"""
Pattern 05.03: Peer Review
Agents review each other's work
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class PeerReviewState(TypedDict):
    """State for Peer Review pattern."""
    input: str
    output: str


def agent_function(state: PeerReviewState) -> PeerReviewState:
    """Peer Review agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process this using Peer Review pattern: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    return state


def create_peer_review():
    """Create Peer Review workflow."""
    workflow = StateGraph(PeerReviewState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

    

Agentic Orchestration Pattern: Peer Review Pattern

03 Peer Review Pattern – Detailed Documentation

Overview

File: 05_collaborative/03_peer_review.py
Pattern Category: 05 Collaborative
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 05.03: Peer Review Agents review each other’s work

---

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 PeerReviewState(TypedDict):
    """State for Peer Review pattern."""
    input: str
    output: str


def agent_function(state: PeerReviewState) -> PeerReviewState:

PeerReviewState: State for Peer Review pattern.

State Fields: – input: Stores input – output: Stores output – def agent_function(state: Stores def agent function(state – prompt = f"Process this using Peer Review pattern: Stores prompt = f”Process this using Peer Review pattern – def create_peer_review(): Stores def create peer review()

---

Functions

agent_function()

Line: 17
Arguments: state

Peer Review agent implementation.

def agent_function(state: PeerReviewState) -> PeerReviewState:
    """Peer Review agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process this using Peer Review pattern: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    return state


def create_peer_review():

create_peer_review()

Line: 26
Arguments: “

Create Peer Review workflow.

def create_peer_review():
    """Create Peer Review workflow."""
    workflow = StateGraph(PeerReviewState)
    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 PeerReviewState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Peer Review pattern."""

Describes the pattern and its purpose.

Line 17: Function agent_function definition

def agent_function(state: PeerReviewState) -> PeerReviewState:

Defines the agent_function function that processes the workflow state.

Line 18: Module docstring

    """Peer Review agent implementation."""

Describes the pattern and its purpose.

Line 26: Function create_peer_review definition

def create_peer_review():

Defines the create_peer_review function that processes the workflow state.

Line 27: Module docstring

    """Create Peer Review workflow."""

Describes the pattern and its purpose.

---

Usage Example

if __name__ == "__main__":
    workflow = create_peer_review()
    result = workflow.invoke({"input": "Test Peer Review", "output": ""})
    print(f"\n=== Peer Review 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

    """
Pattern 05.02: Ensemble
Multiple agents solve same problem, vote on results
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class EnsembleState(TypedDict):
    """State for Ensemble pattern."""
    input: str
    output: str


def agent_function(state: EnsembleState) -> EnsembleState:
    """Ensemble agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process this using Ensemble pattern: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    return state


def create_ensemble():
    """Create Ensemble workflow."""
    workflow = StateGraph(EnsembleState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

    

Agentic Orchestration Pattern: Ensemble Pattern

02 Ensemble Pattern – Detailed Documentation

Overview

File: 05_collaborative/02_ensemble.py
Pattern Category: 05 Collaborative
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 05.02: Ensemble Multiple agents solve same problem, vote on results

---

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 EnsembleState(TypedDict):
    """State for Ensemble pattern."""
    input: str
    output: str


def agent_function(state: EnsembleState) -> EnsembleState:

EnsembleState: State for Ensemble pattern.

State Fields: – input: Stores input – output: Stores output – def agent_function(state: Stores def agent function(state – prompt = f"Process this using Ensemble pattern: Stores prompt = f”Process this using Ensemble pattern – def create_ensemble(): Stores def create ensemble()

---

Functions

agent_function()

Line: 17
Arguments: state

Ensemble agent implementation.

def agent_function(state: EnsembleState) -> EnsembleState:
    """Ensemble agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process this using Ensemble pattern: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    return state


def create_ensemble():

create_ensemble()

Line: 26
Arguments: “

Create Ensemble workflow.

def create_ensemble():
    """Create Ensemble workflow."""
    workflow = StateGraph(EnsembleState)
    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 EnsembleState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Ensemble pattern."""

Describes the pattern and its purpose.

