admin/ruflo
1
0 Fork 0
mirror of https://github.com/ruvnet/ruflo.git synced 2026-11-09 05:35:21 +09:00
Code Issues Packages Projects Releases Wiki Activity
Files
e8637ec7989787c93c017c80358673dee965cdbf
ruflo/docs/api/API_DOCUMENTATION.md
T
rUvandClaude 03c82c5f4c docs: reorganize documentation into subfolders and fix sublinear-time-solver MCP command 
- Move docs files into organized subdirectories (api/, architecture/, development/, guides/, reference/, validation/)
- Fix sublinear-time-solver MCP start command from 'npx sublinear-time-solver@latest start' to 'npx sublinear-time-solver@latest mcp start'
- Update all occurrences in init command including console logs and .mcp.json configuration

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-09-22 21:15:41 +00:00

22 KiB
Raw Blame History

🔗 Claude-Flow v2.0.0 API Documentation

Table of Contents

Overview

Claude-Flow v2.0.0 provides comprehensive AI agent orchestration with 112 MCP tools, 54+ specialized agent types, and advanced swarm intelligence capabilities. This documentation covers the complete API surface for programmatic access to all features.

Key Features

  • 112 MCP Tools - 87 Claude-Flow + 25 Ruv-Swarm integration tools
  • 54+ Agent Types - Specialized agents for every development need
  • Swarm Intelligence - Multi-topology coordination (hierarchical, mesh, ring, star)
  • Neural Networks - WASM-accelerated AI patterns and learning
  • Memory System - Persistent, distributed memory with compression
  • Real-time Coordination - WebSocket-based agent communication
  • GitHub Integration - Native CI/CD and repository management
  • Auto-scaling - Dynamic agent provisioning and resource management

Authentication

CLI Authentication

# Initialize with GitHub authentication (recommended)
npx claude-flow@alpha github init

# Or use API key
export CLAUDE_FLOW_API_KEY="your-api-key"
npx claude-flow@alpha config set --api-key $CLAUDE_FLOW_API_KEY

MCP Integration

// Add Claude-Flow as MCP server
claude mcp add claude-flow npx claude-flow@alpha mcp start

// Available MCP servers:
// - claude-flow: 87 native tools
// - ruv-swarm: 25 advanced coordination tools

Token-based Access

# Generate session token
npx claude-flow@alpha auth login

# Use token in API calls
curl -H "Authorization: Bearer $(npx claude-flow@alpha auth token)" \
  https://api.claude-flow.ai/v2/agents

Command Syntax

Correct Command Format

IMPORTANT: Always use npx claude-flow@alpha (not npx claude-flow)

# ✅ CORRECT - Updated syntax
npx claude-flow@alpha [command] [options]

# ❌ OUTDATED - Do not use
npx claude-flow [command] [options]

Core Commands

# Swarm operations
npx claude-flow@alpha coordination swarm-init --topology hierarchical
npx claude-flow@alpha coordination agent-spawn --type coder
npx claude-flow@alpha coordination task-orchestrate --task "Build API"

# Memory operations
npx claude-flow@alpha memory usage --action store --key project/context
npx claude-flow@alpha memory search --pattern "authentication"

# Performance analysis
npx claude-flow@alpha performance report --timeframe 24h
npx claude-flow@alpha bottleneck analyze --component swarm

# GitHub integration
npx claude-flow@alpha github repo-analyze --repo owner/repo
npx claude-flow@alpha github pr-manage --action create

MCP Tools Reference

Claude-Flow Tools (87 Total)

🐝 Swarm Coordination (12 tools)

