aiologic

GIL-powered* locking library for Python

Stars: 59

Visit

aiologic is a locking library for tasks synchronization and their communication. It provides primitives that are both async-aware and thread-aware, and can be used for interaction between async codes (async <-> async) in one thread as regular async primitives, async codes (async <-> async) in multiple threads, async code and sync one (async <-> sync) in one thread, async code and sync one (async <-> sync) in multiple threads, sync codes (sync <-> sync) in one thread as regular sync primitives, sync codes (sync <-> sync) in multiple threads as regular sync primitives. It offers synchronization primitives like events, barriers, semaphores, capacity limiters, locks, readers-writer locks, condition variables, communication primitives like queues, non-blocking primitives like flags and resource guards, and supports various concurrency libraries like asyncio, curio, trio, anyio, eventlet, gevent, and threading. aiologic is implemented entirely on effectively atomic operations, providing incredible speedup on PyPy compared to alternatives from the threading module. It works in free-threaded mode and ensures atomic operations even with GIL.

README:

.. SPDX-FileCopyrightText: 2024 Ilya Egorov [email protected] SPDX-License-Identifier: CC-BY-4.0

.. role:: class(literal) .. role:: exc(literal) .. role:: mod(literal)

======== aiologic

.. badges-start-marker

.. |pypi-dw| image:: https://img.shields.io/pypi/dw/aiologic :target: https://pypistats.org/packages/aiologic :alt: .. |pypi-impl| image:: https://img.shields.io/pypi/implementation/aiologic :target: #features :alt: .. |pypi-pyv| image:: https://img.shields.io/pypi/pyversions/aiologic :target: #features :alt: .. |pypi-types| image:: https://img.shields.io/pypi/types/aiologic :target: #features :alt:

.. badges-end-marker

.. description-start-marker

aiologic is a locking library for tasks synchronization and their communication. It provides primitives that are both async-aware and thread-aware, and can be used for interaction between:

async codes (async <-> async) in one thread as regular async primitives
async codes (async <-> async) in multiple threads (!)
async code and sync one (async <-> sync) in one thread (!)
async code and sync one (async <-> sync) in multiple threads (!)
sync codes (sync <-> sync) in one thread as regular sync primitives
sync codes (sync <-> sync) in multiple threads as regular sync primitives

Let's take a look at the example:

.. code:: python

import asyncio

from threading import Thread

import aiologic

lock = aiologic.Lock()


async def func(i: int, j: int) -> None:
    print(f"thread={i} task={j} start")

    async with lock:
        await asyncio.sleep(1)

    print(f"thread={i} task={j} end")


async def main(i: int) -> None:
    await asyncio.gather(func(i, 0), func(i, 1))


Thread(target=asyncio.run, args=[main(0)]).start()
Thread(target=asyncio.run, args=[main(1)]).start()

It prints something like this:

.. code-block::

thread=0 task=0 start
thread=1 task=0 start
thread=0 task=1 start
thread=1 task=1 start
thread=0 task=0 end
thread=1 task=0 end
thread=0 task=1 end
thread=1 task=1 end

As you can see, tasks from different event loops are all able to acquire :class:aiologic.Lock. In the same case if you use :class:asyncio.Lock, it will raise a :exc:RuntimeError. And :class:threading.Lock will cause a deadlock.

.. description-end-marker

Features

.. features-start-marker

Python 3.8+ support
CPython <https://www.python.org/>__ and PyPy <https://pypy.org/>__ support
Pickling <https://docs.python.org/3/library/pickle.html>__ and weakrefing <https://docs.python.org/3/library/weakref.html>__ support
Cancellation and timeouts support
Optional Trio-style checkpoints <https://trio.readthedocs.io/en/stable/ reference-core.html#checkpoints>__:
- enabled by default for Trio itself
- disabled by default for all others
Only one checkpoint per asynchronous call:
- exactly one context switch if checkpoints are enabled
- zero or one context switch if checkpoints are disabled
Fairness wherever possible (with some caveats)
Thread-safety wherever possible
Lock-free implementation
Bundled stub files

Synchronization primitives:

Events: one-time, reusable, and countdown
Barriers: single-use, cyclic, and reusable
Semaphores: counting, bounded, and binary
Capacity limiters: borrowable, and reentrant
Locks: ownable, and reentrant
Readers-writer locks (external) <https://gist.github.com/x42005e1f/ a50d0744013b7bbbd7ded608d6a3845b>__
Condition variables

Communication primitives:

Queues: FIFO, LIFO, and priority

Non-blocking primitives:

Flags
Resource guards

Supported concurrency libraries:

.. libraries-start-marker

asyncio, curio, trio, and anyio (coroutine-based)
eventlet, and gevent (greenlet-based)
threading_ (thread-based)

.. _asyncio: https://docs.python.org/3/library/asyncio.html .. _curio: https://curio.readthedocs.io .. _trio: https://trio.readthedocs.io .. _anyio: https://anyio.readthedocs.io .. _eventlet: https://eventlet.readthedocs.io .. _gevent: https://www.gevent.org/ .. _threading: https://docs.python.org/3/library/threading.html

.. libraries-end-marker

All synchronization, communication, and non-blocking primitives are implemented entirely on effectively atomic operations, which gives an incredible speedup on PyPy <https://gist.github.com/x42005e1f/149d3994d5f7bd878def71d5404e6ea4>__ compared to alternatives from the :mod:threading module. All this works because of GIL, but per-object locks also ensure that the same operations are still atomic <https://peps.python.org/pep-0703/#container-thread-safety>, so aiologic also works when running in a free-threaded mode <https:// docs.python.org/3.13/whatsnew/3.13.html#free-threaded-cpython>.

.. features-end-marker

Installation

.. installation-start-marker

Install from PyPI <https://pypi.org/project/aiologic/>__ (stable):

.. code:: console

pip install aiologic

Or from GitHub <https://github.com/x42005e1f/aiologic>__ (latest):

.. code:: console

pip install git+https://github.com/x42005e1f/aiologic.git

You can also use other package managers, such as uv <https://github.com/ astral-sh/uv>__.

.. installation-end-marker

Documentation

Read the Docs: https://aiologic.readthedocs.io (official)

DeepWiki: https://deepwiki.com/x42005e1f/aiologic (AI generated)

Communication channels

GitHub Discussions: https://github.com/x42005e1f/aiologic/discussions (ideas, questions)

GitHub Issues: https://github.com/x42005e1f/aiologic/issues (bug tracker)

You can also send an email to [email protected] with any feedback.

Support

If you like aiologic and want to support its development, please star its repository on GitHub <https://github.com/x42005e1f/aiologic>__.

.. image:: https://starchart.cc/x42005e1f/aiologic.svg?variant=adaptive :target: https://starchart.cc/x42005e1f/aiologic

License

.. license-start-marker

The aiologic library is REUSE-compliant <https://api.reuse.software/info/ github.com/x42005e1f/aiologic>__ and is offered under multiple licenses:

All original source code is licensed under ISC_.
All original test code is licensed under 0BSD_.
All documentation is licensed under CC-BY-4.0_.
All configuration is licensed under CC0-1.0_.

For more accurate information, check the individual files.

.. _ISC: https://choosealicense.com/licenses/isc/ .. _0BSD: https://choosealicense.com/licenses/0bsd/ .. _CC-BY-4.0: https://choosealicense.com/licenses/cc-by-4.0/ .. _CC0-1.0: https://choosealicense.com/licenses/cc0-1.0/

.. license-end-marker

For Tasks:

Click tags to check more tools for each tasks

manage tasks synchronize events control access communicate data ensure atomicity

For Jobs:

software engineer systems engineer devops engineer data engineer ai engineer

Alternative AI tools for aiologic

Similar Open Source Tools

aiologic

github

: 59

Tutel

Tutel MoE is an optimized Mixture-of-Experts implementation that offers a parallel solution with 'No-penalty Parallism/Sparsity/Capacity/Switching' for modern training and inference. It supports Pytorch framework (version >= 1.10) and various GPUs including CUDA and ROCm. The tool enables Full Precision Inference of MoE-based Deepseek R1 671B on AMD MI300. Tutel provides features like all-to-all benchmarking, tensorcore option, NCCL timeout settings, Megablocks solution, and dynamic switchable configurations. Users can run Tutel in distributed mode across multiple GPUs and machines. The tool allows for custom MoE implementations and offers detailed usage examples and reference documentation.

