This tutorial will guide you through practicing the following core skills:

• Integrating OpenRouter + CrewAI + A2A: Complete end-to-end Agent development using OpenRouter as LLM provider, CrewAI as Agent framework, and A2A protocol as standardized interface

• Practicing A2A Agent Image Data Return: Learn how to make Agents generate and return image data, not just text responses

• Using A2A Inspector to Debug A2A Applications: Master professional debugging tools to test and validate your Agent applications

git clone git@github.com:sing1ee/a2a-crewai-charts-agent.git
cd a2a-crewai-charts-agent

Create .env file:

OPENROUTER_API_KEY=sk-or-v1-your-api-key-here
OPENAI_MODEL_NAME=openrouter/anthropic/claude-3.7-sonnet

# Create virtual environment
uv venv

# Activate virtual environment
source .venv/bin/activate

# Run application
uv run .

The application will start at http://localhost:10011.

A2A Inspector is a powerful tool specifically designed for debugging A2A applications.

1. Access A2A Inspector: Open https://inspector.a2aprotocol.ai

2. Connect to Your Agent:

   • Enter your Agent address in Inspector: http://localhost:10011

   • Click "Connect" to establish connection

3. Test Agent Functionality:

   • Send test message: "Generate a chart of revenue: Jan,1000 Feb,2000 Mar,1500"

   • Observe the complete A2A protocol interaction process

   • View returned image data

4. Debug and Monitor:

   • Check Agent's capabilities and skills

   • Monitor complete request and response flow

   • Verify correct image data transmission

Refer to A2A Inspector Documentation for more detailed debugging guides.

sequenceDiagram
    participant U as User
    participant A2A as A2A Inspector
    participant S as A2A Server
    participant H as DefaultRequestHandler
    participant E as ChartGenerationAgentExecutor
    participant CA as ChartGenerationAgent
    participant Crew as CrewAI Crew
    participant Tool as ChartGenerationTool
    participant MP as Matplotlib
    participant Cache as InMemoryCache

    U->>A2A: Send prompt "Generate chart: A,100 B,200"
    A2A->>S: HTTP POST /tasks with A2A message
    S->>H: Handle request with RequestContext
    H->>E: execute(context, event_queue)
    E->>CA: invoke(query, session_id)
    CA->>Crew: kickoff with inputs
    Crew->>Tool: generate_chart_tool(prompt, session_id)
    Tool->>Tool: Parse CSV data
    Tool->>MP: Create bar chart with matplotlib
    MP-->>Tool: Return PNG bytes
    Tool->>Cache: Store image with ID
    Cache-->>Tool: Confirm storage
    Tool-->>Crew: Return image ID
    Crew-->>CA: Return image ID
    CA-->>E: Return image ID
    E->>CA: get_image_data(session_id, image_key)
    CA->>Cache: Retrieve image data
    Cache-->>CA: Return Imagedata
    CA-->>E: Return Imagedata
    E->>H: Create FilePart with image bytes
    H->>S: enqueue completed_task event
    S-->>A2A: Return A2A response with image
    A2A-->>U: Display generated chart

# Define Agent capabilities and skills
capabilities = AgentCapabilities(streaming=False)
skill = AgentSkill(
    id='chart_generator',
    name='Chart Generator',
    description='Generate a chart based on CSV-like data passed in',
    tags=['generate image', 'edit image'],
    examples=['Generate a chart of revenue: Jan,$1000 Feb,$2000 Mar,$1500'],
)

# Create Agent card
agent_card = AgentCard(
    name='Chart Generator Agent',
    description='Generate charts from structured CSV-like data input.',
    url=f'http://{host}:{port}/',
    version='1.0.0',
    defaultInputModes=ChartGenerationAgent.SUPPORTED_CONTENT_TYPES,
    defaultOutputModes=ChartGenerationAgent.SUPPORTED_CONTENT_TYPES,
    capabilities=capabilities,
    skills=[skill],
)

Key Points:

• AgentCapabilities defines supported Agent functions (streaming disabled here)

