How to understand an agentic AI codebase quickly

Building an agentic AI application often starts with reading someone else's. Here is a practical way to find what matters.

Start with the agent loop

Every agentic application has a loop: it gives a model a goal and some context, lets the model choose an action or a tool, runs it, and feeds the result back. Find where that loop lives (often a graph, a state machine or a while loop around a model call) and you have the spine of the system.

Then the tools

Tools are the functions the model may call: search, code execution, databases, other APIs. Read how they are declared, what inputs they accept, and what checks run before they execute. This is where most security risk lives.

Then memory and context

Look for how conversation history, documents and intermediate results are stored and retrieved: a vector store, a database, files, or a checkpointer. How context is trimmed decides cost and quality.

Then prompts and guardrails

Prompts are often spread across files. Collect them, then find the guardrails: input validation, output checks, rate limits, spending caps and human approval steps.

Then evaluation

Mature agentic systems measure themselves: test sets, scoring, regression gates. If you find none, that is the first thing to add.

Let RepoLantern do the first pass

RepoLantern reads the whole repository and writes a guide with the architecture, data flow, modules and a glossary, citing the exact lines of code, so you can verify every claim and focus your reading where it matters. See how it works and how every guide is checked, or try it.