How to Build Autonomous AI Agents Using OpenAGI?

Introduction

Imagine having an assistant who’s always at your fingertips, ready to help at any moment. That’s what an AI agent offers. Unlike your human assistant, who needs coffee breaks and rest, an AI agent is tireless, working around the clock to support you.

Need to schedule a meeting at the last minute? Done. Looking for the latest market trends and reports while you focus on strategy? Consider it handled. Struggling to manage your overflowing inbox? It’s under control.

AI agents are designed to understand your unique needs and adapt to your workflow, making them the perfect partners for your professional and personal life. Imagine delegating repetitive tasks, automating routine processes, and getting insights and recommendations tailored to your goals. The potential is limitless, and the efficiency is unmatched.

Overview

• AI agents understand and adapt to user needs, automating repetitive tasks and providing personalized insights, thus boosting efficiency.
• OpenAGI framework empowers developers to create AI agents capable of independent reasoning and task execution far beyond simple chatbots.
• Key components of OpenAGI include Admin for task management, Workers for execution, Planner for task breakdown, LLMs for language processing, and Tools for external data access.
• Building an AI agent with OpenAGI involves setting up a virtual environment, installing the package, configuring components, and defining worker roles for a cohesive workflow.
• OpenAGI supports seamless integration with existing systems, enhancing workflows and enabling advanced AI applications in the education, finance, and healthcare sectors.

What are AI Agents?

An Artificial Intelligence (AI) agent is a software program that can interact with its environment, collect data, and use the data to perform self-determined tasks to meet predetermined goals. Humans set goals, but an AI agent independently chooses the best actions it needs to perform to achieve those goals. For example, consider a contact centre AI agent that wants to resolve customer queries. The agent will automatically ask the customer different questions, look up information in internal documents, and respond with a solution. Based on the customer responses, it determines if it can resolve the query itself or pass it on to a human.

The diagram below shows the main parts of an AI agent.

1. Environment: The AI agent works within an environment where it interacts with various elements. The environment gives inputs like data and stimuli for the agent to process and respond to. For example, the agent might get weather updates and suggest using an umbrella.
2. Perception: This part involves processing inputs from the environment. The AI agent uses sensors or data feeds to gather information and translate it into a usable format.
3. Brain: The brain of the AI agent is where decisions are made. It includes memory, knowledge storage, and decision-making processes like planning and reasoning. The brain interprets the inputs and uses stored knowledge to make decisions.
4. Action: The AI agent acts after processing the inputs and making decisions. This could be communicating with users, adjusting settings, or performing tasks. For instance, the agent might remind you to carry an umbrella or update your calendar.

By understanding these parts, you can see how AI agents work similarly to human minds, processing information, making decisions, and acting based on those decisions.

How Does OpenAGI Help?

Large Language Models (LLMs) are powerful computer systems that understand and create human-like text. They have helped many industries, such as customer service and content creation. However, LLMs mainly respond to prompts and lack the power to act independently or make decisions.

To address this, OpenAGI introduces a new framework that allows developers to create AI agents capable of independent thought. These AI agents go beyond simple chatbots; they can plan, reason, and perform tasks independently with little to no human intervention. Imagine an AI that answers your questions and finds out your needs, gathers information, and gives solutions—acting as a partner.

OpenAGI offers a complete toolkit for building these agents. It includes pre-trained models, data integration tools, and many development resources, making it easier for a wide range of users, be they AI researchers or curious beginners. It helps anyone create advanced AI systems.

The key features of OpenAGI are customization and flexibility. Developers can tailor AI agents for specific domains and applications, from personalized customer experiences to difficult business process automation. OpenAGI also ensures seamless integration with existing systems, enhancing workflows without causing disruptions.

Also Read: A Comprehensive Guide on Building AI Agents with AutoGPT

Components of OpenAGI

Here are the components of OpenAGI:

1. Admin

The Admin is like the head of the OpenAGI system. It makes all the decisions and ensures everything runs smoothly. It understands what tasks must be done and plans and assigns related tasks to the right workers. Besides managing tasks, the Admin provides resources, prioritizes tasks, and resolves conflicts or errors. It acts as the bridge between what the user wants and what the agent does, ensuring everything is done efficiently. The Admin also monitors the agent’s overall performance and can adjust plans as needed. It manages interactions with other systems and services, checks security and data exchange, and ensures safe communication. 

2. Workers

Workers are the doers within the framework. They carry out the work assigned by the Admin. Each worker is created for a certain task, like getting data, generating text, or performing operations. For example, one worker might be great at gathering information from the web, while another works best at writing text. Workers can be combined to break down big and difficult tasks into smaller, manageable pieces, increasing efficiency and allowing customization. Workers are assigned tasks based on workload and resource availability. By collaborating with the Admin and other components, workers ensure that tasks are executed accurately.

