TGO Multi-Agent Coordinator

A powerful, framework-agnostic multi-agent system that orchestrates AI agents across different frameworks with unified interfaces, memory management, and flexible workflow execution.

🏗️ Architecture Overview

The system is built using the Adapter Pattern + Strategy Pattern + Factory Pattern combination to achieve:

• 🔄 Framework Agnostic: Support for Google ADK, LangGraph, CrewAI, and easy extension to new frameworks
• ⚡ Dynamic Switching: Runtime framework switching with automatic fallback
• 🔀 Multiple Workflows: Hierarchical, sequential, parallel, and custom workflow execution
• 🎯 Unified Interface: Consistent API regardless of underlying framework
• 🧠 Memory Management: Persistent conversation and context memory across sessions
• 🔐 Session Management: Multi-user session handling with group chat support

📊 System Architecture

graph TB
    %% User Layer
    User[👤 User/Application] --> API[🔌 Multi-Agent API]

    %% Core Coordination Layer
    API --> Coordinator[🎯 MultiAgentCoordinator]
    Coordinator --> Registry[📋 AdapterRegistry]
    Coordinator --> WorkflowEngine[⚙️ WorkflowEngine]
    Coordinator --> SessionMgr[🔐 SessionManager]
    Coordinator --> MemoryMgr[🧠 MemoryManager]
    Coordinator --> MCPMgr[🌐 MCPToolManager]

    %% Framework Adapters Layer
    Registry --> GoogleADK[🟦 GoogleADKAdapter]
    Registry --> LangGraph[🟩 LangGraphAdapter]
    Registry --> CrewAI[🟨 CrewAIAdapter]

    %% AI Frameworks
    GoogleADK --> GoogleSDK[Google ADK Framework]
    LangGraph --> LangGraphSDK[LangGraph Framework]
    CrewAI --> CrewAISDK[CrewAI Framework]

    %% Workflow Types
    WorkflowEngine --> Single[👤 Single Agent]
    WorkflowEngine --> Hierarchical[🏢 Hierarchical]
    WorkflowEngine --> Sequential[➡️ Sequential]
    WorkflowEngine --> Parallel[⚡ Parallel]

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✨ Key Features

• 🔄 Multi-Framework Support: Seamlessly switch between Google ADK, LangGraph, CrewAI
• 🎯 Unified API: Consistent interface regardless of underlying framework
• 🧠 Smart Memory: Persistent conversation memory with importance scoring
• 🔐 Session Management: Multi-user sessions with group chat support
• ⚙️ Flexible Workflows: Single, hierarchical, sequential, and parallel execution
• 🌊 Real-time Streaming: Live updates during task execution
• ⚡ Batch Processing: Efficient handling of multiple tasks
• 🔧 Tool Integration: Built-in support for external tools and APIs
• 🌐 MCP Tools: Model Context Protocol integration for external tool access
• 📚 Knowledge Bases: Query and integrate with knowledge repositories
• 🔍 Capability Detection: Automatic adapter selection based on requirements
• 📊 Comprehensive Monitoring: Detailed metrics and health monitoring
• 🛡️ Error Handling: Robust error handling with automatic fallbacks
• 🔌 Extensible Architecture: Easy to add new frameworks and capabilities

🚀 Quick Start

Installation

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

# Install dependencies
poetry install

# Run the example
python example.py

Basic Usage - Multi-Agent Team Collaboration

import asyncio
from tgo.agents import (
    MultiAgentCoordinator, AdapterRegistry, GoogleADKAdapter,
    InMemoryMemoryManager, InMemorySessionManager
)
from tgo.agents.core.models import (
    MultiAgentConfig, AgentConfig, Task, WorkflowConfig, Session
)
from tgo.agents.core.enums import (
    AgentType, WorkflowType, ExecutionStrategy, SessionType
)

async def main():
    # 1. Initialize system components
    memory_manager = InMemoryMemoryManager()
    session_manager = InMemorySessionManager()
    registry = AdapterRegistry()
    registry.register("google-adk", GoogleADKAdapter())

    coordinator = MultiAgentCoordinator(
        registry=registry,
        memory_manager=memory_manager,
        session_manager=session_manager
    )

    # 2. Create session
    session = await session_manager.create_session(
        session_id="session_001",
        user_id="user_123",
        session_type=SessionType.SINGLE_CHAT
    )

