anywidget-graph

Interactive graph visualization for Python notebooks.

Works with Marimo, Jupyter, VS Code, Colab, anywhere anywidget runs.

Features

Universal: One widget, every notebook environment
Backend-agnostic: Grafeo, Neo4j, NetworkX, pandas, or raw dicts
Interactive: Pan, zoom, click, drag, pin, expand neighbors, box select
Customizable: Colors, sizes, layouts, dark mode
Exportable: HTML, JSON

Installation

uv add anywidget-graph

Optional extras:

uv add "anywidget-graph[networkx]"   # NetworkX support
uv add "anywidget-graph[pandas]"     # pandas support
uv add "anywidget-graph[grafeo]"     # Grafeo backend
uv add "anywidget-graph[cosmosdb]"   # CosmosDB / Gremlin support
uv add "anywidget-graph[examples]"   # marimo, to run the apps in examples/

Quick Start

from anywidget_graph import Graph

graph = Graph.from_dict(
    {
        "nodes": [
            {"id": "alice", "label": "Alice", "group": "person"},
            {"id": "bob", "label": "Bob", "group": "person"},
            {"id": "paper", "label": "Graph Theory", "group": "document"},
        ],
        "edges": [
            {"source": "alice", "target": "bob", "label": "knows"},
            {"source": "alice", "target": "paper", "label": "authored"},
        ],
    }
)

graph

Data Sources

Dictionary

graph = Graph.from_dict({"nodes": [{"id": "a"}, {"id": "b"}], "edges": [{"source": "a", "target": "b"}]})

Direct initialization

graph = Graph(
    nodes=[{"id": "a", "label": "Alice"}, {"id": "b", "label": "Bob"}],
    edges=[{"source": "a", "target": "b", "label": "KNOWS"}],
)

Cypher results (Neo4j)

from neo4j import GraphDatabase

driver = GraphDatabase.driver("bolt://localhost:7687", auth=("neo4j", "password"))

with driver.session() as session:
    result = session.run("MATCH (a)-[r]->(b) RETURN a, r, b LIMIT 100")
    graph = Graph.from_cypher(result)

GQL results

graph = Graph.from_gql(result)

SPARQL results

from rdflib import Graph as RDFGraph

g = RDFGraph()
g.parse("data.ttl")
result = g.query("SELECT ?s ?p ?o WHERE { ?s ?p ?o }")
graph = Graph.from_sparql(result)

Gremlin results (CosmosDB, TinkerPop)

graph = Graph.from_gremlin(result)

GraphQL results

graph = Graph.from_graphql(
    response.json(),
    nodes_path="data.characters.results",
    id_field="id",
    label_field="name",
)

NetworkX

import networkx as nx

G = nx.karate_club_graph()
graph = Graph.from_networkx(G)

pandas DataFrames

import pandas as pd

nodes_df = pd.DataFrame({"id": ["alice", "bob"], "group": ["person", "person"]})
edges_df = pd.DataFrame({"source": ["alice"], "target": ["bob"], "weight": [1.0]})

graph = Graph.from_dataframe(nodes_df, edges_df)

Interactivity

Events

graph = Graph.from_dict(data)


@graph.on_node_click
def handle_node(node_id, node_data):
    print(f"Clicked: {node_id}")


@graph.on_edge_click
def handle_edge(edge_data):
    print(f"Edge: {edge_data['label']}")


@graph.on_selection
def handle_selection(node_ids):
    print(f"Selected: {node_ids}")

Selection

graph.selected_nodes  # Current selection (list of IDs)
graph.selection_mode = "box"  # Switch to box-select mode

Node expansion

graph.expand_node("alice")  # Fetch and merge neighbors (requires backend)

Node pinning

graph.pin_nodes(["alice", "bob"])  # Pin at current positions
graph.unpin_nodes(["alice"])  # Release back to layout
graph.toggle_pin("bob")  # Toggle pin state
graph.unpin_all()  # Unpin everything

Clear

graph.clear()  # Remove all nodes, edges, pins, and selection

Linked lanes and live append

Show two graphs side by side in one canvas, each laid out in its own lane, with curved edges between them, and grow them while a process runs:

graph = Graph(
    lanes=[{"id": "source", "title": "Source graph"}, {"id": "target", "title": "Target graph"}],
    nodes=[{"id": "a", "lane": "source"}, {"id": "b", "lane": "source"}, {"id": "x", "lane": "target"}],
    edges=[{"source": "a", "target": "b"}, {"source": "x", "target": "a", "cross": True}],
)
graph.append(nodes=[{"id": "y", "lane": "target"}], edges=[{"source": "y", "target": "b"}])
graph.remove(nodes=["x"])  # fades out, nothing else moves
graph.pulse_nodes = ["b"]  # a soft pulse on these nodes; [] stops it
graph.theme = {
    "background": "#0d1416",
    "panel": "#121b1e",
    "text": "#dbe7e5",
    "muted": "#8ea3a3",
    "border": "#1e2b2f",
    "accent": "#5bb8a9",
}

