Unifies visual creator tools, reusable Skills, and a Codex-powered Agent to handle video translation, image/video generation, voiceovers, and editing in one local multimodal workspace. Desktop and web clients share a synchronized state machine and keep data local by default.
Native desktop client unifying many model providers (OpenAI, Gemini, Anthropic, Ollama, local LLMs) in one app on Windows, macOS, and Linux. Adds 300+ preset assistants, document/PDF chat, MCP server integration, and WebDAV backup, with no subscription.
Captures, transcribes, and summarizes meetings entirely on the user's machine with real-time local transcription and speaker diarization. Privacy-first design keeps audio, transcripts, and models local; supports Ollama, Claude, Groq, OpenRouter or custom OpenAI-compatible endpoints.
Simulates a trading firm using LLM agents in specialized roles — fundamentals, sentiment, news and technical analysts feed bull/bear researcher debates, then a trader and risk team decide. Works across US, global and crypto markets and 10+ LLM providers.
Lets AI agents discover and run Apify Actors as MCP tools for web scraping and automation; supports OAuth/Streamable HTTP, dynamic tool discovery, output-schema inference, and agentic payments (AGI, x402, Skyfire) so agents like Claude, ChatGPT, or VS Code can call ready-made scrapers and automations.
Lets teams build, deploy, and manage AI agents from chat, visual workflows, code, knowledge bases, tables, and more than a thousand integrations.
MCP-native agent framework built around the Model Context Protocol from the start, with end-to-end tested Sampling and Elicitation. Define agents and multi-step workflows in Python, run terminal-first, and swap Anthropic, Google or local models.
Drives your computer from natural language: a vision-language model reads raw screenshots and works the mouse and keyboard like a person, controlling any GUI app without APIs or accessibility hooks. Local or remote operator modes on Windows and macOS.
Generates multi-chapter long-form novels with LLMs, automatically linking context and managing foreshadowing for global coherence. Features vector-based retrieval, character/state tracking and a GUI-driven pipeline; requires LLM/embedding API keys.
Hands-on studio to design, test and deploy declaratively configured multi-agent systems built on the Neuro SAN framework. Ships ready examples, an Agent Network Designer UI (nsflow), CLI tooling, and integrations with major LLMs and external tools for rapid prototyping.
Spins up sandboxed VMs and containers (macOS, Linux, Windows, Android) that an AI agent can fully control through one unified SDK, cloud or local, plus a benchmark suite and background drivers that automate native apps without grabbing the cursor.