Designing multi-agent systems using LangGraph for collaborative problem-solving

Designing Multi-Agent Systems Using LangGraph for Collaborative Problem-Solving

LangGraph has emerged as a powerful framework for building multi-agent systems that can tackle complex problems through collaboration. Having implemented several production-grade multi-agent systems, I'll share practical insights on designing these systems using LangGraph, complete with real-world examples and code implementations.

Understanding Multi-Agent Systems with LangGraph

Multi-agent systems consist of multiple AI agents working together to solve problems that might be too complex for a single agent. LangGraph provides a structured way to orchestrate these interactions through a graph-based approach.

Let's start with a practical example - building a software development team of AI agents that can break down, implement, and review code collaboratively.

from langgraph.graph import Graph
from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage, AIMessage
import operator

# Define our agent roles
class TechLead:
    def __init__(self, llm):
        self.llm = llm

    def break_down_task(self, task_description):
        messages = [
            HumanMessage(content=f"""
            Break down this task into smaller subtasks:
            {task_description}

            Format: List of subtasks with estimates
            """)
        ]
        response = self.llm.invoke(messages)
        return response.content

class Developer:
    def __init__(self, llm):
        self.llm = llm

    def implement_subtask(self, subtask):
        messages = [
            HumanMessage(content=f"""
            Implement this subtask:
            {subtask}

            Provide implementation in Python.
            """)
        ]
        response = self.llm.invoke(messages)
        return response.content

class CodeReviewer:
    def __init__(self, llm):
        self.llm = llm

    def review_code(self, code):
        messages = [
            HumanMessage(content=f"""
            Review this code and provide feedback:
            {code}

            Format: List of issues and suggestions
            """)
        ]
        response = self.llm.invoke(messages)
        return response.content

Building the Workflow Graph

The power of LangGraph lies in its ability to create structured workflows. Here's how we can connect our agents:

def create_development_workflow():
    # Initialize our LLM
    llm = ChatOpenAI(temperature=0.7)

    # Initialize agents
    tech_lead = TechLead(llm)
    developer = Developer(llm)
    reviewer = CodeReviewer(llm)

    # Define the workflow graph
    workflow = Graph()

    # Add nodes
    workflow.add_node("breakdown", tech_lead.break_down_task)
    workflow.add_node("implement", developer.implement_subtask)
    workflow.add_node("review", reviewer.review_code)

    # Define edges
    workflow.add_edge("breakdown", "implement")
    workflow.add_edge("implement", "review")

    return workflow

# Create and compile the workflow
workflow = create_development_workflow()
workflow.compile()

Implementing State Management

One crucial aspect of multi-agent systems is maintaining state across interactions. LangGraph provides elegant solutions for state management:

class DevelopmentState:
    def __init__(self):
        self.task_breakdown = None
        self.implementation = None
        self.review_feedback = None
        self.iteration_count = 0

def state_manager():
    state = DevelopmentState()

    def update_state(action_result, action_name):
        if action_name == "breakdown":
            state.task_breakdown = action_result
        elif action_name == "implement":
            state.implementation = action_result
        elif action_name == "review":
            state.review_feedback = action_result
            state.iteration_count += 1
        return state

    return update_state

Adding Conditional Logic and Iteration

Real-world development often requires iterations based on review feedback. Here's how to implement conditional paths:

def should_iterate(state):
    # Check if we need another iteration based on review feedback
    if state.iteration_count >= 3:
        return False

    if state.review_feedback:
        # Simple check for critical issues in review feedback
        return "critical" in state.review_feedback.lower()

    return False

# Update our workflow with conditional logic
workflow.add_conditional_edge(
    "review",
    should_iterate,
    {
        True: "implement",
        False: None  # End the workflow
    }
)

Handling Agent Communication

Effective communication between agents is crucial. Here's a pattern for structured message passing:

class Message:
    def __init__(self, sender, content, message_type):
        self.sender = sender
        self.content = content
        self.message_type = message_type
        self.timestamp = datetime.now()

def create_message_handler():
    message_queue = []

    def handle_message(message, state):
        message_queue.append(message)

