The primary feature of the Local SDK is the ability to modify code using AI. This is done through the code() method.

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The code() Method

def code(
    prompt: str,
    editable_files: List[str],
    readonly_files: Optional[List[str]] = None
) -> Dict[str, Any]

​
Parameters

  • prompt (str)
    • Natural language instruction for the AI
    • Be specific about what changes you want to make
  • editable_files (List[str])
    • Paths to files the AI may modify
    • Can be relative (to working_dir) or absolute paths
  • readonly_files (List[str], optional)
    • Paths to files the AI may read but not change
    • Useful for providing context to the AI

​
Return Value

The code() method returns a dictionary containing:
  • success (bool)
    • Whether meaningful changes were made
    • True if changes were applied, False otherwise
  • diff (str)
    • Git diff if use_git=True, otherwise a description of changes
  • result (object)
    • The raw result object from the underlying coder
  • cost (Dict[str, float])
    • Contains cost information including:
      • message_cost: cost of this specific operation
      • session_cost: cumulative cost of the session
      • tokens: token usage information
  • marked_up_cost (Dict[str, float])
    • Cost with a 20% markup
  • credits_remaining (float)
    • Your remaining cloudcode credits (if authenticated)

​
Quick Start Example

Run the real Quick Start script included in the examples: 
python cloud-coding/examples/quick-start.py
This script will:
  • Create src/calculator.py
  • Add multiply and divide functions with zero-division handling
  • Print the diff and updated file content
from cloudcode import Local
import os

sdk = Local(working_dir=os.getcwd(), api_key=os.getenv("CLOUD_CODE_API_KEY"))

result = sdk.code(
    prompt="Add multiply and divide functions to the calculator with zero-division handling.",
    editable_files=["src/calculator.py"],
    readonly_files=[]
)

if result["success"]:
    print("Diff:", result["diff"])
    print("Updated content:", sdk.read_file("src/calculator.py"))

​
Multi-File Example

Run the multi-file example script: 
python cloud-coding/examples/example_usage.py
This script demonstrates:
  • Creating and reading files
  • Searching within files
  • Improving the calculator with AI
# excerpt from example_usage.py
from cloudcode import Local
import os

sdk = Local(working_dir="/Users/seansullivan/aider-sdk-testing/", api_key=os.getenv("CLOUD_CODE_API_KEY"))

# Search for a specific string in files
search_results = sdk.search_files("def add", ["src/*.py"])
print("Search results:", search_results)

# Add multiply and divide functions
result = sdk.code(
    prompt="Add multiply and divide function to the calculator.py file. Make sure to handle division by zero in the divide function.",
    editable_files=["src/calculator.py"]
)

​
Advanced Usage

​
With Git Integration

sdk = Local(
    working_dir="/path/to/git/repo",
    model="gpt-4.1",
    api_key=os.getenv("CLOUDCODE_API_KEY"),
    use_git=True
)

result = sdk.code(
    prompt="Refactor the authentication system to use JWT tokens",
    editable_files=["src/auth.py", "src/middleware.py"]
)

if result["success"]:
    print("Changes made and staged in git:")
    print(result["diff"])

    # Now you can commit the changes
    import subprocess
    subprocess.run(["git", "commit", "-m", "Refactor auth to use JWT tokens"])

​
With Architect Mode

sdk = Local(
    working_dir="/path/to/your/project",
    model="gpt-4.1",
    weak_model="gpt-4.1-nano",
    architect_mode=True,
    api_key=os.getenv("CLOUDCODE_API_KEY")
)

# Perform a complex task that benefits from planning
result = sdk.code(
    prompt="Implement a RESTful API with proper error handling and input validation",
    editable_files=["app.py", "routes.py", "models.py", "validators.py"]
)

​
Best Practices

​
Writing Effective Prompts

  • Be specific about what you want to change
  • Mention any libraries, frameworks, or patterns to use
  • Specify error handling and edge cases
  • Reference existing patterns in the codebase
# Good prompt example
result = sdk.code(
    prompt="""
    Add a new function called 'process_batch' to utils.py that:
    1. Takes a list of items and a batch size
    2. Processes items in batches of the specified size
    3. Uses the existing logger to log progress
    4. Handles empty lists and invalid batch sizes
    5. Follows the same error handling pattern as the other utility functions
    """,
    editable_files=["src/utils.py"],
    readonly_files=["src/logger.py"]
)

​
Managing File Context

  • Group related files in the editable_files list
  • Use readonly_files to provide context without allowing changes
  • Limit the number of files for better results
  • Consider the dependencies between files

​
Error Handling

Always check the success status and handle appropriately: 
result = sdk.code(
    prompt="Add a new feature to process payments",
    editable_files=["src/payments.py"]
)

if result["success"]:
    print("Changes applied successfully!")
    # Further actions with the modified code
else:
    print("No significant changes were made. Possible reasons:")
    print("- The prompt may need to be more specific")
    print("- The requested changes might be too small or unnecessary")
    print("- The files might not be relevant to the task")