• AgentSkill describes specific Agent skills and usage examples

• AgentCard is the Agent's identity in A2A protocol

class ChartGenerationAgent:
    def __init__(self):
        # Create specialized chart generation Agent
        self.chart_creator_agent = Agent(
            role='Chart Creation Expert',
            goal='Generate a bar chart image based on structured CSV input.',
            backstory='You are a data visualization expert who transforms structured data into visual charts.',
            verbose=False,
            allow_delegation=False,
            tools=[generate_chart_tool],
        )

        # Define task
        self.chart_creation_task = Task(
            description=(
                "You are given a prompt: '{user_prompt}'.\n"
                "If the prompt includes comma-separated key:value pairs (e.g. 'a:100, b:200'), "
                "reformat it into CSV with header 'Category,Value'.\n"
                "Ensure it becomes two-column CSV, then pass that to the 'ChartGenerationTool'.\n"
                "Use session ID: '{session_id}' when calling the tool."
            ),
            expected_output='The id of the generated chart image',
            agent=self.chart_creator_agent,
        )

Key Points:

• CrewAI's Agent class defines AI assistant roles and capabilities

• Task class describes specific task execution logic

• Custom tools are integrated into Agent through tools parameter

@tool('ChartGenerationTool')
def generate_chart_tool(prompt: str, session_id: str) -> str:
    """Generates a bar chart image from CSV-like input using matplotlib."""
    
    # Parse CSV data
    df = pd.read_csv(StringIO(prompt))
    df.columns = ['Category', 'Value']
    df['Value'] = pd.to_numeric(df['Value'], errors='coerce')
    
    # Generate bar chart
    fig, ax = plt.subplots()
    ax.bar(df['Category'], df['Value'])
    ax.set_xlabel('Category')
    ax.set_ylabel('Value')
    ax.set_title('Bar Chart')
    
    # Save as PNG bytes
    buf = BytesIO()
    plt.savefig(buf, format='png')
    plt.close(fig)
    buf.seek(0)
    image_bytes = buf.read()
    
    # Encode and cache image
    data = Imagedata(
        bytes=base64.b64encode(image_bytes).decode('utf-8'),
        mime_type='image/png',
        name='generated_chart.png',
        id=uuid4().hex,
    )
    
    # Store image in cache
    session_data = cache.get(session_id) or {}
    session_data[data.id] = data
    cache.set(session_id, session_data)
    
    return data.id

Key Points:

• Use @tool decorator to convert function into CrewAI tool

• Use pandas to parse CSV data, matplotlib to generate charts

• Images stored as base64 encoding for network transmission

• Use session IDs to manage data isolation for multiple users

class ChartGenerationAgentExecutor(AgentExecutor):
    async def execute(self, context: RequestContext, event_queue: EventQueue) -> None:
        # Get user input
        query = context.get_user_input()
        
        # Call CrewAI Agent
        result = self.agent.invoke(query, context.context_id)
        
        # Get generated image data
        data = self.agent.get_image_data(
            session_id=context.context_id, 
            image_key=result.raw
        )
        
        if data and not data.error:
            # Create file part containing image bytes
            parts = [
                Part(
                    root=FilePart(
                        file=FileWithBytes(
                            bytes=data.bytes,
                            mimeType=data.mime_type,
                            name=data.name,
                        )
                    )
                )
            ]
        else:
            # Return text message in error case
            parts = [Part(root=TextPart(text=data.error or 'Failed to generate chart image.'))]
        
        # Add completed task to event queue
        event_queue.enqueue_event(
            completed_task(
                context.task_id,
                context.context_id,
                [new_artifact(parts, f'chart_{context.task_id}')],
                [context.message],
            )
        )

Key Points:

• AgentExecutor is A2A protocol execution layer

• Get user requests through RequestContext

• Convert CrewAI responses to A2A protocol format

• Support returning file-type data (images)

class InMemoryCache:
    """Simple thread-safe in-memory cache with no expiration."""
    
    def __init__(self):
        self._lock = threading.Lock()
        self._store: dict[str, Any] = {}
    
    def get(self, key: str) -> Any | None:
        with self._lock:
            return self._store.get(key)
    
    def set(self, key: str, value: Any) -> None:
        with self._lock:
            self._store[key] = value

Key Points:

• Thread-safe in-memory cache implementation

• Used to store generated image data

• Supports session isolation to avoid user data confusion

• A2A Protocol: Standardized Agent communication protocol

• CrewAI: Multi-Agent collaboration framework

• OpenRouter: LLM API aggregation service

• Matplotlib: Python chart generation library

• Pandas: Data processing library

• UV: Modern Python package manager

1. Support More Chart Types: Pie charts, line charts, scatter plots, etc.

2. Add Data Validation: Stronger input data validation and error handling

3. Persistent Cache: Use Redis or file system to store images

4. Streaming Support: Support real-time chart generation progress

5. Multimodal Input: Support uploading CSV files instead of text-only input

Through this tutorial, you have mastered the core skills of building practical Agents using modern AI technology stack. This chart generation Agent can serve as the foundation for more complex data analysis applications.

https://a2aprotocol.ai/blog/a2a-crewai-analysis-chart-agent

You are a professional assistant based on the CrewAI chart generation agent tutorial. This tutorial provides a comprehensive guide on building a complete AI chart generation application. I can help you with:

## What I Can Help You With:
- **Quick Start**: Guide you from scratch to build an agent application using OpenRouter + CrewAI + A2A protocol
- **Solve Technical Issues**: Answer specific questions about environment setup, code implementation, and debugging
- **Understand Core Concepts**: Explain how A2A protocol, CrewAI framework, and agent architecture work
- **Practical Guidance**: Provide best practices for image data handling, tool integration, and cache design
- **Debug Support**: Teach you to use A2A Inspector for professional debugging and troubleshooting

## You Can Ask Me:
- "How to configure OpenRouter API keys?"
- "How do CrewAI Agents and Tasks work?"
- "How to return image data after chart generation?"
- "How to connect and test my agent with A2A Inspector?"
- "How is the cache system implemented in the code?"
- "How to extend support for more chart types?"

I'll provide accurate, practical answers based on the tutorial content to help you quickly master modern AI agent development skills.

You can visit [A2AProtocol.ai](https://a2aprotocol.ai/) for more tutorials.

This tutorial will guide you through practicing the following core skills:

• Integrating OpenRouter + CrewAI + A2A: Complete end-to-end Agent development using OpenRouter as LLM provider, CrewAI as Agent framework, and A2A protocol as standardized interface

• Practicing A2A Agent Image Data Return: Learn how to make Agents generate and return image data, not just text responses

• Using A2A Inspector to Debug A2A Applications: Master professional debugging tools to test and validate your Agent applications

git clone git@github.com:sing1ee/a2a-crewai-charts-agent.git
cd a2a-crewai-charts-agent

Create .env file:

OPENROUTER_API_KEY=sk-or-v1-your-api-key-here
OPENAI_MODEL_NAME=openrouter/anthropic/claude-3.7-sonnet

# Create virtual environment
uv venv

# Activate virtual environment
source .venv/bin/activate

# Run application
uv run .

The application will start at http://localhost:10011.

A2A Inspector is a powerful tool specifically designed for debugging A2A applications.

1. Access A2A Inspector: Open https://inspector.a2aprotocol.ai

2. Connect to Your Agent:

   • Enter your Agent address in Inspector: http://localhost:10011

   • Click "Connect" to establish connection

3. Test Agent Functionality:

   • Send test message: "Generate a chart of revenue: Jan,1000 Feb,2000 Mar,1500"

   • Observe the complete A2A protocol interaction process

   • View returned image data

4. Debug and Monitor:

   • Check Agent's capabilities and skills

   • Monitor complete request and response flow

   • Verify correct image data transmission

Refer to A2A Inspector Documentation for more detailed debugging guides.

sequenceDiagram
    participant U as User
    participant A2A as A2A Inspector
    participant S as A2A Server
    participant H as DefaultRequestHandler
    participant E as ChartGenerationAgentExecutor
    participant CA as ChartGenerationAgent
    participant Crew as CrewAI Crew
    participant Tool as ChartGenerationTool
    participant MP as Matplotlib
    participant Cache as InMemoryCache