Line 17: Function agent_function definition

def agent_function(state: EnsembleState) -> EnsembleState:

Defines the agent_function function that processes the workflow state.

Line 18: Module docstring

    """Ensemble agent implementation."""

Describes the pattern and its purpose.

Line 26: Function create_ensemble definition

def create_ensemble():

Defines the create_ensemble function that processes the workflow state.

Line 27: Module docstring

    """Create Ensemble workflow."""

Describes the pattern and its purpose.

---

Usage Example

if __name__ == "__main__":
    workflow = create_ensemble()
    result = workflow.invoke({"input": "Test Ensemble", "output": ""})
    print(f"\n=== Ensemble 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

    """
Pattern 5.1: Debate Pattern (CrewAI Implementation)
Multiple agents argue different perspectives and synthesize views.
"""
from crewai import Agent, Task, Crew, Process
from langchain_openai import ChatOpenAI
import os


def create_debate_crew(topic: str):
    """Create a debate crew with multiple perspectives."""
    
    # Initialize LLM
    llm = ChatOpenAI(model="gpt-4")
    
    # Create agents with different perspectives
    optimist = Agent(
        role="Optimistic Analyst",
        goal=f"Argue the positive aspects and opportunities of {topic}",
        backstory="You are an optimistic analyst who always sees the bright side and potential benefits.",
        verbose=True,
        llm=llm
    )
    
    pessimist = Agent(
        role="Critical Analyst",
        goal=f"Identify risks, challenges, and potential problems with {topic}",
        backstory="You are a critical thinker who identifies risks and challenges to ensure thorough analysis.",
        verbose=True,
        llm=llm
    )
    
    realist = Agent(
        role="Pragmatic Analyst",
        goal=f"Provide a balanced, practical perspective on {topic}",
        backstory="You are a pragmatic analyst who balances optimism with realism and provides actionable insights.",
        verbose=True,
        llm=llm
    )
    
    synthesizer = Agent(
        role="Synthesis Expert",
        goal="Synthesize different perspectives into a comprehensive analysis",
        backstory="You are an expert at combining different viewpoints into coherent, balanced conclusions.",
        verbose=True,
        llm=llm
    )
    
    # Create tasks
    optimistic_task = Task(
        description=f"Present the optimistic case for {topic}. Focus on benefits, opportunities, and positive outcomes.",
        agent=optimist,
        expected_output="Detailed optimistic analysis with specific benefits and opportunities"
    )
    
    critical_task = Task(
        description=f"Present the critical analysis of {topic}. Focus on risks, challenges, and potential problems.",
        agent=pessimist,
        expected_output="Detailed critical analysis with specific risks and challenges"
    )
    
    realistic_task = Task(
        description=f"Present the pragmatic view of {topic}. Balance pros and cons with practical recommendations.",
        agent=realist,
        expected_output="Balanced pragmatic analysis with actionable recommendations"
    )
    
    synthesis_task = Task(
        description="""Synthesize the optimistic, critical, and pragmatic perspectives into a comprehensive final analysis.
        Include: 1) Summary of each perspective, 2) Key insights, 3) Balanced recommendations""",
        agent=synthesizer,
        expected_output="Comprehensive synthesis with balanced recommendations",
        context=[optimistic_task, critical_task, realistic_task]
    )
    
    # Create crew
    crew = Crew(
        agents=[optimist, pessimist, realist, synthesizer],
        tasks=[optimistic_task, critical_task, realistic_task, synthesis_task],
        process=Process.sequential,
        verbose=True
    )
    
    return crew


def run_debate(topic: str):
    """Run the debate pattern."""
    print(f"\n{'='*60}")
    print(f"Debate Pattern: {topic}")
    print(f"{'='*60}\n")
    
    crew = create_debate_crew(topic)
    result = crew.kickoff()
    
    return result


if __name__ == "__main__":
    # Example usage
    topic = "Implementing AI in healthcare diagnostics"
    result = run_debate(topic)
    
    print(f"\n\n{'='*60}")
    print("FINAL SYNTHESIS")
    print(f"{'='*60}")
    print(result)

    

Agentic Orchestration Pattern: Debate Pattern (CrewAI)

01 Debate Crewai Pattern – Detailed Documentation

Overview

File: 05_collaborative/01_debate_crewai.py
Pattern Category: 05 Collaborative
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 5.1: Debate Pattern (CrewAI Implementation) Multiple agents argue different perspectives and synthesize views.

