Transform your repository into an AI Specialist Team. One command to install role-based AI agents for any tech stack.
TAKT: TAKT Agent Koordination Topology - AI Agent Workflow Orchestration
Graph-based AI agent workflow orchestration. Bring your own agent strategies.
A TypeScript CLI tool for AI agent workflow management
AI Agent Workflow Coordinator — kanban-based pipeline for AI coding agents
AI Agent Workflow System for Spec-Driven Development (SDD)
State machine for AI agent workflow management
Deterministic project migrations for AI-agent workflow packages: plan, apply, check, and explain JSON/text file upgrades without model calls.
AI agent workflow system for Claude Code — installs 12 specialized agents, orchestration commands, and persona-driven product discovery into any repository
Scaffold an AI-agent workflow monorepo (Cursor rules, docs, task board, Vite+React) in one command. 한 줄로 에이전트 워크플로우 모노레포를 만듭니다.
Composable AI agent workflow framework — execution, state, orchestration, runtimes, UI
AI Agent Workflow Static Analyzer — detect infinite loops, cost explosions, PII leaks, prompt injection sinks before execution. CLI wrapper that downloads the platform-specific Go binary from GitHub Releases.
Self-hosted server that gives your engineering team a shared AI agent workflow.
35 production-ready AI Agent workflow templates for customer service, code review, sales, and content strategy
Deterministic project migrations for AI-agent workflow packages: plan, apply, check, and explain JSON/text file upgrades without model calls.
A polished, self-contained chat widget built for [Elia Assistant](https://elia-asistent.com), compatible with [n8n](https://n8n.io)'s Chat Trigger. Drop it in front of any n8n AI Agent workflow — no backend code needed.
Initialize AI agent workflow framework in any project
A portable, tool-agnostic AI agent workflow system built on skills and standards
Structured AI agent workflow framework with Stories, Activities, and Steps
LangGraph
Wellness reminders integrated into your AI agent workflow — water and walk breaks, natively in Claude Code and any MCP-compatible IDE
[](https://www.npmjs.com/package/@google/genai) [](https://www.npmjs.com/package/@google/genai)
SDK for building AI agents with Claude Code's capabilities. Programmatically interact with Claude to build autonomous agents that can understand codebases, edit files, and execute workflows.
A conversational AI-driven telecom multi-agent system for managing call balances, push notifications, marketing, targeting, and sales.
Ruby AI Agents SDK enables creating complex AI workflows with multi-agent orchestration, tool execution, safety guardrails, and provider-agnostic LLM integration.
Phronomy provides composable building blocks — Agents, Workflows, Tools, Guardrails, and Tracing — for building AI agents in Ruby. Powered by RubyLLM for LLM abstraction.
ace-git gives developers and coding agents focused git context commands and guided workflows for rebases, pull requests, and commit reorganization, with smart diff output and Git 2.23+ guardrails.
Named browser sessions, Ruby workflow DSL, and a token-efficient DOM snapshot format. Built on a browser-agnostic driver layer (Ferrum/CDP backend).
Payloop gives AI teams real-time visibility into the true costs of deploying agents - across tasks, workflows, and customers.
Boma Gem for building AI agents, workflows, and service integrations. Boma provides a framework for creating intelligent agents that can perform complex tasks by integrating with various services and APIs. It allows developers to build AI-powered applications with ease, enabling them to automate workflows, make decisions, and interact with users in a natural way.
Bristow provides a flexible framework for creating and managing systems of agents that can work together, hand off tasks between each other, and execute functions. Perfect for building complex AI systems and automation workflows.
Build and manage AI agents with ease using ActAsAgent. This gem provides a robust framework for creating, configuring, and deploying intelligent agents that can perform tasks autonomously. Whether you're looking to automate workflows, enhance user interactions, or develop complex AI systems, ActAsAgent offers the tools and flexibility you need to bring your ideas to life.
RailsOrchestrator provides a structured framework for building AI agents in Ruby on Rails applications. It includes tool support, memory management, and orchestration capabilities for composing complex agent workflows.
Ruby-native AI coding agent with pluggable LLM adapters, persistent memory, and file editing tools. Features automatic retry logic, SQLite-backed conversation history, and user approval workflows for file modifications.
RCrewAI is a powerful Ruby framework for creating autonomous AI agent crews that collaborate to solve complex tasks. Build intelligent workflows with reasoning agents, tool usage, memory systems, and human oversight. Key Features: • Multi-Agent Orchestration: Create crews of specialized AI agents that work together • Multi-LLM Support: OpenAI GPT-4, Anthropic Claude, Google Gemini, Azure OpenAI, Ollama • Rich Tool Ecosystem: Web search, file operations, SQL databases, email, code execution, PDF processing • Agent Memory: Short-term and long-term memory for learning from past executions • Human-in-the-Loop: Interactive approval workflows and collaborative decision making • Advanced Task Management: Dependencies, retries, async execution, and context sharing • Hierarchical Teams: Manager agents that coordinate and delegate to specialist agents • Production Ready: Security controls, error handling, comprehensive logging, and monitoring • Ruby-First Design: Built specifically for Ruby developers with idiomatic patterns • CLI Tools: Command-line interface for creating and managing AI crews
Rubagent is a lightweight Ruby framework for building modular, composable AI agents that can interact with LLMs (like OpenAI), tools, and external APIs. It provides a flexible architecture for defining agents, managing prompts, and orchestrating multi-step workflows using functional patterns. Features: - Streamed LLM responses (OpenAI, etc.) - Plug-and-play agent design - Shared context and memory flow - Support for custom tools and HTTP integrations - Built-in dry-rb and Zeitwerk compatibility
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