AgentRunKit

AgentRunKit

A Swift 6 SDK for building LLM-powered agents with type-safe tool calling.

Zero-dependency core · Full Sendable · Async/await · Cloud + Local · MCP

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Quick Start

import AgentRunKit

let client = OpenAIClient.openAI(apiKey: "sk-...", model: "gpt-5.4")

let weatherTool = try Tool(
    name: "get_weather",
    description: "Get the current weather"
) { params, _ in
    "72°F and sunny in \(params.city)"
}

let agent = Agent(client: client, tools: [weatherTool])
let result = try await agent.run(userMessage: "What's the weather in SF?", context: EmptyContext())
if let content = result.content {
    print(content)
}

result.content is optional. Completed runs return finish-tool content, while structural terminal reasons such as max iterations or token budget exhaustion surface through result.finishReason with no final content.

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Documentation

Full documentation including guides and API reference is available on Swift Package Index.

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Runnable Example

Examples/AgentCode is an interactive terminal coding agent built with AgentRunKit. It demonstrates the full agent loop in a local workspace: streaming events, type-safe tools, approval-gated edits and command execution, bounded file access, transcript export, and deterministic offline mode.

cd Examples/AgentCode
swift run agent-code

By default it opens a bundled broken Swift package so you can ask it to fix failing tests. Set OPENAI_API_KEY for a live OpenAI-compatible provider, or run without a key to exercise the CLI with the offline test client.

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Installation

Add to your Package.swift:

dependencies: [
    .package(url: "https://github.com/Tom-Ryder/AgentRunKit.git", from: "5.3.0")
]

.target(name: "YourApp", dependencies: ["AgentRunKit"])

For on-device inference, additional targets are available:

• AgentRunKitMLX for MLX on Apple Silicon (links mlx-swift-lm)
• AgentRunKitFoundationModels for Apple Foundation Models (iOS 26+ and macOS 26+, no external dependencies)

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Features

• Agent loop with configurable iteration limits and token budgets
• Streaming with AsyncThrowingStream and @Observable SwiftUI wrapper
• Type-safe tools with compile-time JSON schema validation
• Sub-agent composition with depth control and streaming propagation
• Context management: automatic compaction, pruning, token budgets
• Checkpoint and resume: per-iteration snapshots, file or in-memory backends
• Tool approval: human-in-the-loop policies with session allowlists
• Structured output with JSON schema constraints
• Multimodal input: images, audio, video, PDF
• Text-to-speech with concurrent chunking and MP3 concatenation
• MCP client: stdio transport, tool discovery, JSON-RPC
• Extended thinking / reasoning model support

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Providers

| Provider | Description | |----------|-------------| | OpenAIClient | OpenAI and compatible APIs (OpenRouter with reasoning replay, Groq, Together, Ollama) | | AnthropicClient | Anthropic Messages API with same-substrate continuity replay | | GeminiClient | Google Gemini API | | VertexAnthropicClient | Anthropic models on Google Vertex AI with same-substrate continuity replay | | VertexGoogleClient | Google models on Vertex AI | | ResponsesAPIClient | OpenAI Responses API with same-substrate continuity replay and OpenRouter factory | | FoundationModelsClient | Apple on-device (macOS 26+ / iOS 26+) | | MLXClient | On-device via MLX on Apple Silicon |

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Requirements

| Platform | Version | |----------|---------| | iOS | 18.0+ | | macOS | 15.0+ | | Swift | 6.0+ | | Xcode | 26+ for local development and CI |

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License

MIT License. See [LICENSE](LICENSE) for details.

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Tom-Ryder
• Source: Tom-Ryder/AgentRunKit
• License: MIT

Install and usage instructions live in the source repository linked above.