Xirp is an agentic development environment designed to provide AI coding tools with organizational context, including service ownership, dependencies, and architectural history, to improve the accuracy of AI-generated code.
What the product does and how it is positioned
Xirp functions as a context layer for AI coding agents, connecting them to internal organizational knowledge.
The platform captures knowledge during coding sessions and stores this information within a shared workspace, so that both human engineers and AI agents have access to consistent project context.
Source-supported ways to use the product
Maintains work items, sessions, and documentation in a shared workspace so that context is preserved for subsequent engineers or agents working on the same system.
Grounds AI agents in the reality of organizational infrastructure by providing visibility into service ownership, dependencies, and architectural decisions.
Xirp is designed for contexts involving AI agents lacking awareness of the specific systems they are modifying. By connecting to internal services and architectural decisions, the platform grounds AI agents in the reality of the organization's infrastructure.
Checks to run with your own material and workflow
What was checked and when
Answers based on the source-checked product record
Xirp is designed to work with Claude, Gemini, and Codex, allowing teams to switch between these models while maintaining a consistent context layer.
Xirp captures knowledge generated during coding sessions and stores it to ensure that information remains available for future sessions.
Xirp integrates with Portal to access services, ownership, and architectural data, and it supports running coding sessions either locally or remotely.
Context is managed through a Portal plugin called Workspace, which stores work items, sessions, and documentation so they are available to any engineer or agent working on the system.
Xirp is designed to be vendor-independent regarding the underlying AI model, allowing teams to switch between different models while keeping their organizational context layer intact.
