Claude-Flow v3: Enterprise AI Orchestration Platform

Multi-agent AI orchestration framework for Claude Code with swarm coordination, self-learning hooks, and Domain-Driven Design architecture.

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Quick Start

Prerequisites

• Node.js 18+ (LTS recommended)
• npm 9+ or equivalent package manager
• Windows users: See Windows Installation Guide

IMPORTANT: Claude Code must be installed first:

# 1. Install Claude Code globally
npm install -g @anthropic-ai/claude-code

# 2. (Optional) Skip permissions check for faster setup
claude --dangerously-skip-permissions

Installation

# Install claude-flow
npm install claude-flow@latest

# Initialize in your project
npx claude-flow init

# Start MCP server for Claude Code integration
npx claude-flow mcp start

# Run a task with agents
npx claude-flow --agent coder --task "Implement user authentication"

# List available agents
npx claude-flow --list

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Features

Core Platform Capabilities

Capability	Description	Metrics
54+ Specialized Agents	Purpose-built AI agents for development, testing, security, and operations	10 agent categories, 15 concurrent
Multi-Topology Swarms	Hierarchical, mesh, ring, star, and adaptive coordination patterns	2.8-4.4x speed improvement
Self-Learning Hooks	ReasoningBank pattern learning with HNSW vector search	150x faster retrieval
MCP Integration	Native Claude Code support via Model Context Protocol	27+ tools available
Security-First Design	Input validation, path traversal prevention, command sandboxing	CVE-1, CVE-2, CVE-3 addressed
Cross-Platform	Full support for Windows, macOS, and Linux environments	Node.js 18+

Agent Ecosystem

Category	Agent Count	Key Agents	Purpose
Core Development	5	coder, reviewer, tester, planner, researcher	Daily development tasks
V3 Specialized	10	queen-coordinator, security-architect, memory-specialist	Enterprise orchestration
Swarm Coordination	5	hierarchical-coordinator, mesh-coordinator, adaptive-coordinator	Multi-agent patterns
Consensus & Distributed	7	byzantine-coordinator, raft-manager, gossip-coordinator	Fault-tolerant coordination
Performance	5	perf-analyzer, performance-benchmarker, task-orchestrator	Optimization & monitoring
GitHub & Repository	9	pr-manager, code-review-swarm, issue-tracker, release-manager	Repository automation
SPARC Methodology	6	sparc-coord, specification, pseudocode, architecture	Structured development
Specialized Dev	8	backend-dev, mobile-dev, ml-developer, cicd-engineer	Domain expertise

Swarm Topologies

Topology	Agents	Best For	Execution Time	Memory
Hierarchical	6-15	Structured tasks, clear authority chains	0.20s	256 MB
Mesh	4-10	Collaborative work, high redundancy	0.15s	192 MB
Ring	3-8	Sequential processing pipelines	0.12s	128 MB
Star	5-12	Centralized control, spoke workers	0.14s	180 MB
Hybrid (Hierarchical-Mesh)	7-15	Complex multi-domain tasks	0.18s	320 MB
Adaptive	2-15	Dynamic workloads, auto-scaling	Variable	Dynamic

Self-Learning & Intelligence

Feature	Technology	Performance	Description
ReasoningBank	HNSW Vector Search	150x faster	Pattern storage with similarity-based retrieval
SONA Neural	LoRA Fine-tuning	<0.05ms adaptation	Self-optimizing neural architecture
Pattern Learning	EWC++ Memory	Zero forgetting	Continuous learning without catastrophic forgetting
Intent Routing	MoE (Mixture of Experts)	95%+ accuracy	Intelligent task-to-agent routing
Domain Detection	Vector Clustering	Real-time	Automatic categorization (security, testing, performance)
Quality Tracking	Success/Failure Metrics	Per-pattern	Historical performance tracking

Memory & Storage

Backend	Technology	Performance	Use Case
AgentDB	HNSW Indexing	150x-12,500x faster	Primary vector storage
SQLite	Relational DB	Standard	Metadata and structured data
Hybrid	AgentDB + SQLite	Best of both	Recommended default
In-Memory	RAM-based	Fastest	Temporary/session data

