wild-pc/docs/registry.md

Registry

How castle tracks, configures, and manages programs, services, and jobs. This is the central reference for castle.yaml structure and the registry architecture.

Vocabulary (canonical)

Use these terms consistently across code, CLI, API, and docs.

• program — any project castle manages, regardless of what it does. The software catalog (programs:). Every program has a behavior and an optional stack. ("component" was the old name for program — don't use it.)
• behavior — what a program is: tool (a CLI you invoke), daemon (a long-running server), frontend (a web UI). A property of the program, independent of whether/how it's deployed.
• stack — a creation-time toolchain + scaffold template (python-cli, python-fastapi, react-vite). Optional; seeds a program's default dev commands but isn't required at runtime.
• service — a program deployed as a long-running systemd .service (services:).
• job — a program deployed as a scheduled systemd .timer (+ oneshot) (jobs:).
• deployment — the umbrella for "a service or a job" (a program materialized into the runtime). The registry's deployed entries are deployments.

Two orthogonal axes. behavior (tool/daemon/frontend) is what a program is; service/job is how/when it's deployed. They're independent: a program may have neither (a tool you just install), a service (always-on), or a job (scheduled). A daemon-behavior program is usually deployed as a service; a tool-behavior program may back a job or just be installed for manual use.

Configuration Directory Layout

Castle splits its configuration across a root directory (~/.castle/ or your config root) instead of a single file:

~/.castle/
├── castle.yaml        # Global settings (gateway, repo, etc.)
├── programs/          # Program configuration files (one file per program)
│   └── my-tool.yaml
├── services/          # Service configuration files (one file per service)
│   └── my-service.yaml
└── jobs/              # Job configuration files (one file per job)
    └── my-job.yaml

castle.yaml (Globals)

The core castle.yaml contains configuration settings that apply globally to your Castle platform instance:

gateway:
  port: 9000
repo: /data/repos/castle

Resource Configuration Files (programs/, services/, jobs/)

Each resource (program, service, or job) is configured in its own YAML file named after the resource's unique ID (e.g., services/my-service.yaml defines the service my-service).

programs/my-tool.yaml:

description: Does something useful
source: /data/repos/my-tool
stack: python-cli
behavior: tool
system_dependencies: [pandoc]

services/my-service.yaml:

program: my-service
run:
  runner: python
  program: my-service
expose:
  http:
    internal: { port: 9001 }
    health_path: /health
proxy:
  caddy: { path_prefix: /my-service }
manage:
  systemd: {}

jobs/my-job.yaml:

program: my-tool
run:
  runner: command
  argv: [my-tool, sync]
schedule: "0 2 * * *"
manage:
  systemd: {}

Resource Categories

Category	Location	Purpose	Role / Types
programs	programs/*.yaml	Software catalog — what software exists	tool, frontend, daemon
services	services/*.yaml	Long-running daemons — how they run	service
jobs	jobs/*.yaml	Scheduled tasks — when they run	job

Services and jobs can reference a program via program: for description fallthrough and source code linking. They can also exist independently (e.g., castle-gateway runs Caddy — not our software).

Program blocks

Programs define what software exists — identity, source, behavior, builds.

behavior — What role this program plays

behavior: daemon    # or: tool, frontend

Explicit declaration of how the program is used:

• daemon — long-running service (python-fastapi stack)
• tool — CLI utility (python-cli stack)
• frontend — web UI (react-vite stack)

source — Where the source lives

source: /data/repos/my-tool   # your programs, under $CASTLE_REPOS_DIR
source: repo:castle-api        # castle's own programs, inside the git repo

The source path is resolved one of three ways (core/src/castle_core/config.py):

source: value	Resolves to	Used for
/data/repos/my-tool (absolute)	as-is	Your own programs (the default)
repo:castle-api	<repo>/castle-api (via the top-level repo: field)	Castle's built-in programs
code/my-tool (relative)	$CASTLE_HOME/code/my-tool	Legacy — pre-/data/repos layout

