synqed 1.0.2

Synqed - A wrapper around A2A for simplified multi-agent systems interaction and communication

Synqed - Universal Agent Collaboration

Agents that actually talk to each other. Your OpenAI agent delegates to a LangChain agent. A research agent consults specialists. A design agent brainstorms with analysis agents. All automatically. All seamlessly.

Synqed enables true agent-to-agent collaboration across any vendor, any framework, anywhere.

Why Synqed?

• True Agent-to-Agent Collaboration - Agents delegate, consult, brainstorm, and coordinate autonomously to solve complex problems together
• Universal Cross-Vendor Interoperability - OpenAI, Anthropic, LangChain, CrewAI, custom frameworks - they all speak the same language
• Production-Ready in 5 Minutes - Zero protocol knowledge required. Just build agents that collaborate
• Intelligent Orchestration - LLM-powered routing automatically selects the right agents for each task

Installation

pip install synqed

Optional Dependencies

# For gRPC support
pip install synqed[grpc]

# For SQL task store
pip install synqed[sql]

# Everything
pip install synqed[all]

LLM Provider Dependencies

Synqed's Orchestrator works with multiple LLM providers. Install your preferred provider:

# OpenAI
pip install openai

# Anthropic
pip install anthropic

# Google
pip install google-generativeai

Quick Start

Step 1: Create Your First Agent

Create a file my_agent.py:

import synqed
import asyncio

async def main():
    # Create your agent
    agent = synqed.Agent(
        name="Customer Support Assistant",
        description="Automated support agent that handles customer inquiries and service requests efficiently",
        skills=["customer_support", "ticket_routing", "inquiry_handling"],
        executor=agent_logic,  # The function that defines the agent's behavior (LLM choice and reasoning logic)
    )
    
    # Create a server to host your agent
    server = synqed.AgentServer(agent, port=8000)
    
    # Start the server
    print(f"Agent running at {agent.url}")
    await server.start()

"""
Defines the agent's behavior by specifying which LLM to use and implementing the agent's logic.
"""
async def agent_logic(context):
    # Get user's message
    user_message = context.get_user_input()
    
    # Define the agent's abilities and what LLM it is
    from openai import AsyncOpenAI
    client = AsyncOpenAI(api_key="OPENAI_API_KEY")
    
    response = await client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {"role": "system", "content": "You are a helpful customer support assistant."},
            {"role": "user", "content": user_message}
        ]
    )
        
    return response.choices[0].message.content

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

Step 2: Connect a Client

Create a file client.py:

async def main():
    # Connect to the AI agent
    async with synqed.Client("http://localhost:8000") as client:
        
        # Prompt AI agent
        response = await client.ask("What are 3 best practices for customer support?")
        print(f"Response:\n{response}\n")

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

Step 3: Run It

# Terminal 1 - Start your agent
python my_agent.py

# Terminal 2 - Connect your client
python client.py

Congratulations! You just built, deployed and prompted your first AI agent.

---

Core Concepts

The Three Pillars of Synqed

┌─────────────┬──────────────┬─────────────────┐
│   Agents    │    Client    │  Orchestrator   │
│             │              │                 │
│  The brains │  The bridge  │  The director   │
└─────────────┴──────────────┴─────────────────┘

1. Agent - An autonomous AI agent with specific skills
2. Client - Connect to and communicate with agents
3. Orchestrator - Intelligently routes tasks to the right agents

---

Building Agents

Basic Agent

from synqed import Agent

agent = Agent(
    name="WeatherAgent",
    description="Provides weather forecasts and alerts",
    skills=["weather_forecast", "weather_alerts"],
    executor=agent_logic
)

Agent with Detailed Skills

agent = Agent(
    name="RecipeAgent",
    description="Find and recommend recipes",
    skills=[
        {
            "skill_id": "recipe_search",
            "name": "Recipe Search",
            "description": "Search for recipes by ingredients or cuisine type",
            "tags": ["cooking", "recipes", "food", "search"]
        },
        {
            "skill_id": "nutrition_info",
            "name": "Nutrition Information",
            "description": "Get nutritional information for recipes",
            "tags": ["nutrition", "health", "calories"]
        }
    ],
    executor=agent_logic
)

Agent Executor Function

The executor function is where your agent's logic/capability lives:

async def agent_logic(context):
    """
    Args:
        context: RequestContext object with methods:
            - get_user_input() → str: The user's message
            - get_task() → Task: Full task object
            - get_message() → Message: Full message object
    
