptguard's repository](https://github.com/mturac/promptguard). | | [eval-view](/tools/hidai25-eval-view.md) | [promptguard](/tools/mturac-promptguard.md) | | --- | --- | --- | | Tagline | Regression testing for AI agents, snapshots behavior, diffs tool calls, catches regressions in CI | Audits prompts as behavioral contracts for AI agents. | | Stars | 134 | 86 | | Forks | 24 | 1 | | Open issues | 2 | 0 | | 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 | PromptGuard audits prompts for AI agents to ensure safe code generation. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | 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) | [promptguard](/tools/mturac-promptguard.md) | | --- | --- | --- | | Maintenance | Active (82%) | Steady (60%) | | Days since push | 13d | 51d | | Open issues (now) | 2 | 0 | | Stars delta | +8 (30d) | +5 (30d) | | Open issues delta | -1 (30d) | 0 (30d) | | Full report | [trust report](/tools/hidai25-eval-view/trust.md) | [trust report](/tools/mturac-promptguard/trust.md) | ## Shared compatibility - **Python**: [eval-view](/tools/hidai25-eval-view.md) - Python runtime; [promptguard](/tools/mturac-promptguard.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: promptguard - **Adopt for:** PromptGuard audits prompts for AI agents to ensure safe code generation. ## Choose when ### Choose eval-view if… - License: eval-view is Apache-2.0, promptguard is MIT. - 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 promptguard if… - License: promptguard is MIT, eval-view is Apache-2.0. - Tags unique to promptguard: audit, prompt-engineering, safety. - When developing AI agents that generate code and need strict adherence to safety standards ## 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 promptguard - If your project does not involve prompting AI for code generation or execution - In scenarios where real-time prompt auditing is required as it focuses on post-audit analysis ## Common questions ### What is the difference between eval-view and promptguard? eval-view: Regression testing for AI agents, snapshots behavior, diffs tool calls, catches regressions in CI. promptguard: Audits prompts as behavioral contracts for AI agents.. See the comparison table for live GitHub stats and shared categories. ### When should I choose eval-view over promptguard? Choose eval-view over promptguard when License: eval-view is Apache-2.0, promptguard is MIT; 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 promptguard over eval-view? Choose promptguard over eval-view when License: promptguard is MIT, eval-view is Apache-2.0; Tags unique to promptguard: audit, prompt-engineering, safety; When developing AI agents that generate code and need strict adherence to safety standards. ### 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 promptguard? If your project does not involve prompting AI for code generation or execution In scenarios where real-time prompt auditing is required as it focuses on post-audit analysis ### Is eval-view or promptguard more popular on GitHub? eval-view has more GitHub stars (134 vs 86). Stars measure visibility, not whether either tool fits your constraints. ### Are eval-view and promptguard open source? Yes - both are open-source projects on GitHub (eval-view: Apache-2.0, promptguard: MIT). ### Where can I find alternatives to eval-view or promptguard? GraphCanon lists graph-backed alternatives at [eval-view alternatives](/tools/hidai25-eval-view/alternatives) and [promptguard alternatives](/tools/mturac-promptguard/alternatives) ([eval-view markdown twin](/tools/hidai25-eval-view/alternatives.md), [promptguard markdown twin](/tools/mturac-promptguard/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-mturac-promptguard.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, eval-view or promptguard? eval-view: Active. promptguard: Steady. 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 promptguard? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [eval-view trust report](/tools/hidai25-eval-view/trust); [promptguard trust report](/tools/mturac-promptguard/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/_