Packages
Combines vector similarity search with graph traversal over metadata to retrieve documents from existing vector stores, enabling both unstructured and structured retrieval in a single query.
However, the package is in Beta with limited recent activity (last release April 2025), so verify that your vector store is supported and test thoroughly before…
A Python client for Neo4j Graph Data Science that lets you project graphs, run algorithms, and build machine learning pipelines using pure Python code instead of Cypher.
Provides string manipulation functions that work with grapheme clusters—user-perceived characters—rather than individual Unicode code points, enabling correct length calculations and slicing for strings with combining marks, emoji modifiers, and other multi-codepoint characters.
Provides string manipulation functions that work with user-perceived characters (grapheme clusters) as defined by Unicode Standard Annex #29, rather than individual Unicode code points.
Graphene is a Python library for building GraphQL schemas and types declaratively, supporting multiple data sources and integrating with frameworks like Django, SQLAlchemy, and MongoDB.
However, the 643-day gap since the last release raises a maintenance concern—verify that the project's release cadence matches your risk tolerance.
Graphene-Django bridges Django models and GraphQL APIs, automatically generating GraphQL schemas from Django models and integrating with Django's authentication, permissions, and validation systems.
Install it if you're building a GraphQL API on top of Django models and want to avoid manual schema definition and boilerplate.
Automatically optimizes GraphQL queries executed by graphene-django using Django QuerySet methods like select_related, prefetch_related, and only to reduce database round-trips.
However, be aware that maintenance is dormant—last release was 2023-08-05—so compatibility with newer Django or graphene versions is unverified.
Adds Apollo Federation support to Graphene GraphQL schemas, enabling multiple GraphQL services to compose into a federated graph following Apollo Federation specifications.
Adds file upload support to GraphQL mutations in Graphene-based Django and Flask applications, handling multipart requests according to the GraphQL multipart spec.
No—not for new projects.
Converts Pydantic models into GraphQL object types and input types for use with Graphene, enabling you to build GraphQL APIs from your existing Pydantic validation schemas.
Install it if you're building GraphQL APIs on top of existing Pydantic models and want to avoid duplicating type definitions.
Graphene-SQLAlchemy exposes SQLAlchemy database models as GraphQL object types, letting you build GraphQL schemas directly from your existing database layer.
However, be cautious: the package has not been updated since June 2020, so verify that its dependencies (especially graphene and SQLAlchemy) remain compatible with…
GraphFrames provides distributed graph processing and analytics on Apache Spark using DataFrame abstractions, with built-in algorithms and flexible APIs like Pregel and AggregateMessages for custom graph computation.
However, verify that your Spark version and Python environment are compatible, since the latest release date and classifier information suggest the package may not…
GraphFrames Python wrapper provides graph processing and analysis on Apache Spark, enabling operations like centrality metrics, motif finding, community detection, and traversals on distributed graph data.
The main gotcha is the external JVM dependency—you cannot use this package standalone; it requires a working Spark environment.
Parses code, docs, PDFs, images, and video into a queryable knowledge graph for AI coding assistants, using tree-sitter AST for deterministic code analysis and optional semantic passes for other media.
Sends metrics to Graphite's Carbon backend over TCP or UDP, with support for custom prefixes, grouping, and batch operations.
Graphiti builds and queries temporal context graphs for AI agents, tracking how facts change over time with full provenance to source data, supporting both semantic and keyword retrieval alongside graph traversal.
Provides a Python API for creating, serializing, and loading graph data structures conforming to the JSON Graph Spec, with built-in support for Neo4j database integration.
GraphQL-core is a Python port of the GraphQL.js reference implementation, providing tools to define GraphQL schemas and execute queries against them with support for both synchronous and asynchronous resolvers.
Install it if you need to execute GraphQL queries in Python or build a GraphQL server.
Adds promise-based resolver and dataloader support to graphql-core v3+, enabling synchronous promise patterns as an alternative to asyncio for GraphQL execution.