    Returns:
        str or Message: Your agent's response
    """
    user_message = context.get_user_input()
    
    # Your custom logic here
    # - Call LLMs (OpenAI, Anthropic, Google, etc.)
    # - Query databases
    # - Call external APIs
    # - Process data
    # - Whatever your agent needs to do!
    
    return "Agent response"

Agent Capabilities

agent = Agent(
    name="MyAgent",
    description="Does amazing things",
    skills=["skill1"],
    executor=logic,
    capabilities={
        "streaming": True,              # Support real-time streaming
        "push_notifications": False,    # Enable webhook notifications
        "state_transition_history": False  # Track state changes
    }
)

Hosting Your Agent

from synqed import AgentServer

# Create server
server = AgentServer(agent, host="0.0.0.0", port=8000)

# Option 1: Start in foreground (blocking)
await server.start()

# Option 2: Start in background
await server.start_background()
# ... do other things ...
await server.stop()

---

💬 Using the Client

Two Ways to Get Responses

1. Complete Response (ask)

Wait for the full response before continuing.

from synqed import Client

async with Client("http://localhost:8000") as client:
    response = await client.ask("What's 2+2?")
    print(response)  # "4"

Use ask() when:

• You need the complete answer before proceeding
• Response time is reasonable (< 30 seconds)
• You want simpler code without iteration

2. Streaming Response (stream)

Get the response piece by piece as it's generated (like ChatGPT).

async with Client("http://localhost:8000") as client:
    async for chunk in client.stream("Tell me a story"):
        print(chunk, end="", flush=True)  # Creates typing effect

Use stream() when:

• You want to show progress to users
• The response might be long
• You want to process data as it arrives

Pro tip: Use end="" to prevent newlines between chunks and flush=True to display output immediately.

Task Management

async with Client("http://localhost:8000") as client:
    # Submit a task
    task_id = await client.submit_task("Long running operation")
    
    # Check task status
    task = await client.get_task(task_id)
    print(f"Status: {task.state}")
    
    # Cancel if needed
    await client.cancel_task(task_id)

Advanced Client Features

# Custom timeout
client = Client(
    agent_url="http://localhost:8000",
    timeout=120.0  # 2 minutes
)

# Disable streaming
client = Client(
    agent_url="http://localhost:8000",
    streaming=False
)

---

Orchestration (Intelligent Routing)

The Orchestrator uses an LLM to analyze tasks and automatically select the best agent(s) to handle them.

Basic Orchestration

import synqed

# Initialize with your LLM of choice
orchestrator = synqed.Orchestrator(
    provider=LLMProvider.OPENAI,  # or ANTHROPIC, GOOGLE
    api_key="your-api-key",
    model="gpt-4o"
)

# Tell the orchestrator what agents can be used
orchestrator.register_agent(recipe_agent.card, recipe_agent.url)
orchestrator.register_agent(shopping_agent.card, shopping_agent.url)
orchestrator.register_agent(weather_agent.card, weather_agent.url)

# Orchestrator delegates task
result = await orchestrator.orchestrate(
    "I want to cook pasta tonight but need to know what ingredients to buy"
)

# View the results
print(f"Selected Agent: {result.selected_agents[0].agent_name}")
print(f"Confidence: {result.selected_agents[0].confidence:.0%}")
print(f"Reasoning: {result.selected_agents[0].reasoning}")
print(f"Plan: {result.execution_plan}")

Orchestration Result

@dataclass
class OrchestrationResult:
    task: str                                    # The original task
    selected_agents: list[AgentSelection]        # Best agent(s)
    execution_plan: str                          # How to execute
    alternative_agents: list[AgentSelection]     # Backup options

Supported LLM Providers

# OpenAI
orchestrator = Orchestrator(
    provider=LLMProvider.OPENAI,
    api_key="sk-...",
    model="..."  
)

# Anthropic
orchestrator = Orchestrator(
    provider=LLMProvider.ANTHROPIC,
    api_key="sk-ant-...",
    model="..."
)

# Google
orchestrator = Orchestrator(
    provider=LLMProvider.GOOGLE,
    api_key="...",
    model="..."
)

Fine-tune Orchestration

orchestrator = Orchestrator(
    provider=LLMProvider.OPENAI,
    api_key="sk-...",
    model="gpt-4o",
    temperature=0.7,     # Creativity (0.0 - 1.0)
    max_tokens=2000      # Response length limit
)

---

Multi-Agent Delegation

The TaskDelegator coordinates multiple agents working together on complex tasks.

