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LLM AI agents what are they and how can they be used?

what are LLM AI agents and how can they be used
LLM AI agents, powered by large language models (LLMs), represent a new frontier in the world of artificial intelligence. These systems leverage the capabilities of LLMs to reason through problems, formulate plans to resolve them, and reassess these plans if unforeseen issues arise during execution. The applications for LLM AI agents are broad, ranging from question-answering systems to personalized recommendation engines, offering a wealth of possibilities for enterprise settings.

At the heart of every LLM AI agent is the agent core. This is essentially an LLM that follows instructions. It can be assigned a persona, providing it with a personality or general behavioral descriptions that can guide its interactions with users. This imbued persona can give the agent a sense of individuality, making interactions more engaging and human-like.

Another key component of an LLM AI agent is the memory module. This module serves as a store of logs, recording the agent’s thoughts and interactions with users. It can be divided into short-term and long-term memory, allowing the agent to recall past interactions and apply this knowledge to future tasks. This feature enhances the agent’s ability to learn and adapt over time, improving its performance and user experience.

The tools within an LLM AI agent represent well-defined executable workflows that the agent can utilize to execute tasks. These tools might include RAG pipelines, calculators, code interpreters, and various APIs. These tools enable the agent to perform a wide range of tasks, from simple calculations to complex coding tasks, broadening its utility.

What is an AI agent?

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Perhaps one of the most crucial components of an LLM AI agent is the planning module. This module tackles complex problems by using task and question decomposition and reflection or critic techniques. It allows the agent to break down problems into manageable parts, formulate a plan to solve each part, and then reassess and adjust the plan as needed. This ability to plan and adapt is vital for complex problem-solving and is a significant advantage of LLM AI agents.

In enterprise settings, LLM AI agents have a wide range of potential applications. They can serve as question-answering agents, capable of handling complex questions that a straightforward RAG pipeline can’t solve. Their ability to decompose questions and reflect on the best approach can lead to more accurate and comprehensive answers.

LLM AI agents can also function as a swarm of agents, creating a team of AI-powered engineers, designers, product managers, CEOs, and other roles to build basic software at a fraction of the cost. This application of AI agents could revolutionize the way businesses operate, reducing costs and improving efficiency.

In the realm of recommendations and experience design, LLM AI agents can craft personalized experiences. For instance, they can help users compare products on an e-commerce website, providing tailored suggestions based on the user’s past interactions and preferences.

Customized AI author agents represent another potential application. These agents can assist with tasks such as co-authoring emails or preparing for time-sensitive meetings and presentations. They can help users streamline their workflow, saving time and improving productivity.

Multi-Modal AI Agents

Finally, multi-modal agents can process a variety of inputs, such as images and audio files. Unlike traditional models that typically specialize in processing just one type of data, such as text, multi-modal agents are designed to interpret and respond to a variety of input formats, including images, audio, and even videos. This versatility opens up a plethora of new applications and possibilities for AI systems.

  • Enhanced User Interaction: These agents can interact with users in ways that are more natural and intuitive. For example, they can analyze a photo sent by a user and provide relevant information or actions based on that image, creating a more engaging and personalized experience.
  • Broader Accessibility: Multi-modal agents can cater to a wider range of users, including those with disabilities. For instance, they can process voice commands for users who may find typing challenging or analyze images for those who communicate better visually.
  • Richer Data Interpretation: The ability to process multiple types of data simultaneously allows these agents to have a more comprehensive understanding of user requests. For example, in a healthcare setting, an agent could analyze a patient’s verbal symptoms along with their medical images to assist in diagnosis.

Applications of Multi-Modal Agents

  • Customer Service: In customer service, a multi-modal agent can handle queries through text, interpret emotion through voice analysis, and even process images or videos that customers share to better understand their issues.
  • Education and Training: In educational applications, these agents can provide a more interactive learning experience by analyzing and responding to both verbal questions and visual content.
  • Entertainment and Gaming: In the entertainment sector, multi-modal agents can create immersive experiences by responding to users’ actions and inputs across different modes, like voice commands and physical movements captured through a camera.

