Agents CLI

Multi-agent workflow engine for agentic IDEs using OpenAI Agents SDK

🚀 What is Agents CLI?

Agents CLI transforms your IDE into a multi-layered multi-agent orchestration platform. Beyond simple AI assistance, it enables the creation of complex agent ecosystems where specialized AI agents collaborate, coordinate, and evolve together to solve sophisticated problems.

Think of it as turning your development environment into a living, breathing AI organization - where each agent has specific expertise, agents can dynamically spawn other agents, and the collective intelligence emerges from their interactions.

📋 Current Status: Phase 1 Development

We’re currently implementing the foundation and core MCP functionality. View detailed progress →

🧠 Multi-Agent System Capabilities

🏗️ Hierarchical Agent Networks

Create layered agent architectures where manager agents coordinate specialist teams, enabling complex problem decomposition and parallel processing.

🔄 Emergent Intelligence Patterns

• Peer-to-peer collaboration: Agents that debate, fact-check, and improve each other’s work
• Swarm intelligence: Multiple agents working on different aspects simultaneously
• Self-organizing workflows: Agents that dynamically restructure based on task complexity
• Collective learning: Agent networks that improve through shared experiences

🌐 Real-World Applications

🔍 Research & Analysis Network

Research Coordinator → [Web Researcher, Academic Researcher, Market Analyst]
                    ↓
Data Synthesizer → [Fact Checker, Citation Validator, Summary Generator]
                ↓
Report Generator → [Technical Writer, Visual Designer, Quality Reviewer]

🏢 Enterprise Software Development

Project Manager Agent → [Requirements Analyst, Solution Architect]
                     ↓
Development Orchestrator → [Backend Dev, Frontend Dev, DevOps, Tester]
                        ↓
Code Quality Network → [Security Auditor, Performance Optimizer, Documentation Generator]

🎓 Educational Content Creation

Learning Objectives Designer → [Subject Matter Expert, Pedagogical Specialist]
                            ↓
Content Production Team → [Writer, Interactive Designer, Assessment Creator]
                       ↓
Quality Assurance Network → [Accessibility Checker, Learning Effectiveness Validator]

🎯 Core Technical Features

• Multi-Layer Orchestration: Hierarchical and peer-to-peer agent coordination
• Dynamic Agent Spawning: Agents can create specialized sub-agents on demand
• Emergent Behavior Monitoring: Track how agent interactions create unexpected solutions
• Cross-Agent Memory: Shared knowledge bases and learning from collective experiences
• Adaptive Workflows: Self-modifying processes based on success patterns
• Real-time Collaboration: Live agent-to-agent communication and handoffs

🔗 Quick Links

• Installation Guide (Coming Soon)
• IDE Integration
• Configuration Reference
• Example Workflows
• Contributing Guide

⚡ Emergent Properties in Action

When multiple specialized agents interact, something remarkable happens - emergent intelligence that exceeds the sum of its parts:

🌟 Example: Self-Improving Code Review Network

# Simple command that triggers complex multi-agent behavior
agents-cli run --config networks/code-review-network.json \
  --input "Optimize this codebase for production"

What happens behind the scenes:

1. Analysis Agent identifies performance bottlenecks
2. Security Agent discovers potential vulnerabilities
3. Architecture Agent suggests structural improvements
4. Learning Agent notices patterns from previous reviews
5. Coordinator Agent synthesizes insights and creates optimization plan
6. Implementation Agents execute changes in parallel
7. Validation Network tests, benchmarks, and validates changes

The Emergent Magic: The network discovers optimization strategies that no single agent would have found, learns from each review to improve future performance, and adapts its approach based on codebase characteristics.

🚀 Advanced Multi-Layer Example

# Deploy a complete AI-powered development team
agents-cli network deploy --config networks/dev-team.json
agents-cli network scale --agents 50 --auto-spawn

This creates a living development ecosystem where:

• Agents form temporary collaboration groups for specific features
• Senior agents mentor junior agents, improving the overall skill level
• The network self-organizes based on project complexity and deadlines
• Emergent patterns like “design patterns” evolve naturally from agent interactions
• The system develops its own “culture” and coding standards through collective behavior

🏗️ Quick Start Examples

# Simple single-agent task
agents-cli run --config examples/code-review.json \
  --input "Review this pull request" \
  --files "src/**/*.ts"

# Multi-agent network orchestration
agents-cli network start --config networks/research-collective.json

# IDE integration with real-time agent collaboration
agents-cli serve --port 3000 --enable-network-mode

🤝 Contributing

We welcome contributions! This is an open source project under MIT license.

• Development Setup
• Architecture Overview
• Phase Task Breakdown

📄 License

MIT License - see LICENSE