--- title: "olmo-eval vs eval-view" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/allenai-olmo-eval-vs-hidai25-eval-view" tools: ["allenai-olmo-eval", "hidai25-eval-view"] --- # olmo-eval vs eval-view *GraphCanon updated Oct 12, 2026* ## Verdict Pick olmo-eval if olmo-eval is an evaluation framework for large language models, using uv for reproducible builds. It focuses on modular task implementations and integrates with various datasets via defined tasks; 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. [olmo-eval](https://github.com/allenai/olmo-eval) reports 72 GitHub stars, 23 forks, and 81 open issues, last pushed Oct 11, 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 [olmo-eval's repository](https://github.com/allenai/olmo-eval) and [eval-view's repository](https://github.com/hidai25/eval-view). | | [olmo-eval](/tools/allenai-olmo-eval.md) | [eval-view](/tools/hidai25-eval-view.md) | | --- | --- | --- | | Tagline | Olmo Evaluation Framework for LLM Tasks | Regression testing for AI agents, snapshots behavior, diffs tool calls, catches regressions in CI | | Stars | 72 | 134 | | Forks | 23 | 24 | | Open issues | 81 | 2 | | Language | Python | Python | | Adopt for | Olmo-eval is an evaluation framework for large language models, using uv for reproducible builds. It focuses on modular task implementations and integrates with various datasets via defined tasks. | 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 | Evaluation & Observability | AI Agents, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [olmo-eval](/tools/allenai-olmo-eval.md) | [eval-view](/tools/hidai25-eval-view.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 0d | 13d | | Open issues (now) | 81 | 2 | | Stars delta | +3 (30d) | +8 (30d) | | Open issues delta | +25 (30d) | -1 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/allenai-olmo-eval/trust.md) | [trust report](/tools/hidai25-eval-view/trust.md) | ## Shared compatibility - **Python**: [olmo-eval](/tools/allenai-olmo-eval.md) - Python runtime; [eval-view](/tools/hidai25-eval-view.md) - Python runtime ## Decision facts: olmo-eval - **Adopt for:** Olmo-eval is an evaluation framework for large language models, using uv for reproducible builds. It focuses on modular task implementations and integrates with various datasets via defined tasks. ## 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 olmo-eval if… - Tags unique to olmo-eval: datasets, evaluation, llm, python. - When you need a flexible evaluation setup that works with a variety of LLMs and datasets. - More recently updated (last pushed Oct 11, 2026). ### Choose eval-view if… - Tags unique to eval-view: agent-benchmark, agent-evaluation, agentic-ai, ai-agents. - Also covers AI Agents. - 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 olmo-eval - When you require a simpler setup that doesn't need the reproducibility constraints of uv builds. - If your project already has an established evaluation toolchain and does not benefit from introducing a new framework for manageability reasons. ## 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 olmo-eval and eval-view? olmo-eval: Olmo Evaluation Framework for LLM Tasks. 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 olmo-eval over eval-view? Choose olmo-eval over eval-view when Tags unique to olmo-eval: datasets, evaluation, llm, python; When you need a flexible evaluation setup that works with a variety of LLMs and datasets; More recently updated (last pushed Oct 11, 2026). ### When should I choose eval-view over olmo-eval? Choose eval-view over olmo-eval when Tags unique to eval-view: agent-benchmark, agent-evaluation, agentic-ai, ai-agents; Also covers AI Agents; 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 olmo-eval? When you require a simpler setup that doesn't need the reproducibility constraints of uv builds. If your project already has an established evaluation toolchain and does not benefit from introducing a new framework for manageability reasons. ### 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 olmo-eval or eval-view more popular on GitHub? eval-view has more GitHub stars (134 vs 72). Stars measure visibility, not whether either tool fits your constraints. ### Are olmo-eval and eval-view open source? Yes - both are open-source projects on GitHub (olmo-eval: Apache-2.0, eval-view: Apache-2.0). ### Where can I find alternatives to olmo-eval or eval-view? GraphCanon lists graph-backed alternatives at [olmo-eval alternatives](/tools/allenai-olmo-eval/alternatives) and [eval-view alternatives](/tools/hidai25-eval-view/alternatives) ([olmo-eval markdown twin](/tools/allenai-olmo-eval/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/allenai-olmo-eval-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, olmo-eval or eval-view? olmo-eval: Very active. 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 olmo-eval and eval-view? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [olmo-eval trust report](/tools/allenai-olmo-eval/trust); [eval-view trust report](/tools/hidai25-eval-view/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=allenai-olmo-eval`](/api/graphcanon/graph?tool=allenai-olmo-eval) - 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/_