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2026-01-14 03:25:34 +00:00

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Claude-Flow v3: Enterprise AI Orchestration Platform

Production-ready multi-agent AI orchestration for Claude Code

Deploy 54+ specialized agents in coordinated swarms with self-learning capabilities, fault-tolerant consensus, and enterprise-grade security.

Overview

Claude-Flow is a comprehensive AI agent orchestration framework that transforms Claude Code into a powerful multi-agent development platform. It enables teams to deploy, coordinate, and optimize specialized AI agents working together on complex software engineering tasks.

Claude Code: With vs Without Claude-Flow

Capability	Claude Code Alone	Claude Code + Claude-Flow
Agent Collaboration	Agents work in isolation, no shared context	Agents collaborate via swarms with shared memory and consensus
Coordination	Manual orchestration between tasks	Queen-led hierarchy with 5 consensus algorithms (Raft, Byzantine, Gossip)
Memory	Session-only, no persistence	HNSW vector memory with 150x-12,500x faster retrieval
Learning	Static behavior, no adaptation	SONA self-learning with <0.05ms adaptation, improves over time
Task Routing	You decide which agent to use	Intelligent routing based on learned patterns (89% accuracy)
Complex Tasks	Manual breakdown required	Automatic decomposition across 5 domains (Security, Core, Integration, Support)
Background Workers	Nothing runs automatically	12 context-triggered workers auto-dispatch on file changes, patterns, sessions
LLM Provider	Anthropic only	6 providers with automatic failover and cost-based routing (85% savings)
Security	Standard protections	CVE-hardened with bcrypt, input validation, path traversal prevention
Performance	Baseline	2.8-4.4x faster tasks, 4-32x memory reduction via quantization

Key Capabilities

54+ Specialized Agents - Ready-to-use AI agents for coding, code review, testing, security audits, documentation, and DevOps tasks. Each agent is optimized for its specific role.
Coordinated Agent Teams - Run unlimited agents simultaneously in organized swarms. Agents can spawn sub-workers, communicate, share context, and divide work automatically using patterns like hierarchical (queen/workers) or mesh (peer-to-peer).
Learns From Your Workflow - The system remembers what works. Successful patterns are stored and reused, routing similar tasks to the best-performing agents. Gets smarter over time.
Works With Any LLM - Switch between Claude, GPT-4, Gemini, Cohere, or local models like Llama. Automatic failover if one provider is unavailable. Smart routing picks the cheapest option that meets quality requirements.
Plugs Into Claude Code - Native integration via MCP (Model Context Protocol). Use claude-flow commands directly in your Claude Code sessions with full tool access.
Production-Ready Security - Built-in protection against common vulnerabilities: input validation, path traversal prevention, command injection blocking, and safe credential handling.

Why Claude-Flow v3?

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

Feature	Claude Flow v3	CrewAI	LangGraph	AutoGen	MetaGPT
Self-Learning	SONA + EWC++	-	-	-	-
Prevents Forgetting	EWC++ consolidation	-	-	-	-
Expert Routing	MoE (8 experts)	Manual roles	Graph edges	-	Fixed roles
Vector Memory	HNSW (150x faster)	-	Via plugins	-	-
Hyperbolic Embeddings	Poincaré ball model	-	-	-	-
Pattern Learning	From trajectories	-	-	-	-
Work Ownership	Claims system	-	-	-	-
Threat Detection	AIDefence	-	-	-	-
Attention Optimization	Flash Attention	-	-	-	-
Low-Rank Adaptation	LoRA (128x compression)	-	-	-	-
Quantization	Int8 (3.92x savings)	-	-	-	-
Consensus Protocols	5 (Raft, Byzantine, etc.)	-	-	-	-
Background Workers	12 auto-triggered	-	-	-	-
Multi-Provider LLM	6 with failover	2	3	2	1
MCP Integration	Native	-	-	-	-
Swarm Topologies	4 (mesh, hierarchical, etc.)	1	1	1	1

Key differentiators:

SONA (Self-Optimizing Neural Architecture) - Learns optimal agent routing from trajectory outcomes with <0.05ms adaptation
EWC++ (Elastic Weight Consolidation) - Prevents catastrophic forgetting when learning new patterns
MoE (Mixture of Experts) - 8 specialized experts with dynamic gating network for intelligent task routing
Flash Attention - 2.49x-7.47x speedup for attention computations
Hyperbolic Embeddings - Poincaré ball model for hierarchical data representation (better for code structure)
LoRA (Low-Rank Adaptation) - 128x memory compression for efficient fine-tuning
Int8 Quantization - 3.92x memory reduction with minimal accuracy loss
Claims System - Human-agent work ownership with handoff protocols
Byzantine Consensus - Fault-tolerant coordination even with malicious agents

