Comparison
eval-view vs agentcanvas
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.
Markdown twin · eval-view alternatives · agentcanvas alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | eval-view | agentcanvas |
|---|---|---|
| Maintenance | Very active (6d since push) As of 3w · github_public_v1 | Active (7d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 3w · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- eval-view
- Regression testing for AI agents
- agentcanvas
- Visualize Pydantic AI agent workflows using Logfire traces
Stars
- eval-view
- 126
- agentcanvas
- 79
Forks
- eval-view
- 21
- agentcanvas
- 9
Open issues
- eval-view
- 3
- agentcanvas
- 0
Language
- eval-view
- Python
- agentcanvas
- Python
Adopt for
- eval-view
- Regression testing for AI agents to detect behavioral changes and output quality regressions over time.
- agentcanvas
- 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
- eval-view
- -
- agentcanvas
- -
Runtime
- eval-view
- -
- agentcanvas
- -
License
- eval-view
- The software uses the Apache-2.0 license, offering permissive terms for use and distribution.
- agentcanvas
- MIT
Last pushed
- eval-view
- Jul 26, 2026
- agentcanvas
- Aug 1, 2026
Categories
- eval-view
- AI Agents, Evaluation & Observability
- agentcanvas
- AI Agents, Evaluation & Observability
Trust and health
Maintenance
- eval-view
- Very active (96%)
- agentcanvas
- Active (82%)
Days since push
- eval-view
- 6d
- agentcanvas
- 7d
Open issues (now)
- eval-view
- 3
- agentcanvas
- 0
Owner type
- eval-view
- User
- agentcanvas
- Organization
Full report
- eval-view
- Trust report
- agentcanvas
- Trust report
Shared compatibility
- Python · eval-view: Python runtime · agentcanvas: Python runtime
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.
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.
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (hidai25/eval-view) · observed Aug 2, 2026
- GitHub forks (hidai25/eval-view) · observed Aug 2, 2026
- Last push (hidai25/eval-view) · observed Jul 26, 2026
- License file (Apache-2.0) · observed Aug 2, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (vstorm-co/agentcanvas) · observed Aug 9, 2026
- GitHub forks (vstorm-co/agentcanvas) · observed Aug 9, 2026
- Last push (vstorm-co/agentcanvas) · observed Aug 1, 2026
- License file (MIT) · observed Aug 9, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
GitHub stars on cards: eval-view 126 · agentcanvas 79 (synced Aug 2, 2026).
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 and agentcanvas alternatives (eval-view markdown twin, agentcanvas markdown twin), 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 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; agentcanvas trust report.