agentmesh-orchestrator 0.1.7

AgentMesh - Multi-Agent Orchestration Platform

AgentMesh

A production-grade multi-agent orchestration platform built on Microsoft AutoGen, enabling sophisticated agent collaboration through mesh networking with CLI and REST API interfaces.

Features

🤖 Multi-Agent Orchestration

• Sequential: Linear workflows where agents build on each other's work
• Round-Robin: Collaborative discussions with turn-based participation
• Graph-Based: Complex workflows with conditional branching and parallel execution
• Swarm Coordination: Autonomous agent collaboration with self-organizing behavior

🔧 Flexible Communication

• Message passing with context preservation
• Agent handoff mechanisms with specialization routing
• Real-time monitoring and analytics
• Parameter tuning during execution

💻 Dual Interface

• CLI Tool: Complete command-line interface for workflow management
• REST API: Full-featured API with OpenAPI documentation
• Configuration: YAML-based workflow definitions with validation

🚀 Production Ready

• Real-time monitoring and metrics
• Performance optimization and caching
• Comprehensive logging and debugging
• Scalable architecture with AutoGen native integration

📊 Advanced Features

• Graph visualization (ASCII, Mermaid, JSON)
• Swarm analytics and handoff pattern analysis
• Runtime parameter tuning
• Quality gates and conditional routing
• Parallel execution and synchronization

Quick Start

Installation

# Install with Poetry (recommended)
poetry install

# Or with pip
pip install -e .

Basic Usage

CLI Interface

# Create agents
agentmesh agent create --name "architect" --type "assistant" --model "gpt-4o"
agentmesh agent create --name "developer" --type "assistant" --model "gpt-4o"

# List agents
agentmesh agent list

# Create and run a workflow
agentmesh workflow create --config examples/code-review-workflow.yaml
agentmesh workflow run --id workflow-123 --task "Build a REST API for user management"

API Interface

# Start the API server
agentmesh server start --port 8000

# Create agent via API
curl -X POST "http://localhost:8000/api/v1/agents" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "architect",
    "type": "assistant",
    "model": "gpt-4o",
    "system_message": "You are a software architect"
  }'

Orchestration Patterns & Examples

AutoGen A2A supports four distinct orchestration patterns, each optimized for different collaboration scenarios:

1. Sequential Orchestration

Use Case: Linear workflows where each agent builds on the previous agent's work.

# examples/workflows/sequential-code-review.yaml
name: "Code Review Pipeline"
pattern: "sequential"

agents:
  - name: "developer"
    type: "assistant"
    system_message: "You write clean, well-documented code"
  - name: "reviewer"
    type: "assistant"
    system_message: "You review code for best practices and bugs"
  - name: "architect"
    type: "assistant"
    system_message: "You ensure architectural compliance"

execution:
  max_rounds: 3
  timeout: 1800

# Run sequential workflow
agentmesh workflow create --config sequential-code-review.yaml
agentmesh workflow execute workflow-123 --task "Implement user authentication API"

2. Round-Robin Orchestration

Use Case: Collaborative discussions where agents take turns contributing ideas.

# examples/workflows/round-robin-brainstorm.yaml
name: "Product Brainstorming Session"
pattern: "round_robin"

agents:
  - name: "product_manager"
    type: "assistant"
    system_message: "You focus on user needs and business value"
  - name: "designer"
    type: "assistant"
    system_message: "You consider user experience and interface design"
  - name: "engineer"
    type: "assistant"
    system_message: "You evaluate technical feasibility"

termination:
  type: "max_messages"
  value: 12

execution:
  max_rounds: 4
  timeout: 2400

# Run round-robin workflow
agentmesh workflow create --config round-robin-brainstorm.yaml
agentmesh workflow execute workflow-123 --task "Design a mobile app for food delivery"

3. Graph-Based Orchestration

Use Case: Complex workflows with conditional branching, parallel execution, and quality gates.

