Learn to use Agno’s E2B integration to run your Agent-generated code in a secure sandbox.
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.e2b import E2BTools
e2b_tools = E2BTools(
timeout=600, # 10 minutes timeout (in seconds)
filesystem=True,
internet_access=True,
sandbox_management=True,
command_execution=True,
)
agent = Agent(
name="Code Execution Sandbox",
agent_id="e2b-sandbox",
model=OpenAIChat(id="gpt-4o"),
tools=[e2b_tools],
markdown=True,
show_tool_calls=True,
instructions=[
"You are an expert at writing and validating Python code using a secure E2B sandbox environment.",
"Your primary purpose is to:",
"1. Write clear, efficient Python code based on user requests",
"2. Execute and verify the code in the E2B sandbox",
"3. Share the complete code with the user, as this is the main use case",
"4. Provide thorough explanations of how the code works",
],
)
# Example: Generate Fibonacci numbers
agent.print_response(
"Write Python code to generate the first 10 Fibonacci numbers and calculate their sum and average"
)
# Example: Data visualization
agent.print_response(
"Write a Python script that creates a sample dataset of sales by region and visualize it with matplotlib"
)
# Example: Run a web server
agent.print_response(
"Create a simple FastAPI web server that displays 'Hello from E2B Sandbox!' and run it to get a public URL"
)
# Example: Sandbox management
agent.print_response("What's the current status of our sandbox and how much time is left before timeout?")
# Example: File operations
agent.print_response("Create a text file with the current date and time, then read it back")
Create a virtual environment
Terminal
and create a python virtual environment.python3 -m venv .venv
source .venv/bin/activate
Create an E2B account
Install libraries
pip install e2b_code_interpreter
Set your API Key
export E2B_API_KEY=your_api_key_here
Run Agent
python cookbook/tools/e2b_tools.py
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