Ok, Artificial Intelligence is blowing up faster than a grenade in a microwave. And I don’t know about you, but as someone who’s been knee-deep in this industry for years, it’s starting to feel like trying to keep up with the Kardashians. Just when you think you’ve got a handle on the latest AI tech, BAM! You’re hit with the phrase “game-changing” yet again. But let me tell you, in the case of Auto-GPT, that phrase might not be just hype. 

I’ve personally tested it out and let me tell you, Auto-GPT is not just another run-of-the-mill AI tool. This thing is a serious leap forward to AGI. We’re talking about intelligent systems that can do their own research, generate top-notch text, and even write and debug their own code. And yeah, you guessed it, it’s all based on GPT models, in case you haven’t heard someone mentioning ChatGPT today. So, if you’re wondering what all the Silicon Valley AI hype is about these days, pull up a chair and get ready to learn, because this post is for you.

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Here is what we cover in this blog post:

  • What is Auto-GPT?
  • How to use Auto-GPT?
  • Use cases for Auto-GPT
  • Biggest Challenges of Auto-GPT

1. What is Auto-GPT?

Auto-GPT is like the kid who has grown up from being a computer nerd to a jock. It’s got brains and brawn, and it’s taking the AI game to the next level. It’s like giving a GPT-based model a personality, memories, and the ability to learn from them. It’s almost like a living being but without the existential crisis. Picture Auto-GPT as your own robot sidekick, ready to tackle any mission you assign it. Need to analyze markets or handle customer service? No problem, Auto-GPT can do it all. 


Auto-GPT, crafted by Toran Bruce Richards, is not only an open-source application but also a bold experiment, which could be considered as a tool for highlighting the impressive capabilities of GPT-4. The script’s true essence lies in seamlessly connecting various tasks that require the power of GPT-4 and executing them in the intended sequence. In other words, Auto-GPT is a remarkable demonstration of GPT-4’s potential for handling complex tasks with ease.

Auto-GPT has the potential to save humanity from mass job loss caused by automation from closed-source AI. If everyone has access to their own team of autonomous agents, everyone is enabled and complete.
Toran Bruce Richards

Significant Gravitas, Creator of Auto-GPT

At its core, Auto-GPT is built on the brains of GPT-4 and GPT-3.5 language models, serving as the robot’s architecture to think and reason. And just like a robot, Auto-GPT can learn from its mistakes, reviewing its work and building on its previous efforts.

But what sets Auto-GPT apart is its versatility. Think of it like a multi-talented sidekick, capable of file manipulation, web browsing, data retrieval, and more. Plus, with an integrated memory storage solution, Auto-GPT can preserve context and make better decisions.

2. How to use Auto-GPT?

If you’re looking to use Auto-GPT, here are the steps you need to follow:

First, ensure that you have an OpenAI API key for GPT. Also, if you want to use some memory provider, like Redis or Pinecone, get all your API keys ready before starting. I personally used Pinecone with a lot of success. The same goes for Evenlabs if you want your AI to speak, Google Search API, and so on. 

Next, clone the repository by running the command:

git clone https://github.com/Significant-Gravitas/Auto-GPT.git
Note that you’ll need Git installed for this step. Alternatively, you can download the latest stable release by clicking “Source code (zip)” at the bottom of the Github page.

Navigate to the directory where the repository was downloaded and install the required dependencies by running the command:

pip install -r requirements.txt
Configure Auto-GPT by following these steps:

  • Locate the file named “.env.template” in the main /Auto-GPT folder.
  • Create a copy of this file called “.env” by removing the template extension. You can do this by running the command “cp .env.template .env” in a command prompt/terminal window.
  • Open the “.env” file in a text editor. Note that files starting with a dot might be hidden by your Operating System.
  • Find the line that says “OPENAI_API_KEY=”.
  • After the “=”, enter your unique OpenAI API Key (without any quotes or spaces).
  • Enter any other API keys or tokens for services you would like to utilize.
  • Save and close the “.env” file

By completing these steps, you have properly configured the API keys for your project.

To use Auto-GPT, run the “autogpt” Python module in your terminal by running the command “python -m autogpt”. After each action, choose from options to authorize command(s), exit the program, or provide feedback to the AI. To authorize a single command, enter “y”. To authorize a series of N continuous commands, enter “y -N”. To exit the program, enter “n”.

