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Ruflo - Enterprise AI agent orchestration for Claude Code. Deploy 60+ specialized agents in coordinated swarms with self-learning, fault-tolerant consensus, vector memory, and MCP integration

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    Readme

    Ruflo Banner

    Try the UI Beta β€” flo.ruv.io Goal Planner β€” goal.ruv.io Live Agents β€” goal.ruv.io/agents

    npm version (ruflo) npm downloads (ruflo) npm version (claude-flow) npm downloads (claude-flow)

    Star on GitHub MIT License Claude Code Codex Plugin πŸ•ΈοΈ RuVector Graph Ai

    ruFlo Summit β€” Budapest, June 2–3, 2026

    Ruflo

    Multi-agent AI orchestration for Claude Code

    Orchestrate 100+ specialized AI agents across machines, teams, and trust boundaries. Ruflo adds coordinated swarms, self-learning memory, federated comms, and enterprise security to Claude Code β€” so agents don't just run, they collaborate.

    Why Ruflo?

    Claude Flow is now Ruflo β€” named by rUv, who loves Rust, flow states, and building things that feel inevitable. The "Ru" is the rUv. The "flo" is working until 3am. Underneath, powered by Cognitum.One agentic architecture, running a supercharged Rust based AI engine, embeddings, memory, and plugin system.

    What Ruflo Does

    One npx ruvflo init gives Claude Code a nervous system: agents self-organize into swarms, learn from every task, remember across sessions, and β€” with federation β€” securely talk to agents on other machines without leaking data. You keep writing code. Ruflo handles the coordination.

    Self-Learning / Self-Optimizing Agent Architecture
    
    User --> Ruflo (CLI/MCP) --> Router --> Swarm --> Agents --> Memory --> LLM Providers
                              ^                           |
                              +---- Learning Loop <-------+

    New to Ruflo? You don't need to learn 314 MCP tools or 26 CLI commands. After init, just use Claude Code normally -- the hooks system automatically routes tasks, learns from successful patterns, and coordinates agents in the background.


    Ruflo Plugins

    Quick Start

    There are two different install paths with very different surface areas. Pick based on what you need (#1744):

    Claude Code Plugin CLI install (npx ruflo init)
    What it gives you Slash commands + a few skills + agent definitions per-plugin Full Ruflo loop β€” 98 agents, 60+ commands, 30 skills, MCP server, hooks, daemon
    Files in your workspace Zero .claude/, .claude-flow/, CLAUDE.md, helpers, settings
    MCP server registered No (memory_store, swarm_init, etc. unavailable to Claude) Yes
    Hooks installed No Yes
    Best for Try a single plugin's commands without committing to the full install Production use β€” everything works as documented

    Path A β€” Claude Code Plugins (lite, slash commands only)

    # Add the marketplace
    /plugin marketplace add ruvnet/ruflo
    
    # Install core + any plugins you need
    /plugin install ruflo-core@ruflo
    /plugin install ruflo-swarm@ruflo
    /plugin install ruflo-autopilot@ruflo
    /plugin install ruflo-federation@ruflo

    This adds slash commands and agent definitions only. The Ruflo MCP server is NOT registered, so memory_store, swarm_init, agent_spawn, etc. won't be callable from Claude. For the full loop, use Path B below.

    πŸ”Œ All 32 plugins

    Core & Orchestration

    Plugin What it does
    ruflo-core Foundation β€” server, health checks, plugin discovery
    ruflo-swarm Coordinate multiple agents as a team
    ruflo-autopilot Let agents run autonomously in a loop
    ruflo-loop-workers Schedule background tasks on a timer
    ruflo-workflows Reusable multi-step task templates
    ruflo-federation Agents on different machines collaborate securely

