Splunk Agent Observability Instrumentation for LangChain Apps

Add the LangChain Callback

5 minutes

Galileo’s GalileoCallback is a standard LangChain callback handler. When you attach it to a LangChain or LangGraph run, it automatically captures prompts, responses, model names, token usage, timing, and the nesting of each step.

Because the travel planner is a LangGraph workflow, you don’t need to edit every node. Instead, pass a single callback in the run config when the compiled graph is streamed. Splunk Agent Observability then records one trace per request, with a nested LLM span for each agent node (coordinator, flight, hotel, activity, and synthesizer).

Exercise Add the LangChain callback

Import the callback

Add the callback import alongside the other LangChain imports in main.py:

python
from galileo.handlers.langchain import GalileoCallback

Attach the callback to the graph run config

In plan_travel_internal(), create a callback and attach it to the run config passed to compiled_app.stream(...). The existing code should look something like this:

python
    workflow = build_workflow()
    compiled_app = workflow.compile()

    for step in compiled_app.stream(initial_state, config):
        node_name, node_state = next(iter(step.items()))
        final_state = node_state

Update it to build a config that includes the Splunk Agent Observability callback (merging it with any existing config the app already passes). This passes the execution of each node in the agent to Splunk Agent Observability:

python
    workflow = build_workflow()
    compiled_app = workflow.compile()

    # One callback per request keeps each travel plan in its own trace.
    callback = GalileoCallback()
    run_config = {**config, "callbacks": [callback]}

    for step in compiled_app.stream(initial_state, run_config):
        node_name, node_state = next(iter(step.items()))
        final_state = node_state

Passing the callback at the graph level means it propagates to every node’s llm.invoke(...) call automatically. No further instrumentation is needed.

Where do you attach the GalileoCallback in a LangGraph workflow?

Async workflows

If your app streams the graph asynchronously (compiled_app.astream(...)), use GalileoAsyncCallback instead of GalileoCallback. The travel planner runs synchronously, so GalileoCallback is correct here.