github

: 785

hugging-chat-api

Unofficial HuggingChat Python API for creating chatbots, supporting features like image generation, web search, memorizing context, and changing LLMs. Users can log in, chat with the ChatBot, perform web searches, create new conversations, manage conversations, switch models, get conversation info, use assistants, and delete conversations. The API also includes a CLI mode with various commands for interacting with the tool. Users are advised not to use the application for high-stakes decisions or advice and to avoid high-frequency requests to preserve server resources.

github

: 780

AGiXT

AGiXT is a dynamic Artificial Intelligence Automation Platform engineered to orchestrate efficient AI instruction management and task execution across a multitude of providers. Our solution infuses adaptive memory handling with a broad spectrum of commands to enhance AI's understanding and responsiveness, leading to improved task completion. The platform's smart features, like Smart Instruct and Smart Chat, seamlessly integrate web search, planning strategies, and conversation continuity, transforming the interaction between users and AI. By leveraging a powerful plugin system that includes web browsing and command execution, AGiXT stands as a versatile bridge between AI models and users. With an expanding roster of AI providers, code evaluation capabilities, comprehensive chain management, and platform interoperability, AGiXT is consistently evolving to drive a multitude of applications, affirming its place at the forefront of AI technology.

github

: 3.1k

rag-chat

The `@upstash/rag-chat` package simplifies the development of retrieval-augmented generation (RAG) chat applications by providing Next.js compatibility with streaming support, built-in vector store, optional Redis compatibility for fast chat history management, rate limiting, and disableRag option. Users can easily set up the environment variables and initialize RAGChat to interact with AI models, manage knowledge base, chat history, and enable debugging features. Advanced configuration options allow customization of RAGChat instance with built-in rate limiting, observability via Helicone, and integration with Next.js route handlers and Vercel AI SDK. The package supports OpenAI models, Upstash-hosted models, and custom providers like TogetherAi and Replicate.

github

: 203

py-llm-core

PyLLMCore is a light-weighted interface with Large Language Models with native support for llama.cpp, OpenAI API, and Azure deployments. It offers a Pythonic API that is simple to use, with structures provided by the standard library dataclasses module. The high-level API includes the assistants module for easy swapping between models. PyLLMCore supports various models including those compatible with llama.cpp, OpenAI, and Azure APIs. It covers use cases such as parsing, summarizing, question answering, hallucinations reduction, context size management, and tokenizing. The tool allows users to interact with language models for tasks like parsing text, summarizing content, answering questions, reducing hallucinations, managing context size, and tokenizing text.

github

: 118

chat

deco.chat is an open-source foundation for building AI-native software, providing developers, engineers, and AI enthusiasts with robust tools to rapidly prototype, develop, and deploy AI-powered applications. It empowers Vibecoders to prototype ideas and Agentic engineers to deploy scalable, secure, and sustainable production systems. The core capabilities include an open-source runtime for composing tools and workflows, MCP Mesh for secure integration of models and APIs, a unified TypeScript stack for backend logic and custom frontends, global modular infrastructure built on Cloudflare, and a visual workspace for building agents and orchestrating everything in code.

github

: 153

curator

Bespoke Curator is an open-source tool for data curation and structured data extraction. It provides a Python library for generating synthetic data at scale, with features like programmability, performance optimization, caching, and integration with HuggingFace Datasets. The tool includes a Curator Viewer for dataset visualization and offers a rich set of functionalities for creating and refining data generation strategies.

github

: 1.2k

aiosql

aiosql is a Python module that allows you to organize SQL statements in .sql files and load them into your Python application as methods to call. It supports various database drivers like SQLite, PostgreSQL, MySQL, MariaDB, and DuckDB. The project is an implementation of Kris Jenkins' yesql library to the Python ecosystem, allowing users to easily reuse SQL code in SQL GUIs or CLI tools. With aiosql, you can write, version control, comment, and run SQL code using files without losing the ability to use them as you would any other SQL file. It provides support for PEP 249 and asyncio based drivers, enabling users to execute parametric SQL queries from Python methods.

