Giting.
낮의 Giting

홈 · AI 에이전트 · 멀티에이전트 · 에이전트 하네스 · 오케스트레이션 · MCP

ruflo 대표 이미지 b02c0cac
분석 리포트실측 정보사이트적용점원문 README

구조와 정체

ruflo는 Claude Code를 감싸는 실행 계층(에이전트 메타하네스)을 표방한다. 배포물은 단일 앱이 아니라 Claude Code 플러그인 40여 종(ruflo-core·ruflo-swarm·ruflo-federation·ruflo-rag-memory·ruflo-sparc 등)과 풀 CLI, MCP 서버, Rust 크레이트 3종(federation-peer·watermark·agntcy)이 얽힌 모노레포다. package.json 은 아직 이름이 claude-flow(v3.41.2), 실행 바이너리도 claude-flow 라, ruflo 로의 개명이 패키지 메타데이터까지는 내려오지 않았다.

규모는 실제로 크다. 전체 5,716 파일에 텍스트 175.6만 줄, 그중 TypeScript 가 2,171 파일 76.1만 줄로 본체다. Rust 는 39 파일 8,460 줄로 이름값('Ru')에 비하면 작은 비중이다. 첫 커밋은 이 shallow 범위 기준 2026-01-15, 마지막이 2026-09-12 이며 커밋 2,405회는 depth 1000 클론의 하한값이다.

핵심 기능을 파이프라인으로 분해

README 가 제시하는 흐름은 라우터에서 스웜 코디네이터로, 다시 다수 에이전트로, 마지막에 메모리·러닝 계층으로 이어진다. 단계로 끊으면 이렇다. ①라우팅: 작업을 적절한 에이전트로 배분한다. ②스웜 조정: 계층·메시 토폴로지와 합의(Byzantine·Raft·Gossip) 어휘로 다수 에이전트를 묶는다. ③에이전트 실행: 코딩·테스트·보안·문서 등 도메인 특화 에이전트가 붙는다. ④메모리·지능: HNSW 인덱스 벡터 DB(AgentDB)와 자기학습(SONA·ReasoningBank) 어휘로 성공 패턴을 임베딩해 재사용한다고 주장한다. ⑤멀티 프로바이더: Claude·GPT·Gemini·로컬 LLM 라우팅. 설치는 Claude Code 플러그인(경량 슬래시 커맨드)과 풀 CLI(npx 초기화) 두 경로다.

실제로 되는 것과 안 되는 것

실측으로 확인되는 것은 활동성과 화제성이다. 스타 72,322, 포크 8,559, 최근 90일 커밋 744회, 릴리스 태그 1,645개, 이슈 응답 중앙값 0.4시간(표본 25건 중 10건 응답)으로 리포는 매우 뜨겁고 빠르다. CI 워크플로우도 29개로 표면적으로는 검증 파이프라인을 갖췄고 테스트 매칭 파일이 731개 잡힌다.

반대로 신뢰를 유보하게 하는 신호가 셋이다. 첫째, 저자 31명 중 상위가 rUv·ruv·Reuven·ruvnet 으로 사실상 동일 인물의 표기 변형이고 나머지 상위가 Claude 다. 폭넓은 커뮤니티 개발이라기보다 1인+에이전트 생산에 가깝다. 둘째, 태그 1,645개는 마일스톤이 아니라 알파 사이클의 연속 발행으로, 릴리스 규율의 반대극이다. 셋째, 널리 인용되는 제3자 감사는 300+로 광고되는 MCP 도구 다수가 실행 없는 스텁(에이전트 스폰이 subprocess 없이 JSON 레코드만 만들고 영구 idle, 태스크는 큐만 쌓이고 실행 워커 부재, 뉴럴 학습이 하드코딩 라벨 반환, 합의가 단일 프로세스 EventEmitter 시뮬레이션)이라고 지목하며 토큰 절감 주장도 반대로 소비를 늘린다고 반박한다. 이 감사는 외부 주장이므로 우리가 직접 재현하지 않았고, 프로버가 결정적으로 확인한 것은 위의 활동·규모 수치까지다.

