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🪢 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.
The LLM Evaluation Framework
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
A framework for few-shot evaluation of language models.
AI Observability & Evaluation
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)
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.
A full-stack AI Red Teaming platform securing AI ecosystems via Agent Scan, Skills Scan, MCP scan, AI Infra scan and LLM jailbreak evaluation.
Framework for evaluating and improving agents
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.
🐢 Open-Source Evaluation & Testing library for LLM Agents
Democratizing Reinforcement Learning for LLMs
A visual playground for agentic workflows: Iterate over your agents 10x faster
🧩 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.
Train the smallest LM you can that fits in 16MB. Best model wins!
The open source research environment for AI researchers to seamlessly train, evaluate, and scale models from local hardware to GPU clusters.
Open-Source Agent Observability & Evals for AI Platform teams. Trace, test, route and govern every agent & coding assistant in the company.
Open-source AI red teaming toolkit from Microsoft. Test generative AI systems with automated scans, adaptive multi-turn attacks, and a web UI.