{"id":"tom-ryder-agentrunkit","name":"AgentRunKit","af_score":55.0,"security_score":55.2,"reliability_score":31.2,"what_it_does":"AgentRunKit is a lightweight Swift 6 framework for building LLM-powered agents with type-safe tool calling, streaming, context management/compaction, structured outputs, multimodal support, and an MCP client (stdio/JSON-RPC). It supports both cloud providers (e.g., OpenAI-compatible, Anthropic, Gemini, Vertex) and on-device inference via MLX and Apple Foundation Models.","best_when":"You want a Swift-native agent framework (type-safe tool calls + streaming) for Apple platforms, optionally with local inference and/or MCP-based tool discovery.","avoid_when":"You need a network service SDK with its own HTTP API, webhooks, or documented provider-agnostic rate-limit/auth policies.","last_evaluated":"2026-03-30T15:39:52.669334+00:00","has_mcp":false,"has_api":false,"auth_methods":["Provider API keys via client initializers (e.g., OpenAIClient(apiKey:...))","Provider-specific API authentication for supported clients (OpenAI-compatible, Anthropic, Gemini, Vertex, etc.)"],"has_free_tier":false,"known_gotchas":["Agent loops/streaming can increase cost and complexity if iteration limits/token budgets are not configured","Provider-specific behaviors (tool/function calling and response formats) may vary; schema/structured output enforcement quality can depend on the provider","MCP integration details (stdio transport/process lifecycle) can be tricky in real apps, even if supported by an MCP client"],"error_quality":0.0}