    # 3. Configure multi-agent team (Manager + Experts)
    config = MultiAgentConfig(
        framework="google-adk",
        agents=[
            # Manager Agent - coordinates the team
            AgentConfig(
                agent_id="project_manager",
                name="Project Manager",
                agent_type=AgentType.MANAGER,
                model="gemini-2.0-flash",
                instructions="You coordinate tasks between expert agents and synthesize their results."
            ),
            # Research Expert
            AgentConfig(
                agent_id="researcher",
                name="Research Specialist",
                agent_type=AgentType.EXPERT,
                model="gemini-2.0-flash",
                instructions="You are a research expert. Provide thorough market analysis and data insights."
            ),
            # Writing Expert
            AgentConfig(
                agent_id="writer",
                name="Content Writer",
                agent_type=AgentType.EXPERT,
                model="gemini-2.0-flash",
                instructions="You are a content writer. Create clear, engaging reports from research data."
            )
        ],
        workflow=WorkflowConfig(
            workflow_type=WorkflowType.HIERARCHICAL,  # Manager coordinates experts
            execution_strategy=ExecutionStrategy.FAIL_FAST,
            manager_agent_id="project_manager",
            expert_agent_ids=["researcher", "writer"]
        )
    )

    # 4. Create task for the team
    task = Task(
        title="AI Market Analysis Report",
        description="Create a comprehensive report on current AI market trends, including key players, growth projections, and emerging technologies."
    )

    # 5. Execute multi-agent workflow
    print("🚀 Starting multi-agent collaboration...")
    result = await coordinator.execute_task(config, task, session)

    if result.is_successful():
        print("✅ Multi-agent task completed successfully!")
        print(f"📊 Final Result: {result.result}")
        print(f"👥 Agents involved: {', '.join(result.agents_used)}")
    else:
        print(f"❌ Task failed: {result.error_message}")

if __name__ == "__main__":
    asyncio.run(main())

🔄 What happens in this multi-agent workflow:

1. Project Manager receives the task and breaks it down into subtasks
2. Research Specialist analyzes market data and trends
3. Content Writer creates the final report structure
4. Project Manager synthesizes all results into a comprehensive report

This demonstrates true multi-agent collaboration where different specialists work together under coordination.

📁 Directory Structure

tgo/agents/
├── core/                          # 🏗️ Core abstractions
│   ├── interfaces.py              # Core interfaces and protocols
│   ├── models.py                  # Data models and schemas
│   ├── enums.py                   # Enumerations
│   └── exceptions.py              # Exception classes
├── registry/                      # 📋 Adapter registry
│   └── adapter_registry.py        # Framework adapter registry
├── adapters/                      # 🔌 Framework adapters
│   ├── base_adapter.py            # Base adapter implementation
│   ├── google_adk_adapter.py      # Google ADK integration
│   ├── langgraph_adapter.py       # LangGraph integration
│   └── crewai_adapter.py          # CrewAI integration
├── coordinator/                   # 🎯 Multi-agent coordination
│   ├── multi_agent_coordinator.py # Main coordinator
│   ├── workflow_engine.py         # Workflow execution engine
│   ├── task_executor.py           # Task execution logic
│   └── result_aggregator.py       # Result aggregation
├── memory/                        # 🧠 Memory management
│   ├── memory_manager.py          # Memory management implementation
│   └── session_manager.py         # Session management
├── tools/                         # 🌐 MCP tools integration
│   ├── mcp_tool_manager.py        # MCP tool manager
│   ├── mcp_connector.py           # MCP protocol connector
│   ├── mcp_tool_proxy.py          # Framework tool adapter
│   └── mcp_security_manager.py    # Security controls
├── example.py                     # 📖 Complete usage example
└── basic_session_memory_example.py # 🧠 Memory & session example

🔧 Key Components

1. 📋 AdapterRegistry

Centralized management of AI framework adapters with dynamic discovery:

registry = AdapterRegistry()
registry.register("google-adk", GoogleADKAdapter(), is_default=True)
registry.register("langgraph", LangGraphAdapter())
registry.register("crewai", CrewAIAdapter())

# Get adapter by capability
adapter = registry.get_adapter_by_capability(FrameworkCapability.STREAMING)

2. 🎯 MultiAgentCoordinator

Orchestrates multi-agent task execution with memory and session management:

# Initialize with memory and session managers in constructor
coordinator = MultiAgentCoordinator(
    registry=registry,
    memory_manager=memory_manager,
    session_manager=session_manager
)