LLM AI agents, with their complex reasoning capabilities, memory, and ability to execute tasks, offer exciting possibilities for the future of AI. Their potential applications in enterprise settings are vast, promising to revolutionize the way businesses operate and interact with their customers. Whether answering complex questions, crafting personalized experiences, or assisting with time-sensitive tasks, LLM AI agents are poised to become an integral part of the AI landscape.

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Creating AI agents swarms using Assistants API

Creating AI agents swarms using Assistants API for improved automation

AI agent swarms represent a leap forward in efficiency and adaptability. OpenAI’s Assistants API emerges as a pivotal tool for developers looking to harness this power. Here’s an insightful exploration of why and how to create AI agent swarms, using the capabilities of the Assistants API, to revolutionize automation in your applications.

At its core, an AI agent swarm is a collection of AI agents working in unison, much like a well-coordinated orchestra. Each ‘agent’ in this swarm is an instance of an AI model capable of performing tasks autonomously. When these agents work together, they can tackle complex problems more efficiently than a single AI entity. This collaborative effort leads to:

  • Enhanced Problem-Solving: Multiple agents can approach a problem from different angles, leading to innovative solutions.
  • Scalability: Easily adjust the number of agents to match the task’s complexity.
  • Resilience: The swarm’s distributed nature means if one agent fails, others can compensate.

Assistants API for AI Agent Swarms

OpenAI’s Assistants API is a toolkit that facilitates the creation and management of these AI agent swarms. Here’s how you can leverage its features:

  1. Create Diverse Assistants: Each Assistant can be tailored with specific instructions and models, allowing for a diverse range of capabilities within your swarm.
  2. Initiate Conversational Threads: Manage interactions with each AI agent through Threads. This allows for seamless integration of user-specific data and context.
  3. Employ Built-in Tools: Utilize tools like Code Interpreter and Retrieval for enhanced processing and information retrieval by the agents.
  4. Custom Functionality: Define custom function signatures to diversify the swarm’s capabilities.
  5. Monitor and Adapt: Keep track of each agent’s performance and adapt their strategies as needed.

AI Agent Swarms in Automation

Integrating AI agent swarms into your automation processes, facilitated by the Assistants API, offers several key benefits:

  • Efficiency and Speed: Multiple agents can handle various tasks simultaneously, speeding up processes.
  • Flexibility: Adapt to new challenges or changes in the environment without extensive reprogramming.
  • Enhanced Data Processing: Handle large volumes of data more effectively, with each agent specializing in different data types or processing methods.

Imagine deploying an AI agent swarm in a customer service scenario. Each agent, created through the Assistants API, could handle different aspects of customer queries – from technical support to order tracking. This division of labor not only speeds up response times but also ensures more accurate and personalized assistance.

Getting Started

The Assistants API’s playground is a perfect starting point for experimenting with these concepts. And with the API still in beta, there’s a golden opportunity for developers to shape its evolution by providing feedback.

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1. Creating Your Assistant

Your journey begins with crafting your very own Assistant. Think of an Assistant as a digital assistant tailored to respond to specific queries. Here’s what you need to set up:

  • Instructions: Define the behavior and responses of your Assistant.
  • Model Choice: Choose from GPT-3.5 or GPT-4 models, including fine-tuned variants.
  • Enabling Tools: Incorporate tools like Code Interpreter and Retrieval for enhanced functionality.
  • Function Customization: The API allows tailoring of function signatures, akin to OpenAI’s function calling feature.

For instance, imagine creating a personal math tutor. This requires enabling the Code Interpreter tool and selecting an appropriate model like “gpt-4-1106-preview”.

2. Initiating a Thread

Once your Assistant is up and ready, initiate a Thread. This represents a unique conversation, ideally one per user. Here, you can embed user-specific context and files, laying the groundwork for a personalized interaction.

