🌊 RuFlo v3.5: Enterprise AI Orchestration Platform

Multi-agent AI orchestration for Claude Code

Deploy 16 specialized agent roles + custom types in coordinated swarms with self-learning capabilities, fault-tolerant consensus, and enterprise-grade security.

Why Ruflo? Claude Flow is now Ruflo — named by Ruv, who loves Rust, flow states, and building things that feel inevitable. The "Ru" is the Ruv. The "flo" is the flow. Underneath, WASM kernels written in Rust power the policy engine, embeddings, and proof system. 6,000+ commits later, this is v3.5.

Getting into the Flow

Ruflo is a comprehensive AI agent orchestration framework that transforms Claude Code into a powerful multi-agent development platform. It enables teams to deploy, coordinate, and optimize specialized AI agents working together on complex software engineering tasks.

Self-Learning/Self-Optimizing Agent Architecture

User → Ruflo (CLI/MCP) → Router → Swarm → Agents → Memory → LLM Providers
                       ↑                          ↓
                       └──── Learning Loop ←──────┘

flowchart TB
    subgraph USER["👤 User Layer"]
        U[User]
    end

    subgraph ENTRY["🚪 Entry Layer"]
        CLI[CLI / MCP Server]
        AID[AIDefence Security]
    end

    subgraph ROUTING["🧭 Routing Layer"]
        QL[Q-Learning Router]
        MOE[MoE - 8 Experts]
        SK[Skills - 130+]
        HK[Hooks - 27]
    end

    subgraph SWARM["🐝 Swarm Coordination"]
        TOPO[Topologies<br/>mesh/hier/ring/star]
        CONS[Consensus<br/>Raft/BFT/Gossip]
        CLM[Claims<br/>Human-Agent Coord]
    end

    subgraph AGENTS["🤖 100+ Agents"]
        AG1[coder]
        AG2[tester]
        AG3[reviewer]
        AG4[architect]
        AG5[security]
        AG6[...]
    end

    subgraph RESOURCES["📦 Resources"]
        MEM[(Memory<br/>AgentDB)]
        PROV[Providers<br/>Claude/GPT/Gemini/Ollama]
        WORK[Workers - 12<br/>ultralearn/audit/optimize]
    end

    subgraph RUVECTOR["🧠 RuVector Intelligence Layer"]
        direction TB
        subgraph ROW1[" "]
            SONA[SONA<br/>Self-Optimize<br/>&lt;0.05ms]
            EWC[EWC++<br/>No Forgetting]
            FLASH[Flash Attention<br/>2.49-7.47x]
        end
        subgraph ROW2[" "]
            HNSW[HNSW<br/>HNSW-indexed]
            RB[ReasoningBank<br/>Pattern Store]
            HYP[Hyperbolic<br/>Poincaré]
        end
        subgraph ROW3[" "]
            LORA[LoRA/Micro<br/>low-rank adaptation]
            QUANT[Int8 Quant<br/>3.92x memory]
            RL[9 RL Algos<br/>Q/SARSA/PPO/DQN]
        end
    end

    subgraph LEARNING["🔄 Learning Loop"]
        L1[RETRIEVE] --> L2[JUDGE] --> L3[DISTILL] --> L4[CONSOLIDATE] --> L5[ROUTE]
    end

    U --> CLI
    CLI --> AID
    AID --> QL & MOE & SK & HK
    QL & MOE & SK & HK --> TOPO & CONS & CLM
    TOPO & CONS & CLM --> AG1 & AG2 & AG3 & AG4 & AG5 & AG6
    AG1 & AG2 & AG3 & AG4 & AG5 & AG6 --> MEM & PROV & WORK
    MEM --> SONA & EWC & FLASH
    SONA & EWC & FLASH --> HNSW & RB & HYP
    HNSW & RB & HYP --> LORA & QUANT & RL
    LORA & QUANT & RL --> L1
    L5 -.->|loops back| QL

    style RUVECTOR fill:#1a1a2e,stroke:#e94560,stroke-width:2px
    style LEARNING fill:#0f3460,stroke:#e94560,stroke-width:2px
    style USER fill:#16213e,stroke:#0f3460
    style ENTRY fill:#1a1a2e,stroke:#0f3460
    style ROUTING fill:#1a1a2e,stroke:#0f3460
    style SWARM fill:#1a1a2e,stroke:#0f3460
    style AGENTS fill:#1a1a2e,stroke:#0f3460
    style RESOURCES fill:#1a1a2e,stroke:#0f3460

