graphable 0.5.0

A lightweight, type-safe library for building, managing, and visualizing dependency graphs.

graphable

graphable is a lightweight, type-safe Python library for building, managing, and visualizing dependency graphs. It provides a simple API for defining nodes and their relationships, performing topological sorts, and exporting graphs to various formats like Mermaid, Graphviz, and ASCII text trees.

Features

• Type-Safe: Built with modern Python generics and type hints.
• Topological Sorting: Easily get nodes in dependency order.
• Cycle Detection: Built-in protection against circular dependencies.
• Filtering & Tagging: Create subgraphs based on custom predicates or tags.
• Graph Syncing: Automatically expand graphs to include all reachable nodes with discover().
• Transitive Reduction: Automatically remove redundant edges while preserving reachability.
• Reachability Analysis: Easily find ancestors or descendants of any node.
• Node Ordering: Compare nodes using standard operators (a < b means a is an ancestor of b).
• Container Protocols: Use Pythonic idioms like len(graph), node in graph, and for node in graph.
• Integrity: Generate and validate deterministic BLAKE2b checksums of graph structure and metadata.
• Parallel Sorting: Group nodes into execution layers for parallel processing with parallelized_topological_order().
• Orchestration Ready: Track node status and duration for workflow execution.
• Edge Attributes: Support for weights, labels, and custom metadata on relationships.
• Diffing: Compare two graphs structurally and visually with diff() and diff_graph().
• Dependency Algorithms: Native implementations for:
  • Critical Path Method (CPM): Identify bottlenecks based on node durations.
  • BFS & DFS: Perform breadth-first or depth-first traversals with generators.
  • Longest Path: Find the deepest chain of dependencies.
  • All Paths: Explore every possible connection between two nodes.
  • Transitive Closure: Analyze the full "blast radius" of dependencies.
  • Cycle Resolution: Get suggestions for breaking circular dependencies with suggest_cycle_breaks().
• Advanced Slicing: Extract focused subgraphs using upstream_of(), downstream_of(), or subgraph_between().
• Unified I/O: Easy Graph.read() and graph.write() methods with automatic format detection.
• Parsing: Reconstruct graphs from JSON, YAML, TOML, CSV, or GraphML files and strings.
• Clustering: Group nodes into subgraphs/clusters in visualizations based on tags.
• Visualizations:
  • Mermaid: Generate flowchart definitions or export directly to SVG. Supports clustering and color-coded diffs.
  • Graphviz: Generate DOT definitions or export to SVG with custom styling. Supports clustering.
  • D2: Generate D2 definitions or export to SVG with modern styling and layouts. Supports clustering.
  • PlantUML: Generate component or deployment diagram definitions. Supports clustering.
  • TikZ: Generate high-quality LaTeX definitions for academic documents.
  • GraphML: Industrial-standard XML export for professional analysis tools (Gephi, yEd).
  • Interactive HTML: Generate a single, portable HTML file with zooming, panning, and Live Reload when used with the CLI.
  • JSON, YAML & TOML: Export graph structure as machine-readable data.
  • CSV: Export simple edge lists for data processing.
  • NetworkX: Seamless integration with the NetworkX library for advanced analysis.
  • Text Tree & ASCII Flowchart: Generate beautiful ASCII representations.
• Modern Tooling: Managed with uv and just.

Installation

As a Library

Use your preferred package manager to add graphable to your project:

# Using uv (recommended)
uv add graphable

# Using pip
pip install graphable

As a Command Line Tool

To use the graphable CLI globally, we recommend using pipx. You can choose between the bare-bones version or the full-featured "Rich" version:

# Full version with beautiful terminal output (recommended)
pipx install "graphable[cli]"

# Bare-bones version (standard library only)
pipx install graphable

---

Command Line Interface

graphable provides a powerful CLI for analyzing and transforming graph files.

Basic Usage

# Get summary information about a graph
graphable info topology.json

# Validate a graph for cycles and consistency
graphable check topology.yaml

# Convert between any supported formats
graphable convert input.json output.mmd

# Simplify a graph using transitive reduction
graphable reduce complex.graphml simple.svg

# Compare two versions of a graph
graphable diff old.json new.json

# Generate a visual diff (added elements in green, removed in red)
graphable diff old.json new.json -o diff.svg

# Serve a live-reloading interactive visualization
graphable serve architecture.yaml

Automation & CI/CD

If you have the cli extra installed but need plain-text output for logging or automation, use the --bare flag:

graphable --bare info topology.json

Supported Formats

The CLI automatically detects formats based on file extensions:

• Parsers: .json, .yaml/.yml, .toml, .csv, .graphml
• Exporters: .json, .yaml, .toml, .csv, .graphml, .dot, .mmd, .d2, .puml, .html, .tex, .txt, .ascii, .svg

---

Quick Start

from graphable.graph import Graph
from graphable.graphable import Graphable
from graphable.views.texttree import create_topology_tree_txt

# 1. Define your nodes
a = Graphable("Database")
b = Graphable("API Service")
c = Graphable("Web Frontend")

# 2. Build the graph
g = Graph()
g.add_edge(a, b)  # API Service depends on Database
g.add_edge(b, c)  # Web Frontend depends on API Service

# 3. Get topological order
for node in g.topological_order():
    print(node.reference)
# Output: Database, API Service, Web Frontend

