AgentRunKit

Quick Start

import AgentRunKit

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

let weatherTool = try Tool<WeatherParams, String, EmptyContext>(
    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.

Documentation

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

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.

Installation

Add to your Package.swift:

dependencies: [
    .package(url: "https://github.com/Tom-Ryder/AgentRunKit.git", from: "2.4.0")
]

.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)

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
• 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

Providers

Requirements

License

MIT License. See LICENSE for details.