3. Adding Messages to the Thread

In this phase, you incorporate Messages containing text and optional files into the Thread. It’s essential to note that current limitations don’t allow for image uploads via Messages, but enhancements are on the horizon.

4. Running the Assistant

To activate the Assistant’s response to the user’s query, create a Run. This process enables the Assistant to analyze the Thread and decide whether to utilize the enabled tools or respond directly.

5. Monitoring the Run Status

After initiating a Run, it enters a queued status. You can periodically check its status to see when it transitions to completed.

6. Displaying the Assistant’s Response

Upon completion, the Assistant’s responses will be available as Messages in the Thread, offering insights or solutions based on the user’s queries.

The Assistants API is still in its beta phase, so expect continuous updates and enhancements. OpenAI encourages feedback through its Developer Forum, ensuring that the API evolves to meet user needs.

Key Features to Note:

  • Flexibility in Assistant Creation: Tailor your Assistant according to the specific needs of your application.
  • Thread and Message Management: Efficiently handle user interactions and context.
  • Enhanced Tool Integration: Leverage built-in tools for more dynamic responses.
  • Function Customization: Create specific functions for a more personalized experience.

If you are wondering how to get started, simply access the Assistants OpenAI Playground. It’s an excellent resource for exploring the API’s capabilities without delving into coding.

The fusion of AI agent swarms with OpenAI’s Assistants API is a testament to the dynamic future of automation. It’s a future where tasks are not just automated but are executed with a level of sophistication and adaptability that only a swarm of intelligent agents can provide.

You will be pleased to know that, as the technology matures, the applications of AI agent swarms will only expand, offering unprecedented levels of automation and efficiency. OpenAI’s latest offering, the Assistants API, stands as a beacon of innovation for developers and technologists. If you’re keen on integrating AI into your applications, this guide will walk you through the process of building Agent Swarms using the new OpenAI Assistants API. For examples of code jump over to the official OpenAI website and documentation.

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How to use the OpenAI Assistants API to build AI agents & apps

Learn how to use the OpenAI Assistants API

Hubel Labs has created a fantastic introduction to the new OpenAI Assistants API which were recently unveiled at OpenAI’s very first DevDay. The new API tool has been specifically designed to dramatically simplified the process of building custom chatbots, offering more advanced features when compared to the ChatGPT custom GPT Builder which is integrated into the ChatGPT online service.

The API’s advanced features have the potential to significantly streamline the process of retrieving and using information. This quick overview guide and instructional videos created by Hubel Labs will provide more insight into the features of OpenAI’s Assistance API, the new GPTs product, and how developers can use the API to create and manage chatbots.

What is an Assistance API

The Assistants API allows you to build AI assistants within your own applications. An Assistant has instructions and can leverage models, tools, and knowledge to respond to user queries. The Assistants API currently supports three types of tools: Code Interpreter, Retrieval, and Function calling. In the future, we plan to release more OpenAI-built tools, and allow you to provide your own tools on our platform.

diagram of what is an Assistance API

Using Assistants API to build ChatGPT apps

The Assistants API is a powerful tool built on the same capabilities that enable the new GPTs product, custom instructions, and tools such as the code interpreter, retriever, and function calling. Essentially, it allows developers to build custom chatbots on top of the GPT large language model. It eliminates the need for developers to separate files into chunks, use an embedding API to turn chunks into embeddings, and put embeddings into a vector database for a cosine similarity search.

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The API operates on two key concepts: an assistant and a thread. The assistant defines how the custom chatbot works and what resources it has access to, while the thread stores user messages and assistant responses. This structure allows for efficient communication and data retrieval, enhancing the functionality and usability of the chatbot.

