Ruflo: The Leading Agent Orchestration Platform for Claude

Key Takeaways: Ruflo turns Claude Code into a production-ready multi-agent operating system, combining swarm intelligence, RAG, and native MCP tooling in a single orchestration layer.

What is Ruflo?

Ruflo is an enterprise-grade AI agent orchestration platform built specifically to make Claude Code and related tooling the control plane for intelligent multi-agent systems. It allows teams to deploy, coordinate, and optimize swarms of specialized agents that collaborate on complex software engineering and knowledge work tasks. Backed by Cognitum One and released under a permissive open-source license, Ruflo is designed to be self-hostable and production-ready for startups and large enterprises alike.

Why Ruflo matters for Claude builders

Modern AI work increasingly depends on orchestration rather than single prompts, and Ruflo positions itself as the coordination layer that makes agentic systems truly operational. Instead of manually wiring tools, models, and workflows together, developers define agents, swarms, and policies; Ruflo then handles routing, fault-tolerant consensus, and continuous learning from prior executions. Because it integrates natively with Claude Code and OpenAI Codex while also supporting other providers like OpenAI, Google, xAI, Mistral, and local models via Ollama, it avoids vendor lock-in while still feeling “Claude-native” for day-to-day development.

Core capabilities at a glance

Ruflo’s feature set goes well beyond a simple wrapper around LLM APIs.

• Multi-agent swarms with hierarchical and mesh topologies, enabling 60+ specialized agents to collaborate on large tasks in parallel.
• Distributed swarm intelligence with self-learning memory, allowing the system to adapt routing and behavior based on previous task outcomes.
• Advanced RAG (retrieval-augmented generation) and RuVector PostgreSQL integration, combining vector search, knowledge graphs, and collective memory.
• 170+ MCP tools exposed directly into Claude Code for orchestration, governance, diagnostics, Git, security, and more.
• Multi-provider LLM support with intelligent tiered routing to mix Claude, GPT, Gemini, local ONNX/Ollama models, and more under one control plane.
• Built-in hardening against common GenAI security issues such as prompt injection, unsafe command execution, and unsafe filesystem access.

These capabilities make Ruflo especially attractive for teams that want to treat AI agents as a durable, governed platform rather than a series of ad-hoc scripts.

Architecture and swarm intelligence

Under the hood, Ruflo uses an enterprise-grade architecture that treats agents as distributed workers coordinated by a central policy and memory layer. WASM kernels written in Rust power performance-sensitive components such as the policy engine, embedding operations, and parts of the proof and routing system. The platform supports hierarchical “queen and swarm” patterns, fault-tolerant consensus between agents, and self-learning mechanisms that prevent catastrophic forgetting of successful execution paths.

For knowledge-intensive workloads, Ruflo ties in RuVector, a PostgreSQL-based vector and graph subsystem that offers HNSW vector search, 70+ SQL functions, knowledge graph analysis (including PageRank and community detection), and shared collective memory across agents. This gives swarms access to fast semantic search, cross-session memory, and structure-aware reasoning over codebases and documents without relying on a proprietary hosted vector database.

Prerequisites and supported environments

Ruflo is distributed as a Node.js-based CLI and toolbox, and it requires Node.js 20 or newer for full functionality. It runs on all major desktop and server platforms, including Windows, macOS, and Linux, which makes it straightforward to use on laptops, CI servers, or self-hosted infrastructure. For deep IDE integration, Ruflo is typically paired with Claude Code or Claude Desktop, but it can also be wired into other environments such as VS Code, Cursor, Windsurf, JetBrains IDEs, and the OpenAI Codex CLI via MCP and companion packages.

Because Ruflo is just a Node-based tool, it can be containerized easily with Docker and deployed on Kubernetes or similar platforms when teams want to run long-lived agent swarms or shared orchestration backends. Many organizations pair it with cloud-native stacks and GitOps tools for enterprise deployments, but solo developers can just as easily run it locally via npx without any infrastructure overhead.

Installing Ruflo: fastest paths

Ruflo supports several installation paths so developers can get started quickly while still scaling up to more controlled setups later.

One-line curl installer (recommended for full setup)

For a turnkey setup that configures global binaries, MCP integration, and diagnostics, Ruflo exposes a curl-based installer:

# Standard install
curl -fsSL https://cdn.jsdelivr.net/gh/ruvnet/claude-flow@main/scripts/install.sh | bash

# Full install with MCP + diagnostics
curl -fsSL https://cdn.jsdelivr.net/gh/ruvnet/claude-flow@main/scripts/install.sh | bash -s -- --full

This script bootstraps the Ruflo toolchain, aligns versions across packages, and can optionally configure MCP servers automatically for Claude Code.