우리 적용점

첫째, 동향 추적이다. ruflo 는 우리 coxpit-oss(에이전트 플릿)와 Triforge 오케스트레이션이 서 있는 바로 그 '에이전트 하네스' 축의 최대 화제작이라, 플러그인 분해 방식과 기능 어휘(스웜·페더레이션·자기학습 메모리)를 지형도로 관찰할 값이 있다. 채택이 아니라 좌표 확인이다.

둘째, Giting 자신의 존재 증명이다. 스타 7.2만과 도구 300+라는 표면 수치, 그리고 되는 범위 사이의 간극이야말로 '실측 큐레이션'이 필요한 이유를 그대로 보여준다. 결정적 프로버로 활동·규모를 재고 '되는 것/안 되는 것'을 가르는 우리 방식의 실전 케이스로 리포에 남길 만하다.

셋째, 릴리스 규율의 반례다. 태그 1,645개는 우리 워크스페이스가 실측으로 얻은 교훈(태그를 아껴 찍는다, 쌓이는 건 분이 아니라 저장 공간)의 실물 반대극이다. 컨테이너·아티팩트를 미는 CI 에 보존정책을 붙이지 않으면 어떻게 되는지 보여주는 사례로 인용할 수 있다.

가져오지 말 것

수치 마케팅을 그대로 믿고 아키텍처를 이식하지 않는다. 특히 자기학습·합의·페더레이션처럼 검증 비용이 큰 기능은 우리 프로버가 실동을 확인하지 않았고 외부 감사가 스텁이라 지목한 영역이므로 근거로 삼지 않는다. 1인+에이전트 대량 생산의 산출 속도(하루 단위 태그)를 성숙도로 오독하지 않는다. Rust·WASM·벡터 DB 어휘가 코드 비중(rs 8,460줄)과 어긋나는 지점도 유의한다.

실측 정보

HEAD 커밋
b02c0cac
형태
Claude Code 플러그인 40여 종 + 풀 CLI(bin claude-flow) + MCP 서버 + Rust 크레이트 3종
규모
전체 5,716 파일 · 텍스트 175.6만 줄 (TS 2,171파일 76.1만 · mjs 5.9만 · sh 2.8만 · rs 8,460)
테스트 / CI
테스트 매칭 파일 731개 · 워크플로우 29개
이력
커밋 2,405회 하한(shallow depth 1000, 2026-01-15~09-12) · 90일 744회
기여
저자 31명(상위 rUv·ruv·Reuven·ruvnet·Claude) · 릴리스 태그 1,645개
반응성
이슈 25건 표본 중 10건 응답 · 응답 중앙값 0.4시간 · 미해결 이슈 998건
라이선스
MIT

사이트 실측

주소
Cognitum.One
응답 · 첫 화면
200 · 1.6초 (데스크톱)
요청 · 전송량
71개 · 447KB
깨진 요청 · 콘솔 오류
0개 · 0개
폰(390px)
가로 넘침 없음
디자인
어두운 화면 · Outfit

홈페이지를 실제 크롬으로 띄워 첫 화면까지만 쟀습니다(2026-10-16 · 데스크톱 1440px · 폰 390px). 로그인 · 입력 같은 동작은 하지 않았고, 사이트 상태는 리포 품질과 따로 봅니다. 색 칸은 바탕 · 면 · 글자 · 흐린 글자 · 강조 · 선, 점선은 「못 잼」.

적용점

  • P0 · 비용 낮음동향 추적: 우리 coxpit-oss·Triforge와 같은 '에이전트 하네스' 축의 최대 화제작이라 기능 어휘·플러그인 분해 방식(코어/스웜/페더레이션/메모리)을 카탈로그로 관찰. 채택이 아니라 지형 파악.
  • P1 · 비용 낮음안티패턴 교보재: 스타·도구 수를 앞세운 마케팅과 실동 사이의 간극이 우리 Giting '실측 큐레이션'의 존재 이유를 그대로 증명한다. '되는 것/안 되는 것'을 결정적 프로버로 가르는 우리 방식의 레퍼런스 케이스.
  • P1 · 비용 낮음릴리스 규율의 반례: 태그 1,645개는 우리 워크스페이스 교훈('릴리스 태그는 아껴 찍는다')의 실물 반대극. 태그당 아티팩트·이미지가 쌓이는 저장 비용 관점에서 CI 보존정책 부재의 결과를 보여준다.
◇ 관망

