--- title: "eval-view vs Kiln" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hidai25-eval-view-vs-kiln-ai-kiln" tools: ["hidai25-eval-view", "kiln-ai-kiln"] --- # eval-view vs Kiln *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 Kiln if kiln is a versatile AI systems development toolkit that excels in comprehensive evaluation frameworks for agents, RAG components, and fine-tuning processes. [eval-view](https://evalview.com) reports 134 GitHub stars, 24 forks, and 2 open issues, last pushed Sep 5, 2026. [Kiln](https://kiln.tech) has 5.1k stars, 380 forks, and 72 open issues, last pushed Sep 19, 2026. Figures are from public GitHub metadata via [eval-view's repository](https://github.com/hidai25/eval-view) and [Kiln's repository](https://github.com/Kiln-AI/Kiln). | | [eval-view](/tools/hidai25-eval-view.md) | [Kiln](/tools/kiln-ai-kiln.md) | | --- | --- | --- | | Tagline | Regression testing for AI agents, snapshots behavior, diffs tool calls, catches regressions in CI | Build, Evaluate, and Optimize AI Systems | | Stars | 134 | 5,076 | | Forks | 24 | 380 | | Open issues | 2 | 72 | | Language | Python | Python | | 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 | Kiln is a versatile AI systems development toolkit that excels in comprehensive evaluation frameworks for agents, RAG components, and fine-tuning processes. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Other | | Categories | AI Agents, Evaluation & Observability | AI Agents, Data & Retrieval, Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [eval-view](/tools/hidai25-eval-view.md) | [Kiln](/tools/kiln-ai-kiln.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 13d | 0d | | Open issues (now) | 2 | 72 | | Stars delta | +8 (30d) | +105 (30d) | | Open issues delta | -1 (30d) | +6 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/hidai25-eval-view/trust.md) | [trust report](/tools/kiln-ai-kiln/trust.md) | ## Shared compatibility - **Python**: [eval-view](/tools/hidai25-eval-view.md) - Python runtime; [Kiln](/tools/kiln-ai-kiln.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: Kiln - **Adopt for:** Kiln is a versatile AI systems development toolkit that excels in comprehensive evaluation frameworks for agents, RAG components, and fine-tuning processes. ## Choose when ### Choose eval-view if… - License: eval-view is Apache-2.0, Kiln is Other. - Tags unique to eval-view: agent-benchmark, agent-evaluation, agentic-ai, 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 Kiln if… - License: Kiln is Other, eval-view is Apache-2.0. - Tags unique to Kiln: ai, chain-of-thought, collaboration, dataset-generation. - Also covers Data & Retrieval, Model Training. - When you need extensive tools for evaluating custom AI agents ## 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 Kiln - If your project strictly requires a lightweight tool without comprehensive dataset management options - Avoid if you do not require advanced synthetic data generation capabilities ## Common questions ### What is the difference between eval-view and Kiln? eval-view: Regression testing for AI agents, snapshots behavior, diffs tool calls, catches regressions in CI. Kiln: Build, Evaluate, and Optimize AI Systems. See the comparison table for live GitHub stats and shared categories. ### When should I choose eval-view over Kiln? Choose eval-view over Kiln when License: eval-view is Apache-2.0, Kiln is Other; Tags unique to eval-view: agent-benchmark, agent-evaluation, agentic-ai, 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 Kiln over eval-view? Choose Kiln over eval-view when License: Kiln is Other, eval-view is Apache-2.0; Tags unique to Kiln: ai, chain-of-thought, collaboration, dataset-generation; Also covers Data & Retrieval, Model Training; When you need extensive tools for evaluating custom AI agents. ### 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 Kiln? If your project strictly requires a lightweight tool without comprehensive dataset management options Avoid if you do not require advanced synthetic data generation capabilities ### Is eval-view or Kiln more popular on GitHub? Kiln has more GitHub stars (5,076 vs 134). Stars measure visibility, not whether either tool fits your constraints. ### Are eval-view and Kiln open source? Yes - both are open-source projects on GitHub (eval-view: Apache-2.0, Kiln: Other). ### Where can I find alternatives to eval-view or Kiln? GraphCanon lists graph-backed alternatives at [eval-view alternatives](/tools/hidai25-eval-view/alternatives) and [Kiln alternatives](/tools/kiln-ai-kiln/alternatives) ([eval-view markdown twin](/tools/hidai25-eval-view/alternatives.md), [Kiln markdown twin](/tools/kiln-ai-kiln/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-kiln-ai-kiln.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, eval-view or Kiln? eval-view: Active. Kiln: Very 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 Kiln? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [eval-view trust report](/tools/hidai25-eval-view/trust); [Kiln trust report](/tools/kiln-ai-kiln/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/_