--- title: "agent-opt vs eval-view" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/future-agi-agent-opt-vs-hidai25-eval-view" tools: ["future-agi-agent-opt", "hidai25-eval-view"] --- # agent-opt vs eval-view *GraphCanon updated Oct 3, 2026* ## Verdict Pick agent-opt if agent-opt is tailored for teams that require automated optimization of AI workflows and support for continuous integration/continuous delivery (CI/CD), relying on Python and specific library dependencies; 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. [agent-opt](https://app.futureagi.com) reports 75 GitHub stars, 9 forks, and 0 open issues, last pushed Jun 30, 2026. [eval-view](https://evalview.com) has 134 stars, 24 forks, and 2 open issues, last pushed Sep 5, 2026. Figures are from public GitHub metadata via [agent-opt's repository](https://github.com/future-agi/agent-opt) and [eval-view's repository](https://github.com/hidai25/eval-view). | | [agent-opt](/tools/future-agi-agent-opt.md) | [eval-view](/tools/hidai25-eval-view.md) | | --- | --- | --- | | Tagline | Open Source Library for Automated Optimization of AI Agent Workflows | Regression testing for AI agents, snapshots behavior, diffs tool calls, catches regressions in CI | | Stars | 75 | 134 | | Forks | 9 | 24 | | Open issues | 0 | 2 | | Language | Python | Python | | Adopt for | Agent-opt is tailored for teams that require automated optimization of AI workflows and support for continuous integration/continuous delivery (CI/CD), relying on Python and specific library dependencies. | 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 | | 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._ | | [agent-opt](/tools/future-agi-agent-opt.md) | [eval-view](/tools/hidai25-eval-view.md) | | --- | --- | --- | | Maintenance | Archived (8%) | Active (82%) | | Days since push | 95d | 13d | | Archived on GitHub | Yes | No | | Open issues (now) | 0 | 2 | | Stars delta | +4 (30d) | +8 (30d) | | Open issues delta | 0 (30d) | -1 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/future-agi-agent-opt/trust.md) | [trust report](/tools/hidai25-eval-view/trust.md) | ## Shared compatibility - **Python**: [agent-opt](/tools/future-agi-agent-opt.md) - Python runtime; [eval-view](/tools/hidai25-eval-view.md) - Python runtime ## Decision facts: agent-opt - **Adopt for:** Agent-opt is tailored for teams that require automated optimization of AI workflows and support for continuous integration/continuous delivery (CI/CD), relying on Python and specific library dependencies. ## 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 ## Choose when ### Choose agent-opt if… - Tags unique to agent-opt: agent, aioptimization, automation, cicd. - - When your project needs seamless CI/CD integration alongside automated optimization - Leaner open-issue backlog (0). ### 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. ## When NOT to use agent-opt - - If your project does not require Python or if it cannot meet the specific requirement of having Python ≥ 3.10 - - In scenarios where CI/CD integration is not a priority for your AI workflow optimization ## 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. ## Common questions ### What is the difference between agent-opt and eval-view? agent-opt: Open Source Library for Automated Optimization of AI Agent Workflows. eval-view: Regression testing for AI agents, snapshots behavior, diffs tool calls, catches regressions in CI. See the comparison table for live GitHub stats and shared categories. ### When should I choose agent-opt over eval-view? Choose agent-opt over eval-view when Tags unique to agent-opt: agent, aioptimization, automation, cicd; - When your project needs seamless CI/CD integration alongside automated optimization; Leaner open-issue backlog (0). ### When should I choose eval-view over agent-opt? Choose eval-view over agent-opt 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 avoid agent-opt? - If your project does not require Python or if it cannot meet the specific requirement of having Python ≥ 3.10 - In scenarios where CI/CD integration is not a priority for your AI workflow optimization ### 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. ### Is agent-opt or eval-view more popular on GitHub? eval-view has more GitHub stars (134 vs 75). Stars measure visibility, not whether either tool fits your constraints. ### Are agent-opt and eval-view open source? Yes - both are open-source projects on GitHub (agent-opt: Apache-2.0, eval-view: Apache-2.0). ### Where can I find alternatives to agent-opt or eval-view? GraphCanon lists graph-backed alternatives at [agent-opt alternatives](/tools/future-agi-agent-opt/alternatives) and [eval-view alternatives](/tools/hidai25-eval-view/alternatives) ([agent-opt markdown twin](/tools/future-agi-agent-opt/alternatives.md), [eval-view markdown twin](/tools/hidai25-eval-view/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/future-agi-agent-opt-vs-hidai25-eval-view.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, agent-opt or eval-view? agent-opt: Archived. eval-view: 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 agent-opt and eval-view? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agent-opt trust report](/tools/future-agi-agent-opt/trust); [eval-view trust report](/tools/hidai25-eval-view/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=future-agi-agent-opt`](/api/graphcanon/graph?tool=future-agi-agent-opt) - 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/_