---

Code Structure

Imports

from crewai import Agent, Task, Crew, Process
from langchain_openai import ChatOpenAI
import os

Purpose of imports: – ChatOpenAI: OpenAI LLM integration – crewai: Multi-agent collaboration framework

State Definition

---

Functions

create_debate_crew()

Line: 10
Arguments: topic

Create a debate crew with multiple perspectives.

def create_debate_crew(topic: str):
    """Create a debate crew with multiple perspectives."""

    # Initialize LLM
    llm = ChatOpenAI(model="gpt-4")

    # Create agents with different perspectives
    optimist = Agent(
        role="Optimistic Analyst",
        goal=f"Argue the positive aspects and opportunities of {topic}",
        backstory="You are an optimistic analyst who always sees the bright side and potential benefits.",
        verbose=True,
        llm=llm
    )

    pessimist = Agent(
        role="Critical Analyst",
        goal=f"Identify risks, challenges, and potential problems with {topic}",
        backstory="You are a critical thinker who identifies risks and challenges to ensure thorough analysis.",
        verbose=True,
        llm=llm
    )

    realist = Agent(
        role="Pragmatic Analyst",
        goal=f"Provide a balanced, practical perspective on {topic}",
        backstory="You are a pragmatic analyst who balances optimism with realism and provides actionable insights.",
        verbose=True,
        llm=llm
    )

run_debate()

Line: 87
Arguments: topic

Run the debate pattern.

def run_debate(topic: str):
    """Run the debate pattern."""
    print(f"\n{'='*60}")
    print(f"Debate Pattern: {topic}")
    print(f"{'='*60}\n")

    crew = create_debate_crew(topic)
    result = crew.kickoff()

    return result


if __name__ == "__main__":

---

Line-by-Line Explanation

Line 5: Import statement

from crewai import Agent, Task, Crew, Process

Required import for the pattern implementation.

Line 6: Import statement

from langchain_openai import ChatOpenAI

Imports ChatOpenAI for LLM interactions.

Line 7: Import statement

import os

Required import for the pattern implementation.

Line 10: Function create_debate_crew definition

def create_debate_crew(topic: str):

Defines the create_debate_crew function that processes the workflow state.

Line 11: Module docstring

    """Create a debate crew with multiple perspectives."""

Describes the pattern and its purpose.

Line 87: Function run_debate definition

def run_debate(topic: str):

Defines the run_debate function that processes the workflow state.

Line 88: Module docstring

    """Run the debate pattern."""

Describes the pattern and its purpose.

---

Usage Example

if __name__ == "__main__":
    # Example usage
    topic = "Implementing AI in healthcare diagnostics"
    result = run_debate(topic)

    print(f"\n\n{'='*60}")
    print("FINAL SYNTHESIS")
    print(f"{'='*60}")
    print(result)

---

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

    """
Pattern 05.01: Debate
Multiple agents argue different perspectives
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class DebateState(TypedDict):
    """State for Debate pattern."""
    input: str
    output: str


def agent_function(state: DebateState) -> DebateState:
    """Debate agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process this using Debate pattern: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    return state


def create_debate():
    """Create Debate workflow."""
    workflow = StateGraph(DebateState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

    

Agentic Orchestration Pattern: Debate Pattern

01 Debate Pattern – Detailed Documentation

Overview

File: 05_collaborative/01_debate.py
Pattern Category: 05 Collaborative
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 05.01: Debate Multiple agents argue different perspectives

---

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 DebateState(TypedDict):
    """State for Debate pattern."""
    input: str
    output: str


def agent_function(state: DebateState) -> DebateState:

DebateState: State for Debate pattern.