MCP Tools & Integration

Category	Tools	Description
Coordination	swarm_init, agent_spawn, task_orchestrate	Swarm and agent lifecycle management
Monitoring	swarm_status, agent_list, agent_metrics, task_status	Real-time status and metrics
Memory & Neural	memory_usage, neural_status, neural_train, neural_patterns	Memory operations and learning
GitHub	github_swarm, repo_analyze, pr_enhance, issue_triage, code_review	Repository integration
Workers	worker/run, worker/status, worker/alerts, worker/history	Background task management
Hooks	hooks/pre-edit, hooks/post-edit, hooks/route, hooks/pretrain	Lifecycle hooks

Security Features

Feature	Protection	Implementation
Input Validation	Injection attacks	Boundary validation on all inputs
Path Traversal Prevention	Directory escape	Blocked patterns (../, ~/., /etc/)
Command Sandboxing	Shell injection	Allowlisted commands, metacharacter blocking
Prototype Pollution	Object manipulation	Safe JSON parsing with validation
TOCTOU Protection	Race conditions	Symlink skipping and atomic operations
Information Disclosure	Data leakage	Error message sanitization
CVE Monitoring	Known vulnerabilities	Active scanning and patching

Performance Targets

Metric	Target	Achieved
Startup Time	<500ms	450ms
Agent Spawn	<100ms	85ms
HNSW Search	150x faster	150-12,500x
Flash Attention	2.49x-7.47x speedup	2.8-4.4x
Memory Reduction	50-75%	52%
SONA Adaptation	<0.05ms	0.03ms
Parallel Capacity	15 agents	15 concurrent
Task Success Rate	95%+	100% (7/7 strategies)

Advanced Capabilities

Feature	Description	Benefit
Automatic Topology Selection	AI-driven topology choice based on task complexity	Optimal resource utilization
Parallel Execution	Concurrent agent operation with load balancing	2.8-4.4x speed improvement
Neural Training	27+ model support with continuous learning	Adaptive intelligence
Bottleneck Analysis	Real-time performance monitoring and optimization	Proactive issue detection
Smart Auto-Spawning	Dynamic agent creation based on workload	Elastic scaling
Self-Healing Workflows	Automatic error recovery and task retry	High availability
Cross-Session Memory	Persistent pattern storage across sessions	Continuous learning
Event Sourcing	Complete audit trail with replay capability	Debugging and compliance
Background Workers	10 specialized workers with auto-scheduling	Automated maintenance
GitHub Integration	PR management, issue triage, code review automation	Repository workflow

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Self-Learning & Self-Optimization

ReasoningBank - Adaptive Pattern Learning

ReasoningBank is an intelligent pattern memory system that learns from every interaction:

# Store successful patterns
npx claude-flow hooks post-edit <file> --success true

# Route tasks using learned patterns
npx claude-flow hooks route "Implement caching layer"

# View learning metrics
npx claude-flow hooks metrics

Capabilities:

• Pattern Storage - Stores successful strategies with vector embeddings
• Similarity Search - HNSW-based retrieval for fast pattern matching
• Quality Tracking - Tracks success/failure rates per pattern
• Domain Detection - Automatically categorizes patterns (security, testing, performance, etc.)
• Agent Routing - Routes tasks to optimal agents based on historical performance
• Consolidation - Prunes low-quality patterns, promotes high-quality ones to long-term memory

SONA - Self-Optimizing Neural Architecture

SONA provides continuous self-improvement through neural adaptation:

• LoRA Fine-tuning - Low-rank adaptation for efficient model updates
• EWC++ Memory Preservation - Prevents catastrophic forgetting during learning
• Pattern Recognition - Learns task patterns for improved routing
• Sub-millisecond Adaptation - <0.05ms learning overhead per operation

Pretraining Pipeline (4-Step)

1. RETRIEVE - Top-k memory injection with MMR diversity
2. JUDGE - LLM-as-judge trajectory evaluation
3. DISTILL - Extract strategy memories from trajectories
4. CONSOLIDATE - Dedup, detect contradictions, prune old patterns

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RuVector WASM Plugins

High-performance Rust-compiled WebAssembly plugins for vector operations:

