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 - 42+]
        HK[Hooks - 17]
    end

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

    subgraph AGENTS["🤖 60+ 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/>150x-12,500x faster]
            RB[ReasoningBank<br/>Pattern Store]
            HYP[Hyperbolic<br/>Poincaré]
        end
        subgraph ROW3[" "]
            LORA[LoRA/Micro<br/>128x compress]
            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):

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

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.

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

Hive Mind Capabilities:

• 🐝 Queen Types: Strategic (planning), Tactical (execution), Adaptive (optimization)
• 👷 8 Worker Types: Researcher, Coder, Analyst, Tester, Architect, Reviewer, Optimizer, Documenter
• 🗳️ 3 Consensus Algorithms: Majority, Weighted (Queen 3x), Byzantine (f < n/3)
• 🧠 Collective Memory: Shared knowledge, LRU cache, SQLite persistence with WAL
• ⚡ Performance: Fast batch spawning with parallel agent coordination

The system stores successful patterns in vector memory, builds a knowledge graph for structural understanding, learns from outcomes via neural networks, and adapts routing based on what works best.

Skip expensive LLM calls for simple tasks using WebAssembly transforms, and compress tokens to reduce API costs by 30-50%.

Background daemons handle security audits, performance optimization, and session persistence automatically while you work.

Smart routing skips expensive LLM calls when possible. Simple edits use WASM (free), medium tasks use cheaper models. This can extend your Claude Code usage by 250% or save significantly on direct API costs.

Agent Booster uses WebAssembly to handle simple code transformations without calling the LLM at all. When the hooks system detects a simple task, it routes directly to Agent Booster for instant results.

Supported Transform Intents:

Hook Signals:

When you see these in hook output, the system is telling you how to optimize:

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# Agent Booster available - skip LLM entirely
[AGENT_BOOSTER_AVAILABLE] Intent: var-to-const
→ Use Edit tool directly, 352x faster than LLM

# Model recommendation for Task tool
[TASK_MODEL_RECOMMENDATION] Use model="haiku"
→ Pass model="haiku" to Task tool for cost savings

Performance:

The Token Optimizer integrates agentic-flow optimizations to reduce API costs by compressing context and caching results.

Savings Breakdown:

Usage:

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import { getTokenOptimizer } from '@claude-flow/integration';
const optimizer = await getTokenOptimizer();

// Get compact context (32% fewer tokens)
const ctx = await optimizer.getCompactContext("auth patterns");

// Optimized edit (352x faster for simple transforms)
await optimizer.optimizedEdit(file, oldStr, newStr, "typescript");

// Optimal config for swarm (100% success rate)
const config = optimizer.getOptimalConfig(agentCount);

Complex swarms can drift from their original goals. Ruflo V3 includes anti-drift defaults that prevent agents from going off-task.

Recommended Configuration:

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// Anti-drift defaults (ALWAYS use for coding tasks)
swarm_init({
  topology: "hierarchical",  // Single coordinator enforces alignment
  maxAgents: 8,              // Smaller team = less drift surface
  strategy: "specialized"    // Clear roles reduce ambiguity
})

Why This Prevents Drift:

Additional Anti-Drift Measures:

• Frequent checkpoints via post-task hooks
• Shared memory namespace for all agents
• Short task cycles with verification gates
• Hierarchical coordinator reviews all outputs

Task → Agent Routing (Anti-Drift):

Examples:

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# Minimal global install (fastest)
curl ... | bash -s -- --global --minimal

# With MCP auto-setup
curl ... | bash -s -- --global --setup-mcp

# Full setup with diagnostics
curl ... | bash -s -- --full

Speed:

Ruflo supports both Claude Code and OpenAI Codex CLI via the @claude-flow/codex package, following the Agentics Foundation standard.

Quick Start for Codex

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# Initialize for Codex CLI (creates AGENTS.md instead of CLAUDE.md)
npx ruflo@latest init --codex

# Full Codex setup with all 137+ skills
npx ruflo@latest init --codex --full

# Initialize for both platforms (dual mode)
npx ruflo@latest init --dual

Platform Comparison

Key Concept: Execution Model

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┌─────────────────────────────────────────────────────────────────┐
│  CLAUDE-FLOW = ORCHESTRATOR (tracks state, stores memory)       │
│  CODEX = EXECUTOR (writes code, runs commands, implements)      │
└─────────────────────────────────────────────────────────────────┘

Codex does the work. Claude-flow coordinates and learns.