State Fields: – input: Stores input – output: Stores output – def agent_function(state: Stores def agent function(state – prompt = f"Process this using Debate pattern: Stores prompt = f”Process this using Debate pattern – def create_debate(): Stores def create debate()

---

Functions

agent_function()

Line: 17
Arguments: state

Debate agent implementation.

def agent_function(state: DebateState) -> DebateState:
    """Debate agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process this using Debate pattern: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    return state


def create_debate():

create_debate()

Line: 26
Arguments: “

Create Debate workflow.

def create_debate():
    """Create Debate workflow."""
    workflow = StateGraph(DebateState)
    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 DebateState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Debate pattern."""

Describes the pattern and its purpose.

Line 17: Function agent_function definition

def agent_function(state: DebateState) -> DebateState:

Defines the agent_function function that processes the workflow state.

Line 18: Module docstring

    """Debate agent implementation."""

Describes the pattern and its purpose.

Line 26: Function create_debate definition

def create_debate():

Defines the create_debate function that processes the workflow state.

Line 27: Module docstring

    """Create Debate workflow."""

Describes the pattern and its purpose.

---

Usage Example

if __name__ == "__main__":
    workflow = create_debate()
    result = workflow.invoke({"input": "Test Debate", "output": ""})
    print(f"\n=== Debate 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

    """
Pattern 04.04: Tree Orchestration
Multi-level hierarchy with parent-child relationships
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class TreeOrchestrationState(TypedDict):
    """State for Tree Orchestration pattern."""
    input: str
    output: str


def agent_function(state: TreeOrchestrationState) -> TreeOrchestrationState:
    """Tree Orchestration agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process this using Tree Orchestration pattern: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    return state


def create_tree_orchestration():
    """Create Tree Orchestration workflow."""
    workflow = StateGraph(TreeOrchestrationState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

    

Agentic Orchestration Pattern: Tree Orchestration

04 Tree Orchestration Pattern – Detailed Documentation

Overview

File: 04_hierarchical/04_tree_orchestration.py
Pattern Category: 04 Hierarchical
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 04.04: Tree Orchestration Multi-level hierarchy with parent-child relationships

---

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 TreeOrchestrationState(TypedDict):
    """State for Tree Orchestration pattern."""
    input: str
    output: str


def agent_function(state: TreeOrchestrationState) -> TreeOrchestrationState:

TreeOrchestrationState: State for Tree Orchestration pattern.

State Fields: – input: Stores input – output: Stores output – def agent_function(state: Stores def agent function(state – prompt = f"Process this using Tree Orchestration pattern: Stores prompt = f”Process this using Tree Orchestration pattern – def create_tree_orchestration(): Stores def create tree orchestration()

---

Functions

agent_function()

Line: 17
Arguments: state

Tree Orchestration agent implementation.

def agent_function(state: TreeOrchestrationState) -> TreeOrchestrationState:
    """Tree Orchestration agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process this using Tree Orchestration pattern: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    return state


def create_tree_orchestration():

create_tree_orchestration()

Line: 26
Arguments: “

Create Tree Orchestration workflow.

def create_tree_orchestration():
    """Create Tree Orchestration workflow."""
    workflow = StateGraph(TreeOrchestrationState)
    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 TreeOrchestrationState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Tree Orchestration pattern."""

Describes the pattern and its purpose.

Line 17: Function agent_function definition

def agent_function(state: TreeOrchestrationState) -> TreeOrchestrationState:

Defines the agent_function function that processes the workflow state.

Line 18: Module docstring

    """Tree Orchestration agent implementation."""

Describes the pattern and its purpose.

Line 26: Function create_tree_orchestration definition

def create_tree_orchestration():

Defines the create_tree_orchestration function that processes the workflow state.

Line 27: Module docstring

    """Create Tree Orchestration workflow."""

Describes the pattern and its purpose.

