--- title: "AgentGuard vs eval-view" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dipampaul17-agentguard-vs-hidai25-eval-view" tools: ["dipampaul17-agentguard", "hidai25-eval-view"] --- # AgentGuard vs eval-view *GraphCanon updated Sep 20, 2026* ## Verdict Pick AgentGuard if agentGuard is a budget-conscious observer for real-time token spending by AI agents and LLMs, integrating with major providers like OpenAI and Anthropic; 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. [AgentGuard](https://github.com/dipampaul17/AgentGuard) reports 173 GitHub stars, 11 forks, and 2 open issues, last pushed Jul 31, 2025. [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 [AgentGuard's repository](https://github.com/dipampaul17/AgentGuard) and [eval-view's repository](https://github.com/hidai25/eval-view). | | [AgentGuard](/tools/dipampaul17-agentguard.md) | [eval-view](/tools/hidai25-eval-view.md) | | --- | --- | --- | | Tagline | Real-time guardrail that monitors token spend and manages LLM/agent loops in real time | Regression testing for AI agents, snapshots behavior, diffs tool calls, catches regressions in CI | | Stars | 173 | 134 | | Forks | 11 | 24 | | Open issues | 2 | 2 | | Language | JavaScript | Python | | Adopt for | AgentGuard is a budget-conscious observer for real-time token spending by AI agents and LLMs, integrating with major providers like OpenAI and Anthropic. | 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 | MIT | Apache-2.0 | | Categories | Evaluation & Observability, Inference & Serving | AI Agents, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [AgentGuard](/tools/dipampaul17-agentguard.md) | [eval-view](/tools/hidai25-eval-view.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 407d | 13d | | Stars delta | +2 (30d) | +8 (30d) | | Open issues delta | +1 (30d) | -1 (30d) | | Full report | [trust report](/tools/dipampaul17-agentguard/trust.md) | [trust report](/tools/hidai25-eval-view/trust.md) | ## Decision facts: AgentGuard - **Adopt for:** AgentGuard is a budget-conscious observer for real-time token spending by AI agents and LLMs, integrating with major providers like OpenAI and Anthropic. ## 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 AgentGuard if… - AgentGuard is primarily JavaScript; eval-view is Python. - License: AgentGuard is MIT, eval-view is Apache-2.0. - Tags unique to AgentGuard: cost-monitoring, observability. - Also covers Inference & Serving. - When you need precise control over spend and want live updates on token prices ### Choose eval-view if… - eval-view is primarily Python; AgentGuard is JavaScript. - License: eval-view is Apache-2.0, AgentGuard is MIT. - Tags unique to eval-view: agent-benchmark, agent-evaluation, agentic-ai, autogen. - 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 NOT to use AgentGuard - If you prioritize a different language for your project and cannot use JavaScript - In cases requiring more elaborate fallback mechanisms than what AgentGuard offers ## 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 AgentGuard and eval-view? AgentGuard: Real-time guardrail that monitors token spend and manages LLM/agent loops in real time. 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 AgentGuard over eval-view? Choose AgentGuard over eval-view when AgentGuard is primarily JavaScript; eval-view is Python; License: AgentGuard is MIT, eval-view is Apache-2.0; Tags unique to AgentGuard: cost-monitoring, observability; Also covers Inference & Serving; When you need precise control over spend and want live updates on token prices. ### When should I choose eval-view over AgentGuard? Choose eval-view over AgentGuard when eval-view is primarily Python; AgentGuard is JavaScript; License: eval-view is Apache-2.0, AgentGuard is MIT; Tags unique to eval-view: agent-benchmark, agent-evaluation, agentic-ai, autogen; 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 avoid AgentGuard? If you prioritize a different language for your project and cannot use JavaScript In cases requiring more elaborate fallback mechanisms than what AgentGuard offers ### 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 AgentGuard or eval-view more popular on GitHub? AgentGuard has more GitHub stars (173 vs 134). Stars measure visibility, not whether either tool fits your constraints. ### Are AgentGuard and eval-view open source? Yes - both are open-source projects on GitHub (AgentGuard: MIT, eval-view: Apache-2.0). ### Where can I find alternatives to AgentGuard or eval-view? GraphCanon lists graph-backed alternatives at [AgentGuard alternatives](/tools/dipampaul17-agentguard/alternatives) and [eval-view alternatives](/tools/hidai25-eval-view/alternatives) ([AgentGuard markdown twin](/tools/dipampaul17-agentguard/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/dipampaul17-agentguard-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, AgentGuard or eval-view? AgentGuard: Dormant. 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 AgentGuard and eval-view? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [AgentGuard trust report](/tools/dipampaul17-agentguard/trust); [eval-view trust report](/tools/hidai25-eval-view/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=dipampaul17-agentguard`](/api/graphcanon/graph?tool=dipampaul17-agentguard) - 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/_