How Gemini CLI Builds Context and Learns About Your Codebase

July 2, 2025 • Wietse Venema

The Gemini CLI is a great tool for coding tasks, but how does it "learn" about your codebase? I explored its source code to find out how. It's not magic; I'll break down the process for you.

The Initial Context Snapshot

When you start Gemini CLI, it gathers a high-level "snapshot" of the local working directory to provide the starting context. This initial context includes:

  • Basic Environment: The current operating system, the current working directory, and today's date.
  • Directory Structure: It generates a file and folder tree. This gives the model an immediate sense of the project's layout, key files, and overall structure. This does not include the file contents, though.
  • (Optional) Full File Context: If you start the CLI with the --all_files flag, it goes a step further. It reads the entire content of every text-based file in the project (respecting .gitignore and other excludes).

This entire snapshot is sent to the model as the very first message in the chat, but as a user of the Gemini CLI, you won't see it. If you want to explore the entire unfiltered conversation history, use the /chat save command and find the JSON file it stores in .gemini/tmp/[SESSION_HASH]/checkpoint.json

On-Demand Learning with Tools

After the initial snapshot, Gemini CLI relies on its tools to investigate the codebase as needed to answer questions or perform tasks. This is the primary way it "learns" during a conversation. It's analogous to a developer opening files and searching for code as they work.

This searching for relevant files means it might miss important files. I like to start my chat with a few probing questions to make sure it has read the right files, before getting started with the actual task I want to complete.

Another way to influence how the Gemini CLI finds files and navigates your codebase is through the instructional context you can add to GEMINI.md. Refer to Context Files in the docs to learn more.

The key tools Gemini CLI uses to explore the code include:

  • ReadFile and ReadManyFiles: To read the specific contents of one or more files. This is how it understands the actual code, comments, and logic.
  • ReadFolder: To list the names of all files and subdirectories directly in a directory.
  • SearchText: To search for specific strings or regular expressions within files.
  • Shell: To execute shell commands, which can be used to gather information about the codebase, such as the output of git status or npm list.

You can use the command /tools to list all tools available.

The --all_files Flag

The --all_files flag (or its alias -a) is a great feature that allows you to provide the entire codebase as context to the model at the beginning of the session.

This actually works pretty well. Before using Gemini CLI, I have been doing something similar when exploring unfamiliar codebases. I wrote a script to concatenate all text files in a repository and chunk them into 1 Mb files to get around the per-file upload size limit in AI Studio. I would then start a new chat starting with these files, and ask questions about the codebase.

To use it, add this flag when starting the Gemini CLI:

gemini --all_files

Note that there is a 30-second timeout for reading all the files. A large project might hit this limit, causing the CLI to fail silently to add the full context.

While a large context window is a powerful feature, it's not a perfect solution. First, tokens have an associated cost, so putting more tokens in the context of your prompts is more expensive. Second, as the amount of input grows, models can suffer from context degradation, where they struggle to distinguish relevant information from noise if there's too much information in the context. For large codebases, using the --all_files flag might not work as well, and the default way where Gemini CLI explores the codebase using its tools is likely better.

Wrap up

You now know how Gemini CLI learns about your codebase. On startup, it creates a high-level snapshot of your project's directory structure, but does not read the file contents. As you chat with it, it actively uses tools to read files, search for code, and run shell commands to learn about the codebase.

Optionally, you can tell it to ingest all files using the --all_files flag, though this may be less effective for very large projects.

Understanding how this works is key to getting good response quality.