🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
🧪 Evaluación, observabilidad y seguridad
Evaluación de modelos, pruebas de prompts, observabilidad, barreras de seguridad y red teaming
Todo 1040🧠 Modelos de lenguaje 24🤖 Agentes de IA 159🧩 Plataformas de desarrollo de apps 23📚 RAG y bases de conocimiento 47💬 Interfaces de chat y clientes 41🖥️ Inferencia y despliegue local 87👨💻 Programación con IA 97🛠️ Frameworks y SDK 46🔌 MCP y ecosistema de herramientas 36🎨 Generación de imágenes 26🎬 Generación de vídeo 17🎙️ Voz y audio 45👁️ Visión por computador 26📄 Análisis de documentos y scraping 25🗄️ Bases de datos vectoriales 25🏋️ Entrenamiento, ajuste fino y MLOps 71🧪 Evaluación, observabilidad y seguridad 79🚪 Pasarelas y proxies de API 12🔎 Búsqueda e investigación profunda 16⚙️ Bibliotecas de aprendizaje profundo 57🏷️ Datos y anotación 32🔬 IA para industria y ciencia 33⚡ Herramientas de productividad 16
The LLM interactive security toolkit detects clues injected with sensitive information.
PentestAgent is an AI agent framework for black-box security testing, supporting bug bounty, red-team, and penetration testing workflows.
Templates and workflow for generating PRDs, Tech Designs, and MVP and more using LLMs for AI IDEs
Open-source, self-hosted AI penetration testing framework: maps your attack surface into a graph, autonomously exploits it from a Kali sandbox with human approval gates, and opens PRs that fix what it finds. MCP both way
Open-source tools for prompt testing and experimentation, with support for both LLMs (e.g. OpenAI, LLaMA) and vector databases (e.g. Chroma, Weaviate, LanceDB).
An open-source visual programming environment for battle-testing prompts to LLMs.
Inspect: A framework for large language model evaluations
DeepTeam is a framework to red team LLMs and AI agents.
[ACL 2024] An Easy-to-use Knowledge Editing Framework for LLMs.
A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
OpenLIT is the open-source agent harness engineering platform: trace, evaluate, guard, and improve everything around the model in your AI agents, on OTEL.
Self-learning agent browser
Hugging Face's Lightweight LLM Assessment Toolbox.
🤗 Evaluate: A library for easily evaluating machine learning models and datasets.
Python & JS/TS SDK for running AI-generated code/code interpreting in your AI app
A Python package to assess and improve fairness of machine learning models.
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.
ADR secures enterprise AI agents through observability, security benchmarking, and threat detection. Deployed at Uber.
Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower develo
Interpretability and explainability of data and machine learning models
MLE-bench is a benchmark for measuring how well AI agents perform at machine learning engineering
Reference implementations of MLPerf® inference benchmarks
PinchBench is a benchmarking system for evaluating LLM models as OpenClaw coding agents. Made with 🦀 by the humans at https://kilo.ai