> ## Documentation Index
> Fetch the complete documentation index at: https://traceroot.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# LlamaIndex

> Auto-instrument LlamaIndex RAG pipelines and query engines

Automatically capture document ingestion, embedding, retrieval, and LLM synthesis within LlamaIndex pipelines.

## Setup

To capture the complete picture—including both the pipeline structure and the underlying token costs—we highly recommend initializing both the LlamaIndex integration and your specific LLM provider (e.g., OpenAI, Anthropic, Google GenAI).

```python theme={null}
import traceroot
from traceroot import Integration

# Initialize LlamaIndex alongside your LLM provider to capture tokens and costs
traceroot.initialize(integrations=[
    Integration.LLAMA_INDEX,
    Integration.OPENAI  # Or ANTHROPIC, GOOGLE_GENAI, etc.
])
```

## Usage

Once initialized, all LlamaIndex operations are captured automatically:

```python theme={null}
from llama_index.core import Document, Settings, VectorStoreIndex
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding

Settings.llm = OpenAI(model="gpt-4o-mini")
Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small")

documents = [
    Document(text="TraceRoot is an open-source observability platform for AI agents."),
    Document(text="TraceRoot supports OpenTelemetry-compatible tracing via a Python SDK."),
]

# Index construction is automatically traced (SentenceSplitter, Embedding)
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()

# Each query is automatically traced (retrieval + LLM synthesis)
response = query_engine.query("What is TraceRoot?")
print(response)
```

## What Gets Captured

| Attribute         | Description                                              |
| ----------------- | -------------------------------------------------------- |
| Document chunking | Each `SentenceSplitter` call as a span                   |
| Embedding calls   | Each batch embedding with text inputs and vector outputs |
| Retrieval         | Top-k retrieval with query and retrieved documents       |
| LLM synthesis     | Response generation with context and final answer        |
| Tokens & Cost     | Input/output tokens and calculated cost per LLM call     |
| Latency           | Duration of each pipeline stage                          |

## Run the example

Clone the repo and run a complete agent end-to-end.

<Card title="Python" icon="python" href="https://github.com/traceroot-ai/traceroot/tree/main/examples/python/llamaindex-rag">
  Run the Python example
</Card>