Claude Flow V3 - Complete Capabilities Reference

Generated: 2026-02-28T16:04:10.839Z Full documentation: https://github.com/ruvnet/claude-flow

📋 Table of Contents

Overview
Swarm Orchestration
Available Agents (60+)
CLI Commands (26 Commands, 140+ Subcommands)
Hooks System (27 Hooks + 12 Workers)
Memory & Intelligence (RuVector)
Hive-Mind Consensus
Performance Targets
Integration Ecosystem

Overview

Claude Flow V3 is a domain-driven design architecture for multi-agent AI coordination with:

15-Agent Swarm Coordination with hierarchical and mesh topologies
HNSW Vector Search - 150x-12,500x faster pattern retrieval
SONA Neural Learning - Self-optimizing with <0.05ms adaptation
Byzantine Fault Tolerance - Queen-led consensus mechanisms
MCP Server Integration - Model Context Protocol support

Current Configuration

Setting	Value
Topology	hierarchical-mesh
Max Agents	15
Memory Backend	hybrid
HNSW Indexing	Enabled
Neural Learning	Enabled
LearningBridge	Enabled (SONA + ReasoningBank)
Knowledge Graph	Enabled (PageRank + Communities)
Agent Scopes	Enabled (project/local/user)

Swarm Orchestration

Topologies

Topology	Description	Best For
`hierarchical`	Queen controls workers directly	Anti-drift, tight control
`mesh`	Fully connected peer network	Distributed tasks
`hierarchical-mesh`	V3 hybrid (recommended)	10+ agents
`ring`	Circular communication	Sequential workflows
`star`	Central coordinator	Simple coordination
`adaptive`	Dynamic based on load	Variable workloads

Strategies

balanced - Even distribution across agents
specialized - Clear roles, no overlap (anti-drift)
adaptive - Dynamic task routing

Quick Commands

bash

# Initialize swarm
npx @claude-flow/cli@latest swarm init --topology hierarchical --max-agents 8 --strategy specialized

# Check status
npx @claude-flow/cli@latest swarm status

# Monitor activity
npx @claude-flow/cli@latest swarm monitor

Available Agents

Core Development (5)

coder, reviewer, tester, planner, researcher

V3 Specialized (4)

security-architect, security-auditor, memory-specialist, performance-engineer

Swarm Coordination (5)

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

Consensus & Distributed (7)

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

Performance & Optimization (5)

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

GitHub & Repository (9)

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

SPARC Methodology (6)

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

Specialized Development (8)

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

Testing & Validation (2)

tdd-london-swarm, production-validator

Agent Routing by Task

Task Type	Recommended Agents	Topology
Bug Fix	researcher, coder, tester	mesh
New Feature	coordinator, architect, coder, tester, reviewer	hierarchical
Refactoring	architect, coder, reviewer	mesh
Performance	researcher, perf-engineer, coder	hierarchical
Security	security-architect, auditor, reviewer	hierarchical
Docs	researcher, api-docs	mesh

CLI Commands

Core Commands (12)

Command	Subcommands	Description
`init`	4	Project initialization
`agent`	8	Agent lifecycle management
`swarm`	6	Multi-agent coordination
`memory`	11	AgentDB with HNSW search
`mcp`	9	MCP server management
`task`	6	Task assignment
`session`	7	Session persistence
`config`	7	Configuration
`status`	3	System monitoring
`workflow`	6	Workflow templates
`hooks`	17	Self-learning hooks
`hive-mind`	6	Consensus coordination

Advanced Commands (14)

Command	Subcommands	Description
`daemon`	5	Background workers
`neural`	5	Pattern training
`security`	6	Security scanning
`performance`	5	Profiling & benchmarks
`providers`	5	AI provider config
`plugins`	5	Plugin management
`deployment`	5	Deploy management
`embeddings`	4	Vector embeddings
`claims`	4	Authorization
`migrate`	5	V2→V3 migration
`process`	4	Process management
`doctor`	1	Health diagnostics
`completions`	4	Shell completions

