🪢 Open source agent evals & observability: Trace, evaluate, and improve LLM applications with one open platform.
Mastra is the modern TypeScript framework for AI-powered applications and agents.
Framework for evaluating and improving agents
Awesome list for AI agent harness engineering: tools, patterns, evals, memory, MCP, permissions, observability, and orchestration.
Low-level agentic design patterns. Turning old engineering wisdom into reflexes your agent reaches for on its own—on any agent.
AI system design guide for engineers building production AI systems and evals.
AI Observability & Evaluation
Build, Evaluate, and Optimize AI Systems. Includes evals, RAG, agents, fine-tuning, synthetic data generation, dataset management, MCP, and more.
👋 Hello Agent System — learn enterprise AI agent system design by building every production layer from scratch. 17 bilingual lessons (EN/中文) with tested exercises: tools, architectures, reliability, security, evals, distributed execution, cost, RAG, release ops. 企业级 Agent 系统设计训练营
A streamlined and customizable framework for efficient large model (LLM, VLM, AIGC) evaluation and performance benchmarking.
Waku Waku! Waku Agent is a local-first AI agent harness you actually own, including loop, memory, eval, all in code built to stay legible as it grows.
neatroots: see, check and replay your AI agent's runs on your machine while you build it. Local-first, by Neatlogs.
HAEnv — synthetic longitudinal patients with code-derived gold, for evaluating health agents over time
Open-Source Agent Observability & Evals for AI Platform teams. Trace, test, route and govern every agent & coding assistant in the company.
Laminar - open-source observability platform purpose-built for AI agents. YC S24.
Testing and evaluation platform to chat, inspect, and debug MCP servers, MCP apps, and ChatGPT apps.
Skills that guide AI coding agents to help you build product-specific AI evals.
Open source evals for physical AI. Run any LLM/VLA on any arm/humanoid against any real/sim benchmark.
Turn your coding-agent sessions into private evals and find the cheapest setup that still solves your tasks.
Our library for RL environments + evals
Code, Build and Evaluate agents - excellent Model and Skills/MCP/ACP/A2A Support
Open-source, end-to-end platform for evaluating, observing, and improving LLM and AI agent applications. Tracing · Evals · Simulations · Datasets · Gateway · Guardrails. Self-hostable. Apache 2.0.
OpenAI Frontier Evals
Build agentic systems. Run them with confidence. Orchestrate agents, automate business processes, inspect every execution, and keep humans in control. Deploy Heym on your own infrastructure.
Readymade evaluators for your LLM apps
A test runner for agentskills.io-style AI agent skills
Learn harness engineering by building Claude Code from scratch. Free, open-source course: 8 articles, 6 videos, 1 codebase.
The platform for continuously improving AI agents.
Multi-tier framework for evaluating AI agent skills with quality gates, semantic overlap detection, synthetic evaluation dataset generation, and live agent evaluation that measures how skills affect agent behavior.
A tiny TypeScript web agent, hill climbed on real browser evals.
Research-backed, eval-driven skills for AI agents
An eval app which runs locally and uses local models or any OpenAI-compatible API
Observability and enforcement for AI agent harnesses. Capture every run and runtime reliability with policy enforcement.
AI observability platform for production LLM and agent systems.
Evaluation and Tracking for LLM Experiments and AI Agents
🥤 RAGLite is a Python toolkit for Retrieval-Augmented Generation (RAG) with DuckDB or PostgreSQL
The Continuous-Improvement Stack for Agents. Our environment data and evals power agent improvement and monitoring.
AutoEvals is a tool for quickly and easily evaluating AI model outputs using best practices.
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.
Hands-on, framework-free Colab notebooks for the AI Engineer / Forward Deployed Engineer (FDE) skill set — model APIs, structured output, tool calling, RAG, evals-as-the-spine, agents (loop from scratch, tool design, guardrails, MCP, Skills), fine-tuning vs LoRA, prompt-injection/security, LLMOps, and customer craft. Runs on the free Groq API.
Collection of evals for Inspect AI
The definitive OpenAI, Claude, MCP, Harness, Evals, and Production Agent Systems learning roadmap.
Public results and task definitions for FrontierHarness Eval
LLM agent 的有界自纠错长期记忆层:把连续、含矛盾的项目交互日志蒸馏成有界、可自更新的记忆库。Bounded, self-correcting long-term memory for LLM agents — candidate pool + value dynamics + tension adjudication + causal-replay evals.
The model is the engine; the harness is the car. Curated knowledge on harness engineering — context management, tool design, agent loops, memory, sandboxing, evals. EN/中文
Evals and benchmarks for AI models
The trust layer for your AI agent on any platform. Trace and evaluate your agent. Full Observability. Run Model portability analysis. Autotune your agent.
Agent evals and guardrails as Jev decisions: one request per trace, a fraction of a cent, fast enough for the agent loop. Runs locally with Kev or Laya.
Production-grade Agent Skills for AI coding agents—composable workflows for planning, TDD, debugging, review, UI/UX, releases, incidents, and evals.
🚀 Turn ideas and repositories into showcase-ready AI projects with agents, RAG, multimodal AI, local models, evals, safety checks, and polished demos.
Homework assignments for Evaluating and Improving AI Agents
A Ruby harness for running agent benchmarks
Agent eval on one running base. Swap the agent under test with plugins; run the same dataset anywhere.
Reference architecture, contracts, skills, and fixtures for safe evaluator-driven self-improving ecosystems.
Transcript analysis for long-horizon agent evaluations
Curated list of open-source LLMOps tools: tracing, evals, AI gateways, guardrails and prompt management. Sorted by stars, refreshed weekly.
Reproduction package for "Harness as a Language: A Minimalist Agent Framework With Maximal Expressivity" — ten agent arms across AppWorld and StuLife
AI agents for pentesting, code audit, fuzzing, vulnerability discovery, and reverse engineering: harnesses, sandboxes, security MCP servers, benchmarks, and evals.
Calibrate Jev questions against your own labels: tune criteria on labelled examples, confirm on a held-out set, get a verdict per question. Unofficial.