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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, AgentTeam
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]
)

# 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 = AgentTeam(
    agents=[code_agent],
    tasks=[task],
    process="sequential"
)

# 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)

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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.

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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, AgentTeam
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"
)

# 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]
)

# 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 = AgentTeam(
    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],
    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

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