Setting Up Claude Code for a Large Repo

Setting up Claude Code for a large repo requires more than just running a command in your terminal. When a codebase grows to hundreds of thousands of lines across multiple microservices or a massive monorepo, the way an AI agent indexes and interacts with your files changes the quality of its output. Claude Code, as a command line interface agent, excels at reasoning through logic, but it needs a structured environment to prevent context exhaustion and hallucination.

Understanding the Scope of Large Repositories

Large repositories present unique challenges for AI coding agents. Unlike a small utility script where the entire file tree fits into a single prompt, a large repo contains deep dependency chains and complex architectural patterns. If you point Claude Code at a massive directory without preparation, the agent might struggle to find the relevant entry points or waste tokens scanning irrelevant build artifacts.

Effective management involves defining what the agent should see and how it should navigate. Developers often face a trade-off between giving the agent enough context to understand the system and keeping the context window small enough to maintain speed and accuracy.

Initial Configuration and Environment Preparation

The first step in a successful Claude Code setup for a large repo is environment hygiene. You must ensure that the agent does not get bogged down by non-source files. This is usually handled via ignore files, but in large systems, you might need more granular control.

Standard practices for large repo initialization include:

• Creating a specific configuration file to exclude large assets, logs, and temporary build folders.
• Breaking down the workspace into logical sub-directories if you are working on a specific feature.
• Ensuring your local environment has the necessary permissions to allow the agent to run test suites or linting commands.

While Claude Code operates well in a standard terminal, many developers find that terminal-only workflows become cluttered when managing multiple services. This is where a dedicated workspace helps. Deska provides a way to run coding agents like Claude Code inside a visual canvas. By placing your agent terminal next to a live code editor, you can verify the changes the agent suggests in real-time without switching windows constantly.

Optimizing Context for Complex Architectures

Large repositories often suffer from "context drift," where the agent loses track of the primary objective because it has ingested too many unrelated files. To combat this, you should adopt a modular approach to your session.

Strategic Indexing

Instead of asking the agent to "fix the whole app," focus its attention on specific modules. You can use specialized commands to list files or search for patterns before initiating a refactor. This helps the agent build a mental map of only the relevant parts of the large repo.

Using side by side Agents

In complex scenarios, one agent might not be enough. You might want one instance of Claude Code focused on backend logic and another focused on the frontend components. Deska allows you to run coding agents side by side as separate panels. This multi-panel approach prevents the context of one task from polluting the other. You can watch two different instances of Claude Code work on separate parts of a monorepo simultaneously, all within the same infinite canvas.

Comparing Execution Environments

Choosing where to run your AI agent affects your privacy and your workflow speed. Claude Code can be run in various wrappers or directly in the OS terminal.

Integrating Deska into the Workflow

For developers managing large repos, Deska acts as a centralized command center. It is a free desktop app for Mac, Windows, and Linux that uses an infinite canvas. Instead of a rigid grid, you can place terminals, a Monaco code editor, and even browser widgets anywhere on the workspace.

When setting up Claude Code for a large repo in Deska, you can follow these steps:

1. Open a new workspace and add a terminal panel to run your Claude Code commands.
2. Add a code editor panel to view the specific files the agent is modifying.
3. Use the Ask Deska assistant to manage the layout. You can use voice or chat to say "open the auth service terminal" or "zoom out to see all active panels."
4. If you need to step away from your desk, use the mobile app to monitor the progress of a long-running refactor. The mobile app pairs directly with your desktop via a secure relay without exposing ports.

This setup ensures that your code, files, and sessions stay on your machine, adhering to a local-first philosophy. You can use your own API keys for the lifetime tier or managed inference if you prefer a subscription.

Advanced Tips for Large Scale Refactoring

When performing large scale refactoring with Claude Code, follow these guidelines to ensure stability:

• Run tests frequently: Use the agent to execute existing test suites after every major change.
• Use incremental commits: Ask the agent to commit changes in small, logical chunks rather than one giant diff.
• Documentation check: Have the agent read the README.md or architectural docs first to understand the naming conventions and design patterns of the large repo.
• Monitor sessions: Keep an eye on agent threads to ensure the conversation hasn't veered off track.

FAQ

How to limit Claude Code context in a monorepo?

The most effective way is to start the agent within a specific subdirectory rather than the root of the repository. Additionally, you can use a custom .claudecodeignore file to exclude large directories that are not relevant to your current task. This keeps the initial indexing phase fast and prevents the agent from suggesting changes to unrelated services.

Can I run Claude Code alongside other AI agents?

Yes, you can run multiple agents side by side. Tools like Deska allow you to open panels for Claude Code, Codex CLI, and OpenCode simultaneously. This is particularly useful in large repositories where you might want to compare how different models handle a complex bug or use one agent for documentation and another for heavy lifting in the codebase.

Is my code safe when using AI agents on a local machine?

When using a local-first tool, your source code and session data remain on your hardware. While the AI agent sends code snippets to the provider for inference, the workspace itself does not store your code in the cloud. Using your own API keys adds an extra layer of control over your data usage and privacy settings.

Getting Started with a Better Workspace

Setting up Claude Code for a large repo is a significant step toward automating your development lifecycle. By combining the reasoning power of Claude with a flexible, visual workspace, you can manage complexity without feeling overwhelmed. To experience a more organized way of working with AI coding agents, download Deska for your platform and start building your custom developer canvas.