🐢 Open-Source Evaluation & Testing for AI & LLM systems

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

A single interface to use and evaluate different agent frameworks

Ranking LLMs on agentic tasks

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

Mathematical benchmark exposing the massive performance gap between real agents and LLM wrappers. Rigorous multi-dimensional evaluation: stress testing, network resilience, ensemble coordination, failure analysis. Features statistical validation and reproducible methodology for separating architectural theater from real systems.

🤖 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

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

Train a reinforcement learning agent using PPO to balance a pole on a cart in the CartPole-v0 environment using Gymnasium and Stable-Baselines3. Includes model training, evaluation, and rendering using Python and Jupyter Notebook.

Visual dashboard to evaluate multi-agent & RAG-based AI apps. Compare models on accuracy, latency, token usage, and trust metrics - powered by NVIDIA AgentIQ

Browser automation agent for Bunnings website using the browser-use library, orchestrated via the laminar framework, managed with uv for Python environments, and running in Brave Browser for stealth and CAPTCHA bypass.