--- title: "last_layer vs deepeval" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/arekusandr-last-layer-vs-confident-ai-deepeval" tools: ["arekusandr-last-layer", "confident-ai-deepeval"] --- # last_layer vs deepeval *GraphCanon updated Sep 20, 2026* ## Verdict Pick last_layer if an ultra-fast Python tool for detecting prompt injections and jailbreak attempts in large language models suitable for projects requiring rapid security evaluations, with low-latency performance; pick deepeval if deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies. [last_layer](https://vibe-eval.com) reports 133 GitHub stars, 6 forks, and 13 open issues, last pushed Jul 26, 2024. [deepeval](https://deepeval.com) has 18k stars, 2.0k forks, and 624 open issues, last pushed Sep 18, 2026. Figures are from public GitHub metadata via [last_layer's repository](https://github.com/arekusandr/last_layer) and [deepeval's repository](https://github.com/confident-ai/deepeval). | | [last_layer](/tools/arekusandr-last-layer.md) | [deepeval](/tools/confident-ai-deepeval.md) | | --- | --- | --- | | Tagline | Ultra-fast low latency LLM prompt injection jailbreak detection | LLM Evaluation Framework. | | Stars | 133 | 18,342 | | Forks | 6 | 1,953 | | Open issues | 13 | 624 | | Language | Python | Python | | Adopt for | An ultra-fast Python tool for detecting prompt injections and jailbreak attempts in large language models suitable for projects requiring rapid security evaluations, with low-latency performance. | Deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 License | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [last_layer](/tools/arekusandr-last-layer.md) | [deepeval](/tools/confident-ai-deepeval.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 780d | 1d | | Open issues (now) | 13 | 624 | | Stars delta | +2 (30d) | +1.1k (30d) | | Open issues delta | 0 (30d) | +220 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/arekusandr-last-layer/trust.md) | [trust report](/tools/confident-ai-deepeval/trust.md) | ## Shared compatibility - **Python**: [last_layer](/tools/arekusandr-last-layer.md) - Python runtime; [deepeval](/tools/confident-ai-deepeval.md) - Python runtime ## Decision facts: last_layer - **Adopt for:** An ultra-fast Python tool for detecting prompt injections and jailbreak attempts in large language models suitable for projects requiring rapid security evaluations, with low-latency performance. ## 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 ## Choose when ### Choose last_layer if… - License: last_layer is MIT, deepeval is Apache-2.0. - Tags unique to last_layer: chatgpt-prompts, jailbreak, large-language-models, llm-guard. - When you need fast detection of potential security vulnerabilities due to unauthorized prompt manipulations in real-time scenarios involving LLMs ### Choose deepeval if… - License: deepeval is Apache-2.0, last_layer is MIT. - Requirements: Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities.. - Tags unique to deepeval: evaluation, llm-evaluation, metrics. - When developing large language models and you need a comprehensive evaluation framework to measure their performance across various metrics. ## When NOT to use last_layer - If your application does not require ultra-low latency detection and can afford slower, potentially more comprehensive security evaluations - For environments that prefer a broader range of security features beyond prompt injection detection, as last_layer focuses specifically on this aspect with speed in mind ## 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. ## Common questions ### What is the difference between last_layer and deepeval? last_layer: Ultra-fast low latency LLM prompt injection jailbreak detection. deepeval: LLM Evaluation Framework.. See the comparison table for live GitHub stats and shared categories. ### When should I choose last_layer over deepeval? Choose last_layer over deepeval when License: last_layer is MIT, deepeval is Apache-2.0; Tags unique to last_layer: chatgpt-prompts, jailbreak, large-language-models, llm-guard; When you need fast detection of potential security vulnerabilities due to unauthorized prompt manipulations in real-time scenarios involving LLMs. ### When should I choose deepeval over last_layer? Choose deepeval over last_layer when License: deepeval is Apache-2.0, last_layer is MIT; Requirements: Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities.; Tags unique to deepeval: evaluation, 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 avoid last_layer? If your application does not require ultra-low latency detection and can afford slower, potentially more comprehensive security evaluations For environments that prefer a broader range of security features beyond prompt injection detection, as last_layer focuses specifically on this aspect with speed in mind ### 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. ### Is last_layer or deepeval more popular on GitHub? deepeval has more GitHub stars (18,342 vs 133). Stars measure visibility, not whether either tool fits your constraints. ### Are last_layer and deepeval open source? Yes - both are open-source projects on GitHub (last_layer: MIT, deepeval: Apache-2.0). ### Where can I find alternatives to last_layer or deepeval? GraphCanon lists graph-backed alternatives at [last_layer alternatives](/tools/arekusandr-last-layer/alternatives) and [deepeval alternatives](/tools/confident-ai-deepeval/alternatives) ([last_layer markdown twin](/tools/arekusandr-last-layer/alternatives.md), [deepeval markdown twin](/tools/confident-ai-deepeval/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/arekusandr-last-layer-vs-confident-ai-deepeval.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, last_layer or deepeval? last_layer: Dormant. deepeval: Very active. 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 last_layer and deepeval? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [last_layer trust report](/tools/arekusandr-last-layer/trust); [deepeval trust report](/tools/confident-ai-deepeval/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=arekusandr-last-layer`](/api/graphcanon/graph?tool=arekusandr-last-layer) - 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/_