Multimodal agentic frameworks represent a cutting-edge approach in artificial intelligence, integrating various data types\u2014such as text, images, audio, and video\u2014to enhance the capabilities of intelligent systems. These frameworks utilize intelligent agents that can autonomously process and analyze diverse information sources, enabling more nuanced understanding and decision-making. By combining multimodality with agentic functionalities, these systems can adapt in real time to dynamic environments and user interactions. This integration not only improves operational efficiency across industries but also enriches human-computer interactions, making them more intuitive and context-aware. As such, multimodal agentic frameworks are poised to transform how we engage with technology in numerous applications.<\/p>\n
Learning Objectives<\/h3>\n\n- Understanding Agentic AI with Image Generation<\/li>\n
- Exploring Camel AI Functionalities<\/li>\n
- Developing a Multimodal Agentic System with CAMEL AI<\/li>\n
- Benefits to Real Estate Businesses<\/li>\n<\/ul>\n
This article was published as a part of the\u00a0<\/strong><\/em>Data Science Blogathon.<\/strong><\/em><\/a><\/p>\nMultiModal Agentic AI: Agents with Image Generation<\/h2>\n
Agentic AI represents a significant evolution in artificial intelligence<\/a>, characterized by its autonomy and advanced decision-making capabilities. Integrating Agentic Frameworks with Image Generation capabilities can give significant advantages as mentioned below \u2013<\/p>\n\n- Enhanced Creativity: <\/b>These systems can assist in creative processes by generating unique visual content, enabling artists, designers, and marketers to explore new ideas and concepts efficiently.<\/li>\n
- Personalization:<\/b> By generating tailored images based on user preferences or data inputs, agentic systems can create personalized experiences in marketing, advertising, and entertainment.<\/li>\n
- Rapid Prototyping:<\/b> Agentic systems can quickly produce visual prototypes for products or concepts, facilitating faster iterations and feedback during the design process.<\/li>\n
- Data Visualization:<\/b> They can transform complex data sets into intuitive visual representations, aiding in better understanding and communication of information across various fields such as business analytics and scientific research.<\/li>\n
- Accessibility<\/b>: These systems can democratize access to high-quality visual content, allowing individuals and organizations without extensive design resources to create professional-grade images.<\/li>\n
- Automation of Repetitive Tasks: <\/b>By automating the image generation process, agentic systems reduce the time and resources spent on routine design tasks, allowing human creators to focus on more strategic initiatives.<\/li>\n<\/ul>\n
What is Camel AI?<\/h2>\n
Camel AI<\/a> (short for Communicative Agents for Mind Exploration of Large-Scale Language Model Society) is an innovative framework dedicated to the development and research of autonomous, communicative agents. Its primary goal is to examine how AI systems interact and collaborate, reducing the need for human involvement in various tasks. Focusing on the analysis of behaviors, abilities, and potential risks within multi-agent systems, Camel AI is an open-source project designed to foster collaboration and drive innovation within the AI research community.<\/p>\nCore Modules in Camel AI<\/h3>\n
The CAMEL framework is designed for the creation and management of multi-agent systems, incorporating several key components. It includes Models <\/b>for defining agent intelligence, Messages <\/b>for communication, and Memory systems<\/b> for data storage and retrieval. The framework also integrates Tools for specialized tasks, Prompts to guide agent behavior, and Tasks to manage workflows<\/b>. The Workforce <\/b>module enables the formation of agent teams for collaboration, while the Society <\/b>module facilitates interaction among agents. Together, these components enable the development of dynamic, collaborative multi-agent environments.<\/p>\n\n![\"Toolkits](\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/01\/image_uo4AUOq.webp\")
Figure \u2013 Diverse Set of Toolkits Available in Camel AI<\/figcaption><\/figure>\n<\/div>\nOne of the greatest pros of using Camel AI is its integration with a diverse set of toolkits which can be seamlessly leveraged in creating multi-agentic systems. Camel AI includes several toolkits that enhance the capabilities of its multi-agent framework. Key toolkits include:<\/p>\n
