drakeling 0.1.6

A local, lightweight, learning-only companion creature. Drakeling may optionally be linked to the OpenClaw ecosystem

Drakeling

A local, lightweight, learning-only companion creature. Drakeling may optionally be linked to the OpenClaw ecosystem.

Drakeling is a small digital dragon that lives on your machine. It reflects, learns about you, and expresses feelings — but never performs tasks, accesses files, or reaches the network. Safe by architecture.

Prerequisites

• Python 3.12+
• One of: pip, pipx, or uv

Installation

Using pipx (recommended — isolated environment)

pipx install drakeling

Using pip

pip install drakeling

Using uv

uv tool install drakeling

After installation, two commands are available:

Command	Purpose
drakelingd	Start the background daemon (HTTP API on 127.0.0.1:52780)
drakeling	Launch the interactive terminal UI

Getting started

Order matters: Start the daemon first, then the UI in a separate terminal.

1. Start the daemon

drakelingd

On first run, the daemon:

• creates the platform data directory (see Data directory below)
• walks you through an interactive LLM setup — pick your provider, enter your endpoint URL and credentials, and the daemon writes a .env file for you
• generates an ed25519 identity keypair (machine binding)
• generates a local API token
• begins listening on http://127.0.0.1:52780

Leave the daemon running in its own terminal (or set it up as a background service — see Running as a service).

2. Launch the terminal UI

In a separate terminal:

drakeling

If no creature exists, the UI walks you through the birth ceremony: pick a colour, optionally re-roll up to 3 times, name your dragon, and confirm. Your drakeling starts as an egg and progresses through lifecycle stages as you interact with it.

3. Interact

Key	Action	What it does
F2	Care	Show gentle attention — lifts mood, eases loneliness
F3	Rest	Put your creature to sleep — recovers energy and stability
F4 / Ctrl+T	Talk	Focus the text input, type a message and press Enter
F5 / Ctrl+F	Feed	Feed your creature — boosts energy and mood
F1 / ?	Help	Open the in-app help overlay
F8	Release	Say goodbye (irreversible)

Talking requires an LLM provider — see LLM configuration. Talking lifts mood, builds trust, sparks curiosity, and eases loneliness.

Embedded terminals (Zed, VS Code, etc.) may intercept F-keys. Use the alternative bindings shown above (?, Ctrl+T, Ctrl+F) when F-keys do not work.

Data directory

All persistent state lives in a platform-specific data directory:

Platform	Path
Linux	~/.local/share/drakeling/
macOS	~/Library/Application Support/drakeling/
Windows	%APPDATA%\drakeling\drakeling\

Contents:

File	Purpose
drakeling.db	SQLite database (creature state, memory, interaction log, lifecycle events)
identity.key	Ed25519 private key — ties the creature to this machine
api_token	Bearer token for authenticating API requests
.env	Optional — environment variable overrides (see below)

Retrieving the API token

The daemon generates an API token on first run and writes it to the api_token file in the data directory. To read it later:

Platform	Command
Linux	cat ~/.local/share/drakeling/api_token
macOS	cat ~/Library/Application\ Support/drakeling/api_token
Windows	type "%APPDATA%\drakeling\drakeling\api_token"

You need this token for API requests (export, import) and for OpenClaw Skill configuration. See OpenClaw Skill setup.

Upgrading, uninstalling, and reinstalling

Upgrading (keep your creature)

To update the app and keep your creature data:

Installer	Command
pipx	pipx upgrade drakeling
pip	pip install --upgrade drakeling
uv	uv tool upgrade drakeling

Restart the daemon after upgrading.

Uninstalling

1. Stop the daemon (Ctrl+C or stop the service).
2. Uninstall the app:

Installer	Command
pipx	pipx uninstall drakeling
pip	pip uninstall drakeling
uv	uv tool uninstall drakeling

Removing creature data

To delete your creature and all local data (database, identity key, exports), remove the data directory:

Platform	Command
Linux	rm -rf ~/.local/share/drakeling
macOS	rm -rf ~/Library/Application\ Support/drakeling
Windows	rmdir /s /q "%APPDATA%\drakeling\drakeling"

Clean reinstall (start from scratch)

Uninstall the app, remove the data directory (commands above), then install again.