Quick Start

Prerequisites

Node.js 18+ or Bun 1.0+ (Bun is faster)
npm 9+ / pnpm / bun package manager

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

# With npm/npx (Node.js)
npm install claude-flow@v3alpha
npx claude-flow@v3alpha init

# With Bun (faster)
bun add claude-flow@v3alpha
bunx claude-flow@v3alpha init

# Start MCP server for Claude Code integration
npx claude-flow@v3alpha mcp start

# Run a task with agents
npx claude-flow@v3alpha --agent coder --task "Implement user authentication"

# List available agents
npx claude-flow@v3alpha --list

Features

Core Platform Capabilities

Capability	Description	Metrics
54+ Specialized Agents	Purpose-built AI agents for development, testing, security, and operations	10 categories, unlimited 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	Recommended Agents	Best For	Execution Time	Memory/Agent
Hierarchical	6+	Structured tasks, clear authority chains	0.20s	256 MB
Mesh	4+	Collaborative work, high redundancy	0.15s	192 MB
Ring	3+	Sequential processing pipelines	0.12s	128 MB
Star	5+	Centralized control, spoke workers	0.14s	180 MB
Hybrid (Hierarchical-Mesh)	7+	Complex multi-domain tasks	0.18s	320 MB
Adaptive	2+	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-`, `hooks/post-`, `hooks/route`, `hooks/session-`, `hooks/intelligence/`, `hooks/worker/*`	31 lifecycle hooks
Progress	`progress/check`, `progress/sync`, `progress/summary`, `progress/watch`	V3 implementation tracking

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

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	12 auto-triggered workers for analysis and optimization	Automated maintenance
GitHub Integration	PR management, issue triage, code review automation	Repository workflow

Plugin System (`@claude-flow/plugins`)

Component	Description	Key Features
PluginBuilder	Fluent builder for creating plugins	MCP tools, hooks, workers, providers
MCPToolBuilder	Build MCP tools with typed parameters	String, number, boolean, enum params
HookBuilder	Build hooks with conditions and transformers	Priorities, conditional execution, data transformation
WorkerPool	Managed worker pool with auto-scaling	Min/max workers, task queuing, graceful shutdown
ProviderRegistry	LLM provider management with fallback	Cost optimization, automatic failover
AgenticFlowBridge	agentic-flow@alpha integration	Swarm coordination, agent spawning
AgentDBBridge	Vector storage with HNSW indexing	150x faster search, batch operations
Security Utilities	Input validation and protection	Path traversal, injection, rate limiting

Plugin Hook Events

Category	Events	Description
Session	`session:start`, `session:end`	Session lifecycle management
Agent	`agent:pre-spawn`, `agent:post-spawn`, `agent:pre-terminate`, `agent:post-terminate`	Agent lifecycle hooks
Task	`task:pre-execute`, `task:post-complete`, `task:error`	Task execution hooks
Tool	`tool:pre-call`, `tool:post-call`	MCP tool invocation hooks
Memory	`memory:pre-store`, `memory:post-store`, `memory:pre-retrieve`, `memory:post-retrieve`	Memory operation hooks
Swarm	`swarm:initialized`, `swarm:shutdown`, `swarm:consensus-reached`	Swarm coordination hooks
File	`file:pre-read`, `file:post-read`, `file:pre-write`, `file:post-write`	File operation hooks
Command	`command:pre-execute`, `command:post-execute`	Shell command hooks
Learning	`learning:pattern-learned`, `learning:pattern-applied`	Pattern learning hooks

Plugin Worker Types

Worker Type	Purpose	Capabilities
`coder`	Code implementation	Code generation, refactoring
`reviewer`	Code review	Quality analysis, suggestions
`tester`	Test generation/execution	Unit tests, integration tests
`researcher`	Information gathering	Web search, documentation
`planner`	Task planning	Decomposition, scheduling
`coordinator`	Multi-agent coordination	Orchestration, consensus
`security`	Security analysis	Vulnerability scanning, audit
`performance`	Performance optimization	Profiling, benchmarking
`specialized`	Custom capabilities	Domain-specific tasks
`long-running`	Background tasks	Async processing, polling

Plugin Performance

Metric	Target	Achieved
Plugin load time	<50ms	~20ms
Hook execution	<1ms	~0.5ms
Worker spawn	<100ms	~50ms
Vector search (10K)	<10ms	~5ms

RuVector WASM Plugins

Plugin	Description	Performance
SemanticCodeSearchPlugin	Semantic code search with vector embeddings	Real-time indexing
IntentRouterPlugin	Routes user intents to optimal handlers	95%+ accuracy
HookPatternLibraryPlugin	Pre-built patterns for common tasks	Security, testing, performance
MCPToolOptimizerPlugin	Optimizes MCP tool selection	Context-aware suggestions
ReasoningBankPlugin	Vector-backed pattern storage with HNSW	150x faster search
AgentConfigGeneratorPlugin	Generates optimized agent configurations	From pretrain data

Background Workers (12 Auto-Triggered)

Workers run automatically in the background based on context, or can be dispatched manually via MCP tools.

