aiomcache is a Python library that provides an asyncio (PEP 3156) interface to work with memcached. It allows users to interact with memcached servers asynchronously, making it suitable for high-performance applications that require non-blocking I/O operations. The library offers similar functionality to other memcache clients and includes features like setting and getting values, multi-get operations, and deleting keys. Version 0.8 introduces the `FlagClient` class, which enables users to register callbacks for setting or processing flags, providing additional flexibility and customization options for working with memcached servers.

ONNX Runtime Generative AI is a library that provides the generative AI loop for ONNX models, including inference with ONNX Runtime, logits processing, search and sampling, and KV cache management. Users can call a high level `generate()` method, or run each iteration of the model in a loop. It supports greedy/beam search and TopP, TopK sampling to generate token sequences, has built in logits processing like repetition penalties, and allows for easy custom scoring.

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

LazyLLM is a low-code development tool for building complex AI applications with multiple agents. It assists developers in building AI applications at a low cost and continuously optimizing their performance. The tool provides a convenient workflow for application development and offers standard processes and tools for various stages of application development. Users can quickly prototype applications with LazyLLM, analyze bad cases with scenario task data, and iteratively optimize key components to enhance the overall application performance. LazyLLM aims to simplify the AI application development process and provide flexibility for both beginners and experts to create high-quality applications.

OpenPromptInjection is an open-source toolkit for attacks and defenses in LLM-integrated applications, enabling easy implementation, evaluation, and extension of attacks, defenses, and LLMs. It supports various attack and defense strategies, including prompt injection, paraphrasing, retokenization, data prompt isolation, instructional prevention, sandwich prevention, perplexity-based detection, LLM-based detection, response-based detection, and know-answer detection. Users can create models, tasks, and apps to evaluate different scenarios. The toolkit currently supports PaLM2 and provides a demo for querying models with prompts. Users can also evaluate ASV for different scenarios by injecting tasks and querying models with attacked data prompts.

Instructor is a Typescript library for structured extraction in Typescript, powered by llms, designed for simplicity, transparency, and control. It stands out for its simplicity, transparency, and user-centric design. Whether you're a seasoned developer or just starting out, you'll find Instructor's approach intuitive and steerable.

Data-Oriented GOAP (Goal-Oriented Action Planning) is a library that implements GOAP in a data-oriented way, allowing for dynamic setup of states, actions, and goals. It includes bevy_dogoap for Bevy integration. It is useful for NPCs performing tasks dependent on each other, enabling NPCs to improvise reaching goals, and offers a middle ground between Utility AI and HTNs. The library is inspired by the F.E.A.R GDC talk and provides a minimal Bevy example for implementation.

CompressAI-Vision is a tool that helps you develop, test, and evaluate compression models with standardized tests in the context of compression methods optimized for machine tasks algorithms such as Neural-Network (NN)-based detectors. It currently focuses on two types of pipeline: Video compression for remote inference (`compressai-remote-inference`), which corresponds to the MPEG "Video Coding for Machines" (VCM) activity. Split inference (`compressai-split-inference`), which includes an evaluation framework for compressing intermediate features produced in the context of split models. The software supports all the pipelines considered in the related MPEG activity: "Feature Compression for Machines" (FCM).

The AI Fairness 360 toolkit is an open-source library designed to detect and mitigate bias in machine learning models. It provides a comprehensive set of metrics, explanations, and algorithms for bias mitigation in various domains such as finance, healthcare, and education. The toolkit supports multiple bias mitigation algorithms and fairness metrics, and is available in both Python and R. Users can leverage the toolkit to ensure fairness in AI applications and contribute to its development for extensibility.

Zep is a long-term memory service for AI Assistant apps. With Zep, you can provide AI assistants with the ability to recall past conversations, no matter how distant, while also reducing hallucinations, latency, and cost. Zep persists and recalls chat histories, and automatically generates summaries and other artifacts from these chat histories. It also embeds messages and summaries, enabling you to search Zep for relevant context from past conversations. Zep does all of this asyncronously, ensuring these operations don't impact your user's chat experience. Data is persisted to database, allowing you to scale out when growth demands. Zep also provides a simple, easy to use abstraction for document vector search called Document Collections. This is designed to complement Zep's core memory features, but is not designed to be a general purpose vector database. Zep allows you to be more intentional about constructing your prompt: 1. automatically adding a few recent messages, with the number customized for your app; 2. a summary of recent conversations prior to the messages above; 3. and/or contextually relevant summaries or messages surfaced from the entire chat session. 4. and/or relevant Business data from Zep Document Collections.

Delphi OpenAI API is an unofficial library providing Delphi implementation over OpenAI public API. It allows users to access various models, make completions, chat conversations, generate images, and call functions using OpenAI service. The library aims to facilitate tasks such as content generation, semantic search, and classification through AI models. Users can fine-tune models, work with natural language processing, and apply reinforcement learning methods for diverse applications.