        # Process messages based on type
        if message.message_type == "question":
            # Route questions to appropriate agent
            return route_question(message, state)
        elif message.message_type == "update":
            # Handle status updates
            return process_update(message, state)

        return state

    return handle_message

# Add message handling to our workflow
workflow.add_node("message_handler", create_message_handler())

Implementing Error Handling and Recovery

Robust multi-agent systems need proper error handling:

class AgentError(Exception):
    def __init__(self, agent_name, error_message):
        self.agent_name = agent_name
        self.error_message = error_message
        super().__init__(f"Error in {agent_name}: {error_message}")

def create_error_handler():
    def handle_error(error, state):
        if isinstance(error, AgentError):
            # Log the error
            logging.error(f"Agent error: {error.agent_name} - {error.error_message}")

            # Implement recovery strategy
            if error.agent_name == "developer":
                # Retry with simplified task
                return retry_implementation(state)
            elif error.agent_name == "reviewer":
                # Skip review if it fails
                return skip_review(state)

        # Re-raise unknown errors
        raise error

    return handle_error

# Add error handling to our workflow
workflow.add_error_handler(create_error_handler())

Monitoring and Observability

To maintain and improve multi-agent systems, we need good observability:

class WorkflowMetrics:
    def __init__(self):
        self.step_durations = defaultdict(list)
        self.error_counts = defaultdict(int)
        self.iteration_counts = []

    def record_step(self, step_name, duration):
        self.step_durations[step_name].append(duration)

    def record_error(self, step_name):
        self.error_counts[step_name] += 1

    def record_iteration(self, count):
        self.iteration_counts.append(count)

def create_metrics_collector():
    metrics = WorkflowMetrics()

    def collect_metrics(state, step_name):
        # Record metrics for the step
        duration = time.time() - state.step_start_time
        metrics.record_step(step_name, duration)

        if hasattr(state, 'iteration_count'):
            metrics.record_iteration(state.iteration_count)

        return metrics

    return collect_metrics

# Add metrics collection to our workflow
workflow.add_observer(create_metrics_collector())

Running the Multi-Agent System

Here's how to execute our multi-agent development workflow:

# Initialize the workflow with a task
task = """
Create a Python function that implements a cache with LRU (Least Recently Used) eviction policy.
The cache should have a maximum size and automatically remove the least recently used items when full.
"""

# Execute the workflow
try:
    result = workflow.run(
        input_data={"task": task},
        state=DevelopmentState()
    )

    # Process the results
    print("Task Breakdown:", result.task_breakdown)
    print("Implementation:", result.implementation)
    print("Final Review:", result.review_feedback)
    print("Number of Iterations:", result.iteration_count)

except Exception as e:
    print(f"Workflow failed: {str(e)}")

Performance Considerations

When designing multi-agent systems with LangGraph, consider these performance aspects:

Agent Parallelization: LangGraph supports parallel execution of independent agents. Use this for tasks that don't have strict sequential dependencies.
Caching: Implement response caching for frequently performed tasks:

from functools import lru_cache

class CachedDeveloper(Developer):
    @lru_cache(maxsize=100)
    def implement_subtask(self, subtask):
        return super().implement_subtask(subtask)

Batch Processing: Group similar tasks for batch processing when possible:

def batch_process_reviews(code_segments):
    # Process multiple code reviews in one batch
    combined_review = "\n".join(code_segments)
    review_results = reviewer.review_code(combined_review)

    # Split results back into individual reviews
    return split_review_results(review_results)

Multi-agent systems built with LangGraph offer a powerful way to solve complex problems through collaboration. The key is to design clear interfaces between agents, manage state effectively, and implement robust error handling and monitoring. As these systems evolve, we'll likely see more sophisticated patterns emerge for agent coordination and problem-solving strategies.

Designing multi-agent systems using LangGraph for collaborative problem-solving