Basic Delegation

from synqed import TaskDelegator

# Create delegator
delegator = TaskDelegator()

# Register agents
delegator.register_agent(agent=recipe_agent)
delegator.register_agent(agent=shopping_agent)
delegator.register_agent(agent=weather_agent)

# Submit a task - automatically routed to the right agent
result = await delegator.submit_task(
    "Find me a recipe and create a shopping list"
)

Delegation with Orchestrator

For intelligent routing, combine TaskDelegator with Orchestrator:

# Create orchestrator for intelligent routing
orchestrator = Orchestrator(
    provider=LLMProvider.OPENAI,
    api_key="your-key",
    model="gpt-4o"
)

# Create delegator with orchestrator
delegator = TaskDelegator(orchestrator=orchestrator)

# Register agents
delegator.register_agent(agent=recipe_agent)
delegator.register_agent(agent=shopping_agent)

# Now tasks are intelligently routed using LLM analysis
result = await delegator.submit_task(
    "Plan dinner for a cold rainy evening"
)

Remote Agent Registration

# Register a remote agent by URL
delegator.register_agent(
    agent_url="https://recipe-service.example.com",
    agent_card=recipe_agent_card  # Optional pre-loaded card
)

---

Complete Examples

Example 1: Simple Customer Support Agent

import asyncio
import os
from synqed import Agent, AgentServer
from openai import AsyncOpenAI

async def support_logic(context):
    """Customer support agent logic."""
    user_message = context.get_user_input()
    
    client = AsyncOpenAI(api_key=os.getenv("OPENAI_API_KEY"))
    response = await client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {
                "role": "system",
                "content": "You are a helpful customer support agent. "
                          "Be polite, professional, and solve problems efficiently."
            },
            {"role": "user", "content": user_message}
        ]
    )
    
    return response.choices[0].message.content

async def main():
    agent = Agent(
        name="SupportAgent",
        description="Customer support assistant",
        skills=["customer_support", "ticket_routing", "faq"],
        executor=support_logic
    )
    
    server = AgentServer(agent, port=8000)
    print(f"✅ Support agent running at {agent.url}")
    await server.start()

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

Example 2: Multi-Agent System with Orchestration

import asyncio
import os
from synqed import Agent, AgentServer, Orchestrator, LLMProvider
from openai import AsyncOpenAI

# ============================================================================
# Agent 1: Recipe Agent
# ============================================================================

async def recipe_logic(context):
    user_message = context.get_user_input()
    client = AsyncOpenAI(api_key=os.getenv("OPENAI_API_KEY"))
    
    response = await client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {
                "role": "system",
                "content": "You are a recipe expert. Suggest recipes based on "
                          "ingredients, cuisine type, or dietary restrictions."
            },
            {"role": "user", "content": user_message}
        ]
    )
    
    return response.choices[0].message.content

# ============================================================================
# Agent 2: Shopping Agent
# ============================================================================

async def shopping_logic(context):
    user_message = context.get_user_input()
    client = AsyncOpenAI(api_key=os.getenv("OPENAI_API_KEY"))
    
    response = await client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {
                "role": "system",
                "content": "You are a shopping assistant. Create shopping lists, "
                          "compare prices, and suggest where to buy items."
            },
            {"role": "user", "content": user_message}
        ]
    )
    
    return response.choices[0].message.content

# ============================================================================
# Agent 3: Nutrition Agent
# ============================================================================

async def nutrition_logic(context):
    user_message = context.get_user_input()
    client = AsyncOpenAI(api_key=os.getenv("OPENAI_API_KEY"))
    
    response = await client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {
                "role": "system",
                "content": "You are a nutrition expert. Provide nutritional "
                          "information, calculate calories, and give healthy eating advice."
            },
            {"role": "user", "content": user_message}
        ]
    )
    
    return response.choices[0].message.content

# ============================================================================
# Main System
# ============================================================================

async def main():
    # Create agents
    recipe_agent = Agent(
        name="RecipeAgent",
        description="Find and recommend recipes",
        skills=[
            {
                "skill_id": "recipe_search",
                "name": "Recipe Search",
                "description": "Find recipes by ingredient or cuisine",
                "tags": ["cooking", "recipes", "food"]
            }
        ],
        executor=recipe_logic
    )
    
    shopping_agent = Agent(
        name="ShoppingAgent",
        description="Create shopping lists and find products",
        skills=[
            {
                "skill_id": "shopping_list",
                "name": "Shopping List",
                "description": "Create and manage shopping lists",
                "tags": ["shopping", "grocery", "list"]
            }
        ],
        executor=shopping_logic
    )
    
    nutrition_agent = Agent(
        name="NutritionAgent",
        description="Provide nutrition information and advice",
        skills=[
            {
                "skill_id": "nutrition_info",
                "name": "Nutrition Info",
                "description": "Calculate calories and provide nutrition facts",
                "tags": ["nutrition", "health", "calories"]
            }
        ],
        executor=nutrition_logic
    )
    
