Kody serves as a centralized, persistent cloud environment for your AI agents, designed to solve the problem of fragmented agent sessions. Developed as a shared home for packages, secrets, memory, and jobs, it enables users to build functionality in one agent and reuse it across multiple platforms including Claude, Cursor, ChatGPT, and others. By providing a durable execution layer, Kody ensures that the work done by your agents persists beyond individual chat sessions, allowing you to build software that continues to run even when your computer is offline.

Kody provides a structured infrastructure where AI agents can operate as architects of your personal software factory. It separates the thinking process, handled by the agents, from the execution layer, managed by Kody. This allows for a more efficient workflow where expensive inference is avoided for repeated tasks by converting them into durable, triggerable packages. Kody utilizes a set of six primary primitives—memory, secrets, packages, triggers, integrations, and apps—to provide a secure and reusable environment for all your AI-driven tasks.

Some of the key features are:

• Memory: Provides a persistent context layer that carries over between different agents and sessions.
• Secrets: Allows agents to utilize API keys and OAuth credentials through secure references, ensuring models never read the actual values.
• Packages: Enables the conversion of one-off agent tasks into reusable, versioned code that can be inspected, shared, and triggered.
• Triggers: Automates workflows by executing packages based on schedules, webhooks, incoming emails, or other events without requiring active agent input.
• Integrations: Simplifies service connections by allowing users to manage OAuth and API authorizations in one place and reuse them across various packages.
• Apps: Provides a hosted interface layer for packages, allowing users to interact with their automated tools through browser-based surfaces.

Operationally, Kody functions by connecting to your chosen AI agent hosts via MCP (Model Context Protocol). You start by connecting an agent, which can then interact with the Kody primitives to read memory, utilize secrets for API calls, and publish code as packages. Once a package is established, it can be triggered independently through webhooks or schedules, offloading work from the LLM and reducing token consumption. The platform is built with a fair-source philosophy, ensuring code transparency while providing a stable, managed environment for durable automation.

Some common use cases include:

• Personal Productivity: Automatically generating morning briefing digests that aggregate calendar events, weather updates, and feed notifications via email.
• Infrastructure Automation: Creating watchers for system health, such as polling services or feeds and sending alerts only when specific conditions are met.
• Workflow Triage: Handling GitHub notifications, managing PRs, or triaging issues by leveraging saved connections and agent-built logic.
• API Management: Developing a personal API surface that can be invoked via webhooks to log daily habits, save links, or execute custom logic.
• Cross-Agent Portability: Developing a complex utility in Cursor and immediately executing the same logic within a mobile chat client or another IDE without rebuilding the setup.