\n- Function Tool: <\/b>This toolkit allows agents to call functions and interact with various APIs, facilitating complex task execution and integration with external services.<\/li>\n
- Reddit Toolkit:<\/b> This toolkit enables agents to interact with the Reddit API, allowing them to collect top posts, perform sentiment analysis on comments, and monitor discussions across subreddits.<\/li>\n
- Retrieval Toolkit:<\/b> Designed for information retrieval, this toolkit allows agents to query local vector storage systems, retrieving relevant information based on user queries.<\/li>\n
- Media Tools:<\/b> This includes functionalities for processing images and audio, enabling agents to handle multimedia content effectively.<\/li>\n
- Document Tools:<\/b> This toolkit provides capabilities for processing documents in various formats (e.g., PDF, Word) and includes web scraping features.<\/li>\n
- Web Tools: <\/b>These tools enable agents to access and interact with web services, such as search engines and APIs like DuckDuckGo<\/a> and Wikipedia.<\/li>\n
- DALL-E Integration: <\/b>Camel AI also supports integration with image generation models like DALL-E, allowing agents to create images based on textual descriptions, enhancing their creative capabilities.<\/li>\n
- Search Toolkits. <\/b>A toolkit for performing web searches using various search engines like Google, DuckDuckGo, Wikipedia, and Wolfram Alpha<\/a>.<\/li>\n<\/ul>\n
These toolkits collectively empower Camel AI to perform a wide range of tasks, from data retrieval and processing to multimedia handling and creative image generation.<\/p>\n
DALL-E<\/h3>\n
DALL-E is a series of advanced text-to-image models developed by OpenAI that generate digital images based on natural language descriptions, known as prompts. The initial version was released in January 2021, followed by DALL-E 2 in 2022, and the latest iteration, DALL-E 3<\/a>, was integrated into ChatGPT and made available in late 2023.<\/p>\nDALL-E can create images in various styles, including photorealistic images and artistic renditions. It can manipulate and rearrange objects within images and infer details not explicitly mentioned in prompts.<\/p>\n
Hands-On Implementation of a Multi-Modal Agentic System <\/h2>\n
In the following hands-on tutorial, we create a multi-modal agentic system using CAMEL AI for designing brochures for upcoming real estate projects in a city. This could help real estate businesses immensely as this aids in the automated creation of the brochures needed for giving out to clients when any of their new projects come up in a city without minimal human intervention.<\/p>\n
Step 1. Installation of Necessary Libraries<\/h3>\n!pip install 'camel-ai[all]'<\/code><\/pre>\nStep 2. Defining Open AI API Keys<\/h3>\nimport os\nos.environ['OPENAI_API_KEY'] = ''<\/code><\/pre>\nStep 3. Importing Necessary Libraries<\/h3>\nfrom camel.agents.chat_agent import ChatAgent\nfrom camel.messages.base import BaseMessage\nfrom camel.models import ModelFactory\nfrom camel.societies.workforce import Workforce\nfrom camel.tasks.task import Task\nfrom camel.toolkits import (\n FunctionTool,\n GoogleMapsToolkit,\n SearchToolkit,\n)\nfrom camel.toolkits import DalleToolkit\n\nfrom camel.types import ModelPlatformType, ModelType\n\nimport nest_asyncio\nnest_asyncio.apply()<\/code><\/pre>\nStep 4. Defining the Agents<\/h3>\n\n![\"Agents](\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/01\/image_mu9Wtbv.webp\")
Agents & Tools Utilized<\/figcaption><\/figure>\n<\/div>\nsearch_toolkit = SearchToolkit()\nsearch_tools = [\n FunctionTool(search_toolkit.search_duckduckgo)]\n \n\n\n#Define the Model for the Agent as well. Default model is \"gpt-4o-mini\" and model platform type is OpenAI\nguide_agent_model = ModelFactory.create(\n model_platform=ModelPlatformType.DEFAULT,\n model_type=ModelType.DEFAULT,\n ) \n\n#Defining the Real Estate Agent for crafting the brochures\nreal_estate_agent = ChatAgent(\n BaseMessage.make_assistant_message(\n role_name=\"Real Estate Specialist\",\n content=\"You are a Real Estate Specialist who is an expert in creating Description of Upcoming Residential Projects\",\n ),\n model=guide_agent_model,\n )\n\n#Defining the Agent for Real Estate Property Names\nproperty_title_agent = ChatAgent(\n BaseMessage.make_assistant_message(\n role_name=\"Real Estate Project Name Specialist\",\n content=\"You are a