Understanding Codebases with Codex

You inherited a service with 200K lines of code, no architecture docs, and the last person who understood the payment flow left six months ago. Your first ticket is “fix the webhook retry logic,” but you cannot even find where webhooks are processed. You could spend two days reading code. Or you could ask Codex to map the territory for you in twenty minutes.

What You’ll Walk Away With

• A multi-surface approach to codebase exploration — App for deep dives, CLI for quick queries, IDE for file-level context
• Prompts that extract architecture, data flows, and module boundaries from any codebase
• A workflow for producing onboarding documentation that stays current with scheduled automations
• Techniques for tracing specific request flows through unfamiliar code

The Workflow

Choose Your Surface

Codex gives you multiple entry points for codebase exploration, each with different strengths:

Codex App

Best for sustained exploration where you want to follow threads of inquiry. The App maintains full conversation context, syncs with your IDE, and lets you leave inline comments on code you want to revisit.

Open your project in the Codex App, choose Local mode (you are only reading, not modifying), and start asking questions.

Codex CLI

Best for quick, targeted queries when you already know roughly where to look. The CLI reads your working directory and lets you use @ to reference specific files.

codex

Use @ in the composer to fuzzy-search and attach files to your prompt.

IDE Extension

Best for file-level exploration when you are already browsing code. The IDE extension automatically includes your open files as context, so you can select code and ask “what does this do?” without specifying paths.

Step 1: Get the Big Picture

Start with the highest-level question. Do not specify files — let Codex scan the repository structure and figure out what matters.

Tip

Copy-paste prompt for architecture overview:

Explain the architecture of this codebase. Include:

1. What this project does (in one paragraph)
2. The main modules/packages and their responsibilities
3. How requests flow from entry point to response
4. The database schema (tables and key relationships)
5. External services this project depends on
6. One or two architectural decisions that are unusual or surprising

Format as a brief document I could hand to a new team member.

In the Codex App, this prompt in Local mode lets Codex read the entire project tree, scan key files (package.json, entry points, config files, schema definitions), and synthesize an overview. The result is far more useful than grepping through code yourself because Codex connects the dots across files.

Step 2: Trace a Specific Flow

Once you understand the high-level architecture, zoom into the specific area you need to work in. The most effective technique is to describe the behavior you care about and ask Codex to trace it end-to-end.

Tip

Copy-paste prompt for request flow tracing:

Trace the complete flow when a Stripe webhook hits this service:

1. Which file/route handles the incoming webhook POST?
2. How is the webhook signature verified?
3. What happens for each event type (payment_succeeded, payment_failed, subscription_updated)?
4. Which database tables are written to?
5. Are there any retry or idempotency mechanisms?
6. What error handling exists, and what happens on failure?

Include the exact file paths and function names at each step.

In the CLI, you can make this even more targeted by attaching the files you suspect are involved:

I need to understand the webhook retry logic. Read @src/routes/webhooks.ts @src/services/stripe.ts and trace what happens when a webhook delivery fails. Focus on retry behavior and idempotency.

Step 3: Map Module Boundaries

For large monorepos, understanding where one module ends and another begins is critical before making changes. Use the IDE extension for this — open a few files from the area you are investigating, then ask Codex with the auto-context enabled.

Tip

Copy-paste prompt for dependency mapping:

Map the dependency relationships between the modules in this project:

1. Which modules import from which other modules?
2. Are there any circular dependencies?
3. Which modules are "leaf" nodes (depended on by many, depend on few)?
4. Which module boundaries are clean and which are leaky?

Present this as a list of modules with their inbound and outbound dependencies.

Step 4: Generate Onboarding Documentation

Once you understand the codebase, turn that understanding into documentation that helps the next person. Better yet, set up an automation so it stays current.

In the Codex App, create a worktree thread so the generated docs do not touch your working directory until you review them:

Based on your analysis of this codebase, create a docs/ARCHITECTURE.md file that covers:

1. System overview with a text-based component diagram
2. Key data flows (user registration, payment processing, webhook handling)
3. Database schema overview with table relationships
4. Environment variables and configuration
5. Common development tasks (adding a new API endpoint, adding a migration)

Write it for a mid-level developer joining the team. Keep it under 500 lines.

Then set up a weekly automation to keep it fresh:

Review the last week of commits that touch src/. If any architectural changes were made (new modules, changed data flows, new database tables), update docs/ARCHITECTURE.md to reflect them. If nothing architectural changed, report that no updates are needed.

Using Cloud for Deep Analysis

For very large repositories where local analysis hits context limits, delegate to a cloud task. Cloud environments can run longer, have access to the full repository, and support best-of-N attempts for complex analysis.

In the Codex App, switch to Cloud mode and submit your analysis prompt. The cloud agent can run build steps, execute queries against test databases, and take more time to explore the codebase thoroughly.

From the CLI, you can also kick off a cloud analysis:

codex cloud exec --env my-env "Analyze the authentication subsystem in src/auth/. Map every entry point, middleware chain, and session management flow. Report the findings as a structured document."

When This Breaks

Codex misidentifies the main entry point. In monorepos with multiple services, Codex sometimes latches onto the wrong package.json or entry file. Be explicit: “The service I care about is in packages/billing-api/. Ignore all other packages.”

Analysis is too shallow on large codebases. If Codex gives surface-level answers, it likely did not read deep enough. Narrow your scope: instead of “explain the architecture,” ask “explain how the order fulfillment pipeline works, starting from src/services/orders/index.ts.”

Generated documentation is stale by review time. If you generate docs in a worktree but do not merge for a week, the codebase may have changed. Use Sync with local to pull any local changes into the worktree before finalizing.

Cloud task picks the wrong branch. Cloud tasks run against the default branch in your environment’s repo map. If you need analysis of a feature branch, specify the branch in your prompt or update the environment configuration.

What’s Next

Building Features with Parallel Agents Now that you understand the codebase, build features across multiple worktrees simultaneously

AI-Powered Code Review Use your architectural understanding to set up effective review guidelines in AGENTS.md