Example Commands

bash

# Initialize
npx @claude-flow/cli@latest init --wizard

# Spawn agent
npx @claude-flow/cli@latest agent spawn -t coder --name my-coder

# Memory operations
npx @claude-flow/cli@latest memory store --key "pattern" --value "data" --namespace patterns
npx @claude-flow/cli@latest memory search --query "authentication"

# Diagnostics
npx @claude-flow/cli@latest doctor --fix

Hooks System

27 Available Hooks

Core Hooks (6)

Hook	Description
`pre-edit`	Context before file edits
`post-edit`	Record edit outcomes
`pre-command`	Risk assessment
`post-command`	Command metrics
`pre-task`	Task start + agent suggestions
`post-task`	Task completion learning

Session Hooks (4)

Hook	Description
`session-start`	Start/restore session
`session-end`	Persist state
`session-restore`	Restore previous
`notify`	Cross-agent notifications

Intelligence Hooks (5)

Hook	Description
`route`	Optimal agent routing
`explain`	Routing decisions
`pretrain`	Bootstrap intelligence
`build-agents`	Generate configs
`transfer`	Pattern transfer

Coverage Hooks (3)

Hook	Description
`coverage-route`	Coverage-based routing
`coverage-suggest`	Improvement suggestions
`coverage-gaps`	Gap analysis

12 Background Workers

Worker	Priority	Purpose
`ultralearn`	normal	Deep knowledge
`optimize`	high	Performance
`consolidate`	low	Memory consolidation
`predict`	normal	Predictive preload
`audit`	critical	Security
`map`	normal	Codebase mapping
`preload`	low	Resource preload
`deepdive`	normal	Deep analysis
`document`	normal	Auto-docs
`refactor`	normal	Suggestions
`benchmark`	normal	Benchmarking
`testgaps`	normal	Coverage gaps

Memory & Intelligence

RuVector Intelligence System

SONA: Self-Optimizing Neural Architecture (<0.05ms)
MoE: Mixture of Experts routing
HNSW: 150x-12,500x faster search
EWC++: Prevents catastrophic forgetting
Flash Attention: 2.49x-7.47x speedup
Int8 Quantization: 3.92x memory reduction

4-Step Intelligence Pipeline

RETRIEVE - HNSW pattern search
JUDGE - Success/failure verdicts
DISTILL - LoRA learning extraction
CONSOLIDATE - EWC++ preservation

Self-Learning Memory (ADR-049)

Component	Status	Description
LearningBridge	✅ Enabled	Connects insights to SONA/ReasoningBank neural pipeline
MemoryGraph	✅ Enabled	PageRank knowledge graph + community detection
AgentMemoryScope	✅ Enabled	3-scope agent memory (project/local/user)

LearningBridge - Insights trigger learning trajectories. Confidence evolves: +0.03 on access, -0.005/hour decay. Consolidation runs the JUDGE/DISTILL/CONSOLIDATE pipeline.

MemoryGraph - Builds a knowledge graph from entry references. PageRank identifies influential insights. Communities group related knowledge. Graph-aware ranking blends vector + structural scores.

AgentMemoryScope - Maps Claude Code 3-scope directories:

project: <gitRoot>/.claude/agent-memory/<agent>/
local: <gitRoot>/.claude/agent-memory-local/<agent>/
user: ~/.claude/agent-memory/<agent>/

High-confidence insights (>0.8) can transfer between agents.

