--- title: "chain-of-thought-hub vs eval-view" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/franxyao-chain-of-thought-hub-vs-hidai25-eval-view" tools: ["franxyao-chain-of-thought-hub", "hidai25-eval-view"] --- # chain-of-thought-hub vs eval-view *GraphCanon updated Oct 11, 2026* ## Verdict Pick chain-of-thought-hub if chain-of-Thought Hub measures the performance of large language models (LLMs) on complex tasks by using carefully selected datasets across various domains such as math, science, coding, and knowledge. It evaluates if LLM; 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. [chain-of-thought-hub](https://github.com/FranxYao/chain-of-thought-hub) reports 2.8k GitHub stars, 144 forks, and 27 open issues, last pushed Aug 4, 2024. [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 [chain-of-thought-hub's repository](https://github.com/FranxYao/chain-of-thought-hub) and [eval-view's repository](https://github.com/hidai25/eval-view). | | [chain-of-thought-hub](/tools/franxyao-chain-of-thought-hub.md) | [eval-view](/tools/hidai25-eval-view.md) | | --- | --- | --- | | Tagline | Benchmarking large language models' complex reasoning ability with chain-of-thought prompting | Regression testing for AI agents, snapshots behavior, diffs tool calls, catches regressions in CI | | Stars | 2,776 | 134 | | Forks | 144 | 24 | | Open issues | 27 | 2 | | Language | Jupyter Notebook | Python | | Adopt for | Chain-of-Thought Hub measures the performance of large language models (LLMs) on complex tasks by using carefully selected datasets across various domains such as math, science, coding, and knowledge. It evaluates if LLM | 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 | The MIT license permits the use of Chain-of-Thought Hub in both open source and commercial projects with acknowledgment. | Apache-2.0 | | Categories | Evaluation & Observability | AI Agents, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [chain-of-thought-hub](/tools/franxyao-chain-of-thought-hub.md) | [eval-view](/tools/hidai25-eval-view.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 797d | 13d | | Open issues (now) | 27 | 2 | | Stars delta | +2 (30d) | +8 (30d) | | Open issues delta | 0 (30d) | -1 (30d) | | Full report | [trust report](/tools/franxyao-chain-of-thought-hub/trust.md) | [trust report](/tools/hidai25-eval-view/trust.md) | ## Shared compatibility - **Python**: [chain-of-thought-hub](/tools/franxyao-chain-of-thought-hub.md) - Python runtime; [eval-view](/tools/hidai25-eval-view.md) - Python runtime ## Decision facts: chain-of-thought-hub - **Requirements:** Min 8 GB RAM; Chain-of-Thought Hub is designed to be integrated into environments for evaluating LLMs using Jupyter Notebooks - **Adopt for:** Chain-of-Thought Hub measures the performance of large language models (LLMs) on complex tasks by using carefully selected datasets across various domains such as math, science, coding, and knowledge. It evaluates if LLM - **License detail:** The MIT license permits the use of Chain-of-Thought Hub in both open source and commercial projects with acknowledgment. ## 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 chain-of-thought-hub if… - chain-of-thought-hub is primarily Jupyter Notebook; eval-view is Python. - License: chain-of-thought-hub is MIT, eval-view is Apache-2.0. - Requirements: Min 8 GB RAM; Chain-of-Thought Hub is designed to be integrated into environments for evaluating LLMs using Jupyter Notebooks. - Tags unique to chain-of-thought-hub: chain-of-thought prompting, complex reasoning, llm-benchmarking. - Use Chain-of-Thought Hub when you need to benchmark smaller LLMs against larger ones for complex reasoning abilities. ### Choose eval-view if… - eval-view is primarily Python; chain-of-thought-hub is Jupyter Notebook. - License: eval-view is Apache-2.0, chain-of-thought-hub 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 NOT to use chain-of-thought-hub - Do not use Chain-of-Thought Hub if your focus is on general conversational capabilities rather than specific, challenging problem-solving tasks. - Avoid this tool if you are primarily interested in simpler language processing tasks that do not involve chain-of-thought prompting or complex datasets. ## 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 chain-of-thought-hub and eval-view? chain-of-thought-hub: Benchmarking large language models' complex reasoning ability with chain-of-thought prompting. 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 chain-of-thought-hub over eval-view? Choose chain-of-thought-hub over eval-view when chain-of-thought-hub is primarily Jupyter Notebook; eval-view is Python; License: chain-of-thought-hub is MIT, eval-view is Apache-2.0; Requirements: Min 8 GB RAM; Chain-of-Thought Hub is designed to be integrated into environments for evaluating LLMs using Jupyter Notebooks; Tags unique to chain-of-thought-hub: chain-of-thought prompting, complex reasoning, llm-benchmarking; Use Chain-of-Thought Hub when you need to benchmark smaller LLMs against larger ones for complex reasoning abilities. ### When should I choose eval-view over chain-of-thought-hub? Choose eval-view over chain-of-thought-hub when eval-view is primarily Python; chain-of-thought-hub is Jupyter Notebook; License: eval-view is Apache-2.0, chain-of-thought-hub 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 avoid chain-of-thought-hub? Do not use Chain-of-Thought Hub if your focus is on general conversational capabilities rather than specific, challenging problem-solving tasks. Avoid this tool if you are primarily interested in simpler language processing tasks that do not involve chain-of-thought prompting or complex datasets. ### 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 chain-of-thought-hub or eval-view more popular on GitHub? chain-of-thought-hub has more GitHub stars (2,776 vs 134). Stars measure visibility, not whether either tool fits your constraints. ### Are chain-of-thought-hub and eval-view open source? Yes - both are open-source projects on GitHub (chain-of-thought-hub: MIT, eval-view: Apache-2.0). ### Where can I find alternatives to chain-of-thought-hub or eval-view? GraphCanon lists graph-backed alternatives at [chain-of-thought-hub alternatives](/tools/franxyao-chain-of-thought-hub/alternatives) and [eval-view alternatives](/tools/hidai25-eval-view/alternatives) ([chain-of-thought-hub markdown twin](/tools/franxyao-chain-of-thought-hub/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/franxyao-chain-of-thought-hub-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, chain-of-thought-hub or eval-view? chain-of-thought-hub: 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 chain-of-thought-hub and eval-view? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [chain-of-thought-hub trust report](/tools/franxyao-chain-of-thought-hub/trust); [eval-view trust report](/tools/hidai25-eval-view/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=franxyao-chain-of-thought-hub`](/api/graphcanon/graph?tool=franxyao-chain-of-thought-hub) - 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/_