Landing Page Generation Agent

This example demonstrates how to create and use an Upsonic Agent with DeepAgent architecture to generate comprehensive landing page images. The example showcases how to leverage Upsonic’s coordination capabilities to manage multiple specialized agents working together to create conversion-optimized landing pages with professional content, design, and SEO elements.

Overview

Upsonic framework provides seamless integration for multi-agent systems. This example showcases:

DeepAgent Integration — Using DeepAgent to coordinate specialized sub-agents
Image Generation — Creating actual landing page images (PNG format, 1536x1024) using OpenAI’s image generation
Content Creation — Expert copywriting for headlines, value propositions, CTAs, and feature highlights
Design Recommendations — Color schemes, typography, layout structures, and visual element suggestions
SEO Optimization — Meta tags, keywords, header structure, and technical SEO elements
Task Planning — Automatic task decomposition using planning tools
Memory Persistence — SQLite-based session memory for continuity
FastAPI Server — Running the agent as a production-ready API server

The agent uses three specialized sub-agents:

Content Writer — Creates compelling, conversion-focused copy
Designer — Provides design recommendations for visual elements
SEO Specialist — Optimizes landing pages for search engines

Project Structure

landing_page_generation/
├── main.py                    # Entry point with async main() function
├── orchestrator.py             # DeepAgent orchestrator creation
├── subagents.py               # Specialized subagent factory functions
├── schemas.py                 # Pydantic output schemas
├── task_builder.py           # Task description builder
├── upsonic_configs.json       # Upsonic CLI configuration
└── README.md                  # Quick start guide

Environment Variables

You can configure the model using environment variables:

# Required: Set OpenAI API key
export OPENAI_API_KEY="your-api-key"

Installation

# Install dependencies from upsonic_configs.json
upsonic install

Managing Dependencies

# Add a package
upsonic add <package> <section>
upsonic add pandas api

# Remove a package
upsonic remove <package> <section>
upsonic remove streamlit api

Sections: api, streamlit, development

Usage

Option 1: Run Directly

uv run main.py

Runs the agent with default test inputs (AI Writing Assistant landing page).

Option 2: Run as API Server

upsonic run

Server starts at http://localhost:8000. API documentation at /docs. Example API call:

curl -X POST http://localhost:8000/call \
  -H "Content-Type: application/json" \
  -d '{
    "product_name": "AI Writing Assistant",
    "target_audience": "Content creators and marketers",
    "primary_goal": "sign up for free trial",
    "key_features": ["AI-powered content", "Multiple templates", "Real-time collaboration"],
    "brand_tone": "friendly and professional"
  }'

How It Works

Component	Description
DeepAgent	Orchestrator that plans and delegates tasks to subagents
Planning Tool	Automatically breaks down landing page generation into manageable steps
Content Writer	Creates compelling headlines, value propositions, CTAs, and feature highlights
Designer	Recommends color schemes, typography, layout, and visual elements
SEO Specialist	Optimizes for search engines with meta tags, keywords, and structure
Image Generation Tool	Generates the final landing page image (1536x1024 PNG)
Memory	SQLite-based persistence for session continuity

Example Output

Query:

{
  "product_name": "AI Writing Assistant",
  "target_audience": "Content creators and marketers who need to produce high-quality content quickly",
  "primary_goal": "sign up for free trial",
  "key_features": [
    "AI-powered content generation",
    "Multiple writing templates",
    "Real-time collaboration",
    "SEO optimization suggestions"
  ],
  "brand_tone": "friendly and professional"
}

Response:

{
  "product_name": "AI Writing Assistant",
  "image_path": "/path/to/landing_page_images/AI_Writing_Assistant_landing_page.png",
  "generation_completed": true
}

Generated Landing Page Image

Complete Implementation

main.py

"""
Main entry point for Landing Page Generation Agent.

This module provides the entry point that coordinates
the comprehensive landing page generation process.
"""

from __future__ import annotations

from typing import Dict, Any

from upsonic import Task
from upsonic.utils.image import save_image_to_folder, create_images_folder
try:
    from .orchestrator import create_orchestrator_agent
    from .task_builder import build_landing_page_task
except ImportError:
    from orchestrator import create_orchestrator_agent
    from task_builder import build_landing_page_task


async def main(inputs: Dict[str, Any]) -> Dict[str, Any]:
    """
    Main function for landing page generation.
    