Worker	Trigger	Purpose	Auto-Triggers On
UltraLearn	`ultralearn`	Deep knowledge acquisition from codebase	New project, major refactors
Optimize	`optimize`	Performance optimization suggestions	Slow operations detected
Consolidate	`consolidate`	Memory pattern consolidation	Session end, memory threshold
Predict	`predict`	Predictive resource preloading	Usage patterns detected
Audit	`audit`	Security vulnerability analysis	Security-related file changes
Map	`map`	Codebase structure mapping	New directories, large changes
Preload	`preload`	Resource and dependency preloading	Project initialization
DeepDive	`deepdive`	Deep code analysis and understanding	Complex file edits
Document	`document`	Auto-documentation generation	New functions/classes created
Refactor	`refactor`	Refactoring opportunity detection	Code smell patterns
Benchmark	`benchmark`	Performance benchmarking	Performance-critical changes
TestGaps	`testgaps`	Test coverage gap analysis	Code changes without tests

Worker Commands:

# Dispatch a worker manually
npx claude-flow@v3alpha worker dispatch --trigger audit --context "./src"

# Check worker status
npx claude-flow@v3alpha worker status

# View completed results
npx claude-flow@v3alpha worker results --limit 10

LLM Providers (`@claude-flow/providers`)

Provider	Models	Features	Cost
Anthropic	Claude 3.5 Sonnet, Claude 3 Opus, Claude 3 Haiku	Native, streaming, tool calling	$3-15/1M tokens
OpenAI	GPT-4o, GPT-4 Turbo, GPT-3.5, o1-preview, o3-mini	Function calling, vision	$0.50-60/1M tokens
Google	Gemini 2.0 Flash, Gemini 1.5 Pro/Flash	Multimodal, long context	$0.075-7/1M tokens
Cohere	Command R+, Command R, Command Light	RAG optimized	$0.50-15/1M tokens
Ollama	Llama 3.2, Mistral, CodeLlama, DeepSeek	Local, free, offline	Free
RuVector	Custom models via @ruvector/ruvllm	WASM optimized	Custom

Provider Load Balancing

Strategy	Description	Best For
`round-robin`	Rotate through providers sequentially	Even distribution
`least-loaded`	Use provider with lowest current load	High throughput
`latency-based`	Use fastest responding provider	Low latency
`cost-based`	Use cheapest provider that meets requirements	Cost optimization (85%+ savings)

Embedding Providers (`@claude-flow/embeddings`)

Provider	Models	Dimensions	Latency	Cost
Agentic-Flow	ONNX SIMD optimized	384	~3ms	Free (local)
OpenAI	text-embedding-3-small/large, ada-002	1536-3072	~50-100ms	$0.02-0.13/1M tokens
Transformers.js	all-MiniLM-L6-v2, all-mpnet-base-v2, bge-small	384-768	~230ms	Free (local)
Mock	Deterministic hash-based	Configurable	<1ms	Free

Embedding Features

Feature	Description	Performance
Auto-Install	`provider: 'auto'` installs agentic-flow automatically	Zero config
Smart Fallback	agentic-flow → transformers → mock chain	Always works
75x Faster	Agentic-flow ONNX vs Transformers.js	3ms vs 230ms
LRU Caching	Intelligent cache with hit rate tracking	<1ms cache hits
Batch Processing	Efficient batch embedding with partial cache	10 items <100ms
Similarity Functions	Cosine, Euclidean, Dot product	Optimized math

Consensus Strategies (`@claude-flow/swarm`)

Strategy	Algorithm	Fault Tolerance	Latency	Best For
Byzantine (PBFT)	Practical Byzantine Fault Tolerance	f < n/3 faulty nodes	~100ms	Adversarial environments
Raft	Leader-based log replication	f < n/2 failures	~50ms	Strong consistency
Gossip	Epidemic protocol dissemination	High partition tolerance	~200ms	Eventually consistent
CRDT	Conflict-free Replicated Data Types	Strong eventual consistency	~10ms	Concurrent updates
Quorum	Configurable read/write quorums	Flexible	~75ms	Tunable consistency

CLI Commands (`@claude-flow/cli`)

Command	Subcommands	Description
`init`	4	Project initialization (wizard, check, skills, hooks)
`agent`	8	Agent lifecycle (spawn, list, status, stop, metrics, pool, health, logs)
`swarm`	6	Swarm coordination (init, start, status, stop, scale, coordinate)
`memory`	11	Memory operations (store, retrieve, search, list, delete, stats, configure, cleanup, compress, export, import)
`mcp`	9	MCP server (start, stop, status, health, restart, tools, toggle, exec, logs)
`task`	6	Task management (create, list, status, cancel, assign, retry)
`session`	7	Session management (list, save, restore, delete, export, import, current)
`config`	7	Configuration (init, get, set, providers, reset, export, import)
`status`	3	System status with watch mode (agents, tasks, memory)
`workflow`	6	Workflow execution (run, validate, list, status, stop, template)
`hooks`	32	Self-learning hooks (pre/post-edit, pre/post-command, route, explain, pretrain, session-, intelligence/, worker/*, progress)
`hive-mind`	6	Queen-led coordination (init, spawn, status, task, optimize-memory, shutdown)
`migrate`	5	V2→V3 migration (status, run, verify, rollback, breaking)
`neural`	5	Neural pattern training (train, status, patterns, predict, optimize)
`security`	6	Security scanning (scan, audit, cve, threats, validate, report)
`performance`	5	Performance profiling (benchmark, profile, metrics, optimize, report)
`providers`	5	AI providers (list, add, remove, test, configure)
`plugins`	5	Plugin management (list, install, uninstall, enable, disable)
`deployment`	5	Deployment management (deploy, rollback, status, environments, release)
`embeddings`	13	Vector embeddings with ONNX, hyperbolic space, neural substrate
`daemon`	5	Background workers (start, stop, status, trigger, enable)
`progress`	4	V3 implementation progress (check, sync, summary, watch)
`claims`	4	Authorization (check, grant, revoke, list)

