Search Results for "i2b2 shared task"

OpenWork is a framework for building decentralized collaborative work environments powered by AI and human contributions. At its core, the project enables contributors to define tasks, workflows, and goals that can be split, shared, and recombined across distributed nodes while agents and humans cooperate to advance progress. It offers structured templates for work items, decision logic for task allocation, and consensus mechanisms that let groups verify and validate results toward shared objectives. This project also includes moderation and reputation layers so that contributor trust and quality can be assessed and integrated into future task assignments. ...

Kanwas is an open-source shared context board built for teams and AI agents working together in the same workspace. It gives people and agents a common canvas where documents, evidence, decisions, notes, tasks, embeds, and outputs can live side by side. Instead of scattering context across chats, documents, and disconnected tools, Kanwas turns messy collaborative work into a shared visual environment that both humans and agents can read and update. The platform supports real-time...

Alook is an open-source, self-hosted platform for running local AI coding agents as a coordinated workforce. It lets users give agents defined roles, email addresses, task boards, calendars, and shared workflows so they can collaborate more like a real team. The platform connects local agents to external communication channels while keeping the actual agents and codebase running on the user’s own machine. It is designed for developers, solo founders, and teams that want multiple AI agents to handle development, operations, research, and routine work with clearer structure. ...

OpenSpace is a self-evolving agent framework designed to improve the performance, efficiency, and collaboration of AI agents through continuous learning and shared knowledge. It introduces a system where agents develop reusable “skills” based on real task execution, allowing them to improve over time without retraining underlying models. The platform emphasizes collective intelligence, enabling multiple agents to share learned behaviors and benefit from each other’s experiences. It also focuses on cost efficiency by reducing redundant computations and reusing successful workflows, significantly lowering token usage in repeated tasks. ...

...It focuses on enabling developers to create agents that can collaborate, execute tasks, and interact with tools in a structured environment. The framework provides abstractions for agent communication, task orchestration, and tool integration, allowing multiple agents to work together toward shared objectives. It emphasizes openness and interoperability, making it easier to integrate with different models, APIs, and external systems. The project also includes examples and templates that demonstrate how to build and deploy agents for real-world applications. ...