lanes: lanes left to right; a node's lane picks one (a node without a known lane goes to the first). Edges between lanes (or marked cross) are drawn as soft curves above both lanes and light up when either end is hovered or selected; a search also shows a match's partners in the other lanes. Without lanes the widget behaves as before.
append(nodes, edges): adds items without a re-layout; existing nodes keep their positions, a new node is placed at the height of its neighbours in other lanes, where nodes of the same neighbours stack as rows one label line apart with their labels shown (or next to its neighbours in its lane; without lanes, next to its neighbours inside the area already drawn). The items are also merged into nodes and edges, so to_json(), to_html() and a widget shown again include them (a host that sets append_batch itself gets the merged lists written back by the widget). Changing lanes or a style keeps appended items; setting nodes or edges to other data replaces everything drawn. New items fade in and edges draw from source to target, append_stagger_ms (default 60) apart; a large batch is kept under 1.5 s. append_animation="none" turns the animation off.
remove(nodes, edges): takes nodes (with every edge touching them) and edges out without a re-layout; they fade out and leave nodes and edges. An edge is {"source", "target"} (every edge between those ends) or with a "label" (that edge only). A host that sets append_batch itself puts them under remove: {"seq": n, "nodes": [...], "edges": [...], "remove": {"nodes": [ids], "edges": [...]}}; removals come first, so one batch can re-sample (an item removed and sent again stays where it is).
Lane options: width is a lane's share of the standard width (default 1); arrange: "column" stacks the lane's nodes in the middle of its band (for a few roots, such as repositories, whose edges then fan out into the next lane); action: True makes a lane without nodes that shows a button, with the host's icon (SVG markup, where currentColor takes the accent colour, or an image URL), or glyph (a list of colours drawn as stacked bars), else a neutral "open" icon; caption is text under it. An edge whose end is an action lane's id (and no node) is drawn as a curve into its button, like the cross-lane curves. The camera frames all lanes, and the lanes stretch to the canvas's shape.
Lane layout options (all off by default; a lane without them is laid out as before):
- layout: "force": a force layout inside the lane: nodes repel and do not overlap, edges inside the lane are springs, and a node is pulled towards the height of its partners in the lanes to its left. Nodes never leave their lane. A crowded lane draws its nodes smaller so the layout has room. Appended nodes settle from where they land while the older nodes stay put. Deterministic: the same data gives the same picture.
- rows: {"field": "layer", "order": ["business", "application", "technology"]}: splits the lane into rows, top to bottom, by a node field; a node with another value (or none) goes to an extra row at the bottom. Row heights follow the node counts (with a minimum), each row shows its name and a faint separator, and nodes stay inside their row. Implies layout: "force".
- labels: {"count": 12}: at the default zoom, shows the labels of the lane's 12 largest nodes (by size, then degree), and no others (hover or select a node to see its label; zoomed in or out, sigma's label grid decides). A label may cover smaller nodes, on a soft backing so it stays readable, but never a node as large, a labelled node or another label; one that would cross the lane's right edge goes on the left of its node, and a long name is shortened with an ellipsis (hover shows it whole).
- relayout: 0.33: when one batch removes or adds more than this share of the lane's nodes (a re-sample), the lane is laid out again as a whole and its nodes glide to their new places; smaller batches keep older nodes where they are.
lane_action: set to {"lane": <id>, "seq": <n>} when an action lane's glyph is clicked, so the host can respond (open a panel, switch a view). seq counts on from the value the model holds, so every click is a change, also in a widget shown twice.
pulse_nodes: ids that pulse softly.
type_colors: colours per type, {"nodes": {"<type>": "#hex"}, "edges": {"<type>": "#hex"}} (types as the schema panel groups them). They colour the items of that type and the legend swatches; an item's own color still wins, and other types keep the palette.
totals: for a host that sends a sample of a larger graph, {"nodes": {"<type>": n}, "edges": {"<type>": n}} (types as the schema panel groups them: a node's first label, an edge's type). The count badge and the schema panel show "shown / total"; a type the sample lacks shows as 0, a type without a total shows its count (and counts as shown in full).
theme: host colours (background, panel, text, muted, border, accent) that replace the widget's light and dark defaults; a key left out (or theme = {}) brings the default back.
_features: the widget sets the capabilities it supports when it renders, so a host can detect an older build.