Creating an assistant and a thread is a straightforward process. Developers can authenticate with an organization ID and an API key, upload files to give the assistant access to, and create the assistant with specific instructions, model, tools, and file IDs. They can also update the assistant’s configuration, retrieve an existing assistant, create an empty thread, run the assistant to get a response, retrieve the full list of messages from the thread, and delete the assistant. Notably, OpenAI’s platform allows developers to perform all these tasks without any code, making it accessible for people who don’t code.

Creating custom GPT’s with agents

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One of the standout features of the Assistance API is its function calling capability. This feature allows the chatbot to call agents and execute backend tasks, such as fetching user IDs, sending emails, and manually adding game subscriptions to user accounts. The setup for function calling is similar to the retrieval mode, with an assistant that has a name, description, and an underlying model. The assistant can be given up to 128 different tools, which can be proprietary to a company.

OpenAI Assistants API

The assistant can be given files, such as FAQs, that it can refer to. It can also be given functions, such as fetching user IDs, sending emails, and manually adding game subscriptions. The assistant can be given a thread with a user message, which it will run and then pause if it requires action. The assistant will indicate which functions need to be called and what parameters need to be passed in. The assistant will then wait for the output from the called functions before completing the run process and adding a message to the thread.

The Assistance API’s thread management feature helps truncate long threads to fit into the context window. This ensures that the chatbot can effectively handle queries that require information from files, as well as those that require function calls, even if they require multiple function calls.

However, it should be noted that the Assistance API currently does not allow developers to create a chatbot that only answers questions about their knowledge base and nothing else. Despite this limitation, the Assistance API is a groundbreaking tool that has the potential to revolutionize the way developers build and manage chatbots. Its advanced features and user-friendly interface make it a promising addition to OpenAI’s suite of AI tools.

Image Credit : Hubel Labs

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How to use Microsoft AutoGen with multiple prompts and AI agents

How to use Microsoft AutoGen with multiple prompts and AI agents

Very recently Microsoft quietly released it’s multi-agent AutoGen framework that enables the development of Language Learning Model (LLM) applications. These applications can converse with each other, and even with humans, to solve complex tasks. This overview guide will provide a little more information on this amazing new AI agent framework and its workings, and how it can be used to upgrade a Postgres data analytics agent to a multi-agent system. Thanks to a video created by IndyDevDan.

AutoGen is a groundbreaking framework that simplifies the orchestration, automation, and optimization of complex LLM workflows. It maximizes the performance of LLM models and overcomes their weaknesses by enabling the development of applications using multiple agents. These agents are customizable, conversable, and seamlessly allow human participation. They can operate in various modes that employ combinations of LLMs, human inputs, and tools.

The beauty of AutoGen lies in its support for diverse conversation patterns for complex workflows. Developers can use AutoGen to build a wide range of conversation patterns concerning conversation autonomy, the number of agents, and agent conversation topology. This flexibility allows for the creation of systems with varying complexities, spanning a wide range of applications from various domains.

How to use AutoGen to code a Multi-Agent Postgres AI Tool

Consider a Postgres data analytics agent powered by GPT-4. By using AutoGen, this single-agent system can be transformed into a multi-agent system. The process involves splitting up the BI analytics tool into separate agents, each assigned a specific role. For instance, a data analytics agent, a Sr Data Analytics agent, and a Product Manager Agent can be created. Each agent has a specific role and can be assigned special functions that only they can run. This is akin to having a small working software data analytics company, with each agent playing a crucial role in the overall operation.

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Like any technology, AutoGen has its strengths and weaknesses. On the positive side, Autogen simplifies the development of multi-agent systems, making it easier for developers to build complex workflows. It supports diverse conversation patterns and provides a collection of working systems with different complexities. This flexibility and ease of use make AutoGen a powerful tool for developers.

However, AutoGen is not without its challenges. The complexity of multi-agent systems can make them difficult to manage and maintain. Additionally, the need for specialized knowledge to effectively use AutoGen may present a barrier for some developers.

What is AutoGen?