However, the aging maintenance status (525 days since last release) means you should verify compatibility with your target graphql-core version and be prepared to…
Builds GraphQL query strings programmatically in Python using a domain-specific language, letting you construct queries from Python classes instead of writing raw GraphQL strings.
Provides Relay-compliant patterns for GraphQL-core, including connection types for pagination, global object identification, and mutation helpers to simplify building Relay servers in Python.
GraphQL-Server provides helper functions to execute GraphQL queries and subscriptions on top of graphql-core, designed to integrate with Python web frameworks like Django, Flask, FastAPI, and others.
GraphQL-Server-Core provides base utilities and helper functions for building GraphQL servers or integrating GraphQL into existing web frameworks via GraphQL-Core.
Install it if you are building a custom GraphQL server integration and need low-level utilities, or if an existing framework integration you depend on still requires it.
A lightweight GraphQL client that sends queries to GraphQL endpoints and returns results, with support for custom authorization headers.
GraphRAG extracts structured knowledge graphs from unstructured text using LLMs, enabling retrieval-augmented generation (RAG) systems to reason more effectively over private data.
Not recommended if you need a lightweight, zero-configuration RAG solution.
Renders directed graphs as colored ASCII art in the terminal using a pure Python implementation with no external dependencies.
Install it if you need to display directed graphs in the terminal—it's particularly useful for agent workflows and CLI-based tools.
Generates DOT language source code for graph structures and renders them using the Graphviz graph drawing software installed on your system.
Install it if you need to generate or render graphs from Python.
graphyte sends metrics to a Graphite/Carbon server over TCP or UDP, with support for synchronous sends or background-thread batching via the standard library's threading module.
Graspologic provides graph statistical algorithms and utilities for processing, analyzing, and modeling networks using specialized statistical techniques that account for graph structure.
Provides a high-performance Rust implementation of the Leiden community detection algorithm for graph analysis, exposed to Python via a native extension module.
Gravis generates interactive graph visualizations as HTML/CSS/JavaScript output that can be viewed in a web browser, letting you explore network structures and relationships visually.
However, its abandoned status since 2021-12-08 means no updates for Python version compatibility, browser standards, or bug fixes—use it only for one-off…
Gravity manages multiple Galaxy server processes (gunicorn, celery, tusd, and others) through a unified CLI, providing start/stop/logging control via the `galaxyctl` command and a foreground runner via the `galaxy` command.
Install it if you are running a Galaxy server or managing Galaxy from source; it is already a dependency of Galaxy 22.01+.
Converts between two's complement integers and gray code (reflected binary code), where successive values differ by only one bit.
However, the abandoned maintenance status means no bug fixes or Python version updates are forthcoming—install only if the current functionality meets your needs and…
Provides Python logging handlers that format and send log messages to Graylog servers in GELF (Graylog Extended Log Format) via UDP, TCP, TLS, HTTP, or RabbitMQ.
Defines and validates data quality expectations—unit tests for data—and generates documentation of validation results to help teams maintain consistent data standards.
Experimental extensions and early-stage features for Great Expectations, the data quality validation framework that uses Expectations as unit tests for data.
Yes, with conditions: install only if you are already using great-expectations and want to trial experimental features in a non-production environment.
Great Tables transforms Pandas or Polars DataFrames into formatted, publication-quality HTML tables or images with headers, footers, column spanners, and cell-level formatting.
Async Python library for discovering, connecting to, and controlling Gree-based mini-split air conditioning and heat pump systems over the network.
The main gotcha is that device binding is finnicky and requires immediate re-scan if the device key is lost—test discovery and binding thoroughly before relying on it…
Allows you to call async functions from synchronous code that runs within an asyncio or Trio event loop, bridging the gap between sync and async codebases without threading.
Parses and manipulates regular expressions by converting them to finite state machines, enabling operations like computing the intersection of two regexes to find strings both accept.
However, it is aging (no release since 2024-06-08); install it for a specific regex-algebra task, but do not expect active maintenance or rapid updates.