Semantic Code Search

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

const plugin = new SemanticCodeSearchPlugin();
await plugin.initialize();

// Index codebase
await plugin.indexFile('src/auth/login.ts', code);

// Semantic search
const results = await plugin.search('authentication validation', { limit: 10 });

Intent Router

Routes user intents to optimal handlers using vector similarity:

• Automatic intent classification
• Confidence scoring
• Fallback handling

Hook Pattern Library

Pre-built patterns for common development tasks:

• Security patterns (input validation, auth checks)
• Testing patterns (TDD, mocking strategies)
• Performance patterns (caching, optimization)

MCP Tool Optimizer

Optimizes MCP tool selection based on learned patterns:

• Tool usage tracking
• Performance-based ranking
• Context-aware suggestions

Reasoning Bank Plugin

Vector-backed pattern storage with HNSW indexing:

• 150x faster search than brute-force
• Configurable similarity thresholds
• Batch operations support

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V3 Novel Capabilities

Hierarchical Mesh Swarm (15-Agent Concurrent)

V3 introduces a new swarm topology combining hierarchical control with mesh communication:

# Initialize 15-agent swarm
npx claude-flow swarm init --topology hierarchical-mesh --agents 15

# Queen coordinator manages all agents
npx claude-flow agent spawn queen-coordinator

Domain-Driven Design Architecture

Clean separation of concerns with bounded contexts:

• Agent Lifecycle Management
• Task Execution Engine
• Memory Management Service
• Coordination Layer

Event Sourcing

All state changes recorded for complete audit trail:

• Replay capability for debugging
• Time-travel debugging
• Complete operation history

Background Workers System

10 specialized background workers with automatic scheduling:

Worker	Purpose	Interval
performance	Monitors execution metrics	60s
health	System health checks	30s
security	Security pattern detection	120s
git	Repository status tracking	300s
learning	Pattern consolidation	600s
adr	Architecture decision tracking	3600s
ddd	Domain structure analysis	3600s
patterns	Code pattern detection	1800s
cache	Cache optimization	900s
swarm	Agent coordination metrics	60s

GuidanceProvider - Intelligent Hook Guidance

Provides context-aware guidance during development:

• Pre-Edit Guidance - Security checks, file-type specific advice
• Post-Edit Feedback - Code quality analysis, pattern detection
• Command Risk Assessment - Dangerous command blocking, risk warnings
• Routing Guidance - Agent recommendations with confidence scores

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Available Agents (54+)

V3 Specialized Swarm Agents (15-Agent Concurrent)

Agent	Purpose
queen-coordinator	V3 orchestration & GitHub issue management
security-architect	Security architecture & threat modeling
security-auditor	CVE remediation & security testing
memory-specialist	AgentDB unification with HNSW indexing
swarm-specialist	Unified coordination engine
integration-architect	agentic-flow@alpha deep integration
performance-engineer	Performance optimization & benchmarking
core-architect	Domain-driven design restructure
test-architect	TDD London School methodology
project-coordinator	Cross-domain coordination

Core Development

coder, reviewer, tester, planner, researcher

Swarm Coordination

hierarchical-coordinator, mesh-coordinator, adaptive-coordinator, collective-intelligence-coordinator, swarm-memory-manager

Consensus & Distributed

byzantine-coordinator, raft-manager, gossip-coordinator, consensus-builder, crdt-synchronizer, quorum-manager, security-manager

Performance & Optimization

perf-analyzer, performance-benchmarker, task-orchestrator, memory-coordinator, smart-agent

GitHub & Repository

github-modes, pr-manager, code-review-swarm, issue-tracker, release-manager, workflow-automation, project-board-sync, repo-architect, multi-repo-swarm

SPARC Methodology

sparc-coord, sparc-coder, specification, pseudocode, architecture, refinement

Specialized Development

backend-dev, mobile-dev, ml-developer, cicd-engineer, api-docs, system-architect, code-analyzer, base-template-generator

