🤖 LangGraph Multi-Agent Supervisor

A JavaScript library for creating hierarchical multi-agent systems using LangGraph. Hierarchical systems are a type of multi-agent architecture where specialized agents are coordinated by a central supervisor agent. The supervisor controls all communication flow and task delegation, making decisions about which agent to invoke based on the current context and task requirements.

Features

• 🤖 Create a supervisor agent to orchestrate multiple specialized agents
• 🛠️ Tool-based agent handoff mechanism for communication between agents
• 📝 Flexible message history management for conversation control

This library is built on top of LangGraph, a powerful framework for building agent applications, and comes with out-of-box support for streaming, short-term and long-term memory and human-in-the-loop

Installation

npm install @langchain/langgraph-supervisor @langchain/langgraph @langchain/core
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Quickstart

Here's a simple example of a supervisor managing two specialized agents:

npm install @langchain/langgraph-supervisor @langchain/langgraph @langchain/core @langchain/openai

export OPENAI_API_KEY=<your_api_key>
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import { ChatOpenAI } from "@langchain/openai";
import { createSupervisor } from "@langchain/langgraph-supervisor";
import { createReactAgent } from "@langchain/langgraph/prebuilt";
import { tool } from "@langchain/core/tools";
import { z } from "zod";

const model = new ChatOpenAI({ modelName: "gpt-4o" });

// Create specialized agents
const add = tool(
  async (args) => args.a + args.b,
  {
    name: "add",
    description: "Add two numbers.",
    schema: z.object({
      a: z.number(),
      b: z.number()
    })
  }
);

const multiply = tool(
  async (args) => args.a * args.b,
  {
    name: "multiply", 
    description: "Multiply two numbers.",
    schema: z.object({
      a: z.number(),
      b: z.number()
    })
  }
);

const webSearch = tool(
  async (args) => {
    return (
      "Here are the headcounts for each of the FAANG companies in 2024:\n" +
      "1. **Facebook (Meta)**: 67,317 employees.\n" +
      "2. **Apple**: 164,000 employees.\n" +
      "3. **Amazon**: 1,551,000 employees.\n" +
      "4. **Netflix**: 14,000 employees.\n" +
      "5. **Google (Alphabet)**: 181,269 employees."
    );
  },
  {
    name: "web_search",
    description: "Search the web for information.",
    schema: z.object({
      query: z.string()
    })
  }
);

const mathAgent = createReactAgent({
  llm: model,
  tools: [add, multiply],
  name: "math_expert",
  prompt: "You are a math expert. Always use one tool at a time."
});

const researchAgent = createReactAgent({
  llm: model,
  tools: [webSearch],
  name: "research_expert",
  prompt: "You are a world class researcher with access to web search. Do not do any math."
});

// Create supervisor workflow
const workflow = createSupervisor({
  agents: [researchAgent, mathAgent],
  llm: model,
  prompt: 
    "You are a team supervisor managing a research expert and a math expert. " +
    "For current events, use research_agent. " +
    "For math problems, use math_agent."
});

// Compile and run
const app = workflow.compile();
const result = await app.invoke({
  messages: [
    {
      role: "user",
      content: "what's the combined headcount of the FAANG companies in 2024??"
    }
  ]
});
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Message History Management

You can control how agent messages are added to the overall conversation history of the multi-agent system:

Include full message history from an agent:

const workflow = createSupervisor({
  agents: [agent1, agent2],
  outputMode: "full_history"
})
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Include only the final agent response:

const workflow = createSupervisor({
  agents: [agent1, agent2],
  outputMode: "last_message"
})
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Multi-level Hierarchies

You can create multi-level hierarchical systems by creating a supervisor that manages multiple supervisors.

const researchTeam = createSupervisor({
  agents: [researchAgent, mathAgent],
  llm: model,
}).compile({ name: "research_team" })

const writingTeam = createSupervisor({
  agents: [writingAgent, publishingAgent],
  llm: model,
}).compile({ name: "writing_team" })

const topLevelSupervisor = createSupervisor({
  agents: [researchTeam, writingTeam],
  llm: model,
}).compile({ name: "top_level_supervisor" })
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Adding Memory

You can add short-term and long-term memory to your supervisor multi-agent system. Since createSupervisor() returns an instance of StateGraph that needs to be compiled before use, you can directly pass a checkpointer or a store instance to the .compile() method:

import { MemorySaver, InMemoryStore } from "@langchain/langgraph";

const checkpointer = new MemorySaver()
const store = new InMemoryStore()

const model = ...
const researchAgent = ...
const mathAgent = ...

const workflow = createSupervisor({
  agents: [researchAgent, mathAgent],
  llm: model,
  prompt: "You are a team supervisor managing a research expert and a math expert.",
})

// Compile with checkpointer/store
const app = workflow.compile({
  checkpointer,
  store
})
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