Real Estate Project Name Specialist who is an expert in Generating Trendy Names FoR Residental Projects in india\",\n ),\n model=guide_agent_model,\n )\n\n\n#Defining the agent for generating all the amenities near a location\nlocation_benefits_agent = ChatAgent(\n BaseMessage.make_assistant_message(\n role_name=\"Real Estate Location Specialist\",\n content=\"You are a Real Estate Location Specialist who is an expert in Generating All the amenities like malls, airports, markets, metro stations, railway stations etc with distances from a location of the mentioned property\",\n ),\n model=guide_agent_model, tools =search_tools\n )\n\n\n#Define the web search tool for the Agent using Tavily (we need to define the Tavily API Key beforehand)\ndalletool = DalleToolkit()\nimagegen_tools = [\n FunctionTool(dalletool.get_dalle_img),\n \n]\n\n#Define the Image Generation Agent with the pre-defined model and tools and Prompt\nimage_generation_agent = ChatAgent(\n system_message=BaseMessage.make_assistant_message(\n role_name=\"Image Generation Specialist\",\n content=\"You can Generate Images For Upcoming Real Estate Projects For Showing to Clients\",\n ),\n model=guide_agent_model,\n tools=imagegen_tools,\n )<\/code><\/pre>\nThis code snippet defines several agents using a model factory and a chat agent framework.<\/p>\n
\n- Model Creation<\/b>: It first creates a default model (guide_agent_model) for the agents, specifically using the \u201cGPT-4o-mini<\/a>\u201d model from OpenAI.<\/li>\n
- Real Estate Agents:<\/b> Two agents are instantiated: one as a \u201cReal Estate Specialist\u201d focused on creating descriptions for upcoming residential projects, and another as a \u201cReal Estate Project Name Specialist\u201d <\/b>tasked with generating trendy names for residential projects in India.<\/li>\n
- Real Estate Location Specialist : <\/b>This agent is for generating all the amenities like malls, airports, markets, metro stations, railway stations etc with distances from a location of the mentioned property<\/li>\n
- Image Generation Tool: <\/b>An image generation tool (dalletool) which allows the agents to generate images related to real estate projects.<\/li>\n
- Image Generation Agent: <\/b>Finally, an \u201cImage Generation Specialist\u201d agent is created, equipped with the previously defined model and image generation tools to create visuals for upcoming real estate projects to present to clients.<\/li>\n<\/ol>\n
Step 5. Defining the WorkForce<\/h3>\n#Define the workforce that can take case of multiple agents\nworkforce = Workforce('Real Estate Brochure Generator')\nworkforce.add_single_agent_worker(\n \"Real Estate Specialist\",\n worker=real_estate_agent).add_single_agent_worker(\n \"Real Estate Project Name Specialist\",\n worker=property_title_agent).add_single_agent_worker(\n \"Location Amenity Specialist\",worker=location_benefits_agent).add_single_agent_worker(\n \"Image Generation Specialist\",\n worker=image_generation_agent)\n\n # specify the task to be solved Defining the exact task needed\nhuman_task = Task(\n content=(\n \"\"\"Craft a Brochure Content For a Upcoming Residential Real Estate Project in Sector 47,Gurgaon. The content should contain all the types of flats it has, all amenities in it and other such necessary details . \n Provide a Name for this Property as well.\n Generate all the amenities of the location (with respect to its proximity to all public places) to this brochure content.\n Generate an Image of this Upcoming Project as well.\"\"\"\n ),\n id='0',\n )\ntask = workforce.process_task(human_task)<\/code><\/pre>\nThis code defines a \u201cworkforce\u201d that manages multiple agents for generating a real estate brochure. It adds 4 agents: a Real Estate Specialist, a Property Name Specialist, Location Amenity Specialist and an Image Generation Specialist. Then, it specifies a task for the workforce to complete\u2014creating brochure content, providing a project name, and generating an image for a new real estate project in Gurgaon. The workforce processes the task by coordinating the agents to execute their respective roles.<\/p>\n
Outputs<\/h2>\n1. Output from Brochure Content Agent<\/h3>\nUpcoming Residential Project in Sector 47, GurgaonWelcome to Your New Home
---<\/p>
Flat Types Available:<\/p>
1. **1 BHK Flats**
2. **2 BHK Flats**
3. **3 BHK Flats**
4. **Penthouse Suites**
---<\/p>
\u00a0Amenities:
---<\/p>
Location Benefits:
---<\/p>
Conclusion:
2. Output from\u00a0Real Estate Project Name Specialist Agent<\/h3>\n**Gurgaon Heights**Output from\u00a0Location Amenity Specialist\u00a0Agent<\/p>
Amenities and Proximity to Public Places near Gurgaon Heights, Sector 47,
1. Shopping Malls:-<\/p>
**Ambience Mall** - 5 km
2. **Metro Stations:**
3. **Railway Stations:**
4. Airports: -<\/p>
**Indira Gandhi International Airport** - 15 km<\/p>
5. Schools: -<\/p>
**The Shri Ram School** - 2 km
6. Hospitals:-<\/p>
**Medanta - The Medicity** - 6 km
7. Parks and Recreation:-<\/p>
**Aravali Golf Course** - 3 km
8. Restaurants and Cafes:-<\/p>
**Cyber Hub** - 6 km
9. Entertainment: -<\/p>
**PVR Cinemas, Ambience Mall** - 5 km
3. Output From Image Generation Specialist:-<\/h3>\n![\"](\"https:\/\/av-eks-lekhak.s3.amazonaws.com\/media\/__sized__\/article_images\/image_PMHleiJ-thumbnail_webp-600x300.webp\")
Generated Image For Brochure<\/figcaption><\/figure>\nConclusion<\/h2>\n
In conclusion, the integration of agentic AI systems with image generation capabilities, such as those found in the Camel AI framework (MultiModal Agentic Framework), represents a transformative advancement in both creativity and automation. By combining the power of autonomous decision-making with advanced image generation tools, these systems offer significant potential for rapid prototyping, personalized experiences, and enhanced accessibility to high-quality visual content. As Camel AI (MultiModal Agentic Framework) continues to evolve, it can drive innovation across various industries, reducing human involvement in routine tasks while empowering more strategic and creative endeavours.<\/p>\n
Key Takeaways<\/h3>\n\n- Autonomous Creativity: <\/b>Agentic AI systems<\/a> with image generation capabilities enhance creative processes, allowing artists and designers to quickly generate unique and innovative visual content.<\/li>\n
- Personalized Experiences:<\/b> These systems can tailor images based on user preferences, enabling customized marketing, advertising, and entertainment experiences.<\/li>\n
- Efficient Prototyping: <\/b>Agentic AI accelerates the prototyping process by generating visual prototypes rapidly, fostering quicker iterations and feedback in design workflows.<\/li>\n
- Data Visualization:<\/b> Agentic AI systems can convert complex data into clear, visually intuitive representations, aiding in better understanding and communication across diverse fields.<\/li>\n
- Multi-Agent Collaboration: <\/b>Camel AI\u2019s framework promotes collaboration among autonomous agents, enhancing task execution and facilitating the development of advanced, multi-agent systems for a wide range of applications.<\/li>\n<\/ol>\n
The media shown in this article is not owned by Analytics Vidhya and is used at the Author\u2019s discretion.<\/strong><\/p>\nFrequently Asked Questions<\/h2>\n\nQ1. What are Agentic AI systems and how do they work with image generation?<\/strong> <\/p>\nAns. Agentic AI systems are autonomous AI frameworks with advanced decision-making capabilities. When integrated with image generation capabilities, they can create unique visual content, enhance creativity, and automate tasks, making processes like design, marketing, and prototyping more efficient.<\/p>\n<\/p><\/div>\n
This article was published as a part of the\u00a0<\/strong><\/em>Data Science Blogathon.<\/strong><\/em><\/a><\/p>\n Agentic AI represents a significant evolution in artificial intelligence<\/a>, characterized by its autonomy and advanced decision-making capabilities. Integrating Agentic Frameworks with Image Generation capabilities can give significant advantages as mentioned below \u2013<\/p>\n Camel AI<\/a> (short for Communicative Agents for Mind Exploration of Large-Scale Language Model Society) is an innovative framework dedicated to the development and research of autonomous, communicative agents. Its primary goal is to examine how AI systems interact and collaborate, reducing the need for human involvement in various tasks. Focusing on the analysis of behaviors, abilities, and potential risks within multi-agent systems, Camel AI is an open-source project designed to foster collaboration and drive innovation within the AI research community.