github

: 1.3k

agents-starter

A starter template for building AI-powered chat agents using Cloudflare's Agent platform, powered by agents-sdk. It provides a foundation for creating interactive chat experiences with AI, complete with a modern UI and tool integration capabilities. Features include interactive chat interface with AI, built-in tool system with human-in-the-loop confirmation, advanced task scheduling, dark/light theme support, real-time streaming responses, state management, and chat history. Prerequisites include a Cloudflare account and OpenAI API key. The project structure includes components for chat UI implementation, chat agent logic, tool definitions, and helper functions. Customization guide covers adding new tools, modifying the UI, and example use cases for customer support, development assistant, data analysis assistant, personal productivity assistant, and scheduling assistant.

github

: 503

speech-to-speech

This repository implements a speech-to-speech cascaded pipeline with consecutive parts including Voice Activity Detection (VAD), Speech to Text (STT), Language Model (LM), and Text to Speech (TTS). It aims to provide a fully open and modular approach by leveraging models available on the Transformers library via the Hugging Face hub. The code is designed for easy modification, with each component implemented as a class. Users can run the pipeline either on a server/client approach or locally, with detailed setup and usage instructions provided in the readme.

github

: 3.2k

LLM-as-HH

LLM-as-HH is a codebase that accompanies the paper ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution. It introduces Language Hyper-Heuristics (LHHs) that leverage LLMs for heuristic generation with minimal manual intervention and open-ended heuristic spaces. Reflective Evolution (ReEvo) is presented as a searching framework that emulates the reflective design approach of human experts while surpassing human capabilities with scalable LLM inference, Internet-scale domain knowledge, and powerful evolutionary search. The tool can improve various algorithms on problems like Traveling Salesman Problem, Capacitated Vehicle Routing Problem, Orienteering Problem, Multiple Knapsack Problems, Bin Packing Problem, and Decap Placement Problem in both black-box and white-box settings.

github

: 78

react-native-fast-tflite

A high-performance TensorFlow Lite library for React Native that utilizes JSI for power, zero-copy ArrayBuffers for efficiency, and low-level C/C++ TensorFlow Lite core API for direct memory access. It supports swapping out TensorFlow Models at runtime and GPU-accelerated delegates like CoreML/Metal/OpenGL. Easy VisionCamera integration allows for seamless usage. Users can load TensorFlow Lite models, interpret input and output data, and utilize GPU Delegates for faster computation. The library is suitable for real-time object detection, image classification, and other machine learning tasks in React Native applications.

github

: 631

clearml-serving

ClearML Serving is a command line utility for model deployment and orchestration, enabling model deployment including serving and preprocessing code to a Kubernetes cluster or custom container based solution. It supports machine learning models like Scikit Learn, XGBoost, LightGBM, and deep learning models like TensorFlow, PyTorch, ONNX. It provides a customizable RestAPI for serving, online model deployment, scalable solutions, multi-model per container, automatic deployment, canary A/B deployment, model monitoring, usage metric reporting, metric dashboard, and model performance metrics. ClearML Serving is modular, scalable, flexible, customizable, and open source.

github

: 132

litserve

LitServe is a high-throughput serving engine for deploying AI models at scale. It generates an API endpoint for a model, handles batching, streaming, autoscaling across CPU/GPUs, and more. Built for enterprise scale, it supports every framework like PyTorch, JAX, Tensorflow, and more. LitServe is designed to let users focus on model performance, not the serving boilerplate. It is like PyTorch Lightning for model serving but with broader framework support and scalability.

github

: 53

GraphRAG-SDK

Build fast and accurate GenAI applications with GraphRAG SDK, a specialized toolkit for building Graph Retrieval-Augmented Generation (GraphRAG) systems. It integrates knowledge graphs, ontology management, and state-of-the-art LLMs to deliver accurate, efficient, and customizable RAG workflows. The SDK simplifies the development process by automating ontology creation, knowledge graph agent creation, and query handling, enabling users to interact and query their knowledge graphs effectively. It supports multi-agent systems and orchestrates agents specialized in different domains. The SDK is optimized for FalkorDB, ensuring high performance and scalability for large-scale applications. By leveraging knowledge graphs, it enables semantic relationships and ontology-driven queries that go beyond standard vector similarity, enhancing retrieval-augmented generation capabilities.

