--- title: "judgeval vs continuous-eval" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/judgmentlabs-judgeval-vs-relari-ai-continuous-eval" tools: ["judgmentlabs-judgeval", "relari-ai-continuous-eval"] --- # judgeval vs continuous-eval *GraphCanon updated Aug 21, 2026* ## Verdict Pick judgeval if judgeval is a Python tool that aids in the continuous improvement of AI agents through comprehensive environment data and evaluations, supporting methodologies like reinforcement learning and prompt engineering; pick continuous-eval if continuous-eval is a Python framework for evaluating large language models, with emphasis on evaluation metrics and information retrieval. [judgeval](https://judgmentlabs.ai/) reports 1.0k GitHub stars, 95 forks, and 28 open issues, last pushed Jul 27, 2026. [continuous-eval](https://continuous-eval.docs.relari.ai/) has 515 stars, 38 forks, and 14 open issues, last pushed Aug 10, 2026. Figures are from public GitHub metadata via [judgeval's repository](https://github.com/JudgmentLabs/judgeval) and [continuous-eval's repository](https://github.com/relari-ai/continuous-eval). | | [judgeval](/tools/judgmentlabs-judgeval.md) | [continuous-eval](/tools/relari-ai-continuous-eval.md) | | --- | --- | --- | | Tagline | The Continuous-Improvement Stack for Agents | Data-Driven Evaluation for LLM-Powered Applications | | Stars | 1,047 | 515 | | Forks | 95 | 38 | | Open issues | 28 | 14 | | Language | Python | Python | | Adopt for | Judgeval is a Python tool that aids in the continuous improvement of AI agents through comprehensive environment data and evaluations, supporting methodologies like reinforcement learning and prompt engineering. | Continuous-eval is a Python framework for evaluating large language models, with emphasis on evaluation metrics and information retrieval. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Continuous-eval is available under the Apache-2.0 license, allowing free use with attribution and no warranty provided by the authors. | | Categories | AI Agents, Evaluation & Observability | Data & Retrieval, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [judgeval](/tools/judgmentlabs-judgeval.md) | [continuous-eval](/tools/relari-ai-continuous-eval.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 1d | 10d | | Open issues (now) | 28 | 14 | | Stars delta | Unknown | -1 (30d) | | Open issues delta | Unknown | +2 (30d) | | Full report | [trust report](/tools/judgmentlabs-judgeval/trust.md) | [trust report](/tools/relari-ai-continuous-eval/trust.md) | ## Shared compatibility - **Python**: [judgeval](/tools/judgmentlabs-judgeval.md) - Python runtime; [continuous-eval](/tools/relari-ai-continuous-eval.md) - Python runtime ## Decision facts: judgeval - **Adopt for:** Judgeval is a Python tool that aids in the continuous improvement of AI agents through comprehensive environment data and evaluations, supporting methodologies like reinforcement learning and prompt engineering. ## Decision facts: continuous-eval - **Pricing:** freemium - The framework itself is open source and free to use, but enhanced or enterprise features may require additional cost. - **Requirements:** Min 4 GB RAM - **Adopt for:** Continuous-eval is a Python framework for evaluating large language models, with emphasis on evaluation metrics and information retrieval. - **License detail:** Continuous-eval is available under the Apache-2.0 license, allowing free use with attribution and no warranty provided by the authors. ## Choose when ### Choose judgeval if… - Tags unique to judgeval: agent, agentic-ai, agents, grpo. - Also covers AI Agents. - You are working on an AI project where continuous monitoring and enhancement of your agent's performance are critical. ### Choose continuous-eval if… - Pricing: The framework itself is open source and free to use, but enhanced or enterprise features may require additional cost.. - Requirements: Min 4 GB RAM. - Tags unique to continuous-eval: evaluation-framework, evaluation-metrics, information-retrieval, llmops. - Also covers Data & Retrieval. - When developing LLM-powered applications where a continuous evaluation of model performance over time is required. ## When NOT to use judgeval - If you are looking for a tool focused solely on the theoretical aspects of AI development without practical, continuous improvement methodologies. - You require a solution that only supports evaluation metrics and does not offer integrated environment data support, diverging from Judgeval’s comprehensive approach. ## When NOT to use continuous-eval - If your project strictly focuses on small scale or simple applications that do not require robust evaluation metrics or information retrieval features. - When working in environments where Python is not preferred, as continuous-eval is specifically built for Python applications. ## Common questions ### What is the difference between judgeval and continuous-eval? judgeval: The Continuous-Improvement Stack for Agents. continuous-eval: Data-Driven Evaluation for LLM-Powered Applications. See the comparison table for live GitHub stats and shared categories. ### When should I choose judgeval over continuous-eval? Choose judgeval over continuous-eval when Tags unique to judgeval: agent, agentic-ai, agents, grpo; Also covers AI Agents; You are working on an AI project where continuous monitoring and enhancement of your agent's performance are critical. ### When should I choose continuous-eval over judgeval? Choose continuous-eval over judgeval when Pricing: The framework itself is open source and free to use, but enhanced or enterprise features may require additional cost.; Requirements: Min 4 GB RAM; Tags unique to continuous-eval: evaluation-framework, evaluation-metrics, information-retrieval, llmops; Also covers Data & Retrieval; When developing LLM-powered applications where a continuous evaluation of model performance over time is required. ### When should I avoid judgeval? If you are looking for a tool focused solely on the theoretical aspects of AI development without practical, continuous improvement methodologies. You require a solution that only supports evaluation metrics and does not offer integrated environment data support, diverging from Judgeval’s comprehensive approach. ### When should I avoid continuous-eval? If your project strictly focuses on small scale or simple applications that do not require robust evaluation metrics or information retrieval features. When working in environments where Python is not preferred, as continuous-eval is specifically built for Python applications. ### Is judgeval or continuous-eval more popular on GitHub? judgeval has more GitHub stars (1,047 vs 515). Stars measure visibility, not whether either tool fits your constraints. ### Are judgeval and continuous-eval open source? Yes - both are open-source projects on GitHub (judgeval: Apache-2.0, continuous-eval: Apache-2.0). ### Where can I find alternatives to judgeval or continuous-eval? GraphCanon lists graph-backed alternatives at [judgeval alternatives](/tools/judgmentlabs-judgeval/alternatives) and [continuous-eval alternatives](/tools/relari-ai-continuous-eval/alternatives) ([judgeval markdown twin](/tools/judgmentlabs-judgeval/alternatives.md), [continuous-eval markdown twin](/tools/relari-ai-continuous-eval/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/judgmentlabs-judgeval-vs-relari-ai-continuous-eval.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, judgeval or continuous-eval? judgeval: Very active. continuous-eval: 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 judgeval and continuous-eval? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [judgeval trust report](/tools/judgmentlabs-judgeval/trust); [continuous-eval trust report](/tools/relari-ai-continuous-eval/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=judgmentlabs-judgeval`](/api/graphcanon/graph?tool=judgmentlabs-judgeval) - 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/_