Bellman is a unified interface to interact with language and embedding models, supporting various vendors like VertexAI/Gemini, OpenAI, Anthropic, VoyageAI, and Ollama. It consists of a library for direct interaction with models and a service 'bellmand' for proxying requests with one API key. Bellman simplifies switching between models, vendors, and common tasks like chat, structured data, tools, and binary input. It addresses the lack of official SDKs for major players and differences in APIs, providing a single proxy for handling different models. The library offers clients for different vendors implementing common interfaces for generating and embedding text, enabling easy interchangeability between models.

LightRAG is a PyTorch library designed for building and optimizing Retriever-Agent-Generator (RAG) pipelines. It follows principles of simplicity, quality, and optimization, offering developers maximum customizability with minimal abstraction. The library includes components for model interaction, output parsing, and structured data generation. LightRAG facilitates tasks like providing explanations and examples for concepts through a question-answering pipeline.

UniChat is a pipeline tool for creating online and offline chat-bots in Unity. It leverages Unity.Sentis and text vector embedding technology to enable offline mode text content search based on vector databases. The tool includes a chain toolkit for embedding LLM and Agent in games, along with middleware components for Text to Speech, Speech to Text, and Sub-classifier functionalities. UniChat also offers a tool for invoking tools based on ReActAgent workflow, allowing users to create personalized chat scenarios and character cards. The tool provides a comprehensive solution for designing flexible conversations in games while maintaining developer's ideas.

Instructor Go is a library that simplifies working with structured outputs from large language models (LLMs). Built on top of `invopop/jsonschema` and utilizing `jsonschema` Go struct tags, it provides a user-friendly API for managing validation, retries, and streaming responses without changing code logic. The library supports LLM provider APIs such as OpenAI, Anthropic, Cohere, and Google, capturing and returning usage data in responses. Users can easily add metadata to struct fields using `jsonschema` tags to enhance model awareness and streamline workflows.

Whetstone.ChatGPT is a simple light-weight library that wraps the Open AI API with support for dependency injection. It supports features like GPT 4, GPT 3.5 Turbo, chat completions, audio transcription and translation, vision completions, files, fine tunes, images, embeddings, moderations, and response streaming. The library provides a video walkthrough of a Blazor web app built on it and includes examples such as a command line bot. It offers quickstarts for dependency injection, chat completions, completions, file handling, fine tuning, image generation, and audio transcription.

MCP Go is a Go implementation of the Model Context Protocol (MCP), facilitating seamless integration between LLM applications and external data sources and tools. It handles complex protocol details and server management, allowing developers to focus on building tools. The tool is designed to be fast, simple, and complete, aiming to provide a high-level and easy-to-use interface for developing MCP servers. MCP Go is currently under active development, with core features working and advanced capabilities in progress.

LLM Tornado is a .NET library designed to simplify the consumption of various large language models (LLMs) from providers like OpenAI, Anthropic, Cohere, Google, Azure, Groq, and self-hosted APIs. It acts as an aggregator, allowing users to easily switch between different LLM providers with just a change in argument. Users can perform tasks such as chatting with documents, voice calling with AI, orchestrating assistants, generating images, and more. The library exposes capabilities through vendor extensions, making it easy to integrate and use multiple LLM providers simultaneously.

OpenAI-DotNet is a simple C# .NET client library for OpenAI to use through their RESTful API. It is independently developed and not an official library affiliated with OpenAI. Users need an OpenAI API account to utilize this library. The library targets .NET 6.0 and above, working across various platforms like console apps, winforms, wpf, asp.net, etc., and on Windows, Linux, and Mac. It provides functionalities for authentication, interacting with models, assistants, threads, chat, audio, images, files, fine-tuning, embeddings, and moderations.

ModelFusion is an abstraction layer for integrating AI models into JavaScript and TypeScript applications, unifying the API for common operations such as text streaming, object generation, and tool usage. It provides features to support production environments, including observability hooks, logging, and automatic retries. You can use ModelFusion to build AI applications, chatbots, and agents. ModelFusion is a non-commercial open source project that is community-driven. You can use it with any supported provider. ModelFusion supports a wide range of models including text generation, image generation, vision, text-to-speech, speech-to-text, and embedding models. ModelFusion infers TypeScript types wherever possible and validates model responses. ModelFusion provides an observer framework and logging support. ModelFusion ensures seamless operation through automatic retries, throttling, and error handling mechanisms. ModelFusion is fully tree-shakeable, can be used in serverless environments, and only uses a minimal set of dependencies.

com.openai.unity is an OpenAI package for Unity that allows users to interact with OpenAI's API through RESTful requests. It is independently developed and not an official library affiliated with OpenAI. Users can fine-tune models, create assistants, chat completions, and more. The package requires Unity 2021.3 LTS or higher and can be installed via Unity Package Manager or Git URL. Various features like authentication, Azure OpenAI integration, model management, thread creation, chat completions, audio processing, image generation, file management, fine-tuning, batch processing, embeddings, and content moderation are available.

gollm is a Go package designed to simplify interactions with Large Language Models (LLMs) for AI engineers and developers. It offers a unified API for multiple LLM providers, easy provider and model switching, flexible configuration options, advanced prompt engineering, prompt optimization, memory retention, structured output and validation, provider comparison tools, high-level AI functions, robust error handling and retries, and extensible architecture. The package enables users to create AI-powered golems for tasks like content creation workflows, complex reasoning tasks, structured data generation, model performance analysis, prompt optimization, and creating a mixture of agents.

