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Local managed agents provide cloud-style APIs while running on your local infrastructure.

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Quick Start

1

Basic Local Agent

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

managed = ManagedAgent(
    provider="local",
    config=ManagedConfig(model="gpt-4o-mini")
)
agent = Agent(name="local-assistant", backend=managed)
result = agent.start("What is the capital of France?", stream=True)
2

With Custom Configuration

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 helpful assistant. Be concise.",
        name="LocalAgent",
        tools=["web_search", "calculator"]
    )
)
agent = Agent(name="local-advanced", backend=managed)
result = agent.start("Search for today's weather and calculate 25 * 4", stream=True)

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How It Works

Local managed agents provide the same APIs as cloud providers while keeping data on your infrastructure.

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Configuration Options

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ManagedConfig Fields

OptionTypeDefaultDescription
modelstr"gpt-4o-mini"OpenAI model to use
systemstr"You are a helpful assistant."System prompt
namestr"Agent"Agent display name
toolsList[str][]Enabled tool names
temperaturefloat0.7Response randomness
max_tokensint1000Maximum response length

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Session Management

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Persistent Sessions

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

# Create agent with session persistence
managed = ManagedAgent(
    provider="local",
    config=ManagedConfig(
        model="gpt-4o-mini",
        name="PersistentAgent"
    )
)
agent = Agent(name="persistent", backend=managed)

# First conversation
agent.start("Remember: my favorite color is blue")
session_info = managed.retrieve_session()
print(f"Session ID: {session_info['id']}")

# Save session for later
ids = managed.save_ids()
# Store ids to file/database for persistence across restarts

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Resume Sessions

# In another process/restart
managed2 = ManagedAgent(
    provider="local", 
    config=ManagedConfig(model="gpt-4o-mini")
)
managed2.restore_ids(ids)
managed2.resume_session(ids["session_id"])

agent2 = Agent(name="resumed", backend=managed2)
result = agent2.start("What is my favorite color?")  # Knows: blue

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Multi-turn Conversations

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 math tutor. Help students learn step by step.",
        name="MathTutor"
    )
)
agent = Agent(name="tutor", backend=managed)

# Multi-turn conversation in same session
response1 = agent.start("Explain what a derivative is", stream=True)
response2 = agent.start("Now show me how to find the derivative of x²", stream=True)
response3 = agent.start("What about x³?", stream=True)

# Session remembers all previous context

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Usage Tracking

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

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

# Execute some tasks
agent.start("Write a short poem")
agent.start("Explain quantum physics briefly")

# Check usage
session_info = managed.retrieve_session()
if session_info.get("usage"):
    print(f"Input tokens: {session_info['usage']['input_tokens']}")
    print(f"Output tokens: {session_info['usage']['output_tokens']}")

# Also available on instance
print(f"Total input: {managed.total_input_tokens}")
print(f"Total output: {managed.total_output_tokens}")

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Best Practices

Use gpt-4o-mini for cost-effective operations. Upgrade to gpt-4o for complex reasoning tasks. Configure appropriate max_tokens to control costs.
Save session IDs using save_ids() for resuming conversations. Store IDs in a database or file system for persistence across application restarts.
Only enable tools your agent needs. Use selective tool lists like ["web_search", "calculator"] instead of all tools to improve performance.
Implement proper error handling for API failures. Local managed agents depend on OpenAI API availability and rate limits.

Managed Agents

Overview of managed agent concepts

Docker Compute

Containerized execution environments