    Memory & Knowledge

    Plugin What it does
    ruflo-agentdb Fast vector database for agent memory
    ruflo-rag-memory Smart retrieval β€” hybrid search, graph hops, diversity ranking
    ruflo-rvf Save and restore agent memory across sessions
    ruflo-ruvector ruvector β€” GPU-accelerated search, Graph RAG, 103 tools
    ruflo-knowledge-graph Build and traverse entity relationship maps

    Intelligence & Learning

    Plugin What it does
    ruflo-intelligence Agents learn from past successes and get smarter
    ruflo-daa Dynamic agent behavior and cognitive patterns
    ruflo-ruvllm Run local LLMs (Ollama, etc.) with smart routing
    ruflo-goals Break big goals into plans and track progress

    Code Quality & Testing

    Plugin What it does
    ruflo-testgen Find missing tests and generate them automatically
    ruflo-browser Automate browser testing with Playwright
    ruflo-jujutsu Analyze git diffs, score risk, suggest reviewers
    ruflo-docs Generate and maintain documentation automatically

    Security & Compliance

    Plugin What it does
    ruflo-security-audit Scan for vulnerabilities and CVEs
    ruflo-aidefence Block prompt injection, detect PII, safety scanning

    Architecture & Methodology

    Plugin What it does
    ruflo-adr Track architecture decisions with a living record
    ruflo-ddd Scaffold domain-driven design β€” contexts, aggregates, events
    ruflo-sparc Guided 5-phase development methodology with quality gates

    DevOps & Observability

    Plugin What it does
    ruflo-migrations Manage database schema changes safely
    ruflo-observability Structured logs, traces, and metrics in one place
    ruflo-cost-tracker Track token usage, set budgets, get cost alerts

    Extensibility

    Plugin What it does
    ruflo-agent Run agents β€” local WASM sandbox (rvagent) + Anthropic Claude Managed Agents (cloud)
    ruflo-plugin-creator Scaffold, validate, and publish your own plugins

    Domain-Specific

    Plugin What it does
    ruflo-iot-cognitum IoT device management β€” trust scoring, anomaly detection, fleets
    ruflo-neural-trader neural-trader β€” AI trading with 4 agents, backtesting, 112+ tools
    ruflo-market-data Ingest market data, vectorize OHLCV, detect patterns

    CLI Install

    macOS / Linux / WSL / Git-Bash:

    # One-line install (POSIX shells only β€” see Windows note below)
    curl -fsSL https://cdn.jsdelivr.net/gh/ruvnet/ruflo@main/scripts/install.sh | bash

    All platforms (including native Windows PowerShell / cmd):

    # Interactive setup wizard β€” runs identically on every platform
    npx ruflo@latest init wizard
    
    # Quick non-interactive init
    # npx ruflo@latest init
    
    # Or install globally
    npm install -g ruflo@latest

    πŸ’‘ Windows users: the curl ... | bash form needs a POSIX shell (Git-Bash, WSL, MSYS). The npx ruflo@latest init wizard line works natively in PowerShell and cmd. If you hit an 'bash' is not recognized error, use the npx line instead β€” both end up running the same init flow.

    MCP Server

    # Add Ruflo as an MCP server in Claude Code (canonical form, matches USERGUIDE.md)
    claude mcp add ruflo -- npx ruflo@latest mcp start

    What You Get

    Capability Description
    πŸ€– 100+ Agents Specialized agents for coding, testing, security, docs, architecture
    πŸ“‘ Comms Layer Zero-trust federation β€” agents across machines/orgs discover, authenticate, and exchange work securely
    🐝 Swarm Coordination Hierarchical, mesh, and adaptive topologies with consensus
    🧠 Self-Learning SONA neural patterns, ReasoningBank, trajectory learning
    πŸ’Ύ Vector Memory HNSW-indexed AgentDB with 150x-12,500x faster search
    ⚑ Background Workers 12 auto-triggered workers (audit, optimize, testgaps, etc.)
    🧩 Plugin Marketplace 32 native Claude Code plugins + 21 npm plugins
    πŸ”Œ Multi-Provider Claude, GPT, Gemini, Cohere, Ollama with smart routing
    πŸ›‘οΈ Security AIDefence, input validation, CVE remediation, path traversal prevention
    🌐 Agent Federation Cross-installation agent collaboration with zero-trust security
    πŸ’¬ Web UI Beta Multi-model chat at flo.ruv.io with parallel MCP tool calling and an in-browser WASM tool gallery
    🎯 RuFlo Research GOAP A* planner at goal.ruv.io β€” plain-English goals β†’ executable agent plans, with a live agent dashboard at /agents

