Tag: llm

Agentic Orchestration Pattern: Genetic Algorithm Pattern


    """
Pattern 25.02: Genetic Algorithm
Evolution of solutions
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class GeneticAlgorithmState(TypedDict):
    """State for Genetic Algorithm pattern."""
    input: str
    output: str
    metadata: dict


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


def create_genetic_algorithm():
    """Create Genetic Algorithm workflow."""
    workflow = StateGraph(GeneticAlgorithmState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Genetic Algorithm Pattern

02 Genetic Algorithm Pattern – Detailed Documentation

Overview

File: 25_experimental/02_genetic_algorithm.py
Pattern Category: 25 Experimental
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 25.02: Genetic Algorithm Evolution of solutions

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 GeneticAlgorithmState(TypedDict):
    """State for Genetic Algorithm pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: GeneticAlgorithmState) -> GeneticAlgorithmState:

GeneticAlgorithmState: State for Genetic Algorithm 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 Genetic Algorithm: Stores prompt = f”Process using Genetic Algorithm – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

Genetic Algorithm agent implementation.

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


def create_genetic_algorithm():

`create_genetic_algorithm()`

Line: 28
Arguments: “

Create Genetic Algorithm workflow.

def create_genetic_algorithm():
    """Create Genetic Algorithm workflow."""
    workflow = StateGraph(GeneticAlgorithmState)
    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 GeneticAlgorithmState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Genetic Algorithm pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: GeneticAlgorithmState) -> GeneticAlgorithmState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Genetic Algorithm agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_genetic_algorithm definition

def create_genetic_algorithm():

Defines the create_genetic_algorithm function that processes the workflow state.

Line 29: Module docstring

    """Create Genetic Algorithm workflow."""

Describes the pattern and its purpose.

Usage Example

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

Key Concepts

State Management: Uses TypedDict to maintain workflow state
Graph-Based Flow: LangGraph orchestrates execution flow
Agent Functions: Each node processes and updates state
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

November 4, 2025

Agentic Orchestration Pattern: Swarm Pattern


    """
Pattern 25.01: Swarm
Swarm intelligence and collective behavior
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class SwarmState(TypedDict):
    """State for Swarm pattern."""
    input: str
    output: str
    metadata: dict


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


def create_swarm():
    """Create Swarm workflow."""
    workflow = StateGraph(SwarmState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Swarm Pattern

01 Swarm Pattern – Detailed Documentation

Overview

File: 25_experimental/01_swarm.py
Pattern Category: 25 Experimental
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 25.01: Swarm Swarm intelligence and collective behavior

Code Structure

Imports

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

State Definition

class SwarmState(TypedDict):
    """State for Swarm pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: SwarmState) -> SwarmState:

SwarmState: State for Swarm 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 Swarm: Stores prompt = f”Process using Swarm – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

Swarm agent implementation.

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


def create_swarm():

`create_swarm()`

Line: 28
Arguments: “

Create Swarm workflow.

def create_swarm():
    """Create Swarm workflow."""
    workflow = StateGraph(SwarmState)
    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 SwarmState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Swarm pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: SwarmState) -> SwarmState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Swarm agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_swarm definition

def create_swarm():

Defines the create_swarm function that processes the workflow state.

Line 29: Module docstring

    """Create Swarm workflow."""

Describes the pattern and its purpose.

Usage Example

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

Key Concepts

State Management: Uses TypedDict to maintain workflow state
Graph-Based Flow: LangGraph orchestrates execution flow
Agent Functions: Each node processes and updates state
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

November 4, 2025

Agentic Orchestration Pattern: Isolation Pattern


    """
Pattern 24.04: Isolation
Failure isolation and blast radius limitation
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class IsolationState(TypedDict):
    """State for Isolation pattern."""
    input: str
    output: str
    metadata: dict


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


def create_isolation():
    """Create Isolation workflow."""
    workflow = StateGraph(IsolationState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Isolation Pattern

04 Isolation Pattern – Detailed Documentation

Overview

File: 24_resilience/04_isolation.py
Pattern Category: 24 Resilience
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 24.04: Isolation Failure isolation and blast radius limitation

Code Structure

Imports

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

State Definition

class IsolationState(TypedDict):
    """State for Isolation pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: IsolationState) -> IsolationState:

IsolationState: State for Isolation 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 Isolation: Stores prompt = f”Process using Isolation – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

Isolation agent implementation.

