Provides a modular full-stack reinforcement learning stack to train and evaluate long-horizon, multi-turn tool-use LLM agents, including a performant trainer, a Tinker-compatible backend, agent orchestration, and Gymnasium-style environments for task design.
Runs Cloudflare Workers and Durable Objects on self-hosted nodes, storing each object as an independently replicated SQLite database in an S3-compatible bucket—enabling per-object sharding, hibernation, and ownership via object-storage compare-and-swap without a central control plane.
A PyTorch DTensor-native SPMD library for training and fine-tuning LLMs, VLMs, diffusion and retrieval models. Integrates with Hugging Face for day-0 model support, provides YAML-driven recipes, DTensor/FSDP2 parallelism and NVIDIA-optimized kernels (Transformer Engine, DeepEP, FlexAttn).
Practical, full-stack tutorial for building Retrieval-Augmented Generation (RAG) systems—covers data preprocessing, vector embedding and indexing, hybrid and multimodal retrieval, generation integration, evaluation and production-ready engineering. Includes hands-on projects and examples for developers with Python experience.
Evaluates and optimizes AI agents and language models in containerized environments, supporting large-scale parallel benchmarks and RL rollouts. Integrates with third‑party providers for thousands of parallel environments and serves as the official harness for Terminal‑Bench.
Coordinates multiple AI coding agents and persists work state in git-backed hooks; provides convoy-based work tracking, an AI coordinator (Mayor), agent lifecycle/watchdog tooling, and a merge/refinery workflow for reliable multi-agent code work.
Runs background coding agents in isolated sandboxes to autonomously handle development tasks, create pull requests, and integrate with Slack, GitHub, Linear and webhooks. Supports multiplayer sessions, multiple LLM providers, fast startup via snapshots and prebuilt images; designed for single-tenant deployments.
Provides a catalog of NVIDIA-verified, portable “skills” — instruction sets that teach AI agents how to use NVIDIA libraries, models and platform tools. Each skill is published with detached signatures and evaluation artifacts for verifiable reuse in agent workflows.
Orchestrates teams of AI agents to pursue company goals: hire agents, assign tasks, enforce budgets, and audit work from a single dashboard. Combines org charts, persistent agent state, heartbeat scheduling, approval gates and cost controls for long-running autonomous workflows.
Deploy and manage applications and containers to your own servers or Openship Cloud from a single desktop, web, or CLI interface. Built-in CI/CD with push-to-deploy and preview environments, automatic SSL, managed databases, CDN, backups, and multi-node portability for VPS-to-production workflows.
Turns heterogeneous traces (chats, docs, emails, transcripts) into versioned, inspectable agent 'Skills' that capture both Persona and Work behaviors; supports multi-source collection, incremental merges and corrections, and installation across multiple agent hosts.