Docker Compute Agents

Run managed agents in isolated Docker containers with custom environments

Docker compute agents run in isolated containers, providing reproducible environments with custom packages and dependencies.

Diagram

Quick Start

Basic Docker Setup

python

import asyncio
from praisonaiagents import Agent
from praisonai.integrations.managed_agents import ManagedAgent, ManagedConfig

managed = ManagedAgent(
    provider="local",
    config=ManagedConfig(
        model="gpt-4o-mini",
        name="DockerAgent"
    ),
    compute="docker"
)
agent = Agent(name="docker-agent", backend=managed)

# Provision container
info = asyncio.run(managed.provision_compute(image="python:3.12-slim"))
print(f"Container: {info.instance_id}, Status: {info.status}")

# Execute commands
result = asyncio.run(managed.execute_in_compute("python3 --version"))
print(result["stdout"])

With Package Installation

python

import asyncio
from praisonaiagents import Agent
from praisonai.integrations.managed_agents import ManagedAgent, ManagedConfig

managed = ManagedAgent(
    provider="local",
    config=ManagedConfig(model="gpt-4o-mini"),
    compute="docker"
)
agent = Agent(name="data-agent", backend=managed)

# Provision with packages
info = asyncio.run(managed.provision_compute(
    image="python:3.12-slim",
    packages=["pandas", "numpy", "matplotlib"]
))

# Use installed packages
result = asyncio.run(managed.execute_in_compute(
    "python3 -c \"import pandas as pd; print(pd.__version__)\""
))
print(f"Pandas version: {result['stdout']}")

How It Works

Diagram

Docker compute provides isolated, reproducible execution environments with custom package management.

Container Management

Provisioning Containers

python

import asyncio
from praisonaiagents import Agent
from praisonai.integrations.managed_agents import ManagedAgent, ManagedConfig

managed = ManagedAgent(
    provider="local",
    config=ManagedConfig(model="gpt-4o-mini"),
    compute="docker"
)

# Provision with custom image
info = asyncio.run(managed.provision_compute(
    image="python:3.11-slim",
    packages=["requests", "beautifulsoup4", "lxml"]
))

print(f"Container ID: {info.instance_id}")
print(f"Status: {info.status}")
print(f"Image: {info.image}")

Executing Commands

python

# Execute Python scripts
result = asyncio.run(managed.execute_in_compute("""
python3 -c "
import requests
response = requests.get('https://api.github.com/users/octocat')
print(f'Status: {response.status_code}')
print(f'User: {response.json()[\"name\"]}')
"
"""))

print(result["stdout"])
if result["stderr"]:
    print(f"Errors: {result['stderr']}")

Container Lifecycle

python

# Check container status
status = asyncio.run(managed.get_compute_status())
print(f"Container status: {status}")

# Clean up when done
asyncio.run(managed.shutdown_compute())
print("Container cleaned up")

LLM Integration

python

import asyncio
from praisonaiagents import Agent
from praisonai.integrations.managed_agents import ManagedAgent, ManagedConfig

managed = ManagedAgent(
    provider="local",
    config=ManagedConfig(
        model="gpt-4o-mini",
        system="You are a data analysis assistant. Use the containerized environment to run Python code."
    ),
    compute="docker"
)
agent = Agent(name="analyst", backend=managed)

# Provision container with data science packages
info = asyncio.run(managed.provision_compute(
    image="python:3.12-slim",
    packages=["pandas", "matplotlib", "seaborn", "numpy"]
))

# LLM can now use the containerized environment
result = agent.start(
    "Create a bar chart showing sales data: {'Q1': 100, 'Q2': 150, 'Q3': 120, 'Q4': 180}",
    stream=True
)

Common Patterns

Data Processing Pipeline

python

import asyncio
from praisonaiagents import Agent
from praisonai.integrations.managed_agents import ManagedAgent, ManagedConfig

managed = ManagedAgent(
    provider="local",
    config=ManagedConfig(
        model="gpt-4o-mini",
        system="You are a data processing expert."
    ),
    compute="docker"
)
agent = Agent(name="processor", backend=managed)

# Set up data science environment
info = asyncio.run(managed.provision_compute(
    image="python:3.12-slim",
    packages=["pandas", "numpy", "scipy", "scikit-learn"]
))

# Process data through LLM + container
result = agent.start(
    "Load this CSV data and calculate basic statistics: age,salary\n25,50000\n30,60000\n35,70000",
    stream=True
)

# Clean up
asyncio.run(managed.shutdown_compute())

Web Scraping Environment

python

import asyncio
from praisonaiagents import Agent
from praisonai.integrations.managed_agents import ManagedAgent, ManagedConfig

managed = ManagedAgent(
    provider="local",
    config=ManagedConfig(model="gpt-4o-mini"),
    compute="docker"
)
agent = Agent(name="scraper", backend=managed)

# Web scraping environment
info = asyncio.run(managed.provision_compute(
    image="python:3.12-slim",
    packages=["requests", "beautifulsoup4", "selenium", "lxml"]
))

# Execute scraping task
result = agent.start("Scrape the title from https://example.com", stream=True)

Development Environment

python

import asyncio
from praisonaiagents import Agent
from praisonai.integrations.managed_agents import ManagedAgent, ManagedConfig

managed = ManagedAgent(
    provider="local",
    config=ManagedConfig(
        model="gpt-4o-mini",
        system="You are a full-stack developer."
    ),
    compute="docker"
)

# Multi-language environment
info = asyncio.run(managed.provision_compute(
    image="node:18-slim",
    packages=["python3", "pip", "git"]
))

# Install both Python and Node packages
asyncio.run(managed.execute_in_compute("apt-get update && apt-get install -y python3-pip"))
asyncio.run(managed.execute_in_compute("pip install flask"))
asyncio.run(managed.execute_in_compute("npm install express"))

Configuration Options

Compute Configuration

Option	Type	Default	Description
`image`	`str`	`"python:3.12-slim"`	Docker base image
`packages`	`List[str]`	`[]`	Python packages to install
`environment`	`Dict[str, str]`	`{}`	Environment variables
`working_dir`	`str`	`"/workspace"`	Container working directory

Supported Images

Image	Use Case	Pre-installed
`python:3.12-slim`	Python development	Python 3.12, pip
`node:18-slim`	JavaScript/Node.js	Node.js 18, npm
`ubuntu:22.04`	General Linux	Basic Linux utilities
`python:3.12-alpine`	Minimal Python	Python 3.12 (smaller size)

Best Practices

Choose Appropriate Base Images

Use slim images for faster startup. Alpine images for minimal size. Full images when you need system packages and compilation tools.

Package Management

Install packages during provisioning rather than runtime. Cache heavy dependencies by using custom Docker images for repeated use.

Resource Management

Always call shutdown_compute() when done to free container resources. Monitor container resource usage for long-running tasks.

Security Considerations

Containers provide isolation but share the Docker daemon. Don’t run untrusted code without additional sandboxing measures.

Managed Agents

Overview of managed agent concepts

E2B Cloud

Cloud-based sandboxed execution

Docker Compute Agents

Quick Start

How It Works

Container Management

Provisioning Containers

Executing Commands

Container Lifecycle

LLM Integration

Common Patterns

Data Processing Pipeline

Web Scraping Environment

Development Environment

Configuration Options

Compute Configuration

Supported Images

Best Practices

Related