How node-based GPT agents overcome the limitations of AutoGPT

If you are interested in the subject of LLMs and follow the news related to the use of GPT models, then you are certainly familiar with GPT agents such as AutoGPT, AgentGPT, GodMode, etc. Currently, all these agents operate on the same principle: the user defines the agent’s primary goal, assigns a set of tasks, and starts the process.

Why look for alternatives?

Currently existing GPT agents are capable of automating certain tasks, including searching and processing information, creating and editing texts. However, trying to use them for day-to-day tasks is likely to be frustrating, as these applications are currently of little value to the average user.

AutoGPT, based on GPT-3.5, is extremely inefficient, and you can hardly get it to do even one task in a way that doesn’t require you to spend more time patching and editing than if you did it yourself. Using GPT-4 slows things down, and your budget is likely to run out before AutoGPT completes the task.

Even if all these shortcomings are fixed in future versions, a key security issue remains. AutoGPT and similar applications have access to the internet and are able to execute code, which in theory gives AI complete autonomy. This question is subject to much debate, and I am convinced that such a debate is necessary, since underestimating or overestimating the dangers of AI can lead to serious problems.

How to replace AutoGPT?

Now let’s move on to describing a possible alternative to existing GPT agents, namely, the creation of GPT agents using nodes (nodes). Potentially, node-based GPT agents will help overcome some of the limitations and issues associated with current GPT agents. The node-based approach involves the use of a set of nodes, each of which performs a specific function or task. If you are familiar with working with materials in Blender or have used ComfyUI for StableDiffusion, then you already have an idea of ​​how it works.

The use of node-based GPT agents may be of interest to users who want more control over how the AI ​​works. Instead of handing over the entire task to the AI ​​solution, as is done in AutoGPT, the user can break the task into separate steps or operations and assign a separate node to each of them. This allows you to control each stage of AI work and make adjustments if necessary.

Usage example

For example, let’s build an agent that reads Python code from the specified file, finds errors in it and fixes them. Let’s create a simple node that looks for errors in the specified code and returns a list of errors.

Let’s add a few more nodes, such as user input, reading from a file, and so on.

Pros and cons (versus AutoGPT)

Security is definitely one of the strengths of such an interface – AI in this case does not get more independence than is necessary for solving a specific problem. AI spends much less tokens on tasks than in AutoGPT, so this method is cheaper.

It is important to mention that this approach allows for finer tuning of the AI. Instead of trusting a single GPT agent to solve a complex task, you can configure individual nodes to specialize in certain types of tasks. For example, one node can be optimized for working with texts, another for data analysis, a third for performing mathematical operations, and so on.

However, despite all the advantages, node-based GPT agents are not without drawbacks. First, despite the increased level of control, setting up and managing multiple nodes can be challenging, especially for users who are just getting started with AI technologies. This is especially true if the tasks become more complex and require a large number of specialized nodes.

Second, the node-based approach can lead to less flexibility in the solutions. Because each node is specialized to perform a specific task, the ability for AI to “think outside the box” and find new, creative ways to solve problems can be limited. This can be a disadvantage in cases where it is necessary to find non-standard solutions or when the task does not imply a well-defined solution path.

Finally, the use of node-based GPT agents requires the user to have a deeper understanding of the issues and functioning of AI. This may be a barrier to the widespread adoption of this technology among ordinary users who do not have a sufficient level of technical knowledge.

At the moment it’s just a concept and I’m just getting started on the app. I will be glad if you want to join the development – do not hesitate write to me. If you are interested in following the progress of development, welcome to the VK group.