WEBDIENSTE/langgraph
1
0
Fork 0
mirror of https://github.com/langchain-ai/langgraph.git synced 2026-09-12 20:57:52 +02:00
Code Issues Packages Projects Releases Wiki Activity

feat: add docs translations (#5552)

Browse Source 
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Tat Dat Duong <david@duong.cz>
This commit is contained in:
Hunter Lovell
2025-07-30 02:18:30 +00:00
committed by GitHub
co-authored by Eugene Yurtsev Tat Dat Duong
parent 72e418e4d0
commit d59091672f
89 changed files with 21412 additions and 2131 deletions
Download Patch File Download Diff File Expand all files Collapse all files
docs/docs/agents/multi-agent.md
+334 -5
View File
@@ -22,6 +22,7 @@ Two of the most popular multi-agent architectures are:
![Supervisor](./assets/supervisor.png)
:::python
Use [`langgraph-supervisor`](https://github.com/langchain-ai/langgraph-supervisor-py) library to create a supervisor multi-agent system:
```bash
@@ -82,10 +83,76 @@ for chunk in supervisor.stream(
print("\n")
```
:::
:::js
Use [`@langchain/langgraph-supervisor`](https://github.com/langchain-ai/langgraphjs/tree/main/libs/langgraph-supervisor) library to create a supervisor multi-agent system:
```bash
npm install @langchain/langgraph-supervisor
```
```typescript
import { ChatOpenAI } from "@langchain/openai";
import { createReactAgent } from "@langchain/langgraph/prebuilt";
// highlight-next-line
import { createSupervisor } from "langgraph-supervisor";
function bookHotel(hotelName: string) {
/**Book a hotel*/
return `Successfully booked a stay at ${hotelName}.`;
}
function bookFlight(fromAirport: string, toAirport: string) {
/**Book a flight*/
return `Successfully booked a flight from ${fromAirport} to ${toAirport}.`;
}
const flightAssistant = createReactAgent({
llm: "openai:gpt-4o",
tools: [bookFlight],
stateModifier: "You are a flight booking assistant",
// highlight-next-line
name: "flight_assistant",
});
const hotelAssistant = createReactAgent({
llm: "openai:gpt-4o",
tools: [bookHotel],
stateModifier: "You are a hotel booking assistant",
// highlight-next-line
name: "hotel_assistant",
});
// highlight-next-line
const supervisor = createSupervisor({
agents: [flightAssistant, hotelAssistant],
llm: new ChatOpenAI({ model: "gpt-4o" }),
systemPrompt:
"You manage a hotel booking assistant and a " +
"flight booking assistant. Assign work to them.",
});
for await (const chunk of supervisor.stream({
messages: [
{
role: "user",
content: "book a flight from BOS to JFK and a stay at McKittrick Hotel",
},
],
})) {
console.log(chunk);
console.log("\n");
}
```
:::
## Swarm
![Swarm](./assets/swarm.png)
:::python
Use [`langgraph-swarm`](https://github.com/langchain-ai/langgraph-swarm-py) library to create a swarm multi-agent system:
```bash
@@ -143,18 +210,82 @@ for chunk in swarm.stream(
print("\n")
```
:::
:::js
Use [`@langchain/langgraph-swarm`](https://github.com/langchain-ai/langgraphjs/tree/main/libs/langgraph-swarm) library to create a swarm multi-agent system:
```bash
npm install @langchain/langgraph-swarm
```
```typescript
import { createReactAgent } from "@langchain/langgraph/prebuilt";
// highlight-next-line
import { createSwarm, createHandoffTool } from "@langchain/langgraph-swarm";
const transferToHotelAssistant = createHandoffTool({
agentName: "hotel_assistant",
description: "Transfer user to the hotel-booking assistant.",
});
const transferToFlightAssistant = createHandoffTool({
agentName: "flight_assistant",
description: "Transfer user to the flight-booking assistant.",
});
const flightAssistant = createReactAgent({
llm: "anthropic:claude-3-5-sonnet-latest",
// highlight-next-line
tools: [bookFlight, transferToHotelAssistant],
stateModifier: "You are a flight booking assistant",
// highlight-next-line
name: "flight_assistant",
});
const hotelAssistant = createReactAgent({
llm: "anthropic:claude-3-5-sonnet-latest",
// highlight-next-line
tools: [bookHotel, transferToFlightAssistant],
stateModifier: "You are a hotel booking assistant",
// highlight-next-line
name: "hotel_assistant",
});
// highlight-next-line
const swarm = createSwarm({
agents: [flightAssistant, hotelAssistant],
defaultActiveAgent: "flight_assistant",
});
for await (const chunk of swarm.stream({
messages: [
{
role: "user",
content: "book a flight from BOS to JFK and a stay at McKittrick Hotel",
},
],
})) {
console.log(chunk);
console.log("\n");
}
```
:::
## Handoffs
A common pattern in multi-agent interactions is **handoffs**, where one agent *hands off* control to another. Handoffs allow you to specify:
A common pattern in multi-agent interactions is **handoffs**, where one agent _hands off_ control to another. Handoffs allow you to specify:
- **destination**: target agent to navigate to
- **payload**: information to pass to that agent
:::python
This is used both by `langgraph-supervisor` (supervisor hands off to individual agents) and `langgraph-swarm` (an individual agent can hand off to other agents).