<\/p>\n The CAMEL framework is designed for the creation and management of multi-agent systems, incorporating several key components. It includes Models <\/b>for defining agent intelligence, Messages <\/b>for communication, and Memory systems<\/b> for data storage and retrieval. The framework also integrates Tools for specialized tasks, Prompts to guide agent behavior, and Tasks to manage workflows<\/b>. The Workforce <\/b>module enables the formation of agent teams for collaboration, while the Society <\/b>module facilitates interaction among agents. Together, these components enable the development of dynamic, collaborative multi-agent environments.<\/p>\n One of the greatest pros of using Camel AI is its integration with a diverse set of toolkits which can be seamlessly leveraged in creating multi-agentic systems. Camel AI includes several toolkits that enhance the capabilities of its multi-agent framework. Key toolkits include:<\/p>\n These toolkits collectively empower Camel AI to perform a wide range of tasks, from data retrieval and processing to multimedia handling and creative image generation.<\/p>\n DALL-E is a series of advanced text-to-image models developed by OpenAI that generate digital images based on natural language descriptions, known as prompts. The initial version was released in January 2021, followed by DALL-E 2 in 2022, and the latest iteration, DALL-E 3<\/a>, was integrated into ChatGPT and made available in late 2023.<\/p>\n DALL-E can create images in various styles, including photorealistic images and artistic renditions. It can manipulate and rearrange objects within images and infer details not explicitly mentioned in prompts.<\/p>\n In the following hands-on tutorial, we create a multi-modal agentic system using CAMEL AI for designing brochures for upcoming real estate projects in a city. This could help real estate businesses immensely as this aids in the automated creation of the brochures needed for giving out to clients when any of their new projects come up in a city without minimal human intervention.<\/p>\n This code snippet defines several agents using a model factory and a chat agent framework.<\/p>\n This code defines a \u201cworkforce\u201d that manages multiple agents for generating a real estate brochure. It adds 4 agents: a Real Estate Specialist, a Property Name Specialist, Location Amenity Specialist and an Image Generation Specialist. Then, it specifies a task for the workforce to complete\u2014creating brochure content, providing a project name, and generating an image for a new real estate project in Gurgaon. The workforce processes the task by coordinating the agents to execute their respective roles.<\/p>\n Welcome to Your New Home ---<\/p> Flat Types Available:<\/p> 1. **1 BHK Flats** 2. **2 BHK Flats** 3. **3 BHK Flats** 4. **Penthouse Suites** ---<\/p> \u00a0Amenities: ---<\/p> Location Benefits: ---<\/p> Conclusion: Output from\u00a0Location Amenity Specialist\u00a0Agent<\/p> Amenities and Proximity to Public Places near Gurgaon Heights, Sector 47, 1. Shopping Malls:-<\/p> **Ambience Mall** - 5 km 2. **Metro Stations:** 3. **Railway Stations:** 4. Airports: -<\/p> **Indira Gandhi International Airport** - 15 km<\/p> 5. Schools: -<\/p> **The Shri Ram School** - 2 km 6. Hospitals:-<\/p> **Medanta - The Medicity** - 6 km 7. Parks and Recreation:-<\/p> **Aravali Golf Course** - 3 km 8. Restaurants and Cafes:-<\/p> **Cyber Hub** - 6 km 9. Entertainment: -<\/p> **PVR Cinemas, Ambience Mall** - 5 km In conclusion, the integration of agentic AI systems with image generation capabilities, such as those found in the Camel AI framework (MultiModal Agentic Framework), represents a transformative advancement in both creativity and automation. By combining the power of autonomous decision-making with advanced image generation tools, these systems offer significant potential for rapid prototyping, personalized experiences, and enhanced accessibility to high-quality visual content. As Camel AI (MultiModal Agentic Framework) continues to evolve, it can drive innovation across various industries, reducing human involvement in routine tasks while empowering more strategic and creative endeavours.<\/p>\n The media shown in this article is not owned by Analytics Vidhya and is used at the Author\u2019s discretion.<\/strong><\/p>\n Ans. Agentic AI systems are autonomous AI frameworks with advanced decision-making capabilities. When integrated with image generation capabilities, they can create unique visual content, enhance creativity, and automate tasks, making processes like design, marketing, and prototyping more efficient.<\/p>\n<\/p><\/div>\nMultiModal Agentic AI: Agents with Image Generation<\/h2>\n
\n
What is Camel AI?<\/h2>\n
Core Modules in Camel AI<\/h3>\n
\n
DALL-E<\/h3>\n