Memory Commands

bash

# Store pattern
npx @claude-flow/cli@latest memory store --key "name" --value "data" --namespace patterns

# Semantic search
npx @claude-flow/cli@latest memory search --query "authentication"

# List entries
npx @claude-flow/cli@latest memory list --namespace patterns

# Initialize database
npx @claude-flow/cli@latest memory init --force

Hive-Mind Consensus

Queen Types

Type	Role
Strategic Queen	Long-term planning
Tactical Queen	Execution coordination
Adaptive Queen	Dynamic optimization

Worker Types (8)

researcher, coder, analyst, tester, architect, reviewer, optimizer, documenter

Consensus Mechanisms

Mechanism	Fault Tolerance	Use Case
`byzantine`	f < n/3 faulty	Adversarial
`raft`	f < n/2 failed	Leader-based
`gossip`	Eventually consistent	Large scale
`crdt`	Conflict-free	Distributed
`quorum`	Configurable	Flexible

Hive-Mind Commands

bash

# Initialize
npx @claude-flow/cli@latest hive-mind init --queen-type strategic

# Status
npx @claude-flow/cli@latest hive-mind status

# Spawn workers
npx @claude-flow/cli@latest hive-mind spawn --count 5 --type worker

# Consensus
npx @claude-flow/cli@latest hive-mind consensus --propose "task"

Performance Targets

Metric	Target	Status
HNSW Search	150x-12,500x faster	✅ Implemented
Memory Reduction	50-75%	✅ Implemented (3.92x)
SONA Integration	Pattern learning	✅ Implemented
Flash Attention	2.49x-7.47x	🔄 In Progress
MCP Response	<100ms	✅ Achieved
CLI Startup	<500ms	✅ Achieved
SONA Adaptation	<0.05ms	🔄 In Progress
Graph Build (1k)	<200ms	✅ 2.78ms (71.9x headroom)
PageRank (1k)	<100ms	✅ 12.21ms (8.2x headroom)
Insight Recording	<5ms/each	✅ 0.12ms (41x headroom)
Consolidation	<500ms	✅ 0.26ms (1,955x headroom)
Knowledge Transfer	<100ms	✅ 1.25ms (80x headroom)

Integration Ecosystem

Integrated Packages

Package	Version	Purpose
agentic-flow	3.0.0-alpha.1	Core coordination + ReasoningBank + Router
agentdb	3.0.0-alpha.10	Vector database + 8 controllers
@ruvector/attention	0.1.3	Flash attention
@ruvector/sona	0.1.5	Neural learning

Optional Integrations

Package	Command
ruv-swarm	`npx ruv-swarm mcp start`
flow-nexus	`npx flow-nexus@latest mcp start`
agentic-jujutsu	`npx agentic-jujutsu@latest`

MCP Server Setup

bash

# Add Claude Flow MCP
claude mcp add claude-flow -- npx -y @claude-flow/cli@latest

# Optional servers
claude mcp add ruv-swarm -- npx -y ruv-swarm mcp start
claude mcp add flow-nexus -- npx -y flow-nexus@latest mcp start

Quick Reference

Essential Commands

bash

# Setup
npx @claude-flow/cli@latest init --wizard
npx @claude-flow/cli@latest daemon start
npx @claude-flow/cli@latest doctor --fix

# Swarm
npx @claude-flow/cli@latest swarm init --topology hierarchical --max-agents 8
npx @claude-flow/cli@latest swarm status

# Agents
npx @claude-flow/cli@latest agent spawn -t coder
npx @claude-flow/cli@latest agent list

# Memory
npx @claude-flow/cli@latest memory search --query "patterns"

# Hooks
npx @claude-flow/cli@latest hooks pre-task --description "task"
npx @claude-flow/cli@latest hooks worker dispatch --trigger optimize

File Structure

.claude-flow/
├── config.yaml      # Runtime configuration
├── CAPABILITIES.md  # This file
├── data/            # Memory storage
├── logs/            # Operation logs
├── sessions/        # Session state
├── hooks/           # Custom hooks
├── agents/          # Agent configs
└── workflows/       # Workflow templates

Full Documentation: https://github.com/ruvnet/claude-flow Issues: https://github.com/ruvnet/claude-flow/issues