Linux / macOS (pipx):

pipx uninstall drakeling
rm -rf ~/.local/share/drakeling          # Linux
# or: rm -rf ~/Library/Application\ Support/drakeling  # macOS
pipx install drakeling

Windows (pipx, Command Prompt or PowerShell):

pipx uninstall drakeling
rmdir /s /q "%APPDATA%\drakeling\drakeling"
pipx install drakeling

Configuration

The daemon reads configuration from environment variables. For persistent config, place a .env file in the data directory shown above. This is the preferred approach because background services (systemd, launchd) do not inherit shell profiles like ~/.bashrc.

Environment variable reference

Variable	Description	Default
DRAKELING_LLM_BASE_URL	OpenAI-compatible /v1 endpoint URL	(required unless gateway mode)
DRAKELING_LLM_API_KEY	API key for the LLM provider	(required unless gateway mode)
DRAKELING_LLM_MODEL	Model name (e.g. gpt-4o-mini, llama3.3)	(required unless gateway mode)
DRAKELING_USE_OPENCLAW_GATEWAY	Delegate LLM calls to OpenClaw gateway	false
DRAKELING_OPENCLAW_GATEWAY_URL	Gateway URL	http://127.0.0.1:18789
DRAKELING_OPENCLAW_GATEWAY_TOKEN	Bearer token for the gateway	(unset)
DRAKELING_OPENCLAW_GATEWAY_MODEL	Model to request from the gateway (omit to use gateway default)	(unset)
DRAKELING_MAX_TOKENS_PER_CALL	Per-call token cap	300
DRAKELING_MAX_TOKENS_PER_DAY	Daily token budget	10000
DRAKELING_TICK_SECONDS	Background loop interval (seconds, minimum 10)	60
DRAKELING_MIN_REFLECTION_INTERVAL	Minimum seconds between background reflections	600
DRAKELING_PORT	Daemon HTTP port	52780

LLM configuration

Your creature needs an LLM provider to talk and reflect. On first run, drakelingd walks you through setup interactively. You can also configure it manually by editing the .env file in the data directory.

Important base URL rule:

• DRAKELING_LLM_BASE_URL must point to the provider's API root (usually ending in /v1).
• Do not include /chat/completions in DRAKELING_LLM_BASE_URL.
• Drakeling appends /chat/completions automatically.

Examples:

• Correct: http://127.0.0.1:11434/v1
• Wrong: http://127.0.0.1:11434/v1/chat/completions

Common base URL patterns (direct provider mode):

Provider	Base URL (DRAKELING_LLM_BASE_URL)
OpenAI	https://api.openai.com/v1
Ollama (local)	http://127.0.0.1:11434/v1
LM Studio (local server)	http://127.0.0.1:1234/v1
vLLM (default local server)	http://127.0.0.1:8000/v1
OpenRouter	https://openrouter.ai/api/v1

Option A — Any OpenAI-compatible LLM provider

Works with OpenAI, Ollama, vLLM, LiteLLM, or any service that exposes an OpenAI-compatible /v1 endpoint.

DRAKELING_LLM_BASE_URL=https://api.openai.com/v1
DRAKELING_LLM_API_KEY=sk-...
DRAKELING_LLM_MODEL=gpt-4o-mini

For local LLMs (e.g. Ollama), the API key can be any non-empty string:

DRAKELING_LLM_BASE_URL=http://127.0.0.1:11434/v1
DRAKELING_LLM_API_KEY=ollama-local
DRAKELING_LLM_MODEL=llama3.3

Common model name examples (set in DRAKELING_LLM_MODEL):

• Ollama local: qwen3:14b, llama3.3
• OpenAI: gpt-4o-mini
• OpenRouter: openai/gpt-oss-20b
• vLLM (self-hosted): NousResearch/Meta-Llama-3-8B-Instruct

Option B — OpenClaw gateway delegation

If you already run OpenClaw, this is the easiest option. Any model OpenClaw supports (cloud or local) becomes available to Drakeling with no additional provider configuration.

DRAKELING_USE_OPENCLAW_GATEWAY=true
# DRAKELING_OPENCLAW_GATEWAY_URL=    # leave blank for default http://127.0.0.1:18789
# DRAKELING_OPENCLAW_GATEWAY_TOKEN=  # leave blank if gateway has no auth
# DRAKELING_OPENCLAW_GATEWAY_MODEL=openai/gpt-oss-20b

If you set DRAKELING_OPENCLAW_GATEWAY_MODEL, use a model identifier that your OpenClaw gateway can serve (for example cloud models like openai/gpt-oss-20b or local models exposed by your OpenClaw setup).

