--- title: "deepeval vs LLMDebugger" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/confident-ai-deepeval-vs-floridsleeves-llmdebugger" tools: ["confident-ai-deepeval", "floridsleeves-llmdebugger"] --- # deepeval vs LLMDebugger *GraphCanon updated Aug 5, 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 LLMDebugger if lLMDebugger offers step-by-step verification of runtime execution for large language models. [deepeval](https://deepeval.com) reports 17k GitHub stars, 1.7k forks, and 404 open issues, last pushed Jul 27, 2026. [LLMDebugger](https://github.com/FloridSleeves/LLMDebugger) has 587 stars, 56 forks, and 5 open issues, last pushed Sep 10, 2024. Figures are from public GitHub metadata via [deepeval's repository](https://github.com/confident-ai/deepeval) and [LLMDebugger's repository](https://github.com/FloridSleeves/LLMDebugger). | | [deepeval](/tools/confident-ai-deepeval.md) | [LLMDebugger](/tools/floridsleeves-llmdebugger.md) | | --- | --- | --- | | Tagline | LLM Evaluation Framework. | A Large Language Model Debugger verifying runtime execution step by step | | Stars | 17,226 | 587 | | Forks | 1,736 | 56 | | Open issues | 404 | 5 | | 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. | LLMDebugger offers step-by-step verification of runtime execution for large language models. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 License | The LLMDebugger is distributed under the Apache-2.0 license. | | Categories | Evaluation & Observability | Developer Tools, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [deepeval](/tools/confident-ai-deepeval.md) | [LLMDebugger](/tools/floridsleeves-llmdebugger.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 1d | 693d | | Open issues (now) | 404 | 5 | | Owner type | Organization | User | | Full report | [trust report](/tools/confident-ai-deepeval/trust.md) | [trust report](/tools/floridsleeves-llmdebugger/trust.md) | ## Shared compatibility - **Python**: [deepeval](/tools/confident-ai-deepeval.md) - Python runtime; [LLMDebugger](/tools/floridsleeves-llmdebugger.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: LLMDebugger - **Pricing:** freemium - Free for use, based on its open-source nature with an Apache-2.0 license. - **Adopt for:** LLMDebugger offers step-by-step verification of runtime execution for large language models. - **License detail:** The LLMDebugger is distributed under the Apache-2.0 license. ## 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: evaluation, llm-evaluation, metrics. - When developing large language models and you need a comprehensive evaluation framework to measure their performance across various metrics. ### Choose LLMDebugger if… - Pricing: Free for use, based on its open-source nature with an Apache-2.0 license.. - Tags unique to LLMDebugger: acl'24, llm debugging, python debugger for ai, runtime verification. - Also covers Developer Tools. - When detailed step-by-step inspection of the runtime behavior of large language models is required, LLMDebugger can provide precise insights into each execution phase. ## 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 LLMDebugger - Avoid using if you are only interested in higher-level performance metrics rather than the intricate details of runtime behavior, as LLMDebugger emphasizes step-by-step execution. - Not recommended for teams lacking experience with Python or specific to this tool's installation and usage workflow that involves setting up a Conda environment. ## Common questions ### What is the difference between deepeval and LLMDebugger? deepeval: LLM Evaluation Framework.. LLMDebugger: A Large Language Model Debugger verifying runtime execution step by step. See the comparison table for live GitHub stats and shared categories. ### When should I choose deepeval over LLMDebugger? Choose deepeval over LLMDebugger when Requirements: Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities.; Tags unique to deepeval: evaluation, 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 LLMDebugger over deepeval? Choose LLMDebugger over deepeval when Pricing: Free for use, based on its open-source nature with an Apache-2.0 license.; Tags unique to LLMDebugger: acl'24, llm debugging, python debugger for ai, runtime verification; Also covers Developer Tools; When detailed step-by-step inspection of the runtime behavior of large language models is required, LLMDebugger can provide precise insights into each execution phase. ### 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 LLMDebugger? Avoid using if you are only interested in higher-level performance metrics rather than the intricate details of runtime behavior, as LLMDebugger emphasizes step-by-step execution. Not recommended for teams lacking experience with Python or specific to this tool's installation and usage workflow that involves setting up a Conda environment. ### Is deepeval or LLMDebugger more popular on GitHub? deepeval has more GitHub stars (17,226 vs 587). Stars measure visibility, not whether either tool fits your constraints. ### Are deepeval and LLMDebugger open source? Yes - both are open-source projects on GitHub (deepeval: Apache-2.0, LLMDebugger: Apache-2.0). ### Where can I find alternatives to deepeval or LLMDebugger? GraphCanon lists graph-backed alternatives at [deepeval alternatives](/tools/confident-ai-deepeval/alternatives) and [LLMDebugger alternatives](/tools/floridsleeves-llmdebugger/alternatives) ([deepeval markdown twin](/tools/confident-ai-deepeval/alternatives.md), [LLMDebugger markdown twin](/tools/floridsleeves-llmdebugger/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-floridsleeves-llmdebugger.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, deepeval or LLMDebugger? deepeval: Very active. LLMDebugger: 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 LLMDebugger? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [deepeval trust report](/tools/confident-ai-deepeval/trust); [LLMDebugger trust report](/tools/floridsleeves-llmdebugger/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/_