  • mcp__claude-flow__swarm_init - Initialize swarm with topology
  • mcp__claude-flow__agent_spawn - Create specialized agents
  • mcp__claude-flow__task_orchestrate - Coordinate task execution
  • mcp__claude-flow__swarm_status - Monitor swarm health
  • mcp__claude-flow__agent_list - List active agents
  • mcp__claude-flow__agent_metrics - Agent performance data
  • mcp__claude-flow__swarm_monitor - Real-time monitoring
  • mcp__claude-flow__topology_optimize - Optimize coordination
  • mcp__claude-flow__load_balance - Balance agent workload
  • mcp__claude-flow__coordination_sync - Synchronize agents
  • mcp__claude-flow__swarm_scale - Scale agent count
  • mcp__claude-flow__swarm_destroy - Terminate swarm

🧠 Neural Network (15 tools)

  • mcp__claude-flow__neural_status - Neural system status
  • mcp__claude-flow__neural_train - Train AI patterns
  • mcp__claude-flow__neural_predict - Make AI predictions
  • mcp__claude-flow__neural_patterns - Cognitive patterns
  • mcp__claude-flow__model_load - Load AI models
  • mcp__claude-flow__model_save - Save trained models
  • mcp__claude-flow__wasm_optimize - WASM performance
  • mcp__claude-flow__inference_run - Run AI inference
  • mcp__claude-flow__pattern_recognize - Pattern detection
  • mcp__claude-flow__cognitive_analyze - Cognitive analysis
  • mcp__claude-flow__learning_adapt - Adaptive learning
  • mcp__claude-flow__neural_compress - Model compression
  • mcp__claude-flow__ensemble_create - Ensemble models
  • mcp__claude-flow__transfer_learn - Transfer learning
  • mcp__claude-flow__neural_explain - AI explainability

💾 Memory & Persistence (12 tools)

  • mcp__claude-flow__memory_usage - Store/retrieve data
  • mcp__claude-flow__memory_search - Search memory entries
  • mcp__claude-flow__memory_persist - Persistent storage
  • mcp__claude-flow__memory_namespace - Namespace management
  • mcp__claude-flow__memory_backup - Backup memory data
  • mcp__claude-flow__memory_restore - Restore from backup
  • mcp__claude-flow__memory_compress - Compress data
  • mcp__claude-flow__memory_sync - Synchronize memory
  • mcp__claude-flow__cache_manage - Cache operations
  • mcp__claude-flow__state_snapshot - State snapshots
  • mcp__claude-flow__context_restore - Context restoration
  • mcp__claude-flow__memory_analytics - Memory analytics

📊 Analysis & Monitoring (13 tools)

  • mcp__claude-flow__performance_report - Performance reports
  • mcp__claude-flow__bottleneck_analyze - Bottleneck detection
  • mcp__claude-flow__task_status - Task monitoring
  • mcp__claude-flow__task_results - Task results
  • mcp__claude-flow__benchmark_run - Run benchmarks
  • mcp__claude-flow__metrics_collect - Collect metrics
  • mcp__claude-flow__trend_analysis - Trend analysis
  • mcp__claude-flow__cost_analysis - Cost tracking
  • mcp__claude-flow__quality_assess - Quality assessment
  • mcp__claude-flow__error_analysis - Error analysis
  • mcp__claude-flow__usage_stats - Usage statistics
  • mcp__claude-flow__health_check - System health
  • mcp__claude-flow__token_usage - Token tracking

🔄 Workflow & Automation (11 tools)

  • mcp__claude-flow__workflow_create - Create workflows
  • mcp__claude-flow__workflow_execute - Execute workflows
  • mcp__claude-flow__workflow_export - Export workflows
  • mcp__claude-flow__automation_setup - Setup automation
  • mcp__claude-flow__pipeline_create - Create pipelines
  • mcp__claude-flow__scheduler_manage - Manage schedules
  • mcp__claude-flow__trigger_setup - Setup triggers
  • mcp__claude-flow__workflow_template - Workflow templates
  • mcp__claude-flow__batch_process - Batch processing
  • mcp__claude-flow__parallel_execute - Parallel execution
  • mcp__claude-flow__sparc_mode - SPARC workflows