Dual-Mode Integration (Claude Code + Codex)

Run Claude Code for interactive development and spawn headless Codex workers for parallel background tasks:

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┌─────────────────────────────────────────────────────────────────┐
│  CLAUDE CODE (interactive)  ←→  CODEX WORKERS (headless)        │
│  - Main conversation         - Parallel background execution    │
│  - Complex reasoning         - Bulk code generation            │
│  - Architecture decisions    - Test execution                   │
│  - Final integration         - File processing                  │
└─────────────────────────────────────────────────────────────────┘

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# Spawn parallel Codex workers from Claude Code
claude -p "Analyze src/auth/ for security issues" --session-id "task-1" &
claude -p "Write unit tests for src/api/" --session-id "task-2" &
claude -p "Optimize database queries in src/db/" --session-id "task-3" &
wait  # Wait for all to complete

Dual-Mode CLI Commands (NEW)

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# List collaboration templates
npx @claude-flow/codex dual templates

# Run feature development swarm (architect → coder → tester → reviewer)
npx @claude-flow/codex dual run --template feature --task "Add user auth"

# Run security audit swarm (scanner → analyzer → fixer)
npx @claude-flow/codex dual run --template security --task "src/auth/"

# Run refactoring swarm (analyzer → planner → refactorer → validator)
npx @claude-flow/codex dual run --template refactor --task "src/legacy/"

Pre-Built Collaboration Templates

MCP Integration for Codex

When you run init --codex, the MCP server is automatically registered:

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# Verify MCP is registered
codex mcp list

# If not present, add manually:
codex mcp add ruflo -- npx ruflo mcp start

Self-Learning Workflow

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1. LEARN:   memory_search(query="task keywords") → Find similar patterns
2. COORD:   swarm_init(topology="hierarchical") → Set up coordination
3. EXECUTE: YOU write code, run commands       → Codex does real work
4. REMEMBER: memory_store(key, value, namespace="patterns") → Save for future

The Intelligence Loop (ADR-050) automates this cycle through hooks. Each session automatically:

• Builds a knowledge graph from memory entries (PageRank + Jaccard similarity)
• Injects ranked context into every route decision
• Tracks edit patterns and generates new insights
• Boosts confidence for useful patterns, decays unused ones
• Saves snapshots so you can track improvement with node .claude/helpers/hook-handler.cjs stats

MCP Tools for Learning

137+ Skills Available

Vector Search Details

• Embedding Dimensions: 384
• Search Algorithm: HNSW (sub-millisecond)
• Similarity Scoring: 0-1 (higher = better)
  • Score > 0.7: Strong match, use pattern
  • Score 0.5-0.7: Partial match, adapt
  • Score < 0.5: Weak match, create new

Ruflo v3 introduces self-learning neural capabilities that no other agent orchestration framework offers. While competitors require manual agent configuration and static routing, Ruflo learns from every task execution, prevents catastrophic forgetting of successful patterns, and intelligently routes work to specialized experts.

🧠 Neural & Learning

💾 Memory & Embeddings

🐝 Swarm & Coordination

🔧 Developer Experience

🛡️ Security & Platform

_{Comparison updated February 2026. Feature availability based on public documentation.}

What makes Ruflo different from other agent frameworks? These 10 capabilities work together to create a system that learns from experience, runs efficiently on any hardware, and keeps working even when things go wrong.

Not every task needs the most powerful (and expensive) model. Ruflo analyzes each request and automatically routes it to the cheapest handler that can do the job well. Simple code transforms skip the LLM entirely using WebAssembly. Medium tasks use faster, cheaper models. Only complex architecture decisions use Opus.

