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Turns text, images, voice, files, and live context into conversational help across web and mobile. Its edge is tight access to Google Search, Android, Workspace, and multimodal Gemini models; the tradeoff is ecosystem lock-in and uneven reliability.
Interleaves chain-of-thought reasoning with tool-using actions in one LLM loop: the model plans, queries a source like Wikipedia, then revises from results. Cuts hallucination versus reasoning-only prompting and beats trained agents on interactive tasks.
Manages provider configs for seven coding CLIs (Claude Code, Codex, Gemini CLI, OpenCode and more) from one desktop app, so switching API endpoints no longer means hand-editing JSON, TOML, or .env files. Adds tray quick-switch and cloud sync.
Lets developers call Gemini, Nano Banana, Veo, and other Google AI models from apps through SDKs or REST. It is fastest for teams that want hosted multimodal generation without running model infrastructure.
Builds and deploys machine learning models across research, production, web, mobile, and edge environments. Its ecosystem spans Keras, TFX, LiteRT, TensorFlow.js, datasets, model hubs, and visualization tools.
Deploys trained SavedModels behind gRPC and REST endpoints, with hot-swappable versioning so new weights load without downtime. Built around servables, loaders, sources, and a manager, plus request batching to cut accelerator cost.
Notebooks and sample apps demonstrating generative-AI workflows on Google Cloud's Vertex AI and Gemini — covering RAG grounding, multimodal demos, function calling, and agent-building examples, with deployment-ready templates for evaluation and production.
Unifies access to OpenAI, Anthropic, Google and other LLM providers behind one TypeScript API — swap models by changing a string. Adds streaming UI hooks for React, Next.js, Svelte and Vue, plus a tool-calling loop for agentic workflows.
Upload your own documents, PDFs, slides, or web pages and ask questions answered only from that material, with inline citations pointing to the exact passage used. Audio Overview turns your sources into a downloadable two-host podcast discussion.
Provides code and pretrained models for WeatherNext 2 and WeatherNext Cyclones — ML-based global, medium-range atmospheric forecasting models from Google DeepMind/Google Research. Includes Colab demos, pretrained weights, and guides for cloud access and inference.
Detects file content types with a compact deep‑learning model that runs in milliseconds on a single CPU. Trained on ~100M samples across 200+ content types; offered as a Rust CLI plus Python, JS, and Go bindings for large‑scale security and file‑routing use.
Builds stateful LLM agents whose memory persists across sessions: a tiered, self-editing memory system lets an agent rewrite its own context window so it remembers, learns, and improves over time. Model-agnostic, with Python/TypeScript SDKs.