    U->>A2A: Send prompt "Generate chart: A,100 B,200"
    A2A->>S: HTTP POST /tasks with A2A message
    S->>H: Handle request with RequestContext
    H->>E: execute(context, event_queue)
    E->>CA: invoke(query, session_id)
    CA->>Crew: kickoff with inputs
    Crew->>Tool: generate_chart_tool(prompt, session_id)
    Tool->>Tool: Parse CSV data
    Tool->>MP: Create bar chart with matplotlib
    MP-->>Tool: Return PNG bytes
    Tool->>Cache: Store image with ID
    Cache-->>Tool: Confirm storage
    Tool-->>Crew: Return image ID
    Crew-->>CA: Return image ID
    CA-->>E: Return image ID
    E->>CA: get_image_data(session_id, image_key)
    CA->>Cache: Retrieve image data
    Cache-->>CA: Return Imagedata
    CA-->>E: Return Imagedata
    E->>H: Create FilePart with image bytes
    H->>S: enqueue completed_task event
    S-->>A2A: Return A2A response with image
    A2A-->>U: Display generated chart

# Define Agent capabilities and skills
capabilities = AgentCapabilities(streaming=False)
skill = AgentSkill(
    id='chart_generator',
    name='Chart Generator',
    description='Generate a chart based on CSV-like data passed in',
    tags=['generate image', 'edit image'],
    examples=['Generate a chart of revenue: Jan,$1000 Feb,$2000 Mar,$1500'],
)

# Create Agent card
agent_card = AgentCard(
    name='Chart Generator Agent',
    description='Generate charts from structured CSV-like data input.',
    url=f'http://{host}:{port}/',
    version='1.0.0',
    defaultInputModes=ChartGenerationAgent.SUPPORTED_CONTENT_TYPES,
    defaultOutputModes=ChartGenerationAgent.SUPPORTED_CONTENT_TYPES,
    capabilities=capabilities,
    skills=[skill],
)

Key Points:

• AgentCapabilities defines supported Agent functions (streaming disabled here)

• AgentSkill describes specific Agent skills and usage examples

• AgentCard is the Agent's identity in A2A protocol

class ChartGenerationAgent:
    def __init__(self):
        # Create specialized chart generation Agent
        self.chart_creator_agent = Agent(
            role='Chart Creation Expert',
            goal='Generate a bar chart image based on structured CSV input.',
            backstory='You are a data visualization expert who transforms structured data into visual charts.',
            verbose=False,
            allow_delegation=False,
            tools=[generate_chart_tool],
        )

        # Define task
        self.chart_creation_task = Task(
            description=(
                "You are given a prompt: '{user_prompt}'.\n"
                "If the prompt includes comma-separated key:value pairs (e.g. 'a:100, b:200'), "
                "reformat it into CSV with header 'Category,Value'.\n"
                "Ensure it becomes two-column CSV, then pass that to the 'ChartGenerationTool'.\n"
                "Use session ID: '{session_id}' when calling the tool."
            ),
            expected_output='The id of the generated chart image',
            agent=self.chart_creator_agent,
        )

Key Points:

• CrewAI's Agent class defines AI assistant roles and capabilities

• Task class describes specific task execution logic

• Custom tools are integrated into Agent through tools parameter

@tool('ChartGenerationTool')
def generate_chart_tool(prompt: str, session_id: str) -> str:
    """Generates a bar chart image from CSV-like input using matplotlib."""
    
    # Parse CSV data
    df = pd.read_csv(StringIO(prompt))
    df.columns = ['Category', 'Value']
    df['Value'] = pd.to_numeric(df['Value'], errors='coerce')
    
    # Generate bar chart
    fig, ax = plt.subplots()
    ax.bar(df['Category'], df['Value'])
    ax.set_xlabel('Category')
    ax.set_ylabel('Value')
    ax.set_title('Bar Chart')
    
    # Save as PNG bytes
    buf = BytesIO()
    plt.savefig(buf, format='png')
    plt.close(fig)
    buf.seek(0)
    image_bytes = buf.read()
    
    # Encode and cache image
    data = Imagedata(
        bytes=base64.b64encode(image_bytes).decode('utf-8'),
        mime_type='image/png',
        name='generated_chart.png',
        id=uuid4().hex,
    )
    