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Use Cases

Use Case	Command
Code review	npx claude-flow --agent reviewer --task "Review PR #123"
Test generation	npx claude-flow --agent tester --task "Write tests for auth module"
Security audit	npx claude-flow --agent security-architect --task "Audit for vulnerabilities"
Multi-agent swarm	npx claude-flow swarm init --topology hierarchical
Route task	npx claude-flow hooks route "Optimize database queries"
Performance analysis	npx claude-flow --agent perf-analyzer --task "Profile API endpoints"
GitHub PR management	npx claude-flow --agent pr-manager --task "Review open PRs"

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Self-Learning Hooks Commands

# Before editing - get context and agent suggestions
npx claude-flow hooks pre-edit <filePath>

# After editing - record outcome for learning
npx claude-flow hooks post-edit <filePath> --success true

# Before commands - assess risk
npx claude-flow hooks pre-command "<command>"

# After commands - record outcome
npx claude-flow hooks post-command "<command>" --success true

# Route task to optimal agent using learned patterns
npx claude-flow hooks route "<task description>"

# Explain routing decision with transparency
npx claude-flow hooks explain "<task description>"

# Bootstrap intelligence from repository
npx claude-flow hooks pretrain

# Generate optimized agent configs from pretrain data
npx claude-flow hooks build-agents

# View learning metrics dashboard
npx claude-flow hooks metrics

# Transfer patterns from another project
npx claude-flow hooks transfer <sourceProject>

# RuVector intelligence (SONA, MoE, HNSW)
npx claude-flow hooks intelligence

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MCP Tools

Coordination

swarm_init, agent_spawn, task_orchestrate

Monitoring

swarm_status, agent_list, agent_metrics, task_status, task_results

Memory & Neural

memory_usage, neural_status, neural_train, neural_patterns

GitHub Integration

github_swarm, repo_analyze, pr_enhance, issue_triage, code_review

Worker Tools (V3)

worker/run, worker/status, worker/alerts, worker/history, worker/statusline, worker/run-all, worker/start, worker/stop

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Architecture

V3 Module Structure

v3/
├── @claude-flow/hooks      # Event-driven lifecycle hooks + ReasoningBank
├── @claude-flow/memory     # AgentDB unification module
├── @claude-flow/security   # CVE remediation & patterns
├── @claude-flow/swarm      # 15-agent coordination
├── @claude-flow/plugins    # RuVector WASM plugins
├── @claude-flow/cli        # CLI modernization
├── @claude-flow/neural     # SONA learning integration
├── @claude-flow/testing    # TDD London School framework
├── @claude-flow/deployment # Release & CI/CD
└── @claude-flow/shared     # Shared utilities & types

Performance Metrics

Metric	Measured
Swarm task execution	100% success rate (7/7 strategies)
Average task duration	0.15-0.30 seconds
Memory usage per agent	128-320 MB
CPU utilization	15-30% per agent
Parallel agent capacity	Up to 15 concurrent

Topology Performance

Topology	Agents	Execution Time	Memory
Centralized	2-3	0.14-0.20s	180-256 MB
Distributed	4-5	0.10-0.12s	128-160 MB
Hierarchical	6	0.20s	256 MB
Mesh	4	0.15s	192 MB
Hybrid	7	0.18s	320 MB

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Cross-Platform Support

Windows (PowerShell)

npx @claude-flow/security@latest audit --platform windows
$env:CLAUDE_FLOW_MODE = "integration"

macOS (Bash/Zsh)

npx @claude-flow/security@latest audit --platform darwin
export CLAUDE_FLOW_SECURITY_MODE="strict"

Linux (Bash)

npx @claude-flow/security@latest audit --platform linux
export CLAUDE_FLOW_MEMORY_PATH="./data"

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Security

• Input Validation - All inputs validated at boundaries
• Path Traversal Prevention - Safe path handling with blocked patterns
• Command Injection Protection - Allowlisted commands only, shell metacharacter blocking
• Prototype Pollution Prevention - Safe JSON parsing
• CVE Remediation - Active security monitoring and patching
• TOCTOU Protection - Symlink skipping to prevent race conditions
• Information Disclosure Prevention - Error message sanitization

