Home/Compare/athina-evals vs LLM-Knowledge-Conflict

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

athina-evals logo

athina-evals

athina-ai/athina-evals

★ 301pushed Jun 6, 2025
vs
LLM-Knowledge-Conflict logo

LLM-Knowledge-Conflict

OSU-NLP-Group/LLM-Knowledge-Conflict

★ 87pushed Apr 12, 2024

Trust & integrity

Signalathina-evalsLLM-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 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.

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