    Args:
        inputs: Dictionary containing:
            - product_name: Name of the product or service (required)
            - target_audience: Description of target audience (required)
            - primary_goal: Primary conversion goal (required)
            - key_features: Optional list of key features to highlight
            - brand_tone: Optional brand tone (default: "professional")
            - enable_memory: Whether to enable memory persistence (default: True)
            - storage_path: Optional path for SQLite storage (default: "landing_page_generation.db")
            - model: Optional model identifier (default: "openai/gpt-4o")
    
    Returns:
        Dictionary containing comprehensive landing page specification
    """
    product_name = inputs.get("product_name")
    if not product_name:
        raise ValueError("product_name is required in inputs")
    
    target_audience = inputs.get("target_audience")
    if not target_audience:
        raise ValueError("target_audience is required in inputs")
    
    primary_goal = inputs.get("primary_goal")
    if not primary_goal:
        raise ValueError("primary_goal is required in inputs")
    
    key_features = inputs.get("key_features")
    brand_tone = inputs.get("brand_tone", "professional")
    enable_memory = inputs.get("enable_memory", True)
    storage_path = inputs.get("storage_path")
    model = inputs.get("model", "openai-responses/gpt-4o")
    output_folder = inputs.get("output_folder", "landing_page_images")
    
    orchestrator = create_orchestrator_agent(
        model=model,
        storage_path=storage_path,
        enable_memory=enable_memory,
    )
    
    task_description = build_landing_page_task(
        product_name=product_name,
        target_audience=target_audience,
        primary_goal=primary_goal,
        key_features=key_features,
        brand_tone=brand_tone,
    )
    
    task = Task(task_description)
    
    result = await orchestrator.do_async(task)
    
    if not isinstance(result, bytes):
        raise ValueError(f"Expected image bytes, got {type(result)}")
    
    create_images_folder(output_folder)
    safe_product_name = "".join(c for c in product_name if c.isalnum() or c in (' ', '-', '_')).strip().replace(' ', '_')
    image_path = save_image_to_folder(
        image_data=result,
        folder_path=output_folder,
        filename=f"{safe_product_name}_landing_page.png",
        is_base64=False
    )
    
    return {
        "product_name": product_name,
        "image_path": image_path,
        "generation_completed": True,
    }


if __name__ == "__main__":
    import asyncio
    import json
    import sys
    
    test_inputs = {
        "product_name": "AI Writing Assistant",
        "target_audience": "Content creators and marketers who need to produce high-quality content quickly",
        "primary_goal": "sign up for free trial",
        "key_features": [
            "AI-powered content generation",
            "Multiple writing templates",
            "Real-time collaboration",
            "SEO optimization suggestions"
        ],
        "brand_tone": "friendly and professional",
        "enable_memory": False,
        "storage_path": None,
        "model": "openai-responses/gpt-4o",
        "output_folder": "landing_page_images",
    }
    
    if len(sys.argv) > 1:
        try:
            with open(sys.argv[1], "r") as f:
                test_inputs = json.load(f)
        except Exception as e:
            print(f"Error loading JSON file: {e}")
            print("Using default test inputs")
    
    async def run_main():
        try:
            result = await main(test_inputs)
            
            print("\n" + "=" * 80)
            print("Landing Page Generation Completed Successfully!")
            print("=" * 80)
            print(f"\nProduct: {result.get('product_name')}")
            print(f"Generation Status: {'Completed' if result.get('generation_completed') else 'Failed'}")
            print(f"\nImage saved to: {result.get('image_path', 'N/A')}")
            
        except Exception as e:
            print(f"\n❌ Error during execution: {e}")
            import traceback
            traceback.print_exc()
            sys.exit(1)
    
    asyncio.run(run_main())

orchestrator.py

"""
Orchestrator agent creation and configuration.

Creates the main DeepAgent orchestrator that coordinates all specialized
subagents for comprehensive landing page generation.
"""

from __future__ import annotations

from typing import Optional

from upsonic.agent.deepagent import DeepAgent
from upsonic.db.database import SqliteDatabase
from upsonic.tools.builtin_tools import ImageGenerationTool

try:
    from .subagents import (
        create_content_writer_subagent,
        create_designer_subagent,
        create_seo_specialist_subagent,
    )
except ImportError:
    from subagents import (
        create_content_writer_subagent,
        create_designer_subagent,
        create_seo_specialist_subagent,
    )


def create_orchestrator_agent(
    model: str = "openai-responses/gpt-4o",
    storage_path: Optional[str] = None,
    enable_memory: bool = True,
) -> DeepAgent:
    """Create the main orchestrator DeepAgent with all subagents.
    
