localai-cli

Save as quickstart_clock.swift, place localai-cli and localai-playground-run next to it, then run swift quickstart_clock.swift — no Xcode project needed, swift <file>.swift runs a script file directly.

#!/usr/bin/env swift
import Foundation

let env = ProcessInfo.processInfo.environment
let cliPath = env["LOCALAI_CLI_PATH"]
    ?? URL(fileURLWithPath: #filePath).deletingLastPathComponent().appendingPathComponent("localai-cli").path
let configPath = env["LOCALAI_CONFIG_PATH"]
    ?? NSHomeDirectory() + "/Library/Application Support/LocalLM Lab/localai-config.json"

let request: [String: Any] = [
    "system_prompt": "You are a concise assistant.",
    "user_input": "What time is it right now?",
    "connectors": ["clock"],
]
let requestData = try! JSONSerialization.data(withJSONObject: request)

let process = Process()
process.executableURL = URL(fileURLWithPath: cliPath)
process.arguments = ["--config", configPath, "--run"]

let stdinPipe = Pipe(), stdoutPipe = Pipe()
process.standardInput = stdinPipe
process.standardOutput = stdoutPipe
process.standardError = Pipe()

try! process.run()
stdinPipe.fileHandleForWriting.write(requestData)
try! stdinPipe.fileHandleForWriting.close()

let stdoutData = stdoutPipe.fileHandleForReading.readDataToEndOfFile()
process.waitUntilExit()

let response = try! JSONSerialization.jsonObject(with: stdoutData) as! [String: Any]
if let error = response["error"] as? String, !error.isEmpty {
    print("FAIL - \(error)")
    exit(1)
}
print(response["answer"] as? String ?? "")

Full version with error messages tailored to each failure mode: quickstart_clock.swift.

Save as quickstart_clock.py, place localai-cli and localai-playground-run next to it, then run python3 quickstart_clock.py (use python3 explicitly — plain python isn't guaranteed on current macOS).

import json, os, subprocess

CLI_PATH = os.environ.get(
    "LOCALAI_CLI_PATH", os.path.join(os.path.dirname(__file__), "localai-cli")
)
CONFIG_PATH = os.environ.get(
    "LOCALAI_CONFIG_PATH",
    os.path.expanduser("~/Library/Application Support/LocalLM Lab/localai-config.json"),
)

request = {
    "system_prompt": "You are a concise assistant.",
    "user_input": "What time is it right now?",
    "connectors": ["clock"],
}

result = subprocess.run(
    [CLI_PATH, "--config", CONFIG_PATH, "--run"],
    input=json.dumps(request),
    capture_output=True,
    text=True,
)
response = json.loads(result.stdout)

if response.get("error"):
    print(f"FAIL - {response['error']}")
else:
    print(response.get("answer", ""))

Full version with error messages tailored to each failure mode: quickstart_clock.py.

let request: [String: Any] = [
    "system_prompt": "You are a concise assistant. Use the available tool to answer.",
    "user_input": "What documentation topics are available for the GitHub repo "
        + "nickclyde/duckduckgo-mcp-server?",
    "mcp_tools": [["server": "https://mcp.deepwiki.com/mcp", "tool": "read_wiki_structure"]],
]
// ...same Process/Pipe subprocess call as above, using this request instead.

Full script: quickstart_mcp_deepwiki.swift.

request = {
    "system_prompt": "You are a concise assistant. Use the available tool to answer.",
    "user_input": (
        "What documentation topics are available for the GitHub repo "
        "nickclyde/duckduckgo-mcp-server?"
    ),
    "mcp_tools": [{"server": "https://mcp.deepwiki.com/mcp", "tool": "read_wiki_structure"}],
}
# ...same subprocess.run() call as above, using this request instead.

Full script: quickstart_mcp_deepwiki.py (and quickstart_mcp_deepwiki_chat.py for the --chat shape instead of --run).

Save as plate_today.swift, place localai-cli and localai-playground-run next to it, then run swift plate_today.swift.

let requiredConnectors = ["clock", "calendar", "reminders"]

// ...preflight: read localai-config.json, check requiredConnectors are all
// in "connectors_enabled" and the Todoist server/tool are connected +
// enabled, print a checklist, exit(1) before calling localai-cli if
// anything's missing...

let request: [String: Any] = [
    "system_prompt": "You are a friendly, concise personal assistant. Always start by "
        + "calling getCurrentTime to find out today's actual date — never assume or guess "
        + "it. Then use the available tools to check the user's calendar, reminders, and "
        + "Todoist tasks for that date, and summarize their day.",
    "user_input": "What's on my plate today? First check the current date with your clock "
        + "tool, then check my calendar events, my reminders due today, and my Todoist "
        + "tasks due today (exclude overdue tasks), then give me a friendly, concise "
        + "summary of my day.",
    "connectors": requiredConnectors,
    "mcp_tools": [["server": "https://ai.todoist.net/mcp", "tool": "find-tasks-by-date"]],
]
// ...same Process/Pipe subprocess call as the quickstarts above, using this request instead.