# Execute with session context
result = await coordinator.execute_task(config, task, session)

3. 🔌 Framework Adapters

Unified interface to different AI frameworks with capability detection:

• 🟦 GoogleADKAdapter: Google Agent Development Kit integration
• 🟩 LangGraphAdapter: LangGraph framework integration
• 🟨 CrewAIAdapter: CrewAI framework integration

4. ⚙️ Workflow Engine

Flexible execution patterns with streaming and batch support:

• 👤 Single: Single agent execution
• 🏢 Hierarchical: Manager-expert coordination
• ➡️ Sequential: Pipeline-style execution
• ⚡ Parallel: Concurrent execution
• 🎨 Custom: User-defined workflows

5. 🧠 Memory & Session Management

Persistent context and conversation memory:

# Store conversation memory
await memory_manager.store_memory(
    session_id="session_123",
    content="User prefers detailed explanations",
    memory_type="preference",
    session_type=SessionType.SINGLE_CHAT
)

# Retrieve relevant memories
memories = await memory_manager.retrieve_memories(
    session_id="session_123",
    limit=5,
    min_importance=0.3
)

6. 🌐 Elegant Tool Configuration

Unified tool configuration supporting both function tools and MCP tools in one array:

from tgo.agents.core.models import MCPTool

# Define function tools
def calculate_metrics(revenue: float, growth_rate: float) -> dict:
    """Calculate business metrics."""
    return {
        "projected_revenue": revenue * (1 + growth_rate),
        "growth_percentage": growth_rate * 100
    }

async def fetch_data(source: str) -> str:
    """Async function tool for data fetching."""
    # Simulate async data fetching
    await asyncio.sleep(0.1)
    return f"Data from {source}"

# Define MCP tools
web_search_tool = MCPTool(
    name="web_search",
    description="Search the web for information",
    input_schema={
        "type": "object",
        "properties": {
            "query": {"type": "string"},
            "max_results": {"type": "integer", "default": 5}
        },
        "required": ["query"]
    },
    server_id="web_api"
)

file_reader_tool = MCPTool(
    name="read_file",
    description="Read content from a file",
    input_schema={
        "type": "object",
        "properties": {
            "file_path": {"type": "string"}
        },
        "required": ["file_path"]
    },
    server_id="filesystem"
)

# Configure agent with elegant mixed tools
agent_config = AgentConfig(
    agent_id="research_agent",
    name="Research Agent",
    agent_type=AgentType.EXPERT,
    model="gemini-2.0-flash",
    instructions="You have access to calculation functions, data fetching, web search, and file operations.",
    tools=[
        calculate_metrics,    # Function tool
        fetch_data,          # Async function tool
        web_search_tool,     # MCP tool
        file_reader_tool     # MCP tool
    ]  # Elegant: All tools in one array!
)

# Tool type detection
print(f"Function tools: {len(agent_config.get_function_tools())}")  # 2
print(f"MCP tools: {len(agent_config.get_mcp_tools())}")           # 2
print(f"Has MCP tools: {agent_config.has_mcp_tools()}")           # True

💡 Usage Examples

Example 1: Single Agent Execution

import asyncio
from tgo.agents import MultiAgentCoordinator, AdapterRegistry, GoogleADKAdapter
from tgo.agents.core.models import MultiAgentConfig, AgentConfig, Task, WorkflowConfig
from tgo.agents.core.enums import AgentType, WorkflowType, ExecutionStrategy

async def single_agent_example():
    # Setup with memory and session management
    memory_manager = InMemoryMemoryManager()
    session_manager = InMemorySessionManager()
    registry = AdapterRegistry()
    registry.register("google-adk", GoogleADKAdapter())

    coordinator = MultiAgentCoordinator(
        registry=registry,
        memory_manager=memory_manager,
        session_manager=session_manager
    )

    # Create session
    session = await session_manager.create_session(
        session_id="session_001",
        user_id="user_123",
        session_type=SessionType.SINGLE_CHAT
    )

    # Configure single agent
    config = MultiAgentConfig(
        framework="google-adk",
        agents=[
            AgentConfig(
                agent_id="analyst_001",
                name="Market Analyst",
                agent_type=AgentType.EXPERT,
                model="gemini-2.0-flash",
                instructions="You are a market analyst. Provide detailed insights."
            )
        ],
        workflow=WorkflowConfig(
            workflow_type=WorkflowType.SINGLE,
            execution_strategy=ExecutionStrategy.FAIL_FAST
        )
    )

