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Documentation Index

Fetch the complete documentation index at: https://docs.helicone.ai/llms.txt

Use this file to discover all available pages before exploring further.

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Introduction

LangGraph is a framework for building stateful, multi-agent applications with LLMs. The integration with Helicone AI Gateway is nearly identical to the LangChain integration, with the addition of agent-specific features.
This integration requires only two changes to your existing LangGraph code - updating the base URL and API key. See the LangChain AI Gateway docs for full feature details.

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Quick Start

Follow the same setup as LangChain AI Gateway integration, then create your agent: 
import { ChatOpenAI } from "@langchain/openai";
import { createReactAgent } from "@langchain/langgraph/prebuilt";
import { MemorySaver } from "@langchain/langgraph";

const model = new ChatOpenAI({
    model: 'gpt-4.1-mini',
    apiKey: process.env.HELICONE_API_KEY,
    configuration: {
        baseURL: "https://ai-gateway.helicone.ai/v1",
    },
});

const agent = createReactAgent({
    llm: model,
    tools: yourTools,
    checkpointer: new MemorySaver(),
});
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Migration Example

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Before (Direct Provider)

import { ChatOpenAI } from "@langchain/openai";
import { createReactAgent } from "@langchain/langgraph/prebuilt";

const model = new ChatOpenAI({
    model: 'gpt-4o-mini',
    apiKey: process.env.OPENAI_API_KEY,
});

const agent = createReactAgent({
    llm: model,
    tools: myTools,
});

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After (Helicone AI Gateway)

import { ChatOpenAI } from "@langchain/openai";
import { createReactAgent } from "@langchain/langgraph/prebuilt";

const model = new ChatOpenAI({
    model: 'gpt-4.1-mini',                      // 100+ models supported
    apiKey: process.env.HELICONE_API_KEY,      // Your Helicone API key
    configuration: {
        baseURL: "https://ai-gateway.helicone.ai/v1"  // Add this!
    },
});

const agent = createReactAgent({
    llm: model,
    tools: myTools,
});

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Adding Custom Headers to Agent Invocations

You can add custom properties when calling your agent with invoke(): 
import { HumanMessage } from "@langchain/core/messages";
import { v4 as uuidv4 } from 'uuid';

const result = await agent.invoke(
    { messages: [new HumanMessage("What is the weather in San Francisco?")] },
    {
        options: {
            headers: {
                "Helicone-Session-Id": uuidv4(),
                "Helicone-Session-Path": "/weather/query",
                "Helicone-Property-Query-Type": "weather",
            },
        },
    }
);
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AI Gateway Overview

Learn about Helicone’s AI Gateway features and capabilities

Provider Routing

Configure intelligent routing and automatic failover

Model Registry

Browse all available models and providers

LangChain Integration

Full AI Gateway feature documentation

Sessions

Track multi-turn conversations and agent workflows

Custom Properties

Add metadata to track and filter your requests