LlamaIndex Integration with Veritrix Observability

Veritrix gives you deep visibility into LlamaIndex RAG pipelines, query engines, and agents from the moment you call trace.init(). Every query, document retrieval, re-ranking step, LLM synthesis call, and tool invocation is captured as a structured trace — so you can measure retrieval quality, diagnose slow queries, and understand exactly which steps contribute to latency and cost in your production pipelines.

Prerequisites

Python 3.9 or higher
An active Veritrix account and API key
LlamaIndex core installed

Installation & Setup

Install Veritrix and LlamaIndex

pip install veritrix llama-index

For additional LlamaIndex integrations (e.g., specific LLM providers or vector stores), install the relevant packages separately:

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

Initialize Veritrix tracing

Call trace.init() once at the top of your application, before you build any index or query engine.

from veritrix import trace

trace.init("rag-agent")

Build and query your index as normal

Veritrix instruments LlamaIndex’s global settings and callback system automatically after trace.init(). No changes to your index or query engine code are needed.

from llama_index.core import VectorStoreIndex
from veritrix import trace

trace.init("rag-agent")
# Build and query your index normally

Example: RAG Pipeline

The following example builds a simple in-memory vector index and runs a query against it. Veritrix traces the full retrieval-and-synthesis cycle, including the embedding call, retrieved nodes, and the final LLM call.

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
from veritrix import trace

trace.init("rag-agent")

# Configure LlamaIndex settings
Settings.llm = OpenAI(model="gpt-4o")
Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small")

# Load documents and build the index
documents = SimpleDirectoryReader("./data").load_data()
index = VectorStoreIndex.from_documents(documents)

# Query the index — Veritrix traces the entire pipeline
query_engine = index.as_query_engine(similarity_top_k=3)
response = query_engine.query("What are the main benefits of AI agent observability?")
print(response)

Example: LlamaIndex Agent with Tools

For agentic pipelines, Veritrix captures each reasoning step and tool call the agent makes before arriving at a final answer.

from llama_index.core.agent import ReActAgent
from llama_index.core.tools import FunctionTool
from llama_index.llms.openai import OpenAI
from veritrix import trace

trace.init("llamaindex-react-agent")

def get_weather(city: str) -> str:
    """Returns current weather for a city."""
    return f"The weather in {city} is 22°C and sunny."

def get_population(city: str) -> str:
    """Returns the population of a city."""
    return f"The population of {city} is approximately 3.9 million."

weather_tool = FunctionTool.from_defaults(fn=get_weather)
population_tool = FunctionTool.from_defaults(fn=get_population)

llm = OpenAI(model="gpt-4o")
agent = ReActAgent.from_tools(
    [weather_tool, population_tool],
    llm=llm,
    verbose=True,
)

response = agent.chat("What is the weather and population of Toronto?")
print(response)

What Gets Captured Automatically

Signal	Details
LLM calls	Model name, prompt, completion, token counts, latency
Embedding calls	Model name, input text, vector dimensions, latency
Retrieval steps	Query, retrieved nodes, similarity scores, top-k setting
Re-ranking	Re-ranker model, input nodes, output order, scores
Tool calls	Tool name, input arguments, output, duration
Agent reasoning steps	Thought, action, observation per ReAct step
Retries	Retry count, error type, delay

Retrieval traces include the actual document chunks returned for each query, making it easy to audit whether your index is surfacing the most relevant content.

OpenTelemetry Compatibility

Veritrix is built on OpenTelemetry. Every span it generates for your LlamaIndex pipelines is a fully compliant OTel span that can be exported to any OTel-compatible backend alongside your existing infrastructure telemetry.

To configure a custom OTel exporter or forward traces to an existing collector, see the OpenTelemetry integration page.

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LlamaIndex Integration with Veritrix Observability

Prerequisites

Installation & Setup

Example: RAG Pipeline

Example: LlamaIndex Agent with Tools

What Gets Captured Automatically

OpenTelemetry Compatibility

​Prerequisites

​Installation & Setup

​Example: RAG Pipeline

​Example: LlamaIndex Agent with Tools

​What Gets Captured Automatically

​OpenTelemetry Compatibility

Prerequisites

Installation & Setup

Example: RAG Pipeline

Example: LlamaIndex Agent with Tools

What Gets Captured Automatically

OpenTelemetry Compatibility