Visual DAG engine
Build with nodes and edges, run parallel branches, merge results, and watch status update on the canvas as the workflow executes.
Local-first workflow automation
A visual automation console for building real data workflows: webhooks, schedules, AI steps, table transforms, notifications, integrations, and custom code, all on one inspectable canvas.
Why it exists
lxzl is built for the moment when a workflow grows beyond a quick script. The graph shows the shape of the work, the config panel keeps details close to each node, and run history records exactly what happened when data moved through the system.
It also treats communication as part of the work, not a separate email black hole. Teams, vendors, clients, and partners should meet inside shared spaces where docs, boards, calendars, owners, and status are visible together.
It is approachable for non-programmers, but it does not hide the machinery from power users. Every node can be inspected, extended with a sandboxed script override, and connected to local or remote services through explicit credentials.
Build with nodes and edges, run parallel branches, merge results, and watch status update on the canvas as the workflow executes.
Load tables, parse CSV/JSON, filter, sort, group, join, pivot, deduplicate, stitch entities, and persist state between runs.
Use Ollama by default, or connect Anthropic and OpenAI-compatible providers when a workflow needs hosted models.
Schedule cron jobs, receive webhooks, queue work with retry, replay past runs, and monitor failures without adding Redis or a broker.
Architecture
The current deployment model is deliberately compact: SvelteKit on Bun, Drizzle over bun:sqlite in WAL mode, a SQLite-backed job queue, Bun cron for schedules, and Bun Workers for isolated execution. Ollama can be managed by the app or run as a Docker sidecar.
That small shape keeps the system easy to run locally while still leaving room for cloud mode: authenticated workspaces, verified-email gates, credential vault encryption, and per-tenant database paths as the next scaling step.
What it is good at