Packages
Decodes CTC (Connectionist Temporal Classification) logits from speech recognition models using beam search with optional n-gram language model support, handling both standard and byte-pair-encoded vocabularies.
However, be aware the package is abandoned—no updates since early 2023—so use it only in stable production contexts where you can maintain it yourself if needed.
Provides a Python client library for the OpenCTI API, enabling developers to programmatically interact with OpenCTI threat intelligence platform instances through standardized methods and utilities.
Install it if you need to integrate with OpenCTI; the main consideration is ensuring your OpenCTI instance is accessible and that the 18 runtime dependencies align…
PyCUDA provides Pythonic bindings to Nvidia's CUDA API, letting you write GPU-accelerated code in Python with automatic memory management, error handling, and access to CUDA's full driver API.
Provides Python bindings to the CUPS printing system API, allowing Python programs to interact with CUPS for printer management and print job control.
PycURL is a Python wrapper around libcurl that enables fetching URLs and performing multiprotocol file transfers with direct access to most of libcurl's functionality, including SSL, authentication, proxies, and callback support.
PyDaikin provides a Python library and CLI for controlling Daikin air conditioners over the network, supporting multiple WiFi module types and firmware versions with both synchronous and asynchronous APIs.
Pydantic validates Python data structures against type hints, coercing and checking input at runtime to ensure it matches a declared schema.
Pydantic AI is a Python framework for building production-grade AI agents and workflows with LLMs, emphasizing type safety, model-agnostic provider support, and structured validation.
Adds durable execution to Pydantic AI agents by checkpointing model and tool calls to Postgres, allowing crashed workers to resume from the last completed step without token loss.
Provides sandboxed execution and file management tools for Pydantic AI agents, supporting in-memory, local filesystem, Docker, and remote container backends with fine-grained permission control.
Pydantic AI Harness provides pre-built capabilities and tools that extend Pydantic AI agents—including code execution sandboxes, web search, sub-agent orchestration, and integrations with external services—without requiring framework changes.
Pydantic AI Shields provides guardrail capabilities for Pydantic AI agents, including cost tracking, prompt injection detection, PII detection, secret redaction, tool permissions, and async guardrails.
Teaches agents to handle specialized tasks through modular skill packages that bundle instructions, reference documents, and executable scripts with progressive disclosure—skills load their full content only when needed.
Pydantic AI Slim provides core agent framework logic for building AI applications with Pydantic, stripped to minimal required dependencies for lightweight integration.
Adds task planning and tracking tools to Pydantic AI agents, enabling them to create, manage, and complete hierarchical tasks with subtasks, dependencies, and persistent storage.
Generates command-line argument parsers from Pydantic models, automatically handling type validation, help text, and argument aliases based on your model definition.
Converts between pydantic class definitions and Apache Avro schemas in both directions, enabling serialization and schema generation for data validation workflows.
Install it if you need bidirectional conversion between pydantic and Avro—either to export pydantic models for Avro systems or to generate pydantic classes from Avro…
Provides a `BaseCollectionModel` class that wraps collections of pydantic models with validation, serialization, and type safety.
Provides compatibility mixins and adapters to use either pydantic v1 or v2 API names in a single codebase, regardless of which pydantic version is installed.
However, be aware that this is a name adapter, not a full semantic bridge—you will still need to test against both pydantic versions and handle any deeper API…
pydantic-core provides the high-performance validation and serialization engine that powers Pydantic, handling schema-based data validation for Python objects and JSON.
A Python framework and terminal assistant for building autonomous AI agents with tool-calling, sandboxed execution, multi-agent coordination, and live run forking—all type-safe and model-agnostic.
Pydantic Evals provides a framework for defining test cases, custom evaluators, and running evaluations against stochastic functions—particularly those using LLMs—with built-in support for tracing and result reporting.
Provides additional type validators and serializers for Pydantic v2 that are too specialized or niche for the main library, extending Pydantic's type system with domain-specific types.
Install only if you actually need one of its specialized types; it's not a general-purpose enhancement.
Generates mock data objects that conform to pydantic models, dataclasses, and TypedDicts by parsing type annotations and field constraints.
Wraps Python functions to validate their arguments against type hints using Pydantic models, providing a migration path from Pydantic v1's deprecated ValidatedFunction.
However, if you only need basic argument validation without signature introspection, Pydantic's validate_call decorator may be simpler.
Provides Pydantic-based type-safe validation and models for GeoJSON data according to RFC 7946, supporting all standard geometry types, features, and feature collections.
A graph and finite state machine library that lets you define stateful workflows using standard Python type hints and async functions, with nodes connected via return types.
Install it if you need to orchestrate multi-step workflows or state machines with explicit, debuggable control flow.
Renders Handlebars templates for composing LLM prompts, with compile-time validation against Pydantic models to catch typos and missing fields before rendering.
Provides a Repository pattern for MongoDB with Pydantic model integration, supporting both synchronous and asynchronous CRUD operations with built-in validation and serialization.
Provides Python bindings to execute untrusted Python code in a sandboxed subprocess pool, with crash isolation and resource limits enforced by the Monty interpreter.
Provides the `monty` command-line binary, a sandboxed Python interpreter that runs Python code with resource limits and crash isolation, typically spawned as worker subprocesses by pydantic-monty.
Integrates NumPy arrays into Pydantic models with type validation, allowing you to define, validate, serialize, and deserialize NumPy arrays as model fields.
Converts pydantic models into partial variants where all or selected fields become optional, enabling flexible validation for PATCH requests and partial response DTOs.
The main caveat is that type checkers will not understand the partial optionality, so use it only for DTO/serialization contexts, not for type-critical business logic.
Provides Pydantic data models for SCIM (System for Cross-domain Identity Management) protocol types, enabling validation and serialization of identity management data structures.
However, the dormant status and unclear license mean you should verify license compatibility upfront and test pydantic version compatibility before relying on it in…
Loads and validates application settings from environment variables, dotenv files, and other sources using Pydantic's validation framework.
Loads application configuration and secrets from Azure App Configuration and Azure Key Vault into pydantic-settings models, with support for key filtering, prefix trimming, and nested configuration.
However, the aging maintenance status and unclear license pose moderate risk for production use.
Converts Pydantic class definitions to Apache Spark schemas and generates Python code from Spark schemas, bridging type validation and distributed data processing.
However, be aware that the project is dormant—no updates since late 2023—so you should verify compatibility with your current Pydantic and Spark versions before…
Provides Pydantic models and a lightweight HTTP client for the GA4GH Task Execution Service (TES) specification, enabling structured interaction with TES-compliant task execution systems.
Converts pydantic model definitions to pyarrow schemas, enabling direct schema mapping from validated data models to columnar storage formats.
Converts pydantic model definitions into TypeScript interface definitions via a CLI tool, enabling a single source of truth for type definitions across Python and JavaScript applications.