3. Planner

The Planner is the thinker in the OpenAGI system. It breaks down complex tasks into smaller parts, creating a roadmap for the agent. This helps to guide the agent’s actions and prioritizes the tasks based on their importance. The Planner checks available resources, task orders, and potential obstacles to create an efficient plan. It can adapt the plan as conditions change, ensuring the agent remains responsive. 

4. Large Language Models (LLMs)

Large Language Models (LLMs) in OpenAGI agents are models trained on vast amounts of text data, making them able to understand, create, and manipulate human language effectively. They handle tasks involving natural language processing, like summarizing text, translating languages, answering questions, and creative writing. The choice of LLM affects an agent’s performance, as different models have different strengths and weaknesses. OpenAGI supports various LLMs, allowing developers to select the best model for their needs. 

5. Actions

Actions are the building blocks of the OpenAGI system. They represent the functionalities the agent uses to complete tasks. These can be simple, like searching for data, or complex, involving many steps. Actions are flexible so that developers can create custom actions for specific needs. This also helps with reusing code and rapid development. Multiple actions can be combined to handle complex tasks, making the agent adaptable and good at solving problems.

6. Tools

Tools are external resources that increase the capabilities of OpenAGI agents. They give access to data, services, and computing power, enabling agents to do tasks that would be hard or impossible otherwise. Tools include search engines, databases, APIs, and software libraries. Developers can create tools for specific needs, enhancing the agent’s performance in targeted areas. This promotes customization and adaptability.

7. Memory

Memory is important for OpenAGI agents. It allows them to store and retrieve information, learn from past experiences, and make good decisions. Types of memory include short-term memory for current information, long-term memory for future reference, and episodic memory for past events. Good memory management enhances the agent’s reasoning, learning, and adaptability. Developers can improve the agent’s performance by choosing the right types of memory and using efficient storage and retrieval methods.

These components work together to create an intelligent agent. Let’s dive deeper into how they collaborate.

Also Read: Comprehensive Guide to Build AI Agents from Scratch

Building Your First Agent

Now, we will walk through the step-by-step implementation of a blog writer using OpenAGI. We will set up a virtual environment, install the necessary packages, and configure the components of OpenAGI, including creating worker agents with specific roles and an admin to manage the workflow. By the end, we will have an agent capable of researching, writing, and reviewing a blog post about the future of AI.

Explore OpenAGI using these resources:

• The GitHub repo: aiplanethub/openagi
• Documentation for OpenAGI: Documentation

Step 1: Set Up the Virtual Environment

We will set up a virtual environment to ensure our project’s clean and isolated environment.

For Mac Users

python3 -m venv venv
source venv/bin/activate

For Windows Users

python -m venv venv
venv\Scripts\activate

Step 2: Install the OpenAGI Package

Next, we will install the OpenAGI package using pip.

pip install openagi

Step 3: Import the Required Modules

We will import the necessary modules for setting up the agent, including tools for internet searches, content writing, and memory management.

from openagi.actions.files import WriteFileAction
from openagi.actions.tools.ddg_search import DuckDuckGoNewsSearch
from openagi.actions.tools.webloader import WebBaseContextTool
from openagi.agent import Admin
from openagi.llms.azure import AzureChatOpenAIModel
from openagi.memory import Memory
from openagi.planner.task_decomposer import TaskPlanner
from openagi.worker import Worker

Step 4: Set Up the LLM (Large Language Model)

We will load the configuration for the AzureChatOpenAIModel from environment variables.

import os
os.environ["AZURE_BASE_URL"] = "https://<replace-with-your-endpoint>.openai.azure.com/"
os.environ["AZURE_DEPLOYMENT_NAME"] = "<replace-with-your-deployment-name>"
os.environ["AZURE_MODEL_NAME"] = "gpt4–32k"
os.environ["AZURE_OPENAI_API_VERSION"] = "2023–05–15"
os.environ["AZURE_OPENAI_API_KEY"] = "<replace-with-your-key>"
config = AzureChatOpenAIModel.load_from_env_config()
llm = AzureChatOpenAIModel(config=config)

Step 5: Define the Workers

We will create worker agents with specific roles and instructions. Each worker is equipped with tools to perform their designated tasks.

Research Analyst

Conducts research on the latest developments in AI.
researcher = Worker(
role="Research Analyst",
instructions="Uncover cutting-edge developments in AI and data science. You work at a leading tech think tank. Your expertise lies in identifying emerging trends. You have a knack for dissecting complex data and presenting actionable insights.",
actions=[
DuckDuckGoNewsSearch,
WebBaseContextTool,
],
)

Tech Content Strategist

Writes blog posts based on the research.

writer = Worker(
role="Tech Content Strategist",
instructions="Craft compelling content on tech advancements. You are a renowned Content Strategist, known for your insightful and engaging articles. You transform complex concepts into compelling narratives. Finally return the entire article as output.",
actions=[
DuckDuckGoNewsSearch,
WebBaseContextTool,
],
)

Review and Editing Specialist

Reviews and edits the blog post, ensuring clarity and grammatical accuracy.

