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.

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.

Gemini Coder is a free 2M context AI coding assistant that allows users to conveniently copy folders and files for chatbots. It provides FIM completions, file refactoring, and AI-suggested changes. The extension is versatile, private, and lightweight, offering unmatched accuracy, speed, and cost in AI assistance. Users have full control over the context and coding conventions included, ensuring high performance and signal to noise ratio. Gemini Coder supports various chatbots and provides quick start guides for chat and FIM completions. It also offers commands for FIM completions, refactoring, applying changes, chat, and context copying. Users can set up custom model providers for API features and contribute to the project through pull requests or discussions. The tool is licensed under the MIT License.

AgentPress is a collection of simple but powerful utilities that serve as building blocks for creating AI agents. It includes core components for managing threads, registering tools, processing responses, state management, and utilizing LLMs. The tool provides a modular architecture for handling messages, LLM API calls, response processing, tool execution, and results management. Users can easily set up the environment, create custom tools with OpenAPI or XML schema, and manage conversation threads with real-time interaction. AgentPress aims to be agnostic, simple, and flexible, allowing users to customize and extend functionalities as needed.

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.

Crawl4AI is a powerful and free web crawling service that extracts valuable data from websites and provides LLM-friendly output formats. It supports crawling multiple URLs simultaneously, replaces media tags with ALT, and is completely free to use and open-source. Users can integrate Crawl4AI into Python projects as a library or run it as a standalone local server. The tool allows users to crawl and extract data from specified URLs using different providers and models, with options to include raw HTML content, force fresh crawls, and extract meaningful text blocks. Configuration settings can be adjusted in the `crawler/config.py` file to customize providers, API keys, chunk processing, and word thresholds. Contributions to Crawl4AI are welcome from the open-source community to enhance its value for AI enthusiasts and developers.

The Unreal Engine Generative AI Support Plugin is a tool designed to integrate various cutting-edge LLM/GenAI models into Unreal Engine for game development. It aims to simplify the process of using AI models for game development tasks, such as controlling scene objects, generating blueprints, running Python scripts, and more. The plugin currently supports models from organizations like OpenAI, Anthropic, XAI, Google Gemini, Meta AI, Deepseek, and Baidu. It provides features like API support, model control, generative AI capabilities, UI generation, project file management, and more. The plugin is still under development but offers a promising solution for integrating AI models into game development workflows.

MassGen is a cutting-edge multi-agent system that leverages the power of collaborative AI to solve complex tasks. It assigns a task to multiple AI agents who work in parallel, observe each other's progress, and refine their approaches to converge on the best solution to deliver a comprehensive and high-quality result. The system operates through an architecture designed for seamless multi-agent collaboration, with key features including cross-model/agent synergy, parallel processing, intelligence sharing, consensus building, and live visualization. Users can install the system, configure API settings, and run MassGen for various tasks such as question answering, creative writing, research, development & coding tasks, and web automation & browser tasks. The roadmap includes plans for advanced agent collaboration, expanded model, tool & agent integration, improved performance & scalability, enhanced developer experience, and a web interface.

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.

PilottAI is a Python framework for building autonomous multi-agent systems with advanced orchestration capabilities. It provides enterprise-ready features for building scalable AI applications. The framework includes hierarchical agent systems, production-ready features like asynchronous processing and fault tolerance, advanced memory management with semantic storage, and integrations with multiple LLM providers and custom tools. PilottAI offers specialized agents for various tasks such as customer service, document processing, email handling, knowledge acquisition, marketing, research analysis, sales, social media, and web search. The framework also provides documentation, example use cases, and advanced features like memory management, load balancing, and fault tolerance.

A Model Context Protocol (MCP) server that enables secure interaction with MySQL databases. This server allows AI assistants to list tables, read data, and execute SQL queries through a controlled interface, making database exploration and analysis safer and more structured. It provides features such as listing available MySQL tables as resources, reading table contents, executing SQL queries with proper error handling, secure database access through environment variables, and comprehensive logging. The tool ensures security best practices by never committing environment variables or credentials, using a database user with minimal required permissions, implementing query whitelisting for production use, and monitoring and logging all database operations.

Dive is an AI toolkit for Go that enables the creation of specialized teams of AI agents and seamless integration with leading LLMs. It offers a CLI and APIs for easy integration, with features like creating specialized agents, hierarchical agent systems, declarative configuration, multiple LLM support, extended reasoning, model context protocol, advanced model settings, tools for agent capabilities, tool annotations, streaming, CLI functionalities, thread management, confirmation system, deep research, and semantic diff. Dive also provides semantic diff analysis, unified interface for LLM providers, tool system with annotations, custom tool creation, and support for various verified models. The toolkit is designed for developers to build AI-powered applications with rich agent capabilities and tool integrations.

The Wikipedia MCP Server is a Model Context Protocol (MCP) server that provides real-time access to Wikipedia information for Large Language Models (LLMs). It allows AI assistants to retrieve accurate and up-to-date information from Wikipedia to enhance their responses. The server offers features such as searching Wikipedia, retrieving article content, getting article summaries, extracting specific sections, discovering links within articles, finding related topics, supporting multiple languages and country codes, optional caching for improved performance, and compatibility with Google ADK agents and other AI frameworks. Users can install the server using pipx, Smithery, PyPI, virtual environment, or from source. The server can be run with various options for transport protocol, language, country/locale, caching, access token, and more. It also supports Docker and Kubernetes deployment. The server provides MCP tools for interacting with Wikipedia, such as searching articles, getting article content, summaries, sections, links, coordinates, related topics, and extracting key facts. It also supports country/locale codes and language variants for languages like Chinese, Serbian, Kurdish, and Norwegian. The server includes example prompts for querying Wikipedia and provides MCP resources for interacting with Wikipedia through MCP endpoints. The project structure includes main packages, API implementation, core functionality, utility functions, and a comprehensive test suite for reliability and functionality testing.

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.

AI Chunking is a powerful Python library for semantic document chunking and enrichment using AI. It provides intelligent document chunking capabilities with various strategies to split text while preserving semantic meaning, particularly useful for processing markdown documentation. The library offers multiple chunking strategies such as Recursive Text Splitting, Section-based Semantic Chunking, and Base Chunking. Users can configure chunk sizes, overlap, and support various text formats. The tool is easy to extend with custom chunking strategies, making it versatile for different document processing needs.

Probe is an AI-friendly, fully local, semantic code search tool designed to power the next generation of AI coding assistants. It combines the speed of ripgrep with the code-aware parsing of tree-sitter to deliver precise results with complete code blocks, making it perfect for large codebases and AI-driven development workflows. Probe supports various features like AI-friendly code extraction, fully local operation without external APIs, fast scanning of large codebases, accurate code structure parsing, re-rankers and NLP methods for better search results, multi-language support, interactive AI chat mode, and flexibility to run as a CLI tool, MCP server, or interactive AI chat.

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.