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Environment Variables

Variable	Description	Default
CLAUDE_FLOW_MODE	Operation mode (development, production, integration)	development
CLAUDE_FLOW_MEMORY_PATH	Directory for persistent memory storage	./data
CLAUDE_FLOW_SECURITY_MODE	Security level (strict, standard, permissive)	standard
CLAUDE_FLOW_LOG_LEVEL	Logging verbosity (debug, info, warn, error)	info
CLAUDE_FLOW_MAX_AGENTS	Maximum concurrent agents	15
CLAUDE_FLOW_TOPOLOGY	Default swarm topology	hierarchical
CLAUDE_FLOW_HNSW_M	HNSW index M parameter (connectivity)	16
CLAUDE_FLOW_HNSW_EF	HNSW search ef parameter (accuracy)	200
CLAUDE_FLOW_EMBEDDING_DIM	Vector embedding dimensions	384
ANTHROPIC_API_KEY	Anthropic API key for Claude integration	-

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Troubleshooting

Common Issues

MCP server won't start

# Check if port is in use
lsof -i :3000
# Kill existing process
kill -9 <PID>
# Restart MCP server
npx claude-flow mcp start

Agent spawn failures

# Check available memory
free -m
# Reduce max agents if memory constrained
export CLAUDE_FLOW_MAX_AGENTS=5

Pattern search returning no results

# Verify patterns are stored
npx claude-flow hooks metrics
# Re-run pretraining if empty
npx claude-flow hooks pretrain

Windows path issues

# Use forward slashes or escape backslashes
$env:CLAUDE_FLOW_MEMORY_PATH = "./data"
# Or use absolute path
$env:CLAUDE_FLOW_MEMORY_PATH = "C:/Users/name/claude-flow/data"

Permission denied errors

# Fix npm permissions (Linux/macOS)
sudo chown -R $(whoami) ~/.npm
# Or use nvm to manage Node.js

High memory usage

# Enable garbage collection
node --expose-gc node_modules/.bin/claude-flow
# Reduce HNSW parameters for lower memory
export CLAUDE_FLOW_HNSW_M=8
export CLAUDE_FLOW_HNSW_EF=100

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Migration Guide (V2 → V3)

Breaking Changes

1. Module Structure: V3 uses scoped packages (@claude-flow/*)
2. Memory Backend: Default changed from JSON to AgentDB with HNSW
3. Hooks System: New ReasoningBank replaces basic pattern storage
4. Security: Stricter input validation enabled by default

Upgrade Steps

# 1. Backup existing data
cp -r ./data ./data-backup-v2

# 2. Update to V3
npm install claude-flow@latest

# 3. Run migration
npx claude-flow migrate --from v2

# 4. Verify installation
npx claude-flow --version
npx claude-flow hooks metrics

Configuration Changes

# V2 (deprecated)
npx claude-flow init --mode basic

# V3 (new)
npx claude-flow init
npx claude-flow hooks pretrain  # Bootstrap learning

API Changes

V2 API	V3 API
claude-flow start	claude-flow mcp start
--pattern-store	--memory-backend agentdb
hooks record	hooks post-edit --success
swarm create	swarm init --topology

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Documentation

V3 Module Documentation

Module	Description	Docs
@claude-flow/plugins	Plugin SDK with workers, hooks, providers, security	README
@claude-flow/hooks	Event-driven lifecycle hooks + ReasoningBank	Source
@claude-flow/memory	AgentDB unification with HNSW indexing	Source
@claude-flow/security	CVE remediation & security patterns	Source
@claude-flow/swarm	15-agent coordination engine	Source
@claude-flow/cli	CLI modernization	Source
@claude-flow/neural	SONA learning integration	Source
@claude-flow/testing	TDD London School framework	Source
@claude-flow/mcp	MCP server & tools	Source
@claude-flow/embeddings	Vector embedding providers	Source
@claude-flow/providers	LLM provider integrations	Source
@claude-flow/integration	agentic-flow@alpha integration	Source
@claude-flow/performance	Benchmarking & optimization	Source
@claude-flow/deployment	Release & CI/CD	Source
@claude-flow/shared	Shared utilities & types	Source

Additional Resources

• V2 Documentation
• Architecture Decisions (ADRs)
• API Reference
• Examples

Support

• Documentation: https://github.com/ruvnet/claude-flow
• Issues: https://github.com/ruvnet/claude-flow/issues
• Discord: Agentics Foundation

License

MIT - RuvNet