To implement handoffs with `create_react_agent`, you need to:
1. Create a special tool that can transfer control to a different agent
1. Create a special tool that can transfer control to a different agent
```python
def transfer_to_bob():
@@ -173,7 +304,7 @@ To implement handoffs with `create_react_agent`, you need to:
)
```
1. Create individual agents that have access to handoff tools:
2. Create individual agents that have access to handoff tools:
```python
flight_assistant = create_react_agent(
@@ -184,7 +315,7 @@ To implement handoffs with `create_react_agent`, you need to:
)
```
1. Define a parent graph that contains individual agents as nodes:
3. Define a parent graph that contains individual agents as nodes:
```python
from langgraph.graph import StateGraph, MessagesState
@@ -196,8 +327,60 @@ To implement handoffs with `create_react_agent`, you need to:
)
```
:::
:::js
This is used both by `@langchain/langgraph-supervisor` (supervisor hands off to individual agents) and `@langchain/langgraph-swarm` (an individual agent can hand off to other agents).
To implement handoffs with `createReactAgent`, you need to:
1. Create a special tool that can transfer control to a different agent
```typescript
function transferToBob() {
/**Transfer to bob.*/
return new Command({
// name of the agent (node) to go to
// highlight-next-line
goto: "bob",
// data to send to the agent
// highlight-next-line
update: { messages: [...] },
// indicate to LangGraph that we need to navigate to
// agent node in a parent graph
// highlight-next-line
graph: Command.PARENT,
});
}
```
2. Create individual agents that have access to handoff tools:
```typescript
const flightAssistant = createReactAgent({
..., tools: [bookFlight, transferToHotelAssistant]
});
const hotelAssistant = createReactAgent({
..., tools: [bookHotel, transferToFlightAssistant]
});
```
3. Define a parent graph that contains individual agents as nodes:
```typescript
import { StateGraph, MessagesZodState } from "@langchain/langgraph";
const multiAgentGraph = new StateGraph(MessagesZodState)
.addNode("flight_assistant", flightAssistant)
.addNode("hotel_assistant", hotelAssistant)
// ...
```
:::
Putting this together, here is how you can implement a simple multi-agent system with two agents — a flight booking assistant and a hotel booking assistant:
:::python
```python
from typing import Annotated
from langchain_core.tools import tool, InjectedToolCallId
@@ -298,11 +481,157 @@ for chunk in multi_agent_graph.stream(
3. Name of the agent or node to hand off to.
4. Take the agent's messages and **add** them to the parent's **state** as part of the handoff. The next agent will see the parent state.
5. Indicate to LangGraph that we need to navigate to agent node in a **parent** multi-agent graph.
:::
:::js
```typescript
import { tool } from "@langchain/core/tools";
import { ChatAnthropic } from "@langchain/anthropic";
import { createReactAgent } from "@langchain/langgraph/prebuilt";
import {
StateGraph,
START,
MessagesZodState,
Command,
} from "@langchain/langgraph";
import { z } from "zod";
function createHandoffTool({
agentName,
description,
}: {
agentName: string;
description?: string;
}) {
const name = `transfer_to_${agentName}`;
const toolDescription = description || `Transfer to ${agentName}`;
return tool(
async (_, config) => {
const toolMessage = {
role: "tool" as const,
content: `Successfully transferred to ${agentName}`,
name: name,
tool_call_id: config.toolCall?.id!,
};
return new Command({
// (2)!