Cost & Usage Benefits:

Routing Tiers:

Benchmark Results: 100% routing accuracy, 0.57ms avg routing decision latency

Complex projects fail when implementation drifts from the original plan. Ruflo solves this with a spec-first approach: define your architecture through ADRs (Architecture Decision Records), organize code into DDD bounded contexts, and let the system enforce compliance as agents work. The result is implementations that match specifications — even across multi-agent swarms working in parallel.

How It Prevents Drift:

Specification Features:

DDD Bounded Contexts:

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┌─────────────┐  ┌─────────────┐  ┌─────────────┐
│    Core     │  │   Memory    │  │  Security   │
│  Agents,    │  │  AgentDB,   │  │  AIDefence, │
│  Swarms,    │  │  HNSW,      │  │  Validation │
│  Tasks      │  │  Cache      │  │  CVE Fixes  │
└─────────────┘  └─────────────┘  └─────────────┘
┌─────────────┐  ┌─────────────┐
│ Integration │  │Coordination │
│ agentic-    │  │  Consensus, │
│ flow,MCP    │  │  Hive-Mind  │
└─────────────┘  └─────────────┘

Key ADRs:

• ADR-001: agentic-flow@alpha as foundation (eliminates 10,000+ duplicate lines)
• ADR-006: Unified Memory Service with AgentDB
• ADR-008: Vitest testing framework (10x faster than Jest)
• ADR-009: Hybrid Memory Backend (SQLite + HNSW)
• ADR-026: Intelligent 3-tier model routing
• ADR-048: Auto Memory Bridge (Claude Code ↔ AgentDB bidirectional sync)
• ADR-049: Self-Learning Memory with GNN (LearningBridge, MemoryGraph, AgentMemoryScope)

  flowchart TB
    subgraph User["👤 User Layer"]
        CC[Claude Code]
        CLI[CLI Commands]
    end

    subgraph Orchestration["🎯 Orchestration Layer"]
        MCP[MCP Server]
        Router[Intelligent Router]
        Hooks[Self-Learning Hooks]
    end

    subgraph Agents["🤖 Agent Layer"]
        Queen[Queen Coordinator]
        Workers[60+ Specialized Agents]
        Swarm[Swarm Manager]
    end

    subgraph Intelligence["🧠 Intelligence Layer"]
        SONA[SONA Learning]
        MoE[Mixture of Experts]
        HNSW[HNSW Vector Search]
    end

    subgraph Providers["☁️ Provider Layer"]
        Anthropic[Anthropic]
        OpenAI[OpenAI]
        Google[Google]
        Ollama[Ollama]
    end

    CC --> MCP
    CLI --> MCP
    MCP --> Router
    Router --> Hooks
    Hooks --> Queen
    Queen --> Workers
    Queen --> Swarm
    Workers --> Intelligence
    Intelligence --> Providers

  sequenceDiagram
    participant U as User
    participant R as Router
    participant H as Hooks
    participant A as Agent Pool
    participant M as Memory
    participant P as Provider

    U->>R: Submit Task
    R->>H: pre-task hook
    H->>H: Analyze complexity

    alt Simple Task
        H->>A: Agent Booster (WASM)
        A-->>U: Result (<1ms)
    else Medium Task
        H->>A: Spawn Haiku Agent
        A->>M: Check patterns
        M-->>A: Cached context
        A->>P: LLM Call
        P-->>A: Response
        A->>H: post-task hook
        H->>M: Store patterns
        A-->>U: Result
    else Complex Task
        H->>A: Spawn Swarm
        A->>A: Coordinate agents
        A->>P: Multiple LLM calls
        P-->>A: Responses
        A->>H: post-task hook
        A-->>U: Result
    end

  flowchart LR
    subgraph Input["📥 Input"]
        Query[Query/Pattern]
        Insight[New Insight]
    end

    subgraph Processing["⚙️ Processing"]
        Embed[ONNX Embeddings]
        Normalize[Normalization]
        Learn[LearningBridge<br/>SONA + ReasoningBank]
    end

    subgraph Storage["💾 Storage"]
        HNSW[(HNSW Index<br/>150x faster)]
        SQLite[(SQLite Cache)]
        AgentDB[(AgentDB)]
        Graph[MemoryGraph<br/>PageRank + Communities]
    end

    subgraph Retrieval["🔍 Retrieval"]
        Vector[Vector Search]
        Semantic[Semantic Match]
        Rank[Graph-Aware Ranking]
        Results[Top-K Results]
    end

