Benchmarking LLM Accuracy in Real-World API Orchestration (orbitalhq.com)

A recent study has revealed critical insights into the capabilities of Large Language Models (LLMs) for API orchestration, specifically their accuracy in planning workflows that integrate multiple APIs in real-world scenarios. The research focused on assessing how well LLMs can navigate complex API environments, revealing that planning accuracy dramatically declines when the number of endpoints increases, dropping to around 30% with 300 endpoints. However, the study also found that incorporating minimal semantic metadata significantly boosts performance, with accuracy improvements observed when using Taxi annotations alongside OpenAPI specifications. Another key finding demonstrated that employing a declarative query language, TaxiQL, enhanced planning accuracy by as much as 142% while substantially reducing token usage—by up to 80%. This improved efficiency is particularly advantageous for organizations sensitive to token costs. The research highlights the necessity of a semantic layer in LLM applications for reliable enterprise integration, encouraging the adoption of open-source solutions like Taxi and TaxiQL for improved API orchestration. Overall, the study underscores the evolving potential of LLMs in complex technical environments, stressing the importance of context-aware data representation to ensure effective operational workflows.