    # Execute task with session
    task = Task(
        title="Analyze AI Market Trends",
        description="Provide comprehensive analysis of AI market trends"
    )

    result = await coordinator.execute_task(config, task, session)
    print(f"Result: {result.result}")

asyncio.run(single_agent_example())

Example 2: Hierarchical Multi-Agent Workflow

async def hierarchical_example():
    # Configure hierarchical system with manager and experts
    config = MultiAgentConfig(
        framework="google-adk",
        agents=[
            AgentConfig(
                agent_id="manager_001",
                name="Project Manager",
                agent_type=AgentType.MANAGER,
                instructions="Coordinate tasks between expert agents"
            ),
            AgentConfig(
                agent_id="researcher_001",
                name="Research Expert",
                agent_type=AgentType.EXPERT,
                instructions="Provide thorough research and analysis"
            ),
            AgentConfig(
                agent_id="writer_001",
                name="Technical Writer",
                agent_type=AgentType.EXPERT,
                instructions="Create clear technical documentation"
            )
        ],
        workflow=WorkflowConfig(
            workflow_type=WorkflowType.HIERARCHICAL,
            manager_agent_id="manager_001",
            expert_agent_ids=["researcher_001", "writer_001"]
        )
    )

    # Complex task requiring coordination
    task = Task(
        title="Create AI Implementation Guide",
        description="Research and create comprehensive AI implementation guide"
    )

    # Execute with streaming updates
    async for update in coordinator.execute_task_stream(config, task):
        print(f"Update: {update}")

asyncio.run(hierarchical_example())

Example 3: Memory and Session Management

from tgo.agents import (
    MultiAgentCoordinator, AdapterRegistry, GoogleADKAdapter,
    InMemoryMemoryManager, InMemorySessionManager
)
from tgo.agents.core.models import Session
from tgo.agents.core.enums import SessionType

async def memory_example():
    # Setup with memory and session management
    memory_manager = InMemoryMemoryManager()
    session_manager = InMemorySessionManager()
    registry = AdapterRegistry()
    registry.register("google-adk", GoogleADKAdapter())

    coordinator = MultiAgentCoordinator(
        registry=registry,
        memory_manager=memory_manager,
        session_manager=session_manager
    )

    # Create session
    session = await session_manager.create_session(
        session_id="session_123",
        user_id="user_456",
        session_type=SessionType.SINGLE_CHAT
    )

    # Store context memory
    await memory_manager.store_memory(
        session_id="session_123",
        content="User prefers detailed technical explanations",
        memory_type="preference",
        session_type=SessionType.SINGLE_CHAT
    )

    # Execute task with memory context
    result = await coordinator.execute_task(config, task, session)

asyncio.run(memory_example())

Example 4: MCP Tools Integration

from tgo.agents import (
    MultiAgentCoordinator, AdapterRegistry, GoogleADKAdapter,
    MCPToolManager, MCPServerConfig, InMemoryMemoryManager, InMemorySessionManager
)
from tgo.agents.core.models import MultiAgentConfig, AgentConfig, Task, WorkflowConfig
from tgo.agents.core.enums import AgentType, WorkflowType

async def mcp_tools_example():
    # Configure MCP servers
    config = {
        "mcpServers": {
            # A remote HTTP server
            "weather": {
                "url": "https://weather-api.example.com/mcp",
                "transport": "streamable-http"
            },
            # A local server running via stdio
            "math_server": {
                "command": "python",
                "args": ["./fastmcp_simple_server.py"],
                "env": {"DEBUG": "true"}
            }
        }
    }
    # Setup MCP tool manager
    mcp_manager = MCPToolManager(config)

    # Setup coordinator with MCP support
    registry = AdapterRegistry()
    registry.register("google-adk", GoogleADKAdapter())

    coordinator = MultiAgentCoordinator(
        registry=registry,
        memory_manager=InMemoryMemoryManager(),
        session_manager=InMemorySessionManager(),
        mcp_tool_manager=mcp_manager
    )

    # Configure agents with MCP tool access
    calculator_tool = MCPTool(
        name="calculate",
        description="Real MCP calculator via Stdio Transport",
        input_schema={
            "type": "object",
            "properties": {
                "expression": {"type": "string", "description": "Mathematical expression to evaluate"}
            },
            "required": ["expression"]
        },
        server_id="math_server",  # Must match MCPServerConfig server_id
        requires_confirmation=False
    )
    config = MultiAgentConfig(
        framework="google-adk",
        agents=[
            AgentConfig(
                agent_id="data_processor",
                name="Data Processing Agent",
                agent_type=AgentType.EXPERT,
                model="gemini-2.0-flash",
                instructions="You can read files and query databases using MCP tools.",
                tools=[calculator_tool]
            )
        ],
        workflow=WorkflowConfig(
            workflow_type=WorkflowType.SINGLE
        )
    )