우리 제품군(coxpit-oss·Triforge 오케스트레이션)과 정확히 같은 축의 최대 화제작이라 동향은 반드시 추적할 가치가 있다. 다만 저자가 사실상 1인+Claude이고, 릴리스 태그가 1,645개로 폭주하며, 외부 감사가 다수 MCP 도구를 실행 없는 스텁으로 지목한다. 아키텍처 어휘와 안티패턴을 배우는 관찰 대상으로 두고, 코드·수치를 신뢰 근거로 인용하지 않는다.

원문 README

아래는 ruvnet/ruflo 저장소의 README 원문입니다(GitHub 렌더, 수집 시점 기준). 저작권은 원 프로젝트에 있으며 원 저장소의 라이선스를 따릅니다. GitHub에서 보기

README 전문 펼치기

Ruflo Banner

Try the UI Beta — flo.ruv.io npm version (ruflo) MIT License Star on GitHub

Goal Planner Live Agents 🕸️ RuVector Agentic DB Ecosystem downloads Git clones (14d) Claude Code Codex Plugin

Ruflo

An agent meta-harness for Claude Code and Codex.

RuFlo Explained — build an AI team that plans, remembers, tests, and improves

📖 RuFlo Explained — Build an AI Team That Plans, Remembers, Tests, and Improves A 14-chapter guide: from the basic idea to a first useful task, then memory, agent teams, plugins, cost and verification.

Agent = Model + Harness. The model writes; the harness gives it tools, memory, loops, sandboxes, and controls so it can actually work. Ruflo is the harness — the execution layer around Claude Code and Codex that adds 100+ specialized agents, coordinated swarms, self-learning memory, federated comms across machines, and enterprise security guardrails. So agents don't just run, they collaborate.

One npx ruflo init gives Claude Code a nervous system: agents self-organize into swarms, learn from every task, remember across sessions, and — with federation — 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.

📖 Background — where the name comes from

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 working until 3am. Underneath, powered by Cognitum.One agentic architecture, running a supercharged Rust-based AI engine, embeddings, memory, and plugin system.


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 — 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 Only if ruflo-core is installed (it ships its own .mcp.json) — most other plugins don't Yes
Hooks installed No Yes
Best for Try a single plugin's commands without committing to the full install Production use — everything works as documented

Path A — 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-rag-memory@ruflo
/plugin install ruflo-neural-trader@ruflo

This adds slash commands and agent definitions. ruflo-core (installed above) does register its own MCP server on install — its tools are callable as mcp__plugin_ruflo-core_ruflo__* (e.g. mcp__plugin_ruflo-core_ruflo__memory_store), not the bare memory_store/swarm_init/agent_spawn names the CLI-track scaffold uses. Other plugins generally don't ship their own MCP server. For the full loop with the CLI-track tool names, use Path B below.

🔌 All 35 plugins

Core & Orchestration

Plugin What it does
ruflo-core Foundation — 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 — hybrid search, graph hops, diversity ranking
ruflo-rvf Save and restore agent memory across sessions
ruflo-ruvector ruvector — 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-graph-intelligence Sublinear graph reasoning — PageRank, delta updates, complexity-aware execution (ADR-123)
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 — contexts, aggregates, events
ruflo-sparc Guided 5-phase development methodology with quality gates
ruflo-metaharness Grade your agent setup, scan tool configs for security risks, and track changes over time (guide)
ruflo-arena Competitive ruliology — pit agent strategies against each other in tournaments, hill-climb and co-evolve the winners (ADR-147/148)

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 — 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 — trust scoring, anomaly detection, fleets
ruflo-neural-trader neural-trader — 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 — 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 — 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

💡 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 — both end up running the same init flow.

