$ show openclaw/Tachikoma
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.
The LLM identified 7 files worth highlighting for reuse:
AnyEncodable/AnyDecodable wrappers that preserve JSON type distinctions (booleans vs numbers vs nulls) through Codable round-trips. Fully self-contained with zero internal dependencies, directly extractable into any Swift JSON-handling project. Solves the recurring heterogeneous-JSON problem that plagues SDKs bridging tool arguments, MCP values, and provider payloads.
A composable stop-condition protocol with string, regex, token-count, timeout, repetition, and predicate implementations, plus Any/All combinators and a builder. Pure logic with minimal coupling to the provider layer; demonstrates a clean strategy-object design for terminating LLM generation and is portable to any streaming-text consumer.
A chainable async stream transform pipeline (filter, map, buffer, throttle, tap, combined) built on AsyncThrowingStream. Low internal coupling and a clear composable API; extractable as a standalone utility for any Swift streaming use case. Solves the recurring problem of post-processing token deltas without bespoke per-provider stream code.
A generic TTL-based memory cache with pluggable eviction strategies (LRU/LFU/FIFO/priority), memory-pressure eviction, deterministic cache-key hashing that survives composite delimiters, and a CacheAwareProvider wrapper that bypasses unsafe providers. Self-contained and portable; demonstrates sophisticated cache-key design (hashing messages, settings, and reasoning metadata) that is directly reusable in any API-caching layer.
A validated retry policy engine with exponential backoff plus jitter, retry-count validation, cancellation propagation, and an async retryWithCancellation helper. Minimal coupling to Tachikoma internals; demonstrates correct non-obvious details (CancellationToken plumbing, failure classification) that are easy to get wrong and worth copying for any network-resilience layer.
Bidirectional converter between MCP tool schemas and Tachikoma's AgentToolParameters, handling nested objects, enums, defaults, min/max constraints, allOf/oneOf, if-then-else, draft-07 tuples, and dependencies. Solves the hard cross-protocol schema-mapping problem with full JSON type fidelity; extractable for any project bridging two JSON Schema dialects, with strong test coverage proving preservation of edge cases.
The centralized request-encoding and SSE stream-parsing engine shared by OpenAI, Grok, OpenRouter, Kimi, Together, and custom compatible providers. Encodes canonical message conversion, JSON Schema tool serialization, and robust tool-call fragment accumulation (handling fragmented arguments and duplicate IDs). As the codification of a recurring integration pattern, it is the file to study or extract when wrapping any new OpenAI-compatible endpoint, even though it is necessarily coupled to Tachikoma's type system.