    # Create task that requires MCP tools
    task = Task(
        title="Data Analysis Report",
        description="Read data files and query database to create analysis report",
        input_data={
            "data_file": "/workspace/sales_data.csv",
            "query": "SELECT * FROM customers WHERE region = 'North'"
        }
    )

    # Execute task with MCP tools
    result = await coordinator.execute_task(config, task)

    if result.is_successful():
        print("✅ MCP-enabled task completed!")
        print(f"📊 Result: {result.result}")
    else:
        print(f"❌ Task failed: {result.error_message}")

    # Cleanup
    await mcp_manager.shutdown()

asyncio.run(mcp_tools_example())

🚀 Advanced Features

Framework Switching with Fallback

Automatic fallback to alternative frameworks when primary framework fails:

config = MultiAgentConfig(
    framework="google-adk",
    fallback_frameworks=["langgraph", "crewai"],
    agents=[...],
    workflow=WorkflowConfig(...)
)

# System automatically tries fallback frameworks if primary fails
result = await coordinator.execute_task(config, task)

Streaming Execution

Real-time updates during task execution:

async for update in coordinator.execute_task_stream(config, task):
    if update.get("type") == "agent_started":
        print(f"Agent {update.get('agent_id')} started")
    elif update.get("type") == "agent_completed":
        print(f"Agent {update.get('agent_id')} completed")
    elif update.get("type") == "workflow_completed":
        print("Workflow completed successfully")

Batch Processing

Execute multiple tasks efficiently:

tasks = [
    Task(title="Task 1", description="First task"),
    Task(title="Task 2", description="Second task"),
    Task(title="Task 3", description="Third task")
]

results = await coordinator.execute_batch_tasks(config, tasks)
for i, result in enumerate(results):
    print(f"Task {i+1}: {'Success' if result.is_successful() else 'Failed'}")

Capability-Based Adapter Selection

Automatically select adapters based on required capabilities:

# Get adapter that supports streaming
streaming_adapter = registry.get_adapter_by_capability(
    FrameworkCapability.STREAMING
)

# Get adapters supporting multiple capabilities
multi_capable_adapters = registry.get_adapters_by_capabilities([
    FrameworkCapability.TOOL_CALLING,
    FrameworkCapability.KNOWLEDGE_BASE
])

MCP Tools Security and Management

Advanced security controls and management for MCP tools:

from tgo.agents.tools.mcp_security_manager import MCPSecurityManager, SecurityPolicy, SecurityLevel

# Configure security policies
security_manager = MCPSecurityManager()

# Restrictive policy for sensitive agents
restrictive_policy = SecurityPolicy(
    allowed_tools={"read_file", "safe_query"},
    denied_tools={"delete_file", "system_command"},
    max_calls_per_minute=10,
    security_level=SecurityLevel.HIGH,
    require_approval_for_untrusted=True
)

# Set policy for specific agent
security_manager.set_policy("sensitive_agent", restrictive_policy)

# Create MCP manager with security
mcp_manager = MCPToolManager(security_manager=security_manager)

# Check tool permissions
permission = await mcp_manager.check_tool_permission(
    agent_id="sensitive_agent",
    tool_name="read_file",
    context=execution_context
)
print(f"Permission: {permission}")  # "allow", "deny", or "require_approval"

# Get security audit log
audit_log = mcp_manager.get_security_audit_log(limit=50)
for entry in audit_log:
    print(f"{entry['timestamp']}: {entry['event_type']} - {entry['message']}")

Tool Integration and Knowledge Base Queries

Agents can call tools and query knowledge bases:

# Tool calling through agents
tool_result = await adapter.call_tool(
    agent_id="expert_001",
    tool_id="search_tool",
    tool_name="web_search",
    parameters={"query": "latest AI trends"},
    context=execution_context
)

# MCP tool calling with security
mcp_result = await adapter.call_mcp_tool(
    agent_id="expert_001",
    tool_name="filesystem_read",
    arguments={"path": "/data/report.txt"},
    context=execution_context,
    user_approved=True
)

