open-dynamic-workflows

name	open-dynamic-workflows
description	Plan, orchestrate, and adversarially verify parallel AI coding agents with a dynamic multi-agent workflow engine.
category	ai-agents
risk	critical
source	community
source_repo	Suraj1235/open-dynamic-workflows
source_type	community
date_added	2026-06-06
author	Suraj1235
tags	["multi-agent","orchestration","workflow","adversarial-verification","coding-agents"]
tools	["claude","cursor","codex","gemini","antigravity"]
license	MIT
license_source	https://github.com/Suraj1235/open-dynamic-workflows/blob/main/LICENSE

Open Dynamic Workflows

Overview

Open Dynamic Workflows (ODW) is an open-source dynamic multi-agent workflow engine for AI coding agents such as OpenCode, Codex, Antigravity, and VS Code. It lets you plan a task, orchestrate multiple agents working in parallel, and adversarially verify their output before it lands. ODW ships a Codex/Antigravity skill folder (SKILL.md plus a daemon bridge) and an OpenCode plugin, and it is bring-your-own-model (Anthropic, OpenAI-compatible, or Ollama). This skill is adapted from the community project at Suraj1235/open-dynamic-workflows.

When to Use This Skill

• Use when you need to decompose a coding task into independent subtasks and run multiple agents in parallel.
• Use when working across more than one AI coding tool (OpenCode, Codex, Antigravity, VS Code) and want a single orchestration layer.
• Use when the user asks for adversarial review or verification of agent-generated changes before merging.

How It Works

Step 1: Plan

ODW takes a high-level goal and produces a dynamic workflow graph of subtasks, identifying which can run in parallel and which have dependencies.

Step 2: Orchestrate

The engine dispatches subtasks to parallel agents through the OpenCode plugin or the Codex/Antigravity daemon bridge, using your configured model provider (Anthropic, OpenAI-compatible, or Ollama).

Step 3: Adversarially Verify

Completed work is routed through an adversarial verification pass that challenges the output before results are synthesized and returned.

Examples

Example 1: Run a parallel workflow

ODW is installed from source (clone the repo, then npm install). The CLI is odw-daemon — run it as npm run odw -- <args> from inside the repo, or as npx odw-daemon <args> / a global odw-daemon if you link the bin.

# Configure your model provider (bring-your-own-model)
export ANTHROPIC_API_KEY=...        # or an OpenAI-compatible / Ollama endpoint

# One-time setup: generate ~/.odw/config.json
npm run setup

# Start the local workflow daemon (once)
npm run odw -- start

# Plan, orchestrate, and verify a task across parallel agents
npm run odw -- run --prompt "refactor the auth module and add tests"

Example 2: Use the Codex/Antigravity skill bridge