multi-agent-cli 1.1.0

Command-line tool for orchestrating multiple AI agents in parallel or sequence

multi-agent-cli

Command-line tool for orchestrating multiple AI agents in parallel or sequence. Think "kubectl for AI agents."

Features

• Single Agent Execution: Run one agent with specific action and parameters
• Parallel Orchestration: Run multiple agents simultaneously with rate limiting
• Sequential Workflows: Chain agent operations with data flow between steps
• Rich Terminal Output: Beautiful progress bars, tables, and status indicators
• Prometheus Metrics: Production-ready observability and monitoring
• 100% Test Coverage: Thoroughly tested and reliable

Quick Start

# Install
pip install multi-agent-cli

# Initialize configuration
multi-agent-cli init

# Run single agent
multi-agent-cli run pm track_tasks --path ./src

# Run parallel analysis
multi-agent-cli parallel --agents pm,research,index --path ./src

# Execute workflow
multi-agent-cli workflow code-review.yaml

Installation

# From PyPI
pip install multi-agent-cli

# From source
git clone https://github.com/kmcallorum/multi-agent-cli.git
cd multi-agent-cli
pip install -e ".[dev]"

Requirements

• Python 3.11+
• pytest-agents 1.0.0+

Usage

Running a Single Agent

# Basic usage
multi-agent-cli run AGENT ACTION [OPTIONS]

# Examples
multi-agent-cli run pm track_tasks --path ./src
multi-agent-cli run research analyze_document --path README.md
multi-agent-cli run index index_repository --path ./src

# With JSON parameters
multi-agent-cli run pm track_tasks --params '{"include_done": true}'

# Save output to file
multi-agent-cli run pm track_tasks --output results.json

Parallel Execution

# Run multiple agents in parallel
multi-agent-cli parallel --agents pm,research,index --path ./src

# Limit parallel workers
multi-agent-cli parallel --agents pm,research,index --max-workers 2

# Save aggregated results
multi-agent-cli parallel --agents pm,research --output parallel_results.json

Workflow Execution

# Run a workflow file
multi-agent-cli workflow code-review.yaml

# Strict mode (fail on first error)
multi-agent-cli workflow compliance-check.yaml --strict

# Continue on errors
multi-agent-cli workflow analysis.yaml --continue-on-error

Configuration Management

# Show current configuration
multi-agent-cli config show

# Validate configuration file
multi-agent-cli config validate agents.yaml

# Initialize with example workflows
multi-agent-cli config init --example-workflows

Metrics Server

# Start Prometheus metrics server
multi-agent-cli metrics --port 9090

# Access metrics at http://localhost:9090/metrics

Configuration

agents.yaml

agents:
  pm:
    enabled: true
    path: "./pm/dist/index.js"
    timeout: 60

  research:
    enabled: true
    path: "./research/dist/index.js"
    timeout: 90

  index:
    enabled: true
    path: "./index/dist/index.js"
    timeout: 120

settings:
  max_parallel_workers: 3
  default_timeout: 60
  metrics_enabled: true
  metrics_port: 9090

output:
  format: "rich"
  verbose: false
  save_results: true
  results_dir: "./results"

Workflow Definition

name: "Code Quality Analysis"
description: "Comprehensive code quality check"

steps:
  - name: "Track Technical Debt"
    agent: pm
    action: track_tasks
    params:
      path: "./src"
    on_error: continue

  - name: "Analyze Documentation"
    agent: research
    action: analyze_document
    params:
      path: "./README.md"
    on_error: fail

  - name: "Index Codebase"
    agent: index
    action: index_repository
    params:
      path: "./src"
    depends_on:
      - "Track Technical Debt"

quality_gates:
  max_fixmes: 5
  min_documentation_score: 0.8
  max_dead_code_percent: 5

Output Formats

# Rich terminal output (default)
multi-agent-cli run pm track_tasks

# JSON output
multi-agent-cli --format json run pm track_tasks

# Table output
multi-agent-cli --format table run pm track_tasks

Docker

# Build image
docker build -t multi-agent-cli .

# Run single agent
docker run -v $(pwd)/src:/workspace/src multi-agent-cli run pm track_tasks

# Run with docker-compose
docker-compose up multi-agent-cli

Development

# Clone repository
git clone https://github.com/kmcallorum/multi-agent-cli.git
cd multi-agent-cli

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run with coverage
pytest --cov=src/multi_agent_cli --cov-report=term-missing

# Lint
ruff check src tests
ruff format src tests

# Type check
mypy src

Metrics

The CLI exposes Prometheus metrics when using the metrics command:

• agent_invocations_total - Total agent invocations by agent/action
• agent_invocations_success_total - Successful invocations
• agent_invocations_error_total - Failed invocations
• agent_duration_seconds - Execution duration histogram
• workflows_executed_total - Total workflows executed
• workflows_success_total - Successful workflows
• parallel_executions_total - Total parallel executions
• cli_commands_total - CLI commands executed

Contributing

See CONTRIBUTING.md for guidelines.

Security

See SECURITY.md for security policy and reporting vulnerabilities.

License

MIT License - see LICENSE for details.