--- title: "athina-evals vs LLM-Knowledge-Conflict" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/athina-ai-athina-evals-vs-osu-nlp-group-llm-knowledge-conflict" tools: ["athina-ai-athina-evals", "osu-nlp-group-llm-knowledge-conflict"] --- # athina-evals vs LLM-Knowledge-Conflict *GraphCanon updated Sep 20, 2026* ## 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. [athina-evals](https://docs.athina.ai) reports 301 GitHub stars, 22 forks, and 4 open issues, last pushed Jun 6, 2025. [LLM-Knowledge-Conflict](https://github.com/OSU-NLP-Group/LLM-Knowledge-Conflict) has 87 stars, 4 forks, and 1 open issues, last pushed Apr 12, 2024. Figures are from public GitHub metadata via [athina-evals's repository](https://github.com/athina-ai/athina-evals) and [LLM-Knowledge-Conflict's repository](https://github.com/OSU-NLP-Group/LLM-Knowledge-Conflict). | | [athina-evals](/tools/athina-ai-athina-evals.md) | [LLM-Knowledge-Conflict](/tools/osu-nlp-group-llm-knowledge-conflict.md) | | --- | --- | --- | | Tagline | Python SDK for evaluating LLM generated responses | Examines behavior of large language models in knowledge conflicts using ConflictQA dataset | | Stars | 301 | 87 | | Forks | 22 | 4 | | Open issues | 4 | 1 | | Language | Python | Python | | Adopt for | 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 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 | - | - | | Runtime | - | - | | License | - | The tool is licensed under Apache-2.0, allowing for free use, modification, and distribution. | | Categories | Evaluation & Observability | Data & Retrieval, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [athina-evals](/tools/athina-ai-athina-evals.md) | [LLM-Knowledge-Conflict](/tools/osu-nlp-group-llm-knowledge-conflict.md) | | --- | --- | --- | | Days since push | 470d | 889d | | Open issues (now) | 4 | 1 | | Stars delta | 0 (30d) | +3 (30d) | | Open issues delta | +1 (30d) | 0 (30d) | | Full report | [trust report](/tools/athina-ai-athina-evals/trust.md) | [trust report](/tools/osu-nlp-group-llm-knowledge-conflict/trust.md) | ## Decision facts: athina-evals - **Adopt for:** athina-evals is a Python SDK developed for facilitating the evaluation of outputs from large language models through predefined metrics and frameworks. ## Decision facts: LLM-Knowledge-Conflict - **Pricing:** freemium - 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. - **Adopt for:** 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 - **License detail:** The tool is licensed under Apache-2.0, allowing for free use, modification, and distribution. ## Choose when ### 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. ### 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 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 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. ## 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](/tools/athina-ai-athina-evals/alternatives) and [LLM-Knowledge-Conflict alternatives](/tools/osu-nlp-group-llm-knowledge-conflict/alternatives) ([athina-evals markdown twin](/tools/athina-ai-athina-evals/alternatives.md), [LLM-Knowledge-Conflict markdown twin](/tools/osu-nlp-group-llm-knowledge-conflict/alternatives.md)), 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](/compare/athina-ai-athina-evals-vs-osu-nlp-group-llm-knowledge-conflict.md) 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](/tools/athina-ai-athina-evals/trust); [LLM-Knowledge-Conflict trust report](/tools/osu-nlp-group-llm-knowledge-conflict/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=athina-ai-athina-evals`](/api/graphcanon/graph?tool=athina-ai-athina-evals) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_