Programs you create or adopt live under $CASTLE_REPOS_DIR (default /data/repos, override with CASTLE_REPOS_DIR) and are recorded with an absolute source:. Castle's own programs (CLI, core, castle-api, app) live in the git repo and use the repo: prefix. A relative source: still resolves against $CASTLE_HOME for back-compat, but new programs no longer use it.

stack — Development toolchain (optional)

stack: python-fastapi   # or: python-cli, react-vite — OPTIONAL

A stack provides default dev-verb commands (build/test/lint/type-check/…) and a scaffold template for new code. It is optional: a program with no stack works fine as long as it declares its own commands:. Stacks are a creation-time convenience, not a runtime requirement.

commands — Per-program dev verbs

commands:
  lint:  [["ruff", "check", "."]]
  test:  [["pytest", "tests/"]]
  run:   [["./bin/my-tool", "--serve"]]

Each verb is a list of argv lists (run in sequence). A declared verb overrides the stack default; an absent verb falls back to the stack handler (if any), else the verb is unavailable. build is declared via build: (it also carries outputs:); every other verb via commands:. This is what lets a wired-in repo with no stack be linted/tested/run. Verb resolution lives in core/src/castle_core/stacks.py (run_action, available_actions).

repo / ref — Wiring in an existing repo

repo: https://github.com/me/widget.git
ref: v2.1.0          # optional branch/tag/commit

repo records a git URL so castle program clone can provision the source on a fresh machine. When source: points at an existing working copy, that takes precedence. Use castle program add <path|url> to register an existing repo as a program.

system_dependencies — Required system packages

system_dependencies: [pandoc, poppler-utils]

System packages that must be installed for the program to work. Displayed in castle program list --behavior tool and the dashboard.

version — Program version

version: "1.0.0"

Optional version metadata.

build — How to build it

build:
  commands:
    - ["pnpm", "build"]
  outputs:
    - dist/

Programs with build outputs are typically frontends.

Service blocks

Services define how long-running daemons are deployed.

run — How to start it (required)

Discriminated union on runner:

Runner	Sync	Deploy	Key fields
python	(none — uv run self-syncs)	uv run --project <source> --no-dev <program>	program, args
command	(none)	which(argv[0]) → resolved path	argv
container	(none)	docker/podman run	image, command, ports, volumes
node	package_manager install	package_manager run script	script, package_manager
remote	(none)	(none — no local process)	base_url, health_url

A python service runs in place from its own project venv via uv run, which syncs the env to the project's lockfile before launching. There is no separate tool venv and no uv tool install step: a restart picks up both code and dependency changes (the deploy-time ExecStart is deterministic from source, so it never goes stale). uv tool install is reserved for tool-behavior programs, where being on a human's PATH is the point. If a python service declares a program with no resolvable source, deploy falls back to a PATH lookup of the script.

run:
  runner: python
  program: my-service     # name in [project.scripts]

expose — What it exposes

expose:
  http:
    internal:
      port: 9001            # Required for HTTP services
    health_path: /health     # Used by health polling

proxy — How the gateway routes to it

proxy:
  caddy:
    path_prefix: /my-service   # reachable at gateway:9000/my-service/
    host: my-service.lan       # …or by hostname (whole host → backend root)

Castle generates the Caddyfile from these entries. Only needed for services reachable through the gateway.