    # Start agents on different ports
    recipe_server = AgentServer(recipe_agent, port=8001)
    shopping_server = AgentServer(shopping_agent, port=8002)
    nutrition_server = AgentServer(nutrition_agent, port=8003)
    
    await recipe_server.start_background()
    await shopping_server.start_background()
    await nutrition_server.start_background()
    
    print("✅ All agents running")
    print(f"  - Recipe Agent: {recipe_agent.url}")
    print(f"  - Shopping Agent: {shopping_agent.url}")
    print(f"  - Nutrition Agent: {nutrition_agent.url}")
    
    # Create orchestrator
    orchestrator = Orchestrator(
        provider=LLMProvider.OPENAI,
        api_key=os.getenv("OPENAI_API_KEY"),
        model="gpt-4o"
    )
    
    # Register agents
    orchestrator.register_agent(recipe_agent.card, recipe_agent.url)
    orchestrator.register_agent(shopping_agent.card, shopping_agent.url)
    orchestrator.register_agent(nutrition_agent.card, nutrition_agent.url)
    
    print("\n✅ Orchestrator configured with 3 agents\n")
    
    # Test orchestration
    tasks = [
        "Find me a healthy pasta recipe",
        "Create a shopping list for a stir fry dinner",
        "How many calories are in a pepperoni pizza?"
    ]
    
    for task in tasks:
        print(f"📋 Task: {task}")
        result = await orchestrator.orchestrate(task)
        print(f"   🎯 Selected: {result.selected_agents[0].agent_name}")
        print(f"   📊 Confidence: {result.selected_agents[0].confidence:.0%}")
        print(f"   💡 Reasoning: {result.selected_agents[0].reasoning}\n")
    
    # Keep servers running
    print("Press Ctrl+C to stop...")
    try:
        await asyncio.Event().wait()
    except KeyboardInterrupt:
        print("\n\n🛑 Shutting down...")
        await recipe_server.stop()
        await shopping_server.stop()
        await nutrition_server.stop()

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

---

Best Practices

1. Agent Design

DO:

• ✅ Give agents focused, specific skills
• ✅ Write clear, descriptive agent descriptions
• ✅ Use detailed skill definitions with tags
• ✅ Include proper error handling in executor functions

DON'T:

• ❌ Create "do everything" agents
• ❌ Use vague descriptions like "General agent"
• ❌ Skip skill tags (they help with routing)
• ❌ Let exceptions crash your executor

2. Orchestration

DO:

• ✅ Use descriptive agent and skill names
• ✅ Review confidence scores before execution
• ✅ Check alternative agents for complex tasks
• ✅ Tune temperature based on your use case

DON'T:

• ❌ Ignore low confidence scores (< 0.6)
• ❌ Use orchestration for single-agent systems
• ❌ Over-rely on default settings
• ❌ Skip testing with various task types

3. Production Deployment

DO:

• ✅ Use environment variables for API keys
• ✅ Implement comprehensive logging
• ✅ Add health check endpoints
• ✅ Set reasonable timeouts
• ✅ Use async context managers (async with)

DON'T:

• ❌ Hard-code credentials
• ❌ Run without error monitoring
• ❌ Use default ports in production
• ❌ Forget to clean up resources
• ❌ Skip authentication

4. Error Handling

async def robust_executor(context):
    try:
        user_message = context.get_user_input()
        
        # Your logic here
        result = await do_something(user_message)
        
        return result
    
    except ValueError as e:
        # Handle expected errors gracefully
        return f"I couldn't process that: {e}"
    
    except Exception as e:
        # Log unexpected errors
        logger.error(f"Executor error: {e}", exc_info=True)
        return "I encountered an unexpected error. Please try again."