    Query --> Embed
    Embed --> Normalize
    Normalize --> HNSW
    Normalize --> SQLite
    Insight --> Learn
    Learn --> AgentDB
    AgentDB --> Graph
    HNSW --> Vector
    SQLite --> Vector
    AgentDB --> Semantic
    Vector --> Rank
    Semantic --> Rank
    Graph --> Rank
    Rank --> Results

Self-Learning Memory (ADR-049):

Ruflo V3 integrates AgentDB v3 (3.0.0-alpha.10) providing 20+ memory controllers accessible via MCP tools and the CLI.

Core Memory:

Intelligence:

Causal & Explainable:

Security & Integrity:

Optimization:

MCP Tool Examples:

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# Store to hierarchical memory
agentdb_hierarchical-store --key "auth-pattern" --value "JWT refresh" --tier "semantic"

# Recall from memory tiers
agentdb_hierarchical-recall --query "authentication" --topK 5

# Run memory consolidation
agentdb_consolidate

# Batch insert
agentdb_batch --operation insert --entries '[{"key":"k1","value":"v1"}]'

# Synthesize context
agentdb_context-synthesize --query "error handling patterns"

# Semantic routing
agentdb_semantic-route --input "fix auth bug in login"

Hierarchical Memory Tiers:

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┌─────────────────────────────────────────────┐
│  Working Memory                             │  ← Active context, fast access
│  Size-based eviction (1MB limit)            │
├─────────────────────────────────────────────┤
│  Episodic Memory                            │  ← Recent patterns, moderate retention
│  Importance × retention score ranking       │
├─────────────────────────────────────────────┤
│  Semantic Memory                            │  ← Consolidated knowledge, persistent
│  Promoted from episodic via consolidation   │
└─────────────────────────────────────────────┘

  flowchart TB
    subgraph Hierarchical["👑 Hierarchical (Default)"]
        Q1[Queen] --> W1[Worker 1]
        Q1 --> W2[Worker 2]
        Q1 --> W3[Worker 3]
    end

    subgraph Mesh["🕸️ Mesh"]
        M1[Agent] <--> M2[Agent]
        M2 <--> M3[Agent]
        M3 <--> M1[Agent]
    end

    subgraph Ring["💍 Ring"]
        R1[Agent] --> R2[Agent]
        R2 --> R3[Agent]
        R3 --> R1
    end

    subgraph Star["⭐ Star"]
        S1[Hub] --> S2[Agent]
        S1 --> S3[Agent]
        S1 --> S4[Agent]
    end

  flowchart TB
    subgraph Input["📥 Input Validation"]
        Req[Request] --> Scan[AIDefence Scan]
        Scan --> PII[PII Detection]
        Scan --> Inject[Injection Check]
        Scan --> Jailbreak[Jailbreak Detection]
    end

    subgraph Decision["⚖️ Decision"]
        PII --> Risk{Risk Level}
        Inject --> Risk
        Jailbreak --> Risk
    end

    subgraph Action["🎬 Action"]
        Risk -->|Safe| Allow[✅ Allow]
        Risk -->|Warning| Sanitize[🧹 Sanitize]
        Risk -->|Threat| Block[⛔ Block]
    end

    subgraph Learn["📚 Learning"]
        Allow --> Log[Log Pattern]
        Sanitize --> Log
        Block --> Log
        Log --> Update[Update Model]
    end

Ruflo runs as an MCP (Model Context Protocol) server, allowing you to connect it to any MCP-compatible AI client. This means you can use Ruflo’s 60+ agents, swarm coordination, and self-learning capabilities from Claude Desktop, VS Code, Cursor, Windsurf, ChatGPT, and more.