# 4. Visualize as a text tree
print(create_topology_tree_txt(g))
# Output:
# Web Frontend
# └─ API Service
#    └─ Database

Visualizing with ASCII Flowchart

from graphable.views.asciiflow import create_topology_ascii_flow

print(create_topology_ascii_flow(g))
# Output:
# +----------+
# | Database |
# +----------+
#   v
#   +--> API Service
#
# +-------------+
# | API Service |
# +-------------+
#   v
#   +--> Web Frontend

Visualizing with Mermaid

from graphable.views.mermaid import create_topology_mermaid_mmd

mmd = create_topology_mermaid_mmd(g)
print(mmd)
# Output:
# flowchart TD
# Database --> API Service
# API Service --> Web Frontend

Visualizing with Graphviz

from graphable.views.graphviz import create_topology_graphviz_dot

dot = create_topology_graphviz_dot(g)
print(dot)
# Output:
# digraph G {
#     "Database" [label="Database"];
#     "Database" -> "API Service";
#     "API Service" [label="API Service"];
#     "API Service" -> "Web Frontend";
#     "Web Frontend" [label="Web Frontend"];
# }

Advanced Analysis

Edge Attributes & Orchestration

Track durations and weights for workflow optimization:

# Add weighted edges
g.add_edge(task_a, task_b, weight=10, label="critical path")

# Set task metadata
task_a.duration = 5.0
task_a.status = "completed"

Critical Path Method (CPM)

Identify the longest chain of dependencies:

analysis = g.cpm_analysis()
print(f"Project duration: {max(n['EF'] for n in analysis.values())}")

critical_nodes = g.critical_path()
print(f"Critical Path: {[n.reference for n in critical_nodes]}")

Graph Diffing

Compare two versions of a graph:

diff_data = g_v1.diff(g_v2)
print(f"Added nodes: {diff_data['added_nodes']}")

# Generate a visual diff graph for rendering
dg = g_v1.diff_graph(g_v2)
print(dg.render(create_topology_mermaid_mmd))

Advanced Slicing

Focus on specific parts of the graph:

# All nodes required by 'Web UI'
sub = g.upstream_of(ui)

# All nodes impacted by a change in 'Database'
sub = g.downstream_of(db)

# Everything between 'Source' and 'Sink'
sub = g.subgraph_between(source, sink)

Native Traversals (BFS & DFS)

Perform idiomatic iterations over your graph:

from graphable.enums import Direction

# Visit every node reachable from 'Task A' breadth-first
for node in g.bfs(task_a):
    print(f"Visiting: {node.reference}")

# Explore dependencies (upstream) depth-first
for node in g.dfs(task_a, direction=Direction.UP):
    print(f"Required by: {node.reference}")

Advanced Analysis with NetworkX

If you have networkx installed, you can convert your graph for advanced analysis:

import networkx as nx

# Convert to NetworkX DiGraph
dg = g.to_networkx()

# Use NetworkX algorithms
print(nx.dag_longest_path(dg))
# Output: ['Database', 'API Service', 'Web Frontend']

Visualizing with D2

from graphable.views.d2 import create_topology_d2

d2 = create_topology_d2(g)
print(d2)
# Output:
# Database: Database
# Database -> API Service
# API Service: API Service
# API Service -> Web Frontend
# Web Frontend: Web Frontend

Advanced Usage

Pythonic Protocols

print(f"Nodes: {len(g)}")
if "Database" in g:
    node = g["Database"]
    
for node in g:  # Iterates in topological order
    print(node.reference)

Transitive Reduction

Clean up your graphs by removing redundant edges:

reduced_g = g.transitive_reduction()
# Or render directly
print(g.render(create_topology_mermaid_mmd, transitive_reduction=True))

Clustering by Tags

Group nodes in visualizations:

from graphable.views.mermaid import MermaidStylingConfig

a.add_tag("backend")
b.add_tag("backend")
config = MermaidStylingConfig(cluster_by_tag=True)

print(g.render(create_topology_mermaid_mmd, config=config))

Parsing Graphs

Reconstruct graphs from exported data:

# From a file
g = Graph.from_json("graph.json")

# From a string (YAML)
yaml_data = """
nodes:
  - id: Database
  - id: API
edges:
  - source: Database
    target: API
"""
g = Graph.from_yaml(yaml_data)

Equality Comparison

Compare graphs easily:

g1 = Graph.from_json("topology.json")
g2 = Graph.from_yaml("topology.yaml")

if g1 == g2:
    print("Graphs are identical in structure and tags.")

Node Ordering

Nodes support comparison based on reachability:

if db < api:
    print("Database is an ancestor of API")
    
if ui > api:
    print("UI is a descendant of API")

Checksums & Integrity

Verify your graph hasn't changed:

digest = g.checksum()
# ... later ...
if g.validate_checksum(digest):
    print("Graph integrity verified.")

Unified I/O

Read and write any format without thinking about parsers:

# Automatically detects JSON
g = Graph.read("topology.json")

# Automatically detects YAML
g.write("topology.yaml")

Documentation

Full documentation is available in the docs/ directory. You can build it locally:

just docs-view

Development

This project uses uv for dependency management and just as a command runner.

just install    # Install dependencies
just check      # Run linting, type checking, and tests
just coverage   # Run tests with coverage report

License

This project is licensed under the MIT License - see the LICENSE file for details.