--- title: "evals vs code-review-graph" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/openai-evals-vs-tirth8205-code-review-graph" tools: ["openai-evals", "tirth8205-code-review-graph"] --- # evals vs code-review-graph *GraphCanon updated Jul 11, 2026* ## Verdict Pick evals when license: evals is Other, code-review-graph is MIT; pick code-review-graph when license: code-review-graph is MIT, evals is Other. [evals](https://github.com/openai/evals) reports 19k GitHub stars, 3.0k forks, and 217 open issues, last pushed Apr 14, 2026. [code-review-graph](https://code-review-graph.com) has 19k stars, 2.1k forks, and 185 open issues, last pushed Jun 14, 2026. Figures are from public GitHub metadata via [evals's repository](https://github.com/openai/evals) and [code-review-graph's repository](https://github.com/tirth8205/code-review-graph). | | [evals](/tools/openai-evals.md) | [code-review-graph](/tools/tirth8205-code-review-graph.md) | | --- | --- | --- | | Tagline | Framework for evaluating LLMs and LLM systems with an open-source registry of benchmarks. | Local-first code intelligence graph for MCP and CLI. Builds a persistent map of your codebase so AI coding tools read only what matters, with benchmarked context reductions on reviews and large-repo w | | Stars | 18,890 | 19,416 | | Forks | 3,017 | 2,078 | | Open issues | 217 | 185 | | Language | Python | Python | | Adopt for | Evals is an evaluation framework from OpenAI for assessing large language models and systems built with them. It includes an open-source registry of benchmarks and tools to create custom evaluations. | - | | Persona | - | - | | Runtime | - | - | | License | Other | MIT | | Categories | Evaluation & Observability | Developer Tools, Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [evals](/tools/openai-evals.md) | [code-review-graph](/tools/tirth8205-code-review-graph.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Active (82%) | | Days since push | 87d | 26d | | Open issues (now) | 217 | 185 | | Owner type | Organization | User | | Security scan | No lockfile | No MCP manifest | | Full report | [trust report](/tools/openai-evals/trust.md) | [trust report](/tools/tirth8205-code-review-graph/trust.md) | ## Decision facts: evals - **Adopt for:** Evals is an evaluation framework from OpenAI for assessing large language models and systems built with them. It includes an open-source registry of benchmarks and tools to create custom evaluations. ## Choose when ### Choose evals if… - License: evals is Other, code-review-graph is MIT. - Tags unique to evals: benchmarking, custom eval creation, evaluation framework, large-language-models. - * When you need a comprehensive set of pre-existing evals and the ability to create your own tailored tests using specific use cases, especially within the OpenAI model ecosystem. ### Choose code-review-graph if… - License: code-review-graph is MIT, evals is Other. - Tags unique to code-review-graph: ai-coding, claude, claude-code, code-review. - Also covers Developer Tools, LLM Frameworks. ## When NOT to use evals - * When evaluating models or systems that do not benefit from being integrated with the OpenAI API, as some features like direct evals configuration in the OpenAI Dashboard require an OpenAI key. - * If you are looking for an evaluation framework that doesn’t involve external dependencies such as Git Large File Storage (LFS) and specific Python version requirements (Python 3.9 minimum), or if a繁 ## When NOT to use code-review-graph - Developer Tools: A gateway is overkill when you're pinned to a single provider and model. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## Common questions ### What is the difference between evals and code-review-graph? evals: Framework for evaluating LLMs and LLM systems with an open-source registry of benchmarks.. code-review-graph: Local-first code intelligence graph for MCP and CLI. Builds a persistent map of your codebase so AI coding tools read only what matters, with benchmarked context reductions on reviews and large-repo w. See the comparison table for live GitHub stats and shared categories. ### When should I choose evals over code-review-graph? Choose evals over code-review-graph when License: evals is Other, code-review-graph is MIT; Tags unique to evals: benchmarking, custom eval creation, evaluation framework, large-language-models; * When you need a comprehensive set of pre-existing evals and the ability to create your own tailored tests using specific use cases, especially within the OpenAI model ecosystem. ### When should I choose code-review-graph over evals? Choose code-review-graph over evals when License: code-review-graph is MIT, evals is Other; Tags unique to code-review-graph: ai-coding, claude, claude-code, code-review; Also covers Developer Tools, LLM Frameworks. ### When should I avoid evals? * When evaluating models or systems that do not benefit from being integrated with the OpenAI API, as some features like direct evals configuration in the OpenAI Dashboard require an OpenAI key. * If you are looking for an evaluation framework that doesn’t involve external dependencies such as Git Large File Storage (LFS) and specific Python version requirements (Python 3.9 minimum), or if a繁 ### When should I avoid code-review-graph? Developer Tools: A gateway is overkill when you're pinned to a single provider and model. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### Is evals or code-review-graph more popular on GitHub? code-review-graph has more GitHub stars (19,416 vs 18,890). Stars measure visibility, not whether either tool fits your constraints. ### Are evals and code-review-graph open source? Yes - both are open-source projects on GitHub (evals: Other, code-review-graph: MIT). ### Where can I find alternatives to evals or code-review-graph? GraphCanon lists graph-backed alternatives at [evals alternatives](/tools/openai-evals/alternatives) and [code-review-graph alternatives](/tools/tirth8205-code-review-graph/alternatives) ([evals markdown twin](/tools/openai-evals/alternatives.md), [code-review-graph markdown twin](/tools/tirth8205-code-review-graph/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/openai-evals-vs-tirth8205-code-review-graph.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, evals or code-review-graph? evals: Steady. code-review-graph: 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 evals and code-review-graph? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [evals trust report](/tools/openai-evals/trust); [code-review-graph trust report](/tools/tirth8205-code-review-graph/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=openai-evals`](/api/graphcanon/graph?tool=openai-evals) - 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/_