Hummingbird

Article 7 of the Hummingbird series. We measure the LLM gateway against functionally equivalent implementations in FastAPI and Fastify — with a fair middleware stack, a deterministic backend stub, and oha as the load generator. The results are both what you expect and somewhat surprising.

Article 6 of the Hummingbird series. We add Prometheus metrics via swift-metrics and swift-prometheus, a /metrics endpoint, MetricsMiddleware and TracingMiddleware. Plus: cross-compiling to a statically linked Linux binary via the Swift Static Linux SDK, a multi-stage Dockerfile, and a systemd unit for bare-metal Linux deployment.

Article 5 of the Hummingbird series. We add API key auth, token-bucket rate limiting, and spec-compliant error responses — using Hummingbird 2's generic RequestContext to carry typed middleware state without dictionaries.

Article 4 of the Hummingbird series. We add streaming to both endpoints via Server-Sent Events: MLXClient.completeStream as AsyncThrowingStream, Anthropic events, OpenAI chunks, task cancellation. Tokens appear at the client as the model generates them.

Article 3 of the Hummingbird series. We connect the gateway to mlx_lm.server: MLXClient as a Swift actor, format conversion from Anthropic to OpenAI, error mapping with HTTP status codes. After this article, the gateway delivers real model responses.

Article 2 of the Hummingbird series. We implement the Anthropic Messages API and the OpenAI Chat Completions API in the same gateway: Codable types for both protocols, two POST endpoints, input validation, mock inference.

Article 1 · Swift on the Server with Hummingbird. We create the project, write Package.swift, set up the AsyncParsableCommand entry point, and build a minimal router with /healthz and /v1/models until curl gets an answer.

Prologue · Series: Swift on the Server with Hummingbird. Why Swift on the server is no longer a hobby project in 2026, how Hummingbird differs from Vapor and why we chose a local LLM gateway as our demo project.