Full script: plate_today.swift.

Save as plate_today.py, place localai-cli and localai-playground-run next to it, then run python3 plate_today.py.

REQUIRED_CONNECTORS = ["clock", "calendar", "reminders"]

# ...preflight: read localai-config.json, check REQUIRED_CONNECTORS are all
# in "connectors_enabled" and the Todoist server/tool are connected +
# enabled, print a checklist, sys.exit(1) before calling localai-cli if
# anything's missing...

request = {
    "system_prompt": (
        "You are a friendly, concise personal assistant. Always start by calling "
        "getCurrentTime to find out today's actual date — never assume or guess it. "
        "Then use the available tools to check the user's calendar, reminders, and "
        "Todoist tasks for that date, and summarize their day."
    ),
    "user_input": (
        "What's on my plate today? First check the current date with your clock tool, "
        "then check my calendar events, my reminders due today, and my Todoist tasks "
        "due today (exclude overdue tasks), then give me a friendly, concise summary "
        "of my day."
    ),
    "connectors": REQUIRED_CONNECTORS,
    "mcp_tools": [{"server": "https://ai.todoist.net/mcp", "tool": "find-tasks-by-date"}],
}
# ...same subprocess.run() call as the quickstarts above, using this request instead.

Full script: plate_today.py.

struct LocalAIError: Error, CustomStringConvertible {
    let description: String
}

/// Invokes `localai-cli --run` and returns its parsed JSON response.
/// Throws only if localai-cli itself couldn't be executed (e.g. missing
/// binary) - a rejection or model error comes back as a normal dictionary
/// with an "error" key, same as localai-cli's own stdout contract.
func runLocalAI(
    cliPath: String,
    configPath: String,
    systemPrompt: String,
    userInput: String,
    connectors: [String] = [],
    mcpTools: [[String: String]] = []
) throws -> [String: Any] {
    guard FileManager.default.isExecutableFile(atPath: cliPath) else {
        throw LocalAIError(description: "localai-cli not found at \(cliPath)")
    }

    var request: [String: Any] = [
        "system_prompt": systemPrompt,
        "user_input": userInput,
        "connectors": connectors,
    ]
    if !mcpTools.isEmpty { request["mcp_tools"] = mcpTools }
    let requestData = try JSONSerialization.data(withJSONObject: request)

    let process = Process()
    process.executableURL = URL(fileURLWithPath: cliPath)
    process.arguments = ["--config", configPath, "--run"]

    let stdinPipe = Pipe(), stdoutPipe = Pipe(), stderrPipe = Pipe()
    process.standardInput = stdinPipe
    process.standardOutput = stdoutPipe
    process.standardError = stderrPipe

    try process.run()
    stdinPipe.fileHandleForWriting.write(requestData)
    try stdinPipe.fileHandleForWriting.close()

    let stdoutData = stdoutPipe.fileHandleForReading.readDataToEndOfFile()
    process.waitUntilExit()

    guard let json = try? JSONSerialization.jsonObject(with: stdoutData) as? [String: Any] else {
        throw LocalAIError(description: "localai-cli produced no parseable JSON")
    }
    return json
}

Source with the fuller error-handling variant (also reads stderr, supports an --mcp demo mode): run_localai.swift.

import json
import subprocess


def run_localai(cli_path, config_path, system_prompt, user_input, connectors=None, mcp_tools=None):
    """Invoke localai-cli --run and return its parsed JSON response.

    Raises RuntimeError only if localai-cli itself couldn't be executed
    (e.g. missing binary) - a rejection or model error is NOT raised, it
    comes back as a normal dict with an "error" key, same as localai-cli's
    own stdout contract.
    """
    request = {
        "system_prompt": system_prompt,
        "user_input": user_input,
        "connectors": connectors or [],
    }
    if mcp_tools:
        request["mcp_tools"] = mcp_tools

    result = subprocess.run(
        [cli_path, "--config", config_path, "--run"],
        input=json.dumps(request),
        capture_output=True,
        text=True,
    )
    try:
        return json.loads(result.stdout)
    except json.JSONDecodeError:
        raise RuntimeError(
            f"localai-cli produced no parseable JSON (exit {result.returncode}): "
            f"{result.stdout!r} / {result.stderr!r}"
        )

Source with the fuller error-handling variant: run_localai.py.