Folkbench: four checks before using an AI relay API

The guide's starting point is a shift in what terminal-based coding agents such as Claude Code CLI and OpenAI Codex CLI expect from the infrastructure between them and the model. From a software-development view, Folkbench describes a relay not as a simple pipe but as an L7 proxy with a state-machine parser and a context scheduler, which is why the checks focus on what happens to the bytes and the request metadata rather than on network stability or load tolerance.

The four criteria follow from that framing. Escaping and indentation decide whether generated Python, YAML, or C++ arrives parseable, with the pass line set at byte-for-byte agreement with upstream output. Tool-call structure and multi-turn state decide whether an agent can keep calling read_file, edit_file, or run_terminal_command without JSON parse errors. Cache and long-context handling decide whether a one-line edit forces a costly re-prefill, tested with a 150k-token repository that hides a dependency between two distant files. System-prompt integrity is checked with packet capture, requiring that not a single byte changes.

The guide does not name specific relay vendors or report results from running these tests, so it functions as a checklist for teams to apply themselves. Folkbench says its model rankings and evaluation articles can be viewed without registration.