# Using CLI with AI Agents

Let AI coding agents use DeepSource CLI to read analysis data and act on it.

AI coding agents like Claude Code, Cursor, and similar tools can use DeepSource CLI to fetch code health data and act on it directly.

## Setup

### Install CLI

```sh
curl -fsSL https://cli.deepsource.com/install | sh
```

### Authenticate

```sh
deepsource auth login
```

Once authenticated, any AI agent running in your terminal has access to the same CLI commands you do.

## Workflow examples

### Fetch and fix issues

Ask your agent to pull critical issues from the current branch and fix them:

```sh
deepsource issues --severity critical --output json
```

The agent gets back structured data with file paths, line numbers, issue descriptions, and suggested fixes. That's usually enough context to make targeted code changes.

### Check metrics before and after changes

Have the agent check metrics for the current branch to understand the impact of its changes:

```sh
deepsource metrics --output json
```

Returns code health metrics like code coverage, documentation coverage, and overall code health scores for the current branch.

### Review analysis runs

```sh
deepsource runs --output json
```

Returns the status of recent analysis runs, including any new issues introduced or resolved.

### Check vulnerabilities

```sh
deepsource vulnerabilities --output json
```

Returns a list of known vulnerabilities in your dependencies along with severity levels and affected packages.

## Tips

- Always use `--output json`. AI agents work better with structured data than formatted tables.
- Use `--repo gh/org/repo` explicitly if the agent's working directory isn't inside the repository.
- Use `--commit <sha>` to get results for a specific point in time rather than the latest analysis.
- Use flags like `--severity`, `--analyzer`, and `--category` to focus the agent on what matters.