Ruflo: Multi-Agent AI Orchestration for Claude Code

Ruflo is a multi-agent AI orchestration platform for Claude Code that deploys 100+ specialized agents in coordinated swarms with self-learning memory, federated communications, and enterprise-grade security. Whether you are building autonomous coding workflows, managing cross-team agent collaboration, or running background optimization tasks, Ruflo provides the infrastructure to make AI agents work together effectively.

Understanding the Architecture

The architecture diagram above illustrates Ruflo’s layered design, where each component builds on the one below it to create a complete orchestration system.

User / Developer Layer At the top, developers interact with Ruflo through three interfaces: the CLI for terminal-based workflows, the MCP Server for integration with Claude Code and other AI tools, and the Web UI (flo.ruv.io) for browser-based multi-model chat with parallel tool calling. Each interface connects to the same orchestration engine, so commands and context stay consistent regardless of entry point.

Orchestration Layer The orchestration layer is the brain of Ruflo. It includes an intelligent router that achieves 89% accuracy in matching tasks to the right agent, plus 27 hooks that automatically intercept, route, and learn from every task. When you type a command, the router analyzes it, selects the best agent, and dispatches it through the swarm coordinator.

Swarm Coordination Ruflo supports three swarm topologies: Queen-led hierarchy (one coordinator agent dispatches work to workers), Mesh (all agents communicate peer-to-peer), and Adaptive (the system dynamically switches between topologies based on task complexity). Consensus protocols including Raft and Byzantine agreement ensure agents agree on outcomes even when some fail.

100+ Specialized Agents Below the swarm layer sit over 100 specialized agents, each designed for a specific domain: coding, testing, security auditing, documentation generation, architecture review, and more. Agents can work in parallel on different parts of a task, and the swarm coordinator ensures their outputs merge correctly.

Memory and Learning AgentDB provides persistent vector memory using HNSW indexing, delivering 150x to 12,500x faster search than brute force. SONA (Self-Optimizing Neural Architecture) captures successful patterns and feeds them back into the routing system, so the platform gets smarter with every task. ReasoningBank stores trajectory data for future reference.

LLM Providers Ruflo supports five LLM providers out of the box: Claude, GPT, Gemini, Cohere, and Ollama (for local models). Smart routing automatically selects the best provider for each task, with automatic failover if a provider is unavailable.

Agent Federation The federation layer enables agents on different machines, teams, or organizations to collaborate securely. Using mTLS and ed25519 challenge-response for identity verification, plus a 14-type PII detection pipeline, federation ensures that sensitive data never leaves your node while still enabling productive cross-boundary collaboration.

Key Features

Understanding the Features

The features diagram shows how all of Ruflo’s capabilities radiate from the central orchestration engine.

Swarm Coordination Ruflo’s swarm system supports three topologies: hierarchical (Queen-led), mesh (peer-to-peer), and adaptive (dynamic switching). The Queen topology uses Raft consensus for reliability, while mesh enables direct agent-to-agent communication for lower latency. Adaptive mode monitors task complexity and switches topologies in real time.

Vector Memory AgentDB with HNSW indexing provides persistent, sub-millisecond vector search across all agent sessions. Unlike session-only memory in vanilla Claude Code, Ruflo agents remember context across sessions, projects, and even machines. The 150x to 12,500x speed improvement over brute force search means agents retrieve relevant context instantly.

Self-Learning SONA (Self-Optimizing Neural Architecture) captures successful task patterns and feeds them back into the routing system. When an agent completes a task successfully, the trajectory is stored in ReasoningBank. Future tasks with similar characteristics are routed to the same agent type, improving accuracy from 89% baseline to even higher over time.

Agent Federation Federation enables zero-trust collaboration between agents on different machines. Remote agents start untrusted and must prove identity via mTLS and ed25519 challenge-response. A 14-type PII detection pipeline automatically strips sensitive data before it leaves your node. Behavioral trust scoring (0.4x success + 0.2x uptime + 0.2x threat + 0.2x integrity) continuously evaluates peer reliability.

Background Workers Twelve auto-triggered workers run in the background: audit, optimize, testgaps, security scan, documentation, and more. These workers monitor your codebase and proactively suggest improvements without any manual intervention.

Plugin Marketplace Ruflo offers 32 native Claude Code plugins plus 21 npm plugins covering core orchestration, memory, intelligence, code quality, security, architecture, DevOps, and domain-specific tasks. Each plugin adds focused capabilities without bloating the core system.

Multi-Provider LLM Five LLM providers are supported natively: Claude, GPT, Gemini, Cohere, and Ollama. Smart routing selects the best provider for each task, and automatic failover ensures continuity even when a provider experiences downtime.

Enterprise Security AIDefence blocks prompt injection attacks, detects PII, and scans for vulnerabilities. CVE remediation is built in, and path traversal prevention protects your filesystem. All federation traffic is encrypted and auditable for HIPAA, SOC2, and GDPR compliance.

Task Processing Workflow

Understanding the Workflow

The workflow diagram shows how a task moves through Ruflo from initial input to completed result.

Step 1: Init Running ruflo init sets up the orchestration environment, configures hooks, registers agents, and initializes the memory database. This one-time setup gives Claude Code its “nervous system” for agent coordination.

Step 2: Task Input Users describe goals in natural language. There is no need to learn specific commands or JSON schemas. Simply tell Ruflo what you want to accomplish, and the system handles the rest.

