--- title: "athina-evals vs anti-lie" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/athina-ai-athina-evals-vs-lc198707-anti-lie" tools: ["athina-ai-athina-evals", "lc198707-anti-lie"] --- # athina-evals vs anti-lie *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 anti-lie if anti-Lie offers an LLM claim auditing layer using T1-T7 truth gradients to achieve high business effectiveness on fact-checking benchmarks. [athina-evals](https://docs.athina.ai) reports 301 GitHub stars, 22 forks, and 4 open issues, last pushed Jun 6, 2025. [anti-lie](https://github.com/lc198707/anti-lie) has 89 stars, 6 forks, and 0 open issues, last pushed May 10, 2026. Figures are from public GitHub metadata via [athina-evals's repository](https://github.com/athina-ai/athina-evals) and [anti-lie's repository](https://github.com/lc198707/anti-lie). | | [athina-evals](/tools/athina-ai-athina-evals.md) | [anti-lie](/tools/lc198707-anti-lie.md) | | --- | --- | --- | | Tagline | Python SDK for evaluating LLM generated responses | An LLM Claim Auditing Layer with truth gradients for verifying factual claims | | Stars | 301 | 89 | | Forks | 22 | 6 | | Open issues | 4 | 0 | | 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. | Anti-Lie offers an LLM claim auditing layer using T1-T7 truth gradients to achieve high business effectiveness on fact-checking benchmarks. | | Persona | - | - | | Runtime | - | - | | License | - | MIT License, allows easy integration, inspection, forks, embeds, and improvements of the tool in various agent runtimes and audit systems. | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [athina-evals](/tools/athina-ai-athina-evals.md) | [anti-lie](/tools/lc198707-anti-lie.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 470d | 121d | | Open issues (now) | 4 | 0 | | Open issues delta | +1 (30d) | 0 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/athina-ai-athina-evals/trust.md) | [trust report](/tools/lc198707-anti-lie/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: anti-lie - **Pricing:** freemium - The software is free (open source). However, additional compliance documents may incur costs or delays. - **Adopt for:** Anti-Lie offers an LLM claim auditing layer using T1-T7 truth gradients to achieve high business effectiveness on fact-checking benchmarks. - **License detail:** MIT License, allows easy integration, inspection, forks, embeds, and improvements of the tool in various agent runtimes and audit systems. ## 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 89) - visibility, not fit. ### Choose anti-lie if… - Pricing: The software is free (open source). However, additional compliance documents may incur costs or delays.. - Tags unique to anti-lie: agent-skills, ai-safety, anti-lie, audit. - When you require high accuracy in verifying factual claims made by LLMs, achieving a reported 98.1% effectiveness on benchmark testing with LiarBench v0.2 ## 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 anti-lie - When the target platform is not Linux or macOS, as current service definitions are specific to these operating systems (systemd for Linux, launchd for macOS) - If your project does not require an outbound hook bundle including both a Python verifier and a Node.js shadow worker ## Common questions ### What is the difference between athina-evals and anti-lie? athina-evals: Python SDK for evaluating LLM generated responses. anti-lie: An LLM Claim Auditing Layer with truth gradients for verifying factual claims. See the comparison table for live GitHub stats and shared categories. ### When should I choose athina-evals over anti-lie? Choose athina-evals over anti-lie 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 89) - visibility, not fit. ### When should I choose anti-lie over athina-evals? Choose anti-lie over athina-evals when Pricing: The software is free (open source). However, additional compliance documents may incur costs or delays.; Tags unique to anti-lie: agent-skills, ai-safety, anti-lie, audit; When you require high accuracy in verifying factual claims made by LLMs, achieving a reported 98.1% effectiveness on benchmark testing with LiarBench v0.2. ### 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 anti-lie? When the target platform is not Linux or macOS, as current service definitions are specific to these operating systems (systemd for Linux, launchd for macOS) If your project does not require an outbound hook bundle including both a Python verifier and a Node.js shadow worker ### Is athina-evals or anti-lie more popular on GitHub? athina-evals has more GitHub stars (301 vs 89). Stars measure visibility, not whether either tool fits your constraints. ### Are athina-evals and anti-lie open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to athina-evals or anti-lie? GraphCanon lists graph-backed alternatives at [athina-evals alternatives](/tools/athina-ai-athina-evals/alternatives) and [anti-lie alternatives](/tools/lc198707-anti-lie/alternatives) ([athina-evals markdown twin](/tools/athina-ai-athina-evals/alternatives.md), [anti-lie markdown twin](/tools/lc198707-anti-lie/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-lc198707-anti-lie.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, athina-evals or anti-lie? athina-evals: Dormant. anti-lie: Slowing. 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 anti-lie? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [athina-evals trust report](/tools/athina-ai-athina-evals/trust); [anti-lie trust report](/tools/lc198707-anti-lie/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/_