https://a2aprotocol.ai/blog/a2a-travel-planner-openrouter

This is a Python implementation that adheres to the A2A (Agent2Agent) protocol. A travel assistant demo implemented based on Google's official a2a-python SDK and OpenAI Python SDK. It is a travel assistant in line with the specifications of the OpenAI model, capable of providing you with travel planning services.

https://github.com/sing1ee/a2a_travel_planner_openrouter

This project demonstrates how to build an interoperable travel planning agent using the A2A protocol, including the following core components:

• Travel Planner Agent: Core travel assistant logic based on OpenAI-compatible interface

• Agent Executor: A2A protocol adapter that bridges agent logic to A2A server

• A2A Server: A2A protocol-compliant server providing standardized inter-agent communication interface

• Loop Client: Test client for interacting with the A2A server

sequenceDiagram
    participant Client
    participant A2AServer
    participant RequestHandler
    participant Executor as TravelPlannerAgentExecutor
    participant Agent as TravelPlannerAgent
    participant LLM as OpenAI-Compatible LLM

    Client->>A2AServer: Request Agent Card
    A2AServer->>Client: Return Agent Card (skills, capabilities)

    Note over Client,A2AServer: User queries travel planning

    Client->>A2AServer: message/sendStream (streaming request)
    A2AServer->>RequestHandler: Route streaming request
    RequestHandler->>Executor: execute(context, event_queue)
    
    Executor->>Agent: stream(query)
    Agent->>LLM: chat.completions.create(stream=True)
    
    loop Streaming response processing
        LLM-->>Agent: Return streaming content chunk
        Agent-->>Executor: yield {'content': chunk, 'done': False}
        Executor-->>RequestHandler: TaskArtifactUpdateEvent
        RequestHandler-->>A2AServer: Push SSE event
        A2AServer-->>Client: Stream content update
    end
    
    LLM-->>Agent: Final response completed
    Agent-->>Executor: yield {'content': '', 'done': True}
    Executor-->>RequestHandler: Final TaskArtifactUpdateEvent
    RequestHandler-->>A2AServer: Final SSE event
    A2AServer-->>Client: Streaming response completed

1. Agent Card Retrieval: Client first retrieves agent card from A2A server to understand agent capabilities and skills

2. Streaming Request Processing: Client sends streaming message request with user query

3. Agent Execution: Agent executor processes the request and calls travel planner agent core logic

4. LLM Interaction: Agent conducts streaming conversation with OpenAI-compatible LLM

5. Real-time Response: Stream responses to client in real-time via Server-Sent Events (SSE)

1. Configure environment variables:

Copy the example file and configure your API credentials.

cp env.example .env

Edit .env file with your actual values:

# Required: Your API key for the AI model service
API_KEY=your_actual_api_key_here

# Optional: Model name (default: google/gemini-2.0-flash-001)
MODEL_NAME=google/gemini-2.0-flash-001

# Optional: Base URL for the API service
BASE_URL=https://openrouter.ai/api/v1

1. Install dependencies and start the server:

   uv venv
   source .venv/bin/activate
   
   uv sync
   uv run .
   

2. Run the loop client in a new terminal:

   source .venv/bin/activate
   uv run loop_client.py
   

The application uses environment variables for configuration:

• API_KEY (required): Your API key for the AI model service

• MODEL_NAME (optional): The model name to use (default: "google/gemini-2.0-flash-001")

• BASE_URL (optional): The base URL for the API service (default: "https://openrouter.ai/api/v1")

• ✅ A2A Protocol Compliance: Fully compliant with Agent2Agent protocol specification

• ✅ Streaming Response: Supports real-time streaming content generation

• ✅ OpenAI Compatible: Supports any OpenAI-compatible API interface

• ✅ Modular Design: Clear separation between agent logic and protocol adaptation

• ✅ Environment Configuration: Flexible environment variable configuration

Based on Google A2A LangGraph sample, planning to add the following features:

• 🔄 Task Lifecycle Management: Implement complete task state tracking (submitted → working → completed/failed)

• 🔄 Multi-turn Conversation Support: Add input_required state to support complex travel planning scenarios requiring user clarification

• 🔄 Task Persistence: Implement task state persistence for long-running planning tasks

• 🔄 Enhanced Error Handling: More detailed error states and recovery mechanisms

• 🔄 Task Cancellation: Support cancellation of ongoing tasks

# Future state management implementation example
class TravelPlannerTaskManager:
    async def handle_complex_query(self, query: str, context: RequestContext):
        # Detect if more information is needed
        if self.needs_clarification(query):
            return TaskStatus(
                state=TaskState.input_required,
                message="More information needed: Please provide specific destination, dates, and budget range"
            )
        
        # Execute complex multi-step planning
        task_id = await self.create_long_running_task(query)
        return TaskStatus(
            state=TaskState.working,
            taskId=task_id,
            message="Creating detailed travel plan..."
        )

• 📋 Structured Data Support: Add DataPart support for form-based travel preference collection

• 🖼️ Multimedia Support: Support FilePart for generating and processing travel images, maps, etc.

