cypher-graphdb 0.1.2

ORM like library and CLI for cypher query language supporting graph databases.

CypherGraphDB Library

Overview

CypherGraphDB is a unified Python toolkit for working with Cypher-capable graph databases. It provides three core capabilities:

1. Generalized API - Vendor-Neutral Graph Database Access

Work with multiple graph database backends through a unified, consistent interface:

• Vendor Neutrality – Same API works across Apache AGE, Memgraph, and other Cypher databases
• Extensible Architecture – Add new backends by implementing the CypherBackend abstract class
• Connection Management – Pooled connections with automatic reconnection and configuration
• Query Execution – Direct Cypher queries with result processing and statistics
• Data Exchange – Built-in import/export tools for CSV, Excel, JSON formats
• Schema Operations – Graph introspection, schema dumping, and analysis tools

2. Typed ORM-Like Access Layer

Optional Pydantic-based object mapping for type-safe graph operations:

• Model Decorators – @node, @edge, @relation for defining graph schemas
• Type Safety – Automatic validation and IDE support with autocompletion
• Custom Methods – Add business logic directly to your graph models
• Schema Generation – Automatic JSON schema generation from Python models

3. Interactive CLI - Vendor-Neutral Graph Database Operations

Comprehensive command-line interface that works identically across all supported backends:

• 44+ Commands – Full suite of graph operations, schema management, and analysis tools
• Rich Formatting – Colorized output with tables, trees, and JSON highlighting
• Cross-Platform – Same commands work with Apache AGE, Memgraph, or any supported backend
• Interactive Exploration – Real-time graph querying and visualization

Supported Backends

• age – Apache AGE extension on PostgreSQL
• memgraph – Memgraph database via Bolt protocol

Extensible: Add new Cypher-compatible backends by implementing the CypherBackend abstract class.

Prerequisites

Tool	Purpose	Install
Python 3.13+	Runtime	uv python install 3.13
uv	Dependency & venv manager	curl -LsSf https://astral.sh/uv/install.sh | sh
Task	Automation	brew install go-task
Docker	Integration tests	brew install --cask docker

Quick Start

git clone https://github.com/petrarca/cypher-graphdb-core.git
cd cypher-graphdb-core
task install    # Create venv + install library
task test:unit  # Run unit tests
task run:cli    # Launch interactive CLI

Backend Configuration

Apache AGE (backend="age")

Apache AGE runs as a PostgreSQL extension. Connection strings use PostgreSQL format.

Key Parameters:

Parameter	Description	Default
host	PostgreSQL server hostname	localhost
port	PostgreSQL server port	5432
dbname	Database name	(required)
user	Username for authentication	-
password	Password for authentication	-
graph_name	AGE graph name to use/create	-

Connection Examples:

# CLI with connection info
cypher-graphdb --backend age --cinfo "host=localhost port=5432 dbname=postgres"

# Environment variable
export CGDB_CINFO="postgresql://postgres:secret@localhost:5432/postgres"
cypher-graphdb --backend age

Memgraph (backend="memgraph")

Memgraph uses the Bolt protocol for connections.

Key Parameters:

Parameter	Description	Default
host	Memgraph server hostname	127.0.0.1
port	Memgraph Bolt port	7687
username	Username for authentication	(empty)
password	Password for authentication	(empty)

Connection Formats:

• Bolt URI: bolt://[username:password@]hostname:port
• Key=value: host=localhost port=7687 username=user password=secret
• Partial: port=1234 or host=192.168.1.100

Connection Examples:

# Bolt URI
cypher-graphdb --backend memgraph --cinfo "bolt://localhost:7687"

# Environment variable
export CGDB_CINFO="bolt://user:pass@localhost:7687"
cypher-graphdb --backend memgraph

1. Generalized API Usage

Vendor-Neutral Database Access

The unified API provides consistent access to different graph database backends:

Connection (Recommended)

The recommended connection method is connect_url for simplicity:

from cypher_graphdb import CypherGraphDB

# Memgraph - recommended
db = CypherGraphDB(backend="memgraph", connect_url="bolt://localhost:7687")

# Apache AGE
db = CypherGraphDB(backend="age", connect_url="postgresql://postgres:secret@localhost:5432/postgres")

# Context manager (recommended - auto-cleanup)
with CypherGraphDB(backend="memgraph", connect_url="bolt://localhost:7687") as db:
   result = db.execute("RETURN 1 AS value", unnest_result=True)
   print(result)  # 1

Alternative connection methods (CINFO parameter, individual params, environment variables) are also supported. See documentation for details.