Quick Add Command

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# Start Ruflo MCP server in any environment
npx ruflo@v3alpha mcp start

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{
  "mcpServers": {
    "ruflo": {
      "command": "npx",
      "args": ["ruflo@v3alpha", "mcp", "start"],
      "env": {
        "ANTHROPIC_API_KEY": "sk-ant-..."
      }
    }
  }
}

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# Add via CLI (recommended)
claude mcp add ruflo -- npx ruflo@v3alpha mcp start

# Or add with environment variables
claude mcp add ruflo \
  --env ANTHROPIC_API_KEY=sk-ant-... \
  -- npx ruflo@v3alpha mcp start

# Verify installation
claude mcp list

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{
  "mcpServers": {
    "ruflo": {
      "command": "npx",
      "args": ["ruflo@v3alpha", "mcp", "start"],
      "env": {
        "ANTHROPIC_API_KEY": "sk-ant-..."
      }
    }
  }
}

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{
  "mcpServers": {
    "ruflo": {
      "command": "npx",
      "args": ["ruflo@v3alpha", "mcp", "start"],
      "env": {
        "ANTHROPIC_API_KEY": "sk-ant-..."
      }
    }
  }
}

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{
  "mcpServers": {
    "ruflo": {
      "command": "npx",
      "args": ["ruflo@v3alpha", "mcp", "start"],
      "env": {
        "ANTHROPIC_API_KEY": "sk-ant-..."
      }
    }
  }
}

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# Start with HTTP transport
npx ruflo@v3alpha mcp start --transport http --port 3000

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import { GoogleGenAI } from '@google/genai';

const ai = new GoogleGenAI({ apiKey: 'YOUR_API_KEY' });

// MCP definitions are natively supported in the Gen AI SDK
const mcpConfig = {
  servers: [{
    name: 'ruflo',
    command: 'npx',
    args: ['ruflo@v3alpha', 'mcp', 'start']
  }]
};

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{
  "name": "ruflo",
  "command": "npx",
  "args": ["ruflo@v3alpha", "mcp", "start"]
}

Environment Variables

All configurations support these environment variables:

MCP Tool Groups

Control which MCP tools are loaded to reduce latency and token usage:

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# Enable specific tool groups
export CLAUDE_FLOW_TOOL_GROUPS=implement,test,fix,memory

# Or use a preset mode
export CLAUDE_FLOW_TOOL_MODE=develop

Available Groups: create, issue, branch, implement, test, fix, optimize, monitor, security, memory, all, minimal

Preset Modes:

Precedence: CLI args (--tools=X) > Environment vars > Config file > Default (all)

Security Best Practices

⚠️ Never hardcode API keys in config files checked into version control.

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# Use environment variables instead
export ANTHROPIC_API_KEY="sk-ant-..."

# Or use a .env file (add to .gitignore)
echo "ANTHROPIC_API_KEY=sk-ant-..." >> .env

Overview

@claude-flow/guidance turns CLAUDE.md into a runtime governance system with enforcement gates, cryptographic proofs, and feedback loops. Agents that normally drift after 30 minutes can now operate for days — rules are enforced mechanically at every step, not remembered by the model.

7-phase pipeline: Compile → Retrieve → Enforce → Trust → Prove → Defend → Evolve

Install

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npm install @claude-flow/guidance@alpha

Quick Usage

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import {
  createCompiler,
  createRetriever,
  createGates,
  createLedger,
  createProofChain,
} from '@claude-flow/guidance';

// Compile CLAUDE.md into a policy bundle
const compiler = createCompiler();
const bundle = await compiler.compile(claudeMdText);

// Retrieve task-relevant rules
const retriever = createRetriever();
await retriever.loadBundle(bundle);
const { shards, policyText } = await retriever.retrieve({
  taskDescription: 'Fix authentication bug in login flow',
});

// Enforce gates on tool calls
const gates = createGates(bundle);
const result = gates.evaluate({ tool: 'bash', args: { command: 'rm -rf /' } });
// result.blocked === true

// Audit with proof chain
const chain = createProofChain({ signingKey: process.env.PROOF_KEY! });
const envelope = chain.seal(runEvent);
chain.verify(envelope); // true — tamper-evident

Key Modules

Stats

• 1,331 tests across 26 test files
• 27 subpath exports for tree-shaking
• WASM kernel for security-critical hot paths (gates, proof, scoring)
• 25 ADRs documenting every architectural decision

Documentation

• Architecture Overview
• Getting Started
• Enforcement Gates Tutorial
• Proof Audit Trail
• Multi-Agent Security
• API Quick Reference
• Full README

Comprehensive feature set for enterprise-grade AI agent orchestration.

