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README.md

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โœจ Try the UI Beta โ€” flo.ruv.io ๐ŸŽฏ Goal Planner โ€” goal.ruv.io ๐Ÿค– Live Agents โ€” goal.ruv.io/agents

Star on GitHub MIT License Claude Code

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 โ€” so agents don't just run, they collaborate.

Why Ruflo?

Claude Flow is now Ruflo โ€” 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.

What Ruflo Does

One init gives Claude Code a nervous system: agents self-organize into swarms, learn from every task, remember across sessions, and โ€” with federation โ€” 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

Claude Code Plugin (Recommended)

Install Ruflo as a native Claude Code plugin -- adds skills, commands, agents, and MCP tools directly:

# 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
All 32 plugins

Core & Orchestration

PluginWhat it does
ruflo-coreFoundation โ€” server, health checks, plugin discovery
ruflo-swarmCoordinate multiple agents as a team
ruflo-autopilotLet agents run autonomously in a loop
ruflo-loop-workersSchedule background tasks on a timer
ruflo-workflowsReusable multi-step task templates
ruflo-federationAgents on different machines collaborate securely

Memory & Knowledge

PluginWhat it does
ruflo-agentdbFast vector database for agent memory
ruflo-rag-memorySmart retrieval โ€” hybrid search, graph hops, diversity ranking
ruflo-rvfSave and restore agent memory across sessions
ruflo-ruvectorruvector โ€” GPU-accelerated search, Graph RAG, 103 tools
ruflo-knowledge-graphBuild and traverse entity relationship maps

Intelligence & Learning

PluginWhat it does
ruflo-intelligenceAgents learn from past successes and get smarter
ruflo-daaDynamic agent behavior and cognitive patterns
ruflo-ruvllmRun local LLMs (Ollama, etc.) with smart routing
ruflo-goalsBreak big goals into plans and track progress

Code Quality & Testing

PluginWhat it does
ruflo-testgenFind missing tests and generate them automatically
ruflo-browserAutomate browser testing with Playwright
ruflo-jujutsuAnalyze git diffs, score risk, suggest reviewers
ruflo-docsGenerate and maintain documentation automatically

Security & Compliance

PluginWhat it does
ruflo-security-auditScan for vulnerabilities and CVEs
ruflo-aidefenceBlock prompt injection, detect PII, safety scanning

Architecture & Methodology

PluginWhat it does
ruflo-adrTrack architecture decisions with a living record
ruflo-dddScaffold domain-driven design โ€” contexts, aggregates, events
ruflo-sparcGuided 5-phase development methodology with quality gates

DevOps & Observability

PluginWhat it does
ruflo-migrationsManage database schema changes safely
ruflo-observabilityStructured logs, traces, and metrics in one place
ruflo-cost-trackerTrack token usage, set budgets, get cost alerts

Extensibility

PluginWhat it does
ruflo-wasmRun sandboxed WebAssembly agents
ruflo-plugin-creatorScaffold, validate, and publish your own plugins

Domain-Specific

PluginWhat it does
ruflo-iot-cognitumIoT device management โ€” trust scoring, anomaly detection, fleets
ruflo-neural-traderneural-trader โ€” AI trading with 4 agents, backtesting, 112+ tools
ruflo-market-dataIngest market data, vectorize OHLCV, detect patterns

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

What You Get

CapabilityDescription
๐Ÿค– 100+ AgentsSpecialized agents for coding, testing, security, docs, architecture
๐Ÿ“ก Comms LayerZero-trust federation โ€” agents across machines/orgs discover, authenticate, and exchange work securely
๐Ÿ Swarm CoordinationHierarchical, mesh, and adaptive topologies with consensus
๐Ÿง  Self-LearningSONA neural patterns, ReasoningBank, trajectory learning
๐Ÿ’พ Vector MemoryHNSW-indexed AgentDB with 150x-12,500x faster search
โšก Background Workers12 auto-triggered workers (audit, optimize, testgaps, etc.)
๐Ÿงฉ Plugin Marketplace32 native Claude Code plugins + 21 npm plugins
๐Ÿ”Œ Multi-ProviderClaude, GPT, Gemini, Cohere, Ollama with smart routing
๐Ÿ›ก๏ธ SecurityAIDefence, input validation, CVE remediation, path traversal prevention
๐ŸŒ Agent FederationCross-installation agent collaboration with zero-trust security
๐Ÿ’ฌ Web UI BetaMulti-model chat at flo.ruv.io with parallel MCP tool calling and an in-browser WASM tool gallery
๐ŸŽฏ RuFlo ResearchGOAP A* planner at goal.ruv.io โ€” plain-English goals โ†’ executable agent plans, with a live agent dashboard at /agents

RuFlo Web UI executing parallel MCP tool calls at flo.ruv.io โ€” ruflo__memory_store and ruflo__memory_search firing in a single model turn with the 'Step 1 โ€” 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) โ€” 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 โ€” agent orchestration, persistent memory, swarm coordination, code review, GitHub ops โ€” directly from chat. No install, no API key needed to try it.

