Comparison
deepeval vs awesome-hallucination-detection
Verdict
Pick deepeval when tags unique to deepeval: evaluation framework, evaluation-metrics, llm-evaluation, llm-evaluation-framework; pick awesome-hallucination-detection when tags unique to awesome-hallucination-detection: evaluation, hallucination, llms, nlp.
Markdown twin · deepeval alternatives · awesome-hallucination-detection alternatives
GraphCanon updated today
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Trust & integrity
| Signal | deepeval | awesome-hallucination-detection |
|---|---|---|
| Maintenance | Very active (0d since push) As of today · github_public_v1 | Steady (35d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · github_public_v1 | Not a fork · Organization account As of today · github_public_v1 |
| Security (OSV) | No lockfile As of today · none | No lockfile As of today · none |
Tagline
- deepeval
- The LLM Evaluation Framework
- awesome-hallucination-detection
- List of papers on hallucination detection in LLMs.
Stars
- deepeval
- 17k
- awesome-hallucination-detection
- 1.1k
Forks
- deepeval
- 1.6k
- awesome-hallucination-detection
- 89
Open issues
- deepeval
- 334
- awesome-hallucination-detection
- 0
Language
- deepeval
- Python
- awesome-hallucination-detection
- -
Adopt for
- deepeval
- -
- awesome-hallucination-detection
- awesome-hallucination-detection provides a curated list of research papers focused on techniques to detect and mitigate hallucinations in large language models (LLMs), including process supervision methods for factual QA
Persona
- deepeval
- -
- awesome-hallucination-detection
- -
Runtime
- deepeval
- -
- awesome-hallucination-detection
- -
License
- deepeval
- Apache-2.0
- awesome-hallucination-detection
- Apache-2.0
Last pushed
- deepeval
- Jul 10, 2026
- awesome-hallucination-detection
- Jun 6, 2026
Categories
- deepeval
- Evaluation & Observability, LLM Frameworks
- awesome-hallucination-detection
- Evaluation & Observability
Trust and health
Maintenance
- deepeval
- Very active (96%)
- awesome-hallucination-detection
- Steady (60%)
Days since push
- deepeval
- 0d
- awesome-hallucination-detection
- 35d
Open issues (now)
- deepeval
- 334
- awesome-hallucination-detection
- 0
Full report
- deepeval
- Trust report
- awesome-hallucination-detection
- Trust report
Choose deepeval if…
- Tags unique to deepeval: evaluation framework, evaluation-metrics, llm-evaluation, llm-evaluation-framework.
- Also covers LLM Frameworks.
- More GitHub stars (17k vs 1.1k) - visibility, not fit.
When NOT to use deepeval
- Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers.
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
Choose awesome-hallucination-detection if…
- Tags unique to awesome-hallucination-detection: evaluation, hallucination, llms, nlp.
- - When focusing on specific methodologies like Corpus Verify (CorVer) from the paper 'Verifiable Rewards Beyond Math and Code' which utilizes lightweight, process-based rewards to mitigate hallucinat
- Leaner open-issue backlog (0).
When NOT to use awesome-hallucination-detection
- - When the need is for immediate implementation or code rather than research papers — this repository only curates information about methodologies and benchmarks
- - If your focus is on general LLM training techniques without a specific emphasis on hallucination detection or calibration
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 11, 2026
- GitHub forks (confident-ai/deepeval) · observed Jul 11, 2026
- Last push (confident-ai/deepeval) · observed Jul 10, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (EdinburghNLP/awesome-hallucination-detection) · observed Jul 11, 2026
- GitHub forks (EdinburghNLP/awesome-hallucination-detection) · observed Jul 11, 2026
- Last push (EdinburghNLP/awesome-hallucination-detection) · observed Jun 6, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: deepeval 17k · awesome-hallucination-detection 1.1k (synced Jul 11, 2026).
Common questions
- What is the difference between deepeval and awesome-hallucination-detection?
- deepeval: The LLM Evaluation Framework. awesome-hallucination-detection: List of papers on hallucination detection in LLMs.. See the comparison table for live GitHub stats and shared categories.
- When should I choose deepeval over awesome-hallucination-detection?
- Choose deepeval over awesome-hallucination-detection when Tags unique to deepeval: evaluation framework, evaluation-metrics, llm-evaluation, llm-evaluation-framework; Also covers LLM Frameworks; More GitHub stars (17k vs 1.1k) - visibility, not fit.
- When should I choose awesome-hallucination-detection over deepeval?
- Choose awesome-hallucination-detection over deepeval when Tags unique to awesome-hallucination-detection: evaluation, hallucination, llms, nlp; - When focusing on specific methodologies like Corpus Verify (CorVer) from the paper 'Verifiable Rewards Beyond Math and Code' which utilizes lightweight, process-based rewards to mitigate hallucinat; Leaner open-issue backlog (0).
- When should I avoid deepeval?
- Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- When should I avoid awesome-hallucination-detection?
- - When the need is for immediate implementation or code rather than research papers — this repository only curates information about methodologies and benchmarks - If your focus is on general LLM training techniques without a specific emphasis on hallucination detection or calibration
- Is deepeval or awesome-hallucination-detection more popular on GitHub?
- deepeval has more GitHub stars (16,767 vs 1,116). Stars measure visibility, not whether either tool fits your constraints.
- Are deepeval and awesome-hallucination-detection open source?
- Yes - both are open-source projects on GitHub (deepeval: Apache-2.0, awesome-hallucination-detection: Apache-2.0).
- Where can I find alternatives to deepeval or awesome-hallucination-detection?
- GraphCanon lists graph-backed alternatives at deepeval alternatives and awesome-hallucination-detection alternatives (deepeval markdown twin, awesome-hallucination-detection 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 awesome-hallucination-detection?
- deepeval: Very active. awesome-hallucination-detection: Steady. 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 awesome-hallucination-detection?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: deepeval trust report; awesome-hallucination-detection trust report.