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Documentation Index

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

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

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LlamaIndex

Use the Adaline LlamaIndex callback handler to send LlamaIndex events into Adaline. The integration attaches through LlamaIndex’s callback manager and works well for LLM, retrieval, and workflow events emitted by the framework.

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Prerequisites

Before you start, make sure you have:
  • An Adaline account.
  • A workspace API key — create one under Settings → API keys.
  • Your project ID — copy it from Monitor → Copy Project ID.
See Integrate your AI agent for a full walkthrough. Set both as environment variables before running the examples on this page: 
export ADALINE_API_KEY="your-api-key"
export ADALINE_PROJECT_ID="your-project-id"

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Install

pip install adaline-client adaline-llama-index llama-index llama-index-llms-openai
Install LlamaIndex and the model-provider packages your application already uses alongside the Adaline integration package. If you use the OpenAI example below, make sure the corresponding LlamaIndex OpenAI package is also installed in your environment.

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Initialize Adaline

Create an Adaline client, then initialize a monitor for the target project. 
import os

from adaline import Adaline

adaline = Adaline(api_key=os.environ["ADALINE_API_KEY"])
monitor = adaline.init_monitor(project_id=os.environ["ADALINE_PROJECT_ID"])
For production guidance — buffering, batching, retries, serverless flushing, and graceful shutdown — see Instrument with the Adaline SDK.

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Attach the LlamaIndex callback handler

Create an AdalineLlamaIndexCallbackHandler, then add it to a CallbackManager. 
from llama_index.core.callbacks import CallbackManager

from adaline_llama_index import AdalineLlamaIndexCallbackHandler

handler = AdalineLlamaIndexCallbackHandler(monitor=monitor)
callback_manager = CallbackManager([handler])

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Basic example

This example keeps the integration intentionally small: one OpenAI completion with the Adaline callback handler attached through the callback manager. 
import asyncio
from llama_index.core.callbacks import CallbackManager
from llama_index.llms.openai import OpenAI as LlamaOpenAI

from adaline_llama_index import AdalineLlamaIndexCallbackHandler

async def main():
    parent_trace = monitor.log_trace(
        name="llamaindex-run",
        reference_id="llamaindex-run-1",
    )

    handler = AdalineLlamaIndexCallbackHandler(
        monitor=monitor,
        parent_trace=parent_trace,
        tags=["llamaindex"],
    )
    callback_manager = CallbackManager([handler])

    llm = LlamaOpenAI(
        model="gpt-4o-mini",
        max_tokens=20,
        callback_manager=callback_manager,
    )

    response = llm.complete("Say hello in one word.")

    parent_trace.end()
    await monitor.flush()


asyncio.run(main())

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Use an existing parent trace or span

The LlamaIndex handler accepts:
  • parent_trace
  • parent_span
Pass one or the other, but not both.

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Optional event filtering

The handler also accepts:
  • event_starts_to_ignore
  • event_ends_to_ignore
Use these when you want to suppress specific LlamaIndex callback event types from being observed.

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What the handler captures

The LlamaIndex callback handler is designed to capture LlamaIndex callback events, including:
  • root trace creation for a LlamaIndex run
  • nested event observations created during that run
  • framework and trace metadata stored as Adaline attributes
  • child spans under an Adaline parent when provided
This page focuses on wiring the callback manager. The exact span tree depends on the LlamaIndex events your application emits.

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Next steps

Instrument with the Adaline SDK

Monitor lifecycle, buffering and batching, retries, serverless flushing, and graceful shutdown.

SDK reference

Full class and type reference for the TypeScript and Python SDKs.

All integrations

Browse every framework and AI-provider integration Adaline supports.

View your logs

Open Adaline to see traces and spans land in your project.