agentfab

agentfab is a robust distributed AI platform designed for seamless multi-agent orchestration, enabling specialized AI agents to collaborate effectively through task graphs, persistent knowledge, and bounded review loops. This platform allows users to define agents using YAML configurations that specify individual models, tools, and knowledge, creating an efficient system for managing complex AI tasks.

Key Features

• Task Decomposition: agentfab intelligently decomposes requests into a Directed Acyclic Graph (DAG), enabling parallel scheduling of agents to improve performance.
• Durable Control Plane: A persistent control plane maintains state and runtime details, ensuring reliability across agent executions, even during failures.
• Custom Agent Configurations: Users can easily compile Markdown descriptions into YAML or write YAML directly to define custom agents tailored to specific workflows.
• Flexible Runtime Modes: Supports various operational modes, from local environments for quick iterations to external-node configurations for deploying across distributed systems like VMs and Kubernetes.

Quick Start Example

To initiate a new project, simply run:

make build
agentfab init
agentfab run

This command sequences specialized agents that break down the project idea into actionable tasks, outputting results to a designated data directory.

Runtime Modes Explained

• Local Mode: Utilizes in-memory processing for all agents, ideal for fast development cycles.
• External-node Mode: Operates with the Conductor and multiple node hosts as separate processes, suitable for production environments with enhanced security and reliability features.

CLI Commands Overview

agentfab provides a comprehensive command-line interface that includes:

This platform is designed to streamline the orchestration of multiple AI agents, making it an efficient tool for developers looking to optimize their AI workflows. For more details, guides, and examples, visit agentfab's documentation and explore the underlying architecture.