# Codebase Analysis

> Understand codebases with semantic analysis and call graphs

- 网址：https://funcoding.ai/agents/goose/guides/codebase-analysis/
- 来源：goose 官方文档原文（英文），Apache-2.0 许可，同步于 2026-10-11
- 官方原文：https://goose-docs.ai/docs/guides/codebase-analysis

---
The Analyze platform extension provides an `analyze` tool that helps you understand code structure, track symbol usage, and explore call graphs across your codebase. It is enabled by default and supports file types for [multiple programming languages](https://github.com/aaif-goose/goose/blob/main/crates/goose/src/agents/platform_extensions/analyze/languages.rs).

<details>
<summary>Example analysis: Tracking a function across files</summary>

**Prompt:**

"Show me the call graph for the authenticate function"

**Output:**

<div class="callout callout-note">

**Desktop**

I'll analyze how the `authenticate` function is used across your codebase.

Based on my analysis, here's how the `authenticate` function is used in your codebase:

#### Function Definition
The `authenticate` function is defined in `auth.py` at line 15. It takes username and password parameters and handles user authentication.

#### Incoming Calls (Functions that call authenticate)
- **login_handler()** in `api.py:25` - The API endpoint that processes login requests
- **admin_login()** in `admin.py:102` - Special authentication flow for admin users
- **test_auth()** in `tests/test_auth.py:45` - Test case for authentication

#### Outgoing Calls (Functions that authenticate calls)
- **validate_credentials()** in `db.py:102` - Checks credentials against the database
- **log_attempt()** in `logger.py:45` - Records authentication attempts
- **check_rate_limit()** in `security.py:78` - Prevents brute force attacks

This shows that `authenticate` is a central function in your authentication flow, called by multiple entry points and depending on database validation, logging, and security checks.

</div>

</details>

## Analysis Modes

The `analyze` tool operates in three modes&mdash;Structure, Semantic, and Focus&mdash;depending on whether you’re analyzing directories, files, or symbols. Invoke it through natural language or direct commands with [parameters](#common-parameters).

### Understanding Project Organization

Get a structural overview of your codebase by analyzing a directory—understand project organization, identify large files, and view codebase metrics.

**Natural language:**
- "Can you analyze the structure of my src/ directory?"
- "Give me an overview of this project's code structure"
- "What's the main entry point of this Python project?"

**Direct commands:**
```bash
# Get overview with default depth (3 levels)
analyze path="src/"

# Get overview limited to 2 subdirectory levels
analyze path="." max_depth=2
```

### Inspecting a File

Get semantic details for a single file—see its functions, classes, and imports to understand structure and find specific implementations.

**Natural language:**
- "What functions are in main.py?"
- "Show me the structure of src/utils.py"

**Direct commands:**
```bash
# Get file details
analyze path="main.py"

# Analyze specific file
analyze path="src/utils.py"
```

### Tracking a Symbol Across Files

Focus on a specific function, class, or method to see where it’s defined and how it’s called across files—useful for refactoring and debugging.

**Natural language:**
- "Trace the dependencies for the authenticate function"
- "Show me the call graph for UserClass"

**Direct commands:**
```bash
# Track function usage
analyze path="src/" focus="authenticate"

# Track with deeper call chains
analyze path="." focus="UserClass" follow_depth=3
```

## Common Parameters

| Parameter | Default | Description |
|-----------|---------|-------------|
| `path` | None (required) | Absolute or relative path to the file or directory to analyze |
| `focus` | None | Name of the symbol to track. For cross-file tracking, `path` must be a directory. |
| `follow_depth` | 2 | How many steps to trace from the focused symbol (0=where defined, 1=immediate callers/callees, 2=their callers/callees, etc.). Used with the `focus` parameter. |
| `max_depth` | 3 | How many subdirectory levels to analyze when `path` is a directory (0=unlimited) |
| `force` | false | Receive full analysis results (otherwise, only a warning message is shown when the results exceed 50,000 characters) |

## Best Practices

### Handling Large Outputs

If the analysis results exceed 50,000 characters, the tool returns a warning message instead of the analysis. Options for managing large outputs:

- **Use `force=true`** to bypass the warning and see the full output (may consume significant conversation context)
- **Narrow your scope** by analyzing a specific subdirectory or file
- **Reduce depth** with `max_depth=1` or `max_depth=2` for directories
- **Delegate to a [subagent](https://funcoding.ai/agents/goose/guides/context-engineering/subagents/)** to analyze and summarize without filling your conversation history, for example: "Use a subagent to analyze the entire src/ directory and summarize the main components"

### Performance Tips

- Start with smaller scopes (specific files or subdirectories) before analyzing entire projects
- Use `max_depth=1` or `max_depth=2` to limit directory traversal depth
- Use `.gitignore` files to exclude unnecessary files from analysis, such as `node_modules/` and build artifacts