Multi-Agent Systems

Designing Multi-Agent Systems with LangGraph

Multi-agent systems in LangGraph require careful coordination of roles, communication, and task delegation to ensure seamless collaboration. This section outlines patterns for structuring agent interactions, including role-based workflows, message-passing protocols, and dynamic task routing.

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Role Assignment Patterns

Agents must be assigned roles that align with their capabilities and the task requirements. Roles can be static or dynamic, depending on the workflow. Use state machines or role-based workflows to define responsibilities.

Example: A "Researcher" agent might gather data, while a "Summarizer" agent condenses findings. Roles can be determined by task context or user input.

from langgraph import Agent, Role

class Researcher(Agent):
    role = Role("Researcher")
    def execute(self, query):
        # Fetch data from external sources
        return "Data collected: [X]"

class Summarizer(Agent):
    role = Role("Summarizer")
    def execute(self, data):
        # Generate summary
        return "Summary: [Y]"

Diagram:

[User Query] --> [Researcher] --> [Summarizer] --> [Final Answer]

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Communication Protocols

Agents communicate via structured messages, often using JSON or custom schemas. LangGraph supports synchronous and asynchronous interactions, with state management to track progress.

Key Patterns: - Message Passing: Agents send/receive structured payloads (e.g., {"query": "What is X?", "context": "Previous findings"}). - Error Handling: Retry mechanisms for failed steps or fallback agents. - State Synchronization: Use shared state stores (e.g., Redis, SQLite) to track agent progress.

Example: A query router directs requests to the appropriate agent.

def route_query(query):
    if "data" in query:
        return "Researcher"
    elif "summary" in query:
        return "Summarizer"
    return "DefaultAgent"

Diagram:

[User] --> [Query Router] --> [Researcher/Summarizer] --> [Response]

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Task Delegation Strategies

Complex tasks should be split into subtasks, with a task router or priority queue to assign work dynamically. LangGraph supports parallel execution and dependency management.

Patterns: - Task Splitting: Break tasks into independent subtasks (e.g., "Analyze X", "Analyze Y"). - Priority Queues: Assign higher-priority tasks to faster agents. - Feedback Loops: Use intermediate results to adjust task allocation.

Example: A router assigns tasks based on agent availability.

def assign_task(task_queue, agents):
    for agent in agents:
        if task_queue and agent.is_available():
            task = task_queue.pop(0)
            agent.execute(task)

Diagram:

[Task Queue] --> [Agent Pool] --> [Task Execution] --> [Results]

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Key takeaways

• Role-based design ensures agents focus on specific responsibilities.
• Structured communication (e.g., message schemas) prevents ambiguity in interactions.
• Dynamic task delegation optimizes resource use and handles complex workflows.
• Use LangGraph's state management and routing capabilities to build scalable, collaborative agent systems.