Independent Auditing of AI Agents. Run by human or the agent itself, to answer the most crucial question in the AI Agent Economy. Is the agent doing what is supposed to do? With iFixAi you can have this answer in less than 120 seconds.
- ai
- diagnostic-tool
- agent-evaluation
- ai-alignment
🪢 Open source AI engineering platform: AI Gateway, agent observability, evals, and prompt/skill management, built on OpenTelemetry. One open platform for every AI call and every agent. Self-host or use Langfuse Cloud.
The open source AI engineering platform for agents, LLMs, and ML models. MLflow enables teams of all sizes to debug, evaluate, monitor, and optimize production-quality AI applications while controlling costs and managing access to models and data.
Test your prompts, agents, and RAGs. Red teaming/pentesting/vulnerability scanning for AI. Compare performance of GPT, Claude, Gemini, DeepSeek, and more. Simple declarative configs with command line and CI/CD integration. Used by OpenAI and Anthropic.
Debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards.
Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks.
Python SDK for Agent AI Observability, Monitoring and Evaluation Framework. Includes features like agent, llm and tools tracing, debugging multi-agentic system, self-hosted dashboard and advanced analytics with timeline and execution graph view
- agentneo
- agents
- ai-performance-optimization
- llm-testing
AI Observability & Evaluation
- llmops
- ai-monitoring
- ai-observability
- llm-eval
Generates original ARC-AGI-1-style tasks distribution-matched to the public eval set.
Easily fine-tune (SFT/RL), evaluate, and deploy Qwen, Gemma, or any open agentic LLM/VLM!
TranslatedBELLE: Be Everyone's Large Language model Engine (open-source Chinese conversational large language model)
- bloom
- instruction-set
- llama
- open-models
Evidently is an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.
AgentPilot: Make Your Agents Move Better and Faster
OpenCompass is an LLM evaluation platform, supporting a wide range of models from OpenAI, Anthropic, Gemini, Qwen, GLM, DeepSeek, etc, across 100+ datasets covering knowledge, reasoning, coding, science, language, long-context, and safety.
This repo powers my experiment where ChatGPT manages a real-money micro-cap stock portfolio.
Translated非线智能 NoneLinear (Non-linear Intelligence) - ReLE evaluation: capability evaluation of Chinese AI large models (continuously updated): it currently covers 374 large models, including commercial models such as chatgpt, gpt-5.4, Google gemini-3.1-pro, Claude-4.6, 文心 ERNIE-X1.1, ERNIE-5.0, qwen3.6-max, qwen3.6-plus, Baichuan, iFlytek Spark, and SenseTime senseChat, as well as open-source large models such as step3.5-flash, kimi-k2.6, ernie4.5, MiniMax-M2.7, deepseek-v4, Qwen3.6, llama4, Zhipu GLM-5.1, MiMo-V2, LongCat, gemma4, and mistral. It provides not only leaderboards but also a large model defect library with more than 2,000,000 entries! This makes it convenient for the broad community to research, analyze, and improve large models.
Agent OS: the agent gets smarter on its own. We just hold the line: Interview-gated, staged evaluation, budgeted evolution loop. MCP server, 14 runtimes: Claude Code, Codex CLI, Gemini CLI, OpenCode, Copilot, Kiro and more.
SWE-bench: Can Language Models Resolve Real-world Github Issues?
- benchmark
- language-model
- software-engineering
Democratizing Reinforcement Learning for LLMs
A visual playground for agentic workflows: Iterate over your agents 10x faster
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.
- agent
- ai
- agent-evaluation
- agent-observability
🧩 FrontierAgent, our agent framework, open-sourced alongside it — native command-line TUI, ReAct and Agent Team modes, one command on macOS and Linux, no preinstall, no hard Docker dependency.
Build, Evaluate, and Optimize AI Systems. Includes evals, RAG, agents, fine-tuning, synthetic data generation, dataset management, MCP, and more.
- ai
- chain-of-thought
- collaboration
- dataset-generation
Train the smallest LM you can that fits in 16MB. Best model wins!