Run AI-Generated Code Safely with Daytona Sandboxes

Have you ever wondered how to run AI-generated code in production safely? As AI agents become more sophisticated at generating code, the challenge isn't just about creating code—it's about executing it safely. To solve this critical challenge, this guide combines LangChain's powerful AI capabilities with Daytona's secure sandbox environments.

Picture LangChain as your AI code chef, cooking up sophisticated solutions, while Daytona acts as your secure test kitchen where these creations can be safely tried and tested. Together, they provide a robust framework for generating and executing AI code without compromising security.

Whether you're building AI-powered developer tools or implementing automated code generation in your pipeline, this integration provides the security and flexibility you need.

What is Daytona?

Daytona is an open-source development environment manager designed to create instant, fully-configured development environments across both local and cloud infrastructures. Its architecture supports deployment on all major cloud providers and platforms, while offering a self-hosted option ideal for enterprise and security-sensitive applications.

What sets Daytona apart is its comprehensive API for automated sandbox management through "Workspaces" - isolated development environments that provide secure boundaries for code execution. The platform offers native SDK support for both Python and TypeScript, making it seamless to integrate Daytona's capabilities into existing projects. With its robust infrastructure, Daytona enables automated environment provisioning, resource management, and secure code execution at scale.

Why Sandbox AI-Generated Code?

When working with AI-generated code, security is paramount. AI models might generate code that could:

• Access system resources inappropriately

• Make unauthorized network calls

• Consume excessive memory or CPU resources

• Perform changes to the system configuration

Daytona's workspace isolation provides a secure boundary for executing such code, making it an ideal solution for AI agent implementations.

What is LangChain?

LangChain is a framework designed to simplify the creation of applications using large language models (LLMs). It provides a modular architecture for combining LLMs with other tools and data sources, making it easier to build complex AI applications. The framework excels at tasks like prompt management, LLM output handling, and creating sophisticated chains of operations. For our use case, LangChain's ability to manage structured interactions with language models makes it an ideal choice for generating and processing code.

Building an AI Code Generator with LangChain and Daytona

Let's walk through creating a practical example that combines LangChain's code generation capabilities with Daytona's secure execution environment.

Setting Up the Environment

First, install the required packages:

1pip install langchain langchain-community openai daytona_sdk

The Code Generation Component

Here's our implementation of a code generator using LangChain:

1from langchain_community.llms import OpenAI
2from langchain.chains import LLMChain
3from langchain.prompts import PromptTemplate
4from langchain.chains import SimpleSequentialChain
5import os
6
7# Set your OpenAI API key
8os.environ["OPENAI_API_KEY"] = "your-api-key-here"
9
10# Create a prompt template for code generation
11code_generation_prompt = PromptTemplate(
12    input_variables=["language", "task"],
13    template="""
14You are an expert {language} developer. Write clean, efficient, and well-documented code for the following task:
15
16Task: {task}
17
18Please provide:
191. A complete implementation
202. Brief comments explaining key parts
213. Example usage if applicable
22
23Code:
24"""
25)
26
27# Initialize the LLM with lower temperature for more focused code generation
28llm = OpenAI(
29    temperature=0.2,
30    max_tokens=1000
31)
32
33# Create the code generation chain
34code_chain = LLMChain(
35    llm=llm,
36    prompt=code_generation_prompt,
37    verbose=True
38)
39
40def generate_code(language: str, task: str):
41    """
42    Generate code for a given task in the specified programming language.
43    """
44    return code_chain.run({
45        "language": language,
46        "task": task
47    })

Adding Daytona Integration

To safely execute the generated code, we'll now integrate Daytona into our workflow. The first step is to locate your Daytona API key and server URL. You can easily retrieve this information by running the command 'daytona config -k' in your terminal. This command will display both the URL and API key you need.

1from daytona_sdk import Daytona, DaytonaConfig, CreateWorkspaceParams
2
3def execute_in_sandbox(code: str):
4    """
5    Execute generated code in a Daytona workspace sandbox.
6    """
7    # Initialize Daytona with your configuration
8    config = DaytonaConfig(
9        api_key="DAYTONA_API_KEY",
10        server_url="DAYTONA_SERVER_URL",
11        target="local"
12    )
13    daytona = Daytona(config=config)
14    
15    # Create a Python workspace
16    params = CreateWorkspaceParams(language="python")
17    workspace = daytona.create()
18    
19    try:
20        # Execute the code in the sandbox
21        response = workspace.process.code_run(code)
22        
23        if response.code != 0:
24            return f"Error: {response.code} {response.result}"
25        return response.result
26    finally:
27        # Always clean up the workspace
28        daytona.remove(workspace)
29
30# Example usage
31if __name__ == "__main__":
32    # Generate Python code
33    task = "Create a function that takes a list of numbers and returns the moving average with a specified window size"
34    generated_code = generate_code("Python", task)
35    
36    # Execute in sandbox
37    result = execute_in_sandbox(generated_code)
38    print("Code execution result:", result)

Benefits of This Approach

1. Security: All code execution happens in isolated Daytona workspaces, preventing any potential system access or resource abuse.

2. Resource Management: Daytona workspaces can be configured with specific resource limits, ensuring controlled execution.

3. Clean Environment: Each execution starts in a fresh workspace, eliminating any potential side effects from previous runs.

4. Automated Cleanup: The workspace is automatically removed after execution, maintaining system cleanliness.

Built-in Features for Best Practices

Daytona simplifies the implementation of essential security and operational practices through its built-in features:

• Resource Management and Scaling: Daytona's native infrastructure handles horizontal scaling across cloud resources automatically. Its simple configuration interface lets you set CPU, memory, and storage limits with just a few parameters, making resource management effortless.

• Logging and Audit Controls: Built-in comprehensive logging captures all workspace activities automatically. Daytona provides ready-to-use APIs for tracking execution flows, resource usage patterns, and system interactions, giving you immediate visibility into your AI agent activities.

• API Integration and Automation: Daytona comes with a full suite of APIs for file operations, git workflows, process management, and code analysis. These pre-built integrations make it straightforward to automate workflows and connect with existing systems without additional development effort.

• Cost Control and Resource Optimization: Managing costs is simplified through Daytona's built-in spend controls and resource monitoring capabilities. The platform automatically tracks resource usage and provides easy-to-implement limits to optimize performance while controlling costs.

• Security and Isolation: Security comes standard with Daytona's workspace isolation system. The platform automatically handles clean environment states, process timeouts, and security boundaries, ensuring safe code execution without manual configuration.

Conclusion

Combining AI code generation with Daytona's sandboxed execution creates a powerful and secure system for automated code generation and testing. This approach provides the flexibility to experiment with AI-generated code while maintaining robust security boundaries.

The solution we've built demonstrates how modern development tools can work together to create secure, efficient systems for AI-assisted development. Whether you're building an AI coding assistant or implementing automated code generation in your pipeline, Daytona provides the secure foundation needed for safe code execution.

Remember to replace the placeholder API keys with your actual Daytona and OpenAI credentials when implementing this solution in your environment.

What's Next?

In Part 2 of this series, we'll explore more advanced integration features of Daytona, focusing on using the filesystem API to manipulate files within the sandbox environment. We'll demonstrate how to:

• Create and modify files within the workspace

• Handle file uploads and downloads

• Search for files and replace in files

• Change file permissions and ownership

Stay tuned for more advanced implementations and best practices for working with Daytona's file manipulation capabilities.