# examples/workflows/graph-product-development.yaml
name: "Product Development Pipeline"
pattern: "graph"

agents:
  - name: "product_manager"
    type: "assistant"
  - name: "designer"
    type: "assistant"
  - name: "frontend_dev"
    type: "assistant"
  - name: "backend_dev"
    type: "assistant"
  - name: "qa_engineer"
    type: "assistant"

graph:
  nodes:
    - id: "requirements"
      agent: "product_manager"
      description: "Define product requirements"
    - id: "design"
      agent: "designer"
      description: "Create UI/UX designs"
    - id: "frontend"
      agent: "frontend_dev"
      description: "Implement frontend"
    - id: "backend"
      agent: "backend_dev"
      description: "Implement backend"
    - id: "testing"
      agent: "qa_engineer"
      description: "Test and validate"
  
  edges:
    - id: "req_to_design"
      source: "requirements"
      target: "design"
      type: "sequential"
    - id: "design_to_frontend"
      source: "design"
      target: "frontend"
      type: "parallel"
    - id: "design_to_backend"
      source: "design"
      target: "backend"
      type: "parallel"
    - id: "frontend_to_testing"
      source: "frontend"
      target: "testing"
      type: "synchronize"
    - id: "backend_to_testing"
      source: "backend"
      target: "testing"
      type: "synchronize"
  
  conditions:
    quality_gate:
      type: "evaluation"
      criteria: ["code_quality", "test_coverage"]

# Run graph workflow with visualization
agentmesh workflow create --config graph-product-development.yaml
agentmesh workflow visualize workflow-123 --format mermaid
agentmesh workflow execute workflow-123 --task "Build an e-commerce checkout system"

4. Swarm Coordination

Use Case: Autonomous agent collaboration with self-organizing behavior and dynamic handoffs.

# examples/workflows/swarm-research-analysis.yaml
name: "Research Analysis Swarm"
pattern: "swarm"

agents:
  - name: "data_collector"
    type: "assistant"
    system_message: "You excel at gathering research data from multiple sources"
  - name: "analyst"
    type: "assistant"
    system_message: "You analyze data and identify patterns and insights"
  - name: "statistician"
    type: "assistant"
    system_message: "You perform statistical analysis and validation"
  - name: "report_generator"
    type: "assistant"
    system_message: "You create comprehensive research reports"

swarm:
  participants:
    - agent_id: "data_collector"
      specializations: ["data_collection", "web_scraping", "research"]
      handoff_targets: ["analyst", "statistician"]
      participation_weight: 1.2
      max_consecutive_turns: 3
    
    - agent_id: "analyst"
      specializations: ["data_analysis", "pattern_recognition", "insights"]
      handoff_targets: ["statistician", "report_generator"]
      participation_weight: 1.0
      max_consecutive_turns: 4
    
    - agent_id: "statistician"
      specializations: ["statistics", "validation", "hypothesis_testing"]
      handoff_targets: ["analyst", "report_generator"]
      participation_weight: 0.8
      max_consecutive_turns: 2
    
    - agent_id: "report_generator"
      specializations: ["writing", "reporting", "documentation"]
      handoff_targets: ["analyst"]
      participation_weight: 1.0
      max_consecutive_turns: 3

  termination:
    max_messages: 25
    timeout_seconds: 1800

  handoff_config:
    autonomous_threshold: 0.7
    broadcast_threshold: 0.3
    load_balancing: true

# Run swarm workflow with monitoring
agentmesh swarm create --config swarm-research-analysis.yaml
agentmesh swarm monitor --id swarm-123 --metrics all --refresh 5
agentmesh swarm tune --id swarm-123 --parameter participation_balance --value 0.8
agentmesh swarm analytics --id swarm-123 --output json

Pattern Comparison

Pattern	Best For	Complexity	Control	Autonomy
Sequential	Linear workflows, step-by-step processes	Low	High	Low
Round-Robin	Collaborative discussions, brainstorming	Low	Medium	Low
Graph	Complex workflows, conditional logic	High	High	Medium
Swarm	Dynamic collaboration, emergent behavior	Medium	Medium	High