    Args:
        model: Model identifier for the orchestrator agent
        storage_path: Optional path for SQLite storage database
        enable_memory: Whether to enable memory persistence
        
    Returns:
        Configured DeepAgent instance with all subagents
    """
    db = None
    
    if enable_memory:
        if storage_path is None:
            storage_path = "landing_page_generation.db"
        db = SqliteDatabase(
            db_file=storage_path,
            session_table="agent_sessions",
            session_id="landing_page_session",
            user_id="landing_page_user",
            full_session_memory=True,
            summary_memory=True,
            model=model,
        )
    
    subagents = [
        create_content_writer_subagent(),
        create_designer_subagent(),
        create_seo_specialist_subagent(),
    ]
    
    orchestrator = DeepAgent(
        model=model,
        name="Landing Page Generation Orchestrator",
        role="Senior Landing Page Strategist",
        goal="Plan and orchestrate landing page image generation by coordinating subagents and generating the final visual",
        system_prompt="""You are a senior landing page strategist orchestrating a landing page image generation process. 
        Your role is to plan the generation process, coordinate with specialized subagents to gather all necessary 
        specifications, synthesize the information into a detailed visual description, and generate the final landing 
        page image. Coordinate parallel execution when tasks are independent to maximize efficiency.""",
        db=db,
        subagents=subagents,
        tools=[ImageGenerationTool(size="1536x1024", quality="high", output_format="png")],
        enable_planning=True,
        enable_filesystem=True,
        tool_call_limit=25,
        debug=False,
    )
    
    return orchestrator

subagents.py

"""
Specialized subagent creation functions.

Each function creates a specialized agent for a specific domain:
- Content writing
- Design recommendations
- SEO optimization
"""

from __future__ import annotations

from typing import TYPE_CHECKING

from upsonic import Agent

if TYPE_CHECKING:
    pass


def create_content_writer_subagent(model: str = "openai/gpt-4o-mini") -> Agent:
    """Create specialized subagent for landing page content creation.
    
    Args:
        model: Model identifier for the subagent
        
    Returns:
        Configured Agent instance for content writing
    """
    return Agent(
        model=model,
        name="content-writer",
        role="Landing Page Content Specialist",
        goal="Create compelling, conversion-focused copy for landing pages including headlines, value propositions, CTAs, and feature highlights",
        system_prompt="""You are an expert copywriter specializing in high-converting landing pages. Your role is to create 
        compelling, clear, and persuasive copy that drives action. Focus on:
        - Attention-grabbing headlines that communicate value immediately
        - Clear value propositions that differentiate the offering
        - Benefit-focused content that addresses customer pain points
        - Strong, action-oriented call-to-action buttons
        - Social proof and trust-building elements
        - Concise, scannable content that works on mobile devices
        
        Write copy that is specific, benefit-driven, and speaks directly to the target audience. Keep it concise and 
        conversion-focused.""",
        tool_call_limit=10,
    )


def create_designer_subagent(model: str = "openai/gpt-4o-mini") -> Agent:
    """Create specialized subagent for landing page design recommendations.
    
    Args:
        model: Model identifier for the subagent
        
    Returns:
        Configured Agent instance for design recommendations
    """
    return Agent(
        model=model,
        name="designer",
        role="Landing Page Design Specialist",
        goal="Provide design recommendations for landing pages including color schemes, typography, layout, and visual elements",
        system_prompt="""You are a UI/UX designer specializing in high-converting landing pages. Your role is to recommend 
        design elements that enhance user experience and conversion rates. Focus on:
        - Color schemes that align with brand and psychology
        - Typography that improves readability and hierarchy
        - Layout structures that guide user attention
        - Visual elements that support the message
        - Spacing and whitespace for clarity
        - Mobile-first responsive design principles
        
        Provide practical, implementable design recommendations that balance aesthetics with conversion optimization.""",
        tool_call_limit=10,
    )


def create_seo_specialist_subagent(model: str = "openai/gpt-4o-mini") -> Agent:
    """Create specialized subagent for SEO optimization.
    