Step 3: Intelligent Router The router analyzes the task description and matches it to the best agent type. With 89% baseline accuracy (improving over time through SONA learning), the router considers task complexity, agent availability, and historical success rates to make optimal assignments.

Step 4: Swarm Dispatch The swarm coordinator assigns the task to one or more agents using the appropriate topology. Simple tasks go to a single agent. Complex tasks get decomposed and distributed across multiple agents working in parallel.

Step 5: Agent Execution Specialized agents execute their assigned subtasks. Coder agents write code, tester agents generate tests, reviewer agents analyze quality, and security agents scan for vulnerabilities. All agents can operate simultaneously.

Step 6: Memory Store Results, context, and trajectory data are stored in AgentDB with HNSW indexing. This persistent memory means agents can recall relevant information from previous sessions, projects, and even other team members’ work.

Step 7: Self-Learning SONA captures the patterns of successful task completions and stores them in ReasoningBank. The learning loop feeds back into the router, continuously improving task-agent matching accuracy.

Step 8: Result The completed task, with full context and documentation, is delivered back to the user. The feedback loop from Step 7 back to Step 3 ensures that future similar tasks are routed even more efficiently.

Installation

Claude Code Plugin (Recommended)

# Add the marketplace
/plugin marketplace add ruvnet/ruflo

# Install core + plugins you need
/plugin install ruflo-core@ruflo
/plugin install ruflo-swarm@ruflo
/plugin install ruflo-autopilot@ruflo
/plugin install ruflo-federation@ruflo

CLI Install

# One-line install
curl -fsSL https://cdn.jsdelivr.net/gh/ruvnet/ruflo@main/scripts/install.sh | bash

# Or via npx
npx ruflo@latest init --wizard

# Or install globally
npm install -g ruflo@latest

MCP Server

# Add Ruflo as an MCP server in Claude Code
claude mcp add ruflo -- npx -y @claude-flow/cli@latest

Usage

Basic Agent Orchestration

# Initialize Ruflo in your project
ruflo init

# Spawn a swarm of agents for a task
ruflo swarm spawn --task "Refactor the authentication module with tests"

# Check swarm status
ruflo swarm status

# View agent memory
ruflo memory search "authentication patterns"

Federation Setup

# Initialize federation on your machine
ruflo federation init

# Join a remote federation endpoint
ruflo federation join wss://partner.example.com:8443

# Send a task to a federated agent
ruflo federation send --to partner-team --type task-request \
  --message "Review the API changes for security issues"

# Check trust levels
ruflo federation status

Goal-Oriented Planning

# Describe a goal in plain English
ruflo goals create "Ship the payment integration with full test coverage"

# View the generated plan
ruflo goals plan

# Execute the plan with agents
ruflo goals execute

Plugin Ecosystem

Ruflo provides 32 native plugins organized by category:

Category	Plugins	Purpose
Core and Orchestration	core, swarm, autopilot, loop-workers, workflows, federation	Agent coordination and task management
Memory and Knowledge	agentdb, rag-memory, rvf, ruvector, knowledge-graph	Persistent storage and retrieval
Intelligence and Learning	intelligence, daa, ruvllm, goals	Self-improvement and planning
Code Quality and Testing	testgen, browser, jujutsu, docs	Automated quality assurance
Security and Compliance	security-audit, aidefence	Vulnerability scanning and PII protection
Architecture and Methodology	adr, ddd, sparc	Design patterns and development methodology
DevOps and Observability	migrations, observability, cost-tracker	Infrastructure and monitoring
Extensibility	wasm, plugin-creator	Sandbox and custom plugin development
Domain-Specific	iot-cognitum, neural-trader, market-data	IoT, trading, and financial data

Comparison: Claude Code With vs Without Ruflo

Capability	Claude Code Alone	With Ruflo
Agent Collaboration	Isolated, no shared context	Swarms with shared memory and consensus
Coordination	Manual orchestration	Queen-led hierarchy with Raft consensus
Memory	Session-only	HNSW vector memory with sub-ms retrieval
Learning	Static behavior	SONA self-learning with pattern matching
Task Routing	You decide	Intelligent routing (89% accuracy)
Background Workers	None	12 auto-triggered workers
LLM Providers	Anthropic only	5 providers with failover
Security	Standard	CVE-hardened with AIDefence
Cross-Machine	Not supported	Zero-trust federation with mTLS

Troubleshooting

Issue	Solution
ruflo init fails	Ensure Node.js 20+ is installed: node --version
Agents not spawning	Check swarm status with ruflo swarm status and verify MCP server is running
Memory search returns nothing	Run ruflo memory index to rebuild the HNSW index
Federation connection refused	Verify the remote endpoint is accessible and mTLS certificates are valid
Plugin install errors	Clear npm cache with npm cache clean --force and retry
High token usage	Use ruflo cost-tracker to monitor and set budget limits

Conclusion

Ruflo transforms Claude Code from a single-agent tool into a full orchestration platform. With 100+ specialized agents, swarm coordination, self-learning memory, zero-trust federation, and a rich plugin ecosystem, Ruflo enables teams to deploy AI agents that collaborate, learn, and scale across trust boundaries. The one-command init setup and natural language interface make it accessible to developers at any level, while the enterprise security features ensure production readiness.

Check out the project on GitHub: https://github.com/ruvnet/ruflo

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