--- title: "eval-view vs qwed-verification" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hidai25-eval-view-vs-qwed-ai-qwed-verification" tools: ["hidai25-eval-view", "qwed-ai-qwed-verification"] --- # eval-view vs qwed-verification *GraphCanon updated Aug 2, 2026* ## Verdict Pick eval-view if regression testing for AI agents to detect behavioral changes and output quality regressions over time; pick qwed-verification if qWED-verification employs math and formal methods like Z3, SMT, SymPy to verify AI outputs, creating an auditable trust boundary specifically for agentic AI. [eval-view](https://evalview.com) reports 126 GitHub stars, 21 forks, and 3 open issues, last pushed Jul 26, 2026. [qwed-verification](https://docs.qwedai.com/) has 57 stars, 11 forks, and 26 open issues, last pushed Aug 2, 2026. Figures are from public GitHub metadata via [eval-view's repository](https://github.com/hidai25/eval-view) and [qwed-verification's repository](https://github.com/QWED-AI/qwed-verification). | | [eval-view](/tools/hidai25-eval-view.md) | [qwed-verification](/tools/qwed-ai-qwed-verification.md) | | --- | --- | --- | | Tagline | Regression testing for AI agents | A deterministic verification layer for AI systems | | Stars | 126 | 57 | | Forks | 21 | 11 | | Open issues | 3 | 26 | | Language | Python | Python | | Adopt for | Regression testing for AI agents to detect behavioral changes and output quality regressions over time. | QWED-verification employs math and formal methods like Z3, SMT, SymPy to verify AI outputs, creating an auditable trust boundary specifically for agentic AI. | | Persona | - | - | | Runtime | - | - | | License | The software uses the Apache-2.0 license, offering permissive terms for use and distribution. | Apache-2.0 | | Categories | AI Agents, Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [eval-view](/tools/hidai25-eval-view.md) | [qwed-verification](/tools/qwed-ai-qwed-verification.md) | | --- | --- | --- | | Days since push | 6d | 0d | | Open issues (now) | 3 | 26 | | Owner type | User | Organization | | Full report | [trust report](/tools/hidai25-eval-view/trust.md) | [trust report](/tools/qwed-ai-qwed-verification/trust.md) | ## Shared compatibility - **Python**: [eval-view](/tools/hidai25-eval-view.md) - Python runtime; [qwed-verification](/tools/qwed-ai-qwed-verification.md) - Python runtime ## 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. ## Decision facts: qwed-verification - **Adopt for:** QWED-verification employs math and formal methods like Z3, SMT, SymPy to verify AI outputs, creating an auditable trust boundary specifically for agentic AI. ## Choose when ### 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, ai-agents, regression-testing. - Also covers AI Agents. - When you need to track and assess the behavior consistency of your AI agent across versions without involving live API calls. ### Choose qwed-verification if… - Tags unique to qwed-verification: ai safety, ai-accuracy, ai-security, code-security. - When deterministic verification is needed for ai output accuracy - More recently updated (last pushed Aug 2, 2026). ## 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. ## When NOT to use qwed-verification - If looking to generate content rather than verify outputs - In scenarios where flexible, non-mathematical verification methods suffice ## Common questions ### What is the difference between eval-view and qwed-verification? eval-view: Regression testing for AI agents. qwed-verification: A deterministic verification layer for AI systems. See the comparison table for live GitHub stats and shared categories. ### When should I choose eval-view over qwed-verification? Choose eval-view over qwed-verification 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, ai-agents, regression-testing; Also covers AI Agents; When you need to track and assess the behavior consistency of your AI agent across versions without involving live API calls. ### When should I choose qwed-verification over eval-view? Choose qwed-verification over eval-view when Tags unique to qwed-verification: ai safety, ai-accuracy, ai-security, code-security; When deterministic verification is needed for ai output accuracy; More recently updated (last pushed Aug 2, 2026). ### 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. ### When should I avoid qwed-verification? If looking to generate content rather than verify outputs In scenarios where flexible, non-mathematical verification methods suffice ### Is eval-view or qwed-verification more popular on GitHub? eval-view has more GitHub stars (126 vs 57). Stars measure visibility, not whether either tool fits your constraints. ### Are eval-view and qwed-verification open source? Yes - both are open-source projects on GitHub (eval-view: Apache-2.0, qwed-verification: Apache-2.0). ### Where can I find alternatives to eval-view or qwed-verification? GraphCanon lists graph-backed alternatives at [eval-view alternatives](/tools/hidai25-eval-view/alternatives) and [qwed-verification alternatives](/tools/qwed-ai-qwed-verification/alternatives) ([eval-view markdown twin](/tools/hidai25-eval-view/alternatives.md), [qwed-verification markdown twin](/tools/qwed-ai-qwed-verification/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-qwed-ai-qwed-verification.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, eval-view or qwed-verification? eval-view: Very active. qwed-verification: 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 qwed-verification? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [eval-view trust report](/tools/hidai25-eval-view/trust); [qwed-verification trust report](/tools/qwed-ai-qwed-verification/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/_