Comparison
deepeval vs instruct-eval
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 instruct-eval if key facts about instruct-eval.
Markdown twin · deepeval alternatives · instruct-eval alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | deepeval | instruct-eval |
|---|---|---|
| Maintenance | Very active (1d since push) As of 4w · github_public_v1 | Dormant (879d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 4w · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | Published findings 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.
- instruct-eval
- Quantitative evaluation for instruction-tuned language models
Stars
- deepeval
- 17k
- instruct-eval
- 552
Forks
- deepeval
- 1.7k
- instruct-eval
- 45
Open issues
- deepeval
- 404
- instruct-eval
- 24
Language
- deepeval
- Python
- instruct-eval
- Python
Adopt for
- deepeval
- Deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies.
- instruct-eval
- Key facts about instruct-eval
Persona
- deepeval
- -
- instruct-eval
- -
Runtime
- deepeval
- -
- instruct-eval
- -
License
- deepeval
- Apache-2.0 License
- instruct-eval
- The tool is distributed under Apache-2.0 license
Last pushed
- deepeval
- Jul 27, 2026
- instruct-eval
- Mar 10, 2024
Categories
- deepeval
- Evaluation & Observability
- instruct-eval
- Evaluation & Observability
Trust and health
Maintenance
- deepeval
- Very active (96%)
- instruct-eval
- Dormant (18%)
Days since push
- deepeval
- 1d
- instruct-eval
- 879d
Open issues (now)
- deepeval
- 404
- instruct-eval
- 24
OSV dependency advisories
- deepeval
- No lockfile (source not queried)
- instruct-eval
- Published findings
Full report
- deepeval
- Trust report
- instruct-eval
- Trust report
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.
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 instruct-eval if…
- Requirements: Min 8 GB RAM; Requires Python environment setup and specific dependencies as outlined in the repository's documentation..
- Tags unique to instruct-eval: benchmarking, instruct-tuning, llm, safety.
- When you need to quantitatively evaluate the performance of instruction-tuned large language models such as Alpaca and Flan-T5 on held-out tasks.
When NOT to use instruct-eval
- When primarily interested in general model evaluation without a focus on instruction-tuned LMs.
- If your primary interest lies in qualitative assessment rather than quantitative metrics.
- If you need support for non-HuggingFace Transformer models, as instruct-eval mainly supports models from the HuggingFace ecosystem.
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 (declare-lab/instruct-eval) · observed Aug 7, 2026
- GitHub forks (declare-lab/instruct-eval) · observed Aug 7, 2026
- Last push (declare-lab/instruct-eval) · observed Mar 10, 2024
- License file (Apache-2.0) · observed Aug 7, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: deepeval 17k · instruct-eval 552 (synced Jul 28, 2026).
Common questions
- What is the difference between deepeval and instruct-eval?
- deepeval: LLM Evaluation Framework.. instruct-eval: Quantitative evaluation for instruction-tuned language models. See the comparison table for live GitHub stats and shared categories.
- When should I choose deepeval over instruct-eval?
- Choose deepeval over instruct-eval 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 instruct-eval over deepeval?
- Choose instruct-eval over deepeval when Requirements: Min 8 GB RAM; Requires Python environment setup and specific dependencies as outlined in the repository's documentation.; Tags unique to instruct-eval: benchmarking, instruct-tuning, llm, safety; When you need to quantitatively evaluate the performance of instruction-tuned large language models such as Alpaca and Flan-T5 on held-out tasks.
- 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 instruct-eval?
- When primarily interested in general model evaluation without a focus on instruction-tuned LMs. If your primary interest lies in qualitative assessment rather than quantitative metrics. If you need support for non-HuggingFace Transformer models, as instruct-eval mainly supports models from the HuggingFace ecosystem.
- Is deepeval or instruct-eval more popular on GitHub?
- deepeval has more GitHub stars (17,226 vs 552). Stars measure visibility, not whether either tool fits your constraints.
- Are deepeval and instruct-eval open source?
- Yes - both are open-source projects on GitHub (deepeval: Apache-2.0, instruct-eval: Apache-2.0).
- Where can I find alternatives to deepeval or instruct-eval?
- GraphCanon lists graph-backed alternatives at deepeval alternatives and instruct-eval alternatives (deepeval markdown twin, instruct-eval 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 instruct-eval?
- deepeval: Very active. instruct-eval: 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 instruct-eval?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: deepeval trust report; instruct-eval trust report.