Claude‑Flow (by Reuven Cohen / “ruvnet”) , agent orchestration platform, guide for quickstart

What it is

Claude-Flow is described as an enterprise-grade “agent orchestration platform” built around the Claude Code ecosystem (the CLI/agent environment from Anthropic). From its README:

It enables deployment of “intelligent multi-agent swarms, coordinate autonomous workflows, … build conversational AI systems.” GitHub

It supports features like: agent memory systems, vector search, swarm/hive-mind architectures, GitHub integration for code review automation, hooks for automation workflows. GitHub

The emphasis is on “agent first” development: instead of a single AI assistant, you spawn many specialized agents (workers, researcher, coder, reviewer) that coordinate. Example blog article shows someone built ~150k lines of code with a swarm in two days using Claude-Flow.

What you’ll need

Before diving, make sure you have:

• Node.js version 18+ (LTS recommended) — as Claude-Flow is a CLI built on Node. ([GitHub][1])

• A package manager (npm or yarn)

• The companion tool Claude Code installed globally:

  npm install -g @anthropic-ai/claude-code
  claude --dangerously-skip-permissions
  

  (this is required by Claude-Flow) ([GitHub][2])

• (Optional) If you want advanced memory features: set up the memory backend later.

Installing Claude-Flow & initial setup

Follow these steps:

# Install globally (alpha release)
npm install -g claude-flow@alpha

# Or use npx to always run latest: (recommended for quick experiments)
npx claude-flow@alpha init --force

# Verify installation
claude-flow --version

These steps are pulled from the “Quick Start” docs. ([GitHub][2])

After init, a project-folder (or config) will be created with default settings.

Your first “swarm” / orchestrated task

Claude-Flow uses the concept of a swarm of agents (multiple AI workers) to do tasks in parallel (orchestrated).

Here’s a minimal example:

# Initialize a hive-mind (swarm) with topology mesh (3 agents)
claude-flow hive init --topology mesh --agents 3

# Status check
claude-flow hive status

# Orchestrate a simple task: build a “hello world” API with tests
claude-flow orchestrate "create a hello world API with tests" --agents 3 --parallel

What this will do:

• spawn e.g., 3 agents (architect, coder, tester)
• they’ll coordinate to scaffold an API, write code, write tests
• you can monitor progress. These steps appear in the docs. ([GitHub][2])

Tip: For your context (you do full-stack, dev pipelines, Node/Redis/Bull etc), you might adapt the task description to something like:

“build a Node.js microservice with Redis caching, Bull Queue integration, and endpoint to process job batches”

Then let the swarm generate skeleton code, tests, and maybe integrate with your queue logic.

Memory & Persistent Context

One of the powerful bits: Claude-Flow supports a memory system so that agents can “remember” past tasks, decisions, patterns.

Basic usage:

# Store a decision or note
claude-flow memory store "architecture/decisions" "Use micro-services + Redis cache"

# Query memory
claude-flow memory query "microservices Redis" --namespace backend

Semantic & advanced mode (ReasoningBank):

You can enable semantic search of memory (vector embeddings, namespace aware).

claude-flow memory init --reasoningbank
claude-flow memory store api_pattern "Use environment variables for config" --namespace backend --reasoningbank
claude-flow memory query "configuration best practice" --namespace backend --reasoningbank

This uses vector search so you can recall meaningful items, not just exact matches. ([GitHub][3])

In your workflow, you might store patterns like: “Shopify payment plugin architecture”, “Yamato shipping micro-service flow”, “Node.js cluster + Bull queue pattern” — then later agents can recall these patterns when working on new tasks.

GitHub integration & automation

If you also want to tie into your GitHub workflow (which you do, given you’re working on a plugin, PR review agent, etc), Claude-Flow has agents and tools for GitHub automation: repository analysis, PR review, workflow generation. ([GitHub][4])

Example:

# Analyze a repository
claude-flow github analyze --repo yourorg/yourrepo --deep

# Enhance a pull-request with tests and docs
claude-flow github pr enhance --pr 123 --add-tests --improve-docs

You could integrate this into your PR-review agent: spawn a Claude-Flow swarm that reviews incoming PRs, uses memory of past issues, suggests improvements, etc.

Bringing it all together: Mini “inspired” workflow

Given your interests, here is a small workflow you could try to see how Claude-Flow fits you:

1. Create a new project folder, e.g., shopify-credit-plugin.

2. npx claude-flow@alpha init --force inside that folder.

3. Store some baseline memory:

   claude-flow memory store project/overview "Shopify credit plugin integrating Yamato shipping and B2B companies" --namespace project
   

4. Spawn a swarm task:

   claude-flow orchestrate "Design architecture for Shopify credit plugin with Redis-cache, Bull Queue for job scheduling, multi-tenant B2B support, Yamato shipping integration" --agents 4 --parallel
   

   The four agents could be: architect, coder, tester, devops.

5. After code scaffolded, spawn GitHub review swarm:

   claude-flow swarm init --topology mesh --agents 3
   claude-flow swarm spawn pr-manager "Review PR for security and caching logic" --repo yourorg/shopify-credit-plugin --pr 42
   

6. After implementation, store architectural decisions:

   claude-flow memory store architecture/decisions "Used Redis pub/sub pattern with Bull queue to decouple job submission from processing" --namespace project
   

7. Later, when you start another plugin or feature, you can query:

   claude-flow memory query "Redis pub/sub Bull queue pattern" --namespace project --reasoningbank
   

   To reuse what you learned.

✅ 7. Quick checklist

• [ ] Node.js 18+ installed
• [ ] Claude Code globally installed
• [ ] Claude-Flow installed (via npx or global)
• [ ] Initialized a project (init)
• [ ] Spawned a swarm task (architecture / code / test)
• [ ] Used memory store/query for context
• [ ] Integrated GitHub analysis/review for your repo

🚀 8. Why this matters for you

Given your work:

• Multi-agent systems & workflows: Claude-Flow gives you a framework for that (spawn many agents, orchestrate tasks).
• RAG/memory: The memory system aligns with your retrieval-augmented generation work.
• Developer pipeline: Your focus on Shopify, Node/Bull/Redis etc fits well with the architecture-first style of Claude-Flow.
• GitHub & automation: You’re building tools for PR automation — Claude-Flow already supports GitHub agents, hooks etc.