Tachikoma is a production-grade Swift SDK/library that provides a unified typed interface to a broad spectrum of LLM providers: OpenAI (Chat Completions, the newer Responses API for GPT-5+ generations, and a ChatGPT Codex OAuth backend), Anthropic (Claude with streaming signed thinking blocks), Google Gemini, Mistral, Groq, Grok/xAI, Kimi, MiniMax, OpenRouter, Together, Replicate, Ollama, LM Studio, Azure OpenAI, and arbitrary OpenAI/Anthropic-compatible endpoints. Its public surface is deliberately modeled on the Vercel AI SDK: generateText/streamText, generateObject/streamObject with partial-JSON repair, embeddings, vision analysis, and multi-step agent tool-calling loops. Beyond generation, it ships composable stop conditions, response caching, retry policies, usage/cost accounting, reasoning-effort controls, and a provider feature-parity adapter that normalizes differences in system roles, vision, and tool limits across backends. The repo is strictly an application SDK in character: no datasets, notebooks, or data pipelines. The one data-like facet is its manually curated model capability catalog (context lengths, pricing, per-generation reasoning support), which is embedded as Swift enums rather than external registries. Architecturally it is a SwiftPM monorepo of five products: Tachikoma (core generation/provider layer), TachikomaAgent (conversations, dynamic tool registry, MCP tool binding), TachikomaAudio (transcription, TTS, and an OpenAI Realtime WebSocket voice client), TachikomaMCP (a client for Model Context Protocol servers over stdio/SSE/HTTP with full JSON Schema bridging), and TachikomaConfigCLI (auth/credential helper), plus two example CLIs (ai-cli, gpt5cli). All providers implement a common ModelProvider protocol with ProviderRequest/ProviderResponse and AsyncThrowingStream-based streaming, with shared SSE/NDJSON parsing funneled through OpenAICompatibleHelper and provider-specific adapters for Anthropic, Google, and Ollama. The code is Swift 6.2, heavily async/await, and covered by a substantial test suite (Swift Testing, mock URLProtocol fixtures, snapshot tests, serialized mutex-guarded tests for shared state), with CI across macOS and Linux. The codebase exhibits strong engineering practice: clean provider abstraction, genuinely reusable utilities (stream transforms, retry policies, TTL caches, typed JSON Schema views), modular product boundaries, and disciplined error modeling. Its principal weaknesses are the enormous provider surface that must be manually maintained (hardcoded capability matrices, context-length string heuristics like isOpus48/isSonnet5, static pricing tables in ModelCostCalculator), a handful of @unchecked Sendable singletons and NSLock-guarded global state, Linux/Apple streaming divergence (Linux buffers the full response instead of streaming bytes), some dead placeholder APIs (the legacy generate() returning a mock string), and a rapid feature-churn cadence visible in the changelog (GPT-5.6/6, Claude 5, reasoning replay). These are maintainability risks more than correctness defects; the tested behavior is consistently thorough. Overall this is a high-craft SDK that an engineer building AI application features in Swift would benefit from studying, particularly its OpenAI-compatible adapter pattern, cache-key determinism, and MCP schema translation.