LangChain

Setup

Install Braintrust alongside the LangChain JS packages.

pnpm add braintrust @langchain/core@^1 @langchain/openai@^1

npm install braintrust @langchain/core@^1 @langchain/openai@^1

Auto-instrumentation

To trace LangChain calls without modifying your application code, initialize Braintrust normally, then run your app with Braintrust’s import hook to patch @langchain/core at runtime. Requires @langchain/core v0.3.42 or later. The same hook traces LangGraph graphs, which run through the LangChain callback path.

Initialize Braintrust and call LangChain

import { initLogger } from "braintrust";
import { ChatOpenAI } from "@langchain/openai";

initLogger({
  projectName: "My Project",
  apiKey: process.env.BRAINTRUST_API_KEY,
});

const model = new ChatOpenAI({ model: "gpt-5-mini" });

const response = await model.invoke("What is the capital of France?");
console.log(response.content);

Run with the import hook

node --import braintrust/hook.mjs trace-langchain-auto.js

The auto-instrumentation example uses plain JavaScript so node --import can run the file directly. The Braintrust APIs work the same in TypeScript projects. Compile your TypeScript to JavaScript and run the compiled file with the import hook, or run TypeScript directly with a TypeScript loader.

If you’re using a bundler, see Trace LLM calls for plugin and loader setup.

Manual instrumentation

To trace LangChain calls manually, attach BraintrustLangChainCallbackHandler to each call’s callbacks option yourself.

import { ChatOpenAI } from "@langchain/openai";
import { BraintrustLangChainCallbackHandler, initLogger } from "braintrust";

initLogger({
  projectName: "My Project",
  apiKey: process.env.BRAINTRUST_API_KEY,
});

const handler = new BraintrustLangChainCallbackHandler();

async function main() {
  const model = new ChatOpenAI({ modelName: "gpt-5-mini" });

  await model.invoke("What is the capital of France?", {
    callbacks: [handler],
  });
}

main();

Run it:

npx ts-node trace-langchain.ts

What Braintrust traces

Braintrust captures:

LLM and chat model calls, chains, agents, tools, and retrievers as separate spans, with inputs and outputs preserved
Token usage on LLM spans (prompt_tokens, completion_tokens, total_tokens, prompt_cached_tokens, prompt_cache_creation_tokens, completion_reasoning_tokens)
time_to_first_token on streaming responses, captured on the first new-token callback
Model name and serialized invocation parameters in span metadata
Errors from any callback captured on the span

AI providers

SDKs

Developer tools

Runtime environments

Setup

Auto-instrumentation

Manual instrumentation

What Braintrust traces

Tracing resources

​Setup

​Auto-instrumentation

​Manual instrumentation

​What Braintrust traces

​Tracing resources

Setup

Auto-instrumentation

Manual instrumentation

What Braintrust traces

Tracing resources