“AutoGen is a framework that enables the development of LLM applications using multiple agents that can converse with each other to solve tasks. AutoGen agents are customizable, conversable, and seamlessly allow human participation. They can operate in various modes that employ combinations of LLMs, human inputs, and tools.”

The potential of AutoGen in enhancing the multi-agent Postgres data analytics agent is immense. Future plans include further customization of the agents to improve their efficiency and effectiveness. Additionally, there are plans to incorporate more advanced features into the agents, such as the ability to learn and adapt to new tasks and environments.

AutoGen represents a significant step forward in the development of multi-agent systems. Its ability to simplify complex workflows and support diverse conversation patterns makes it a valuable tool for developers. As we continue to explore its potential, we can expect to see even more innovative applications of this technology in the future.

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Building autonomous AI Agents and potential applications

Building autonomous AI Agents and potential applications

Thanks to the explosion of large language models both open source and those released by companies such as OpenAI, Stability AI and others, Autonomous AI agents have emerged as a significant area of interest and advancement for a wide variety of different applications.

These agents, defined by their ability to operate independently, make decisions, and perform tasks without human intervention, are poised to revolutionize various sectors, from content creation to finance. This article delves into the functionality of autonomous AI agents, their potential applications, and the framework for building such systems. It also explores the potential future of AI agents in various industries, with examples of AI agent systems like AutoGPT and BabyAGI.

“BabyAGI is an example of an AI-powered task management system. The system uses OpenAI and vector databases such as Chroma or Weaviate to create, prioritize, and execute tasks. The main idea behind this system is that it creates tasks based on the result of previous tasks and a predefined objective. The script then uses OpenAI’s natural language processing (NLP) capabilities to create new tasks based on the objective, and Chroma/Weaviate to store and retrieve task results for context. “

Autonomous AI Agents and their potential applications

Autonomous AI agents are essentially self-governing entities within a system. They are designed to perceive their environment, process information, and take actions to achieve specific goals. These agents are not merely reactive; they possess the ability to learn from their experiences and adapt their strategies accordingly. This ability to learn and adapt is what sets them apart from traditional software and makes them a powerful tool in various fields.

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One of the most promising applications of autonomous AI agents is in the field of content creation. These agents can generate text, images, and even videos, offering a new way to create content that is both efficient and cost-effective. For instance, AI agents can be used to write articles, design graphics, or produce promotional videos, freeing up human creators to focus on more complex tasks.

Workflow automation

In the realm of personal assistance, autonomous AI agents can perform tasks ranging from scheduling appointments to managing emails. They can also provide personalized recommendations based on user preferences, making them an invaluable tool for enhancing productivity and efficiency.

The gaming industry is another sector where autonomous AI agents hold immense potential. They can be used to create intelligent non-player characters (NPCs), design complex game environments, or even develop entire games. The use of AI agents in gaming not only enhances the gaming experience but also opens up new possibilities for game design and development.

In the finance sector, autonomous AI agents can analyze vast amounts of data to make predictions, identify trends, and provide insights. They can also automate trading activities, manage portfolios, and even advise on investment strategies. The use of AI agents in finance can lead to more informed decision-making and improved financial outcomes.

Building AI agents

Building an autonomous AI agent system requires a robust framework that can support the agent’s learning and decision-making processes. This includes a learning algorithm that allows the agent to learn from its experiences, a decision-making mechanism that enables it to make informed decisions, and a reward system that encourages the agent to achieve its goals. Here a few other articles that delve deeper into  how to build your very own AI agents :

The potential future of AI agents in various industries is vast. Systems like Auto GPT and Baby AGI are just the tip of the iceberg. As AI technology continues to evolve, we can expect to see more sophisticated AI agents that can perform increasingly complex tasks.

The transformative potential of autonomous AI agents in various sectors is undeniable. From content creation to finance, these agents are set to revolutionize the way we work, play, and interact with the world. As we continue to explore the possibilities of AI, the role of autonomous AI agents will undoubtedly become increasingly significant.

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