🐙 GitHub Integration (8 tools)

  • mcp__claude-flow__github_repo_analyze - Repository analysis
  • mcp__claude-flow__github_pr_manage - Pull request management
  • mcp__claude-flow__github_issue_track - Issue tracking
  • mcp__claude-flow__github_release_coord - Release coordination
  • mcp__claude-flow__github_workflow_auto - Workflow automation
  • mcp__claude-flow__github_code_review - Code review
  • mcp__claude-flow__github_sync_coord - Sync coordination
  • mcp__claude-flow__github_metrics - GitHub metrics

🤖 DAA (Dynamic Agent Architecture) (8 tools)

  • mcp__claude-flow__daa_agent_create - Create dynamic agents
  • mcp__claude-flow__daa_capability_match - Match capabilities
  • mcp__claude-flow__daa_resource_alloc - Resource allocation
  • mcp__claude-flow__daa_lifecycle_manage - Lifecycle management
  • mcp__claude-flow__daa_communication - Agent communication
  • mcp__claude-flow__daa_consensus - Consensus algorithms
  • mcp__claude-flow__daa_fault_tolerance - Fault tolerance
  • mcp__claude-flow__daa_optimization - Agent optimization

🛠️ System & Utilities (8 tools)

  • mcp__claude-flow__terminal_execute - Terminal execution
  • mcp__claude-flow__config_manage - Configuration management
  • mcp__claude-flow__features_detect - Feature detection
  • mcp__claude-flow__security_scan - Security scanning
  • mcp__claude-flow__backup_create - Create backups
  • mcp__claude-flow__restore_system - System restoration
  • mcp__claude-flow__log_analysis - Log analysis
  • mcp__claude-flow__diagnostic_run - Run diagnostics

Ruv-Swarm Tools (25 Total)

🌊 Advanced Swarm Operations

  • mcp__ruv-swarm__swarm_init - Advanced swarm initialization
  • mcp__ruv-swarm__swarm_status - Detailed swarm status
  • mcp__ruv-swarm__swarm_monitor - Real-time monitoring
  • mcp__ruv-swarm__agent_spawn - Spawn ruv-swarm agents
  • mcp__ruv-swarm__agent_list - List ruv-swarm agents
  • mcp__ruv-swarm__agent_metrics - Agent performance metrics

🎯 Task Coordination

  • mcp__ruv-swarm__task_orchestrate - Advanced task orchestration
  • mcp__ruv-swarm__task_status - Task status monitoring
  • mcp__ruv-swarm__task_results - Retrieve task results

🧠 Neural Intelligence

  • mcp__ruv-swarm__neural_status - Neural system status
  • mcp__ruv-swarm__neural_train - Train neural models
  • mcp__ruv-swarm__neural_patterns - Cognitive patterns

💾 Memory Management

  • mcp__ruv-swarm__memory_usage - Memory operations

⚡ Performance

  • mcp__ruv-swarm__benchmark_run - Performance benchmarks
  • mcp__ruv-swarm__features_detect - Feature detection

🤖 Dynamic Agent Architecture (DAA)

  • mcp__ruv-swarm__daa_init - Initialize DAA
  • mcp__ruv-swarm__daa_agent_create - Create DAA agents
  • mcp__ruv-swarm__daa_agent_adapt - Adapt agent behavior
  • mcp__ruv-swarm__daa_workflow_create - Create DAA workflows
  • mcp__ruv-swarm__daa_workflow_execute - Execute DAA workflows
  • mcp__ruv-swarm__daa_knowledge_share - Share knowledge
  • mcp__ruv-swarm__daa_learning_status - Learning status
  • mcp__ruv-swarm__daa_cognitive_pattern - Cognitive patterns
  • mcp__ruv-swarm__daa_meta_learning - Meta-learning
  • mcp__ruv-swarm__daa_performance_metrics - Performance metrics