Styling

Property-based coloring

graph = Graph.from_dict(
    data,
    color_field="group",  # Color nodes by field
    color_scale="viridis",  # Scale: viridis, plasma, inferno, magma, cividis, turbo
    size_field="score",  # Size nodes by field
    size_range=[5, 30],  # Min/max node size
)

Edge styling

graph.edge_color_field = "type"
graph.edge_color_scale = "plasma"
graph.edge_size_field = "weight"
graph.edge_size_range = [1, 8]

Layouts

Graph.from_dict(data, layout="force")  # ForceAtlas2 (default)
Graph.from_dict(data, layout="circular")
Graph.from_dict(data, layout="random")
Graph(nodes=[{"id": "a", "x": 0, "y": 0}, {"id": "b", "x": 10, "y": 5}], layout="preset")  # The nodes' own x and y

Options

graph = Graph(
    nodes=nodes,
    edges=edges,
    width=800,  # Widget width (px)
    height=600,  # Widget height (px)
    fill=False,  # Take the host's size and follow resizes (ignores width/height)
    background="#fafafa",  # Background color
    show_labels=True,  # Node labels
    show_edge_labels=False,  # Edge labels
    edge_arrows=False,  # Edges as arrows from source to target
    highlight_neighbors=False,  # A selected node keeps its neighbours and their edges lit
    show_toolbar=True,  # Toolbar visibility
    show_settings=True,  # Settings panel
    show_query_input=True,  # Query input box
    dark_mode=True,  # Dark theme
    show_tooltip=True,  # Hover tooltips
    tooltip_fields=["label", "id"],
    max_nodes=300,  # Limit for node expansion
)

Database Backends

Grafeo (default)

Grafeo runs in one of three modes, picked with grafeo_connection_mode or in the settings panel.

Embedded (Python, the default): queries run in the notebook kernel. Requires the grafeo extra (grafeo>=0.5.44,<0.7).

import grafeo

db = grafeo.GrafeoDB()
graph = Graph(database_backend="grafeo", grafeo_db=db, query_language="gql")

Server: the browser talks to grafeo-server over HTTP (POST /query, GET /db/{name}/schema). The server must allow the notebook's origin, for example grafeo-server --cors-origins http://localhost:8888.

graph = Graph(database_backend="grafeo", grafeo_connection_mode="server", grafeo_server_url="http://localhost:7474")

WASM: the engine runs in the browser. @grafeo-db/wasm (0.5.44) is downloaded from jsDelivr when you click "Initialize WASM", so nothing extra is installed in Python.

graph = Graph(database_backend="grafeo", grafeo_connection_mode="wasm", query_language="gql")

Query languages: GQL, Cypher, Gremlin, GraphQL and SPARQL. Nodes and edges are taken from every result column, including lists (collect(n), variable-length relationships, nodes(p)) and paths (RETURN p); scalar columns are ignored. Run one statement per query: Grafeo rejects several ;-separated statements in one call.

Try it without any setup:

from anywidget_graph.demo import demo_graph

demo_graph()  # WASM mode with a small movie graph loaded

Neo4j (browser-side)

graph = Graph(
    database_backend="neo4j",
    connection_uri="neo4j+s://demo.neo4jlabs.com",
    connection_username="neo4j",
    connection_password="password",
)

Generic backend

graph = Graph(backend=my_backend)  # Any object implementing DatabaseBackend protocol

Embedding in an app

The widget knows graphs, not your domain. The host (a notebook, or an app that mounts the widget's front end) decides how its data looks and behaves, and the widget's own defaults stay neutral:

Looks: colours per type (type_colors), the host's colours (theme), action lane icons (icon, glyph), lane titles and captions.
What to show: when a graph is too large to draw whole, the host picks the sample and sends it, with the totals behind it (totals); the widget never makes counts up. append() and remove() keep the drawing up to date without a re-layout.
Layout per lane: width, arrange, layout, rows, relayout and labels are lane options the host sets.

New behaviour is opt-in and off by default: an update does not change a view that does not ask for it. _features lists what a build supports, so a host can tell an older build apart and fall back.

Without Python

An app can mount the bundled front end (anywidget_graph.ui.get_esm() and get_css()) with any model object that has get, set, on, off, save_changes and send, as anywidget's model does.

The host sets nodes, edges, lanes, append_batch, pulse_nodes, theme, type_colors, totals, dark_mode, and the size (width and height, or fill).
The widget sets selected_node, selected_nodes, selected_edge, hovered_node, lane_action and _features. After an append_batch it writes the merged nodes and edges back, unless they already hold the batch (as Graph.append() sends them).
A setting the host leaves out reads as the widget's default, the same as a fresh Python Graph() has it.
The widget sends no query when it mounts: the first view is the host's.

Export

graph.to_json()  # JSON string with nodes and edges
graph.to_html()  # Self-contained HTML string
graph.to_html(title="My Graph")  # Custom title
graph.save_html("graph.html")  # Write HTML to file

Environment Support

Environment	Supported
Marimo	Yes
JupyterLab	Yes
Jupyter Notebook	Yes
VS Code	Yes
Google Colab	Yes
Databricks	Yes

License

Apache-2.0

Name		Name	Last commit message	Last commit date
.github/workflows		.github/workflows
assets		assets
examples		examples
scripts/vendor		scripts/vendor
src/anywidget_graph		src/anywidget_graph
tests		tests
.gitignore		.gitignore
.python-version		.python-version
CHANGELOG.md		CHANGELOG.md
LICENSE		LICENSE
README.md		README.md
pyproject.toml		pyproject.toml
uv.lock		uv.lock

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