    Args:
        model: Model identifier for the subagent
        
    Returns:
        Configured Agent instance for SEO optimization
    """
    return Agent(
        model=model,
        name="seo-specialist",
        role="SEO Optimization Specialist",
        goal="Optimize landing pages for search engines with proper meta tags, keywords, structure, and technical SEO elements",
        system_prompt="""You are an SEO expert specializing in landing page optimization. Your role is to ensure landing pages 
        are discoverable and rank well in search engines. Focus on:
        - Compelling meta titles and descriptions
        - Strategic keyword placement and density
        - Proper header hierarchy (H1, H2, H3)
        - Alt text for images
        - Clean URL structures
        - Schema markup for rich snippets
        - Mobile-friendly optimization
        
        Balance SEO requirements with user experience and conversion goals. Avoid keyword stuffing.""",
        tool_call_limit=10,
    )

schemas.py

"""
Output schemas for landing page generation agent.

Defines structured Pydantic models for type-safe outputs from different
landing page generation components.
"""

from __future__ import annotations

from typing import List, Optional
from pydantic import BaseModel


class ContentOutput(BaseModel):
    """Structured output for landing page content."""
    headline: str
    subheadline: str
    value_proposition: str
    key_benefits: List[str]
    call_to_action_primary: str
    call_to_action_secondary: Optional[str] = None
    feature_highlights: List[str]
    social_proof: Optional[str] = None
    footer_text: Optional[str] = None


class DesignOutput(BaseModel):
    """Structured output for landing page design recommendations."""
    color_scheme: str
    primary_color: str
    secondary_color: str
    typography_style: str
    layout_structure: str
    visual_elements: List[str]
    spacing_recommendations: str
    mobile_responsiveness: str


class SEOOutput(BaseModel):
    """Structured output for SEO optimization."""
    meta_title: str
    meta_description: str
    focus_keywords: List[str]
    header_structure: List[str]
    alt_text_suggestions: List[str]
    url_structure: str
    schema_markup: Optional[str] = None


class LandingPageOutput(BaseModel):
    """Final comprehensive landing page specification."""
    content: ContentOutput
    design: DesignOutput
    seo: SEOOutput
    implementation_notes: str
    priority_features: List[str]

task_builder.py

"""
Task description builder for landing page generation.

Constructs comprehensive task descriptions based on input parameters.
"""

from __future__ import annotations

from typing import Optional


def build_landing_page_task(
    product_name: str,
    target_audience: str,
    primary_goal: str,
    key_features: Optional[list[str]] = None,
    brand_tone: Optional[str] = None,
) -> str:
    """Build comprehensive task description for landing page generation.
    
    Args:
        product_name: Name of the product or service
        target_audience: Description of the target audience
        primary_goal: Primary conversion goal (e.g., "sign up", "purchase", "download")
        key_features: Optional list of key features to highlight
        brand_tone: Optional brand tone (e.g., "professional", "friendly", "bold")
        
    Returns:
        Comprehensive task description string
    """
    features_text = ""
    if key_features:
        features_list = "\n".join([f"       - {feature}" for feature in key_features])
        features_text = f"\n    Key features to highlight:\n{features_list}"
    
    tone_text = f"\n    Brand tone: {brand_tone}" if brand_tone else ""
    
    task_description = f"""Generate a landing page image for {product_name} targeting {target_audience} with the goal of {primary_goal}.
    
    Coordinate with specialized subagents to gather content, design, and SEO specifications, then create a detailed visual description 
    and generate the final landing page image. The image should incorporate all gathered specifications including headlines, 
    value propositions, color schemes, layout structure, and visual elements.{features_text}{tone_text}"""
    