---

Usage Example

if __name__ == "__main__":
    workflow = create_tree_orchestration()
    result = workflow.invoke({"input": "Test Tree Orchestration", "output": ""})
    print(f"\n=== Tree Orchestration 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

    """
Pattern 04.03: Delegation
High-level agent delegates to specialized agents
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class DelegationState(TypedDict):
    """State for Delegation pattern."""
    input: str
    output: str


def agent_function(state: DelegationState) -> DelegationState:
    """Delegation agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process this using Delegation pattern: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    return state


def create_delegation():
    """Create Delegation workflow."""
    workflow = StateGraph(DelegationState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

    

Agentic Orchestration Pattern: Delegation Pattern

03 Delegation Pattern – Detailed Documentation

Overview

File: 04_hierarchical/03_delegation.py
Pattern Category: 04 Hierarchical
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 04.03: Delegation High-level agent delegates to specialized agents

---

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 DelegationState(TypedDict):
    """State for Delegation pattern."""
    input: str
    output: str


def agent_function(state: DelegationState) -> DelegationState:

DelegationState: State for Delegation pattern.

State Fields: – input: Stores input – output: Stores output – def agent_function(state: Stores def agent function(state – prompt = f"Process this using Delegation pattern: Stores prompt = f”Process this using Delegation pattern – def create_delegation(): Stores def create delegation()

---

Functions

agent_function()

Line: 17
Arguments: state

Delegation agent implementation.

def agent_function(state: DelegationState) -> DelegationState:
    """Delegation agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process this using Delegation pattern: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    return state


def create_delegation():

create_delegation()

Line: 26
Arguments: “

Create Delegation workflow.

def create_delegation():
    """Create Delegation workflow."""
    workflow = StateGraph(DelegationState)
    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 DelegationState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Delegation pattern."""

Describes the pattern and its purpose.

Line 17: Function agent_function definition

def agent_function(state: DelegationState) -> DelegationState:

Defines the agent_function function that processes the workflow state.

Line 18: Module docstring

    """Delegation agent implementation."""

Describes the pattern and its purpose.

Line 26: Function create_delegation definition

def create_delegation():

Defines the create_delegation function that processes the workflow state.

Line 27: Module docstring

    """Create Delegation workflow."""

Describes the pattern and its purpose.

---

Usage Example

if __name__ == "__main__":
    workflow = create_delegation()
    result = workflow.invoke({"input": "Test Delegation", "output": ""})
    print(f"\n=== Delegation 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

    """
Pattern 04.02: Supervisor-Subordinate
Hierarchical command structure with escalation
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class SupervisorSubordinateState(TypedDict):
    """State for Supervisor-Subordinate pattern."""
    input: str
    output: str


def agent_function(state: SupervisorSubordinateState) -> SupervisorSubordinateState:
    """Supervisor-Subordinate agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process this using Supervisor-Subordinate pattern: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    return state


def create_supervisor_subordinate():
    """Create Supervisor-Subordinate workflow."""
    workflow = StateGraph(SupervisorSubordinateState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

    

Agentic Orchestration Pattern: Supervisor-Subordinate

02 Supervisor Subordinate Pattern – Detailed Documentation

Overview

File: 04_hierarchical/02_supervisor_subordinate.py
Pattern Category: 04 Hierarchical
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 04.02: Supervisor-Subordinate Hierarchical command structure with escalation

---

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 SupervisorSubordinateState(TypedDict):
    """State for Supervisor-Subordinate pattern."""
    input: str
    output: str


def agent_function(state: SupervisorSubordinateState) -> SupervisorSubordinateState:

SupervisorSubordinateState: State for Supervisor-Subordinate pattern.

State Fields: – input: Stores input – output: Stores output – def agent_function(state: Stores def agent function(state – prompt = f"Process this using Supervisor-Subordinate pattern: Stores prompt = f”Process this using Supervisor-Subordinate pattern – def create_supervisor_subordinate(): Stores def create supervisor subordinate()

---

Functions

agent_function()

Line: 17
Arguments: state

Supervisor-Subordinate agent implementation.

def agent_function(state: SupervisorSubordinateState) -> SupervisorSubordinateState:
    """Supervisor-Subordinate agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process this using Supervisor-Subordinate pattern: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    return state


def create_supervisor_subordinate():

create_supervisor_subordinate()

Line: 26
Arguments: “

Create Supervisor-Subordinate workflow.

def create_supervisor_subordinate():
    """Create Supervisor-Subordinate workflow."""
    workflow = StateGraph(SupervisorSubordinateState)
    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 SupervisorSubordinateState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Supervisor-Subordinate pattern."""