5. Resource Management

# Good: Use context manager
async with Client("http://localhost:8000") as client:
    response = await client.ask("Hello")

# Good: Manual cleanup
client = Client("http://localhost:8000")
try:
    response = await client.ask("Hello")
finally:
    await client.close()

# Bad: No cleanup
client = Client("http://localhost:8000")
response = await client.ask("Hello")
# Resources leak!

---

🔒 Security Considerations

Environment Variables

Never hard-code credentials:

# ❌ BAD
orchestrator = Orchestrator(
    provider=LLMProvider.OPENAI,
    api_key="sk-proj-abc123...",  # DON'T DO THIS!
    model="gpt-4o"
)

# ✅ GOOD
import os

orchestrator = Orchestrator(
    provider=LLMProvider.OPENAI,
    api_key=os.getenv("OPENAI_API_KEY"),
    model="gpt-4o"
)

Authentication

agent = Agent(
    name="SecureAgent",
    description="Requires authentication",
    skills=["secure_skill"],
    executor=logic,
    security_schemes={
        "api_key": {
            "type": "apiKey",
            "in": "header",
            "name": "X-API-Key"
        }
    }
)

---

🐛 Troubleshooting

Common Issues

1. "Connection refused"

Problem: Client can't connect to agent

Solution:

# Make sure agent is running
# Check the port matches
# Try: curl http://localhost:8000/agent-card

2. "Module not found: openai"

Problem: Missing LLM provider package

Solution:

pip install openai  # or anthropic, google-generativeai

3. Streaming not working

Problem: No streaming support or agent doesn't stream

Solution:

# Enable streaming in agent
agent = Agent(
    name="MyAgent",
    description="...",
    skills=["..."],
    executor=logic,
    capabilities={"streaming": True}  # Add this
)

# Enable in client
client = Client("http://localhost:8000", streaming=True)

4. Low orchestration confidence

Problem: Orchestrator is unsure which agent to select

Solution:

• Improve agent descriptions
• Add more detailed skills with tags
• Make agent names more descriptive
• Use a more powerful LLM (e.g., GPT-4o vs GPT-4o-mini)

5. "Task timeout"

Problem: Agent takes too long to respond

Solution:

# Increase client timeout
client = Client("http://localhost:8000", timeout=120.0)

# Or implement progress updates in your executor

---

📊 Performance Tips

1. Use Background Servers for Multiple Agents

# Start all agents in background
await recipe_server.start_background()
await shopping_server.start_background()
await weather_server.start_background()

# Now they all run concurrently

2. Reuse Clients

# ❌ BAD: Create new client for each request
for task in tasks:
    async with Client(url) as client:
        await client.ask(task)

# ✅ GOOD: Reuse client
async with Client(url) as client:
    for task in tasks:
        await client.ask(task)

3. Use Streaming for Long Responses

# Streaming shows progress and feels faster
async for chunk in client.stream("Long task..."):
    print(chunk, end="", flush=True)

4. Parallel Task Submission

# Submit multiple tasks in parallel
tasks = [
    client1.ask("Task 1"),
    client2.ask("Task 2"),
    client3.ask("Task 3")
]

results = await asyncio.gather(*tasks)

---

🆘 Getting Help

Documentation & Resources

• Examples: Check the examples/ directory for complete working code
• Tests: Review tests/ for usage patterns and edge cases
• License: See LICENSE file for terms and conditions

Common Questions

Q: Do I need to understand A2A protocol?
A: No! That's the whole point of Synqed - it abstracts the protocol away.

Q: Can I use any LLM provider?
A: In your agent executor, yes! For orchestration, we support OpenAI, Anthropic, and Google.

Q: How many agents can I run?
A: As many as your system resources allow. Each agent runs on its own port.

Q: Can agents call other agents?
A: Yes! Use the Client within your executor function to call other agents.

Q: Is Synqed production-ready?
A: Yes, with proper error handling, logging, and monitoring in place.

Q: What's the difference between Orchestrator and TaskDelegator?
A: Orchestrator routes tasks intelligently using an LLM. TaskDelegator executes the routing decision and manages the actual delegation.

---

📄 License

This software is proprietary and confidential. See LICENSE file for full terms.

Copyright © 2025 Synq Team. All rights reserved.

---

🚀 Next Steps

Ready to build something amazing?

1. Install: pip install synqed
2. Try examples: Explore the examples/ directory
3. Build your first agent: Start with the Quick Start above
4. Scale up: Add orchestration and delegation
5. Deploy: Take it to production

Happy building! 🎉