io focuses on providing automated evaluation methods for AI system outputs. The platform is designed to help users define and run 'evals', which are described as automated checks on AI outputs, ranging from simple… Below are 10 llm eval & observability apps with similar functionality to AI-Evals, matched by what each product actually does — not ranked or scored. Explore each to find the closest fit for your use case.
AgentEvals is an open-source tool designed to evaluate and score the behavior of AI agents using telemetry data captured from real production or test environments. By analyzing OpenTelemetry Protocol (OTLP) streams and Jaeger JSON traces, it enables users to assess agent performance and inference quality without the need to rerun or replay expensive large language model (LLM) calls. This approach allows for benchmarking agents before deployment and provides insights based on actual agent traces rather than synthetic replays. The platform offers several evaluation features, including the ability to define golden evaluation sets that describe expected agent behaviors, tool calls, and trajectories. AgentEvals supports flexible trajectory matching with strict, unordered, subset, or superset modes, enabling nuanced comparisons between expected and observed agent actions. Users can also create custom evaluators in Python, JavaScript, or any language of their choice and share them through a community registry. AgentEvals is accessible through both a command-line interface (CLI) and a web user interface (Web UI). The CLI is tailored for automation and integration into CI/CD pipelines, enabling teams to gate deployments based on agent behavior quality scores. The Web UI provides interactive capabilities for visually inspecting traces, browsing results, comparing runs, and drilling into detailed evaluations. Installation is available via Python wheel, and evaluations can be run directly against trace files. 0 license. Its focus on trace-driven evaluation and support for both automated and interactive workflows make it suitable for developers and teams seeking to ensure the reliability and quality of AI agent behavior before production deployment.
EvalsHub AI is a platform designed for automated quality assurance of AI models, focusing on streamlining the evaluation process and reducing the need for manual review. The service leverages large language models (LLMs) as evaluators, referred to as "LLM-as-a-judge," to assess, compare, and monitor AI models according to user-defined standards and criteria. Users can connect their data, define evaluation rubrics in natural language, and rely on automated scorers to deliver consistent, repeatable pass/fail metrics, eliminating manual spot-checks. The platform supports the creation of custom evaluation rubrics, allowing users to specify criteria such as accuracy, hallucination, and safety, assign weights or thresholds, and generate a weighted quality score for each model output. EvalsHub AI provides real-time evaluation results and maintains a global quality index, enabling users to monitor model drift, compare different versions, and track performance over time. The platform also features dashboards for sharing metrics and accuracy gains with stakeholders. Security and robustness are emphasized through automated adversarial testing, which exposes vulnerabilities like prompt injection, jailbreak attempts, and safety violations. The system employs both heuristic and LLM-based detection methods to identify malicious inputs and stress-test models against evolving attack strategies. Compliance checks for content filtering, PII leakage, and policy adherence are also integrated into the evaluation workflow. EvalsHub AI is delivered via integration with the user's existing codebase through a lightweight SDK, allowing seamless incorporation into current prompt engineering workflows. It supports the use of built-in evaluation models as well as custom models from providers such as OpenAI, Anthropic, or open-source alternatives. The platform offers CI/CD integration for automated evaluation pipelines and provides options for enterprise deployment, including zero-retention logging and VPC deployments to ensure data privacy. Users can begin using the platform for free without requiring a credit card, and enterprise plans with additional security options are available. EvalsHub AI positions itself as a comprehensive solution for teams seeking rigorous, automated quality assurance in the development and deployment of generative AI systems.
evalmedia is an open-source framework designed to provide structured quality assessments for AI-generated images. Its primary focus is to deliver actionable, decomposed feedback to AI agents, enabling them to determine whether an image is suitable for use or requires further refinement. Rather than offering a single overall score, the framework breaks down evaluation into specific checks, such as detecting face or hand artifacts, assessing prompt adherence, and evaluating text legibility. The tool includes eight built-in checks: face artifacts, hand artifacts, prompt adherence, text legibility, aesthetic quality, style consistency, CLIP similarity, and resolution adequacy. 1 as judges. It also offers classical evaluation methods, such as CLIP similarity and resolution checks, which do not require external APIs. The framework supports rubrics—weighted collections of checks tailored to particular use cases, such as portraits or marketing assets. evalmedia is designed to integrate with AI agents, providing tool schemas compatible with OpenAI and Anthropic function calling. gather. The framework can be used via a command-line interface for image evaluation or integrated directly into Python workflows. 1. As an open-source project, evalmedia is available for use and modification without licensing fees. Its focus on agent-native, structured evaluation distinguishes it within the broader class of AI media quality assessment tools.
Benchmark your AI agent / RAG pipeline on the AI01 leaderboard.
Evalta AI is a web-based tool designed for agencies to audit websites, detect SEO and technical issues, and provide step-by-step AI-guided fixes. It tracks site health, performance, and structured data, helping users resolve problems quickly and improve search visibility.
AI Voting is a web platform where users can evaluate and compare AI models by voting on their outputs. Participants earn points for their contributions, helping improve model selection and benchmarking. The tool is aimed at AI researchers and enthusiasts interested in model performance.
Zero-dependency eval harness for LLM and agent regression testing. Scores outputs with exact, contains, regex, JSON, citation, and token-F1 checks. Compares two runs to flag regressions.
tool-eval is a command-line framework for evaluating the tool usage of AI agents. It provides researchers and developers with tools to analyze and benchmark agent interactions with external tools.
Agent evaluation toolkit
Evaluator is a technical assessment platform designed to grade how effectively engineering candidates collaborate with AI tools in the context of software development. The platform addresses the evolving hiring landscape, where the ability to work skillfully with AI-generated code is considered essential. Rather than screening for or penalizing AI use, Evaluator focuses on identifying engineers who can read, fix, prompt, and override AI-generated solutions, alongside traditional skills such as code reading, writing, and debugging. The assessment process is structured around six core dimensions, with a particular emphasis on AI collaboration. Five dedicated sub-tests evaluate prompt quality, the ability to read AI-written code, fixing specific bugs in AI-generated functions, critiquing code for multiple flaws or hallucinations, and live collaboration with an AI assistant within the coding environment. For example, candidates may be asked to write prompts for an AI, analyze and critique AI-generated code for non-idiomatic patterns or over-engineering, surgically fix planted bugs, and identify all issues in code containing multiple errors. The live collaboration section records every candidate prompt, suggestion accepted or rejected, and manual edits, providing a transcript for review. Evaluator customizes each assessment to the specific role and tech stack described by the hiring team, generating unique questions while maintaining consistent evaluation criteria. Beyond AI collaboration, the platform also tests code reading, code writing, debugging, communication (such as explaining refactors or writing PR descriptions), and tradeoff reasoning (like evaluating build vs. buy decisions). The process begins with the hiring manager pasting a job description or role summary, after which a tailored assessment is generated in approximately 30 seconds. The platform offers a free plan allowing up to ten assessments per month, with no credit card required to get started. Evaluator is delivered as a web-based service, accessible through login and sign-up options. It is positioned as a tool for companies and hiring managers seeking to identify engineering talent proficient in leveraging AI as part of their workflow.