Describes the pattern and its purpose.

Line 17: Function agent_function definition

def agent_function(state: SupervisorSubordinateState) -> SupervisorSubordinateState:

Defines the agent_function function that processes the workflow state.

Line 18: Module docstring

    """Supervisor-Subordinate agent implementation."""

Describes the pattern and its purpose.

Line 26: Function create_supervisor_subordinate definition

def create_supervisor_subordinate():

Defines the create_supervisor_subordinate function that processes the workflow state.

Line 27: Module docstring

    """Create Supervisor-Subordinate workflow."""

Describes the pattern and its purpose.

---

Usage Example

if __name__ == "__main__":
    workflow = create_supervisor_subordinate()
    result = workflow.invoke({"input": "Test Supervisor-Subordinate", "output": ""})
    print(f"\n=== Supervisor-Subordinate 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

    """
Pattern 04.01: Master-Worker
Central coordinator delegates to workers
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class MasterWorkerState(TypedDict):
    """State for Master-Worker pattern."""
    input: str
    output: str


def agent_function(state: MasterWorkerState) -> MasterWorkerState:
    """Master-Worker agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process this using Master-Worker pattern: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    return state


def create_master_worker():
    """Create Master-Worker workflow."""
    workflow = StateGraph(MasterWorkerState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

    

Agentic Orchestration Pattern: Master-Worker Pattern

01 Master Worker Pattern – Detailed Documentation

Overview

File: 04_hierarchical/01_master_worker.py
Pattern Category: 04 Hierarchical
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 04.01: Master-Worker Central coordinator delegates to workers

---

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 MasterWorkerState(TypedDict):
    """State for Master-Worker pattern."""
    input: str
    output: str


def agent_function(state: MasterWorkerState) -> MasterWorkerState:

MasterWorkerState: State for Master-Worker pattern.

State Fields: – input: Stores input – output: Stores output – def agent_function(state: Stores def agent function(state – prompt = f"Process this using Master-Worker pattern: Stores prompt = f”Process this using Master-Worker pattern – def create_master_worker(): Stores def create master worker()

---

Functions

agent_function()

Line: 17
Arguments: state

Master-Worker agent implementation.

def agent_function(state: MasterWorkerState) -> MasterWorkerState:
    """Master-Worker agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process this using Master-Worker pattern: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    return state


def create_master_worker():

create_master_worker()

Line: 26
Arguments: “

Create Master-Worker workflow.

def create_master_worker():
    """Create Master-Worker workflow."""
    workflow = StateGraph(MasterWorkerState)
    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 MasterWorkerState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Master-Worker pattern."""

Describes the pattern and its purpose.

Line 17: Function agent_function definition

def agent_function(state: MasterWorkerState) -> MasterWorkerState:

Defines the agent_function function that processes the workflow state.

Line 18: Module docstring

    """Master-Worker agent implementation."""

Describes the pattern and its purpose.

Line 26: Function create_master_worker definition

def create_master_worker():

Defines the create_master_worker function that processes the workflow state.

Line 27: Module docstring

    """Create Master-Worker workflow."""

Describes the pattern and its purpose.

---

Usage Example

if __name__ == "__main__":
    workflow = create_master_worker()
    result = workflow.invoke({"input": "Test Master-Worker", "output": ""})
    print(f"\n=== Master-Worker 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

    """
Pattern 03.04: Refinement Loop
Iterative improvement of results with quality assessment
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class RefinementLoopState(TypedDict):
    """State for Refinement Loop pattern."""
    input: str
    output: str


def agent_function(state: RefinementLoopState) -> RefinementLoopState:
    """Refinement Loop agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process this using Refinement Loop pattern: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    return state


def create_refinement_loop():
    """Create Refinement Loop workflow."""
    workflow = StateGraph(RefinementLoopState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

    

Agentic Orchestration Pattern: Refinement Loop

04 Refinement Loop Pattern – Detailed Documentation

Overview

File: 03_iterative/04_refinement_loop.py
Pattern Category: 03 Iterative
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 03.04: Refinement Loop Iterative improvement of results with quality assessment

---

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 RefinementLoopState(TypedDict):
    """State for Refinement Loop pattern."""
    input: str
    output: str


def agent_function(state: RefinementLoopState) -> RefinementLoopState:

RefinementLoopState: State for Refinement Loop pattern.

