flograph

A visual node-based Python environment: dataflow on an infinite Blueprint-style canvas, where every node is real, editable Python — and the same graph also gives you interactive dashboards and printable reports.

Build the pipeline on the model canvas, put its live results on a dashboard page for someone who will never open the model, and write the write-up on a report page that pulls the same charts and numbers into markdown and prints to PDF. One file, three surfaces, no export step between them.

Install

flograph is a standard pip-installable package (hatchling build backend):

pip install flograph        # or, from a checkout: pip install -e .

That puts a flograph command on your PATH and makes python -m flograph work. Optional extras pull in what individual nodes need:

pip install "flograph[matplotlib,plotly,excel]"

You don't have to decide up front — Tools > Manage Packages installs into the running environment, and a node can use a package the moment it's there.

The project was renamed from flopy to flograph: flopy was already taken on PyPI (USGS MODFLOW).

One-file script (no install)

If you can't install packages on a given machine — locked-down work laptop, no index access — but PySide6, pandas, jedi and psutil are already there, build a single self-contained .py:

python scripts/build_onefile.py       # -> dist/flograph_onefile_<version>.py
python flograph_onefile_<version>.py  # run it anywhere, no install

It embeds flograph's own source as a base64 zip and unpacks to a temp dir at startup. It does not bundle the third-party dependencies themselves.

Run it

flograph                                # after install
python -m flograph                      # equivalent
python main.py project.flograph         # open a project from a checkout
python -m flograph.engine.headless project.flograph   # run it with no GUI

File > Create Desktop Shortcut… writes a desktop shortcut that starts flograph the same way it's running right now — a .lnk on Windows, a .desktop entry on Linux, a .command script on macOS. It names this environment's interpreter by absolute path (so nothing depends on what python means to the desktop) and the entry point you're actually on: the one-file bundle's own .py if that's how you started, otherwise -m flograph. If a project is open it can be baked into the shortcut, so double-clicking opens flograph with that flow loaded.

File > Open Example ships eleven worked projects — filter-and-visualise, an aggregate dashboard, a custom-script chart, join/group-by comparison, an interactive slicer dashboard, a scripted pipeline in a frame, a retail ops command centre, two geo/folium maps, an SVG retrofit workbench that diffs a redrawn SVG against the page already wired to it, and a Goto/From workflow that runs a three-branch report off one source prep with no wires crossing the page. They're the fastest way in.

The idea

Nodes are Python scripts. Every node — the shipped library included — is one small module: a NODE dict declaring typed ports, an optional PARAMS list that auto-generates its properties form, and a run(ctx, **inputs) function. Double-click any node to read or fork its code in the built-in editor, with syntax highlighting, jedi completion, find/replace, and error markers on the failing line. There is no privileged built-in tier: the Group By node is a file you can open and change.

Dataflow semantics. Data flows through typed ports; execution is a topological walk of the dirty subgraph on a background thread, so re-runs only recompute what actually changed. Outputs are cached per node. Status LEDs read at a glance: grey idle, yellow queued, pulsing blue running, green done, red error — and a node that reports ctx.progress(0..1) from its loop fills that LED as a ring instead of pulsing, with the percentage beside the node's name in the status bar. Cancellation is cooperative (ctx.check_cancelled()).

Inspect everything. Click any node or wire to see the data on it — a paged table view for DataFrames (millions of rows are fine), matplotlib figures with a toolbar, pretty-printed objects. Per-node stdout and tracebacks land in the Log dock, with the traceback mapped back to the line in your node script.

Projects are plain JSON (.flograph) — diffable, reviewable, and small. Node output caches are written to a side-car <project>.flograph.cache/ directory keyed by a fingerprint of each node's source, params and everything upstream, so reopening a project restores the results you had without a re-run. A stale or corrupt entry just leaves that node dirty; it can never block a load.

Canvas

Nodes can be recoloured, aligned and distributed, locked into frames, and stacked in a deliberate front-to-back order. Goto / From nodes give you a wire without the wire: name a value at the Goto, pick it up at any number of Froms, and keep a busy canvas readable. A minimap (toggleable in Settings) and a status-bar resource monitor — system memory, the open project's cache footprint, the selected node's own — keep an eye on the scale of things.

