AI Observability & Evaluation
- llmops
- ai-monitoring
- ai-observability
- llm-eval
47,133 repositories indexed
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AI Observability & Evaluation
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.
The Time Series Visualization Tool that you deserve.
Open-Source Agent Observability & Evals for AI Platform teams. Trace, test, route and govern every agent & coding assistant in the company.
Blazing-fast, Neuro-symbolic Data-Wrangling toolkit
Fast numerical array expression evaluator for Python, NumPy, Pandas, PyTables and more
WFGY is heading toward WFGY 5.0 Polaris Protocol, a major open-source release for AI reasoning, RAG, agents, and real-world workflows. Includes Problem Map, Global Debug Card, WFGY 4.0, and the CFV Easter Egg.
SimpleITK: a layer built on top of the Insight Toolkit (ITK), intended to simplify and facilitate ITK's use in rapid prototyping, education and interpreted languages.
This repository introduces PIXIU, an open-source resource featuring the first financial large language models (LLMs), instruction tuning data, and evaluation benchmarks to holistically assess financial LLMs. Our goal is to continually push forward the open-source development of financial artificial intelligence (AI).
Fluent navigation and management of markdown notebooks
🍇 GRAPE is a Rust/Python Graph Representation Learning library for Predictions and Evaluations
A library of metrics for evaluating recommender systems
The bridge between Neovim and Jupyterlab
📊 🌐 🧑🏫 Website for graduate-level course on program evaluation and causal inference using R, built with Quarto
A plugin for neovim to render pandas dataframes in nvim-dap using the power of visidata.
Neovim plugin that launches a Jupyter/IPython console in a split to run files, lines, and Spyder‑IDE‑style runcells, with a ZMQ‑powered variable explorer and data viewer.
A Neovim plugin for seamless integration with Jupyter Notebooks through Jupyter Ascending
Advanced introduction to R in French.
Machine learning model evaluation made easy: plots, tables, HTML reports, experiment tracking and Jupyter notebook analysis.