--- title: "deepeval vs uptrain" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/confident-ai-deepeval-vs-uptrain-ai-uptrain" tools: ["confident-ai-deepeval", "uptrain-ai-uptrain"] --- # deepeval vs uptrain *GraphCanon updated Sep 20, 2026* ## Verdict Pick deepeval if deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies; pick uptrain if upTrain, an open-source platform, evaluates and enhances Generative AI applications with preconfigured checks, root cause analysis, and actionable insights. [deepeval](https://deepeval.com) reports 18k GitHub stars, 2.0k forks, and 624 open issues, last pushed Sep 18, 2026. [uptrain](https://uptrain.ai/) has 2.4k stars, 205 forks, and 65 open issues, last pushed Aug 18, 2024. Figures are from public GitHub metadata via [deepeval's repository](https://github.com/confident-ai/deepeval) and [uptrain's repository](https://github.com/uptrain-ai/uptrain). | | [deepeval](/tools/confident-ai-deepeval.md) | [uptrain](/tools/uptrain-ai-uptrain.md) | | --- | --- | --- | | Tagline | LLM Evaluation Framework. | Unified platform for evaluating and improving Generative AI applications | | Stars | 18,342 | 2,365 | | Forks | 1,953 | 205 | | Open issues | 624 | 65 | | Language | Python | Python | | Adopt for | Deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies. | UpTrain, an open-source platform, evaluates and enhances Generative AI applications with preconfigured checks, root cause analysis, and actionable insights. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 License | The tool is available under the Apache-2.0 license, suitable for both free and commercial use with appropriate attribution. | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [deepeval](/tools/confident-ai-deepeval.md) | [uptrain](/tools/uptrain-ai-uptrain.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 1d | 762d | | Open issues (now) | 624 | 65 | | Stars delta | +1.1k (30d) | +6 (30d) | | Open issues delta | +220 (30d) | +7 (30d) | | Full report | [trust report](/tools/confident-ai-deepeval/trust.md) | [trust report](/tools/uptrain-ai-uptrain/trust.md) | ## Shared compatibility - **Python**: [deepeval](/tools/confident-ai-deepeval.md) - Python runtime; [uptrain](/tools/uptrain-ai-uptrain.md) - Python runtime ## 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 ## Decision facts: uptrain - **Hosting:** self hosted - UpTrain can be installed on-premises using pip or accessed through a managed version. - **Adopt for:** UpTrain, an open-source platform, evaluates and enhances Generative AI applications with preconfigured checks, root cause analysis, and actionable insights. - **License detail:** The tool is available under the Apache-2.0 license, suitable for both free and commercial use with appropriate attribution. ## Choose when ### Choose deepeval if… - Requirements: Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities.. - Tags unique to deepeval: llm-evaluation, metrics. - When developing large language models and you need a comprehensive evaluation framework to measure their performance across various metrics. ### Choose uptrain if… - UpTrain can be installed on-premises using pip or accessed through a managed version. - Tags unique to uptrain: autoevaluation, experimentation, hallucination-detection, jailbreak-detection. - uptrain ships Docker support for self-hosted deployment. - - When you need to evaluate Generative AI applications across various use-cases including language models, code generation, and embeddings. ## 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. ## When NOT to use uptrain - - When your application does not require extensive monitoring or do not need insights into improving Generative AI performance through root cause analysis. - - If you prioritize a highly hands-off user experience without the capability to customize evaluation checks, consider using UpTrain's managed version instead of self-managing it. ## Common questions ### What is the difference between deepeval and uptrain? deepeval: LLM Evaluation Framework.. uptrain: Unified platform for evaluating and improving Generative AI applications. See the comparison table for live GitHub stats and shared categories. ### When should I choose deepeval over uptrain? Choose deepeval over uptrain when Requirements: Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities.; Tags unique to deepeval: llm-evaluation, metrics; When developing large language models and you need a comprehensive evaluation framework to measure their performance across various metrics. ### When should I choose uptrain over deepeval? Choose uptrain over deepeval when UpTrain can be installed on-premises using pip or accessed through a managed version; Tags unique to uptrain: autoevaluation, experimentation, hallucination-detection, jailbreak-detection; uptrain ships Docker support for self-hosted deployment; - When you need to evaluate Generative AI applications across various use-cases including language models, code generation, and embeddings. ### 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. ### When should I avoid uptrain? - When your application does not require extensive monitoring or do not need insights into improving Generative AI performance through root cause analysis. - If you prioritize a highly hands-off user experience without the capability to customize evaluation checks, consider using UpTrain's managed version instead of self-managing it. ### Is deepeval or uptrain more popular on GitHub? deepeval has more GitHub stars (18,342 vs 2,365). Stars measure visibility, not whether either tool fits your constraints. ### Are deepeval and uptrain open source? Yes - both are open-source projects on GitHub (deepeval: Apache-2.0, uptrain: Apache-2.0). ### Where can I find alternatives to deepeval or uptrain? GraphCanon lists graph-backed alternatives at [deepeval alternatives](/tools/confident-ai-deepeval/alternatives) and [uptrain alternatives](/tools/uptrain-ai-uptrain/alternatives) ([deepeval markdown twin](/tools/confident-ai-deepeval/alternatives.md), [uptrain markdown twin](/tools/uptrain-ai-uptrain/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/confident-ai-deepeval-vs-uptrain-ai-uptrain.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, deepeval or uptrain? deepeval: Very active. uptrain: Dormant. 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 deepeval and uptrain? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [deepeval trust report](/tools/confident-ai-deepeval/trust); [uptrain trust report](/tools/uptrain-ai-uptrain/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=confident-ai-deepeval`](/api/graphcanon/graph?tool=confident-ai-deepeval) - 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/_