Testing Framework (`@claude-flow/testing`)

Component	Description	Features
London School TDD	Behavior verification with mocks	Mock-first, interaction testing
Vitest Integration	ADR-008 compliant test runner	10x faster than Jest
Fixture Library	Pre-defined test data	Agents, memory, swarm, MCP
Mock Factory	Application and service mocks	Auto-reset, state tracking
Async Utilities	waitFor, retry, withTimeout	Reliable async testing
Performance Assertions	V3 target validation	Speedup, memory, latency checks

Testing Fixtures

Fixture Type	Contents	Use Case
`agentConfigs`	15 V3 agent configurations	Agent testing
`memoryEntries`	Patterns, rules, embeddings	Memory testing
`swarmConfigs`	V3 default, minimal, mesh, hierarchical	Swarm testing
`mcpTools`	27+ tool definitions	MCP testing

Deployment & CI/CD (`@claude-flow/deployment`)

Feature	Description	Automation
Version Bumping	major, minor, patch, prerelease	Automatic semver
Changelog Generation	Conventional commits parsing	Auto-generated
Git Integration	Tagging, committing	Automatic
NPM Publishing	alpha, beta, rc, latest tags	Tag-based
Validation	Lint, test, build, dependency checks	Pre-release
Dry Run Mode	Test releases without changes	Safe testing

Release Channels

Channel	Version Format	Purpose
`alpha`	1.0.0-alpha.1	Early development
`beta`	1.0.0-beta.1	Feature complete, testing
`rc`	1.0.0-rc.1	Release candidate
`latest`	1.0.0	Stable production

Integration (`@claude-flow/integration`)

Component	Description	Performance
AgenticFlowBridge	agentic-flow@alpha integration	ADR-001 compliant
SONA Adapter	Learning system integration	<0.05ms adaptation
Flash Attention	Attention mechanism coordinator	2.49x-7.47x speedup
SDK Bridge	Version negotiation, API compatibility	Auto-detection
Feature Flags	Dynamic feature management	9 configurable flags
Runtime Detection	NAPI, WASM, JS auto-selection	Optimal performance

Integration Runtimes

Runtime	Performance	Requirements
NAPI	Optimal	Native bindings, x64
WASM	Good	WebAssembly support
JS	Fallback	Always available

Performance Benchmarking (`@claude-flow/performance`)

Capability	Description	Output
Statistical Analysis	Mean, median, P95, P99, stddev	Comprehensive metrics
Memory Tracking	Heap, RSS, external, array buffers	Resource monitoring
Auto-Calibration	Automatic iteration adjustment	Statistical significance
Regression Detection	Baseline comparison	Change detection
V3 Target Validation	Built-in performance targets	Pass/fail checking

V3 Benchmark Targets

Category	Benchmark	Target
Startup	CLI cold start	<500ms
Startup	MCP server init	<400ms
Startup	Agent spawn	<200ms
Memory	Vector search	<1ms
Memory	HNSW indexing	<10ms
Memory	Memory write	<5ms
Swarm	Agent coordination	<50ms
Swarm	Consensus latency	<100ms
Neural	SONA adaptation	<0.05ms

Neural & SONA (`@claude-flow/neural`)

Feature	Description	Performance
SONA Learning	Self-Optimizing Neural Architecture	<0.05ms adaptation
5 Learning Modes	real-time, balanced, research, edge, batch	Mode-specific optimization
9 RL Algorithms	PPO, A2C, DQN, Q-Learning, SARSA, Decision Transformer, etc.	Comprehensive RL
LoRA Integration	Low-Rank Adaptation for efficient fine-tuning	Minimal memory overhead
MicroLoRA	Ultra-lightweight LoRA for edge/real-time modes	<5MB memory footprint
EWC++ Memory	Elastic Weight Consolidation prevents catastrophic forgetting	Zero knowledge loss
Trajectory Tracking	Execution path recording for pattern extraction	Continuous learning

Memory & Vector Optimization

Feature	Description	Improvement
Scalar Quantization	Reduce vector precision for memory savings	4x memory reduction
Product Quantization	Compress vectors into codebooks	8-32x memory reduction
HNSW Indexing	Hierarchical Navigable Small World graphs	150x-12,500x faster search
LRU Caching	Intelligent embedding cache with TTL	<1ms cache hits
Batch Processing	Process multiple embeddings in single call	10x throughput
Memory Compression	Pattern distillation and pruning	50-75% reduction

