Ideas that push
the field forward

Open large language models (LLMs) like Meta's LLaMA, Google's Gemma, and OpenAI's GPT-OSS now support tool-calling (aka function-calling) to let the model invoke external APIs or tools. We survey how each model supports tool-calling, methods for improving its reliability (prompt engineering, supervised fine-tuning, instruction tuning, RLHF, parameter-efficient tuning like LoRA), and when to use each approach. We cover failure modes, benchmarks, integration architecture, training requirements, and a decision table comparing approaches on performance, cost, latency, safety, dev effort, and maintainability.