Troubleshooting common URL mistakes

If daemon logs show an error like:

404 Not Found ... /v1/chat/completions/chat/completions

your base URL is too specific. This usually means DRAKELING_LLM_BASE_URL was set to include /chat/completions.

Fix:

• Set DRAKELING_LLM_BASE_URL to the provider root only (for example http://127.0.0.1:11434/v1).
• Keep /chat/completions out of the .env value.
• Restart drakelingd after updating .env.

Export and import

Export (backup)

Your creature can be exported as an encrypted .drakeling bundle file containing the database and identity key.

curl -X POST http://127.0.0.1:52780/export \
  -H "Authorization: Bearer $(cat ~/.local/share/drakeling/api_token)" \
  -H "Content-Type: application/json" \
  -d '{"passphrase": "your-secret-passphrase", "output_path": "/tmp/my-dragon.drakeling"}'

Import (restore / migrate)

To import a bundle onto a new machine, start the daemon in import-ready mode:

drakelingd --allow-import

Then send the import request:

curl -X POST http://127.0.0.1:52780/import \
  -H "Authorization: Bearer $(cat ~/.local/share/drakeling/api_token)" \
  -H "Content-Type: application/json" \
  -d '{"passphrase": "your-secret-passphrase", "bundle_path": "/tmp/my-dragon.drakeling"}'

The daemon creates a .bak backup before importing and rolls back automatically if anything goes wrong. After a successful import, restart the daemon normally (without --allow-import).

CLI reference

drakelingd

Flag	Description
(no flags)	Normal production mode
--dev	Development mode: verbose stdout logging, no background reflection, import always permitted
--allow-import	Enable the POST /import endpoint (disabled by default for safety)

drakeling

No flags. Connects to the local daemon and launches the interactive terminal UI.

Running as a service

For production use, the daemon should run as a background service that starts automatically on login. Template files are provided in deploy/.

Linux — systemd

cp deploy/drakeling.service ~/.config/systemd/user/
systemctl --user daemon-reload
systemctl --user enable --now drakeling

Check status: systemctl --user status drakeling

macOS — launchd

cp deploy/drakeling.plist ~/Library/LaunchAgents/
launchctl load ~/Library/LaunchAgents/drakeling.plist

Windows — Task Scheduler

schtasks /create /tn "Drakeling" /tr "drakelingd" /sc onlogon /rl limited /f

Or import deploy/drakeling-task.xml via the Task Scheduler GUI.

OpenClaw Skill setup

This lets OpenClaw agents check on your drakeling and give it care autonomously.

1. Install the skill: clawhub install drakeling (or copy skill/ to ~/.openclaw/skills/drakeling/)
2. Start the daemon at least once: drakelingd
3. Read the API token:
   • Linux: cat ~/.local/share/drakeling/api_token
   • macOS: cat ~/Library/Application\ Support/drakeling/api_token
   • Windows: type "%APPDATA%\drakeling\drakeling\api_token"
4. Add to ~/.openclaw/openclaw.json under skills.entries.drakeling:

   {
     "skills": {
       "entries": {
         "drakeling": {
           "env": {
             "DRAKELING_API_TOKEN": "paste-token-here"
           }
         }
       }
     }
   }
   

See docs/openclaw_integration.md for the full OpenClaw integration guide (config format, gateway delegation, and references).

The skill only uses /status (read) and /care (write). It never calls /talk, /rest, /export, or /import.

Development

Setup

git clone https://github.com/BVisagie/drakeling.git
cd drakeling

Using pip:

python3 -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"

Using pipx:

pipx install --editable .

Using uv:

uv venv
source .venv/bin/activate
uv pip install -e ".[dev]"

Running in dev mode

drakelingd --dev

Dev mode:

• Logs all lifecycle events and token usage to stdout
• Disables background reflection (tick loop still runs for stat decay)
• Permits import without --allow-import

Running tests

pytest

The test suite covers domain models, trait generation, stat decay/boost, lifecycle transitions, crypto (identity, tokens, encrypted bundles), sprites, and API integration tests.

Project structure

src/drakeling/
  domain/         Pure domain logic (models, traits, decay, lifecycle, sprites)
  crypto/         Ed25519 identity, API tokens, encrypted bundles
  storage/        SQLAlchemy models and database init
  llm/            LLM wrapper and prompt construction
  daemon/         Daemon entry point, config, background tick loop
  api/            FastAPI endpoints (birth, status, care, talk, rest, export/import)
  ui/             Textual terminal UI (birth ceremony, main screen, widgets)