LiteGraph is a property graph database designed for knowledge and artificial intelligence applications. It supports graph relationships, tags, labels, metadata, data, and vectors. LiteGraph can be used in-process with LiteGraphClient or as a standalone RESTful server with LiteGraph.Server. The latest version includes major internal refactor, batch APIs, enumeration APIs, statistics APIs, database caching, vector search enhancements, and bug fixes. LiteGraph allows for simple embedding into applications without user configuration. Users can create tenants, graphs, nodes, edges, and perform operations like finding routes and exporting to GEXF file. It also provides features for working with object labels, tags, data, and vectors, enabling filtering and searching based on various criteria. LiteGraph offers REST API deployment with LiteGraph.Server and Docker support with a Docker image available on Docker Hub.

SwarmGo is a Go package designed to create AI agents capable of interacting, coordinating, and executing tasks. It focuses on lightweight agent coordination and execution, offering powerful primitives like Agents and handoffs. SwarmGo enables building scalable solutions with rich dynamics between tools and networks of agents, all while keeping the learning curve low. It supports features like memory management, streaming support, concurrent agent execution, LLM interface, and structured workflows for organizing and coordinating multiple agents.

Letta is an open source framework for building stateful LLM applications. It allows users to build stateful agents with advanced reasoning capabilities and transparent long-term memory. The framework is white box and model-agnostic, enabling users to connect to various LLM API backends. Letta provides a graphical interface, the Letta ADE, for creating, deploying, interacting, and observing with agents. Users can access Letta via REST API, Python, Typescript SDKs, and the ADE. Letta supports persistence by storing agent data in a database, with PostgreSQL recommended for data migrations. Users can install Letta using Docker or pip, with Docker defaulting to PostgreSQL and pip defaulting to SQLite. Letta also offers a CLI tool for interacting with agents. The project is open source and welcomes contributions from the community.

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.

AgentKit is a framework for creating and orchestrating AI Agents, enabling developers to build, test, and deploy reliable AI applications at scale. It allows for creating networked agents with separate tasks and instructions to solve specific tasks, as well as simple agents for tasks like writing content. The framework requires the Inngest TypeScript SDK as a dependency and provides documentation on agents, tools, network, state, and routing. Example projects showcase AgentKit in action, such as the Test Writing Network demo using Workflow Kit, Supabase, and OpenAI.

Bellman is a unified interface to interact with language and embedding models, supporting various vendors like VertexAI/Gemini, OpenAI, Anthropic, VoyageAI, and Ollama. It consists of a library for direct interaction with models and a service 'bellmand' for proxying requests with one API key. Bellman simplifies switching between models, vendors, and common tasks like chat, structured data, tools, and binary input. It addresses the lack of official SDKs for major players and differences in APIs, providing a single proxy for handling different models. The library offers clients for different vendors implementing common interfaces for generating and embedding text, enabling easy interchangeability between models.

Ollama is a powerful tool for running large language models locally or on your own infrastructure. It provides a full implementation of the Ollama API, support for streaming requests, and tool use capability. Users can interact with Ollama in Elixir to generate completions, chat messages, and perform streaming requests. The tool also supports function calling on compatible models, allowing users to define tools with clear descriptions and arguments. Ollama is designed to facilitate natural language processing tasks and enhance user interactions with language models.

LLM Agents is a small library designed to build agents controlled by large language models. It aims to provide a better understanding of how such agents work in a concise manner. The library allows agents to be instructed by prompts, use custom-built components as tools, and run in a loop of Thought, Action, Observation. The agents leverage language models to generate Thought and Action, while tools like Python REPL, Google search, and Hacker News search provide Observations. The library requires setting up environment variables for OpenAI API and SERPAPI API keys. Users can create their own agents by importing the library and defining tools accordingly.

Kagent is a Kubernetes native framework for building AI agents, designed to be easy to understand and use. It provides a flexible and powerful way to build, deploy, and manage AI agents in Kubernetes. The framework consists of agents, tools, and model configurations defined as Kubernetes custom resources, making them easy to manage and modify. Kagent is extensible, flexible, observable, declarative, testable, and has core components like a controller, UI, engine, and CLI.

AgentFly is an extensible framework for building LLM agents with reinforcement learning. It supports multi-turn training by adapting traditional RL methods with token-level masking. It features a decorator-based interface for defining tools and reward functions, enabling seamless extension and ease of use. To support high-throughput training, it implemented asynchronous execution of tool calls and reward computations, and designed a centralized resource management system for scalable environment coordination. A suite of prebuilt tools and environments are provided.

VectorFlow is an open source, high throughput, fault tolerant vector embedding pipeline. It provides a simple API endpoint for ingesting large volumes of raw data, processing, and storing or returning the vectors quickly and reliably. The tool supports text-based files like TXT, PDF, HTML, and DOCX, and can be run locally with Kubernetes in production. VectorFlow offers functionalities like embedding documents, running chunking schemas, custom chunking, and integrating with vector databases like Pinecone, Qdrant, and Weaviate. It enforces a standardized schema for uploading data to a vector store and supports features like raw embeddings webhook, chunk validation webhook, S3 endpoint, and telemetry. The tool can be used with the Python client and provides detailed instructions for running and testing the functionalities.

PaperQA is a minimal package for question and answering from PDFs or text files, providing very good answers with in-text citations. It uses OpenAI Embeddings to embed and search documents, and includes a process of embedding docs, queries, searching for top passages, creating summaries, using an LLM to re-score and select relevant summaries, putting summaries into prompt, and generating answers. The tool can be used to answer specific questions related to scientific research by leveraging citations and relevant passages from documents.

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.

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.