Embedding System (`@claude-flow/embeddings`)

Feature	Description	Performance
Multi-Provider	Agentic-Flow (ONNX), OpenAI, Transformers.js, Mock	4 providers
Auto-Install	`claude-flow embeddings init` or `createEmbeddingServiceAsync()`	Zero config
75x Faster	Agentic-flow ONNX SIMD vs Transformers.js	3ms vs 230ms
Hyperbolic Space	Poincaré ball model for hierarchical data	Exponential capacity
Dimensions	384 to 3072 configurable	Quality vs speed tradeoff
Similarity Metrics	Cosine, Euclidean, Dot product, Hyperbolic distance	Task-specific matching
Neural Substrate	Drift detection, memory physics, swarm coordination	agentic-flow integration
LRU + SQLite Cache	Persistent cross-session caching	<1ms cache hits

# Initialize ONNX embeddings with hyperbolic config
claude-flow embeddings init

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

# Semantic search
claude-flow embeddings search -q "authentication patterns"

SONA Learning Modes

Mode	Adaptation	Quality	Memory	Use Case
`real-time`	<0.5ms	70%+	25MB	Production, low-latency
`balanced`	<18ms	75%+	50MB	General purpose
`research`	<100ms	95%+	100MB	Deep exploration
`edge`	<1ms	80%+	5MB	Resource-constrained
`batch`	<50ms	85%+	75MB	High-throughput

RL Algorithms

Algorithm	Type	Best For
PPO	Policy Gradient	Stable continuous learning
A2C	Actor-Critic	Balanced exploration/exploitation
DQN	Value-based	Discrete action spaces
Q-Learning	Tabular	Simple state spaces
SARSA	On-policy	Online learning
Decision Transformer	Sequence modeling	Long-horizon planning

Hive-Mind Coordination

Feature	Description	Capability
Queen-Led Topology	Hierarchical command structure	Unlimited agents + sub-workers
Byzantine Consensus	Fault-tolerant agreement	f < n/3 tolerance
Collective Memory	Shared pattern storage	Distillation, compression
Specialist Spawning	Domain-specific agents	Security, performance, etc.
Adaptive Topology	Dynamic structure changes	Load-based optimization

agentic-flow@alpha Integration

Feature	Description	Benefit
ADR-001 Compliance	Build on agentic-flow, don't duplicate	Eliminates 10,000+ duplicate lines
Core Foundation	Use agentic-flow as the base layer	Unified architecture
SONA Integration	Seamless learning system connection	<0.05ms adaptation
Flash Attention	Optimized attention mechanisms	2.49x-7.47x speedup
AgentDB Bridge	Vector storage integration	150x-12,500x faster search
Feature Flags	Dynamic capability management	9 configurable features
Runtime Detection	NAPI/WASM/JS auto-selection	Optimal performance per platform
Graceful Fallback	Works with or without agentic-flow	Always functional

MCP Server (`@claude-flow/mcp`)

Feature	Description	Spec
MCP 2025-11-25	Full specification compliance	Latest MCP standard
Multiple Transports	stdio, HTTP, WebSocket, in-process	Flexible connectivity
Resources	list, read, subscribe with caching	Dynamic content
Prompts	Templates with arguments and embedding	Reusable prompts
Tasks	Async operations with progress/cancel	Long-running ops
Tool Registry	O(1) lookup, <10ms registration	Fast tool access
Connection Pooling	Max 10 connections, configurable	Resource management
Session Management	Timeout handling, authentication	Secure sessions

MCP Methods

Method	Description
`initialize`	Initialize connection
`tools/list`	List available tools
`tools/call`	Execute a tool
`resources/list`	List resources with pagination
`resources/read`	Read resource content
`resources/subscribe`	Subscribe to updates
`prompts/list`	List prompts with pagination
`prompts/get`	Get prompt with arguments
`tasks/status`	Get task status
`tasks/cancel`	Cancel running task
`completion/complete`	Auto-complete arguments

Security Module (`@claude-flow/security`)

Feature	CVE/Issue	Description
Password Hashing	CVE-2	Secure bcrypt with 12+ rounds
Credential Generation	CVE-3	Cryptographically secure API keys
Safe Command Execution	HIGH-1	Allowlist-based command execution
Path Validation	HIGH-2	Path traversal and symlink protection
Input Validation	General	Zod-based schema validation
Token Generation	General	HMAC-signed secure tokens
HTML Sanitization	XSS	Script and injection prevention

Security Validation Schemas

Schema	Purpose
`SafeStringSchema`	Basic safe string with length limits
`IdentifierSchema`	Alphanumeric identifiers
`FilenameSchema`	Safe filenames
`EmailSchema`	Email addresses
`PasswordSchema`	Secure passwords (8-72 chars)
`UUIDSchema`	UUID v4 format
`HttpsUrlSchema`	HTTPS URLs only
`SpawnAgentSchema`	Agent spawn requests
`TaskInputSchema`	Task definitions