RuVector Components (included with Ruflo):

| Component | Purpose | Performance | |-----------|---------|-------------| | SONA | Self-Optimizing Pattern Learning - learns optimal routing | Fast adaptation | | EWC++ | Elastic Weight Consolidation - prevents catastrophic forgetting | Preserves learned patterns | | Flash Attention | Optimized attention computation | 2-7x speedup (benchmarked) | | HNSW | Hierarchical Navigable Small World vector search | Sub-millisecond retrieval | | ReasoningBank | Pattern storage with trajectory learning | RETRIEVE→JUDGE→DISTILL | | Hyperbolic | Poincare ball embeddings for hierarchical data | Better code relationships | | LoRA/MicroLoRA | Low-Rank Adaptation for efficient fine-tuning | Lightweight adaptation | | Int8 Quantization | Memory-efficient weight storage | ~4x memory reduction | | SemanticRouter | Semantic task routing with cosine similarity | Fast intent routing | | 9 RL Algorithms | Q-Learning, SARSA, A2C, PPO, DQN, Decision Transformer, etc. | Task-specific learning |

# Use RuVector via Ruflo
npx ruflo@latest hooks intelligence --status

Get Started Fast

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

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

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

New to Ruflo? You don't need to learn 310+ MCP tools or 26 CLI commands. After running init, just use Claude Code normally — the hooks system automatically routes tasks to the right agents, learns from successful patterns, and coordinates multi-agent work in the background. The advanced tools exist for fine-grained control when you need it.

Key Capabilities

🤖 100+ Specialized Agents - Ready-to-use AI agents for coding, code review, testing, security audits, documentation, and DevOps. Each agent is optimized for its specific role.

🐝 Coordinated Agent Teams - Run unlimited agents simultaneously in organized swarms. Agents spawn sub-workers, communicate, share context, and divide work automatically using hierarchical (queen/workers) or mesh (peer-to-peer) patterns.

🧠 Learns From Your Workflow - The system remembers what works. Successful patterns are stored and reused, routing similar tasks to the best-performing agents. Gets smarter over time.

🔌 Works With Any LLM - Switch between Claude, GPT, Gemini, Cohere, or local models like Llama. Automatic failover if one provider is unavailable. Smart routing picks the cheapest option that meets quality requirements.

⚡ Plugs Into Claude Code - Native integration via MCP (Model Context Protocol). Use ruflo commands directly in your Claude Code sessions with full tool access.

🔒 Production-Ready Security - Built-in protection against prompt injection, input validation, path traversal prevention, command injection blocking, and safe credential handling.

🧩 Extensible Plugin System - Add custom capabilities with the plugin SDK. Create workers, hooks, providers, and security modules. Share plugins via the decentralized IPFS marketplace.

A multi-purpose Agent Tool Kit

Every request flows through four layers: from your CLI or Claude Code interface, through intelligent routing, to specialized agents, and finally to LLM providers for reasoning.

| Layer | Components | What It Does | |-------|------------|--------------| | User | Claude Code, CLI | Your interface to control and run commands | | Orchestration | MCP Server, Router, Hooks | Routes requests to the right agents | | Agents | 100+ types | Specialized workers (coder, tester, reviewer...) | | Providers | Anthropic, OpenAI, Google, Ollama | AI models that power reasoning |

Agents organize into swarms led by queens that coordinate work, prevent drift, and reach consensus on decisions—even when some agents