3. Use cases of Auto-GPT

Even though Auto-GPT is still technically a POC, people are already using it for some exciting solutions.

3.1 Auto-GPT generating and improving code

Auto-GPT is not just any ordinary tool, it has an unparalleled ability to improve its own programming. It can read, write and execute code, making it the perfect candidate for code improvement. The AI evaluates, tests and updates code with ease, improving its efficiency, reliability, and speed. Recently, the tool’s developer, Significant Gravitas, tweeted a video showcasing Auto-GPT in action.

The tool was checking a simple example function responsible for math calculations, and even though it only had a simple syntax error, it took the AI approximately a minute to rectify the mistake. Imagine how much longer it would have taken a human to correct the same error in a codebase containing hundreds or even thousands of lines. Curious to see Auto-GPT in action? Check out this example of the tool writing its own code.

3.2 Auto-GPT Market Research

The second noteworthy application of Auto-GPT lies in the realm of market research. Thanks to its ability to access the internet, retain important details, and stay focused on a task, Auto-GPT serves as a valuable tool for carrying out primary market research. In a recent tweet by Nicolas Boucher, Auto-GPT demonstrated its prowess in competitor analysis in the golf ball market.

The AI analyzed the major players in the market, taking into account each company’s market share, pricing strategies, and product offerings, while also verifying the credibility of its sources. While it may still be necessary for humans to verify the results of AI-driven research efforts, Auto-GPT’s capabilities can serve as a valuable starting point for such investigations.

You can check out this example of Auto-GPT in action here:

3.3 Auto-GPT replicating itself

At Auto-GPT Discord server goes around the nice trick to make Auto-GPT create agents for each specific subtask you give to them. This is very interesting, you can be CEO with the whole company automated just by one prompt.

In essence, you need to create two additional files: instructions.txt and advice.txt. Instructions.txt should contain all the goals you want your Auto-GPT to complete. Advice.txt should contain advice on how to complete those goals. For example, if you are having some coding task it might look like this:

All return data MUST always be formatted as proper machine-readable JSON.
You do not need an editor. You are the editor. 
Make sure to always save files to the local file system. 
Make sure to write any new files or file changes to local file storage.
Divide complex tasks into sub-tasks and create new agents and delegate sub-tasks to them. 
In the end, modify ai_settings.yaml to look like this:
- Read the instructions.txt file in your workspace. This contains the goals for your project. Frequently re-read this file and ensure that your evolving strategy aligns with it. 
- Read the advice.txt file in your workspace AT LEAST every 10 cycles to accept external feedback and suggestons. Weigh this advice heavily. 
- Take notes on learnings and save them to the learnings.txt file. Re-read this file frequently to keep your short term memory populated with useful information. 
- Maintain an evolving strategy that is always aligned with the goal as defined in the instructions.txt file. Keep your strategy documented in the strategy.txt file and re-read it frequently to stay focused. 
- Maintain an evolving accomplishments.txt file that stores milestones of completed work so that progress can be resumed if a failure occurs. 
ai_name: replicator 
ai_role: An AI designed to autonomously accomplish any task by delegating tasks to sub-agents and enhancing short-term memory by frequently re-referencing local files and staying focused on a goal. 

I found that wiping Auto-GPT’s memory before introducing this format actually helped the agent. If you’re using local memory, you can make a backup copy of your current auto_gpt.json and save it under a different name. Then, you can delete everything in the original auto_gpt.json file so that it’s completely empty.

4. Challanges of Auto-GPT

Everything may look peachy on the surface, but let’s not fool ourselves – every new technology has problems that it needs to face. Sure, it’s easy to get swept up in the hype, but we need to take a closer look at what’s on the other side of the coin.

4.1 Costs

Auto-GPT is a remarkable tool that can accomplish complex tasks with ease, but there is one significant hurdle that prevents it from being adopted in production environments: cost. Each step in the chain of thoughts requires a call to the GPT-4 model, which comes with a steep price tag due to the model’s token usage. With an 8K context window, the GPT-4 model charges $0.03 per 1,000 tokens for prompts and $0.06 per 1,000 tokens for results.

