- Implemented comprehensive multi-agent orchestration system - Added sophisticated task scheduling with priority management - Built persistent memory management with hybrid SQLite/Markdown backends - Created terminal session management with pooling capabilities - Implemented resource coordination with deadlock detection - Added MCP integration for tool management - Built interactive CLI with REPL mode - Created comprehensive test suite with 100% coverage goal - Added CI/CD pipeline with GitHub Actions - Implemented comprehensive documentation and guides - Updated package.json for npm deployment - Created claude.md comprehensive system guide 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
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Getting Started with Claude-Flow
Welcome to Claude-Flow, an advanced AI agent orchestration system designed for sophisticated multi-agent collaboration, task coordination, and memory management. This guide will help you get up and running quickly.
Quick Installation
Option 1: NPX (Recommended for First-Time Users)
# Run directly without installation
npx claude-flow
# Or install globally for persistent use
npm install -g claude-flow
claude-flow --version
Option 2: Deno Installation
# Install with Deno
deno install --allow-all --name claude-flow https://raw.githubusercontent.com/ruvnet/claude-code-flow/main/src/cli/index.ts
# Verify installation
claude-flow --help
Option 3: From Source
# Clone the repository
git clone https://github.com/ruvnet/claude-code-flow.git
cd claude-code-flow
# Install dependencies and build
deno task install
# Run from source
deno task dev
Initial Setup
1. Initialize Configuration
# Create default configuration file
claude-flow config init
# Verify configuration
claude-flow config show
2. Start the Orchestrator
# Basic start
claude-flow start
# Start with daemon mode for background operation
claude-flow start --daemon
# Start with custom port
claude-flow start --port 3000
3. Verify System Health
# Check system status
claude-flow agent list
claude-flow memory stats
claude-flow mcp status
Your First Workflow
Let's create a simple research workflow to demonstrate Claude-Flow's capabilities:
Step 1: Spawn a Research Agent
# Create a research agent
claude-flow agent spawn researcher --name "Research Assistant"
# Verify agent is active
claude-flow agent list
Step 2: Create a Research Task
# Create a research task
claude-flow task create research "Analyze current trends in AI development tools" \
--priority high \
--estimated-duration 2h
# Check task status
claude-flow task list
Step 3: Monitor Progress
# Monitor task execution
claude-flow task monitor --follow
# Check agent activity
claude-flow agent info <agent-id>
Step 4: Review Results
# Query memory for research findings
claude-flow memory query --filter "AI development tools" --recent
# Export findings
claude-flow memory export --filter "research-results" --output research-findings.json
Interactive Exploration
For learning and experimentation, use the interactive REPL mode:
# Start interactive session
claude-flow repl
In REPL mode, you can:
# Get help
> help
# Spawn agents interactively
> agent spawn coordinator --name "Project Manager"
# Create tasks
> task create analysis "Evaluate system performance"
# Query memory
> memory query --recent --limit 5
# Exit REPL
> exit
Basic Concepts
Agents
Agents are specialized AI workers with specific capabilities:
- Researcher: Information gathering and analysis
- Implementer: Code development and technical tasks
- Analyst: Data analysis and pattern recognition
- Coordinator: Planning and task delegation
Tasks
Tasks represent work to be done:
- Research: Information gathering
- Implementation: Code development
- Analysis: Data processing
- Coordination: Planning and management
Memory Bank
The memory system stores:
- Agent discoveries and insights
- Task progress and results
- Shared knowledge across agents
- Project history and context
MCP Integration
Model Context Protocol enables:
- External tool integration
- API connectivity
- Custom tool development
- Secure tool execution
Common Commands Reference
Agent Management
# List all agents
claude-flow agent list
# Get detailed agent info
claude-flow agent info <agent-id>
# Terminate an agent
claude-flow agent terminate <agent-id>
Task Management
# List all tasks
claude-flow task list
# Check task status
claude-flow task status <task-id>
# Cancel a task
claude-flow task cancel <task-id>
Memory Operations
# Search memory
claude-flow memory query --search "keyword"
# View memory stats
claude-flow memory stats
# Clean up old entries
claude-flow memory cleanup --older-than 30d
Configuration
# View current config
claude-flow config show
# Update settings
claude-flow config set orchestrator.maxConcurrentAgents 15
# Reset to defaults
claude-flow config init --force
Next Steps
- Explore the Architecture: Read 02-architecture-overview.md to understand how Claude-Flow works
- Configure Your System: See 03-configuration-guide.md for detailed configuration options
- Learn Agent Management: Check 04-agent-management.md for advanced agent patterns
- Create Complex Workflows: Study 05-task-coordination.md for workflow orchestration
Getting Help
- Use
claude-flow helpfor command-line help - Join our Discord community
- Check GitHub Issues
- Review the full documentation
Troubleshooting Common Issues
Installation Problems
If you encounter permission issues:
# For NPM
npm config set prefix ~/.npm-global
export PATH=~/.npm-global/bin:$PATH
# For Deno
export PATH="$HOME/.deno/bin:$PATH"
Configuration Issues
If configuration fails to initialize:
# Check directory permissions
ls -la $(pwd)
# Manually create config
touch claude-flow.config.json
claude-flow config init --force
Agent Startup Issues
If agents fail to start:
# Check system resources
claude-flow system resources
# Increase limits
claude-flow config set orchestrator.maxConcurrentAgents 5
# Check logs
claude-flow logs --level debug
You're now ready to start using Claude-Flow! Continue to the next sections for more advanced features and configuration options.