Agent Types

Core Development Agents

Agent Type Capabilities
coder Implementation Code generation, refactoring, debugging
reviewer Quality Assurance Code review, best practices, standards
tester Testing Unit tests, integration tests, TDD
researcher Investigation Research, analysis, documentation
planner Planning Project planning, task breakdown

Specialized Agents

Agent Type Capabilities
code-analyzer Analysis Code quality, performance, security
system-architect Architecture System design, patterns, scalability
backend-dev Development API development, databases, services
mobile-dev Development React Native, mobile platforms
ml-developer ML/AI Machine learning, data science
api-docs Documentation API documentation, OpenAPI specs
cicd-engineer DevOps CI/CD pipelines, automation
performance-benchmarker Performance Load testing, optimization
production-validator Validation Production readiness, deployment
task-orchestrator Coordination Task management, workflow coordination

Swarm Coordination Agents

Agent Type Capabilities
hierarchical-coordinator Coordination Queen-led hierarchical swarms
mesh-coordinator Coordination Peer-to-peer mesh networks
adaptive-coordinator Coordination Dynamic topology switching
collective-intelligence-coordinator Coordination Hive-mind intelligence
swarm-memory-manager Memory Distributed memory coordination
consensus-builder Consensus Distributed decision making

GitHub Integration Agents

Agent Type Capabilities
github-modes Integration Comprehensive GitHub operations
pr-manager Pull Requests PR creation, review, management
issue-tracker Issues Issue management, tracking
release-manager Releases Release coordination, automation
code-review-swarm Code Review Multi-agent code review
repo-architect Repository Repository structure, organization
workflow-automation Automation GitHub Actions, CI/CD
sync-coordinator Synchronization Multi-repo coordination

Performance & Consensus Agents

Agent Type Capabilities
perf-analyzer Performance Bottleneck identification, optimization
byzantine-coordinator Consensus Byzantine fault tolerance
raft-manager Consensus Raft consensus algorithm
gossip-coordinator Communication Gossip protocol coordination
quorum-manager Consensus Quorum-based decisions
crdt-synchronizer Synchronization CRDT-based data sync
security-manager Security Security validation, auditing

SPARC Agents

Agent Type Capabilities
sparc-coder SPARC Implementation TDD-driven development
sparc-coordinator SPARC Coordination SPARC workflow management

WebSocket Integration

Connection Setup

const ws = new WebSocket('wss://api.claude-flow.ai/v2/ws');

// Authentication
ws.on('open', () => {
  ws.send(JSON.stringify({
    type: 'auth',
    token: 'your-session-token'
  }));
});

// Subscribe to agent events
ws.send(JSON.stringify({
  type: 'subscribe',
  channels: ['agents', 'swarms', 'tasks', 'memory']
}));

Event Types

Agent Events

// Agent spawned
{
  "type": "agent.spawned",
  "data": {
    "agentId": "agent_123",
    "type": "coder",
    "name": "Backend Developer",
    "status": "active"
  }
}

// Agent status change
{
  "type": "agent.status",
  "data": {
    "agentId": "agent_123",
    "status": "busy",
    "currentTask": "implement-auth",
    "progress": 0.65
  }
}

Swarm Events

// Swarm coordination event
{
  "type": "swarm.coordination",
  "data": {
    "swarmId": "swarm_456",
    "topology": "hierarchical",
    "agentCount": 8,
    "efficiency": 0.94
  }
}

// Task orchestration
{
  "type": "swarm.task",
  "data": {
    "taskId": "task_789",
    "assignedAgents": ["agent_123", "agent_456"],
    "strategy": "parallel",
    "progress": 0.45
  }
}

Memory Events

// Memory synchronization
{
  "type": "memory.sync",
  "data": {
    "namespace": "project-alpha",
    "entriesSync": 1247,
    "compressionRatio": 0.65,
    "latency": "12ms"
  }
}