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npx ruflo hive-mind init                    # Initialize hive mind
npx ruflo hive-mind spawn "Build API"       # Spawn with objective
npx ruflo hive-mind spawn "..." --queen-type strategic --consensus byzantine
npx ruflo hive-mind status                  # Check status
npx ruflo hive-mind metrics                 # Performance metrics
npx ruflo hive-mind memory                  # Collective memory stats
npx ruflo hive-mind sessions                # List active sessions

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# Automatically enabled with ruflo init
npx ruflo@latest init

# Or manually add to .claude/settings.json
{
  "env": {
    "CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS": "1"
  }
}

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// Create a team
TeamCreate({ team_name: "feature-dev", description: "Building feature" })

// Create shared tasks
TaskCreate({ subject: "Design API", description: "..." })
TaskCreate({ subject: "Implement endpoints", description: "..." })

// Spawn teammates (parallel background work)
Task({ prompt: "Work on task #1...", subagent_type: "architect",
       team_name: "feature-dev", name: "architect", run_in_background: true })
Task({ prompt: "Work on task #2...", subagent_type: "coder",
       team_name: "feature-dev", name: "developer", run_in_background: true })

// Message teammates
SendMessage({ type: "message", recipient: "developer",
              content: "Prioritize auth", summary: "Priority update" })

// Cleanup when done
SendMessage({ type: "shutdown_request", recipient: "developer" })
TeamDelete()

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# Handle idle teammate
npx ruflo@latest hooks teammate-idle --auto-assign true

# Handle task completion
npx ruflo@latest hooks task-completed --task-id <id> --train-patterns

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# Install Quality Engineering plugin
npm install @claude-flow/plugin-agentic-qe

# Install AI Interpretability plugin
npm install @claude-flow/plugin-prime-radiant

# Install Gas Town Bridge plugin (WASM-accelerated orchestration)
npx ruflo@latest plugins install -n @claude-flow/plugin-gastown-bridge

# Install domain-specific plugins
npm install @claude-flow/plugin-healthcare-clinical
npm install @claude-flow/plugin-financial-risk
npm install @claude-flow/plugin-legal-contracts

# Install development intelligence plugins
npm install @claude-flow/plugin-code-intelligence
npm install @claude-flow/plugin-test-intelligence
npm install @claude-flow/plugin-perf-optimizer

# Install advanced AI/reasoning plugins
npm install @claude-flow/plugin-neural-coordination
npm install @claude-flow/plugin-cognitive-kernel
npm install @claude-flow/plugin-quantum-optimizer
npm install @claude-flow/plugin-hyperbolic-reasoning

# List all installed plugins
npx ruflo plugins list --installed

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import { createRuVectorBridge } from '@claude-flow/plugins';

const bridge = createRuVectorBridge({
  host: 'localhost',
  port: 5432,
  database: 'vectors',
  user: 'postgres',
  password: 'secret',
  pool: { min: 2, max: 10 },
  ssl: true
});

// Enable the plugin
await registry.register(bridge);
await registry.loadAll();

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import { createHyperbolicSpace } from '@claude-flow/plugins';

const space = createHyperbolicSpace('poincare', { curvature: -1.0 });

// Embed hierarchical data (trees, taxonomies)
const embedding = await space.embed(vector);
const distance = await space.distance(v1, v2);  // Geodesic distance
const midpoint = await space.geodesicMidpoint(v1, v2);

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import { createSelfLearningSystem } from '@claude-flow/plugins';

const learning = createSelfLearningSystem(bridge);

// Automatic optimization
await learning.startLearningLoop();  // Runs in background

// Manual optimization
const suggestions = await learning.queryOptimizer.analyze(query);
await learning.indexTuner.tune('my_index');

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npx ruflo@v3alpha worker dispatch --trigger audit --context "./src"
npx ruflo@v3alpha worker status