Configuration

The library reads configuration from environment variables (can be overridden by CLI flags or constructor parameters):

Setting	Env Var	CLI Flag	Default	Description
backend	CGDB_BACKEND	-b / --backend	None	Backend type (memgraph, age)
cinfo	CGDB_CINFO	-c / --cinfo	None	Connection string / DSN
graph	CGDB_GRAPH	-g / --graph	None	Graph name (backend-specific)
read_only	CGDB_READ_ONLY	-r / --read-only	False	Block write operations
create_graph	CGDB_CREATE_GRAPH_IF_NOT_EXISTS	-	False	Auto-create graph if missing (AGE only)

Precedence: CLI arguments > Environment variables > Constructor parameters > Defaults

Example .env files:

• .env.memgraph.example - Pre-configured for Memgraph Docker Compose stack
• .env.age.example - Pre-configured for Apache AGE Docker Compose stack

Copy the appropriate example file to .env and the library will automatically load it:

# For Memgraph
cp .env.memgraph.example .env

# For Apache AGE
cp .env.age.example .env

Basic Queries

from cypher_graphdb import CypherGraphDB

db = CypherGraphDB(backend="memgraph", connect_url="bolt://localhost:7687")

# Execute query with multiple results
rows = db.execute("MATCH (n:Person) RETURN n.name, n.age LIMIT 5")
for name, age in rows:
    print(f"{name}: {age}")

# Unnest single scalar result
count = db.execute("MATCH (n) RETURN count(n)", unnest_result=True)
print(f"Total nodes: {count}")  # 42

# Fetch one row
result = db.execute("RETURN 1 AS x, 2 AS y", fetch_one=True, unnest_result=True)
print(result)  # (1, 2)

Create & Merge Nodes and Edges

from cypher_graphdb import CypherGraphDB, GraphNode, GraphEdge

db = CypherGraphDB(backend="memgraph", connect_url="bolt://localhost:7687")

# Create nodes
person = GraphNode(label_="Person", properties_={"name": "Alice", "age": 30})
person = db.create_or_merge(person)  # Returns node with ID assigned
print(f"Created person with ID: {person.id_}")

company = GraphNode(label_="Company", properties_={"name": "TechCorp"})
company = db.create_or_merge(company)

# Create relationship
rel = GraphEdge.build(person, company, label_="WORKS_FOR", properties_={"since": 2020})
rel = db.create_or_merge(rel)
print(f"Created relationship: {person.properties_['name']} WORKS_FOR {company.properties_['name']}")

Fetch Nodes (Multiple Methods)

from cypher_graphdb import CypherGraphDB

db = CypherGraphDB(backend="memgraph", connect_url="bolt://localhost:7687")

# Fetch by database ID
node = db.fetch_nodes(12345, unnest_result=True)

# Fetch by GID (string identifier)
node = db.fetch_nodes("6f628f1e7tFZHfis", unnest_result=True)

# Fetch by properties
nodes = db.fetch_nodes({"label_": "Product", "name": "CypherGraph"})

# Fetch all nodes of a type (with typed models)
from my_models import Product
products = db.fetch_nodes({"label_": Product})

Bulk Data Setup

from cypher_graphdb import CypherGraphDB

db = CypherGraphDB(backend="memgraph", connect_url="bolt://localhost:7687")

# Create multiple nodes and relationships in one query
db.execute("""
    CREATE (p1:Person {name: 'Alice', age: 30, email: '[e-mail]'})
    CREATE (p2:Person {name: 'Bob', age: 25, email: '[e-mail]'})
    CREATE (c:Company {name: 'TechCorp', founded: 2010})
    CREATE (p1)-[:WORKS_FOR {since: 2020, role: 'Engineer'}]->(c)
    CREATE (p2)-[:WORKS_FOR {since: 2021, role: 'Designer'}]->(c)
    CREATE (p1)-[:KNOWS {since: 2019}]->(p2)
""")

3. Interactive CLI - Vendor-Neutral Graph Operations

The library includes a comprehensive interactive CLI with 44+ commands for exploring graph databases. The CLI provides identical functionality across all supported backends.

