--- title: "deepeval vs auto-evaluator" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/confident-ai-deepeval-vs-rlancemartin-auto-evaluator" tools: ["confident-ai-deepeval", "rlancemartin-auto-evaluator"] --- # deepeval vs auto-evaluator *GraphCanon updated Sep 20, 2026* ## Verdict Pick deepeval if deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies; pick auto-evaluator if auto-evaluator is a Python-based tool designed for evaluating LLM QA chains with the capability to auto-generate question-answer pairs from user-provided documents and evaluate answers using configurations chosen via UI. [deepeval](https://deepeval.com) reports 18k GitHub stars, 2.0k forks, and 624 open issues, last pushed Sep 18, 2026. [auto-evaluator](https://autoevaluator.langchain.com/) has 1.1k stars, 92 forks, and 3 open issues, last pushed May 10, 2023. Figures are from public GitHub metadata via [deepeval's repository](https://github.com/confident-ai/deepeval) and [auto-evaluator's repository](https://github.com/rlancemartin/auto-evaluator). | | [deepeval](/tools/confident-ai-deepeval.md) | [auto-evaluator](/tools/rlancemartin-auto-evaluator.md) | | --- | --- | --- | | Tagline | LLM Evaluation Framework. | A lightweight evaluation tool for question-answering using Langchain | | Stars | 18,342 | 1,102 | | Forks | 1,953 | 92 | | Open issues | 624 | 3 | | Language | Python | Python | | Adopt for | Deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies. | Auto-evaluator is a Python-based tool designed for evaluating LLM QA chains with the capability to auto-generate question-answer pairs from user-provided documents and evaluate answers using configurations chosen via UI. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 License | - | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [deepeval](/tools/confident-ai-deepeval.md) | [auto-evaluator](/tools/rlancemartin-auto-evaluator.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 1d | 1216d | | Open issues (now) | 624 | 3 | | Stars delta | +1.1k (30d) | -3 (30d) | | Open issues delta | +220 (30d) | 0 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/confident-ai-deepeval/trust.md) | [trust report](/tools/rlancemartin-auto-evaluator/trust.md) | ## Shared compatibility - **Python**: [deepeval](/tools/confident-ai-deepeval.md) - Python runtime; [auto-evaluator](/tools/rlancemartin-auto-evaluator.md) - Python runtime ## Decision facts: deepeval - **Requirements:** Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities. - **Adopt for:** Deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies. - **License detail:** Apache-2.0 License ## Decision facts: auto-evaluator - **Adopt for:** Auto-evaluator is a Python-based tool designed for evaluating LLM QA chains with the capability to auto-generate question-answer pairs from user-provided documents and evaluate answers using configurations chosen via UI. ## Choose when ### Choose deepeval if… - Requirements: Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities.. - Tags unique to deepeval: llm-evaluation, metrics. - When developing large language models and you need a comprehensive evaluation framework to measure their performance across various metrics. ### Choose auto-evaluator if… - Tags unique to auto-evaluator: gpt-3.5-turbo, langchain, llm, question-answering. - Use when you need a lightweight solution for testing question-answering capabilities of Langchain models. - Leaner open-issue backlog (3). ## When NOT to use deepeval - For small-scale applications that do not require the depth of metrics and evaluations offered by Deepeval, as it might be overkill. - In situations where there is a need for real-time performance monitoring, since Deepeval focuses more on post-development evaluation rather than continuous runtime analysis. ## When NOT to use auto-evaluator - Avoid using this tool when you do not have access to an OpenAI API key providing access to GPT-4, as it uses that by default for optimal settings. - If you are looking for a tool that does not require you to input documents for question generation and prefer a more customized prompt approach rather than the auto-generation feature. ## Common questions ### What is the difference between deepeval and auto-evaluator? deepeval: LLM Evaluation Framework.. auto-evaluator: A lightweight evaluation tool for question-answering using Langchain. See the comparison table for live GitHub stats and shared categories. ### When should I choose deepeval over auto-evaluator? Choose deepeval over auto-evaluator when Requirements: Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities.; Tags unique to deepeval: llm-evaluation, metrics; When developing large language models and you need a comprehensive evaluation framework to measure their performance across various metrics. ### When should I choose auto-evaluator over deepeval? Choose auto-evaluator over deepeval when Tags unique to auto-evaluator: gpt-3.5-turbo, langchain, llm, question-answering; Use when you need a lightweight solution for testing question-answering capabilities of Langchain models; Leaner open-issue backlog (3). ### When should I avoid deepeval? For small-scale applications that do not require the depth of metrics and evaluations offered by Deepeval, as it might be overkill. In situations where there is a need for real-time performance monitoring, since Deepeval focuses more on post-development evaluation rather than continuous runtime analysis. ### When should I avoid auto-evaluator? Avoid using this tool when you do not have access to an OpenAI API key providing access to GPT-4, as it uses that by default for optimal settings. If you are looking for a tool that does not require you to input documents for question generation and prefer a more customized prompt approach rather than the auto-generation feature. ### Is deepeval or auto-evaluator more popular on GitHub? deepeval has more GitHub stars (18,342 vs 1,102). Stars measure visibility, not whether either tool fits your constraints. ### Are deepeval and auto-evaluator open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to deepeval or auto-evaluator? GraphCanon lists graph-backed alternatives at [deepeval alternatives](/tools/confident-ai-deepeval/alternatives) and [auto-evaluator alternatives](/tools/rlancemartin-auto-evaluator/alternatives) ([deepeval markdown twin](/tools/confident-ai-deepeval/alternatives.md), [auto-evaluator markdown twin](/tools/rlancemartin-auto-evaluator/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/confident-ai-deepeval-vs-rlancemartin-auto-evaluator.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, deepeval or auto-evaluator? deepeval: Very active. auto-evaluator: 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 deepeval and auto-evaluator? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [deepeval trust report](/tools/confident-ai-deepeval/trust); [auto-evaluator trust report](/tools/rlancemartin-auto-evaluator/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=confident-ai-deepeval`](/api/graphcanon/graph?tool=confident-ai-deepeval) - 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/_