Hands-On Implementation of a Multi-Modal Agentic System <\/h2>\n
Step 1. Installation of Necessary Libraries<\/h3>\n
!pip install 'camel-ai[all]'<\/code><\/pre>\nStep 2. Defining Open AI API Keys<\/h3>\n
import os\nos.environ['OPENAI_API_KEY'] = ''<\/code><\/pre>\nStep 3. Importing Necessary Libraries<\/h3>\n
from camel.agents.chat_agent import ChatAgent\nfrom camel.messages.base import BaseMessage\nfrom camel.models import ModelFactory\nfrom camel.societies.workforce import Workforce\nfrom camel.tasks.task import Task\nfrom camel.toolkits import (\n FunctionTool,\n GoogleMapsToolkit,\n SearchToolkit,\n)\nfrom camel.toolkits import DalleToolkit\n\nfrom camel.types import ModelPlatformType, ModelType\n\nimport nest_asyncio\nnest_asyncio.apply()<\/code><\/pre>\nStep 4. Defining the Agents<\/h3>\n
search_toolkit = SearchToolkit()\nsearch_tools = [\n FunctionTool(search_toolkit.search_duckduckgo)]\n \n\n\n#Define the Model for the Agent as well. Default model is \"gpt-4o-mini\" and model platform type is OpenAI\nguide_agent_model = ModelFactory.create(\n model_platform=ModelPlatformType.DEFAULT,\n model_type=ModelType.DEFAULT,\n ) \n\n#Defining the Real Estate Agent for crafting the brochures\nreal_estate_agent = ChatAgent(\n BaseMessage.make_assistant_message(\n role_name=\"Real Estate Specialist\",\n content=\"You are a Real Estate Specialist who is an expert in creating Description of Upcoming Residential Projects\",\n ),\n model=guide_agent_model,\n )\n\n#Defining the Agent for Real Estate Property Names\nproperty_title_agent = ChatAgent(\n BaseMessage.make_assistant_message(\n role_name=\"Real Estate Project Name Specialist\",\n content=\"You are a Real Estate Project Name Specialist who is an expert in Generating Trendy Names FoR Residental Projects in india\",\n ),\n model=guide_agent_model,\n )\n\n\n#Defining the agent for generating all the amenities near a location\nlocation_benefits_agent = ChatAgent(\n BaseMessage.make_assistant_message(\n role_name=\"Real Estate Location Specialist\",\n content=\"You are a Real Estate Location Specialist who is an expert in Generating All the amenities like malls, airports, markets, metro stations, railway stations etc with distances from a location of the mentioned property\",\n ),\n model=guide_agent_model, tools =search_tools\n )\n\n\n#Define the web search tool for the Agent using Tavily (we need to define the Tavily API Key beforehand)\ndalletool = DalleToolkit()\nimagegen_tools = [\n FunctionTool(dalletool.get_dalle_img),\n \n]\n\n#Define the Image Generation Agent with the pre-defined model and tools and Prompt\nimage_generation_agent = ChatAgent(\n system_message=BaseMessage.make_assistant_message(\n role_name=\"Image Generation Specialist\",\n content=\"You can Generate Images For Upcoming Real Estate Projects For Showing to Clients\",\n ),\n model=guide_agent_model,\n tools=imagegen_tools,\n )<\/code><\/pre>\n\n
Step 5. Defining the WorkForce<\/h3>\n
#Define the workforce that can take case of multiple agents\nworkforce = Workforce('Real Estate Brochure Generator')\nworkforce.add_single_agent_worker(\n \"Real Estate Specialist\",\n worker=real_estate_agent).add_single_agent_worker(\n \"Real Estate Project Name Specialist\",\n worker=property_title_agent).add_single_agent_worker(\n \"Location Amenity Specialist\",worker=location_benefits_agent).add_single_agent_worker(\n \"Image Generation Specialist\",\n worker=image_generation_agent)\n\n # specify the task to be solved Defining the exact task needed\nhuman_task = Task(\n content=(\n \"\"\"Craft a Brochure Content For a Upcoming Residential Real Estate Project in Sector 47,Gurgaon. The content should contain all the types of flats it has, all amenities in it and other such necessary details . \n Provide a Name for this Property as well.\n Generate all the amenities of the location (with respect to its proximity to all public places) to this brochure content.\n Generate an Image of this Upcoming Project as well.\"\"\"\n ),\n id='0',\n )\ntask = workforce.process_task(human_task)<\/code><\/pre>\nOutputs<\/h2>\n
1. Output from Brochure Content Agent<\/h3>\n
Upcoming Residential Project in Sector 47, Gurgaon
2. Output from\u00a0Real Estate Project Name Specialist Agent<\/h3>\n
**Gurgaon Heights**
3. Output From Image Generation Specialist:-<\/h3>\n
Conclusion<\/h2>\n
Key Takeaways<\/h3>\n
\n
Frequently Asked Questions<\/h2>\n
![\"Nibedita](\"https:\/\/av-eks-lekhak.s3.amazonaws.com\/media\/lekhak-profile-images\/converted_image_ZkJo4gb.webp\")
<\/p>\n