Build LLM-powered agents and applications from modular components: provider-agnostic model abstractions, tool integrations, retrievers for RAG, and agent orchestration primitives. Suited for prototyping and production agent workflows; requires developer wiring and dependency management.
Build full‑stack web apps entirely in Python — write frontend components and backend state as Python classes with a reactive model. Provides fast refresh, deployment tooling, and AI-focused integrations such as an AI Builder and an Agent Toolkit for connecting LLMs and image models.
Connects LLMs to private and domain-specific data with ingestion, indexing, and retrieval primitives for RAG and agentic apps. Centers on document parsing via LlamaParse for 90+ file formats, schema-based extraction, and composable queries.
GPU-accelerated robot-learning framework on NVIDIA Isaac Sim, running thousands of parallel environments on one GPU for reinforcement and imitation learning. Ships 30+ ready-to-train tasks and 16+ robot models wired to RSL RL, SKRL, and RL Games.
Provides reusable computer-vision utilities for dataset loading/conversion, visualization/annotation of detections and segmentation, and connectors to popular detection frameworks—aimed at quick prototyping, dataset work, and visualization.
Connect LLMs to major chat platforms so teams can build, deploy, and operate multi-platform AI chatbots and agents. Provides multi-platform adapters, a plugin marketplace, an MCP server and built-in RAG plus production features like access control, rate limiting and monitoring.
Reproduces GPT-2 (124M) from scratch on OpenWebText in ~4 days on an 8xA100 node, with the whole stack kept to two ~300-line files: train.py for the loop and model.py for the architecture. A char-level Shakespeare run finishes in ~3 minutes on one GPU.
Lets you write compositional Python programs that compile into self‑improving LLM pipelines — replacing brittle prompt engineering with a declarative, programmatic approach and built‑in algorithms to optimize prompts and weights for RAG, multi‑stage pipelines, and agent loops.
Create and run node-based generative AI workflows for images, video, 3D, and audio — reusable, shareable node graphs with custom nodes, live previews, and local/cloud runtime options. Open-source with Comfy Cloud and Hub for creators.
Aggregates alerts from dozens of monitoring tools into a single pane of glass, then deduplicates, correlates, and enriches them. Automates incident response with declarative YAML workflows — like GitHub Actions for your monitoring stack.
Runs an agentic RAG loop over scientific papers: searches literature, gathers and re-ranks evidence chunks, then answers with in-text citations. Adds metadata-aware embeddings, retraction checks, and contradiction detection across full PDFs.