LlamaIndex

Trace LlamaIndex pipelines with Rius: query engines, retrievers, embeddings, and the model calls inside them.

Setup

pip install "glassflow-ai[llama-index]"

import glassflow
 
glassflow.init(api_key="glassflow_...", service_name="my-agent")
# build indexes and query engines exactly as before

What gets captured

A query becomes a nested trace of typed spans: retrieval steps as RETRIEVER spans, embedding calls as EMBEDDING, synthesis model calls as LLM spans with model, token usage, and messages (cost computed server-side). RAG debugging usually starts here: the retriever span’s output shows what the model was given to work with.

Verify

Run one query against an index, then open the trace in the console: the pipeline appears as nested spans with the LLM leaf carrying model, tokens, and cost.