--- title: "eval-view vs heron" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hidai25-eval-view-vs-netis-heron" tools: ["hidai25-eval-view", "netis-heron"] --- # eval-view vs heron *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 heron if an open-source network traffic analysis tool for monitoring the performance of LLMs and AI agents without requiring SDK changes. [eval-view](https://evalview.com) reports 134 GitHub stars, 24 forks, and 2 open issues, last pushed Sep 5, 2026. [heron](https://heron-ai.pages.dev) has 101 stars, 10 forks, and 3 open issues, last pushed Aug 18, 2026. Figures are from public GitHub metadata via [eval-view's repository](https://github.com/hidai25/eval-view) and [heron's repository](https://github.com/Netis/heron). | | [eval-view](/tools/hidai25-eval-view.md) | [heron](/tools/netis-heron.md) | | --- | --- | --- | | Tagline | Regression testing for AI agents, snapshots behavior, diffs tool calls, catches regressions in CI | Performance monitoring tool for LLM APIs and AI agents | | Stars | 134 | 101 | | Forks | 24 | 10 | | Open issues | 2 | 3 | | Language | Python | Rust | | 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 | An open-source network traffic analysis tool for monitoring the performance of LLMs and AI agents without requiring SDK changes. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | 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) | [heron](/tools/netis-heron.md) | | --- | --- | --- | | Days since push | 13d | 23d | | Open issues (now) | 2 | 3 | | Stars delta | +8 (30d) | +27 (30d) | | Open issues delta | -1 (30d) | 0 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/hidai25-eval-view/trust.md) | [trust report](/tools/netis-heron/trust.md) | ## 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: heron - **Adopt for:** An open-source network traffic analysis tool for monitoring the performance of LLMs and AI agents without requiring SDK changes. - **License detail:** Apache-2.0 ## Choose when ### Choose eval-view if… - eval-view is primarily Python; heron is Rust. - Tags unique to eval-view: agent-benchmark, agent-evaluation, ai-agents, anthropic. - 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 heron if… - heron is primarily Rust; eval-view is Python. - Tags unique to heron: ai-agent-development, libpcap, llm-monitoring, rust. - When you need a provider-side solution that does not require altering existing codebases or SDKs to monitor performance metrics. ## 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 heron - When the need is for an in-agent monitoring tool rather than a network packet-based solution, as Heron operates on traffic. - In environments where live capture requires administrative privileges that are not available to the user performing the installation. - For real-time performance insights without prior deployment because Heron involves a setup phase and typically uses pre-collected `.pcap` files. ## Common questions ### What is the difference between eval-view and heron? eval-view: Regression testing for AI agents, snapshots behavior, diffs tool calls, catches regressions in CI. heron: Performance monitoring tool for LLM APIs and AI agents. See the comparison table for live GitHub stats and shared categories. ### When should I choose eval-view over heron? Choose eval-view over heron when eval-view is primarily Python; heron is Rust; Tags unique to eval-view: agent-benchmark, agent-evaluation, ai-agents, anthropic; 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 heron over eval-view? Choose heron over eval-view when heron is primarily Rust; eval-view is Python; Tags unique to heron: ai-agent-development, libpcap, llm-monitoring, rust; When you need a provider-side solution that does not require altering existing codebases or SDKs to monitor performance metrics. ### 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 heron? When the need is for an in-agent monitoring tool rather than a network packet-based solution, as Heron operates on traffic. In environments where live capture requires administrative privileges that are not available to the user performing the installation. For real-time performance insights without prior deployment because Heron involves a setup phase and typically uses pre-collected `.pcap` files. ### Is eval-view or heron more popular on GitHub? eval-view has more GitHub stars (134 vs 101). Stars measure visibility, not whether either tool fits your constraints. ### Are eval-view and heron open source? Yes - both are open-source projects on GitHub (eval-view: Apache-2.0, heron: Apache-2.0). ### Where can I find alternatives to eval-view or heron? GraphCanon lists graph-backed alternatives at [eval-view alternatives](/tools/hidai25-eval-view/alternatives) and [heron alternatives](/tools/netis-heron/alternatives) ([eval-view markdown twin](/tools/hidai25-eval-view/alternatives.md), [heron markdown twin](/tools/netis-heron/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-netis-heron.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, eval-view or heron? eval-view: Active. heron: 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 heron? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [eval-view trust report](/tools/hidai25-eval-view/trust); [heron trust report](/tools/netis-heron/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/_