ᗣᗣ Milo

from milo import CLI, Context, Action, Quit, SpecialKey
from milo.streaming import Progress
from typing import Annotated
from milo import MinLen

cli = CLI(name="deployer", description="Deploy services")

@cli.command("deploy", description="Deploy a service", annotations={"destructiveHint": True})
def deploy(
    environment: Annotated[str, MinLen(1)],
    service: Annotated[str, MinLen(1)],
    ctx: Context = None,
) -> dict:
    """Deploy a service to an environment."""
    # Interactive mode: show confirmation UI
    if ctx and ctx.is_interactive:
        if not ctx.confirm(f"Deploy {service} to {environment}?"):
            return {"status": "cancelled"}

    # Stream progress (MCP clients see real-time notifications)
    yield Progress(status=f"Deploying {service}", step=0, total=2)
    yield Progress(status="Verifying health", step=1, total=2)

    return {"status": "deployed", "environment": environment, "service": service}

Run by a human: interactive confirmation, then progress output. Called via MCP: progress notifications stream, then structured JSON result.

Every Milo CLI is automatically an MCP server:

# Run as MCP server (stdin/stdout JSON-RPC)
myapp --mcp

# Register with an AI host directly
claude mcp add myapp -- uv run python examples/deploy/app.py --mcp

For multiple CLIs, register them and run a single gateway:

# Register CLIs
taskman --mcp-install
deployer --mcp-install

# Run the unified gateway
uv run python -m milo.gateway --mcp

# Or register the gateway with your AI host
claude mcp add milo -- uv run python -m milo.gateway --mcp

The gateway namespaces tools automatically: taskman.add, deployer.deploy, etc. Implements MCP 2025-11-25 with outputSchema, structuredContent, tool annotations, and streaming Progress notifications.

Built-in milo://stats resource exposes request latency, error counts, and throughput.

from typing import Annotated
from milo import CLI, MinLen, MaxLen, Gt, Lt, Pattern, Description

cli = CLI(name="app")

@cli.command("create-user", description="Create a user account")
def create_user(
    name: Annotated[str, MinLen(1), MaxLen(100), Description("Full name")],
    age: Annotated[int, Gt(0), Lt(200)],
    email: Annotated[str, Pattern(r"^[^@]+@[^@]+$")],
) -> dict:
    return {"name": name, "age": age, "email": email}

Generates JSON Schema with minLength, maxLength, exclusiveMinimum, exclusiveMaximum, pattern, and description — AI agents validate inputs before calling.

from milo import App, Action

def reducer(state, action):
    if state is None:
        return {"count": 0}
    if action.type == "@@KEY" and action.payload.char == " ":
        return {**state, "count": state["count"] + 1}
    return state

app = App(template="counter.kida", reducer=reducer, initial_state=None)
final_state = app.run()

counter.kida:

Count: {{ count }}

Press SPACE to increment, Ctrl+C to quit.

from milo import App
from milo.flow import FlowScreen

welcome = FlowScreen("welcome", "welcome.kida", welcome_reducer)
config = FlowScreen("config", "config.kida", config_reducer)
confirm = FlowScreen("confirm", "confirm.kida", confirm_reducer)

flow = welcome >> config >> confirm
app = App.from_flow(flow)
app.run()

Navigate between screens by dispatching @@NAVIGATE actions from your reducers. Add custom transitions with flow.with_transition("welcome", "confirm", on="@@SKIP").

from milo import form, FieldSpec, FieldType

result = form(
    FieldSpec("name", "Your name"),
    FieldSpec("env", "Environment", field_type=FieldType.SELECT,
              choices=("dev", "staging", "prod")),
    FieldSpec("confirm", "Deploy?", field_type=FieldType.CONFIRM),
)
# result = {"name": "Alice", "env": "prod", "confirm": True}

Tab/Shift+Tab navigates fields. Arrow keys cycle select options. Falls back to plain input() prompts when stdin is not a TTY.

from milo import Call, Put, Select, ReducerResult

def fetch_saga():
    url = yield Select(lambda s: s["url"])
    data = yield Call(fetch_json, (url,))
    yield Put(Action("FETCH_DONE", payload=data))

def reducer(state, action):
    if action.type == "@@KEY" and action.payload.char == "f":
        return ReducerResult({**state, "loading": True}, sagas=(fetch_saga,))
    if action.type == "FETCH_DONE":
        return {**state, "loading": False, "data": action.payload}
    return state

Saga effects: Call, Put, Select, Fork, Delay, Retry, Timeout, TryCall, Race, All, Take, Debounce, TakeEvery, TakeLatest.

For one-shot effects, use Cmd instead — no generator needed:

from milo import Cmd, ReducerResult

def fetch_status():
    return Action("STATUS", payload=urllib.request.urlopen(url).status)

def reducer(state, action):
    if action.type == "CHECK":
        return ReducerResult(state, cmds=(Cmd(fetch_status),))
    return state

from milo.testing import assert_renders, assert_state, assert_saga
from milo import Action, Call

# Snapshot test: render state through template, compare to file
assert_renders({"count": 5}, "counter.kida", snapshot="tests/snapshots/count_5.txt")

# Reducer test: feed actions, assert final state
assert_state(reducer, None, [Action("@@INIT"), Action("INCREMENT")], {"count": 1})

# Saga test: step through generator, assert each yielded effect
assert_saga(my_saga(), [(Call(fetch, ("url",), {}), {"data": 42})])

Set MILO_UPDATE_SNAPSHOTS=1 to regenerate snapshot files.

                    ┌──────────────┐
                    │   Terminal    │
                    │   (View)     │
                    └──────┬───────┘
                           │ Key events
                           ▼
┌──────────┐    ┌──────────────────┐    ┌──────────────┐
│  Kida    │◄───│      Store       │◄───│   Reducer    │
│ Template │    │  (State Tree)    │    │  (Pure fn)   │
└──────────┘    └──────────┬───────┘    └──────────────┘
                           │
                           ▼
                    ┌──────────────┐
                    │    Sagas     │
                    │ (Side Effects│
                    │  on ThreadPool)
                    └──────────────┘

1. Model — Immutable state (plain dicts or frozen dataclasses)
2. View — Kida templates render state to terminal output
3. Update — Pure reducer(state, action) -> state functions
4. Effects — Cmd thunks (one-shot) or generator-based sagas (multi-step) on ThreadPoolExecutor

App.run()
  ├── Store(reducer, initial_state)
  ├── KeyReader (raw mode, escape sequences → Key objects)
  ├── TerminalRenderer (alternate screen buffer, flicker-free updates)
  ├── Optional: tick thread (@@TICK at interval)
  ├── Optional: SIGWINCH handler (@@RESIZE)
  └── Loop:
        read key → dispatch @@KEY → reducer → re-render
        until state.submitted or @@QUIT