    RuFlo Web UI executing parallel MCP tool calls at flo.ruv.io β€” ruflo__memory_store and ruflo__memory_search firing in a single model turn with the 'Step 1 β€” 2 tools completed' parallel-execution indicator, thinking process panel visible, Qwen 3.6 Max as the active model. Multi-agent AI chat with Model Context Protocol (MCP) tool calling, persistent vector memory via AgentDB + HNSW, swarm coordination, and 6 frontier models including Claude Sonnet 4.6, Gemini 2.5 Pro, and OpenAI through OpenRouter.

    Web UI (Beta) β€” self-hostable, hosted demo at flo.ruv.io

    RuFlo's web UI is a multi-model AI chat with built-in Model Context Protocol (MCP) tool calling. Talk to Qwen, Claude, Gemini, or OpenAI while RuFlo invokes the same MCP tools the CLI uses β€” agent orchestration, persistent memory, swarm coordination, code review, GitHub ops β€” directly from chat. No install, no API key needed to try it.

    What it is Why it matters
    🧠 Any model, local or remote 6 curated frontier models out-of-the-box β€” Qwen 3.6 Max (default), Claude Sonnet 4.6, Claude Haiku 4.5, Gemini 2.5 Pro, Gemini 2.5 Flash, OpenAI β€” via OpenRouter. Add your own: any OpenAI-compatible endpoint (vLLM, Ollama, LM Studio, Together, Groq, self-hosted).
    🦾 ruvLLM self-learning AI Native support for ruvLLM (lives in ruvnet/RuVector/examples/ruvLLM) β€” RuFlo's self-improving local model layer. Routes to MicroLoRA adapters, learns from your trajectories via SONA, and stays on your machine. Pair with the cloud models or run fully offline.
    πŸ› οΈ ~210 tools, ready to call 5 server groups (Core, Intelligence, Agents, Memory, DevTools) plus an 18-tool gallery that runs entirely in your browser β€” works offline.
    πŸ”Œ Bring your own MCP servers Click the MCP (n) pill in the chat input β†’ Add Server and paste any MCP endpoint (HTTP, SSE, or stdio). Your tools join RuFlo's native ones in the same parallel-execution flow. Run a local MCP server on localhost:3000 and it just works.
    ⚑ Tools run in parallel One model response can fire 4–6+ tools at the same time. The UI shows them as cards with a Step 1 β€” 2 tools completed badge so you can see exactly what ran.
    πŸ’Ύ Memory that sticks Say "remember my favorite color is indigo" and ask weeks later β€” RuFlo recalls it. Backed by AgentDB + HNSW vector search (β‰₯150Γ— faster than brute force).
    πŸ“˜ Built-in capabilities tour Click the question-mark icon in the sidebar β€” a "RuFlo Capabilities" modal opens with the full tool list, model strengths, architecture, and keyboard shortcuts.
    🏠 Self-hostable Web UI is shipped as Docker (ruflo/src/ruvocal/Dockerfile) with embedded Mongo. Deploy to your own Cloud Run / Fly / Kubernetes / docker-compose. The hosted flo.ruv.io demo is one option; running your own is fully supported.
    πŸš€ Zero install to try Open the hosted URL, pick a model, type a question. That's the whole onboarding.

