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

Catch your AI's mistakes and blind spots before your customers or regulators do. iFixAi runs 45 inspections, 32 graded core plus 13 extended for frontier risks like sabotage, sandbagging, and oversight evasion. It returns a letter grade in under 5 minutes. Industry and model agnostic.

Open-source benchmark for browser AI agents on daily tasks.

open source agent engineering platform: traces, evals, and metrics to debug and improve your AI agents. Integrates with LangGraph, CrewAI, Claude Agent SDK, and more.

Ranking LLMs on agentic tasks

AdaRubric: Adaptive Dynamic Rubric Evaluator for Agent Trajectories

Lightweight, auditable Python code agent (~1500 LOC) — ReAct + Planner + Reflexion + Hybrid RAG, with SWE-bench Lite eval and trace replay.

Open skill for capturing AI agent work as structured traces.

Regression testing for AI agents. Snapshot behavior,diff tool calls,catch regressions in CI. Works with LangGraph, CrewAI, OpenAI, Anthropic.

Every Eval Ever is a shared schema and crowdsourced eval database. It defines a standardized metadata format for storing AI evaluation results — from leaderboard scrapes and research papers to local evaluation runs — so that results from different frameworks can be compared, reproduced, and reused.

Benchmarking the gap between AI agent hype and architecture. Three agent archetypes, 73-point performance spread, stress testing, network resilience, and ensemble coordination analysis with statistical validation.

Crash-test insurance claim AI agents before production.

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

Synthetic cloud computers for training and evaluating long-horizon agents on Cloudflare. Persona → filesystem → artifacts → collaborators → simulation → retrospective.

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

LLM and agent evaluation for Java & Kotlin. Runs in JUnit and CI. Spring AI, LangChain4j, Koog, Embabel, and any LLM client.

🤖 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