Quick start via npx (no global install)

If a global install is not desired, the easiest entry point is npx:

# Initialize a new Ruflo project
npx ruflo@latest init

# Or start with a guided wizard
npx ruflo@latest init --wizard

Using npx keeps everything project-local while still giving access to the full Ruflo CLI, and it works well inside disposable environments or cloud workspaces. For developers who prefer Bun, an equivalent bunx ruflo@latest init workflow is also supported.

Global npm install

For teams standardizing on Ruflo across many repositories, a global installation is often more convenient:

# Install Ruflo globally
npm install -g ruflo@latest

# Initialize a project using the global binary
ruflo init

There is also a “minimal” flavor that skips optional ML/embedding packages for lightweight environments:

npm install -g ruflo@latest --omit=optional

This approach works well in long-lived containers or developer images where the Ruflo CLI should always be available.

Integrating Ruflo with Claude Code and Codex

The real power of Ruflo appears once it is wired directly into Claude Code via the Model Context Protocol (MCP). After installing the CLI, add Ruflo as an MCP server so that all of its agents and tools show up inside Claude Code sessions:

# Register Ruflo as an MCP server for Claude Code
claude mcp add ruflo -- npx -y ruflo@latest mcp start

# Verify that Ruflo is listed
claude mcp list

This makes more than 170 Ruflo MCP tools and 60+ agents available directly inside Claude Code, including swarm management commands, RAG tools, diagnostics, and governance utilities. Ruflo can also integrate with the OpenAI Codex CLI using similar patterns, for example:

# Initialize a Ruflo project for Codex
npx ruflo@latest init --codex

# Or enable both Claude Code and Codex
npx ruflo@latest init --dual

# Add Ruflo as an MCP server for Codex
codex mcp add ruflo -- npx ruflo mcp start

This dual-mode setup is particularly useful for teams that want to orchestrate agents across multiple LLM providers from a single codebase.

Creating your first multi-agent swarm

Once installed and connected to Claude Code, spinning up a simple swarm-based workflow with Ruflo only requires a few commands.

# Initialize project metadata and default config
npx ruflo@latest init

# Start the MCP server for IDE integration
npx ruflo@latest mcp start

# List available agents
npx ruflo@latest --list

# Run a task with a specific agent
npx ruflo@latest --agent coder --task "Implement user authentication"

The init step scaffolds configuration files such as CLAUDE.md or AGENTS.md, defines default agents, and wires in recommended skills. Starting the MCP server then exposes those capabilities to Claude Code, while the --agent and --task flags let developers invoke targeted agents or swarms from the CLI for batch-style workflows.

Inside Claude Code, the same Ruflo agents become available as tools or slash commands that the model can call autonomously as it works through development tasks. This tight feedback loop—where Claude plans work and Ruflo agents execute it—underpins many of the productivity gains reported by early adopters.

RAG and knowledge workflows with RuVector

For teams building knowledge-heavy systems, Ruflo’s RAG and RuVector capabilities are a major differentiator.

• RuVector provides a PostgreSQL-backed vector store with HNSW indexing, offering sub-millisecond semantic search and high QPS throughput for large codebases and document sets.
• A knowledge graph layer applies algorithms like PageRank and community detection to identify influential nodes, concepts, or files, enabling more targeted reasoning.
• Collective memory stores cross-session learnings and insights, shared across agents but bounded by LRU caches and persistence strategies to control growth.

Combined with Ruflo’s swarm orchestration, these features enable workflows where one set of agents curates and indexes knowledge while others consume that knowledge to design architectures, write code, or answer complex questions with strong grounding in project-specific data.

Example use cases for Ruflo

Because Ruflo is both Claude-native and multi-provider, it supports a wide range of real-world use cases.

• Full-stack software delivery: Specialized agents handle requirements clarification, architecture design, implementation, testing, and documentation, all coordinated as a governed swarm.
• Legacy code modernization: Swarms can map and refactor large monoliths into services, using RuVector to understand call graphs and dependency clusters.
• Data and analytics workflows: Agents orchestrate ETL jobs, data quality checks, and analytics notebook generation, combining RAG over warehouse schemas with LLM-powered documentation.
• Enterprise knowledge assistants: Ruflo acts as the backend for conversational agents that draw on internal wikis, ticket systems, and code repositories, providing explainable, grounded responses through RAG.

In each of these cases, Ruflo’s value comes from aligning orchestration, memory, and tooling rather than from any single model or prompt.

Where to go next

Developers who want to go deeper with Ruflo should start by initializing a small project with npx ruflo@latest init --wizard, wiring it into Claude Code via claude mcp add ruflo -- npx -y ruflo@latest mcp start, and experimenting with a few of the built-in agents on a real repository. From there, teams can layer on RuVector-backed RAG, custom agents, and more advanced swarm patterns as their comfort grows.

Between its open-source model, deep Claude integration, and focus on production-grade swarm intelligence, Ruflo has quickly become one of the most important tools for anyone serious about building AI-native development workflows on top of Claude and other leading LLMs.