    Try the hosted demo: https://flo.ruv.io/ β€” no account, no API key. Run your own: the source lives in ruflo/src/ruvocal/ with a multi-stage Dockerfile (INCLUDE_DB=true builds in MongoDB) and a cloudbuild.yaml for Google Cloud Run. See ADR-033 for the architecture and issue #1689 for the roadmap.

    goal.ruv.io/agents β€” RuFlo Goal-Oriented Action Planning (GOAP) UI for autonomous AI agents. Visual goal decomposition, A* search through state spaces, multi-agent task assignment, and live agent telemetry.

    Goal Planner UI β€” autonomous agents at goal.ruv.io

    Turn high-level goals into executable agent plans. goal.ruv.io is RuFlo's hosted Goal-Oriented Action Planning (GOAP) front-end β€” describe an outcome in plain English and watch RuFlo decompose it into preconditions, actions, and an A* path through state space, then dispatch the work to live agents at /agents.

    What it is Why it matters
    🎯 Plain-English goals Type "ship the auth refactor with tests and a PR" β€” RuFlo extracts the success criteria, the constraints, and the implicit preconditions. No JSON, no DSL.
    🧭 GOAP A* planner Classic gaming-AI planning ported to software work: state-space search through actions with preconditions/effects to find the shortest viable path. Replans on the fly when state changes.
    πŸ€– Live agent dashboard goal.ruv.io/agents shows every spawned agent β€” role, current step, memory namespace, token budget, status. Click in to inspect trajectories, kill runaway workers, or reassign.
    🌳 Visual plan tree Goals render as collapsible action trees with progress, blocked branches, and rollbacks highlighted. See exactly why an agent picked a path β€” no opaque chain-of-thought.
    ♻️ Adaptive replanning When an action fails or new info arrives, the planner re-runs A* from the current state instead of restarting. Failures become learning, not loops.
    🧠 Shared memory + SONA Plans, trajectories, and outcomes flow into AgentDB. Future plans retrieve past solutions via HNSW β€” the planner gets smarter with every run.
    πŸ”— Wired to MCP tools Every action node maps to a tool call (RuFlo's ~210 MCP tools, your custom servers, or shell). The planner schedules them in parallel where the dependency graph allows.
    πŸš€ Zero install to try Open goal.ruv.io, describe a goal, watch it run. Source lives in v3/goal_ui/ β€” Vite + Supabase, self-hostable.

    Try it: https://goal.ruv.io/ for goals Β· https://goal.ruv.io/agents for live agents. Run your own: clone the goal branch and cd v3/goal_ui && npm install && npm run dev.

    Agent Federation β€” Slack for Agents

    Your Agent --> [ Remove secrets ] --> [ Sign message ] --> [ Encrypted channel ]
                     Emails, SSNs,        Proves it came       No one reads it
                     keys stripped         from you              in transit
                                                                    |
                                                                    v
    Their Agent <-- [ Block attacks ] <-- [ Check identity ] <------+
                     Stops prompt          Rejects forgeries
                     injection
    
                              Audit trail on both sides.
                      Trust builds over time. Bad behavior = instant downgrade.

    Slack gave teams channels. Federation gives agents the same thing β€” shared workspaces across trust boundaries, where agents on different machines, orgs, or cloud regions can discover each other, prove who they are, and collaborate on tasks.

    The difference: some channels are trusted, some aren't. @claude-flow/plugin-agent-federation handles that automatically. Your agents join a federation, get verified via mTLS + ed25519, and start exchanging work β€” with PII stripped before anything leaves your node and every message auditable. Untrusted agents can still participate at lower privilege: they see discovery info, not your memory. As they prove reliable, trust upgrades. If they misbehave, they get downgraded instantly β€” no human in the loop required.

    You don't configure handshakes or manage certificates. You federation init, federation join, and your agents start talking. The protocol handles identity, the PII pipeline handles data safety, and the audit trail handles compliance.

