What is Auto-GPT?
Auto-GPT is an experimental, open source autonomous AI agent based on the GPT-4 language model. Auto-GPT autonomously chains together tasks to achieve a big-picture goal set by the user.
It automates the multi-step prompting process typically required to operate a chatbot such as ChatGPT. The user provides one prompt or set of natural language instructions, and Auto-GPT works by breaking the goal into subtasks to reach its objective.
The open-source model was created by Toran Bruce Richards and is publicly available for download on GitHub. To use Auto-GPT it needs to be installed in a containerized development platform, such as Docker, and be registered with OpenAI's API key.
Auto-GPT can be used in many of the same ways as ChatGPT , but it automates those tasks to achieve them faster. Auto-GPT integrates with the internet to give it access to real-time data. Some general tasks Auto-GPT can do include the following:
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- Analyze investments. Prompt the model to do market research and perform sentiment analysis on online conversations to determine smart investments.
- Create content. Prompt Auto-GPT to create articles, blogs and social media posts.
- Generate leads. Prompt the model to help research new leads and prospects for sales.
- Create a business plan. Prompt the model to help grow a business, and it will come up with a plan to do so.
- Automate product reviews. Prompt the model to research new products, provide sources and write reviews for them.
- Create a podcast. Prompt the model to write a podcast outline by doing research and drafting questions for the hosts.
Some real-world examples of applications using Auto-GPT include the following:
- Agent-GPT. Agent-GPT is an in-browser AI tool for creating and deploying autonomous AI agents. Agent-GPT creates a more user-friendly interface for Auto-GPT, which requires some coding knowledge.
- Godmode. Godmode is another tool that essentially performs the same functions as Auto-GPT but runs in the browser and is more user-friendly.
Auto-GPT vs. ChatGPT
Auto-GPT runs on the same basic backend infrastructure as ChatGPT: GPT-3.5 and GPT-4 language models developed by OpenAI. Despite Auto-GPT and ChatGPT being chatbot tools that use OpenAI's technology, there are several differences between the two.
ChatGPT was developed by OpenAI. Auto-GPT was developed by Toran Bruce Richards using OpenAI's APIs.
Unlike ChatGPT, Auto-GPT runs in a loop. It breaks activities into subtasks, prompts itself, responds to the prompt and repeats the process until it achieves the provided goal. ChatGPT requires repeated prompting from an end user. The user prompts the model, it responds and then the user must prompt it again. There is no overarching goal that ChatGPT can follow -- just the string of prompts provided by the user.
Auto-GPT also uses short-term memory management to preserve context and help the model work through long prompt chains. ChatGPT by itself does not have memory. Information does not carry over between sessions.
Auto-GPT is also multimodal, meaning it can handle both text and images as input. ChatGPT can only handle text.
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What are some potential challenges of Auto-GPT?
One potential challenge that Auto-GPT users face is that running the application in continuous mode can rack up significant costs. Auto-GPT relies on access to the OpenAI API key, which needs a paid OpenAI account. As of this writing, GPT-4 costs $0.03/1,000 prompt tokens and $0.06/1,000 results tokens.
Another challenge is that Auto-GPT can get distracted or get caught in a loop. For example, when asked to perform research on waterproof shoes, the tool might only focus on shoelaces because it misunderstands the scope of its task and gets distracted.
What are the limitations of Auto-GPT?
There are several limitations to using Auto-GPT, including:
- Experimental. Auto-GPT is just an experiment. Like other AI systems, it is prone to hallucination.
- Cost. The cost of using Auto-GPT combined with its technical flaws make it difficult to use on its own in a production environment at scale. Auto-GPT's authors point this out in the Github repository. There is an active community on Discord and Github where developers share their progress and ideas using Auto-GPT.
- No long-term memory. Auto-GPT also has no long-term memory. It usually cannot remember how to complete a task after doing so. When it can, it doesn't understand the context it should use it in. With a lack of long-term memory and contextual understanding, it also struggles to break complex tasks into subtasks.
What are the benefits of Auto-GPT?
One benefit of Auto-GPT is that it demonstrates the boundaries of AI and its ability to act autonomously. Users can see how the model works on its own and prompts itself, where it goes awry and what it gets right. Auto-GPT is also open source and free to download, although using it costs money.
How will Auto-GPT affect the future of AI?
While it's not clear exactly how Auto-GPT will affect the future of AI, the application highlights the potential of autonomous agents and moves the field one step closer to artificial general intelligence. Artificial general intelligence is a term for a sentient machine.
Auto-GPT could be one way to measure progress toward artificial general intelligence through task complexity or the number of complex steps a model can complete autonomously before it veers away from the intended output.
Theoretically, a more adept version of Auto-GPT could spin up other autonomous agents to interact with and remove humans from the loop completely.
Another example of autonomous AI is BabyAGI. BabyAGI is a Python script that uses both OpenAI and Pinecone APIs to create, organize, prioritize and execute tasks using predefined objectives. BabyAGI is not connected to the internet.
While Auto-GPT is far from business-ready, other AI tools are being integrated professionally across industries. Many of them are still new. Learn key performance indicators to measure AI success in the enterprise.