Contents

christopherkarani/Swarm

🐦‍🔥 A Swifty Agent Orchestration Framework Purpose built for on-device Models

Install

.package(url: "https://github.com/christopherkarani/Swarm.git", from: "0.5.0")

Quick Start

import Swarm

// The @Tool macro generates the JSON schema at compile time
@Tool("Looks up the current stock price")
struct PriceTool {
    @Parameter("Ticker symbol") var ticker: String
    func execute() async throws -> String { "182.50" }
}

// Create an agent with unlabeled instructions first and tools in the trailing @ToolBuilder closure
let agent = try Agent("Answer finance questions using real data.",
    configuration: .init(name: "Analyst"),
    inferenceProvider: .anthropic(key: "sk-...")) {
    PriceTool()
    CalculatorTool()
}

let result = try await agent.run("What is AAPL trading at?")
print(result.output) // "Apple (AAPL) is currently trading at $182.50."

That is a working agent with type-safe tool calling. The rest of this README covers workflows, memory, guardrails, and the surrounding runtime pieces.

On-Device Workspace

Swarm now supports a file-backed on-device workspace with:

  • AGENTS.md for workspace-wide instructions
  • .swarm/agents/<id>.md for per-agent specs
  • standard .swarm/skills/<name>/SKILL.md folders for reusable skills
  • .swarm/memory/ for durable writable notes

Code-first setup:

let workspace = try AgentWorkspace.appDefault()

let agent = try Agent.onDevice(
    "You are a concise local assistant.",
    workspace: workspace,
    inferenceProvider: .foundationModels
)

Markdown-first setup:

let workspace = try AgentWorkspace.appDefault()

let agent = try Agent.spec(
    "support",
    in: workspace,
    inferenceProvider: .foundationModels
)

Workspace layout:

AgentWorkspace/
  AGENTS.md
  .swarm/
    agents/
      support.md
    skills/
      refund-policy/
        SKILL.md
    memory/
      facts/
      decisions/
      tasks/
      lessons/
      handoffs/

Use try await workspace.validate() in development or CI to catch malformed specs and skills before runtime.

Why Swarm

  • Swift concurrency is part of the surface. Swift 6.2 StrictConcurrency is enabled across the package.
  • Tools stay type-safe. The @Tool macro generates JSON schemas from Swift structs.
  • Workflows can survive crashes. Durable workflow checkpointing lets you resume from an explicit checkpoint ID.
  • Cloud and on-device models use the same abstractions. Foundation Models, Anthropic, OpenAI, Ollama, Gemini, OpenRouter, and MLX all fit the same shape.
  • It is written in Swift all the way down. AsyncThrowingStream, actors, result builders, and macros are first-class here.

Examples

Capability matrix showcase

Swarm now ships with an in-repo capability showcase that exercises the stable surface area in one deterministic matrix:

  • agents and tools
  • streaming
  • conversation plus session persistence
  • sequential, parallel, routed, and repeat-until workflows
  • handoffs
  • memory
  • on-device workspace loading
  • guardrails
  • resilience helpers
  • durable checkpoint and resume
  • observability
  • MCP discovery and tool bridging
  • provider selection

Run it locally:

swift run SwarmCapabilityShowcase list
swift run SwarmCapabilityShowcase matrix
swift run SwarmCapabilityShowcase run handoff
swift run SwarmCapabilityShowcase smoke

The deterministic matrix is CI-safe. Live-provider smoke coverage is opt-in through environment variables. See docs/guide/capability-showcase.md for the scenario catalog and smoke-mode details.

Multi-agent pipeline

let researcher = try Agent("Research the topic and extract key facts.",
    inferenceProvider: .anthropic(key: "sk-...")) {
    WebSearchTool()
}

let writer = try Agent("Write a concise summary from the research.",
    inferenceProvider: .anthropic(key: "sk-..."))

let result = try await Workflow()
    .step(researcher)
    .step(writer)
    .run("Latest advances in on-device ML")

Parallel fan-out

let result = try await Workflow()
    .parallel([bullAgent, bearAgent, analystAgent], merge: .structured)
    .run("Evaluate Apple's Q4 earnings.")
// Three perspectives, merged into one output.

Dynamic routing

let result = try await Workflow()
    .route { input in
        if input.contains("$") { return mathAgent }
        if input.contains("weather") { return weatherAgent }
        return generalAgent
    }
    .run("What is 15% of $240?")

Streaming

for try await event in agent.stream("Summarize the changelog.") {
    switch event {
    case .output(.token(let t)):           print(t, terminator: "")
    case .tool(.completed(let call, _)):   print("\n[tool: \(call.toolName)]")
    case .lifecycle(.completed(let r)):     print("\nDone in \(r.duration)")
    default: break
    }
}

<details> <summary><strong>More examples</strong></summary>

Semantic memory
let agent = try Agent("You remember past conversations.",
    inferenceProvider: .anthropic(key: "sk-..."),
    memory: .vector(embeddingProvider: myEmbedder, threshold: 0.75)) {
    // tools
}
Guardrails
let agent = try Agent("You are a helpful assistant.",
    inputGuardrails: [GuardrailSpec.maxInput(5000), GuardrailSpec.inputNotEmpty],
    outputGuardrails: [GuardrailSpec.maxOutput(2000)])
Closure tools
let reverse = FunctionTool(
    name: "reverse",
    description: "Reverses a string",
    parameters: [ToolParameter(name: "text", description: "Text to reverse", type: .string, isRequired: true)]
) { args in
    let text = try args.require("text", as: String.self)
    return .string(String(text.reversed()))
}

let agent = try Agent("Text utilities.", tools: [reverse])
Crash-resumable workflows
let workflow = Workflow()
    .step(monitor)
    .durable.checkpoint(id: "monitor-v1", policy: .everyStep)
    .durable.checkpointing(.fileSystem(directory: checkpointsURL))

let resumed = try await workflow.durable.execute("watch", resumeFrom: "monitor-v1")
Provider switching
// On-device, private, no API key needed
let local = try Agent("Be helpful.", inferenceProvider: .foundationModels)