reviewer = Worker(
role="Review and Editing Specialist",
instructions="Review the content for clarity, engagement, grammatical accuracy, and alignment with company values and refine it to ensure perfection. A meticulous editor with an eye for detail, ensuring every piece of content is clear, engaging, and grammatically perfect. Finally write the blog post to a file and return the same as output.",
actions=[
DuckDuckGoNewsSearch,
WebBaseContextTool,
WriteFileAction,
],
)

Step 6: Set Up the Admin

We will configure the Admin to manage and coordinate the tasks. The Admin assigns tasks to the workers and oversees the entire workflow.

admin = Admin(
planner=TaskPlanner(human_intervene=False),
memory=Memory(),
llm=llm,
)
admin.assign_workers([researcher, writer, reviewer])

Step 7: Run the Task

The Admin executes the task by providing a query and description. The task involves researching, writing, and reviewing a blog post about the future of AI.

res = admin.run(
query="Write a blog post about future of AI. Feel free to write files to maintain the context.",
description="Conduct a comprehensive analysis of the latest advancements in AI in 2024. Identify key trends, breakthrough technologies, and potential industry impacts. Using the insights provided, develop an engaging blog post that highlights the most significant AI advancements. Your post should be informative yet accessible, catering to a tech-savvy audience. Make it sound cool, avoid complex words so it doesn't sound like AI.",
)

Step 8: Print the Results

Finally, print the results from the OpenAGI, displaying the agent-generated content.

print(res)

Output

The agent will create a file with the following content:

The Future of AI: Key Trends and Innovations in 2024

Introduction

Artificial Intelligence (AI) continues to transform businesses, industries,
 and various aspects of our daily lives. As we move into 2024, the
 advancements in AI are set to shape the future in unprecedented ways. This
 blog post explores the key trends, breakthrough technologies, and potential
 industry impacts of AI in 2024.

Key Trends and Breakthrough Technologies

1. **AI Market Growth**: The AI market is projected to reach USD 2575.16
 billion by 2032, driven by innovations in educational tools, natural
 language processing (NLP), and healthcare applications.

2. **Transformative AI Innovations**: AI is rapidly integrating into various
 sectors, transforming businesses and industries while raising potential
 challenges like energy consumption.

Industry and Safety Concerns

1. **Transparency and Ethics**: Former OpenAI employees have called for
 increased transparency and safety measures in AI development, emphasizing
 the importance of ethical considerations.

2. **Ethical Use in Legal and Healthcare Sectors**: The ethical use of AI is
 crucial, particularly in the legal and healthcare sectors, to avoid
 potential legal implications and ensure quality patient outcomes.

Notable Company Announcements and Market Movements

1. **Nvidia's Leadership**: Nvidia's announcements at Computex 2024
 highlighted significant AI advancements and partnerships, showcasing their
 leadership in AI technology.

2. **Market Performance**: Nvidia surpassed Apple in market cap, with both
 companies reaching a $3 trillion valuation, underscoring Nvidia's dominance
 in the AI market.

Sector-Specific Impacts

1. **AI in Finance**: AI is revolutionizing banking and financial software
 development, enhancing financial services and customer experiences.

2. **AI in Healthcare**: AI holds great potential in healthcare for improving
 patient outcomes but requires careful implementation and ethical guidelines.

3. **AI/ML-Enabled Medical Devices**: Innovators like Tejesh Marsale are 
leading advancements in AI/ML-enabled medical devices, pushing the boundaries
 of healthcare technology.

More OpenAGI Use Cases

With OpenAGI, you can revolutionize education, finance, healthcare, and more sectors. Some of the potential use cases include:

• Education: In education, agents can provide personalized learning experiences. They adapt and tailor learning content based on student’s progress, performance, and interests. This can extend to automating various other administrative tasks and assisting teachers in improving their productivity.
• Finance and Banking: Financial services can use agents for fraud detection, risk assessment, personalized banking advice, automating trading, and customer service. Agents help analyze large numbers of transactions to check suspicious activities and offer good investment advice.
• Healthcare: Agents can monitor patients, provide health recommendations, manage patient data, and automate administrative tasks. They can also help diagnose diseases based on symptoms and medical history.
• IT & Software Development: There are multiple use cases here, from code generation and bug fixing to test case automation, efficient documentation writing, and process improvement.

Conclusion

OpenAGI stands out as a powerful, flexible, and user-friendly AI framework that meets the needs of modern businesses and developers. By offering streamlined workflow integration, robust performance, and comprehensive support, OpenAGI enables users to effectively leverage AI technology to drive innovation and efficiency in their projects.

Frequently Asked Questions

Shivaya Pandey

Hey, I’m Shivaya, a second-year student specializing in Data Science. I'm a DevRel Intern at AI Planet. Passionate about cutting-edge AI technology, I love exploring new advancements and sharing my insights through blogs. Enthusiastic and curious, I'm always eager to learn and contribute to the evolving world of AI.