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Quick Start

1

Install Package

First, install the required packages:

pip install praisonaiagents e2b_code_interpreter
2

Set API Key

Set your OpenAI API key and E2B API key as an environment variable in your terminal:

export OPENAI_API_KEY=your_api_key_here
export E2B_API_KEY=your_e2b_api_key_here
3

Create a file

Create a new file app.py with the basic setup:

from praisonaiagents import Agent, Task, PraisonAIAgents
from e2b_code_interpreter import Sandbox

def code_interpreter(code: str):
    print(f"\n{'='*50}\n> Running following AI-generated code:\n{code}\n{'='*50}")
    exec_result = Sandbox().run_code(code)
    if exec_result.error:
        print("[Code Interpreter error]", exec_result.error)
        return {"error": str(exec_result.error)}
    else:
        results = []
        for result in exec_result.results:
            if hasattr(result, '__iter__'):
                results.extend(list(result))
            else:
                results.append(str(result))
        logs = {"stdout": list(exec_result.logs.stdout), "stderr": list(exec_result.logs.stderr)}
        return json.dumps({"results": results, "logs": logs})

# Create code agent
code_agent = Agent(
    role="Code Developer",
    goal="Write and execute Python code",
    backstory="Expert Python developer with strong coding skills",
    tools=[code_interpreter],
    verbose=True
)

# Create a task
task = Task(
    description="Write and execute a Python script to analyze data",
    expected_output="Working Python script with execution results",
    agent=code_agent
)

# Create and start the agents
agents = PraisonAIAgents(
    agents=[code_agent],
    tasks=[task],
    process="sequential",
    verbose=2
)

# Start execution
agents.start()
4

Start Agents

Type this in your terminal to run your agents:

python app.py

Requirements

  • Python 3.10 or higher
  • OpenAI API key. Generate OpenAI API key here. Use Other models using this guide.
  • e2b_code_interpreter package installed

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Understanding Code Agents

What are Code Agents?

Code agents are specialized AI agents that can:

  • Write Python code based on requirements
  • Execute code safely in a sandboxed environment
  • Handle code execution results and errors
  • Work together in a pipeline (writer → executor)

​
Features

Code Writer

Writes Python code based on requirements.

Safe Execution

Executes code in a sandboxed environment.

Error Handling

Manages code execution errors and debugging.

Results Processing

Processes and formats execution results.

​
Multi-Agent Code Development

1

Install Package

First, install the required packages:

pip install praisonaiagents e2b_code_interpreter
2

Set API Key

Set your OpenAI API key as an environment variable in your terminal:

export OPENAI_API_KEY=your_api_key_here
3

Create a file

Create a new file app.py with the basic setup:

from praisonaiagents import Agent, Task, PraisonAIAgents
from e2b_code_interpreter import Sandbox

def code_interpreter(code: str):
    print(f"\n{'='*50}\n> Running following AI-generated code:\n{code}\n{'='*50}")
    exec_result = Sandbox().run_code(code)
    if exec_result.error:
        print("[Code Interpreter error]", exec_result.error)
        return {"error": str(exec_result.error)}
    else:
        results = []
        for result in exec_result.results:
            if hasattr(result, '__iter__'):
                results.extend(list(result))
            else:
                results.append(str(result))
        logs = {"stdout": list(exec_result.logs.stdout), "stderr": list(exec_result.logs.stderr)}
        return json.dumps({"results": results, "logs": logs})

# Create first agent for writing code
code_writer = Agent(
    role="Code Writer",
    goal="Write efficient Python code",
    backstory="Expert Python developer specializing in code writing",
    verbose=True
)

# Create second agent for code execution
code_executor = Agent(
    role="Code Executor",
    goal="Execute and validate Python code",
    backstory="Expert in code execution and testing",
    tools=[code_interpreter],
    verbose=True
)

# Create first task
writing_task = Task(
    description="Write a Python script for data analysis",
    expected_output="Complete Python script",
    agent=code_writer
)

# Create second task
execution_task = Task(
    description="Execute and validate the Python script",
    expected_output="Execution results and validation",
    agent=code_executor
)

# Create and start the agents
agents = PraisonAIAgents(
    agents=[code_writer, code_executor],
    tasks=[writing_task, execution_task],
    process="sequential"
)

# Start execution
agents.start()
4

Start Agents

Type this in your terminal to run your agents:

python app.py

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Configuration Options

# Create an agent with advanced code execution configuration
agent = Agent(
    role="Code Developer",
    goal="Write and execute Python code",
    backstory="Expert in Python development",
    tools=[code_interpreter],
    verbose=True,  # Enable detailed logging
    llm="gpt-4o"  # Language model to use
)

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Troubleshooting

Code Errors

If code execution fails:

  • Check syntax errors
  • Verify package imports
  • Enable verbose mode for debugging

Sandbox Issues

If sandbox execution fails:

  • Check environment setup
  • Verify permissions
  • Review resource limits

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Next Steps

AutoAgents

Learn about automatically created and managed AI agents

Mini Agents

Explore lightweight, focused AI agents

For optimal results, ensure code is properly formatted and tested in the sandbox environment before production use.

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