vecs is a Python client for managing and querying vector stores in PostgreSQL with the pgvector extension. It allows users to create collections of vectors with associated metadata, index the collections for fast search performance, and query the collections based on specified filters. The tool simplifies the process of working with vector data in a PostgreSQL database, making it easier to store, retrieve, and analyze vector information.

Educhain is a powerful Python package that leverages Generative AI to create engaging and personalized educational content. It enables users to generate multiple-choice questions, create lesson plans, and support various LLM models. Users can export questions to JSON, PDF, and CSV formats, customize prompt templates, and generate questions from text, PDF, URL files, youtube videos, and images. Educhain outperforms traditional methods in content generation speed and quality. It offers advanced configuration options and has a roadmap for future enhancements, including integration with popular Learning Management Systems and a mobile app for content generation on-the-go.

Funcchain is a Python library that allows you to easily write cognitive systems by leveraging Pydantic models as output schemas and LangChain in the backend. It provides a seamless integration of LLMs into your apps, utilizing OpenAI Functions or LlamaCpp grammars (json-schema-mode) for efficient structured output. Funcchain compiles the Funcchain syntax into LangChain runnables, enabling you to invoke, stream, or batch process your pipelines effortlessly.

Cognee is an open-source framework designed for creating self-improving deterministic outputs for Large Language Models (LLMs) using graphs, LLMs, and vector retrieval. It provides a platform for AI engineers to enhance their models and generate more accurate results. Users can leverage Cognee to add new information, utilize LLMs for knowledge creation, and query the system for relevant knowledge. The tool supports various LLM providers and offers flexibility in adding different data types, such as text files or directories. Cognee aims to streamline the process of working with LLMs and improving AI models for better performance and efficiency.

Redis Vector Library (RedisVL) is a Python client library for building AI applications on top of Redis. It provides a high-level interface for managing vector indexes, performing vector search, and integrating with popular embedding models and providers. RedisVL is designed to make it easy for developers to build and deploy AI applications that leverage the speed, flexibility, and reliability of Redis.

LangChain Go is a Go language implementation of LangChain, a framework for building applications with LLMs through composability. It provides a simple and easy-to-use API for interacting with LLMs, making it easy to add language-based features to your applications.

Open-Source Evaluation for LLM Applications. `continuous-eval` is an open-source package created for granular and holistic evaluation of GenAI application pipelines. It offers modularized evaluation, a comprehensive metric library covering various LLM use cases, the ability to leverage user feedback in evaluation, and synthetic dataset generation for testing pipelines. Users can define their own metrics by extending the Metric class. The tool allows running evaluation on a pipeline defined with modules and corresponding metrics. Additionally, it provides synthetic data generation capabilities to create user interaction data for evaluation or training purposes.

The Mistral Python Client is a tool inspired by cohere-python that allows users to interact with the Mistral AI API. It provides functionalities to access and utilize the AI capabilities offered by Mistral. Users can easily install the client using pip and manage dependencies using poetry. The client includes examples demonstrating how to use the API for various tasks, such as chat interactions. To get started, users need to obtain a Mistral API Key and set it as an environment variable. Overall, the Mistral Python Client simplifies the integration of Mistral AI services into Python applications.

LanceDB is an open-source database for vector-search built with persistent storage, which greatly simplifies retrieval, filtering, and management of embeddings. The key features of LanceDB include: Production-scale vector search with no servers to manage. Store, query, and filter vectors, metadata, and multi-modal data (text, images, videos, point clouds, and more). Support for vector similarity search, full-text search, and SQL. Native Python and Javascript/Typescript support. Zero-copy, automatic versioning, manage versions of your data without needing extra infrastructure. GPU support in building vector index(*). Ecosystem integrations with LangChain 🦜️🔗, LlamaIndex 🦙, Apache-Arrow, Pandas, Polars, DuckDB, and more on the way. LanceDB's core is written in Rust 🦀 and is built using Lance, an open-source columnar format designed for performant ML workloads.

ChatRex is a Multimodal Large Language Model (MLLM) designed to seamlessly integrate fine-grained object perception and robust language understanding. By adopting a decoupled architecture with a retrieval-based approach for object detection and leveraging high-resolution visual inputs, ChatRex addresses key challenges in perception tasks. It is powered by the Rexverse-2M dataset with diverse image-region-text annotations. ChatRex can be applied to various scenarios requiring fine-grained perception, such as object detection, grounded conversation, grounded image captioning, and region understanding.

