--- title: "contextcheck vs deepeval" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/addepto-contextcheck-vs-confident-ai-deepeval" tools: ["addepto-contextcheck", "confident-ai-deepeval"] --- # contextcheck vs deepeval *GraphCanon updated Sep 20, 2026* ## Verdict Pick contextcheck if contextcheck, an MIT-licensed Python framework for evaluating large language models and RAG systems through configurable YAML settings that integrate with CI pipelines; pick deepeval if deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies. [contextcheck](https://addepto.com/) reports 97 GitHub stars, 11 forks, and 1 open issues, last pushed Dec 11, 2024. [deepeval](https://deepeval.com) has 18k stars, 2.0k forks, and 624 open issues, last pushed Sep 18, 2026. Figures are from public GitHub metadata via [contextcheck's repository](https://github.com/Addepto/contextcheck) and [deepeval's repository](https://github.com/confident-ai/deepeval). | | [contextcheck](/tools/addepto-contextcheck.md) | [deepeval](/tools/confident-ai-deepeval.md) | | --- | --- | --- | | Tagline | Framework for LLMs and RAGs testing in Python | LLM Evaluation Framework. | | Stars | 97 | 18,342 | | Forks | 11 | 1,953 | | Open issues | 1 | 624 | | Language | Python | Python | | Adopt for | Contextcheck, an MIT-licensed Python framework for evaluating large language models and RAG systems through configurable YAML settings that integrate with CI pipelines. | Deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 License | | Categories | Evaluation & Observability, Model Training | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [contextcheck](/tools/addepto-contextcheck.md) | [deepeval](/tools/confident-ai-deepeval.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 635d | 1d | | Open issues (now) | 1 | 624 | | Stars delta | +1 (30d) | +1.1k (30d) | | Open issues delta | 0 (30d) | +220 (30d) | | Full report | [trust report](/tools/addepto-contextcheck/trust.md) | [trust report](/tools/confident-ai-deepeval/trust.md) | ## Decision facts: contextcheck - **Adopt for:** Contextcheck, an MIT-licensed Python framework for evaluating large language models and RAG systems through configurable YAML settings that integrate with CI pipelines. ## Decision facts: deepeval - **Requirements:** Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities. - **Adopt for:** Deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies. - **License detail:** Apache-2.0 License ## Choose when ### Choose contextcheck if… - License: contextcheck is MIT, deepeval is Apache-2.0. - Tags unique to contextcheck: ai-chat, ai-testing, chatbot-framework, ci-integration. - Also covers Model Training. - When you require a framework specifically designed to test the robustness of both LLMs and Retrieval-Augmented Generation systems within Python projects. ### Choose deepeval if… - License: deepeval is Apache-2.0, contextcheck is MIT. - Requirements: Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities.. - Tags unique to deepeval: evaluation, metrics. - When developing large language models and you need a comprehensive evaluation framework to measure their performance across various metrics. ## When NOT to use contextcheck - Avoid if you aim for a framework without configuration flexibility through YAML as contextcheck strictly relies on this format for setting up test environments. - Skip contextcheck if your project environment or requirements do not align with the MIT license terms, especially in contexts where licensing compatibility must be strictly observed. ## When NOT to use deepeval - For small-scale applications that do not require the depth of metrics and evaluations offered by Deepeval, as it might be overkill. - In situations where there is a need for real-time performance monitoring, since Deepeval focuses more on post-development evaluation rather than continuous runtime analysis. ## Common questions ### What is the difference between contextcheck and deepeval? contextcheck: Framework for LLMs and RAGs testing in Python. deepeval: LLM Evaluation Framework.. See the comparison table for live GitHub stats and shared categories. ### When should I choose contextcheck over deepeval? Choose contextcheck over deepeval when License: contextcheck is MIT, deepeval is Apache-2.0; Tags unique to contextcheck: ai-chat, ai-testing, chatbot-framework, ci-integration; Also covers Model Training; When you require a framework specifically designed to test the robustness of both LLMs and Retrieval-Augmented Generation systems within Python projects. ### When should I choose deepeval over contextcheck? Choose deepeval over contextcheck when License: deepeval is Apache-2.0, contextcheck is MIT; Requirements: Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities.; Tags unique to deepeval: evaluation, metrics; When developing large language models and you need a comprehensive evaluation framework to measure their performance across various metrics. ### When should I avoid contextcheck? Avoid if you aim for a framework without configuration flexibility through YAML as contextcheck strictly relies on this format for setting up test environments. Skip contextcheck if your project environment or requirements do not align with the MIT license terms, especially in contexts where licensing compatibility must be strictly observed. ### When should I avoid deepeval? For small-scale applications that do not require the depth of metrics and evaluations offered by Deepeval, as it might be overkill. In situations where there is a need for real-time performance monitoring, since Deepeval focuses more on post-development evaluation rather than continuous runtime analysis. ### Is contextcheck or deepeval more popular on GitHub? deepeval has more GitHub stars (18,342 vs 97). Stars measure visibility, not whether either tool fits your constraints. ### Are contextcheck and deepeval open source? Yes - both are open-source projects on GitHub (contextcheck: MIT, deepeval: Apache-2.0). ### Where can I find alternatives to contextcheck or deepeval? GraphCanon lists graph-backed alternatives at [contextcheck alternatives](/tools/addepto-contextcheck/alternatives) and [deepeval alternatives](/tools/confident-ai-deepeval/alternatives) ([contextcheck markdown twin](/tools/addepto-contextcheck/alternatives.md), [deepeval markdown twin](/tools/confident-ai-deepeval/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/addepto-contextcheck-vs-confident-ai-deepeval.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, contextcheck or deepeval? contextcheck: Dormant. deepeval: 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 contextcheck and deepeval? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [contextcheck trust report](/tools/addepto-contextcheck/trust); [deepeval trust report](/tools/confident-ai-deepeval/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=addepto-contextcheck`](/api/graphcanon/graph?tool=addepto-contextcheck) - 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/_