Server Operations

This page covers operating the Fabro server once it’s running, whether locally on your laptop or self-hosted in a container. For where to run it, see Deployment.

Starting the server

fabro server start

This starts the server on a Unix socket at ~/.fabro/fabro.sock by default. Use --bind 127.0.0.1 for TCP.

First run: web install wizard

If ~/.fabro/settings.toml does not yet exist, fabro server start enters install mode: it prints an install URL and a one-time install token, attempts to open the URL in your default browser, and serves a web wizard that walks you through configuring your server URL, shared object store, LLM provider, and GitHub integration. The LLM step can be completed with one or more provider keys, or explicitly skipped so you can finish server setup first and add model credentials later. A skipped LLM step writes no LLM vault credentials; LLM-dependent workflows keep failing with provider-not-configured errors until credentials are added. Optional integration secrets collected by install mode, including LLM keys and GitHub App secrets, are written to the server vault rather than server.env. When Fabro can construct a direct install URL, the token is embedded in the URL and also printed on its own line for copying. If you open the server root through a reverse proxy or another machine, paste the printed install token when prompted. The Object store step offers two wizard-managed modes:

Local disk for a host-local object-store root, detected by default and editable before continuing
AWS S3 for one bucket with the fixed artifacts/ prefix

The wizard’s manual-credential path stores only AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY in server.env. It does not collect STS/session tokens or S3-compatible endpoint settings. If you need MinIO, Cloudflare R2, path-style options, or custom endpoints, finish install with local defaults and then edit [server.artifacts] in settings.toml manually. When you finish the wizard, the server writes ~/.fabro/settings.toml and exits cleanly. Start it again to boot in configured mode:

fabro server start

Under a process supervisor with a restart policy (for example docker-compose restart: unless-stopped, systemd, or Railway’s restart-on-exit) this second start happens automatically. For headless or scripted environments where no browser is available, run fabro install instead — it’s the same wizard as a CLI prompt flow. Common flags:

Flag	Default	Description
`--bind`	`~/.fabro/fabro.sock`	Address to bind: `IP` or `IP:port` for TCP, or a path for Unix socket
`--model`	—	Override default LLM model
`--environment`	—	Override default environment slug
`--max-concurrent-runs`	`5`	Maximum concurrent run executions

See Server Configuration for the full settings.toml reference.

Upgrading from SlateDB

Fabro stores current run history and content-addressed blobs in SQLite. Upgrading from a release that stored these in SlateDB does not automatically import them, so those older runs are not available in the current application. The upgrade leaves existing SlateDB run history and blobs in their original local storage directory or object-store location. Retain that data if you may need to recover historical runs using a reader compatible with the older storage format. Artifact files use the separate local or S3 object store configured through [server.artifacts] in Server Configuration.

Legacy SlateDB settings

When Fabro loads active server settings that still contain [server.slatedb], it backs up the settings file and removes that section and its subtables. For settings.toml, the backup is settings.toml.server-slatedb-migration.bak beside the original file. Existing backups are preserved; further backups use numbered suffixes such as settings.toml.server-slatedb-migration.1.bak. A warning identifies the rewritten file and its backup. Other settings are preserved by this cleanup, and subsequent loads do nothing once the section is absent. This cleanup changes configuration only. The settings backup contains no run history or blobs.

SQLite schema upgrades

On startup, Fabro applies pending SQLite schema migrations before serving normal traffic. Before changing a database that has previously applied migrations, it writes a snapshot beside the database as fabro.sqlite3.pre-migration.bak. A fresh database or a restart with no pending migrations does not create a new snapshot. Each later schema upgrade replaces this snapshot; failure to create it stops the upgrade. The Petri transition drops the former SQL run_events table and its activation bookkeeping. Those events are not converted into Petri run history. Current runs use Petri records and Fabro platform records in SQLite. For a database rollback, stop the server and restore the .pre-migration.bak snapshot, then remove any -wal and -shm siblings before starting the corresponding older binary. The snapshot holds the database state immediately before the most recent schema upgrade. Restoring it loses database writes accepted after that snapshot.

Submitting runs

Register the workflow content, create a run from that version, then request execution. This example uses a clone-based default environment and an empty workspace target; add the authentication headers required by your server:

version_id=$(curl --fail-with-body -sS http://localhost:3000/api/v1/workflow-versions \
  -H 'Content-Type: application/json' \
  -d '{"entrypoint":"workflow.fabro","files":{"workflow.fabro":"digraph Demo { start [shape=Mdiamond]; exit [shape=Msquare]; start -> exit; }"},"workflow_dependencies":{}}' \
  | jq -r '.workflow_version_id')

run_id=$(curl --fail-with-body -sS http://localhost:3000/api/v1/runs \
  -H 'Content-Type: application/json' \
  -d "$(jq -n --arg id "$version_id" '{workflow_version_id:$id,target:{kind:"none"},args:{}}')" \
  | jq -r '.id')

curl --fail-with-body -X POST "http://localhost:3000/api/v1/runs/$run_id/start"

The server returns immediately with a run ID. After a start request, a background scheduler promotes runnable runs to running in FIFO order, up to the concurrency limit. Parent-generated child runs can remain pending until a user approves them.