Advanced Examples

For complete examples with full configurations, see the examples/workflows/ directory:

• Sequential: sequential-document-processing.yaml, sequential-data-pipeline.yaml
• Round-Robin: round-robin-creative-writing.yaml, round-robin-problem-solving.yaml
• Graph: graph-code-review.yaml, graph-content-creation-pipeline.yaml, graph-advanced-product-development.yaml
• Swarm: swarm-product-development.yaml, swarm-content-creation.yaml, swarm-financial-analysis.yaml

Documentation

• Getting Started Guide
• API Reference
• CLI Reference
• Orchestration Patterns
• Graph Workflows
• Swarm Coordination
• Examples

Pattern-Specific Guides

• Sequential & Round-Robin: Built-in patterns for linear and collaborative workflows
• Graph Workflows: Complex workflows with conditional branching, parallel execution, and quality gates
• Swarm Coordination: Autonomous agent collaboration with self-organizing behavior

CLI Quick Reference

# Workflow Management
agentmesh workflow create --config config.yaml
agentmesh workflow list --status running
agentmesh workflow execute workflow-123 --task "Your task here"
agentmesh workflow get workflow-123 --format json

# Graph Workflows
agentmesh workflow visualize workflow-123 --format mermaid
agentmesh workflow pause workflow-123
agentmesh workflow resume workflow-123

# Swarm Coordination
agentmesh swarm create --config swarm-config.yaml
agentmesh swarm monitor --id swarm-123 --metrics all
agentmesh swarm tune --id swarm-123 --parameter participation_balance --value 0.8
agentmesh swarm analytics --id swarm-123 --output json

# Agent Management
agentmesh agent create --name "agent" --type "assistant" --model "gpt-4o"
agentmesh agent list --format table

Development

Prerequisites

• Python 3.11+
• Poetry
• Redis (for message queuing)
• PostgreSQL (for persistence)

Setup Development Environment

# Clone the repository
git clone <repository-url>
cd autogen-agent

# Install dependencies
poetry install

# Setup pre-commit hooks
poetry run pre-commit install

# Start development services
docker-compose up -d redis postgres

# Run tests
poetry run pytest

# Start development server
poetry run agentmesh server start --reload

Project Structure

agentmesh/
├── src/
│   └── agentmesh/
│       ├── api/           # FastAPI application
│       ├── cli/           # CLI commands
│       ├── core/          # Core agent logic
│       ├── models/        # Data models
│       ├── services/      # Business logic
│       └── utils/         # Utilities
├── tests/                 # Test suite
├── docs/                  # Documentation
├── examples/              # Example workflows
└── deployment/            # Deployment configs

Implementation Status

This project is currently under active development following the Sprint Planning Document.

✅ Completed Sprints

• Sprint 1: Core Foundation & CLI Bootstrap
• Sprint 2: REST API Foundation & Agent Registry
• Sprint 3: Message Infrastructure & Communication
• Sprint 4: Security, Monitoring & Handoff Management
• Sprint 5: Sequential & Round-Robin Orchestration
• Sprint 6: Graph-based Workflows
• Sprint 7: Swarm Coordination

🔄 Current Focus: Sprint 8 - Performance Optimization & Caching

System Status: Production-ready for orchestration patterns, monitoring, and CLI/API interfaces.

Key Features Available:

• ✅ Multi-Agent Orchestration: All four patterns (Sequential, Round-Robin, Graph, Swarm)
• ✅ CLI Interface: Complete command-line tool with workflow management
• ✅ REST API: Full API with OpenAPI documentation
• ✅ Monitoring: Real-time metrics, analytics, and visualization
• ✅ Configuration: YAML-based workflow definitions with validation
• ✅ Examples: Comprehensive examples for all orchestration patterns

See the full implementation roadmap for detailed progress tracking.

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Related Projects

• Microsoft AutoGen
• AutoGen Studio
• AutoGen Core