    # Store image in cache
    session_data = cache.get(session_id) or {}
    session_data[data.id] = data
    cache.set(session_id, session_data)
    
    return data.id

Key Points:

• Use @tool decorator to convert function into CrewAI tool

• Use pandas to parse CSV data, matplotlib to generate charts

• Images stored as base64 encoding for network transmission

• Use session IDs to manage data isolation for multiple users

class ChartGenerationAgentExecutor(AgentExecutor):
    async def execute(self, context: RequestContext, event_queue: EventQueue) -> None:
        # Get user input
        query = context.get_user_input()
        
        # Call CrewAI Agent
        result = self.agent.invoke(query, context.context_id)
        
        # Get generated image data
        data = self.agent.get_image_data(
            session_id=context.context_id, 
            image_key=result.raw
        )
        
        if data and not data.error:
            # Create file part containing image bytes
            parts = [
                Part(
                    root=FilePart(
                        file=FileWithBytes(
                            bytes=data.bytes,
                            mimeType=data.mime_type,
                            name=data.name,
                        )
                    )
                )
            ]
        else:
            # Return text message in error case
            parts = [Part(root=TextPart(text=data.error or 'Failed to generate chart image.'))]
        
        # Add completed task to event queue
        event_queue.enqueue_event(
            completed_task(
                context.task_id,
                context.context_id,
                [new_artifact(parts, f'chart_{context.task_id}')],
                [context.message],
            )
        )

Key Points:

• AgentExecutor is A2A protocol execution layer

• Get user requests through RequestContext

• Convert CrewAI responses to A2A protocol format

• Support returning file-type data (images)

class InMemoryCache:
    """Simple thread-safe in-memory cache with no expiration."""
    
    def __init__(self):
        self._lock = threading.Lock()
        self._store: dict[str, Any] = {}
    
    def get(self, key: str) -> Any | None:
        with self._lock:
            return self._store.get(key)
    
    def set(self, key: str, value: Any) -> None:
        with self._lock:
            self._store[key] = value

Key Points:

• Thread-safe in-memory cache implementation

• Used to store generated image data

• Supports session isolation to avoid user data confusion

• A2A Protocol: Standardized Agent communication protocol

• CrewAI: Multi-Agent collaboration framework

• OpenRouter: LLM API aggregation service

• Matplotlib: Python chart generation library

• Pandas: Data processing library

• UV: Modern Python package manager

1. Support More Chart Types: Pie charts, line charts, scatter plots, etc.

2. Add Data Validation: Stronger input data validation and error handling

3. Persistent Cache: Use Redis or file system to store images

4. Streaming Support: Support real-time chart generation progress

5. Multimodal Input: Support uploading CSV files instead of text-only input

Through this tutorial, you have mastered the core skills of building practical Agents using modern AI technology stack. This chart generation Agent can serve as the foundation for more complex data analysis applications.

https://a2aprotocol.ai/blog/a2a-crewai-analysis-chart-agent

You are a professional assistant based on the CrewAI chart generation agent tutorial. This tutorial provides a comprehensive guide on building a complete AI chart generation application. I can help you with:

## What I Can Help You With:
- **Quick Start**: Guide you from scratch to build an agent application using OpenRouter + CrewAI + A2A protocol
- **Solve Technical Issues**: Answer specific questions about environment setup, code implementation, and debugging
- **Understand Core Concepts**: Explain how A2A protocol, CrewAI framework, and agent architecture work
- **Practical Guidance**: Provide best practices for image data handling, tool integration, and cache design
- **Debug Support**: Teach you to use A2A Inspector for professional debugging and troubleshooting

## You Can Ask Me:
- "How to configure OpenRouter API keys?"
- "How do CrewAI Agents and Tasks work?"
- "How to return image data after chart generation?"
- "How to connect and test my agent with A2A Inspector?"
- "How is the cache system implemented in the code?"
- "How to extend support for more chart types?"

I'll provide accurate, practical answers based on the tutorial content to help you quickly master modern AI agent development skills.

You can visit [A2AProtocol.ai](https://a2aprotocol.ai/) for more tutorials.