// highlight-next-line
goto: agentName, // (3)!
// highlight-next-line
update: { messages: [toolMessage] }, // (4)!
// highlight-next-line
graph: Command.PARENT, // (5)!
});
},
{
name,
description: toolDescription,
schema: z.object({}),
}
);
}
// Handoffs
const transferToHotelAssistant = createHandoffTool({
agentName: "hotel_assistant",
description: "Transfer user to the hotel-booking assistant.",
});
const transferToFlightAssistant = createHandoffTool({
agentName: "flight_assistant",
description: "Transfer user to the flight-booking assistant.",
});
// Simple agent tools
const bookHotel = tool(
async ({ hotelName }) => {
/**Book a hotel*/
return `Successfully booked a stay at ${hotelName}.`;
},
{
name: "book_hotel",
description: "Book a hotel",
schema: z.object({
hotelName: z.string().describe("Name of the hotel to book"),
}),
}
);
const bookFlight = tool(
async ({ fromAirport, toAirport }) => {
/**Book a flight*/
return `Successfully booked a flight from ${fromAirport} to ${toAirport}.`;
},
{
name: "book_flight",
description: "Book a flight",
schema: z.object({
fromAirport: z.string().describe("Departure airport code"),
toAirport: z.string().describe("Arrival airport code"),
}),
}
);
// Define agents
const flightAssistant = createReactAgent({
llm: new ChatAnthropic({ model: "anthropic:claude-3-5-sonnet-latest" }),
// highlight-next-line
tools: [bookFlight, transferToHotelAssistant],
stateModifier: "You are a flight booking assistant",
// highlight-next-line
name: "flight_assistant",
});
const hotelAssistant = createReactAgent({
llm: new ChatAnthropic({ model: "anthropic:claude-3-5-sonnet-latest" }),
// highlight-next-line
tools: [bookHotel, transferToFlightAssistant],
stateModifier: "You are a hotel booking assistant",
// highlight-next-line
name: "hotel_assistant",
});
// Define multi-agent graph
const multiAgentGraph = new StateGraph(MessagesZodState)
.addNode("flight_assistant", flightAssistant)
.addNode("hotel_assistant", hotelAssistant)
.addEdge(START, "flight_assistant")
.compile();
// Run the multi-agent graph
for await (const chunk of multiAgentGraph.stream({
messages: [
{
role: "user",
content: "book a flight from BOS to JFK and a stay at McKittrick Hotel",
},
],
})) {
console.log(chunk);
console.log("\n");
}
```
1. Access agent's state
2. The `Command` primitive allows specifying a state update and a node transition as a single operation, making it useful for implementing handoffs.
3. Name of the agent or node to hand off to.
4. Take the agent's messages and **add** them to the parent's **state** as part of the handoff. The next agent will see the parent state.
5. Indicate to LangGraph that we need to navigate to agent node in a **parent** multi-agent graph.
:::
!!! Note
This handoff implementation assumes that:
- each agent receives overall message history (across all agents) in the multi-agent system as its input
- each agent outputs its internal messages history to the overall message history of the multi-agent system
Check out LangGraph [supervisor](https://github.com/langchain-ai/langgraph-supervisor-py#customizing-handoff-tools) and [swarm](https://github.com/langchain-ai/langgraph-swarm-py#customizing-handoff-tools) documentation to learn how to customize handoffs.
:::python
Check out LangGraph [supervisor](https://github.com/langchain-ai/langgraph-supervisor-py#customizing-handoff-tools) and [swarm](https://github.com/langchain-ai/langgraph-swarm-py#customizing-handoff-tools) documentation to learn how to customize handoffs.
:::
:::js
Check out LangGraph [supervisor](https://github.com/langchain-ai/langgraphjs/tree/main/libs/langgraph-supervisor#customizing-handoff-tools) and [swarm](https://github.com/langchain-ai/langgraphjs/tree/main/libs/langgraph-swarm#customizing-handoff-tools) documentation to learn how to customize handoffs.
:::