davy-jones-locker (engine)

Early release. This is 0.1: the API may change between minor versions until 1.0, so pin an exact version if you depend on it.

DAVY JONES' LOCKER's Python engine, which executes flowgraphs on the server (FastAPI, port 3000), and the runner, the sandbox it runs Python in. An app built on the framework runs everything it can in the browser; the engine is the second engine, for:

• PythonScript nodes, which can't run in a browser.
• SQL over the backend database: a SQL node whose query reads a table its wired inputs don't supply runs here, over Postgres (attached read-only as db) and the inputs the browser sends.
• Whole graphs on the server, for schedulers (n8n, say) to trigger.

There's no sign-in yet (identity is the next part): keep the engine off the public internet until there is. Until then, anyone who can reach it can use its AI assistant, and so its LiteLLM key's budget.

An app's engine

An app builds its engine on this one. Its entry point makes the FastAPI app:

from davy_jones_locker import create_app

app = create_app(title="engine-myapp")

and its image starts from the framework's (Dockerfile), adding that entry point:

FROM davy-jones-locker-engine
COPY main.py ./
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "3000"]

The runner is used as it is (runner/Dockerfile), until an app adds libraries of its own. ../compose.yaml runs both, with the runner's safeguards; copy it into the app and name the services for it (engine-myapp, engine-myapp-runner): browsers reach the engine through the app's own origin at /engines/<service>/.

The package is davy-jones-locker (Python 3.12+); pip install -e . here for development. The images don't install it: they copy the package and install the pinned, hashed versions in requirements.txt and runner/requirements.txt.

Settings

.env.example (the engine) and runner.env.example (the runner's limits: never credentials, since scripts run beside it). The app's compose file usually sets RUNNER_URL (the runner's address, default http://runner:3000) and PUBLIC_HOST (what /health reports, default engine).

API

Errors are JSON { error }, their message meant for the user. /run/python's result is { output, value, variables } or { output, error }:

• output: what the script printed.
• value: its last expression's value: { kind: 'table', rows } (the table is the table part), { kind: 'json', type, value }, { kind: 'text', type, text } (anything JSON can't hold), or { kind: 'none' }.
• variables: its top-level variables, [{ name, type, summary }] (for promoting them to outputs of their own, later).

A graph run (/execute, /graphs/{id}/execute, e.g. from n8n) runs targets (default: every node with Auto Run on) and everything upstream, as the browser does, and answers { runId, status, failed, results }: per node { status, error? | rowCount, output?, value?, export? }, an export's file as { filename, contentType, size, base64 }. Runs are recorded in runs when there's a database. On the server:

AI assistant

The Assistant field in SEAMONSTER's Manage panel (SQLQuery, PythonScript and JavaScript nodes) asks POST /assist (assist.py), which asks a LiteLLM proxy's /chat/completions, OpenAI-style. Set it up in .env:

Off (and hidden in the app) unless all three of the first settings are set. What the model is sent: the request; the node's kind and current code; each input's reference name, columns (name and type), row count and first ASSIST_SAMPLE_ROWS rows (values cut to 200 characters), which the browser works out by running what's upstream; and for SQL, the query database's tables and columns, not their rows. For Python, it's told the packages in runner/requirements.in, so enabling one there tells it too. It answers by calling a write_code tool (code and a note), or with a fenced code block. The code only fills the node's editor: nothing runs until the user runs it. Prompts and answers aren't logged, only their size and time.

A LiteLLM proxy on the Docker host rather than on the network: from inside the container, that's host.docker.internal, which needs extra_hosts: ["host.docker.internal:host-gateway"] on the engine's service.

Database

The engine owns its tables, in schema FLOW_SCHEMA (flow), created the first time the database answers (database.py): graphs, files (stored data by key), runs. Without DATABASE_URL, or while the database can't be reached, those endpoints answer 503 and graph runs aren't recorded; everything else still works, and the engine starts either way.

SQL reads the query database (QUERY_DATABASE_URL, else DATABASE_URL), attached read-only as catalog db: db.public.orders, or just orders (plain names look in the inputs, then db.public). Its role should be read-only: engine-shared's reader role is, and its script makes the same roles on any Postgres.

Safeguards

User code runs in two places here, and neither is trusted.

SQL (sql.py). Each query or graph run gets its own in-memory DuckDB. Once the query database is attached, DuckDB is locked down: no file access, no new extensions or attachments, settings frozen. That doesn't stop postgres_scan() connecting to any server by connection string, so a query must also be one SELECT that calls none of the functions that open connections or run SQL given as text (postgres_*, query, query_table), checked with DuckDB's own parser. Queries stop after QUERY_TIMEOUT_SECONDS and are held to QUERY_MEMORY_LIMIT.

Python (sandbox/). The engine never runs it: the runner does, in a separate container that holds no credentials (its settings file is separate, and nothing secret goes in it) and can reach nothing but the engine. The container is read-only, unprivileged, capability-free and resource-limited (../compose.yaml). Each script runs in its own process with time, CPU, memory and file limits, in a scratch directory deleted afterwards, one at a time per container (concurrent runs share a user, so could read each other's files: scale with replicas). After each run, every process it started is killed, even ones that left its process group, so nothing a script leaves running sees the next run; the container's init reaps them, so they can't use up its process limit either. Python can't be sandboxed from inside itself, so these limits are the safeguard, not anything the script is told. In production, Azure Container Apps' dynamic sessions (Hyper-V isolated) could replace the runner behind the same API.

Libraries: scripts get exactly what runner/requirements.in lists (pandas, numpy, pyarrow), compiled to a pinned, hashed runner/requirements.txt and installed at image build; nothing installs at run time. To enable a library, add it there, recompile (the command is in the file), and rebuild. An app adds its own the same way, in a runner image built on this one.

Layout

davy_jones_locker/
  main.py        the API, and create_app
  settings.py    environment settings (.env.example)
  database.py    the engine's tables in Postgres
  sql.py         locked-down DuckDB sessions, query checks
  runner.py      calls to the runner
  executor.py    whole-graph runs
  assist.py      the AI assistant (through LiteLLM)
  formdata.py    multipart/form-data both ways (the runner uses it too)
  sandbox/
    server.py    the runner's API, limits, one process per run
    worker.py    runs one script
runner/          the runner's Dockerfile and libraries
Dockerfile       the engine's image

Developing without Docker

With Python 3.12 and the packages from both lock files installed, from this folder:

RUNNER_URL=http://127.0.0.1:9002 uvicorn davy_jones_locker.main:app --port 9001
uvicorn davy_jones_locker.sandbox.server:app --port 9002

(There's no container around the runner here: development only. On macOS it can't limit a script's memory (the system refuses RLIMIT_AS), and on Windows it sets no process limits at all. In the Linux container every limit applies, and a run fails rather than go without one.)