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Initialization

To integrate with Runloop and your preferred LLM provider, initialize the respective SDK clients. 
from anthropic import Anthropic
from runloop_api_client import Runloop
import os

# Initialize clients
anthropic = Anthropic(api_key=os.environ.get("ANTHROPIC_API_KEY"))
runloop = Runloop(bearer_token=os.environ.get("RUNLOOP_API_KEY"))

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Defining Prompts

Defining a clear, actionable prompt ensures accurate LLM responses. 
system_prompt = "You are a helpful coding assistant that can generate and execute python code. "
"You only respond with the code to be executed and nothing else."
"Strip backticks in code blocks."

prompt = 
"Write a Python script that generates a maze. The script should:"
"1. Accept a size parameter from command line arguments"
"2. Generate a random maze of the specified size. Remember to make the maze solvable "
"and easy and to make it clear the outer borders of the maze."
"3. Print the maze where '#' represents walls and ' ' represents paths."
"Mark the Maze start with 'S' and end with 'E'"
"4. Use argparse for command line argument parsing"
"The code should be in the format of a Python script that can be run directly"
"with 'python gen_maze.py --size 5'."
"ONLY output the code and do NOT wrap the code in markdown! The code should begin "
"with an import and end with a print statement."

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Generating Code

Send the defined prompts to the LLM’s message endpoint, configure parameters and extract the generated code from the response. 
# Generate code using Claude
response = anthropic.messages.create(
    model="claude-3-5-sonnet-20240620",
    max_tokens=1024,
    messages=[
        {"role": "assistant", "content": system_prompt},
        {"role": "user", "content": prompt}
    ]
)
maze_generation_script = response.content[0].text

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Running Code on a Devbox

After retrieving the code from the LLM, execute it in a Runloop Devbox. 
devbox = runloop.devboxes.create_and_await_running()
print("Devbox ID:", devbox.id)

runloop.devboxes.write_file_contents(
  devbox.id,
  file_path="gen_maze.py",
  contents=code
)

result = runloop.devboxes.execute_and_await_completion(
  devbox.id,
  command=f"python gen_maze.py --size {size}"
)

if not result.exit_status:
  print("Maze generated successfully\n", result.stdout)
  return result.stdout
else:
  print("Script execution failed:", result.stderr)
  return result.stderr
The examples below show Integration of Runloop with popular frameworks and LLM providers. The examples follow this structure:
  1. Client Initialization: Set up SDK clients with environment variables.
  2. Prompt Definition: Use pre-defined system and user prompts.
  3. Code Generation: Generate code based on the prompts.
  4. Execution: Run the code in a secure Runloop Devbox.
Prompts are defined above and reused across examples.
Handle the non-null ”!” operator in examples with default values or as needed.

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TypeScript Integrations

import Runloop from '@runloop/api-client';
import Anthropic from '@anthropic-ai/sdk';

// Initialize clients
const anthropic = new Anthropic({apiKey: process.env.ANTHROPIC_API_KEY,});
const runloop = new Runloop({bearerToken: process.env.RUNLOOP_API_KEY});


async function generateMazeCreator() {
  try{
    const {content} = await anthropic.messages.create({
      model: "claude-3-5-sonnet-20241022",
      max_tokens: 1000,
      temperature: 0,
      system: "Respond only with code. Do not include any markdown or comments.",
      messages: [{
        "role": "user",
        "content": [
          {
          "type": "text",
          "text": prompt
          }]
        }
    ]
    });
    const mazeGenerationScript = (content[0] as { text: string }).text;

    // Execute the script in a Devbox
    const devbox = await runloop.devboxes.createAndAwaitRunning();
    console.log(`Devbox ID: ${devbox.id}`);

    await runloop.devboxes.writeFileContents(devbox.id, {
      file_path: "gen_maze.py",
      contents:  mazeGenerationScript,
    });

    const { exit_status, stdout, stderr } = await runloop.devboxes.executeAndAwaitCompletion(devbox.id, {
      command: "python gen_maze.py --size 11",
    });

    exit_status === 0
      ? console.log("Maze generated successfully\n", stdout)
      : console.error("Maze generation failed\n", stderr);

    } catch (error) {
      console.error("Error:", error);
    }
}

generateMazeCreator();

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Python Integrations

from anthropic import Anthropic
from runloop_api_client import Runloop
import os

# Initialize clients
anthropic = Anthropic(api_key=os.environ.get("ANTHROPIC_API_KEY"))
runloop = Runloop(bearer_token=os.environ.get("RUNLOOP_API_KEY"))

def generate_maze_creator():
    try:
        # Generate code using Claude
        response = anthropic.messages.create(
            model="claude-3-5-sonnet-20240620",
            max_tokens=1024,
            messages=[
                {"role": "assistant", "content": system_prompt},
                {"role": "user", "content": prompt}
            ]
        )
        maze_generation_script = response.content[0].text

        # Execute the script in a Devbox
        devbox = runloop.devboxes.create_and_await_running()
        print("Devbox ID:", devbox.id)

        runloop.devboxes.write_file_contents(devbox.id,
         file_path= "gen_maze.py",
         contents= maze_generation_script
         )

        result = runloop.devboxes.execute_and_await_completion(devbox.id,
            command= "python gen_maze.py --size 10"
        )

        if not result.exit_status:
            print("Maze generated successfully\n", result.stdout)
        else:
            print("Script execution failed:", result.stderr)

    except Exception as e:
        print("Error:", e)

if __name__ == "__main__":
    generate_maze_creator()