How to add a custom system prompt to the prebuilt ReAct agent¶
This tutorial will show how to add a custom system prompt to the prebuilt ReAct agent. Please see this tutorial for how to get started with the prebuilt ReAct agent
You can add a custom system prompt by passing a string to the stateModifier param.
Compatibility
The stateModifier parameter was added in @langchain/langgraph>=0.2.27.
If you are on an older version, you will need to use the deprecated messageModifier parameter.
For help upgrading, see this guide.
Setup¶
First, we need to install the required packages.
This guide will use OpenAI's GPT-4o model. We will optionally set our API key for LangSmith tracing, which will give us best-in-class observability.
// process.env.OPENAI_API_KEY = "sk_...";
// Optional, add tracing in LangSmith
// process.env.LANGCHAIN_API_KEY = "ls__..."
// process.env.LANGCHAIN_CALLBACKS_BACKGROUND = "true";
process.env.LANGCHAIN_CALLBACKS_BACKGROUND = "true";
process.env.LANGCHAIN_TRACING_V2 = "true";
process.env.LANGCHAIN_PROJECT = "ReAct Agent with system prompt: LangGraphJS";
Code¶
Now we can use the prebuilt createReactAgent function to setup our agent with a system prompt:
import { ChatOpenAI } from "@langchain/openai";
import { tool } from '@langchain/core/tools';
import { z } from 'zod';
import { createReactAgent } from "@langchain/langgraph/prebuilt";
const model = new ChatOpenAI({
model: "gpt-4o",
});
const getWeather = tool((input) => {
if (input.location === 'sf') {
return 'It\'s always sunny in sf';
} else {
return 'It might be cloudy in nyc';
}
}, {
name: 'get_weather',
description: 'Call to get the current weather.',
schema: z.object({
location: z.enum(['sf','nyc']).describe("Location to get the weather for."),
})
})
// We can add our system prompt here
const prompt = "Respond in Italian"
const agent = createReactAgent({ llm: model, tools: [getWeather], stateModifier: prompt });
Usage¶
Let's verify that the agent does indeed respond in Italian!
let inputs = { messages: [{ role: "user", content: "what is the weather in NYC?" }] };
let stream = await agent.stream(inputs, {
streamMode: "values",
});
for await (
const { messages } of stream
) {
let msg = messages[messages?.length - 1];
if (msg?.content) {
console.log(msg.content);
} else if (msg?.tool_calls?.length > 0) {
console.log(msg.tool_calls);
} else {
console.log(msg);
}
console.log("-----\n");
}