// Cloud
let cloud = try Agent("Be helpful.", inferenceProvider: .anthropic(key: k))

// Or swap at runtime via environment
let modified = agent.environment(\.inferenceProvider, .ollama(model: "mistral"))
Conversation
let conversation = Conversation(with: agent)

let response1 = try await conversation.send("What's the weather?")
let response2 = try await conversation.send("And tomorrow?") // Context preserved

for message in await conversation.messages {
    print("\(message.role): \(message.text)")
}

</details>

How Swarm Compares

| | Swarm | LangChain | AutoGen | |---|---|---|---| | Language | Swift 6.2 | Python | Python | | Data race safety | Compile-time | Runtime | Runtime | | On-device LLM | Foundation Models | n/a | n/a | | Execution engine | Compiled DAG | Loop-based | Loop-based | | Crash recovery | Checkpoints | n/a | Partial | | Type-safe tools | @Tool macro (compile-time) | Decorators (runtime) | Runtime | | Streaming | AsyncThrowingStream | Callbacks | Callbacks | | iOS / macOS native | First-class | n/a | n/a |

What's Included

| | | |---|---| | Agents | Agent struct with @ToolBuilder trailing closure, AgentRuntime protocol | | Workflows | Workflow: .step(), .parallel(), .route(), .repeatUntil(), .timeout() | | Tools | @Tool macro, FunctionTool, @ToolBuilder, parallel execution | | Memory | MemoryOption.conversation(limit:), MemoryOption.vector(embeddingProvider:), MemoryOption.slidingWindow(count:), MemoryOption.summary(summarizer:) | | Guardrails | GuardrailSpec.maxInput(), GuardrailSpec.maxOutput(), GuardrailSpec.inputNotEmpty, GuardrailSpec.outputNotEmpty, GuardrailSpec.customInput(), GuardrailSpec.customOutput() | | Conversation | Conversation actor for stateful multi-turn dialogue | | Resilience | 7 backoff strategies, circuit breaker, fallback chains, rate limiting | | Observability | AgentObserver, Tracer, SwiftLogTracer, per-agent token metrics | | MCP | Model Context Protocol client and server support | | Providers | Foundation Models, Anthropic, OpenAI, Ollama, Gemini, OpenRouter, MLX via Conduit | | Macros | @Tool, @Parameter, @Traceable, #Prompt |

Architecture

┌─────────────────────────────────────────────────────────────┐
│                      Your Application                       │
│          iOS 26+  ·  macOS 26+  ·  Linux (Ubuntu 22.04+)   │
├─────────────────────────────────────────────────────────────┤
│     Workflow  ·  Conversation  ·  .run()  ·  .stream()      │
├─────────────────────────────────────────────────────────────┤
│  Agents              Memory              Tools              │
│  Agent (struct)      MemoryOption        @Tool macro        │
│  AgentRuntime        Conversation        FunctionTool       │
│                      (dot-syntax)        @ToolBuilder       │
├─────────────────────────────────────────────────────────────┤
│  GuardrailSpec  ·  Resilience  ·  Observability  ·  MCP    │
├─────────────────────────────────────────────────────────────┤
│              Durable Graph Runtime (internal)               │
│   Compiled DAG  ·  Checkpointing  ·  Deterministic retry   │
├─────────────────────────────────────────────────────────────┤
│              InferenceProvider (pluggable)                   │
│   Foundation Models · Anthropic · OpenAI · Ollama · MLX     │
└─────────────────────────────────────────────────────────────┘

Requirements

| Platform | Minimum | |----------|---------| | Swift | 6.2+ | | iOS | 26.0+ | | macOS | 26.0+ | | tvOS | 26.0+ | | Linux | Ubuntu 22.04+ with Swift 6.2 |

Foundation Models require iOS 26 / macOS 26. Cloud providers work on any Swift 6.2 platform including Linux.

Documentation

| | | |---|---| | Getting Started | Installation, first agent, workflows | | API Reference | Every type, protocol, and API | | Front-Facing API | Public API surface | | Why Swarm? | Design philosophy and architecture |

Contributing

  1. Fork → branch → swift test → PR
  2. All public types must be Sendable; the compiler enforces it
  3. Format with swift package plugin --allow-writing-to-package-directory swiftformat

Bug reports and feature requests: GitHub Issues

Community

GitHub Issues · Discussions · @ckarani7

If Swarm saves you time, a star helps others find it.

License

Released under the MIT License.

Package Metadata

Repository: christopherkarani/Swarm

Homepage: https://christopherkarani.github.io/Swarm/

Stars: 460

Forks: 32

Open issues: 5

Default branch: main

Primary language: swift

License: MIT

Topics: agent, agentic-ai, agentic-systems, agents, ai, ai-agents, foundation-models, ios, langchain, machine-learning, macos, memory, offline-first, server-side, swift, swift-agents, swift-library, swiftui, watchos

README: README.md