Hooks System (`@claude-flow/hooks`)

Component	Description	Performance
ReasoningBank	Pattern storage with HNSW indexing	150x faster retrieval
GuidanceProvider	Context-aware development guidance	Real-time suggestions
PatternLearning	Automatic strategy extraction	Continuous improvement
QualityTracking	Success/failure rate per pattern	Performance metrics
DomainDetection	Auto-categorization of patterns	Security, testing, etc.
AgentRouting	Task-to-agent optimization	Historical performance
Consolidation	Prune low-quality, promote high-quality	Memory optimization

Hook Lifecycle Events

Phase	Hooks	Purpose
Pre-Edit	`pre-edit`	Context gathering, security checks
Post-Edit	`post-edit`	Outcome recording, pattern learning
Pre-Command	`pre-command`	Risk assessment, validation
Post-Command	`post-command`	Success/failure tracking
Pre-Task	`pre-task`	Setup, resource allocation
Post-Task	`post-task`	Cleanup, learning
Session	`session-end`, `session-restore`	State management

V3 Statusline (`@claude-flow/hooks`)

Real-time development status display for Claude Code integration showing DDD progress, swarm activity, security status, and system metrics.

Output Format:

▊ Claude Flow V3 ● ruvnet  │  ⎇ v3  │  Opus 4.5
─────────────────────────────────────────────────────
🏗️  DDD Domains    [●●●●●]  5/5    ⚡ 1.0x → 2.49x-7.47x
🤖 Swarm  ◉ [58/15]  👥 0    🟢 CVE 3/3    💾 22282MB    📂  47%    🧠  10%
🔧 Architecture    DDD ● 98%  │  Security ●CLEAN  │  Memory ●AgentDB  │  Integration ●

Indicator	Description	Values
`▊ Claude Flow V3`	Project header	Always shown
`● ruvnet`	GitHub user (via `gh` CLI)	Dynamic
`⎇ v3`	Current git branch	Dynamic
`Opus 4.5`	Claude model name	From Claude Code
`[●●●●●]`	DDD domain progress bar	0-5 domains
`⚡ 1.0x → 2.49x-7.47x`	Performance speedup target	Current → Target
`◉/○`	Swarm coordination status	Active/Inactive
`[58/15]`	Active agents / max agents	Process count
`👥 0`	Sub-agents spawned	Task tool agents
`🟢 CVE 3/3`	Security CVE remediation	Fixed/Total
`💾 22282MB`	Memory usage (Node.js processes)	Real-time
`📂 47%`	Context window usage	From Claude Code
`🧠 10%`	Intelligence score (patterns learned)	0-100%
`DDD ● 98%`	Domain-Driven Design progress	Percentage
`Security ●CLEAN`	Security audit status	CLEAN/PENDING/FAILED
`Memory ●AgentDB`	Memory backend in use	AgentDB/SQLite/Hybrid
`Integration ●`	agentic-flow integration status	Active/Inactive

Usage:

# V3 statusline (Node.js)
node v3/@claude-flow/hooks/bin/statusline.js

# JSON output for scripting
node v3/@claude-flow/hooks/bin/statusline.js --json

# Compact JSON (single line)
node v3/@claude-flow/hooks/bin/statusline.js --compact

# Help
node v3/@claude-flow/hooks/bin/statusline.js --help

Claude Code Integration:

Add to .claude/settings.json:

{
  "statusLine": {
    "type": "command",
    "command": "node v3/@claude-flow/hooks/bin/statusline.js"
  }
}

Data Sources:

.claude-flow/metrics/v3-progress.json - DDD domain progress
.claude-flow/metrics/swarm-activity.json - Active agent counts
.claude-flow/security/audit-status.json - CVE remediation status
.claude-flow/learning/patterns.db - Intelligence score (pattern count)
Process detection via ps aux - Real-time memory and agent counts
Git branch via git branch --show-current
GitHub user via gh api user

Background Daemons

V3 Node.js Worker Daemon (Recommended)

Cross-platform TypeScript-based daemon service with auto-scheduling:

Worker	Interval	Priority	Description
`map`	5min	normal	Codebase structure mapping
`audit`	10min	critical	Security vulnerability scanning
`optimize`	15min	high	Performance optimization
`consolidate`	30min	low	Memory consolidation
`testgaps`	20min	normal	Test coverage analysis

Commands:

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

# Check status with worker history
npx claude-flow@v3alpha daemon status

# Manually trigger a worker
npx claude-flow@v3alpha daemon trigger map

# Enable/disable workers
npx claude-flow@v3alpha daemon enable map audit optimize

# Stop daemon
npx claude-flow@v3alpha daemon stop

Daemon Status Output:

+-- 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   |
+-------------+----+----------+------+---------+----------+----------+

Legacy Shell Daemons (V2)

Shell-based daemons for monitoring (Linux/macOS only):

