--- title: "eval-view vs superagent" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hidai25-eval-view-vs-superagent-ai-superagent" tools: ["hidai25-eval-view", "superagent-ai-superagent"] --- # eval-view vs superagent *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 superagent if superagent is an open-source SDK designed to protect AI applications from prompt injections, data leaks, and harmful outputs. It supports integration directly within. [eval-view](https://evalview.com) reports 134 GitHub stars, 24 forks, and 2 open issues, last pushed Sep 5, 2026. [superagent](https://superagent.sh) has 6.8k stars, 964 forks, and 17 open issues, last pushed Aug 25, 2026. Figures are from public GitHub metadata via [eval-view's repository](https://github.com/hidai25/eval-view) and [superagent's repository](https://github.com/superagent-ai/superagent). | | [eval-view](/tools/hidai25-eval-view.md) | [superagent](/tools/superagent-ai-superagent.md) | | --- | --- | --- | | Tagline | Regression testing for AI agents, snapshots behavior, diffs tool calls, catches regressions in CI | Superagent SDK | | Stars | 134 | 6,752 | | Forks | 24 | 964 | | Open issues | 2 | 17 | | Language | Python | TypeScript | | 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 | Superagent is an open-source SDK designed to protect AI applications from prompt injections, data leaks, and harmful outputs. It supports integration directly within TypeScript or Python applications. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT License permits use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the SDK, provided that copyright notices and license texts accompany any distributed parts. | | 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) | [superagent](/tools/superagent-ai-superagent.md) | | --- | --- | --- | | Days since push | 13d | 25d | | Open issues (now) | 2 | 17 | | Stars delta | +8 (30d) | +39 (30d) | | Open issues delta | -1 (30d) | +5 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/hidai25-eval-view/trust.md) | [trust report](/tools/superagent-ai-superagent/trust.md) | ## Shared compatibility - **Python**: [eval-view](/tools/hidai25-eval-view.md) - Python runtime; [superagent](/tools/superagent-ai-superagent.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: superagent - **Hosting:** managed - Superagent requires integrating their API via an SDK for TypeScript or Python applications. Users must obtain an API key for accessing these services. - **Adopt for:** Superagent is an open-source SDK designed to protect AI applications from prompt injections, data leaks, and harmful outputs. It supports integration directly within TypeScript or Python applications. - **License detail:** MIT License permits use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the SDK, provided that copyright notices and license texts accompany any distributed parts. ## Choose when ### Choose eval-view if… - eval-view is primarily Python; superagent is TypeScript. - License: eval-view is Apache-2.0, superagent is MIT. - Tags unique to eval-view: agent-benchmark, agent-evaluation, agentic-ai, ai-agents. - Also covers AI Agents. - 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 superagent if… - superagent is primarily TypeScript; eval-view is Python. - License: superagent is MIT, eval-view is Apache-2.0. - Superagent requires integrating their API via an SDK for TypeScript or Python applications. Users must obtain an API key for accessing these services. - Tags unique to superagent: ai, guardrails, llm, openai. - Use Superagent when you need a proactive solution that can detect and block malicious activities such as prompt injections and redact personal information automatically. ## 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 superagent - Avoid using Superagent when your project does not require the specific safety measures it offers, such as prompt injection prevention or scanning GitHub repositories for malicious activities. - If another tool in the same category provides better coverage or more advanced features that are critical to your application's security needs and aligns closer with your tech stack. ## Common questions ### What is the difference between eval-view and superagent? eval-view: Regression testing for AI agents, snapshots behavior, diffs tool calls, catches regressions in CI. superagent: Superagent SDK. See the comparison table for live GitHub stats and shared categories. ### When should I choose eval-view over superagent? Choose eval-view over superagent when eval-view is primarily Python; superagent is TypeScript; License: eval-view is Apache-2.0, superagent is MIT; Tags unique to eval-view: agent-benchmark, agent-evaluation, agentic-ai, ai-agents; Also covers AI Agents; 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 superagent over eval-view? Choose superagent over eval-view when superagent is primarily TypeScript; eval-view is Python; License: superagent is MIT, eval-view is Apache-2.0; Superagent requires integrating their API via an SDK for TypeScript or Python applications. Users must obtain an API key for accessing these services; Tags unique to superagent: ai, guardrails, llm, openai; Use Superagent when you need a proactive solution that can detect and block malicious activities such as prompt injections and redact personal information automatically. ### 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 superagent? Avoid using Superagent when your project does not require the specific safety measures it offers, such as prompt injection prevention or scanning GitHub repositories for malicious activities. If another tool in the same category provides better coverage or more advanced features that are critical to your application's security needs and aligns closer with your tech stack. ### Is eval-view or superagent more popular on GitHub? superagent has more GitHub stars (6,752 vs 134). Stars measure visibility, not whether either tool fits your constraints. ### Are eval-view and superagent open source? Yes - both are open-source projects on GitHub (eval-view: Apache-2.0, superagent: MIT). ### Where can I find alternatives to eval-view or superagent? GraphCanon lists graph-backed alternatives at [eval-view alternatives](/tools/hidai25-eval-view/alternatives) and [superagent alternatives](/tools/superagent-ai-superagent/alternatives) ([eval-view markdown twin](/tools/hidai25-eval-view/alternatives.md), [superagent markdown twin](/tools/superagent-ai-superagent/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-superagent-ai-superagent.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, eval-view or superagent? eval-view: Active. superagent: 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 superagent? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [eval-view trust report](/tools/hidai25-eval-view/trust); [superagent trust report](/tools/superagent-ai-superagent/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/_