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ML experiment tracking and model monitoring

Weights and Biases is the AI developer platform for experiment tracking, model monitoring, and ML workflow orchestration. Weave extends W&B with LLM ops capabilities for prompt engineering, evaluation, and deployment. Enables teams to track experiments, monitor model performance in production, manage datasets, log LLM application traces, and collaborate on ML projects with visualization dashboards, automated logging, and enterprise SSO and RBAC compliance.

Data factory for AI teams and model training

Labelbox is a comprehensive data platform for AI teams handling reinforcement learning, evaluations, robotics, and human feedback workflows. Core capabilities include RL data generation with knowledge work rubrics, custom evaluations for private benchmarks and model comparisons, robotics data with full-stack video and trajectories, and an expert network of 1.5M+ knowledge workers including 50K+ PhDs. Trusted by 80% of leading AI labs for production data operations.

Embedding-first search and discovery engine for AI-powered product experiences.

Marqo is an open-source tensor search engine that combines embedding generation and vector search in a single API, removing the need to manage separate embedding pipelines and vector databases. Built for product discovery and multi-modal search, it lets teams index text, images, and structured data together, returning ranked results based on semantic similarity rather than keyword overlap.

AI-powered file-type detection at Google scale

Open-source AI-powered file-type detection tool from Google that uses a custom deep-learning model under a few megabytes to identify more than 200 binary and textual content types in milliseconds, even on a single CPU. Magika ships as a CLI, Python package, JavaScript/TypeScript library, and an ONNX model, achieves around 99% accuracy on its test set, and is already used at Google scale across Gmail, Drive, and Safe Browsing as well as by VirusTotal and abuse.ch.

Context engineering platform for AI agents with temporal knowledge graphs

Zep is a context engineering platform that assembles relationship-aware context for AI agents from conversations, business data, documents, and events. It maintains a temporal knowledge graph that automatically extracts entities and relationships, tracking how context evolves over time. Zep delivers formatted context blocks optimized for LLMs with sub-200ms latency, integrating with LangChain, LlamaIndex, AutoGen, and Google ADK through Python, TypeScript, and Go SDKs.

Agent memory system that learns, not just remembers

Hindsight is an agent memory system that enables AI agents to learn from experience rather than just store conversations. It organizes memories into three biomimetic categories: World knowledge for facts, Experiences for agent events, and Mental Models for learned understanding. The system provides retain, recall, and reflect operations backed by a temporal knowledge graph with parallel retrieval strategies including semantic, keyword, graph traversal, and temporal search.

AI Lakehouse with Feature Store for real-time ML

Hopsworks is a data-intensive AI platform combining a Python-centric Feature Store with MLOps capabilities for production ML systems. Provides sub-millisecond feature retrieval powered by RonDB, dual offline and online storage for batch and real-time inference, experiment tracking, model registry, and deployment pipelines. Available as managed cloud on AWS, Azure, and GCP, self-hosted on Kubernetes, or serverless platform.

On-device AI inference for React Native apps

Declarative framework for running AI models on-device in React Native applications, powered by Meta ExecuTorch runtime. Supports LLMs including Llama 3.2, computer vision, OCR, embeddings, and vision-language models on iOS 17+ and Android 13+. Developed by Software Mansion with pre-built optimized models, custom model export support, and privacy-first inference without any cloud dependency for mobile AI development.