State Fields: – input: Stores input – output: Stores output – def agent_function(state: Stores def agent function(state – prompt = f"Process this using Refinement Loop pattern: Stores prompt = f”Process this using Refinement Loop pattern – def create_refinement_loop(): Stores def create refinement loop()

---

Functions

agent_function()

Line: 17
Arguments: state

Refinement Loop agent implementation.

def agent_function(state: RefinementLoopState) -> RefinementLoopState:
    """Refinement Loop agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process this using Refinement Loop pattern: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    return state


def create_refinement_loop():

create_refinement_loop()

Line: 26
Arguments: “

Create Refinement Loop workflow.

def create_refinement_loop():
    """Create Refinement Loop workflow."""
    workflow = StateGraph(RefinementLoopState)
    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 RefinementLoopState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Refinement Loop pattern."""

Describes the pattern and its purpose.

Line 17: Function agent_function definition

def agent_function(state: RefinementLoopState) -> RefinementLoopState:

Defines the agent_function function that processes the workflow state.

Line 18: Module docstring

    """Refinement Loop agent implementation."""

Describes the pattern and its purpose.

Line 26: Function create_refinement_loop definition

def create_refinement_loop():

Defines the create_refinement_loop function that processes the workflow state.

Line 27: Module docstring

    """Create Refinement Loop workflow."""

Describes the pattern and its purpose.

---

Usage Example

if __name__ == "__main__":
    workflow = create_refinement_loop()
    result = workflow.invoke({"input": "Test Refinement Loop", "output": ""})
    print(f"\n=== Refinement Loop 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

    """
Pattern 03.03: Retry
Automatic retry on failure with exponential backoff
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class RetryState(TypedDict):
    """State for Retry pattern."""
    input: str
    output: str


def agent_function(state: RetryState) -> RetryState:
    """Retry agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process this using Retry pattern: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    return state


def create_retry():
    """Create Retry workflow."""
    workflow = StateGraph(RetryState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

    

Agentic Orchestration Pattern: Retry Pattern

03 Retry Pattern – Detailed Documentation

Overview

File: 03_iterative/03_retry.py
Pattern Category: 03 Iterative
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 03.03: Retry Automatic retry on failure with exponential backoff

---

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 RetryState(TypedDict):
    """State for Retry pattern."""
    input: str
    output: str


def agent_function(state: RetryState) -> RetryState:

RetryState: State for Retry pattern.

State Fields: – input: Stores input – output: Stores output – def agent_function(state: Stores def agent function(state – prompt = f"Process this using Retry pattern: Stores prompt = f”Process this using Retry pattern – def create_retry(): Stores def create retry()

---

Functions

agent_function()

Line: 17
Arguments: state

Retry agent implementation.

def agent_function(state: RetryState) -> RetryState:
    """Retry agent implementation."""
    llm = ChatOpenAI()
    prompt = f"Process this using Retry pattern: {state['input']}"
    response = llm.invoke([HumanMessage(content=prompt)])
    state["output"] = str(response.content)
    return state


def create_retry():

create_retry()

Line: 26
Arguments: “

Create Retry workflow.

def create_retry():
    """Create Retry workflow."""
    workflow = StateGraph(RetryState)
    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 RetryState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Retry pattern."""

Describes the pattern and its purpose.

Line 17: Function agent_function definition

def agent_function(state: RetryState) -> RetryState:

Defines the agent_function function that processes the workflow state.

Line 18: Module docstring

    """Retry agent implementation."""

Describes the pattern and its purpose.

Line 26: Function create_retry definition

def create_retry():

Defines the create_retry function that processes the workflow state.

Line 27: Module docstring

    """Create Retry workflow."""

Describes the pattern and its purpose.

---

Usage Example

if __name__ == "__main__":
    workflow = create_retry()
    result = workflow.invoke({"input": "Test Retry", "output": ""})
    print(f"\n=== Retry 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