LlamaIndex

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

Install

Python

pip install traceroot llama-index-core llama-index-llms-openai llama-index-embeddings-openai

Set up

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

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:

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 AI agent improvement platform."),
    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)

Verify

Run your code and confirm the trace arrives:

In the TraceRoot UI: Navigate to Tracing > Traces in TraceRoot Cloud. You should see a trace with root span RetrieverQueryEngine.query, the class and method that as_query_engine().query() resolves to.
Via the CLI: Run traceroot traces list to inspect your recent traces from the terminal.

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; cost when the model ID matches TraceRoot’s pricing table
Latency	Duration of each pipeline stage

Limitations

Initializing both Integration.LLAMA_INDEX and your underlying LLM provider integration (such as Integration.OPENAI) is recommended to capture full token usage and pricing.

Run the example

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

Python

Run the Python example

Next steps

Python SDK Reference

Explore the full Python SDK reference and instrumentation options.

Cost Tracking

Learn how TraceRoot calculates token costs and tracks model spend.

Overview

Native

Frameworks

Model Providers

Internal Model Providers

Install

Set up

Usage

Verify

What gets captured

Limitations

Run the example

Python

Next steps

Python SDK Reference

Cost Tracking

​Install

​Set up

​Usage

​Verify

​What gets captured

​Limitations

​Run the example

Python

​Next steps

Python SDK Reference

Cost Tracking

Install

Set up

Usage

Verify

What gets captured

Limitations

Run the example

Next steps