Gateway routes — one concept, three target kinds. The gateway (:9000) maps a public address (a path prefix /foo, or a host foo.lan) to a target:

Kind	Target	Declared by
proxy	a local service on a port — Caddy reverse_proxy localhost:PORT	a service's proxy.caddy
remote	a service on another node — reverse_proxy host:PORT	mesh discovery
static	a built frontend's dist/ — Caddy file_server (no process)	a frontend program with build.outputs and no service (implicit; served at /<name>/, castle-app at /)

"Serving a frontend" and "proxying a service" are the same thing — a route — differing only in whether the target is files on disk or a live process. The complete table (all kinds) is shown by castle gateway status, the dashboard Gateway panel, and GET /gateway; the Caddyfile is generated from it.

manage — How to manage it

manage:
  systemd: {}

Enables castle service enable/disable and castle service logs. An empty {} uses defaults (enable=true, restart=on-failure, restart_sec=2).

Full options:

manage:
  systemd:
    description: Custom unit description
    restart: always          # on-failure | always | no
    restart_sec: 2
    no_new_privileges: true
    after: [network.target, castle-other.service]
    wanted_by: [default.target]
    exec_reload: "caddy reload ..."

defaults — Environment

defaults.env is the single, explicit source of the env a service/job runs with — what you write here is exactly what lands in the systemd unit. Castle does not inject hidden convention vars; whatever env var your program reads for its port, data dir, etc., you map here.

expose: { http: { internal: { port: 9001 }, health_path: /health } }
defaults:
  env:
    MY_SERVICE_PORT: ${port}          # the program's own port var ← expose.port
    MY_SERVICE_DATA_DIR: ${data_dir}  # = $CASTLE_DATA_DIR/<name>
    CENTRAL_CONTEXT_URL: http://localhost:9001
    API_KEY: ${secret:MY_API_KEY}

Values may contain placeholders that castle resolves at deploy:

Placeholder	Expands to
${port}	the service's expose.http.internal.port (so it can't drift)
${data_dir}	$CASTLE_DATA_DIR/<program-or-name> (the dedicated data volume)
${name}	the deployment name
${secret:NAME}	the contents of ~/.castle/secrets/NAME

Hardcode the values instead if you prefer; the placeholders just save you from repeating castle's computed paths/ports. castle program create scaffolds the ${port}/${data_dir} lines for new services. Never store secrets in castle.yaml — use ${secret:…}.

Job blocks

Jobs define how scheduled tasks run. Same blocks as services plus schedule and timezone.

schedule — Cron expression (required)

schedule: "*/5 * * * *"
timezone: America/Los_Angeles    # default

Castle generates a systemd .timer file alongside the .service unit.

Other blocks

Jobs also support run (required), manage, and defaults — same semantics as services.

How programs get into /data/repos/

Every program's source lives under $CASTLE_REPOS_DIR (default /data/repos/<name>/). It can arrive there a few ways:

1. Scaffold a new one with castle program create — writes the project into /data/repos/<name>/ and registers it in castle.yaml with an absolute source: /data/repos/<name>.
2. Adopt an existing repo — castle program add <path|git-url> registers it in place (or records its repo: URL for castle program clone).
3. Drop files in directly — a /data/repos/<name>/ directory is just a working tree; it doesn't have to be under version control to be run.

/data/repos/ holds independent repos — each program directory manages its own version control (or none); some are standalone git clones, others loose files.

Castle's own programs (CLI, core, castle-api, app) are the exception: they live inside the castle git repo and are referenced with source: repo:<name>.

Registering a new program

Via castle program create (recommended)

# Service — scaffolds into /data/repos/, assigns port, registers in castle.yaml
castle program create my-service --stack python-fastapi --description "Does something"

# Tool — scaffolds into /data/repos/
castle program create my-tool --stack python-cli --description "Does something"

Manually

Clone or create the project under /data/repos/, then add entries to the appropriate sections of castle.yaml:

# Tool — only needs a program entry
programs:
  my-tool:
    description: Does something useful
    source: /data/repos/my-tool
    stack: python-cli
    behavior: tool

# Service — needs both program and service entries
programs:
  my-service:
    description: Does something useful
    source: /data/repos/my-service
    stack: python-fastapi
    behavior: daemon

services:
  my-service:
    program: my-service
    run:
      runner: python
      program: my-service
    expose:
      http:
        internal: { port: 9001 }
        health_path: /health
    proxy:
      caddy: { path_prefix: /my-service }
    manage:
      systemd: {}