• 🔍 Tool Integration: Integrate external APIs (weather, flights, hotels, etc.) as tool calls

• 🌐 Multi-language Support: Extend multi-language travel planning capabilities

• 📊 Analytics Metrics: Add collection of task execution time, success rate metrics

Go A2A

https://a2aprotocol.ai/blog/a2a-travel-planner-openrouter

This is a Python implementation that adheres to the A2A (Agent2Agent) protocol. A travel assistant demo implemented based on Google's official a2a-python SDK and OpenAI Python SDK. It is a travel assistant in line with the specifications of the OpenAI model, capable of providing you with travel planning services.

https://github.com/sing1ee/a2a_travel_planner_openrouter

This project demonstrates how to build an interoperable travel planning agent using the A2A protocol, including the following core components:

• Travel Planner Agent: Core travel assistant logic based on OpenAI-compatible interface

• Agent Executor: A2A protocol adapter that bridges agent logic to A2A server

• A2A Server: A2A protocol-compliant server providing standardized inter-agent communication interface

• Loop Client: Test client for interacting with the A2A server

sequenceDiagram
    participant Client
    participant A2AServer
    participant RequestHandler
    participant Executor as TravelPlannerAgentExecutor
    participant Agent as TravelPlannerAgent
    participant LLM as OpenAI-Compatible LLM

    Client->>A2AServer: Request Agent Card
    A2AServer->>Client: Return Agent Card (skills, capabilities)

    Note over Client,A2AServer: User queries travel planning

    Client->>A2AServer: message/sendStream (streaming request)
    A2AServer->>RequestHandler: Route streaming request
    RequestHandler->>Executor: execute(context, event_queue)
    
    Executor->>Agent: stream(query)
    Agent->>LLM: chat.completions.create(stream=True)
    
    loop Streaming response processing
        LLM-->>Agent: Return streaming content chunk
        Agent-->>Executor: yield {'content': chunk, 'done': False}
        Executor-->>RequestHandler: TaskArtifactUpdateEvent
        RequestHandler-->>A2AServer: Push SSE event
        A2AServer-->>Client: Stream content update
    end
    
    LLM-->>Agent: Final response completed
    Agent-->>Executor: yield {'content': '', 'done': True}
    Executor-->>RequestHandler: Final TaskArtifactUpdateEvent
    RequestHandler-->>A2AServer: Final SSE event
    A2AServer-->>Client: Streaming response completed

1. Agent Card Retrieval: Client first retrieves agent card from A2A server to understand agent capabilities and skills

2. Streaming Request Processing: Client sends streaming message request with user query

3. Agent Execution: Agent executor processes the request and calls travel planner agent core logic

4. LLM Interaction: Agent conducts streaming conversation with OpenAI-compatible LLM

5. Real-time Response: Stream responses to client in real-time via Server-Sent Events (SSE)

1. Configure environment variables:

Copy the example file and configure your API credentials.

cp env.example .env

Edit .env file with your actual values:

# Required: Your API key for the AI model service
API_KEY=your_actual_api_key_here

# Optional: Model name (default: google/gemini-2.0-flash-001)
MODEL_NAME=google/gemini-2.0-flash-001

# Optional: Base URL for the API service
BASE_URL=https://openrouter.ai/api/v1

1. Install dependencies and start the server:

   uv venv
   source .venv/bin/activate
   
   uv sync
   uv run .
   

2. Run the loop client in a new terminal:

   source .venv/bin/activate
   uv run loop_client.py
   

The application uses environment variables for configuration:

• API_KEY (required): Your API key for the AI model service

• MODEL_NAME (optional): The model name to use (default: "google/gemini-2.0-flash-001")

• BASE_URL (optional): The base URL for the API service (default: "https://openrouter.ai/api/v1")

• ✅ A2A Protocol Compliance: Fully compliant with Agent2Agent protocol specification

• ✅ Streaming Response: Supports real-time streaming content generation

• ✅ OpenAI Compatible: Supports any OpenAI-compatible API interface

• ✅ Modular Design: Clear separation between agent logic and protocol adaptation

• ✅ Environment Configuration: Flexible environment variable configuration

Based on Google A2A LangGraph sample, planning to add the following features:

• 🔄 Task Lifecycle Management: Implement complete task state tracking (submitted → working → completed/failed)

• 🔄 Multi-turn Conversation Support: Add input_required state to support complex travel planning scenarios requiring user clarification

• 🔄 Task Persistence: Implement task state persistence for long-running planning tasks

• 🔄 Enhanced Error Handling: More detailed error states and recovery mechanisms

• 🔄 Task Cancellation: Support cancellation of ongoing tasks

# Future state management implementation example
class TravelPlannerTaskManager:
    async def handle_complex_query(self, query: str, context: RequestContext):
        # Detect if more information is needed
        if self.needs_clarification(query):
            return TaskStatus(
                state=TaskState.input_required,
                message="More information needed: Please provide specific destination, dates, and budget range"
            )
        
        # Execute complex multi-step planning
        task_id = await self.create_long_running_task(query)
        return TaskStatus(
            state=TaskState.working,
            taskId=task_id,
            message="Creating detailed travel plan..."
        )

• 📋 Structured Data Support: Add DataPart support for form-based travel preference collection

• 🖼️ Multimedia Support: Support FilePart for generating and processing travel images, maps, etc.

• 🔍 Tool Integration: Integrate external APIs (weather, flights, hotels, etc.) as tool calls

• 🌐 Multi-language Support: Extend multi-language travel planning capabilities

• 📊 Analytics Metrics: Add collection of task execution time, success rate metrics

Go A2A