🐢 Open-Source Evaluation & Testing library for LLM Agents

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.

Evaluation and Tracking for LLM Experiments and AI Agents

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.

A single interface to use and evaluate different agent frameworks

AuditPilot: auditable enterprise AI agents for evidence-grounded workflows, governed tools, evaluation harnesses, human review, and remediation delivery.

A curated, non-BS library of the best resources for building and evaluating AI agents — papers, blogs, talks, tools, benchmarks. Maintained by BenchFlow.

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.

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

AdaRubric: Adaptive Dynamic Rubric Evaluator for Agent Trajectories

Ranking LLMs on agentic tasks

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

Learn AI Agents step by step, from scratch - from function calling to agent loops to multi-agent systems, orchestration, and evaluation.

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

Verification-gated skill routing and self-improvement harness for Hermes-style agent skills

Crash-test insurance claim AI agents before production.

Evaluate & benchmark AI coding agents and Claude Code skills — sandboxed, reproducible YAML eval suites for Claude Code, Codex & Gemini, with A/B experiments and CI gates.

AgentKernelArena provides an end-to-end siloed-benchmarking environment where different LLM-powered agents—such as Cursor Agent, Claude Code, Codex, SWE-agent, and GEAK—can be evaluated side-by-side on the same GPU kernel tasks, using objective and reproducible metrics.

Open skill for capturing AI agent work as structured traces.

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.