Run lifecycle

Submit — POST /api/v1/runs creates the run with status submitted.
Start request — POST /api/v1/runs/{id}/start makes normal runs runnable; parent-generated child runs may become pending with approval_required.
Approve if needed — Approving a pending child run makes it runnable; denying it fails with approval_denied.
Schedule — The scheduler picks up runnable runs up to max_concurrent_runs.
Execute — The engine walks the graph, streaming events to all subscribers.
Complete — The run transitions to succeeded, failed, or dead.

Web UI

The web UI connects to the API server and provides:

Runs board — Monitor all active runs organized by status
Run detail — Real-time stage progress, event stream, diffs, and usage stats
Files Changed — Browse changed files with a searchable tree, per-file status, aggregate diff stats, and split or stacked diffs
Settings — Inspect server configuration, enabled integrations, storage, auth, and capacity settings
Start new run — Submit workflows from the browser
Human-in-the-loop — Answer agent questions through the web interface
Workflows — Browse available workflows, view their graphs, and see run history
Insights — SQL-based analysis across runs via DuckDB

Fabro web UI Runs board with Working, Pending, Verify, and Merge columns — The Runs board shows all active runs organized by status.

Fabro web UI run detail showing stages and workflow graph — The run detail view shows stage progress alongside the workflow graph.

Event streaming

The API streams run events via Server-Sent Events (SSE). Every stage start, LLM call, tool invocation, and edge selection is emitted as a structured JSON event. Any HTTP client that supports SSE can subscribe — the web UI is just one consumer.

Human-in-the-loop

Human-in-the-loop questions are served over HTTP. The engine blocks the current stage until an answer is submitted, then continues execution. See the list questions and submit answer API reference pages.

Authentication

The server configures auth with server.auth.methods:

dev-token — Operators can call the API directly with Authorization: Bearer fabro_dev_..., and the web login page can accept the dev token too.
github — End users sign in through GitHub OAuth and receive a browser session cookie.

Both methods can be enabled simultaneously:

settings.toml

[server.auth]
methods = ["dev-token", "github"]

[server.auth.github]
allowed_usernames = ["alice", "bob"]

Demo mode

Send the X-Fabro-Demo: 1 header on any API request to get static mock data. To enable demo mode in the web UI, set the fabro-demo=1 cookie in your browser devtools (Application → Cookies). This lets you explore the UI without API keys or real workflow execution.

Pointing the CLI at a server

The CLI can target a running Fabro server for commands that support a remote API. Configure ~/.fabro/settings.toml:

settings.toml

[cli.target]
type = "http"
url = "https://fabro.example.com/api/v1"

Or use the --server flag:

fabro model list --server https://fabro.example.com/api/v1

For dev-token servers, save the token in the CLI auth store instead of exporting it for every command:

fabro auth login --server https://fabro.example.com/api/v1 --dev-token fabro_dev_...

fabro model list and fabro model test honor [cli.target] by default unless you explicitly pass --storage-dir. fabro exec remains a local agent session and only uses the server when you pass --server. See User Configuration for the full connection options, including client certificates for proxy-terminated HTTPS endpoints.

Next steps

Deployment

Choose where the server runs: laptop or self-hosted Docker container.

Server Configuration

Full settings.toml reference — authentication, reverse-proxy TLS, run defaults, and more.

API Reference

REST API for submitting runs, streaming events, and managing resources.

How Fabro Works

The workflow engine and architecture.

Getting Started

Core Concepts

Defining Workflows

Executing Workflows

Human Tools

Agents

Integrations

Reference

Server Operations

Starting the server

First run: web install wizard

Upgrading from SlateDB

Legacy SlateDB settings

SQLite schema upgrades

Submitting runs

Run lifecycle

Web UI

Event streaming

Human-in-the-loop

Authentication

Demo mode

Pointing the CLI at a server

Next steps

Deployment

Server Configuration

API Reference

How Fabro Works

​Starting the server

​First run: web install wizard

​Upgrading from SlateDB

​Legacy SlateDB settings

​SQLite schema upgrades

​Submitting runs

​Run lifecycle

​Web UI

​Event streaming

​Human-in-the-loop

​Authentication

​Demo mode

​Pointing the CLI at a server

​Next steps

Deployment

Server Configuration

API Reference

How Fabro Works

Starting the server

First run: web install wizard

Upgrading from SlateDB

Legacy SlateDB settings

SQLite schema upgrades

Submitting runs

Run lifecycle

Web UI

Event streaming

Human-in-the-loop

Authentication

Demo mode

Pointing the CLI at a server

Next steps