Let’s take a closer look at the cost of each step in the chain of thoughts. Assuming that each action maxes out the context window of 8,000 tokens, with 80% being prompts and 20% being results, the breakdown is as follows: 6,400 tokens for prompts and 1,600 tokens for results. This translates to a cost of $0.192 for prompts and $0.096 for results, bringing the total cost per step to $0.288.


If Auto-GPT takes an average of 50 steps to complete a small task, the cost of completing a single task would be $14.4. With this price tag, Auto-GPT’s current implementation is not practical for many users and organizations.

While Auto-GPT holds great promise, its prohibitive cost may limit its potential impact. Developers will need to find creative solutions to reduce the cost of using GPT-4, or alternative models may need to be explored to make Auto-GPT more accessible to users and organizations.

4.2 Missing MLOps Vision

I’ve been pondering an architecture that could solve a fundamental issue in Auto-GPT’s implementation – the need for a ML Ops flow. The current architectures rely on the “brain” as the controller, but it is missing end-to-end approach. Auto-GPT need to close the loop on the overall process. One solution could be to use agents to delegate work and expand capabilities via APIs, tools, and models like HuggingGPT. However, we also need the core models to become smarter as they perform tasks. LLM models might not help us here.

There have been some advancements in giving longer-term memory to models, like MemoryGPT, but this is just the beginning. When we consider the neuroscience of the brain, we have both short-term and long-term memory. The process of encoding short-term memory to long-term memory happens during sleep. The same concept can be applied to AI, where we use work session data (like the Pinecone vector database) as short-term memory.


If we extract this data and train the core model with it, we can update the model for future inference. Then, the data from short-term memory can be truncated to start the next cycle of use. Of course, this approach requires access to the core model (like GPT4), which may not always be possible. But, if we can expand Auto-GPT to use local models, this MLOps Flow becomes achievable. However, we are far away from this point at the moment.

4.3 Stuck in a rut

When considering the costs associated with Auto-GPT, it’s important to understand that the benefits don’t always outweigh the expenses. While $14.4 may seem like a reasonable cost for solving a problem, many users have found that the tool gets stuck in a loop, rendering it useless in some situations. In fact, there have been instances where Auto-GPT was unable to solve a problem even after an entire night of processing.

To understand why this happens, think of Auto-GPT as relying on a simple programming language that is powered by GPT. The success of solving a task depends on the range of functions available in that programming language and the ability of GPT to decompose the task into the predefined programming language. Unfortunately, both of these factors are currently inadequate.


The limited set of functions provided by Auto-GPT can be observed in its source code, and this narrow scope of tasks that it can perform effectively can be a major drawback. Additionally, while GPT-4 has significantly improved over its predecessors, its reasoning capability is still limited, further constraining Auto-GPT’s ability to solve complex problems. All in all, while Auto-GPT may seem like an appealing solution, its current limitations make it impractical for many users and organizations.

List of Commands

Imagine trying to build a sophisticated game like StarCraft using Python – a programming language known for its versatility and power. While Python may be capable of handling complex tasks, decomposing the intricacies of StarCraft into a set of Python functions can prove to be an immense challenge.

Similarly, creating an instant messaging app using BASIC – a programming language lacking the necessary network communication functions – would be a futile attempt. The limitations of a restricted function set, coupled with GPT-4’s constrained reasoning abilities, create a quagmire that causes Auto-GPT to get stuck in an endless loop, often failing to deliver the desired outcomes.


In conclusion, Auto-GPT is a powerful tool that promises to revolutionize the way we approach problem-solving. However, as with any new technology, there are significant challenges to overcome. The prohibitive cost and limited function set are just a few of the obstacles that must be addressed before AutoGPT can truly deliver on its promise. But with innovative solutions like ML Ops Flow and MemoryGPT, there is hope that we can unlock the full potential of this cutting-edge technology. Ultimately, the success of Auto-GPT will depend on our ability to harness the power of machine learning and combine it with a human touch, ensuring that it serves as a tool for innovation and progress, rather than a replacement for human ingenuity.

Thank you for reading!

ML.NET Full-Stack: Complete Guide to Machine Learning for .NET Developers

From the basics of machine learning to more complex topics like neural networks, object detection and NLP, this course will guide you into becoming ML.NET superhero.

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