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


def create_isolation():

`create_isolation()`

Line: 28
Arguments: “

Create Isolation workflow.

def create_isolation():
    """Create Isolation workflow."""
    workflow = StateGraph(IsolationState)
    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 IsolationState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Isolation pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: IsolationState) -> IsolationState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Isolation agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_isolation definition

def create_isolation():

Defines the create_isolation function that processes the workflow state.

Line 29: Module docstring

    """Create Isolation workflow."""

Describes the pattern and its purpose.

Usage Example

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

Key Concepts

State Management: Uses TypedDict to maintain workflow state
Graph-Based Flow: LangGraph orchestrates execution flow
Agent Functions: Each node processes and updates state
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

November 4, 2025

Agentic Orchestration Pattern: Health Check Pattern


    """
Pattern 24.03: Health Check
Regular health monitoring and recovery
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class HealthCheckState(TypedDict):
    """State for Health Check pattern."""
    input: str
    output: str
    metadata: dict


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


def create_health_check():
    """Create Health Check workflow."""
    workflow = StateGraph(HealthCheckState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Health Check Pattern

03 Health Check Pattern – Detailed Documentation

Overview

File: 24_resilience/03_health_check.py
Pattern Category: 24 Resilience
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 24.03: Health Check Regular health monitoring and recovery

Code Structure

Imports

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

State Definition

class HealthCheckState(TypedDict):
    """State for Health Check pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: HealthCheckState) -> HealthCheckState:

HealthCheckState: State for Health Check 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 Health Check: Stores prompt = f”Process using Health Check – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

Health Check agent implementation.

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


def create_health_check():

`create_health_check()`

Line: 28
Arguments: “

Create Health Check workflow.

def create_health_check():
    """Create Health Check workflow."""
    workflow = StateGraph(HealthCheckState)
    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 HealthCheckState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Health Check pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: HealthCheckState) -> HealthCheckState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Health Check agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_health_check definition

def create_health_check():

Defines the create_health_check function that processes the workflow state.

Line 29: Module docstring

    """Create Health Check workflow."""

Describes the pattern and its purpose.

Usage Example

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

Key Concepts

State Management: Uses TypedDict to maintain workflow state
Graph-Based Flow: LangGraph orchestrates execution flow
Agent Functions: Each node processes and updates state
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

November 4, 2025

Agentic Orchestration Pattern: Graceful Degradation


    """
Pattern 24.02: Graceful Degradation
Reduced functionality on failure
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class GracefulDegradationState(TypedDict):
    """State for Graceful Degradation pattern."""
    input: str
    output: str
    metadata: dict


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


def create_graceful_degradation():
    """Create Graceful Degradation workflow."""
    workflow = StateGraph(GracefulDegradationState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Graceful Degradation

02 Graceful Degradation Pattern – Detailed Documentation

Overview

File: 24_resilience/02_graceful_degradation.py
Pattern Category: 24 Resilience
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 24.02: Graceful Degradation Reduced functionality on failure

Code Structure

Imports

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

State Definition

class GracefulDegradationState(TypedDict):
    """State for Graceful Degradation pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: GracefulDegradationState) -> GracefulDegradationState:

GracefulDegradationState: State for Graceful Degradation 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 Graceful Degradation: Stores prompt = f”Process using Graceful Degradation – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

Graceful Degradation agent implementation.

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


def create_graceful_degradation():

`create_graceful_degradation()`

Line: 28
Arguments: “

Create Graceful Degradation workflow.

def create_graceful_degradation():
    """Create Graceful Degradation workflow."""
    workflow = StateGraph(GracefulDegradationState)
    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 GracefulDegradationState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Graceful Degradation pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: GracefulDegradationState) -> GracefulDegradationState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Graceful Degradation agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_graceful_degradation definition

def create_graceful_degradation():

Defines the create_graceful_degradation function that processes the workflow state.