Running the CLI

# Using task automation
task run:cli

# Direct execution
.venv/bin/cypher-graphdb

Command Categories

Connection Management:

• connect memgraph bolt://localhost:7687 – Connect to Memgraph
• connect age postgresql://... – Connect to Apache AGE
• disconnect – Close current connection

Query Execution:

• MATCH (n) RETURN n LIMIT 5; – Execute any Cypher query
• stats – Show last query execution statistics
• format_output – Configure result display format

Graph Operations:

• create_node – Create nodes with properties
• create_edge – Create relationships between nodes
• fetch_nodes – Retrieve nodes by criteria
• fetch_edges – Retrieve edges by criteria
• delete_graphobj – Remove nodes or edges

Schema & Introspection:

• dump_schema – Export graph schema definitions
• dump_labels – List all node and edge labels
• dump_models – Show registered model classes
• load_models – Import model definitions

Import/Export:

• import_graph – Load data from CSV, Excel, JSON files
• export_graph – Export graph data to various formats

Schema Generation:

• schema generate – Generate JSON schema from Python models (standalone, no DB required)

Transaction Control:

• commit – Commit current transaction
• rollback – Rollback current transaction

Utilities:

• help – Show all available commands
• exit – Exit CLI (aliases: quit, q, bye)
• search – Full-text search across graph data
• graph_to_tree – Visualize graph structure as tree

Vendor Neutrality: All CLI commands work identically across Apache AGE, Memgraph, and any other supported backend.

Schema Generation (Standalone)

Generate JSON schema files from Python graph models without requiring a database connection:

# Generate from a single Python file
cypher-graphdb schema generate --models ./models.py --output ./schemas/

# Generate from a package directory (with __init__.py)
cypher-graphdb schema generate -m ./graph_models/ -o ./output/

# Specify output filename
cypher-graphdb schema generate -m ./models.py -o ./my-schema.json

# With verbose output
cypher-graphdb schema generate -m ./models.py -o ./schemas/ --verbose

# Skip confirmation prompt
cypher-graphdb schema generate -m ./models.py -o ./schemas/ --yes

Options:

• -m, --models – Path to Python file or directory with @node/@edge decorated models
• -o, --output – Output path (directory uses graph.schema.json, or specify filename)
• -v, --verbose – Show detailed output
• --overwrite – Overwrite existing file without prompting
• -y, --yes – Skip confirmation prompt

Package Support: Directories with __init__.py are loaded as packages, enabling relative imports between model files.

2. Typed ORM-Like Access Layer

Define typed models with decorators for better type safety and metadata:

from cypher_graphdb import GraphNode, GraphEdge, node, edge, relation

@node(label="Product")
@relation(rel_type="USES_TECHNOLOGY", to_type="Technology")
class Product(GraphNode):
    name: str
    multi_tenancy: bool | None = None

@node(label="Technology")
class Technology(GraphNode):
    name: str

@edge(label="USES_TECHNOLOGY")
class UsesTechnology(GraphEdge):
    version: str | None = None

Using Typed Models

from cypher_graphdb import CypherGraphDB

db = CypherGraphDB(backend="memgraph", connect_url="bolt://localhost:7687")

# Create typed nodes
demo_product = Product(name="CypherGraph Demo", multi_tenancy=True)
db.create_or_merge(demo_product)

demo_technology = Technology(name="Python")
db.create_or_merge(demo_technology)

# Create typed relationship
demo_relation = UsesTechnology.build(demo_product, demo_technology, version="3.13")
db.create_or_merge(demo_relation)

# Fetch typed nodes
products = db.fetch_nodes({"label_": Product})
for product in products:
    print(f"Product: {product.name}, Multi-tenant: {product.multi_tenancy}")

Result Unnesting

Simplify common query patterns:

# Single scalar
value = db.execute("RETURN 42", unnest_result=True)  # 42 (not [(42,)])

# Single row
row = db.execute("RETURN 1, 2", fetch_one=True, unnest_result=True)  # (1, 2)

Task Automation

Task	Purpose
install	Install library with dependencies
test:unit	Run unit tests
test:integration	Integration tests (requires Docker)
format	Format code with Ruff
check	Lint and check code quality
run:cli	Run interactive CLI
fct	Format + check + test

Development Workflow

See CONTRIBUTING.md for the development workflow and guidelines.

Documentation

Library documentation is built with MkDocs:

task build:docs   # Build docs site

Output: site/ directory. API reference auto-generated from docstrings.

License

Apache License 2.0 -- see LICENSE.md.

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Happy graph hacking!