MCP Server

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

What You Get

Capability Description
🤖 100+ Agents Specialized agents for coding, testing, security, docs, architecture
📡 Comms Layer Zero-trust federation — 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
💾 Vector Memory HNSW-indexed AgentDB — measured ~1.9x faster at N=20k, ~3.2x–4.7x at N=5k vs brute force (recall@10 ~0.99); ANN wins above the crossover, ties/loses at small N. See audit + scripts/benchmark-intelligence.mjs
⚡ Background Workers 12 auto-triggered workers (audit, optimize, testgaps, etc.)
🧩 Plugin Marketplace 33 native Claude Code plugins + 21 npm plugins
🔌 Multi-Provider Claude, GPT, Gemini, Cohere, Ollama with smart routing
🛡️ Security AIDefence, input validation, CVE remediation, path traversal prevention
🌐 Agent Federation Cross-installation agent collaboration with zero-trust security
🔬 MetaHarness Audit your AI agent setup before you ship. Grade readiness (1-100), scan tool configs for security issues, snapshot the whole project to catch regressions over time, and find templates that match your repo. ruflo eject turns a ruflo project into a standalone agent toolkit with its own name. Full guide.
💬 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 — plain-English goals → executable agent plans, with a live agent dashboard at /agents

RuFlo Web UI executing parallel MCP tool calls at flo.ruv.io — ruflo__memory_store and ruflo__memory_search firing in a single model turn with the 'Step 1 — 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) — 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 — agent orchestration, persistent memory, swarm coordination, code review, GitHub ops — 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 — Qwen 3.6 Max (default), Claude Sonnet 4.6, Claude Haiku 4.5, Gemini 2.5 Pro, Gemini 2.5 Flash, OpenAI — 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) — 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.
🛠️ ~210 tools, ready to call 5 server groups (Core, Intelligence, Agents, Memory, DevTools) plus an 18-tool gallery that runs entirely in your browser — works offline.
🔌 Bring your own MCP servers Click the MCP (n) pill in the chat input → 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 — 2 tools completed badge so you can see exactly what ran.
💾 Memory that sticks Say "remember my favorite color is indigo" and ask weeks later — RuFlo recalls it. Backed by AgentDB + HNSW vector search (measured ~1.9x–4.7x faster than brute force above the crossover, recall@10 ~0.99).
📘 Built-in capabilities tour Click the question-mark icon in the sidebar — 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.
🚀 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/ — 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 — 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 — 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 — 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" — 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.
🤖 Live agent dashboard goal.ruv.io/agents shows every spawned agent — 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 — 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 — the planner gets smarter with every run.
🔗 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.
🚀 Zero install to try Open goal.ruv.io, describe a goal, watch it run. Source lives in v3/goal_ui/ — Vite + Supabase, self-hostable.

Try it: https://goal.ruv.io/ for goals · 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 — 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 — 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 — 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 — 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.

📘 Full user guide: docs/federation/ — 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
🔒 Zero-trust federation Remote agents start untrusted. Identity proven via mTLS + ed25519 challenge-response. No API keys, no shared secrets.
🛡️ 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.
📊 Behavioral trust scoring Formula (0.4×success + 0.2×uptime + 0.2×threat + 0.2×integrity) continuously evaluates peers. Upgrades require history; downgrades are instant.
📋 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 — 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

Four docs for four audiences:

Doc When to read it
Status See what currently works — capability counts, test baselines, recent fixes, what's next. The is-it-ready doc.
User Guide Daily reference — every command, every config flag, every plugin. The how-do-I doc.
MetaHarness Guide How to grade your agent setup, scan tool configs for security, detect changes between runs, and eject a project into a standalone agent toolkit. The audit-my-setup doc.
Benchmarks v3.8.0 SOTA matrix vs LangGraph / AutoGen / CrewAI on darwin-arm64 + linux-x64. ruflo wins cold start, single turn, RSS by 1.3×–1953×. The is-it-fast doc.
Verification Cryptographically prove your installed bytes match the signed witness — ruflo verify. The trust-but-verify doc.
Team Gateway Checklist Before-merge gates, dual-mode handoff, memory namespace sharing, and witness manifest entry per merge. The safer-team-workflows doc.

Benchmark internals (for reproduction): sota-workload-spec.md · SOTA-PROGRESS.md · raw matrix JSON: darwin · linux

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
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License

MIT - RuvNet

의견

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