MSCCL++ is a GPU-driven communication stack for scalable AI applications. It provides a highly efficient and customizable communication stack for distributed GPU applications. MSCCL++ redefines inter-GPU communication interfaces, delivering a highly efficient and customizable communication stack for distributed GPU applications. Its design is specifically tailored to accommodate diverse performance optimization scenarios often encountered in state-of-the-art AI applications. MSCCL++ provides communication abstractions at the lowest level close to hardware and at the highest level close to application API. The lowest level of abstraction is ultra light weight which enables a user to implement logics of data movement for a collective operation such as AllReduce inside a GPU kernel extremely efficiently without worrying about memory ordering of different ops. The modularity of MSCCL++ enables a user to construct the building blocks of MSCCL++ in a high level abstraction in Python and feed them to a CUDA kernel in order to facilitate the user's productivity. MSCCL++ provides fine-grained synchronous and asynchronous 0-copy 1-sided abstracts for communication primitives such as `put()`, `get()`, `signal()`, `flush()`, and `wait()`. The 1-sided abstractions allows a user to asynchronously `put()` their data on the remote GPU as soon as it is ready without requiring the remote side to issue any receive instruction. This enables users to easily implement flexible communication logics, such as overlapping communication with computation, or implementing customized collective communication algorithms without worrying about potential deadlocks. Additionally, the 0-copy capability enables MSCCL++ to directly transfer data between user's buffers without using intermediate internal buffers which saves GPU bandwidth and memory capacity. MSCCL++ provides consistent abstractions regardless of the location of the remote GPU (either on the local node or on a remote node) or the underlying link (either NVLink/xGMI or InfiniBand). This simplifies the code for inter-GPU communication, which is often complex due to memory ordering of GPU/CPU read/writes and therefore, is error-prone.

Numerapi is a Python client to the Numerai API that allows users to automatically download and upload data for the Numerai machine learning competition. It provides functionalities for downloading training data, uploading predictions, and accessing user, submission, and competitions information for both the main competition and Numerai Signals competition. Users can interact with the API using Python modules or command line interface. Tokens are required for certain actions like uploading predictions or staking, which can be obtained from Numer.ai account settings. The tool also supports features like checking new rounds, getting leaderboards, and managing stakes.

Kafka-ML is a framework designed to manage the pipeline of Tensorflow/Keras and PyTorch machine learning models on Kubernetes. It enables the design, training, and inference of ML models with datasets fed through Apache Kafka, connecting them directly to data streams like those from IoT devices. The Web UI allows easy definition of ML models without external libraries, catering to both experts and non-experts in ML/AI.

Prism is a Laravel package for integrating Large Language Models (LLMs) into applications. It simplifies text generation, multi-step conversations, and AI tools integration. Focus on developing exceptional AI applications without technical complexities.

OpenVoiceChat is an open-source tool designed for having natural voice conversations with an LLM model. It supports various speech-to-text (STT), text-to-speech (TTS), and large language model (LLM) models. The tool aims to provide an alternative to closed commercial implementations, with well-abstracted APIs that are easy to use and extend. Users can install base and functionality-specific packages using pip, and the tool supports interruptions during conversations. The project encourages contributions through bounties and has a detailed roadmap available for reference.

aiomcache is a Python library that provides an asyncio (PEP 3156) interface to work with memcached. It allows users to interact with memcached servers asynchronously, making it suitable for high-performance applications that require non-blocking I/O operations. The library offers similar functionality to other memcache clients and includes features like setting and getting values, multi-get operations, and deleting keys. Version 0.8 introduces the `FlagClient` class, which enables users to register callbacks for setting or processing flags, providing additional flexibility and customization options for working with memcached servers.

MinIO is a High Performance Object Storage released under GNU Affero General Public License v3.0. It is API compatible with Amazon S3 cloud storage service. Use MinIO to build high performance infrastructure for machine learning, analytics and application data workloads.

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.

Deep Lake is a Database for AI powered by a storage format optimized for deep-learning applications. Deep Lake can be used for: 1. Storing data and vectors while building LLM applications 2. Managing datasets while training deep learning models Deep Lake simplifies the deployment of enterprise-grade LLM-based products by offering storage for all data types (embeddings, audio, text, videos, images, pdfs, annotations, etc.), querying and vector search, data streaming while training models at scale, data versioning and lineage, and integrations with popular tools such as LangChain, LlamaIndex, Weights & Biases, and many more. Deep Lake works with data of any size, it is serverless, and it enables you to store all of your data in your own cloud and in one place. Deep Lake is used by Intel, Bayer Radiology, Matterport, ZERO Systems, Red Cross, Yale, & Oxford.

