--- title: "eval-view vs agentcanvas" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hidai25-eval-view-vs-vstorm-co-agentcanvas" tools: ["hidai25-eval-view", "vstorm-co-agentcanvas"] --- # eval-view vs agentcanvas *GraphCanon updated Aug 9, 2026* ## Verdict Pick eval-view if regression testing for AI agents to detect behavioral changes and output quality regressions over time; pick agentcanvas if agentcanvas is for developers and AI practitioners who require detailed visual representations of Pydantic AI agent workflows, including cost breakdowns, extracted from Logfire traces. [eval-view](https://evalview.com) reports 126 GitHub stars, 21 forks, and 3 open issues, last pushed Jul 26, 2026. [agentcanvas](https://vstorm.co) has 79 stars, 9 forks, and 0 open issues, last pushed Aug 1, 2026. Figures are from public GitHub metadata via [eval-view's repository](https://github.com/hidai25/eval-view) and [agentcanvas's repository](https://github.com/vstorm-co/agentcanvas). | | [eval-view](/tools/hidai25-eval-view.md) | [agentcanvas](/tools/vstorm-co-agentcanvas.md) | | --- | --- | --- | | Tagline | Regression testing for AI agents | Visualize Pydantic AI agent workflows using Logfire traces | | Stars | 126 | 79 | | Forks | 21 | 9 | | Open issues | 3 | 0 | | Language | Python | Python | | Adopt for | Regression testing for AI agents to detect behavioral changes and output quality regressions over time. | agentcanvas is for developers and AI practitioners who require detailed visual representations of Pydantic AI agent workflows, including cost breakdowns, extracted from Logfire traces. | | Persona | - | - | | Runtime | - | - | | License | The software uses the Apache-2.0 license, offering permissive terms for use and distribution. | 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) | [agentcanvas](/tools/vstorm-co-agentcanvas.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 6d | 7d | | Open issues (now) | 3 | 0 | | Owner type | User | Organization | | Full report | [trust report](/tools/hidai25-eval-view/trust.md) | [trust report](/tools/vstorm-co-agentcanvas/trust.md) | ## Shared compatibility - **Python**: [eval-view](/tools/hidai25-eval-view.md) - Python runtime; [agentcanvas](/tools/vstorm-co-agentcanvas.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: agentcanvas - **Adopt for:** agentcanvas is for developers and AI practitioners who require detailed visual representations of Pydantic AI agent workflows, including cost breakdowns, extracted from Logfire traces. ## Choose when ### Choose eval-view if… - License: eval-view is Apache-2.0, agentcanvas is MIT. - 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, 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. ### Choose agentcanvas if… - License: agentcanvas is MIT, eval-view is Apache-2.0. - Tags unique to agentcanvas: agents, genai, llm, logfire. - To generate interactive HTML diagrams that provide visibility into the tools, sub-agents, tokens, and costs involved in running a Pydantic AI agent. ## 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 agentcanvas - If your AI agents are not built using Pydantic or do not utilize Logfire for tracing, as agentcanvas specifically works with these technologies. - When the level of detail provided in the HTML diagrams is unnecessary or when you lack access to the required Logfire traces and associated tokens. ## Common questions ### What is the difference between eval-view and agentcanvas? eval-view: Regression testing for AI agents. agentcanvas: Visualize Pydantic AI agent workflows using Logfire traces. See the comparison table for live GitHub stats and shared categories. ### When should I choose eval-view over agentcanvas? Choose eval-view over agentcanvas when License: eval-view is Apache-2.0, agentcanvas is MIT; 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, 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 choose agentcanvas over eval-view? Choose agentcanvas over eval-view when License: agentcanvas is MIT, eval-view is Apache-2.0; Tags unique to agentcanvas: agents, genai, llm, logfire; To generate interactive HTML diagrams that provide visibility into the tools, sub-agents, tokens, and costs involved in running a Pydantic AI agent. ### 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 agentcanvas? If your AI agents are not built using Pydantic or do not utilize Logfire for tracing, as agentcanvas specifically works with these technologies. When the level of detail provided in the HTML diagrams is unnecessary or when you lack access to the required Logfire traces and associated tokens. ### Is eval-view or agentcanvas more popular on GitHub? eval-view has more GitHub stars (126 vs 79). Stars measure visibility, not whether either tool fits your constraints. ### Are eval-view and agentcanvas open source? Yes - both are open-source projects on GitHub (eval-view: Apache-2.0, agentcanvas: MIT). ### Where can I find alternatives to eval-view or agentcanvas? GraphCanon lists graph-backed alternatives at [eval-view alternatives](/tools/hidai25-eval-view/alternatives) and [agentcanvas alternatives](/tools/vstorm-co-agentcanvas/alternatives) ([eval-view markdown twin](/tools/hidai25-eval-view/alternatives.md), [agentcanvas markdown twin](/tools/vstorm-co-agentcanvas/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-vstorm-co-agentcanvas.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, eval-view or agentcanvas? eval-view: Very active. agentcanvas: 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 agentcanvas? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [eval-view trust report](/tools/hidai25-eval-view/trust); [agentcanvas trust report](/tools/vstorm-co-agentcanvas/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/_