Agentarium is a powerful Python framework for managing and orchestrating AI agents with ease. It provides a flexible and intuitive way to create, manage, and coordinate interactions between multiple AI agents in various environments. The framework offers advanced agent management, robust interaction management, a checkpoint system for saving and restoring agent states, data generation through agent interactions, performance optimization, flexible environment configuration, and an extensible architecture for customization.

Jido is a toolkit for building autonomous, distributed agent systems in Elixir. It provides the foundation for creating smart, composable workflows that can evolve and respond to their environment. Geared towards Agent builders, it contains core state primitives, composable actions, agent data structures, real-time sensors, signal system, skills, and testing tools. Jido is designed for multi-node Elixir clusters and offers rich helpers for unit and property-based testing.

GPT Computer Assistant (GCA) is an open-source framework designed to build vertical AI agents that can automate tasks on Windows, macOS, and Ubuntu systems. It leverages the Model Context Protocol (MCP) and its own modules to mimic human-like actions and achieve advanced capabilities. With GCA, users can empower themselves to accomplish more in less time by automating tasks like updating dependencies, analyzing databases, and configuring cloud security settings.

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.

Daydreams is a generative agent library designed for playing onchain games by injecting context. It is chain agnostic and allows users to perform onchain tasks, including playing any onchain game. The tool is lightweight and powerful, enabling users to define game context, register actions, set goals, monitor progress, and integrate with external agents. Daydreams aims to be 'lite' and 'composable', dynamically generating code needed to play games. It is currently in pre-alpha stage, seeking feedback and collaboration for further development.

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.

Flo AI is a Python framework that enables users to build production-ready AI agents and teams with minimal code. It allows users to compose complex AI architectures using pre-built components while maintaining the flexibility to create custom components. The framework supports composable, production-ready, YAML-first, and flexible AI systems. Users can easily create AI agents and teams, manage teams of AI agents working together, and utilize built-in support for Retrieval-Augmented Generation (RAG) and compatibility with Langchain tools. Flo AI also provides tools for output parsing and formatting, tool logging, data collection, and JSON output collection. It is MIT Licensed and offers detailed documentation, tutorials, and examples for AI engineers and teams to accelerate development, maintainability, scalability, and testability of AI systems.

The starknet-agent-kit is a toolkit designed for creating AI agents that can interact with the Starknet blockchain. It provides support for multiple AI providers such as Anthropic, OpenAI, Google Gemini, and Ollama. The kit includes an NPM package and a NestJS server with a web interface. Users can run the server in different modes like Chat Mode for conversations, checking balances, executing transfers, and managing accounts, as well as Autonomous Mode for automated monitoring. Additionally, the kit offers a library mode for more advanced usage, allowing users to interact with the StarknetAgent class for executing specific actions. The kit aims to simplify the process of integrating AI capabilities with blockchain interactions.

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.

VITA is an open-source interactive omni multimodal Large Language Model (LLM) capable of processing video, image, text, and audio inputs simultaneously. It stands out with features like Omni Multimodal Understanding, Non-awakening Interaction, and Audio Interrupt Interaction. VITA can respond to user queries without a wake-up word, track and filter external queries in real-time, and handle various query inputs effectively. The model utilizes state tokens and a duplex scheme to enhance the multimodal interactive experience.

Inferable is an open source platform that helps users build reliable LLM-powered agentic automations at scale. It offers a managed agent runtime, durable tool calling, zero network configuration, multiple language support, and is fully open source under the MIT license. Users can define functions, register them with Inferable, and create runs that utilize these functions to automate tasks. The platform supports Node.js/TypeScript, Go, .NET, and React, and provides SDKs, core services, and bootstrap templates for various languages.

This is a super customizable, concise, user-friendly, and efficient toolkit for training and aligning LLMs. It provides support for various methods such as SFT, Distillation, DPO, ORPO, CPO, SimPO, SMPO, Non-pair Reward Modeling, Special prompts basket format, Rejection Sampling, Scoring using RM, Effective FAISS Map-Reduce Deduplication, LLM scoring using RM, NER, CLIP, Classification, and STS. The toolkit offers key libraries like PyTorch, Transformers, TRL, Accelerate, FSDP, DeepSpeed, and tools for result logging with wandb or clearml. It allows mixing datasets, generation and logging in wandb/clearml, vLLM batched generation, and aligns models using the SMPO method.

VideoKit is a full-featured user-generated content solution for Unity Engine, enabling video recording, camera streaming, microphone streaming, social sharing, and conversational interfaces. It is cross-platform, with C# source code available for inspection. Users can share media, save to camera roll, pick from camera roll, stream camera preview, record videos, remove background, caption audio, and convert text commands. VideoKit requires Unity 2022.3+ and supports Android, iOS, macOS, Windows, and WebGL platforms.