Line 29: Module docstring

    """Create Graceful Degradation workflow."""

Describes the pattern and its purpose.

Usage Example

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

Key Concepts

State Management: Uses TypedDict to maintain workflow state
Graph-Based Flow: LangGraph orchestrates execution flow
Agent Functions: Each node processes and updates state
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

November 4, 2025

Agentic Orchestration Pattern: Redundancy Pattern


    """
Pattern 24.01: Redundancy
Multiple redundant agents for failover
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class RedundancyState(TypedDict):
    """State for Redundancy pattern."""
    input: str
    output: str
    metadata: dict


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


def create_redundancy():
    """Create Redundancy workflow."""
    workflow = StateGraph(RedundancyState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Redundancy Pattern

01 Redundancy Pattern – Detailed Documentation

Overview

File: 24_resilience/01_redundancy.py
Pattern Category: 24 Resilience
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 24.01: Redundancy Multiple redundant agents for failover

Code Structure

Imports

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

State Definition

class RedundancyState(TypedDict):
    """State for Redundancy pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: RedundancyState) -> RedundancyState:

RedundancyState: State for Redundancy 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 Redundancy: Stores prompt = f”Process using Redundancy – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

Redundancy agent implementation.

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


def create_redundancy():

`create_redundancy()`

Line: 28
Arguments: “

Create Redundancy workflow.

def create_redundancy():
    """Create Redundancy workflow."""
    workflow = StateGraph(RedundancyState)
    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 RedundancyState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Redundancy pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: RedundancyState) -> RedundancyState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Redundancy agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_redundancy definition

def create_redundancy():

Defines the create_redundancy function that processes the workflow state.

Line 29: Module docstring

    """Create Redundancy workflow."""

Describes the pattern and its purpose.

Usage Example

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

Key Concepts

State Management: Uses TypedDict to maintain workflow state
Graph-Based Flow: LangGraph orchestrates execution flow
Agent Functions: Each node processes and updates state
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

November 4, 2025

Agentic Orchestration Pattern: Reasoning Pattern


    """
Pattern 23.04: Reasoning
Chain-of-thought and logical inference
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class ReasoningState(TypedDict):
    """State for Reasoning pattern."""
    input: str
    output: str
    metadata: dict


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


def create_reasoning():
    """Create Reasoning workflow."""
    workflow = StateGraph(ReasoningState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Reasoning Pattern

04 Reasoning Pattern – Detailed Documentation

Overview

File: 23_domain_specific/04_reasoning.py
Pattern Category: 23 Domain Specific
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 23.04: Reasoning Chain-of-thought and logical inference

Code Structure

Imports

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

State Definition

class ReasoningState(TypedDict):
    """State for Reasoning pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: ReasoningState) -> ReasoningState:

ReasoningState: State for Reasoning 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 Reasoning: Stores prompt = f”Process using Reasoning – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

Reasoning agent implementation.

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


def create_reasoning():

`create_reasoning()`

Line: 28
Arguments: “

Create Reasoning workflow.

def create_reasoning():
    """Create Reasoning workflow."""
    workflow = StateGraph(ReasoningState)
    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 ReasoningState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Reasoning pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: ReasoningState) -> ReasoningState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Reasoning agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_reasoning definition

def create_reasoning():

Defines the create_reasoning function that processes the workflow state.

Line 29: Module docstring

    """Create Reasoning workflow."""

Describes the pattern and its purpose.