What it isWhy it matters
๐Ÿง Any model, local or remote6 curated frontier models out-of-the-box โ€” Qwen 3.6 Max (default), Claude Sonnet 4.6, Claude Haiku 4.5, Gemini 2.5 Pro, Gemini 2.5 Flash, OpenAI โ€” via OpenRouter. Add your own: any OpenAI-compatible endpoint (vLLM, Ollama, LM Studio, Together, Groq, self-hosted).
๐ŸฆพruvLLM self-learning AINative support for ruvLLM (lives in ruvnet/RuVector/examples/ruvLLM) โ€” 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.
๐Ÿ› ๏ธ~210 tools, ready to call5 server groups (Core, Intelligence, Agents, Memory, DevTools) plus an 18-tool gallery that runs entirely in your browser โ€” works offline.
๐Ÿ”ŒBring your own MCP serversClick the MCP (n) pill in the chat input โ†’ 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.
โšกTools run in parallelOne model response can fire 4โ€“6+ tools at the same time. The UI shows them as cards with a Step 1 โ€” 2 tools completed badge so you can see exactly what ran.
๐Ÿ’พMemory that sticksSay "remember my favorite color is indigo" and ask weeks later โ€” RuFlo recalls it. Backed by AgentDB + HNSW vector search (โ‰ฅ150ร— faster than brute force).
๐Ÿ“˜Built-in capabilities tourClick the question-mark icon in the sidebar โ€” a "RuFlo Capabilities" modal opens with the full tool list, model strengths, architecture, and keyboard shortcuts.
๐Ÿ Self-hostableWeb 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.
๐Ÿš€Zero install to tryOpen the hosted URL, pick a model, type a question. That's the whole onboarding.

Try the hosted demo: https://flo.ruv.io/ โ€” 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 โ€” 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 โ€” 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 โ€” 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 isWhy it matters
๐ŸŽฏPlain-English goalsType "ship the auth refactor with tests and a PR" โ€” RuFlo extracts the success criteria, the constraints, and the implicit preconditions. No JSON, no DSL.
๐ŸงญGOAP A* plannerClassic 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.
๐Ÿค–Live agent dashboardgoal.ruv.io/agents shows every spawned agent โ€” role, current step, memory namespace, token budget, status. Click in to inspect trajectories, kill runaway workers, or reassign.
๐ŸŒณVisual plan treeGoals render as collapsible action trees with progress, blocked branches, and rollbacks highlighted. See exactly why an agent picked a path โ€” no opaque chain-of-thought.
โ™ป๏ธAdaptive replanningWhen an action fails or new info arrives, the planner re-runs A* from the current state instead of restarting. Failures become learning, not loops.
๐Ÿง Shared memory + SONAPlans, trajectories, and outcomes flow into AgentDB. Future plans retrieve past solutions via HNSW โ€” the planner gets smarter with every run.
๐Ÿ”—Wired to MCP toolsEvery 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.
๐Ÿš€Zero install to tryOpen goal.ruv.io, describe a goal, watch it run. Source lives in v3/goal_ui/ โ€” Vite + Supabase, self-hostable.

Try it: https://goal.ruv.io/ for goals ยท 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 โ€” 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 โ€” 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 โ€” 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 โ€” 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.

Federation capabilities
CapabilityHow it works
๐Ÿ”’Zero-trust federationRemote agents start untrusted. Identity proven via mTLS + ed25519 challenge-response. No API keys, no shared secrets.
๐Ÿ›ก๏ธPII-gated data flow14-type detection pipeline scans every outbound message. Per-trust-level policies: BLOCK, REDACT, HASH, or PASS. Adaptive calibration reduces false positives.
๐Ÿ“ŠBehavioral trust scoringFormula (0.4ร—success + 0.2ร—uptime + 0.2ร—threat + 0.2ร—integrity) continuously evaluates peers. Upgrades require history; downgrades are instant.
๐Ÿ“‹Compliance built-inHIPAA, SOC2, GDPR audit trails as compliance modes. Every federation event produces a structured record searchable via HNSW.
๐Ÿค9 MCP tools + 10 CLI commandsFull 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 โ€” 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
CapabilityClaude Code Alone+ Ruflo
Agent CollaborationIsolated, no shared contextSwarms with shared memory and consensus
CoordinationManual orchestrationQueen-led hierarchy (Raft, Byzantine, Gossip)
MemorySession-onlyHNSW vector memory with sub-ms retrieval
LearningStatic behaviorSONA self-learning with pattern matching
Task RoutingYou decideIntelligent routing (89% accuracy)
Background WorkersNone12 auto-triggered workers
LLM ProvidersAnthropic only5 providers with failover
SecurityStandardCVE-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

Full documentation including architecture, configuration, CLI reference, API usage, plugin development, and advanced topics:

User Guide -- Complete reference documentation

SectionTopics
Quick StartInstallation, prerequisites, install profiles
Core FeaturesMCP tools, agents, memory, neural learning
Intelligence & LearningHooks, workers, SONA, model routing
Swarm & CoordinationTopologies, consensus, hive mind
SecurityAIDefence, CVE remediation, validation
EcosystemRuVector, agentic-flow, Flow Nexus
ConfigurationEnvironment variables, config schema
Plugin MarketplaceBrowse and install plugins

Support

ResourceLink
DocumentationUser Guide
Issues & BugsGitHub Issues
Enterpriseruv.io
CommunityAgentics Foundation Discord
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License

MIT - RuvNet

ๅ…ณไบŽ About

๐ŸŒŠ The leading agent orchestration platform for Claude. Deploy intelligent multi-agent swarms, coordinate autonomous workflows, and build conversational AI systems. Features enterprise-grade architecture, distributed swarm intelligence, RAG integration, and native Claude Code / Codex Integration
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