Unified TypeScript toolkit for building AI apps and agents: swap between 100+ models from OpenAI, Anthropic, Google and others by changing one line. Ships streaming, tool calling, and framework-agnostic UI hooks for React, Next.js, Vue, and Svelte.
Calls 100+ LLM providers — OpenAI, Anthropic, Gemini, Bedrock, Azure — through one OpenAI-compatible API, as a Python SDK or self-hosted proxy. The proxy adds virtual keys, spend tracking, rate limits, and load balancing across models and providers.
Open-source AI coding assistant for VS Code and JetBrains that bundles autocomplete, chat, inline edit, and an agent mode behind one config, letting each capability use any model provider rather than a single locked-in vendor.
Runnable Jupyter notebooks for building with the Claude API: tool use, RAG, vision, prompt caching, sub-agents, classification, summarization, and integrations like Pinecone and Voyage embeddings. Copy-paste recipes that drop into real projects.
Provides a memory-first library and managed service that stores, reasons about, and serves long-term state for agents and users — offering continual representations, session context, vector search, and a chat-style API for personalized behavior.
Converts videos between languages by transcribing audio, translating subtitles, and producing AI dubbing—supports local and online ASR/LLM/TTS providers, speaker diarization, voice cloning, and GUI/CLI workflows for batch or headless 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.
Turns any website into structured data or an API without code: record clicks once to capture lists and tables, or describe fields in plain language for AI extraction. Also crawls full sites, scrapes pages to Markdown, and runs filtered searches.
Bundles AI features and coding agents into JetBrains IDEs, using IDE code intelligence for completion, refactoring, and chat. Runs on the proprietary Mellum model or your choice of OpenAI, Gemini, Anthropic, and local models via Ollama or LM Studio.
Builds custom AI inference servers in pure Python on top of FastAPI, keeping full control over request logic while batching, GPU autoscaling, streaming, and OpenAI-spec endpoints come built in. Claims a 2x+ throughput edge over plain FastAPI.
Orchestrates teams of role-based autonomous agents that collaborate on multi-step tasks, plus event-driven Flows for deterministic control. Built from scratch with no LangChain dependency; runs 450M+ agentic workflows monthly.
Lets AI agents place and answer business phone calls, holding spoken conversations to collect structured data, answer questions, and escalate to humans. Built on Azure Communication Services and Azure OpenAI, with RAG over your own documents.