Connect LangChain to the AI Gateway

Send requests from your LangChain agents through the Logfire AI Gateway.

LangChain is a framework for building LLM-powered applications. Both examples keep the agent in LangChain and point ChatOpenAI at an OpenAI-compatible gateway route.

Before you start

• Complete the AI Gateway prerequisites, including setting LOGFIRE_GATEWAY_API_KEY in your terminal.
• Use an existing LangChain project with the packages imported by your chosen example installed.

Model data passes through Logfire

This configuration sends prompts, tool inputs, and model responses through the Logfire AI Gateway and the selected model provider. If gateway telemetry is enabled, Logfire stores the conversation content in your selected project. Calls to built-in providers count toward your gateway spend.

TypeScript

Set apiKey to your gateway key and baseURL to the OpenAI-compatible gateway route. Copy the route and a supported model name from the Gateway Connect tab.

langchain-gateway.mts

import { createAgent, tool } from 'langchain';
import { ChatOpenAI } from '@langchain/openai';
import { z } from 'zod';

const envSchema = z.object({
  LOGFIRE_GATEWAY_API_KEY: z.string(),
});

const env = envSchema.parse(process.env);

const model = new ChatOpenAI({
  model: 'gpt-5.4-mini',
  apiKey: env.LOGFIRE_GATEWAY_API_KEY,
  configuration: {
    baseURL: 'https://gateway-us.pydantic.dev/proxy/openai',
  },
});

const getWeather = tool(
  ({ city }) => `It's always sunny in ${city}!`,
  {
    name: 'get_weather',
    description: 'Get weather for a city',
    schema: z.object({
      city: z.string(),
    }),
  }
);

const agent = createAgent({
  model,
  tools: [getWeather],
});

const result = await agent.invoke({
  messages: [{ role: 'user', content: 'What is the weather in London?' }],
});

console.log(result);

Python

Set the equivalent api_key and base_url values on the Python client.

langchain-gateway.py

import os

from langchain.agents import create_agent
from langchain_openai import ChatOpenAI


def get_weather(city: str) -> str:
    """Get weather for a city."""
    return f"It's always sunny in {city}!"


model = ChatOpenAI(
    model='gpt-5.4-mini',
    api_key=os.environ['LOGFIRE_GATEWAY_API_KEY'],
    base_url='https://gateway-us.pydantic.dev/proxy/openai',
)

agent = create_agent(model=model, tools=[get_weather])

result = agent.invoke({'messages': [{'role': 'user', 'content': 'What is the weather in London?'}]})

print(result['messages'][-1].content)

Verify it worked

Run either example from your terminal. It prints the agent’s final response. That confirms the client reached the gateway. Organization admins can also open AI Engineering > Gateway > Spending to see usage for the key. If telemetry is enabled, open the selected project’s Live view to inspect the request trace.

Troubleshooting

• The example cannot read LOGFIRE_GATEWAY_API_KEY: set the environment variable in the same terminal where you run the example.
• The request returns an authentication or model error: copy the URL and model name again from the Gateway Connect tab, and confirm that the selected route supports the OpenAI request format.