langchain-ai/langgraph-supervisor-js
1
0
Fork 0
mirror of https://github.com/langchain-ai/langgraph-supervisor-js.git synced 2026-07-01 16:21:20 -04:00
Code Issues Packages Projects Releases Wiki Activity

chore(README): add notice that repo has moved (#4)

Browse Source
This commit is contained in:
Ben Burns
2025-02-28 17:56:43 +13:00
committed by GitHub
parent 56ec29164c
commit fcdd6de777
1 changed files with 4 additions and 181 deletions
Download Patch File Download Diff File Expand all files Collapse all files
README.md
+4 -181
View File
@@ -1,3 +1,6 @@
> [!IMPORTANT]
> The code for this project has moved into the [LangGraph JS monorepo](https://github.com/langchain-ai/langgraphjs/tree/main/libs/langgraph-supervisor).
# 🤖 LangGraph Multi-Agent Supervisor
A JavaScript library for creating hierarchical multi-agent systems using [LangGraph](https://github.com/langchain-ai/langgraphjs). Hierarchical systems are a type of [multi-agent](https://langchain-ai.github.io/langgraphjs/concepts/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.
@@ -10,184 +13,4 @@ A JavaScript library for creating hierarchical multi-agent systems using [LangGr
This library is built on top of [LangGraph](https://github.com/langchain-ai/langgraphjs), a powerful framework for building agent applications, and comes with out-of-box support for [streaming](https://langchain-ai.github.io/langgraphjs/how-tos/#streaming), [short-term and long-term memory](https://langchain-ai.github.io/langgraphjs/concepts/memory/) and [human-in-the-loop](https://langchain-ai.github.io/langgraphjs/concepts/human_in_the_loop/)
## Installation
```bash
npm install @langchain/langgraph-supervisor
```
## Quickstart
Here's a simple example of a supervisor managing two specialized agents:
![Supervisor Architecture](static/img/supervisor.png)
```bash
npm install @langchain/langgraph-supervisor @langchain/openai
export OPENAI_API_KEY=<your_api_key>
```
```ts
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??"
}
]
});
```
## 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:
![Full History](static/img/full_history.png)
```ts
const workflow = createSupervisor({
agents: [agent1, agent2],
outputMode: "full_history"
})
```
Include only the final agent response:
![Last Message](static/img/last_message.png)
```ts
const workflow = createSupervisor({
agents: [agent1, agent2],
outputMode: "last_message"
})
```
## Multi-level Hierarchies
You can create multi-level hierarchical systems by creating a supervisor that manages multiple supervisors.
```ts
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" })
```
## Adding Memory
You can add [short-term](https://langchain-ai.github.io/langgraphjs/how-tos/persistence/) and [long-term](https://langchain-ai.github.io/langgraphjs/how-tos/cross-thread-persistence/) [memory](https://langchain-ai.github.io/langgraphjs/concepts/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](https://langchain-ai.github.io/langgraphjs/reference/classes/checkpoint.BaseCheckpointSaver.html) or a [store](https://langchain-ai.github.io/langgraphjs/reference/classes/checkpoint.BaseStore.html) instance to the `.compile()` method:
```ts
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
})
```
## Installation