Comparison
athina-evals vs LLM-Knowledge-Conflict
Verdict
Pick athina-evals if athina-evals is a Python SDK developed for facilitating the evaluation of outputs from large language models through predefined metrics and frameworks; pick LLM-Knowledge-Conflict if lLM-Knowledge-Conflict is a tool for evaluating how large language models handle knowledge conflicts using the ConflictQA dataset. It provides a framework for analyzing model responses and their alignment with ground-tru.
Markdown twin · athina-evals alternatives · LLM-Knowledge-Conflict alternatives
GraphCanon updated Sep 20, 2026
9views this month
Trust & integrity
| Signal | athina-evals | LLM-Knowledge-Conflict |
|---|---|---|
| Maintenance | Dormant (470d since push) As of Sep 20, 2026 · github_public_v1 | Dormant (889d since push) As of Sep 18, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Sep 18, 2026 · 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
- athina-evals
- Python SDK for evaluating LLM generated responses
- LLM-Knowledge-Conflict
- Examines behavior of large language models in knowledge conflicts using ConflictQA dataset
Stars
- athina-evals
- 301
- LLM-Knowledge-Conflict
- 87
Forks
- athina-evals
- 22
- LLM-Knowledge-Conflict
- 4
Open issues
- athina-evals
- 4
- LLM-Knowledge-Conflict
- 1
Language
- athina-evals
- Python
- LLM-Knowledge-Conflict
- Python
Adopt for
- athina-evals
- athina-evals is a Python SDK developed for facilitating the evaluation of outputs from large language models through predefined metrics and frameworks.
- LLM-Knowledge-Conflict
- LLM-Knowledge-Conflict is a tool for evaluating how large language models handle knowledge conflicts using the ConflictQA dataset. It provides a framework for analyzing model responses and their alignment with ground-tru
Persona
- athina-evals
- -
- LLM-Knowledge-Conflict
- -
Runtime
- athina-evals
- -
- LLM-Knowledge-Conflict
- -
License
- athina-evals
- -
- LLM-Knowledge-Conflict
- The tool is licensed under Apache-2.0, allowing for free use, modification, and distribution.
Last pushed
- athina-evals
- Jun 6, 2025
- LLM-Knowledge-Conflict
- Apr 12, 2024
Categories
- athina-evals
- Evaluation & Observability
- LLM-Knowledge-Conflict
- Data & Retrieval, Evaluation & Observability
Trust and health
Days since push
- athina-evals
- 470d
- LLM-Knowledge-Conflict
- 889d
Open issues (now)
- athina-evals
- 4
- LLM-Knowledge-Conflict
- 1
Stars delta
- athina-evals
- 0 (30d)
- LLM-Knowledge-Conflict
- +3 (30d)
Open issues delta
- athina-evals
- +1 (30d)
- LLM-Knowledge-Conflict
- 0 (30d)
Full report
- athina-evals
- Trust report
- LLM-Knowledge-Conflict
- Trust report
Choose athina-evals if…
- Tags unique to athina-evals: evaluation, evaluation-framework, evaluation-metrics, llm-eval.
- When comprehensive evaluation of LLM responses is required, leveraging athina's specific tools and metrics
- More GitHub stars (301 vs 87) - visibility, not fit.
When NOT to use athina-evals
- If open-source alternatives with transparent customization options are preferred over athina-evals' approach
- In scenarios where API access requirements limit the ability to perform evaluations offline or in private environments
Choose LLM-Knowledge-Conflict if…
- Pricing: The tool is free to use under the Apache-2.0 license, but users may need to consider costs associated with running large language models..
- Requirements: Min 8 GB RAM; Python environment is required.; The dataset can be loaded using Huggingface datasets..
- Tags unique to LLM-Knowledge-Conflict: conflictqa, dataset, iclr'24, knowledge conflict.
- Also covers Data & Retrieval.
- When you need to assess how large language models handle conflicting information and their alignment with ground truth.
When NOT to use LLM-Knowledge-Conflict
- If you are looking for a tool that focuses on general performance metrics of large language models without considering knowledge conflicts.
- When your primary interest is in the speed or efficiency of large language models rather than their handling of conflicting information.
- If your project does not require an in-depth analysis of how models respond to conflicting evidence and align with ground truth.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (athina-ai/athina-evals) · observed Sep 20, 2026
- GitHub forks (athina-ai/athina-evals) · observed Sep 20, 2026
- Last push (athina-ai/athina-evals) · observed Jun 6, 2025
- License file (unknown) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (OSU-NLP-Group/LLM-Knowledge-Conflict) · observed Sep 20, 2026
- GitHub forks (OSU-NLP-Group/LLM-Knowledge-Conflict) · observed Sep 20, 2026
- Last push (OSU-NLP-Group/LLM-Knowledge-Conflict) · observed Apr 12, 2024
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
GitHub stars on cards: athina-evals 301 · LLM-Knowledge-Conflict 87 (synced Sep 20, 2026).
Common questions
- What is the difference between athina-evals and LLM-Knowledge-Conflict?
- athina-evals: Python SDK for evaluating LLM generated responses. LLM-Knowledge-Conflict: Examines behavior of large language models in knowledge conflicts using ConflictQA dataset. See the comparison table for live GitHub stats and shared categories.
- When should I choose athina-evals over LLM-Knowledge-Conflict?
- Choose athina-evals over LLM-Knowledge-Conflict when Tags unique to athina-evals: evaluation, evaluation-framework, evaluation-metrics, llm-eval; When comprehensive evaluation of LLM responses is required, leveraging athina's specific tools and metrics; More GitHub stars (301 vs 87) - visibility, not fit.
- When should I choose LLM-Knowledge-Conflict over athina-evals?
- Choose LLM-Knowledge-Conflict over athina-evals when Pricing: The tool is free to use under the Apache-2.0 license, but users may need to consider costs associated with running large language models.; Requirements: Min 8 GB RAM; Python environment is required.; The dataset can be loaded using Huggingface datasets.; Tags unique to LLM-Knowledge-Conflict: conflictqa, dataset, iclr'24, knowledge conflict; Also covers Data & Retrieval; When you need to assess how large language models handle conflicting information and their alignment with ground truth.
- When should I avoid athina-evals?
- If open-source alternatives with transparent customization options are preferred over athina-evals' approach In scenarios where API access requirements limit the ability to perform evaluations offline or in private environments
- When should I avoid LLM-Knowledge-Conflict?
- If you are looking for a tool that focuses on general performance metrics of large language models without considering knowledge conflicts. When your primary interest is in the speed or efficiency of large language models rather than their handling of conflicting information. If your project does not require an in-depth analysis of how models respond to conflicting evidence and align with ground truth.
- Is athina-evals or LLM-Knowledge-Conflict more popular on GitHub?
- athina-evals has more GitHub stars (301 vs 87). Stars measure visibility, not whether either tool fits your constraints.
- Are athina-evals and LLM-Knowledge-Conflict open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to athina-evals or LLM-Knowledge-Conflict?
- GraphCanon lists graph-backed alternatives at athina-evals alternatives and LLM-Knowledge-Conflict alternatives (athina-evals markdown twin, LLM-Knowledge-Conflict 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, athina-evals or LLM-Knowledge-Conflict?
- athina-evals: Dormant. LLM-Knowledge-Conflict: 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 athina-evals and LLM-Knowledge-Conflict?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: athina-evals trust report; LLM-Knowledge-Conflict trust report.