Python
GateCtr + LlamaIndex
Add budget control and token optimization to LlamaIndex pipelines
1
Install
No additional packages required. Use your existing LlamaIndex installation.
2
Configure
Before
from llama_index.llms.openai import OpenAI llm = OpenAI(model="gpt-4o", api_key="sk-...")
After GateCtr
from llama_index.llms.openai import OpenAI
llm = OpenAI(
model="gpt-4o",
api_key="sk-...",
api_base="https://api.gatectr.com/v1"
)3
Test
Make a test call and check the GateCtr dashboard for token savings and cost data.
What GateCtr does under the hood for LlamaIndex
When you route LlamaIndex calls through GateCtr, every request is automatically compressed (up to 40% fewer tokens), scored for complexity (to select the optimal model), and checked against your budget cap before reaching the LLM provider. You get full observability — tokens, cost, latency — in the GateCtr dashboard.