Daemon	Interval	Purpose	Output
Swarm Monitor	3s	Process detection, agent counting	`swarm-activity.json`
Metrics Daemon	30s	V3 progress sync, SQLite metrics	`metrics.db`

Commands:

# 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

Worker Manager (7 Scheduled Workers)

Worker	Interval	Purpose
`perf`	5 min	Performance benchmarks
`health`	5 min	Disk, memory, CPU monitoring
`patterns`	15 min	Pattern dedup & pruning
`ddd`	10 min	DDD progress tracking
`adr`	15 min	ADR compliance checking
`security`	30 min	Security vulnerability scans
`learning`	30 min	Learning pattern optimization

Commands:

# 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

Use Cases

Use Case	Command
Code review	`npx claude-flow@v3alpha --agent reviewer --task "Review PR #123"`
Test generation	`npx claude-flow@v3alpha --agent tester --task "Write tests for auth module"`
Security audit	`npx claude-flow@v3alpha --agent security-architect --task "Audit for vulnerabilities"`
Multi-agent swarm	`npx claude-flow@v3alpha swarm init --topology hierarchical`
Route task	`npx claude-flow@v3alpha hooks route "Optimize database queries"`
Performance analysis	`npx claude-flow@v3alpha --agent perf-analyzer --task "Profile API endpoints"`
GitHub PR management	`npx claude-flow@v3alpha --agent pr-manager --task "Review open PRs"`
Check V3 progress	`npx claude-flow@v3alpha progress --detailed`
Sync progress metrics	`npx claude-flow@v3alpha progress sync`

Self-Learning Hooks Commands (27 Hooks)

Core Tool Lifecycle Hooks

# Before/after file editing
npx claude-flow@v3alpha hooks pre-edit <filePath>
npx claude-flow@v3alpha hooks post-edit <filePath> --success true --train-patterns

# Before/after commands
npx claude-flow@v3alpha hooks pre-command "<command>"
npx claude-flow@v3alpha hooks post-command "<command>" --success true

# Before/after tasks
npx claude-flow@v3alpha hooks pre-task --description "<task>"
npx claude-flow@v3alpha hooks post-task --task-id "<id>" --success true

Intelligence & Routing Hooks

# Route task to optimal agent using learned patterns
npx claude-flow@v3alpha hooks route "<task description>" --include-explanation

# Explain routing decision with transparency
npx claude-flow@v3alpha hooks explain "<topic>" --depth comprehensive

# Bootstrap intelligence from repository
npx claude-flow@v3alpha hooks pretrain --model-type moe --epochs 10

# Generate optimized agent configs from pretrain data
npx claude-flow@v3alpha hooks build-agents --agent-types coder,tester --config-format yaml

# Transfer patterns from another project
npx claude-flow@v3alpha hooks transfer <sourceProject>

# Initialize hooks system
npx claude-flow@v3alpha hooks init

# View learning metrics dashboard
npx claude-flow@v3alpha hooks metrics

# List all registered hooks
npx claude-flow@v3alpha hooks list

Session Management Hooks

# Start session with context loading
npx claude-flow@v3alpha hooks session-start --session-id "<id>" --load-context

# End session with persistence
npx claude-flow@v3alpha hooks session-end --export-metrics true --persist-patterns

# Restore previous session context
npx claude-flow@v3alpha hooks session-restore --session-id "<id>"

# Send notifications to swarm
npx claude-flow@v3alpha hooks notify --message "<message>" --swarm-status

RuVector Intelligence Hooks (Reinforcement Learning)

# Trajectory-based learning (4-step pipeline: RETRIEVE, JUDGE, DISTILL, CONSOLIDATE)
npx claude-flow@v3alpha hooks intelligence trajectory-start --session "<session>"
npx claude-flow@v3alpha hooks intelligence trajectory-step --action "<action>" --reward 0.9
npx claude-flow@v3alpha hooks intelligence trajectory-end --verdict success

# Pattern storage with HNSW indexing (150x faster search)
npx claude-flow@v3alpha hooks intelligence pattern-store --pattern "<pattern>" --embedding "[...]"
npx claude-flow@v3alpha hooks intelligence pattern-search --query "<query>" --limit 10

# Learning stats and attention focus
npx claude-flow@v3alpha hooks intelligence stats
npx claude-flow@v3alpha hooks intelligence learn --experience '{"type":"success"}'
npx claude-flow@v3alpha hooks intelligence attention --focus "<task>"

# Full intelligence system (SONA, MoE, HNSW, EWC++, Flash Attention)
npx claude-flow@v3alpha hooks intelligence
npx claude-flow@v3alpha hooks intelligence reset --confirm

# ═══════════════════════════════════════════════════════════════
# Background Worker Commands (12 workers for analysis/optimization)
# ═══════════════════════════════════════════════════════════════

# List all available workers
npx claude-flow@v3alpha hooks worker list

# Detect triggers from prompt text
npx claude-flow@v3alpha hooks worker detect --prompt "optimize performance"

# Auto-dispatch workers when triggers match (confidence ≥0.6)
npx claude-flow@v3alpha hooks worker detect --prompt "deep dive into auth" --auto-dispatch --min-confidence 0.6

