Ruflo v3 is an enterprise AI orchestration platform for Claude-based multi-agent swarms, closely related to Claude Flow. It features an inter-agent consensus algorithm, vector database integration for persistent memory, self-learning workflows, and native Claude Code SDK integration. Designed for deploying autonomous agent pipelines at scale with shared state management.

ruflo

Active·★ 74.1k·MIT·Updated 2026-10-08

★ Most Popular★ Multi-Agent★ Workflow Automation

Ruflo (formerly Claude Flow) is an agent "meta-harness" for Claude Code and Codex. Its premise is that an agent is a model plus a harness, and Ruflo is the harness layer: it adds specialised agents, commands, skills, hooks and an MCP server so that agents can organise into swarms, remember across sessions and learn from earlier runs, while you keep working in Claude Code as usual.

It is aimed at heavy users of Claude Code or Codex who want more structure around long or parallel work: coordinating several agents on one task, tracking cost and tokens, and reviewing what each agent did. A visual console shows workflows, agents, models and cost, lets you replay and compare runs, and can be driven by Claude itself within permission levels you set (read, write, manage or full, with ask or auto approval). Federation lets agents on different machines communicate.

Getting started: in Claude Code 2.1.287 or later, add the `ruvnet/ruflo` plugin marketplace and install `ruflo-core` (plus the console and mods if wanted). For the full setup with hooks and the MCP server, run `npx ruflo@latest init wizard` in your project; other MCP clients can launch `npx -y ruflo@latest mcp start`.

#AI Orchestration#Multi-Agent System#Self-Learning AI#Claude Code Integration#Vector Database#Consensus Algorithms#WebAssembly (WASM)#Distributed Systems

Features

01Adds a large set of specialised agents, commands, skills, hooks and an MCP server around Claude Code and Codex

02Agent swarms that self-organise and coordinate in the background through hooks

03Memory across sessions with local vector retrieval and learning from past runs

04Visual console to track workflows, agents, models, tokens and cost, with replay of runs

05Federation for agents on different machines to communicate

Why choose it

+Deep integration with Claude Code through plugins, hooks and a console, plus MCP support for Codex and other clients.

+Multi-agent coordination and cross-session memory without changing how you use Claude Code day to day.

+Console with cost and token tracking, run replay and adjustable control levels for the agent.

+Very actively developed and MIT licensed.

Trade-offs

!The full CLI install adds many files and hooks to your workspace (`.claude/`, `.claude-flow/`, CLAUDE.md and more); the plugin path is lighter but more limited.

!Mods run with your account's permissions and are not sandboxed; the project advises reading their code before installing.

!A very large surface (hundreds of MCP tools, many commands and agents) means a steep learning curve and more context overhead.

!Closely tied to Claude Code; hook support varies in other clients.

Compatibility

Claude Code

Native

Verified via docs

Node.js

Verified via docs

Quick start

$ npx ruflo@latest init wizard

Use cases

↳Running parallel Claude agent swarms for large-scale document processing

↳Building autonomous workflows that require inter-agent coordination and consensus

↳Deploying production AI pipelines with persistent vector memory across sessions

How it compares

≈vs CrewAI: CrewAI is a general Python framework for building multi-agent applications; Ruflo orchestrates coding agents inside Claude Code and Codex.

≈vs AWS CLI Agent Orchestrator: both coordinate multiple coding CLIs; Ruflo adds memory, learning and a console, while the AWS project focuses on isolated tmux sessions.

≈vs Headroom: Headroom reduces the tokens an agent reads; Ruflo adds orchestration and memory around the agent.

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ruflo

Active·★ 74.1k·MIT·Updated 2026-10-08

★ Most Popular★ Multi-Agent★ Workflow Automation

#AI Orchestration#Multi-Agent System#Self-Learning AI#Claude Code Integration#Vector Database#Consensus Algorithms#WebAssembly (WASM)#Distributed Systems

Features

01Adds a large set of specialised agents, commands, skills, hooks and an MCP server around Claude Code and Codex

02Agent swarms that self-organise and coordinate in the background through hooks

03Memory across sessions with local vector retrieval and learning from past runs

04Visual console to track workflows, agents, models, tokens and cost, with replay of runs

05Federation for agents on different machines to communicate

Why choose it

+Deep integration with Claude Code through plugins, hooks and a console, plus MCP support for Codex and other clients.

+Multi-agent coordination and cross-session memory without changing how you use Claude Code day to day.

+Console with cost and token tracking, run replay and adjustable control levels for the agent.

+Very actively developed and MIT licensed.

Trade-offs

!The full CLI install adds many files and hooks to your workspace (`.claude/`, `.claude-flow/`, CLAUDE.md and more); the plugin path is lighter but more limited.

!Mods run with your account's permissions and are not sandboxed; the project advises reading their code before installing.

!A very large surface (hundreds of MCP tools, many commands and agents) means a steep learning curve and more context overhead.

!Closely tied to Claude Code; hook support varies in other clients.

Compatibility

Claude Code

Native

Verified via docs

Node.js

Verified via docs

Quick start

$ npx ruflo@latest init wizard

Use cases

↳Running parallel Claude agent swarms for large-scale document processing

↳Building autonomous workflows that require inter-agent coordination and consensus

↳Deploying production AI pipelines with persistent vector memory across sessions

How it compares

≈vs CrewAI: CrewAI is a general Python framework for building multi-agent applications; Ruflo orchestrates coding agents inside Claude Code and Codex.

≈vs AWS CLI Agent Orchestrator: both coordinate multiple coding CLIs; Ruflo adds memory, learning and a console, while the AWS project focuses on isolated tmux sessions.

≈vs Headroom: Headroom reduces the tokens an agent reads; Ruflo adds orchestration and memory around the agent.