Next-generation AI Agent Optimization Platform: Cozeloop addresses challenges in AI agent development by providing full-lifecycle management capabilities from development, debugging, and evaluation to monitoring.

🐢 Open-Source Evaluation & Testing library for LLM Agents

Evaluation and Tracking for LLM Experiments and AI Agents

A single interface to use and evaluate different agent frameworks

Ranking LLMs on agentic tasks

Mathematical benchmark exposing the massive performance gap between real agents and LLM wrappers. Rigorous multi-dimensional evaluation with statistical validation (95% CI, Cohen's h) and reproducible methodology. Separates architectural theater from real systems through stress testing, network resilience, and failure analysis.

Learn How To Observe, Manage, and Scale, Agentic AI Apps Using Azure AI Foundry - with this hands-on workshop

Catch AI agent regressions before you ship. YAML test cases, golden baselines, execution tracing, cost tracking, CI integration. LangGraph, CrewAI, Anthropic, OpenAI.

Tune your AI Agent to best meet its KPI with a cyclic process of analyze, improve and simulate

🤖 A curated list of resources for testing AI agents - frameworks, methodologies, benchmarks, tools, and best practices for ensuring reliable, safe, and effective autonomous AI systems

Evaluation Framework for LLM applications in Java

Intelligent Context Engineering Assistant for Multi-Agent Systems. Analyze, optimize, and enhance your AI agent configurations with AI-powered insights

A minimal sandbox to run, score, and compare AI agent outputs locally.

End-to-end TeamCity framework to run AI agents on SWE-Bench Lite. Spin up isolated Docker images per task, extract patches, score with the official harness, and aggregate success rates. As an example, we'll look at Junie and Google Gemini CLI

Multi-agent customer support system with Google ADK & Gemini 2.5 Flash Lite. Kaggle capstone demonstrating 11+ concepts. Automates 80%+ queries, <10s response time.

Benchmark framework for evaluating LLM agent continual learning in stateful environments. Features production-realistic CRM workflows with multi-turn conversations, state mutations, and cross-entity relationships. Extensible to additional domains

Evaluate AI agents with Unix-style pipeline commands. Schema-driven adapters for any CLI agent, trajectory capture, pass@k metrics, and multi-run comparison.

Neurosim is a Python framework for building, running, and evaluating AI agent systems. It provides core primitives for agent evaluation, cloud storage integration, and an LLM-as-a-judge system for automated scoring.

Qualitative benchmark suite for evaluating AI coding agents and orchestration paradigms on realistic, complex development tasks