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Planning enables agents to think before acting - decomposing complex tasks into manageable steps, then executing them systematically.

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Quick Start

1

Enable Planning

from praisonaiagents import Agent

agent = Agent(
    name="Planner Agent",
    instructions="You solve complex problems step by step",
    planning=True  # Enable planning
)

agent.start("Build a REST API with authentication")
2

With Configuration

from praisonaiagents import Agent, PlanningConfig

agent = Agent(
    name="Advanced Planner",
    instructions="You are a strategic problem solver",
    planning=PlanningConfig(
        llm="gpt-4o",           # Use powerful model for planning
        reasoning=True,         # Show reasoning process
        auto_approve=False,     # Require approval before execution
    )
)

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How Planning Works

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The Planning Process

PhaseWhat HappensOutput
AnalyzeUnderstand the task requirementsGoal definition
DecomposeBreak into subtasksList of steps
OrderDetermine dependenciesExecution order
ExecuteRun each stepIntermediate results
SynthesizeCombine resultsFinal output

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Configuration Options

from praisonaiagents import PlanningConfig

config = PlanningConfig(
    llm="gpt-4o",           # LLM for planning (can differ from execution)
    tools=[search_tool],    # Tools available during planning
    reasoning=True,         # Enable reasoning display
    auto_approve=False,     # Require user approval
    read_only=False,        # Read-only mode (no modifications)
)
OptionTypeDefaultDescription
llmstrNoneLLM model for planning (uses agent’s LLM if not set)
toolslistNoneTools available during planning phase
reasoningboolFalseShow reasoning process
auto_approveboolFalseAuto-approve plans without confirmation
read_onlyboolFalseOnly allow read operations

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Planning Modes

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Simple Planning

# Just enable it
agent = Agent(
    instructions="You are a helpful assistant",
    planning=True
)

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Configured Planning

# With specific settings
agent = Agent(
    instructions="You are a code architect",
    planning=PlanningConfig(
        llm="gpt-4o",
        reasoning=True,
        auto_approve=True,
    )
)

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Read-Only Planning

# Safe mode - no file modifications
agent = Agent(
    instructions="You analyze code",
    planning=PlanningConfig(
        read_only=True,  # Only read operations allowed
    )
)

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Multi-Agent Planning

Enable planning for multi-agent workflows: 
from praisonaiagents import Agent, Task, PraisonAIAgents

researcher = Agent(name="Researcher", instructions="Research topics")
writer = Agent(name="Writer", instructions="Write content")

agents = PraisonAIAgents(
    agents=[researcher, writer],
    planning=True,  # Enable planning for the workflow
)

agents.start("Write a blog post about AI trends")

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When to Use Planning

✅ Use Planning For

  • Complex multi-step tasks
  • Tasks requiring coordination
  • Code refactoring projects
  • Research and analysis
  • Content creation workflows

❌ Skip Planning For

  • Simple Q&A
  • Single-step operations
  • Real-time responses needed
  • Trivial tasks

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Best Practices

Planning benefits from stronger reasoning. Use gpt-4o or similar for planning even if using a smaller model for execution.
Set reasoning=True to see the agent’s thought process and catch issues early.
Keep auto_approve=False for critical tasks to review and modify plans.
When analyzing code or data, use read_only=True to prevent accidental modifications.

Reflection

Self-evaluation after execution

Autonomy

Control agent independence