# Manually dispatch a worker (ultralearn, optimize, audit, map, deepdive, document, refactor, benchmark, testgaps, etc.)
npx claude-flow@v3alpha hooks worker dispatch --trigger refactor --context "auth module"

# Check worker status
npx claude-flow@v3alpha hooks worker status

# Cancel a running worker
npx claude-flow@v3alpha hooks worker cancel --id worker_refactor_1_abc123

Progress Tracking Hooks

# Check V3 implementation progress
npx claude-flow@v3alpha hooks progress

# Detailed breakdown by category (CLI, MCP, Hooks, Packages, DDD)
npx claude-flow@v3alpha hooks progress --detailed

# Sync progress and persist to file
npx claude-flow@v3alpha hooks progress --sync

# Get human-readable summary
npx claude-flow@v3alpha hooks progress --summary

# JSON output for scripting
npx claude-flow@v3alpha progress --json

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	Unlimited (resource-dependent)

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

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"

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`	Default concurrent agent limit (increase for more parallelism)	`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	-

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@v3alpha 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@v3alpha hooks metrics
# Re-run pretraining if empty
npx claude-flow@v3alpha 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

Migration Guide (V2 → V3)

Breaking Changes

Module Structure: V3 uses scoped packages (@claude-flow/*)
Memory Backend: Default changed from JSON to AgentDB with HNSW
Hooks System: New ReasoningBank replaces basic pattern storage
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@v3alpha migrate --from v2

# 4. Verify installation
npx claude-flow@v3alpha --version
npx claude-flow@v3alpha hooks metrics

Configuration Changes

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

# V3 (new)
npx claude-flow@v3alpha init
npx claude-flow@v3alpha 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`

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 & V3ProgressService	Source

53 KiB Raw Blame History

Claude-Flow v3: Enterprise AI Orchestration Platform

Overview

Claude Code: With vs Without Claude-Flow

Key Capabilities

Why Claude-Flow v3?

Quick Start

Prerequisites

Installation

Features

Core Platform Capabilities

Agent Ecosystem

Swarm Topologies

Self-Learning & Intelligence

Memory & Storage

MCP Tools & Integration

Security Features

Advanced Capabilities

Plugin System (@claude-flow/plugins)

Plugin Hook Events

Plugin Worker Types

Plugin Performance

RuVector WASM Plugins

Background Workers (12 Auto-Triggered)

LLM Providers (@claude-flow/providers)

Provider Load Balancing

Embedding Providers (@claude-flow/embeddings)

Embedding Features

Consensus Strategies (@claude-flow/swarm)

CLI Commands (@claude-flow/cli)

Testing Framework (@claude-flow/testing)

Testing Fixtures

Deployment & CI/CD (@claude-flow/deployment)

Release Channels

Integration (@claude-flow/integration)

Integration Runtimes

Performance Benchmarking (@claude-flow/performance)

V3 Benchmark Targets

Neural & SONA (@claude-flow/neural)

Memory & Vector Optimization

Embedding System (@claude-flow/embeddings)

SONA Learning Modes

RL Algorithms

Hive-Mind Coordination

agentic-flow@alpha Integration

MCP Server (@claude-flow/mcp)

MCP Methods

Security Module (@claude-flow/security)

Security Validation Schemas

Hooks System (@claude-flow/hooks)

Hook Lifecycle Events

V3 Statusline (@claude-flow/hooks)

Background Daemons

V3 Node.js Worker Daemon (Recommended)

Legacy Shell Daemons (V2)

Worker Manager (7 Scheduled Workers)

Use Cases

Self-Learning Hooks Commands (27 Hooks)

Core Tool Lifecycle Hooks

Intelligence & Routing Hooks

Session Management Hooks

RuVector Intelligence Hooks (Reinforcement Learning)

Progress Tracking Hooks

Architecture

V3 Module Structure

Performance Metrics

Topology Performance

Cross-Platform Support

Windows (PowerShell)

macOS (Bash/Zsh)

Linux (Bash)

Environment Variables

Troubleshooting

Common Issues

Migration Guide (V2 → V3)

Breaking Changes

Upgrade Steps

Configuration Changes

API Changes

Documentation

53 KiB

Raw Blame History

Plugin System (`@claude-flow/plugins`)

LLM Providers (`@claude-flow/providers`)

Embedding Providers (`@claude-flow/embeddings`)

Consensus Strategies (`@claude-flow/swarm`)

CLI Commands (`@claude-flow/cli`)

Testing Framework (`@claude-flow/testing`)

Deployment & CI/CD (`@claude-flow/deployment`)

Integration (`@claude-flow/integration`)

Performance Benchmarking (`@claude-flow/performance`)

Neural & SONA (`@claude-flow/neural`)

Embedding System (`@claude-flow/embeddings`)

MCP Server (`@claude-flow/mcp`)

Security Module (`@claude-flow/security`)

Hooks System (`@claude-flow/hooks`)

V3 Statusline (`@claude-flow/hooks`)