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

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

rl

TorchRL is an open-source Reinforcement Learning (RL) library for PyTorch. It provides pytorch and **python-first** , low and high level abstractions for RL that are intended to be **efficient** , **modular** , **documented** and properly **tested**. The code is aimed at supporting research in RL. Most of it is written in python in a highly modular way, such that researchers can easily swap components, transform them or write new ones with little effort.

github

: 3.1k

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

: 170

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

sdk-python

Strands Agents is a lightweight and flexible SDK that takes a model-driven approach to building and running AI agents. It supports various model providers, offers advanced capabilities like multi-agent systems and streaming support, and comes with built-in MCP server support. Users can easily create tools using Python decorators, integrate MCP servers seamlessly, and leverage multiple model providers for different AI tasks. The SDK is designed to scale from simple conversational assistants to complex autonomous workflows, making it suitable for a wide range of AI development needs.

github

: 3.5k

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

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

LEANN

LEANN is an innovative vector database that democratizes personal AI, transforming your laptop into a powerful RAG system that can index and search through millions of documents using 97% less storage than traditional solutions without accuracy loss. It achieves this through graph-based selective recomputation and high-degree preserving pruning, computing embeddings on-demand instead of storing them all. LEANN allows semantic search of file system, emails, browser history, chat history, codebase, or external knowledge bases on your laptop with zero cloud costs and complete privacy. It is a drop-in semantic search MCP service fully compatible with Claude Code, enabling intelligent retrieval without changing your workflow.

github

: 2.6k

ppl.llm.kernel.cuda

Primitive cuda kernel library for ppl.nn.llm, part of PPL.LLM system, tested on Ampere and Hopper, requires Linux on x86_64 or arm64 CPUs, GCC >= 9.4.0, CMake >= 3.18, Git >= 2.7.0, CUDA Toolkit >= 11.4. 11.6 recommended. Provides cuda kernel functionalities for deep learning tasks.

github

: 140

candle-vllm

Candle-vllm is an efficient and easy-to-use platform designed for inference and serving local LLMs, featuring an OpenAI compatible API server. It offers a highly extensible trait-based system for rapid implementation of new module pipelines, streaming support in generation, efficient management of key-value cache with PagedAttention, and continuous batching. The tool supports chat serving for various models and provides a seamless experience for users to interact with LLMs through different interfaces.

github

: 329

serve

Jina-Serve is a framework for building and deploying AI services that communicate via gRPC, HTTP and WebSockets. It provides native support for major ML frameworks and data types, high-performance service design with scaling and dynamic batching, LLM serving with streaming output, built-in Docker integration and Executor Hub, one-click deployment to Jina AI Cloud, and enterprise-ready features with Kubernetes and Docker Compose support. Users can create gRPC-based AI services, build pipelines, scale services locally with replicas, shards, and dynamic batching, deploy to the cloud using Kubernetes, Docker Compose, or JCloud, and enable token-by-token streaming for responsive LLM applications.

github

: 21.4k

aiohttp-debugtoolbar

aiohttp_debugtoolbar provides a debug toolbar for aiohttp web applications. It is a port of pyramid_debugtoolbar and offers basic functionality such as basic panels, intercepting redirects, pretty printing exceptions, an interactive python console, and showing source code. The library is still in early development stages and offers various debug panels for monitoring different aspects of the web application. It is a useful tool for developers working with aiohttp to debug and optimize their applications.

github

: 197

litdata

LitData is a tool designed for blazingly fast, distributed streaming of training data from any cloud storage. It allows users to transform and optimize data in cloud storage environments efficiently and intuitively, supporting various data types like images, text, video, audio, geo-spatial, and multimodal data. LitData integrates smoothly with frameworks such as LitGPT and PyTorch, enabling seamless streaming of data to multiple machines. Key features include multi-GPU/multi-node support, easy data mixing, pause & resume functionality, support for profiling, memory footprint reduction, cache size configuration, and on-prem optimizations. The tool also provides benchmarks for measuring streaming speed and conversion efficiency, along with runnable templates for different data types. LitData enables infinite cloud data processing by utilizing the Lightning.ai platform to scale data processing with optimized machines.

github

: 432

mcphub.nvim

MCPHub.nvim is a powerful Neovim plugin that integrates MCP (Model Context Protocol) servers into your workflow. It offers a centralized config file for managing servers and tools, with an intuitive UI for testing resources. Ideal for LLM integration, it provides programmatic API access and interactive testing through the `:MCPHub` command.

github

: 448

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

: 96

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

: 668

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