# Knowledge base queries
kb_result = await adapter.query_knowledge_base(
    agent_id="expert_001",
    kb_id="company_kb",
    kb_name="Company Knowledge Base",
    query="AI implementation best practices",
    context=execution_context
)

Key Design Decisions

1. Adapter Pattern

• Why: Provides unified interface across different AI frameworks
• Benefit: Easy to add new frameworks without changing existing code

2. Registry Pattern

• Why: Centralized management of framework adapters
• Benefit: Dynamic discovery and switching of frameworks

3. Strategy Pattern for Workflows

• Why: Different execution strategies for different use cases
• Benefit: Flexible workflow execution without tight coupling

4. Pydantic Models

• Why: Type safety and validation
• Benefit: Catch errors early and provide clear interfaces

5. Async/Await Throughout

• Why: Non-blocking execution for better performance
• Benefit: Handle multiple agents and tasks concurrently

Extension Points

Adding New Frameworks

1. Create new adapter inheriting from BaseFrameworkAdapter
2. Implement required abstract methods
3. Register with the registry

Adding New Workflow Types

1. Add new workflow type to WorkflowType enum
2. Implement handler in WorkflowEngine
3. Update coordinator to support new type

Adding New Capabilities

1. Add capability to FrameworkCapability enum
2. Update adapters to declare support
3. Use capability checks in coordination logic

Migration from Old Architecture

The old architecture used a single GoogleADKAdapter with tight coupling. The new architecture:

1. Abstracts frameworks behind unified interfaces
2. Separates concerns with dedicated components
3. Enables extensibility through adapter pattern
4. Provides type safety with Pydantic models
5. Supports multiple workflows beyond single-agent execution

📈 Performance and Monitoring

The architecture provides comprehensive monitoring and performance features:

Execution Metrics

• Timing: Detailed execution time tracking
• Resource Usage: Memory and CPU monitoring
• Token Counting: LLM token usage tracking
• Success Rates: Task and agent success metrics

Health Monitoring

# Check adapter health
health_status = await registry.get_health_status()
for adapter_name, status in health_status.items():
    print(f"{adapter_name}: {status}")

# Monitor execution metrics
metrics = result.get_execution_metrics()
print(f"Execution time: {metrics.total_duration_ms}ms")
print(f"Token usage: {metrics.total_tokens}")

Concurrent Execution

• Parallel task processing across multiple agents
• Asynchronous execution with proper resource management
• Configurable concurrency limits

🔍 Troubleshooting

Common Issues

1. Framework Not Available

   # Check if adapter is registered
   if not registry.is_registered("google-adk"):
       registry.register("google-adk", GoogleADKAdapter())

2. Configuration Errors

   # Validate configuration before execution
   try:
       config.model_validate(config_dict)
   except ValidationError as e:
       print(f"Configuration error: {e}")

3. Memory Issues

   # Check memory manager status
   if not coordinator._memory_manager:
       await coordinator.set_memory_manager(InMemoryMemoryManager())

Debug Mode

Enable comprehensive debugging:

import logging
logging.basicConfig(
    level=logging.DEBUG,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)

# Enable specific logger
logger = logging.getLogger('src.coordinator')
logger.setLevel(logging.DEBUG)

🚀 Future Enhancements

Completed ✅

1. Multi-Framework Support: Google ADK, LangGraph, CrewAI adapters
2. Memory Management: Persistent conversation and context memory
3. Session Management: Multi-user session handling
4. Workflow Engine: Multiple execution patterns
5. Streaming Support: Real-time execution updates

Planned 🔄

1. Caching Layer: Result caching for improved performance
2. Security Layer: Authentication and authorization
3. Configuration Management: Environment-based configuration
4. Distributed Execution: Multi-node agent coordination
5. Advanced Monitoring: Grafana/Prometheus integration
6. Plugin System: Dynamic capability extension

📄 License

[Add license information here]

🤝 Contributing

We welcome contributions! Please see our contributing guidelines for details.

Development Setup

# Clone and setup development environment
git clone <repository-url>
cd tgo-agent-coordinator
pip install -r requirements-dev.txt

# Run tests
python -m pytest tests/

# Run linting
python -m flake8 src/
python -m mypy src/

📞 Support

For questions or support:

• Create an issue on GitHub
• Check the documentation
• Contact the maintainer team

🙏 Acknowledgments

Special thanks to:

• Google ADK team for the excellent framework
• LangGraph and CrewAI communities
• Contributors and early adopters