Large graphs get a GPU-accelerated viewport option and zoom-based level of detail, so cards stop rendering their contents when they're too small to read.

Cards, dashboards and controls

Any node can declare NODE["card"] and become a live card on the canvas — not a preview pane elsewhere, the node is the chart. Card kinds shipped today: figure, webview, table_viewer, kpi, grid, slicer, button, note, control, report, plus the structural reroute, goto and from.

Click + on the page bar to add a dashboard page. Drag nodes onto it and each becomes a tile — the same widget as the canvas card, resizable and arrangeable, showing STALE when its node is dirty. Tiles maximise to fullscreen; pages can be renamed, recoloured, reordered by dragging, and duplicated.

Input controls are the other half of that: a whole node category that you set rather than compute. Slider, Number, Text, Date, Toggle and Choice each carry a caption you write, are typed properly so wires still validate, and re-run everything downstream when you move them. A Slicer does the same for picking values out of a column. The result is a dashboard you can hand to someone who will never open the model canvas: they turn the knobs, the charts answer.

A control's options and bounds can come from its own optional input ports — wire a column into a Choice node and its dropdown is that column's values.

Report pages

The other page kind is a report: markdown that you write, with your results dropped in by name.

# Q3 review

Revenue came to ![[Total Revenue]] across ![[Region Count]] regions.

![[Revenue by Region]]

![[Sales Table|filtered]]

![[Label]] embeds a node's output — a figure, a table, a scalar, a markdown string — resolved by node label, with ![[Label|port]] picking a specific output port. Embeds render inline mid-sentence for scalars and as blocks for charts and tables, update when the flow re-runs, and warn visibly when a name doesn't resolve. The page prints to PDF at 300dpi; the preview and the PDF are literally the same document, so they can't disagree.

There is also a Report card (Viz > Report) — the same markdown, but as a node inside the flow, embedding its own wired inputs. It edits in place on the canvas, has a right-click Insert menu listing everything embeddable, and tiles onto a dashboard. That gives you rich prose on a dashboard, which a chart tile can't do.

Node library

The library dock shows every node type in its category. Right-click any node to Add to Favorites (or Ctrl+Shift+F on a selected row) and it is pinned in a ★ Favorites section at the top; the same star puts favorites first in the Tab search popup. The star button next to the search box narrows the whole tree to favorites only. Favorites persist per-machine in settings.

Input — Slider, Number, Text, Date, Toggle, Choice.

IO — Read/Write CSV, Excel, Parquet, JSON (incl. JSONL) and SQLite (query in, table out); drag a file onto the canvas to get the right reader already configured. Table is a real spreadsheet you edit on the canvas, with formulas (=SUM(A1:A9), plus AVERAGE, ROUND, POWER, CONCAT, LEFT/MID/RIGHT, AND/OR/NOT and the rest of the usual set), fill, copy/paste, and an optional linked input that keeps its contents when you disconnect.

Transform — Select Columns, Filter Rows, Sort, Join, Group By, Expression, Concatenate, Missing Values, Duplicate Row Filter, Rename Columns, Pivot, Unpivot, Row Sampling, Convert Types, String Manipulation, Statistics, Data Profile.

Viz — Show Table, Show Plot (matplotlib, live on-canvas), Show Plotly (a real interactive plotly.js chart embedded on the canvas — hover, zoom and pan in place), Show Web View (render anything that produces HTML: folium maps, altair, bokeh, your own template), Card (a Power BI-style KPI number), Table Spec (the incoming table's structure), Chart per Value and Chart per Value (Plotly) — one chart per distinct value of a column, as a stack, in either backend — Slicer, Report.

Any web-view node has Open in Browser on its right-click menu — the same document, in a real browser, refreshed in place when the flow re-runs.

Util — Constant, Reroute, Note, Action Button, Goto, From.

Scripting — Python Script, plus Node Template and Control Template to fork when you're writing your own.