NucliaDB is a robust database that allows storing and searching on unstructured data. It is an out of the box hybrid search database, utilizing vector, full text and graph indexes. NucliaDB is written in Rust and Python. We designed it to index large datasets and provide multi-teanant support. When utilizing NucliaDB with Nuclia cloud, you are able to the power of an NLP database without the hassle of data extraction, enrichment and inference. We do all the hard work for you.

OpenIO SDS is a software solution for object storage, targeting very large-scale unstructured data volumes.

Venice is a derived data storage platform, providing the following characteristics: 1. High throughput asynchronous ingestion from batch and streaming sources (e.g. Hadoop and Samza). 2. Low latency online reads via remote queries or in-process caching. 3. Active-active replication between regions with CRDT-based conflict resolution. 4. Multi-cluster support within each region with operator-driven cluster assignment. 5. Multi-tenancy, horizontal scalability and elasticity within each cluster. The above makes Venice particularly suitable as the stateful component backing a Feature Store, such as Feathr. AI applications feed the output of their ML training jobs into Venice and then query the data for use during online inference workloads.

Aiocache is an asyncio cache library that supports multiple backends such as memory, redis, and memcached. It provides a simple interface for functions like add, get, set, multi_get, multi_set, exists, increment, delete, clear, and raw. Users can easily install and use the library for caching data in Python applications. Aiocache allows for easy instantiation of caches and setup of cache aliases for reusing configurations. It also provides support for backends, serializers, and plugins to customize cache operations. The library offers detailed documentation and examples for different use cases and configurations.

aiomcache is a Python library that provides an asyncio (PEP 3156) interface to work with memcached. It allows users to interact with memcached servers asynchronously, making it suitable for high-performance applications that require non-blocking I/O operations. The library offers similar functionality to other memcache clients and includes features like setting and getting values, multi-get operations, and deleting keys. Version 0.8 introduces the `FlagClient` class, which enables users to register callbacks for setting or processing flags, providing additional flexibility and customization options for working with memcached servers.

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.

Bee is an easy and high efficiency ORM framework that simplifies database operations by providing a simple interface and eliminating the need to write separate DAO code. It supports various features such as automatic filtering of properties, partial field queries, native statement pagination, JSON format results, sharding, multiple database support, and more. Bee also offers powerful functionalities like dynamic query conditions, transactions, complex queries, MongoDB ORM, cache management, and additional tools for generating distributed primary keys, reading Excel files, and more. The newest versions introduce enhancements like placeholder precompilation, default date sharding, ElasticSearch ORM support, and improved query capabilities.

claude-api is a web conversation library for ClaudeAI implemented in GoLang. It provides functionalities to interact with ClaudeAI for web-based conversations. Users can easily integrate this library into their Go projects to enable chatbot capabilities and handle conversations with ClaudeAI. The library includes features for sending messages, receiving responses, and managing chat sessions, making it a valuable tool for developers looking to incorporate AI-powered chatbots into their applications.

Aide is a code-first API documentation and utility library for Rust, along with other related utility crates for web-servers. It provides tools for creating API documentation and handling JSON request validation. The repository contains multiple crates that offer drop-in replacements for existing libraries, ensuring compatibility with Aide. Contributions are welcome, and the code is dual licensed under MIT and Apache-2.0. If Aide does not meet your requirements, you can explore similar libraries like paperclip, utoipa, and okapi.

Amadeus Java SDK provides a rich set of APIs for the travel industry, allowing developers to access various functionalities such as flight search, booking, airport information, and more. The SDK simplifies interaction with the Amadeus API by providing self-contained code examples and detailed documentation. Developers can easily make API calls, handle responses, and utilize features like pagination and logging. The SDK supports various endpoints for tasks like flight search, booking management, airport information retrieval, and travel analytics. It also offers functionalities for hotel search, booking, and sentiment analysis. Overall, the Amadeus Java SDK is a comprehensive tool for integrating Amadeus APIs into Java applications.

Rig is a Rust library designed for building scalable, modular, and user-friendly applications powered by large language models (LLMs). It provides full support for LLM completion and embedding workflows, offers simple yet powerful abstractions for LLM providers like OpenAI and Cohere, as well as vector stores such as MongoDB and in-memory storage. With Rig, users can easily integrate LLMs into their applications with minimal boilerplate code.

Celery AsyncIO Pool is a free software tool licensed under GNU Affero General Public License v3+. It provides an AsyncIO worker pool for Celery, enabling users to leverage the power of AsyncIO in their Celery applications. The tool allows for easy installation using Poetry, pip, or directly from GitHub. Users can configure Celery to use the AsyncIO pool provided by celery-aio-pool, or they can wait for the upcoming support for out-of-tree worker pools in Celery 5.3. The tool is actively maintained and welcomes contributions from the community.