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.

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.

Preswald is a full-stack platform for building, deploying, and managing interactive data applications in Python. It simplifies the process by combining ingestion, storage, transformation, and visualization into one lightweight SDK. With Preswald, users can connect to various data sources, customize app themes, and easily deploy apps locally. The platform focuses on code-first simplicity, end-to-end coverage, and efficiency by design, making it suitable for prototyping internal tools or deploying production-grade apps with reduced complexity and cost.

Agentarium is a powerful Python framework for managing and orchestrating AI agents with ease. It provides a flexible and intuitive way to create, manage, and coordinate interactions between multiple AI agents in various environments. The framework offers advanced agent management, robust interaction management, a checkpoint system for saving and restoring agent states, data generation through agent interactions, performance optimization, flexible environment configuration, and an extensible architecture for customization.

MeeseeksAI is a framework designed to orchestrate AI agents using a mermaid graph and networkx. It provides a structured approach to managing and coordinating multiple AI agents within a system. The framework allows users to define the interactions and dependencies between agents through a visual representation, making it easier to understand and modify the behavior of the AI system. By leveraging the power of networkx, MeeseeksAI enables efficient graph-based computations and optimizations, enhancing the overall performance of AI workflows. With its intuitive design and flexible architecture, MeeseeksAI simplifies the process of building and deploying complex AI systems, empowering users to create sophisticated agent interactions with ease.

Honcho is a platform for creating personalized AI agents and LLM powered applications for end users. The repository is a monorepo containing the server/API for managing database interactions and storing application state, along with a Python SDK. It utilizes FastAPI for user context management and Poetry for dependency management. The API can be run using Docker or manually by setting environment variables. The client SDK can be installed using pip or Poetry. The project is open source and welcomes contributions, following a fork and PR workflow. Honcho is licensed under the AGPL-3.0 License.

Sagentic.ai Agent Framework is a tool for creating AI agents with hot reloading dev server. It allows users to spawn agents locally by calling specific endpoint. The framework comes with detailed documentation and supports contributions, issues, and feature requests. It is MIT licensed and maintained by Ahyve Inc.

tinyllm is a lightweight framework designed for developing, debugging, and monitoring LLM and Agent powered applications at scale. It aims to simplify code while enabling users to create complex agents or LLM workflows in production. The core classes, Function and FunctionStream, standardize and control LLM, ToolStore, and relevant calls for scalable production use. It offers structured handling of function execution, including input/output validation, error handling, evaluation, and more, all while maintaining code readability. Users can create chains with prompts, LLM models, and evaluators in a single file without the need for extensive class definitions or spaghetti code. Additionally, tinyllm integrates with various libraries like Langfuse and provides tools for prompt engineering, observability, logging, and finite state machine design.

Council is an open-source platform designed for the rapid development and deployment of customized generative AI applications using teams of agents. It extends the LLM tool ecosystem by providing advanced control flow and scalable oversight for AI agents. Users can create sophisticated agents with predictable behavior by leveraging Council's powerful approach to control flow using Controllers, Filters, Evaluators, and Budgets. The framework allows for automated routing between agents, comparing, evaluating, and selecting the best results for a task. Council aims to facilitate packaging and deploying agents at scale on multiple platforms while enabling enterprise-grade monitoring and quality control.

Mentals AI is a tool designed for creating and operating agents that feature loops, memory, and various tools, all through straightforward markdown syntax. This tool enables you to concentrate solely on the agent’s logic, eliminating the necessity to compose underlying code in Python or any other language. It redefines the foundational frameworks for future AI applications by allowing the creation of agents with recursive decision-making processes, integration of reasoning frameworks, and control flow expressed in natural language. Key concepts include instructions with prompts and references, working memory for context, short-term memory for storing intermediate results, and control flow from strings to algorithms. The tool provides a set of native tools for message output, user input, file handling, Python interpreter, Bash commands, and short-term memory. The roadmap includes features like a web UI, vector database tools, agent's experience, and tools for image generation and browsing. The idea behind Mentals AI originated from studies on psychoanalysis executive functions and aims to integrate 'System 1' (cognitive executor) with 'System 2' (central executive) to create more sophisticated agents.

Agent Pilot is an open source desktop app for creating, managing, and chatting with AI agents. It features multi-agent, branching chats with various providers through LiteLLM. Users can combine models from different providers, configure interactions, and run code using the built-in Open Interpreter. The tool allows users to create agents, manage chats, work with multi-agent workflows, branching workflows, context blocks, tools, and plugins. It also supports a code interpreter, scheduler, voice integration, and integration with various AI providers. Contributions to the project are welcome, and users can report known issues for improvement.

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.