Comparison
deepeval vs LLMDebugger
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.
Markdown twin · deepeval alternatives · LLMDebugger alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | deepeval | LLMDebugger |
|---|---|---|
| Maintenance | Very active (1d since push) As of 4w · github_public_v1 | Dormant (693d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 4w · github_public_v1 | Not a fork · Personal account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No published findings from this source as of 2026-07-11 As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- deepeval
- LLM Evaluation Framework.
- LLMDebugger
- A Large Language Model Debugger verifying runtime execution step by step
Stars
- deepeval
- 17k
- LLMDebugger
- 587
Forks
- deepeval
- 1.7k
- LLMDebugger
- 56
Open issues
- deepeval
- 404
- LLMDebugger
- 5
Language
- deepeval
- Python
- LLMDebugger
- Python
Adopt for
- deepeval
- Deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies.
- LLMDebugger
- LLMDebugger offers step-by-step verification of runtime execution for large language models.
Persona
- deepeval
- -
- LLMDebugger
- -
Runtime
- deepeval
- -
- LLMDebugger
- -
License
- deepeval
- Apache-2.0 License
- LLMDebugger
- The LLMDebugger is distributed under the Apache-2.0 license.
Last pushed
- deepeval
- Jul 27, 2026
- LLMDebugger
- Sep 10, 2024
Categories
- deepeval
- Evaluation & Observability
- LLMDebugger
- Developer Tools, Evaluation & Observability
Trust and health
Maintenance
- deepeval
- Very active (96%)
- LLMDebugger
- Dormant (18%)
Days since push
- deepeval
- 1d
- LLMDebugger
- 693d
Open issues (now)
- deepeval
- 404
- LLMDebugger
- 5
Owner type
- deepeval
- Organization
- LLMDebugger
- User
OSV dependency advisories
- deepeval
- No lockfile (source not queried)
- LLMDebugger
- No published findings from this source as of 2026-07-11
Full report
- deepeval
- Trust report
- LLMDebugger
- Trust report
Shared compatibility
- Python · deepeval: Python runtime · LLMDebugger: Python runtime
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.
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.
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (confident-ai/deepeval) · observed Jul 28, 2026
- GitHub forks (confident-ai/deepeval) · observed Jul 28, 2026
- Last push (confident-ai/deepeval) · observed Jul 27, 2026
- License file (Apache-2.0) · observed Jul 28, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (FloridSleeves/LLMDebugger) · observed Aug 5, 2026
- GitHub forks (FloridSleeves/LLMDebugger) · observed Aug 5, 2026
- Last push (FloridSleeves/LLMDebugger) · observed Sep 10, 2024
- License file (Apache-2.0) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: deepeval 17k · LLMDebugger 587 (synced Jul 28, 2026).
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 and LLMDebugger alternatives (deepeval markdown twin, LLMDebugger markdown twin), 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 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; LLMDebugger trust report.