Builds a knowledge graph from a text corpus by extracting entities and relations, clusters it into communities with the Leiden algorithm, and summarizes them — so queries can synthesize across scattered documents instead of retrieving isolated chunks.
Splits autonomous R&D into two cooperating agents: one proposes hypotheses, the other writes and tests code — iterating on quant-finance factors, Kaggle pipelines, and model research. Hits a ~30% medal rate on MLE-Bench, nearly double AIDE's.
Combines drag-and-drop field binding with natural-language prompts so an AI agent derives the transformations behind charts your raw tables can't produce. Reads from databases, files, images, and websites; 30+ chart types and branchable threads.
Turns a UI screenshot into structured elements so a vision LLM can act without HTML or accessibility trees. A fine-tuned detector finds interactable icons; a caption model describes their function, lifting GPT-4V grounding on ScreenSpot and Mind2Web.
Official inference framework for 1-bit and ternary (1.58-bit) LLMs such as BitNet b1.58, with optimized CPU kernels. Delivers 1.37x-6.17x speedups and 55-82% lower energy on x86 and ARM, and runs a 100B model on a single CPU at 5-7 tokens/sec.
Runs AI models on user devices with native SDKs, optimized model management, hardware acceleration, and OpenAI-compatible APIs for apps that need offline, private inference.
Converts PDF, Office docs, EPUB, images, audio, HTML and ZIP archives into structured Markdown for LLM pipelines, preserving headings, tables and links instead of visual layout. Adds optional OCR, audio transcription and LLM image captions.
Generates high-quality, editable 3D assets from text or images and decodes to radiance fields, 3D Gaussians, or textured meshes. Ships pretrained models up to 2B parameters, a 500K asset dataset and training code; best used with image conditioning and a ≥16GB NVIDIA GPU.
Gives an LLM a browser via Playwright's accessibility tree instead of screenshots, so the model reads structured snapshots, not pixels. Actions target named elements deterministically, cutting token use and removing any need for a vision model.
Builds production-grade AI agents and multi-agent workflows in .NET and Python, with graph-based orchestration for sequential, concurrent, and handoff patterns. Unifies Microsoft's Semantic Kernel and AutoGen lineages, adding durable, checkpointed runs.
Curated marketplace of community-contributed GitHub Copilot customizations — reusable agents, file-scoped instructions, skills, plugins, hooks, and agentic workflows — installable via the Copilot CLI and searchable on a companion site.
Trains and optimizes AI agents with reinforcement learning using almost zero code change. Works with any agent framework (LangChain, OpenAI Agents SDK, AutoGen, CrewAI) or none, and can selectively optimize a single agent inside a multi-agent system.