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# Initialize ONNX embeddings with hyperbolic config
ruflo embeddings init

# Use larger model for higher quality
ruflo embeddings init --model all-mpnet-base-v2

# Semantic search
ruflo embeddings search -q "authentication patterns"

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# Initialize RuVector in PostgreSQL
ruflo ruvector init --database mydb --user admin

# Check connection and schema status
ruflo ruvector status --verbose

# Run pending migrations
ruflo ruvector migrate --up

# Performance benchmark
ruflo ruvector benchmark --iterations 1000

# Optimize indices and vacuum
ruflo ruvector optimize --analyze

# Backup vector data
ruflo ruvector backup --output ./backup.sql

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npx ruflo hive-mind init                                    # Initialize
npx ruflo hive-mind spawn "Build API" --queen-type tactical # Spawn swarm
npx ruflo hive-mind spawn "Research AI" --consensus byzantine --claude
npx ruflo hive-mind status                                  # Check status

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▊ Ruflo V3 ● ruvnet  │  ⎇ main  │  Opus 4.6  | ●42% ctx  | $0.15
🏗️ DDD [●●●●○] 4/5  ⚡ HNSW 150x  🤖 ◉ [12/8]  👥 3  🟢 CVE 3/3  💾 512MB  🧠 15%  📦 AgentDB ●1.2K vectors

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{
  "statusLine": {
    "type": "command",
    "command": "node .claude/helpers/statusline.cjs"
  }
}

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npx ruflo@latest init --update --settings

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{
  "model": { "display_name": "Opus 4.6" },
  "context_window": { "used_percentage": 42, "remaining_percentage": 58 },
  "cost": { "total_cost_usd": 0.15, "total_duration_ms": 45000 },
  "workspace": { "current_dir": "/path/to/project" },
  "session_id": "abc-123"
}

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# Start daemon (auto-runs on SessionStart hooks)
npx ruflo@v3alpha daemon start

# Check status with worker history
npx ruflo@v3alpha daemon status

# Manually trigger a worker
npx ruflo@v3alpha daemon trigger map

# Enable/disable workers
npx ruflo@v3alpha daemon enable map audit optimize

# Stop daemon
npx ruflo@v3alpha daemon stop

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+-- Worker Daemon ---+
| Status: ● RUNNING  |
| PID: 12345         |
| Workers Enabled: 5 |
| Max Concurrent: 3  |
+--------------------+

Worker Status
+-------------+----+----------+------+---------+----------+----------+
| Worker      | On | Status   | Runs | Success | Last Run | Next Run |
+-------------+----+----------+------+---------+----------+----------+
| map         | ✓  | idle     | 12   | 100%    | 2m ago   | in 3m    |
| audit       | ✓  | idle     | 6    | 100%    | 5m ago   | in 5m    |
| optimize    | ✓  | running  | 4    | 100%    | now      | -        |
| consolidate | ✓  | idle     | 2    | 100%    | 15m ago  | in 15m   |
| testgaps    | ✓  | idle     | 3    | 100%    | 8m ago   | in 12m   |
+-------------+----+----------+------+---------+----------+----------+

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# Start all daemons
.claude/helpers/daemon-manager.sh start 3 5

# Check daemon status
.claude/helpers/daemon-manager.sh status

# Stop all daemons
.claude/helpers/daemon-manager.sh stop

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# Start worker manager
.claude/helpers/worker-manager.sh start 60

# Force run all workers immediately
.claude/helpers/worker-manager.sh force

# Check worker status
.claude/helpers/worker-manager.sh status

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# Initialize project with wizard
npx ruflo@v3alpha init --wizard

# Start daemon with background workers
npx ruflo@v3alpha daemon start

# Spawn an agent with specific type
npx ruflo@v3alpha agent spawn -t coder --name my-coder

# Initialize swarm with V3 mode
npx ruflo@v3alpha swarm init --v3-mode

# Search memory (HNSW-indexed, 150x faster)
npx ruflo@v3alpha memory search -q "authentication patterns"

# Run security scan
npx ruflo@v3alpha security scan --depth full

# Performance benchmark
npx ruflo@v3alpha performance benchmark --suite all