"""
Pattern 23.03: Planning
Generate and optimize execution plans
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class PlanningState(TypedDict):
    """State for Planning pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: PlanningState) -> PlanningState:
    """Planning agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process using Planning: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    state["metadata"] = {"pattern": "Planning", "status": "completed"}
    return state


def create_planning():
    """Create Planning workflow."""
    workflow = StateGraph(PlanningState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


if __name__ == "__main__":
    workflow = create_planning()
    result = workflow.invoke({"input": "Test Planning", "output": "", "metadata": {}})
    print(f"\n=== Planning Pattern ===\nOutput: {result['output'][:200]}...")

    

Agentic Orchestration Pattern: Planning Pattern

03 Planning Pattern – Detailed Documentation

Overview

File: 23_domain_specific/03_planning.py
Pattern Category: 23 Domain Specific
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 23.03: Planning Generate and optimize execution plans

---

Code Structure

Imports

from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage

Purpose of imports: – TypedDict: For type-safe state definitions – StateGraph: LangGraph’s graph-based workflow orchestration – ChatOpenAI: OpenAI LLM integration – HumanMessage: LangChain message formatting

State Definition

class PlanningState(TypedDict):
    """State for Planning pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: PlanningState) -> PlanningState:

PlanningState: State for Planning pattern.

State Fields: – input: Stores input – output: Stores output – metadata: Stores metadata – def agent_function(state: Stores def agent function(state – prompt = f"Process using Planning: Stores prompt = f”Process using Planning – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

---

Functions

agent_function()

Line: 18
Arguments: state

Planning agent implementation.

def agent_function(state: PlanningState) -> PlanningState:
    """Planning agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process using Planning: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    state["metadata"] = {"pattern": "Planning", "status": "completed"}
    return state


def create_planning():

create_planning()

Line: 28
Arguments: “

Create Planning workflow.

def create_planning():
    """Create Planning workflow."""
    workflow = StateGraph(PlanningState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


if __name__ == "__main__":

---

Line-by-Line Explanation

Line 5: Import statement

from typing import TypedDict

Imports TypedDict for creating type-safe state dictionaries.

Line 6: Import statement

from langgraph.graph import StateGraph, END

Imports StateGraph – the core LangGraph class for building workflows.

Line 7: Import statement

from langchain_openai import ChatOpenAI

Imports ChatOpenAI for LLM interactions.

Line 8: Import statement

from langchain_core.messages import HumanMessage

Imports HumanMessage for formatting prompts to the LLM.

Line 11: Class definition

class PlanningState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Planning pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: PlanningState) -> PlanningState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Planning agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_planning definition

def create_planning():

Defines the create_planning function that processes the workflow state.

Line 29: Module docstring

    """Create Planning workflow."""

Describes the pattern and its purpose.

---

Usage Example

if __name__ == "__main__":
    workflow = create_planning()
    result = workflow.invoke({"input": "Test Planning", "output": "", "metadata": {}})
    print(f"\n=== Planning Pattern ===\nOutput: {result['output'][:200]}...")

---

Key Concepts

1. State Management: Uses TypedDict to maintain workflow state
2. Graph-Based Flow: LangGraph orchestrates execution flow
3. Agent Functions: Each node processes and updates state
4. LLM Integration: Uses ChatOpenAI for intelligent processing

Pattern Benefits

• ✅ Type-safe state management
• ✅ Clear workflow visualization
• ✅ Modular and reusable components
• ✅ Easy to test and debug
• ✅ Scalable architecture

Related Patterns

• See other patterns in the same category
• Explore combination possibilities
• Check the main documentation for pattern relationships