github

: 292

For similar tasks

aiologic

github

: 59

MiniAI-Face-Recognition-LivenessDetection-AndroidSDK

MiniAiLive provides system integrators with fast, flexible and extremely precise facial recognition with 3D passive face liveness detection (face anti-spoofing) that can be deployed across a number of scenarios, including security, access control, public safety, fintech, smart retail and home protection.

github

: 307

aiolimiter

An efficient implementation of a rate limiter for asyncio using the Leaky bucket algorithm, providing precise control over the rate a code section can be entered. It allows for limiting the number of concurrent entries within a specified time window, ensuring that a section of code is executed a maximum number of times in that period.

github

: 474

foundationallm

FoundationaLLM is a platform designed for deploying, scaling, securing, and governing generative AI in enterprises. It allows users to create AI agents grounded in enterprise data, integrate REST APIs, experiment with various large language models, centrally manage AI agents and their assets, deploy scalable vectorization data pipelines, enable non-developer users to create their own AI agents, control access with role-based access controls, and harness capabilities from Azure AI and Azure OpenAI. The platform simplifies integration with enterprise data sources, provides fine-grain security controls, scalability, extensibility, and addresses the challenges of delivering enterprise copilots or AI agents.

github

: 95

ragna

Ragna is a RAG orchestration framework designed for managing workflows and orchestrating tasks. It provides a comprehensive set of features for users to streamline their processes and automate repetitive tasks. With Ragna, users can easily create, schedule, and monitor workflows, making it an ideal tool for teams and individuals looking to improve their productivity and efficiency. The framework offers extensive documentation, community support, and a user-friendly interface, making it accessible to users of all skill levels. Whether you are a developer, data scientist, or project manager, Ragna can help you simplify your workflow management and boost your overall performance.

github

: 185

tegon

Tegon is an open-source AI-First issue tracking tool designed for engineering teams. It aims to simplify task management by leveraging AI and integrations to automate task creation, prioritize tasks, and enhance bug resolution. Tegon offers features like issues tracking, automatic title generation, AI-generated labels and assignees, custom views, and upcoming features like sprints and task prioritization. It integrates with GitHub, Slack, and Sentry to streamline issue tracking processes. Tegon also plans to introduce AI Agents like PR Agent and Bug Agent to enhance product management and bug resolution. Contributions are welcome, and the product is licensed under the MIT License.

github

: 1.5k

Advanced-GPTs

Nerority's Advanced GPT Suite is a collection of 33 GPTs that can be controlled with natural language prompts. The suite includes tools for various tasks such as strategic consulting, business analysis, career profile building, content creation, educational purposes, image-based tasks, knowledge engineering, marketing, persona creation, programming, prompt engineering, role-playing, simulations, and task management. Users can access links, usage instructions, and guides for each GPT on their respective pages. The suite is designed for public demonstration and usage, offering features like meta-sequence optimization, AI priming, prompt classification, and optimization. It also provides tools for generating articles, analyzing contracts, visualizing data, distilling knowledge, creating educational content, exploring topics, generating marketing copy, simulating scenarios, managing tasks, and more.

github

: 212

aioclock

An asyncio-based scheduling framework designed for execution of periodic tasks with integrated support for dependency injection, enabling efficient and flexible task management. Aioclock is 100% async, light, fast, and resource-friendly. It offers features like task scheduling, grouping, trigger definition, easy syntax, Pydantic v2 validation, and upcoming support for running the task dispatcher on a different process and backend support for horizontal scaling.

github

: 119

For similar jobs

sweep

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

github

: 7.1k

teams-ai

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

github

: 502

ai-guide

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

github

: 159

classifai

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

github

: 620

chatbot-ui

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

github

: 27.7k

BricksLLM

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

github

: 953

uAgents

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

github

: 1.3k

griptape

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.

github

: 2.2k