    πŸ“˜ Full user guide: docs/federation/ β€” setup, MCP tools, trust levels, circuit breaker, and the (opt-in) WireGuard mesh layer that ties packet-layer reachability to federation trust. ADR-111 deep-dive at docs/federation/phase7-mesh-bringup.md.

    Federation capabilities
    Capability How it works
    πŸ”’ Zero-trust federation Remote agents start untrusted. Identity proven via mTLS + ed25519 challenge-response. No API keys, no shared secrets.
    πŸ›‘οΈ PII-gated data flow 14-type detection pipeline scans every outbound message. Per-trust-level policies: BLOCK, REDACT, HASH, or PASS. Adaptive calibration reduces false positives.
    πŸ“Š Behavioral trust scoring Formula (0.4Γ—success + 0.2Γ—uptime + 0.2Γ—threat + 0.2Γ—integrity) continuously evaluates peers. Upgrades require history; downgrades are instant.
    πŸ“‹ Compliance built-in HIPAA, SOC2, GDPR audit trails as compliance modes. Every federation event produces a structured record searchable via HNSW.
    🀝 9 MCP tools + 10 CLI commands Full lifecycle: federation_init, federation_send, federation_trust, federation_audit, and more.
    Example: two teams sharing fraud signals without sharing customer data
    # Team A: initialize federation and generate keypair
    npx claude-flow@latest federation init
    
    # Team A: join Team B's federation endpoint
    npx claude-flow@latest federation join wss://team-b.example.com:8443
    
    # Team A: send a task β€” PII is stripped automatically before it leaves
    npx claude-flow@latest federation send --to team-b --type task-request \
      --message "Analyze transaction patterns for account anomalies"
    
    # Team A: check peer trust levels and session health
    npx claude-flow@latest federation status

    See issue #1669 for the complete architecture, trust model, and implementation roadmap.

    # Claude Code plugin
    /plugin install ruflo-federation@ruflo
    
    # Or via CLI
    npx claude-flow@latest plugins install @claude-flow/plugin-agent-federation
    Claude Code: With vs Without Ruflo
    Capability Claude Code Alone + Ruflo
    Agent Collaboration Isolated, no shared context Swarms with shared memory and consensus
    Coordination Manual orchestration Queen-led hierarchy (Raft, Byzantine, Gossip)
    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
    Architecture overview
    User --> Claude Code / CLI
              |
              v
        Orchestration Layer
        (MCP Server, Router, 27 Hooks)
              |
              v
        Swarm Coordination
        (Queen, Topology, Consensus)
              |
              v
        100+ Specialized Agents
        (coder, tester, reviewer, architect, security...)
              |
              v
        Memory & Learning
        (AgentDB, HNSW, SONA, ReasoningBank)
              |
              v
        LLM Providers
        (Claude, GPT, Gemini, Cohere, Ollama)

    Documentation

    Three docs for three audiences:

    Doc When to read it
    Status See what currently works β€” capability counts, test baselines, recent fixes, what's next. The is-it-ready doc.
    User Guide Daily reference β€” every command, every config flag, every plugin. The how-do-I doc.
    Verification Cryptographically prove your installed bytes match the signed witness β€” ruflo verify. The trust-but-verify doc.

    User Guide section index:

    Section Topics
    Quick Start Installation, prerequisites, install profiles
    Core Features MCP tools, agents, memory, neural learning
    Intelligence & Learning Hooks, workers, SONA, model routing
    Swarm & Coordination Topologies, consensus, hive mind
    Security AIDefence, CVE remediation, validation
    Ecosystem RuVector, agentic-flow, Flow Nexus
    Configuration Environment variables, config schema
    Plugin Marketplace Browse and install plugins

    Support

    Resource Link
    Documentation User Guide
    Issues & Bugs GitHub Issues
    Enterprise ruv.io
    Community Agentics Foundation Discord
    Powered by Cognitum.one

    License

    MIT - RuvNet