Langgraph Multi Agent Supervisor

LangGraph Multi-Agent Supervisor Skill

Build LangGraph multi-agent systems with a supervisor that orchestrates specialized worker agents. The supervisor intelligently routes user queries to the appropriate agent and manages the overall conversation flow.

Core Architecture

• Supervisor node: LLM-powered routing that selects the best worker agent for each query
• Worker agents: Specialized agents (Genie, RAG, MCP, custom) that handle domain-specific tasks
• State management: Shared state across agents with result aggregation
• Routing strategies: LLM-based (semantic), rule-based (keywords), sequential, or parallel

Supervisor Patterns

Choose the right pattern based on your use case:

See references/patterns.md for complete implementation code for all 4 patterns.

Worker Agent Types

See references/worker-agents.md for implementation code and configuration-driven agent creation.

Quick Start

1. Define State and Workers

from typing import TypedDict, Annotated, Sequence
import operator
from langchain_core.messages import BaseMessage, AIMessage
from langgraph.graph import StateGraph, END
from databricks_langchain import ChatDatabricks

class SupervisorState(TypedDict):
    messages: Annotated[Sequence[BaseMessage], operator.add]
    next_agent: str
    agent_results: dict
    final_response: str

WORKER_AGENTS = {
    "sales_agent": {"description": "Sales data queries", "type": "genie", "space_id": "..."},
    "docs_agent": {"description": "Documentation search", "type": "rag", "index_name": "..."},
}

2. Build Graph

graph = StateGraph(SupervisorState)
graph.add_node("supervisor", supervisor_node)
for name, config in WORKER_AGENTS.items():
    graph.add_node(name, create_worker_node(name, config))
    graph.add_edge(name, END)

graph.set_entry_point("supervisor")
graph.add_conditional_edges("supervisor", route_to_agent, routes)
agent = graph.compile()

3. Enable MLflow Tracking

import mlflow
mlflow.langchain.autolog()
supervisor = create_supervisor_agent()

Best Practices

1. Agent design: Keep agents focused and specialized with clear descriptions for routing
2. Routing: Use semantic routing (LLM) for flexibility, add rule-based for known patterns
3. State: Keep shared state minimal, use agent-specific state for details
4. Performance: Cache results, use parallel execution when possible, implement timeouts
5. Observability: Enable MLflow tracing, log routing decisions, track execution time

References

• references/patterns.md - Complete implementations of all 4 supervisor patterns
• references/worker-agents.md - Worker agent implementations and config-driven creation
• examples/example_agents.json - Example agent configurations
• Databricks Multi-Agent Framework: https://docs.databricks.com/generative-ai/agent-framework/multi-agent-genie
• LangGraph Supervisor: https://github.com/langchain-ai/langgraph-supervisor-py