# Understand a Large Codebase with Agents {% embed url="" %} ### 1. The Challenge Kevin, who worked on Warp’s Windows and Linux builds, wanted to jump into a feature he hadn’t touched before: Block Sharing.\ \ This feature spans two codebases — Warp’s client (Rust) and server (Go) — making onboarding tough. That’s where Codebase Context comes in. *** ### 2. What Is Codebase Context? Warp’s Codebase Context uses semantic search to understand your code.\ It doesn’t rely on exact function or variable names — instead, it searches based on meaning. You can use it through a shared workflow in Warp Drive. This prompt tells Warp to: * Search across both client and server codebases * Summarize how a feature works end-to-end * Include clickable links to relevant files *** ### 3. Real Example: Block Sharing Kevin types `block sharing` into Warp’s shared workflow.\ Warp: 1. Searches the client codebase for the rendering logic 2. Searches the server codebase for GraphQL handlers 3. Generates a summary combining both perspectives The output includes: * Architecture overview * Linked file paths * Function and module summaries No more manual onboarding or guessing file names. *** ### 4. Incremental Syncing Whenever you change a file in an indexed repo: * Warp detects the update automatically * Re-embeds just that file * Keeps your code context fresh That means agents never reference stale code. *** ### 5. Why It’s Game-Changing Codebase Context helps teams: * Understand large or unfamiliar codebases * Onboard faster * Jump between client and server logic seamlessly * Generate accurate, clickable documentation > “This saved us hours of one-on-one walkthroughs.” — Lucy --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.warp.dev/guides/agent-workflows/understanding-your-codebase.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.