🪢 Open source agent evals & observability: Trace, evaluate, and improve LLM applications with one open platform.
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Updated
Sep 21, 2026 - TypeScript
🪢 Open source agent evals & observability: Trace, evaluate, and improve LLM applications with one open platform.
Open-source LLM knowledge platform: turn raw documents into a queryable RAG, an autonomous reasoning agent, and a self-maintaining Wiki.
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.
🤘 awesome-semantic-segmentation
Easily fine-tune, evaluate and deploy Qwen, Gemma, or any open weight LLM!
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.
🧊 Open source LLM observability platform. One line of code to monitor, evaluate, and experiment. YC W23 🍓
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.
Build, Evaluate, and Optimize AI Systems. Includes evals, RAG, agents, fine-tuning, synthetic data generation, dataset management, MCP, and more.
The platform for LLM evaluations and AI agent testing
One-for-All Multimodal Evaluation Toolkit Across Text, Image, Video, and Audio Tasks
Open-source evaluation toolkit of large multi-modality models (LMMs), support 220+ LMMs, 80+ benchmarks
Python package for the evaluation of odometry and SLAM
Building a modern functional compiler from first principles. (http://dev.stephendiehl.com/fun/)
A streamlined and customizable framework for efficient large model (LLM, VLM, AIGC) evaluation and performance benchmarking.
MTEB: State-of-the-art evaluation of embeddings across languages and modalities
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