Command Examples

Complete Development Workflow

# 1. Initialize project with GitHub integration
npx claude-flow@alpha github init

# 2. Set up swarm for development
npx claude-flow@alpha coordination swarm-init \
  --topology hierarchical \
  --max-agents 8 \
  --strategy adaptive

# 3. Spawn development team (concurrent)
npx claude-flow@alpha coordination agent-spawn --type system-architect --name "Lead Architect"
npx claude-flow@alpha coordination agent-spawn --type backend-dev --name "API Developer"
npx claude-flow@alpha coordination agent-spawn --type coder --name "Frontend Dev"
npx claude-flow@alpha coordination agent-spawn --type tester --name "QA Engineer"
npx claude-flow@alpha coordination agent-spawn --type code-analyzer --name "Code Reviewer"

# 4. Store project context in memory
npx claude-flow@alpha memory usage \
  --action store \
  --key "project/architecture" \
  --value "Microservices with event sourcing and CQRS" \
  --namespace "development" \
  --ttl 86400

# 5. Orchestrate development task
npx claude-flow@alpha coordination task-orchestrate \
  --task "Build complete REST API with authentication and testing" \
  --strategy parallel \
  --priority high

# 6. Monitor swarm performance
npx claude-flow@alpha coordination swarm-status
npx claude-flow@alpha performance report --timeframe 24h --format detailed

# 7. Analyze GitHub repository
npx claude-flow@alpha github repo-analyze \
  --repo "myorg/my-project" \
  --analysis-type code_quality

# 8. Create workflow for automation
npx claude-flow@alpha workflow create \
  --name "full-stack-pipeline" \
  --steps '[
    {"type": "swarm_init", "topology": "hierarchical"},
    {"type": "agent_spawn", "agents": ["architect", "coder", "tester"]},
    {"type": "task_orchestrate", "strategy": "parallel"},
    {"type": "github_integration", "automate": true}
  ]'

Neural Network Training

# 1. Check neural system status
npx claude-flow@alpha neural status

# 2. Train coordination patterns
npx claude-flow@alpha neural train \
  --pattern-type coordination \
  --training-data "./data/coordination-patterns.json" \
  --epochs 100

# 3. Make AI predictions
npx claude-flow@alpha neural predict \
  --model-id coordination_model_v1.2 \
  --input "complex microservices architecture with event sourcing"

# 4. Analyze cognitive patterns
npx claude-flow@alpha neural patterns \
  --pattern convergent \
  --analysis detailed

Memory and State Management

# 1. Store complex project data
npx claude-flow@alpha memory usage \
  --action store \
  --key "decisions/architecture" \
  --value '{
    "pattern": "microservices",
    "database": "postgres",
    "auth": "jwt",
    "caching": "redis"
  }' \
  --namespace "project-alpha" \
  --ttl 604800

# 2. Search for related information
npx claude-flow@alpha memory search \
  --pattern "microservices|architecture" \
  --namespace "project-alpha" \
  --limit 10

# 3. Create memory backup
npx claude-flow@alpha memory backup \
  --namespace "project-alpha" \
  --format compressed

# 4. Analyze memory usage
npx claude-flow@alpha memory analytics \
  --timeframe 7d \
  --include-compression-stats

Performance Monitoring

# 1. Run comprehensive performance report
npx claude-flow@alpha performance report \
  --timeframe 24h \
  --format detailed \
  --include-recommendations

# 2. Identify bottlenecks
npx claude-flow@alpha bottleneck analyze \
  --component swarm_coordination \
  --metrics "response_time,throughput,error_rate" \
  --severity all

# 3. Optimize swarm topology
npx claude-flow@alpha topology optimize \
  --swarm-id "swarm_123" \
  --target-efficiency 0.95

# 4. Health check all systems
npx claude-flow@alpha health-check \
  --components '["swarm", "neural", "memory", "mcp"]' \
  --detailed true