Writing a node

"""My Node

The first paragraph of the docstring shows in the properties panel.
"""
NODE = {
    "label": "My Node",
    "category": "Transform",
    "inputs":  [("table", "dataframe")],
    "outputs": [("result", "dataframe")],
}
PARAMS = [
    {"name": "factor", "type": "float", "default": 1.0},
]

def run(ctx, table):
    ctx.log(f"scaling by {ctx.params['factor']}")
    ctx.check_cancelled()          # cooperative cancellation
    ctx.progress(0.5)              # 0..1 through a long loop; throttled
    return {"result": table * ctx.params["factor"]}

Port types: any, dataframe, series, number, string, bool, object, figure. Param types include string, text, int, float, bool, choice, columns, date, password. A columns param renders with a ▾ picker listing the columns of the DataFrames cached on the node's inputs (run upstream once to populate it); add "multi": False so picking replaces instead of toggling a comma list.

Rules that matter:

• Treat inputs as read-only — outputs are cached by reference, so a write that escapes your node rewrites what every other branch reads. The engine guards what it can guard for free: a pandas input arrives as a copy-on-write shallow copy, and a list, dict, set or bytearray is rebuilt one level deep, so appending to a list or assigning a column stays local to your node. A numpy array arrives read-only and raises if you write to it — arr = arr.copy() first. Reaching through an input (rows[0]["x"] = 1), and anything else you pass between nodes, remain yours to copy.
• Heavy imports go inside run(). Node scripts are executed to be read, so a top-level import runs at library-load time.
• matplotlib: the OO API only (matplotlib.figure.Figure()), never pyplot — it isn't thread-safe from the worker.
• A list output renders as a stack. Return a list of figures and every surface that draws one figure draws them stacked. That's the whole "one chart per value" mechanism — the loop lives in your script, not in a faceting UI.

Add "card": "figure" (or webview, table_viewer, kpi, grid, …) to give the node a live card. For an input control, "card": "control" plus "control": "slider" — one host renders every control shape from that and your PARAMS, so a new control node is usually just a script.

Drop new .py files under src/flograph/nodes/<category>/ (or your user nodes directory) and they appear in the library on next launch. If a node's import is missing, it loads as a broken placeholder that keeps its code and params — install the package, re-apply the code, and it repairs itself.

AI assistant (optional)

Tools > AI Assistant Settings points flograph at any local OpenAI-compatible chat server — Ollama, LM Studio, llama.cpp. You can then describe a change in English ("filter out rows where price is negative") and get a rewritten node script. It is never applied automatically: the reply lands in the editor for you to read, and Apply stays a separate, explicit action. Nothing leaves your machine unless you point it somewhere that isn't local.

Packages

Tools > Manage Packages installs, upgrades and uninstalls pip packages in flograph's own environment. Nodes execute in-process, so anything installed there is importable from a node's run() immediately — no restart for new installs; upgrades of already-imported modules take effect next launch. The dialog uses pip when the interpreter has it and falls back to uv pip (uv-made venvs ship without pip). flograph's own core dependencies are protected from uninstall.

Settings

Ctrl+, opens a searchable two-column settings grid with a navigation tree: General (window behaviour, resets), Canvas (display, snapping, colour muting strength, GPU viewport, previews, page-bar position), Table Node, and About. Selecting a group narrows the grid; the search box filters across the page.

Development

uv pip install -p .venv/bin/python -e ".[dev]"
QT_QPA_PLATFORM=offscreen .venv/bin/python -m pytest tests/ -q

Architecture (src layout):

• flograph/core — Qt-free model: graph, typed ports, script contract, registry, JSON serialization, spreadsheet engine, report parsing, layering. Fully unit-testable; a poison test keeps Qt and pandas out of its import graph.
• flograph/engine — background execution: plan builder, single-thread pool worker, output cache and its on-disk persistence, cancellation, per-node stdout capture, tracebacks mapped to node script lines.
• flograph/nodes — the standard library; each node is a script file loaded as text through the same contract as user code.
• flograph/ui — canvas (QGraphicsView from scratch), dashboard and report pages, code editor, inspector, properties, console.

Two invariants hold everywhere:

1. core/ is Qt-free, enforced by a test that imports it in a subprocess and asserts PySide6 and pandas never appear.
2. QUndoCommands are the sole writers to the graph. UI items react to graph events; nothing mutates the graph from a click handler. That is why Ctrl+Z works on literally everything.

See AGENTS.md for the full contributor briefing.

Changelog

See CHANGELOG.md for what's new in each version.

License

MIT — free for commercial and private use, modification and redistribution; just keep the copyright and license notice.