LangGraph for Cloud Functions for Firebase

Import name: Sentry.langGraphIntegration

This integration is enabled by default when tracing is enabled. If you'd like to modify your default integrations, read this.

The langGraphIntegration adds instrumentation for @langchain/langgraph to capture agent tracing gen_ai spans, recording model, token usage, latency, and (when enabled) inputs and outputs.

Sentry.init({
  dsn: "___PUBLIC_DSN___",
  // Tracing must be enabled for agent tracing to work
  tracesSampleRate: 1.0,
  integrations: [Sentry.langGraphIntegration()],
});

Sentry.init({
  dsn: "___PUBLIC_DSN___",
  // Tracing must be enabled for agent tracing to work
  tracesSampleRate: 1.0,
  integrations: [Sentry.langGraphIntegration()],
});

Supported Operations

By default, tracing support is added to the following LangGraph operations:

• Agent Creation (gen_ai.create_agent) - Captures spans when compiling a StateGraph into an executable agent
• Agent Invocation (gen_ai.invoke_agent) - Captures spans for agent execution via invoke()

Supported Versions

• @langchain/langgraph: >=0.2.0 <2.0.0

Options

recordInputs

Type: boolean (optional)

Records inputs to LangGraph operations (such as messages and state data passed to the graph).

Defaults to true if dataCollection.genAI.inputs is true, which is the default.

recordOutputs

Type: boolean (optional)

Records outputs from LangGraph operations (such as generated responses, agent outputs, and final state).

Defaults to true if dataCollection.genAI.outputs is true, which is the default.

Sentry.init({
  dsn: "___PUBLIC_DSN___",
  tracesSampleRate: 1.0,
  integrations: [
    Sentry.langGraphIntegration({
      recordInputs: false,
      recordOutputs: false,
    }),
  ],
});

Sentry.init({
  dsn: "___PUBLIC_DSN___",
  tracesSampleRate: 1.0,
  integrations: [
    Sentry.langGraphIntegration({
      recordInputs: false,
      recordOutputs: false,
    }),
  ],
});

Manual Instrumentation

Import names: Sentry.instrumentStateGraph, Sentry.instrumentCreateReactAgent

If your application does not use automatic instrumentation, wrap your agent after setting up Sentry. Two helpers are available, depending on how you build your agent:

• instrumentStateGraph: for graphs you build yourself with StateGraph. Call it on the graph before calling .compile().
• instrumentCreateReactAgent: for LangGraph's prebuilt createReactAgent. Wrap the factory, then use the wrapped version exactly like createReactAgent. This also captures the model and wraps tools, so you get execute_tool spans as well.

Both accept the same recordInputs and recordOutputs options as the integration.

Instrumenting a StateGraph:

import { ChatOpenAI } from "@langchain/openai";
import {
  StateGraph,
  MessagesAnnotation,
  START,
  END,
} from "@langchain/langgraph";
import { SystemMessage, HumanMessage } from "@langchain/core/messages";

// Create LLM call
const llm = new ChatOpenAI({
  modelName: "gpt-4o",
  apiKey: "your-api-key",
});

async function callLLM(state) {
  const response = await llm.invoke(state.messages);

  return {
    messages: [...state.messages, response],
  };
}

// Create the agent
const agent = new StateGraph(MessagesAnnotation)
  .addNode("agent", callLLM)
  .addEdge(START, "agent")
  .addEdge("agent", END);

// Instrument the graph before compiling
Sentry.instrumentStateGraph(agent, {
  recordInputs: false,
  recordOutputs: false,
});

const graph = agent.compile({ name: "my_agent" });

// Invoke the agent
const result = await graph.invoke({
  messages: [
    new SystemMessage("You are a helpful assistant."),
    new HumanMessage("Hello!"),
  ],
});

import { ChatOpenAI } from "@langchain/openai";
import {
  StateGraph,
  MessagesAnnotation,
  START,
  END,
} from "@langchain/langgraph";
import { SystemMessage, HumanMessage } from "@langchain/core/messages";

// Create LLM call
const llm = new ChatOpenAI({
  modelName: "gpt-4o",
  apiKey: "your-api-key",
});

async function callLLM(state) {
  const response = await llm.invoke(state.messages);

  return {
    messages: [...state.messages, response],
  };
}

// Create the agent
const agent = new StateGraph(MessagesAnnotation)
  .addNode("agent", callLLM)
  .addEdge(START, "agent")
  .addEdge("agent", END);

// Instrument the graph before compiling
Sentry.instrumentStateGraph(agent, {
  recordInputs: false,
  recordOutputs: false,
});

const graph = agent.compile({ name: "my_agent" });

// Invoke the agent
const result = await graph.invoke({
  messages: [
    new SystemMessage("You are a helpful assistant."),
    new HumanMessage("Hello!"),
  ],
});

Instrumenting a prebuilt createReactAgent:

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

// Instrument createReactAgent, then use the wrapped version in its place
const instrumentedCreateReactAgent =
  Sentry.instrumentCreateReactAgent(createReactAgent);

const agent = instrumentedCreateReactAgent({ llm, tools });

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

// Instrument createReactAgent, then use the wrapped version in its place
const instrumentedCreateReactAgent =
  Sentry.instrumentCreateReactAgent(createReactAgent);

const agent = instrumentedCreateReactAgent({ llm, tools });