    return task_description

upsonic_configs.json

{
    "envinroment_variables": {
        "UPSONIC_WORKERS_AMOUNT": {
            "type": "number",
            "description": "The number of workers for the Upsonic API",
            "default": 1
        },
        "API_WORKERS": {
            "type": "number",
            "description": "The number of workers for the Upsonic API",
            "default": 1
        },
        "RUNNER_CONCURRENCY": {
            "type": "number",
            "description": "The number of runners for the Upsonic API",
            "default": 1
        }
    },
    "machine_spec": {
        "cpu": 2,
        "memory": 4096,
        "storage": 1024
    },
    "agent_name": "Landing Page Generation Agent",
    "description": "AI agent system that generates landing page images by coordinating specialized subagents for content, design, and SEO, then creating the final visual using DeepAgent",
    "icon": "file-text",
    "language": "python",
    "streamlit": false,
    "proxy_agent": false,
    "dependencies": {
        "api": [
            "upsonic",
            "upsonic[tools]",
            "upsonic[storage]"
        ],
        "development": [
            "python-dotenv",
            "pytest"
        ]
    },
    "entrypoints": {
        "api_file": "main.py",
        "streamlit_file": "streamlit_app.py"
    },
    "input_schema": {
        "inputs": {
            "product_name": {
                "type": "string",
                "description": "Name of the product or service to create a landing page for (required)",
                "required": true,
                "default": null
            },
            "target_audience": {
                "type": "string",
                "description": "Description of the target audience for the landing page (required)",
                "required": true,
                "default": null
            },
            "primary_goal": {
                "type": "string",
                "description": "Primary conversion goal (e.g., 'sign up', 'purchase', 'download') (required)",
                "required": true,
                "default": null
            },
            "key_features": {
                "type": "array",
                "items": {
                    "type": "string"
                },
                "description": "Optional list of key features to highlight on the landing page",
                "required": false,
                "default": null
            },
            "brand_tone": {
                "type": "string",
                "description": "Brand tone for the landing page (e.g., 'professional', 'friendly', 'bold')",
                "required": false,
                "default": "professional"
            },
            "enable_memory": {
                "type": "boolean",
                "description": "Whether to enable memory persistence for session history",
                "required": false,
                "default": true
            },
            "storage_path": {
                "type": "string",
                "description": "Optional path for SQLite storage database file",
                "required": false,
                "default": "landing_page_generation.db"
            },
            "model": {
                "type": "string",
                "description": "Optional model identifier (e.g., openai-responses/gpt-4o, openai-responses/gpt-4o-mini)",
                "required": false,
                "default": "openai-responses/gpt-4o"
            },
            "output_folder": {
                "type": "string",
                "description": "Optional folder path for saving generated image",
                "required": false,
                "default": "landing_page_images"
            }
        }
    },
    "output_schema": {
        "product_name": {
            "type": "string",
            "description": "The product name for which the landing page was generated"
        },
        "image_path": {
            "type": "string",
            "description": "Path to the generated landing page image file"
        },
        "generation_completed": {
            "type": "boolean",
            "description": "Whether the landing page image generation was successfully completed"
        }
    }
}

Key Features

DeepAgent Orchestration

The orchestrator uses DeepAgent’s planning capabilities to automatically break down complex landing page generation tasks into manageable steps and delegate them to specialized subagents.

Specialized Subagents

Each subagent is optimized for its specific domain:

Content Writer: Creates compelling headlines, value propositions, CTAs, and feature highlights
Designer: Recommends color schemes, typography, layout structures, and visual elements
SEO Specialist: Optimizes for search engines with meta tags, keywords, and structure

Image Generation

Uses OpenAI’s image generation capabilities to create high-quality landing page visuals (1536x1024 PNG format) based on all gathered specifications from content, design, and SEO subagents.

Memory Persistence

Uses SQLite database for session persistence, allowing the agent to:

Maintain conversation history
Store generation specifications
Build upon previous sessions
Generate summaries for context

Repository

View the complete example: Landing Page Generation Agent

Overview

Video Examples

Business & Sales

Document Analysis

Code & Development

Model Integrations

Safety & Compliance

Landing Page Generation Agent

Overview

Project Structure

Environment Variables

Installation

Managing Dependencies

Usage

Option 1: Run Directly

Option 2: Run as API Server

How It Works

Example Output

Generated Landing Page Image

Complete Implementation

main.py

orchestrator.py

subagents.py

schemas.py

task_builder.py

upsonic_configs.json

Key Features

DeepAgent Orchestration

Specialized Subagents

Image Generation

Memory Persistence

Repository

Overview

Video Examples

Business & Sales

Document Analysis

Code & Development

Model Integrations

Safety & Compliance

​Overview

​Project Structure

​Environment Variables

​Installation

​Managing Dependencies

​Usage

​Option 1: Run Directly

​Option 2: Run as API Server

​How It Works

​Example Output

​Generated Landing Page Image

​Complete Implementation

​main.py

​orchestrator.py

​subagents.py

​schemas.py

​task_builder.py

​upsonic_configs.json

​Key Features

​DeepAgent Orchestration

​Specialized Subagents

​Image Generation

​Memory Persistence

​Repository

Overview

Project Structure

Environment Variables

Installation

Managing Dependencies

Usage

Option 1: Run Directly

Option 2: Run as API Server

How It Works

Example Output

Generated Landing Page Image

Complete Implementation

main.py

orchestrator.py

subagents.py

schemas.py

task_builder.py

upsonic_configs.json

Key Features

DeepAgent Orchestration

Specialized Subagents

Image Generation

Memory Persistence

Repository