Repository History
5 repositories tagged with Agent Framework
Declarative Agents: Profile-Driven LLM Agent Runtime in Go
Declarative Agents by Nokia Bell Labs offers a profile-driven runtime and design patterns for building tool-augmented LLM agents. It allows defining agents, their tools, states, and transitions via YAML profiles, eliminating the need for code changes for workflow alterations. This Go-based framework promotes flexible and dependable agent development.
Ralph Orchestrator, An Advanced Framework for Autonomous AI Agent Orchestration
Ralph Orchestrator is a robust, Rust-based framework designed for autonomous AI agent orchestration. It implements the innovative "Ralph Wiggum technique," a methodology focused on continuous iteration to ensure AI agents complete complex tasks effectively. This powerful tool supports multiple AI backends and offers features like a "hat system" for specialized personas and human-in-the-loop interaction via Telegram.
taOS: Self-Hosted AI Agent OS for Your Hardware and Data Sovereignty
taOS is a self-hosted AI agent operating system designed to keep your AI's memory, chats, agents, and files on your own hardware, prioritizing privacy and data sovereignty. It offers a full web desktop, a multi-framework group chat, and the ability to auto-cluster various consumer hardware like Raspberry Pi, Mac mini, and gaming PCs into a distributed AI compute mesh. This platform empowers users with local-first AI capabilities, offline by default and cloud by choice.
Deep Agents: The Batteries-Included Agent Harness for Complex AI Tasks
Deep Agents is an agent harness built on LangChain and LangGraph, designed to simplify the creation of complex AI agents. It comes equipped with essential tools like planning, filesystem access, and the ability to spawn sub-agents, enabling it to handle sophisticated agentic tasks out of the box. This framework provides a ready-to-run agent that can be easily customized with additional tools, models, and prompts.
ROMA: Recursive Open Meta-Agents for High-Performance Multi-Agent Systems
ROMA is a powerful meta-agent framework designed for building high-performance multi-agent systems using recursive hierarchical structures. It simplifies complex problem-solving by breaking tasks into parallelizable components, offering transparent development and proven performance. This open-source framework is extensible, allowing developers to customize agents and benefit from community-driven improvements.