Lifecycle

Service lifecycle

castle program create my-service --stack python-fastapi   # 1. Scaffold + register
cd /data/repos/my-service && uv sync   # 2. Install deps
# ... implement ...
castle program test my-service                    # 3. Run tests
castle service enable my-service          # 4. Generate systemd unit, start
castle gateway reload                     # 5. Update Caddy routes

After service enable, the service starts automatically on boot and restarts on failure. Manage with:

castle logs my-service -f         # Tail logs
castle service run my-service     # Run in foreground (for debugging)
castle service disable my-service # Stop and remove systemd unit

Tool lifecycle

castle program create my-tool --stack python-cli        # 1. Scaffold + register
cd /data/repos/my-tool && uv sync     # 2. Install deps
# ... implement ...
castle program test my-tool                      # 3. Run tests
uv tool install --editable /data/repos/my-tool/   # 4. Install to PATH

Job lifecycle

Jobs are defined in the jobs: section with a run spec and schedule:

jobs:
  my-job:
    description: Runs nightly
    run:
      runner: command
      argv: ["my-job"]
    schedule: "0 2 * * *"
    manage:
      systemd: {}

castle job enable my-job generates both a .service (Type=oneshot) and a .timer file.

Infrastructure paths

Castle uses two independent roots, each overridable by an environment variable (both expand ~ and resolve relative paths):

• CASTLE_HOME — config, code, artifacts, and secrets. Default ~/.castle.
• CASTLE_DATA_DIR — program/service data I/O (potentially large; lives on a dedicated volume). Default /data/castle. Decoupled from CASTLE_HOME on purpose so bulk data doesn't sit in the home directory.

What	Where
Castle home	$CASTLE_HOME (default ~/.castle)
Registry	$CASTLE_HOME/castle.yaml
Program source (yours)	$CASTLE_HOME/code/<name>/
Program source (castle's)	<repo>/<name> (via source: repo:<name>)
Secrets	$CASTLE_HOME/secrets/<NAME>
Generated Caddyfile	$CASTLE_HOME/artifacts/specs/Caddyfile
Built frontends	served in place from <source>/<dist>/ (no copy)
Service data	$CASTLE_DATA_DIR/<name>/ (default /data/castle/<name>/)
Systemd units	~/.config/systemd/user/castle-*.service
Systemd timers	~/.config/systemd/user/castle-*.timer

Defined in core/src/castle_core/config.py: CASTLE_HOME (with derived CODE_DIR, SECRETS_DIR, SPECS_DIR, CONTENT_DIR) and the independent DATA_DIR (CASTLE_DATA_DIR). A service reaches its data path by mapping ${data_dir} (= $CASTLE_DATA_DIR/<name>) to the env var its program reads, in defaults.env. Systemd unit/timer paths are fixed by systemd's user-unit convention.

Manifest models

The Pydantic models live in core/src/castle_core/manifest.py. Key classes:

• ProgramSpec — software catalog entry (source, behavior, stack, build, system_dependencies)
• ServiceSpec — long-running daemon (run, expose, proxy, manage, defaults)
• JobSpec — scheduled task (run, schedule, manage, defaults)
• RunSpec — discriminated union (RunPython, RunCommand, RunContainer, RunNode, RunRemote)
• ExposeSpec, ProxySpec, ManageSpec, BuildSpec
• CaddySpec, SystemdSpec, HttpExposeSpec, HttpInternal

Config loading: core/src/castle_core/config.py — load_config() parses castle.yaml into CastleConfig with typed programs, services, and jobs dicts.

Infrastructure generators: core/src/castle_core/generators/ — systemd unit/timer generation (systemd.py) and Caddyfile generation (caddyfile.py).