Error Handling

Common Error Codes

# Agent spawn failure
{
  "error": "AGENT_SPAWN_FAILED",
  "message": "Maximum agent limit reached",
  "details": {
    "currentAgents": 8,
    "maxAgents": 8,
    "swarmId": "swarm_123"
  }
}

# Memory operation failure
{
  "error": "MEMORY_STORAGE_FULL",
  "message": "Memory storage limit exceeded",
  "details": {
    "usedMemory": "512MB",
    "maxMemory": "512MB",
    "namespace": "project-alpha"
  }
}

# Neural training failure
{
  "error": "NEURAL_TRAINING_FAILED",
  "message": "Insufficient training data",
  "details": {
    "requiredSamples": 100,
    "providedSamples": 45,
    "patternType": "coordination"
  }
}

Error Recovery

# Retry with backoff
npx claude-flow@alpha coordination agent-spawn \
  --type coder \
  --retry-attempts 3 \
  --retry-delay 1000

# Graceful degradation
npx claude-flow@alpha coordination swarm-init \
  --topology hierarchical \
  --fallback-topology mesh \
  --max-agents 8 \
  --min-agents 3

# Error notification
npx claude-flow@alpha hooks post-edit \
  --file "error.log" \
  --memory-key "errors/$(date +%s)" \
  --notify-on-failure true

Best Practices

1. Efficient Agent Management

# Always batch agent operations
# ✅ Good - Single message with multiple spawns
npx claude-flow@alpha coordination agent-spawn --type architect &
npx claude-flow@alpha coordination agent-spawn --type coder &
npx claude-flow@alpha coordination agent-spawn --type tester &
wait

# ❌ Bad - Sequential spawning
npx claude-flow@alpha coordination agent-spawn --type architect
npx claude-flow@alpha coordination agent-spawn --type coder
npx claude-flow@alpha coordination agent-spawn --type tester

2. Memory Management

# Use namespaces effectively
npx claude-flow@alpha memory usage \
  --action store \
  --key "config/database" \
  --namespace "project-$(date +%Y%m%d)" \
  --ttl 86400

# Regular cleanup
npx claude-flow@alpha memory compress \
  --namespace "temporary" \
  --threshold 0.8

3. Performance Optimization

# Monitor before scaling
npx claude-flow@alpha performance report --format summary
npx claude-flow@alpha coordination swarm-scale --target-size 12

# Use appropriate topologies
# Complex tasks -> hierarchical
# Collaborative work -> mesh
# Sequential processing -> ring
# Centralized control -> star

4. Integration Patterns

# Hook integration for automation
npx claude-flow@alpha hooks pre-task \
  --description "Auto-spawn agents based on task complexity"

npx claude-flow@alpha hooks post-edit \
  --file "src/**/*.js" \
  --memory-key "code-changes/$(date +%s)"

# Workflow templates for reusability
npx claude-flow@alpha workflow template \
  --name "api-development" \
  --export "./templates/api-dev-workflow.json"

5. Security and Access Control

# Secure authentication
npx claude-flow@alpha github init --secure-mode
npx claude-flow@alpha config set --api-key-encryption enabled

# Resource limits
npx claude-flow@alpha coordination swarm-init \
  --max-agents 10 \
  --memory-limit "1GB" \
  --cpu-limit "4 cores"

# Audit logging
npx claude-flow@alpha log-analysis \
  --include-security-events \
  --format audit

Support & Resources

Documentation Links

CLI Help

# Get help for any command
npx claude-flow@alpha --help
npx claude-flow@alpha coordination --help
npx claude-flow@alpha github --help

# Version information
npx claude-flow@alpha --version

Community

Claude-Flow v2.0.0-alpha.59

Intelligent AI Agent Orchestration

🚀 Get Started | 🔧 Configure | 🤝 Contribute

Reference in new issue View Git Blame Copy Permalink