CatAI is a tool that allows users to run GGUF models on their computer with a chat UI. It serves as a local AI assistant inspired by Node-Llama-Cpp and Llama.cpp. The tool provides features such as auto-detecting programming language, showing original messages by clicking on user icons, real-time text streaming, and fast model downloads. Users can interact with the tool through a CLI that supports commands for installing, listing, setting, serving, updating, and removing models. CatAI is cross-platform and supports Windows, Linux, and Mac. It utilizes node-llama-cpp and offers a simple API for asking model questions. Additionally, developers can integrate the tool with node-llama-cpp@beta for model management and chatting. The configuration can be edited via the web UI, and contributions to the project are welcome. The tool is licensed under Llama.cpp's license.

LionAGI is a powerful intelligent workflow automation framework that introduces advanced ML models into any existing workflows and data infrastructure. It can interact with almost any model, run interactions in parallel for most models, produce structured pydantic outputs with flexible usage, automate workflow via graph based agents, use advanced prompting techniques, and more. LionAGI aims to provide a centralized agent-managed framework for "ML-powered tools coordination" and to dramatically lower the barrier of entries for creating use-case/domain specific tools. It is designed to be asynchronous only and requires Python 3.10 or higher.

aiomqtt is an idiomatic asyncio MQTT client that allows users to interact with MQTT brokers using asyncio in Python. It eliminates the need for callbacks and return codes, providing a more streamlined experience. The tool supports MQTT versions 5.0, 3.1.1, and 3.1, and offers graceful disconnection handling. It is fully type-hinted, making it easier to work with. Users can publish and subscribe to MQTT topics with ease, making it a versatile tool for MQTT communication in Python.

Acte is a framework designed to build GUI-like tools for AI Agents. It aims to address the issues of cognitive load and freedom degrees when interacting with multiple APIs in complex scenarios. By providing a graphical user interface (GUI) for Agents, Acte helps reduce cognitive load and constraints interaction, similar to how humans interact with computers through GUIs. The tool offers APIs for starting new sessions, executing actions, and displaying screens, accessible via HTTP requests or the SessionManager class.

Aioshelly is an asynchronous library designed to control Shelly devices. It is currently under development and requires Python version 3.11 or higher, along with dependencies like bluetooth-data-tools, aiohttp, and orjson. The library provides examples for interacting with Gen1 devices using CoAP protocol and Gen2/Gen3 devices using RPC and WebSocket protocols. Users can easily connect to Shelly devices, retrieve status information, and perform various actions through the provided APIs. The repository also includes example scripts for quick testing and usage guidelines for contributors to maintain consistency with the Shelly API.

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.

vecs is a Python client for managing and querying vector stores in PostgreSQL with the pgvector extension. It allows users to create collections of vectors with associated metadata, index the collections for fast search performance, and query the collections based on specified filters. The tool simplifies the process of working with vector data in a PostgreSQL database, making it easier to store, retrieve, and analyze vector information.

Chroma is an open-source embedding database that simplifies building LLM apps by enabling the integration of knowledge, facts, and skills for LLMs. The Ruby client for Chroma Database, chroma-rb, facilitates connecting to Chroma's database via its API. Users can configure the host, check server version, create collections, and add embeddings. The gem supports Chroma Database version 0.3.22 or newer, requiring Ruby 3.1.4 or later. It can be used with the hosted Chroma service at trychroma.com by setting configuration options like api_key, tenant, and database. Additionally, the gem provides integration with Jupyter Notebook for creating embeddings using Ollama and Nomic embed text with a Ruby HTTP client.

Community plugins repository for Google Firebase Genkit, containing various plugins for AI APIs and Vector Stores. Developed by The Fire Company, this repository offers plugins like genkitx-anthropic, genkitx-cohere, genkitx-groq, genkitx-mistral, genkitx-openai, genkitx-convex, and genkitx-hnsw. Users can easily install and use these plugins in their projects, with examples provided in the documentation. The repository also showcases products like Fireview and Giftit built using these plugins, and welcomes contributions from the community.

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.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.