Usage Example

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

Key Concepts

State Management: Uses TypedDict to maintain workflow state
Graph-Based Flow: LangGraph orchestrates execution flow
Agent Functions: Each node processes and updates state
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

November 4, 2025

Agentic Orchestration Pattern: Planning Pattern


    """
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

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

State Management: Uses TypedDict to maintain workflow state
Graph-Based Flow: LangGraph orchestrates execution flow
Agent Functions: Each node processes and updates state
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

November 4, 2025

Agentic Orchestration Pattern: Tool-Use Pattern


    """
Pattern 23.02: Tool-Use
Agents use external tools and APIs
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class ToolUseState(TypedDict):
    """State for Tool-Use pattern."""
    input: str
    output: str
    metadata: dict


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


def create_tool_use():
    """Create Tool-Use workflow."""
    workflow = StateGraph(ToolUseState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Tool-Use Pattern

02 Tool Use Pattern – Detailed Documentation

Overview

File: 23_domain_specific/02_tool_use.py
Pattern Category: 23 Domain Specific
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 23.02: Tool-Use Agents use external tools and APIs

Code Structure

Imports

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

State Definition

class ToolUseState(TypedDict):
    """State for Tool-Use pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: ToolUseState) -> ToolUseState:

ToolUseState: State for Tool-Use 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 Tool-Use: Stores prompt = f”Process using Tool-Use – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

Tool-Use agent implementation.

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


def create_tool_use():

`create_tool_use()`

Line: 28
Arguments: “

Create Tool-Use workflow.

def create_tool_use():
    """Create Tool-Use workflow."""
    workflow = StateGraph(ToolUseState)
    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 ToolUseState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for Tool-Use pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: ToolUseState) -> ToolUseState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """Tool-Use agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_tool_use definition

def create_tool_use():

Defines the create_tool_use function that processes the workflow state.

Line 29: Module docstring

    """Create Tool-Use workflow."""

Describes the pattern and its purpose.

Usage Example

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

Key Concepts

State Management: Uses TypedDict to maintain workflow state
Graph-Based Flow: LangGraph orchestrates execution flow
Agent Functions: Each node processes and updates state
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

November 4, 2025

Agentic Orchestration Pattern: Retrieval-Augmented Generation (RAG)


    """
Pattern 23.01: RAG
Retrieval-Augmented Generation pattern
"""
from typing import TypedDict
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage


class RagState(TypedDict):
    """State for RAG pattern."""
    input: str
    output: str
    metadata: dict


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


def create_rag():
    """Create RAG workflow."""
    workflow = StateGraph(RagState)
    workflow.add_node("agent", agent_function)
    workflow.set_entry_point("agent")
    workflow.add_edge("agent", END)
    return workflow.compile()


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

Agentic Orchestration Pattern: Retrieval-Augmented Generation (RAG)

01 Rag Pattern – Detailed Documentation

Overview

File: 23_domain_specific/01_rag.py
Pattern Category: 23 Domain Specific
Implementation: LangGraph/CrewAI

Pattern Description

Pattern 23.01: RAG Retrieval-Augmented Generation pattern

Code Structure

Imports

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

State Definition

class RagState(TypedDict):
    """State for RAG pattern."""
    input: str
    output: str
    metadata: dict


def agent_function(state: RagState) -> RagState:

RagState: State for RAG 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 RAG: Stores prompt = f”Process using RAG – state["metadata"] = {"pattern": Stores state[“metadata”] = {“pattern”

Functions

`agent_function()`

Line: 18
Arguments: state

RAG agent implementation.

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


def create_rag():

`create_rag()`

Line: 28
Arguments: “

Create RAG workflow.

def create_rag():
    """Create RAG workflow."""
    workflow = StateGraph(RagState)
    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 RagState(TypedDict):

Defines the state structure using TypedDict for type safety.

Line 12: Module docstring

    """State for RAG pattern."""

Describes the pattern and its purpose.

Line 18: Function agent_function definition

def agent_function(state: RagState) -> RagState:

Defines the agent_function function that processes the workflow state.

Line 19: Module docstring

    """RAG agent implementation."""

Describes the pattern and its purpose.

Line 28: Function create_rag definition

def create_rag():

Defines the create_rag function that processes the workflow state.

Line 29: Module docstring

    """Create RAG workflow."""

Describes the pattern and its purpose.

Usage Example

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

Key Concepts

State Management: Uses TypedDict to maintain workflow state
Graph-Based Flow: LangGraph orchestrates execution flow
Agent Functions: Each node processes and updates state
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

November 4, 2025