LlamaIndex

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.
])

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)