A Model Context Provider (MCP) server with accompanying Chrome extension that allows AI applications to control and automate your real browser using the MCP protocol.
Pulls up-to-date, version-specific docs and code examples straight from a library's source into your AI coding agent's prompt, killing hallucinated APIs from stale training data. Works as an MCP server or via a ctx7 CLI plus skill.
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.
Exposes a self-hosted WhatsApp HTTP/REST API that runs a real WhatsApp Web instance so apps and AI agents can read/send messages, manage contacts, and automate flows. Offers three engine modes (WEBJS, NOWEB, GOWS), Docker images, and MCP support; relies on WhatsApp Web so blocking risk exists.
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.
Visual canvas for composing, testing, and deploying LLM-based pipelines and multi-agent workflows. Supports major LLMs and vector databases, exports flows as APIs or MCP servers, and offers a desktop bundle for local experimentation and iteration.
Build and deploy enterprise-grade conversational agents with integrated RAG pipelines, workflow orchestration, multi-modal IO, and model-agnostic integrations (private and public LLMs). Designed for self-hosted production with vector stores and tooling integrations.
Connects AI coding clients to multiple model providers through MCP, adding multi-model review, planning, debugging, and CLI-to-CLI delegation while keeping the main agent in control.
An MCP server giving Claude and other AI assistants direct control of the local terminal and file system: run shell commands, manage long-running processes, and search and diff-edit files across the whole OS, not just one project folder.
Continuously records your screen and audio 24/7 to a local, searchable timeline you can query in natural language. Stores screenshots with accessibility data in SQLite, and a plugin system runs scheduled AI agents on what it captures.
Packs a Git repository into a single AI-friendly file for easy ingestion by LLMs. Offers per-file and total token counts, optional Tree-sitter compression, secret scanning, and multiple interfaces (CLI, web, browser extension, Docker, MCP) for AI-driven code review and analysis.