What is AgentGPT?

Unlike current iterations of OpenAI’s ChatGPT, AgentGPT lets you create autonomous AI agents that can think, plan, act and adapt toward a user-defined goal, with minimal manual prompting. One feature that makes AgentGPT special is its user-friendly interface, which makes AI agent functionality accessible for all sorts of use cases like research and data gathering, exploratory problem-solving, idea generation, automating repetitive tasks, creating chatbots and more.

AgentGPT is a no-code tool, meaning users do not need to write software or set up a development environment to define a goal, select a model and deploy agents. Templates are provided to help beginners understand how to frame effective goals.

AgentGPT is an open-source project primarily developed by a team at Reworkd AI. The San Francisco-based startup launched the tool in April 2023 and continues to maintain it. In terms of pricing, Reworkd offers a free version as well as a hosted service with paid tiers. The project gained early attention in part because it was open and accessible, with code and documentation available on Github.

It’s worth noting that the “GPT” in AgentGPT stands for generative pretrained transformer, a family of large language models (LLMs) based on a transformer deep learning architecture. It’s called AgentGPT because the GPT is acting as an autonomous agent, not just a chatbot, as in ChatGPT, although both rely on the same OpenAI models under the hood, like GPT-3.5 and GPT-4.

AgentGPT should not be confused with other agentic AI tools such as AutoGPT, which is a similar tool that runs on users’ personal machines rather than the web. BabyAGI is another agentic platform that is a more technical, Python-based platform, often used as a foundational tool for developers.

An agent is an AI-powered system that autonomously performs tasks by designing workflows with available tools. Traditional LLMs such as IBM® Granite® models, produce their responses based on the data used to train them and are bounded by knowledge and reasoning limitations.

Agents, in contrast, can do so much more than just talk to users. An LLM can be thought of as a kind of brain. An agent is a system that uses an LLM brain to think in order to take action. Agents use their brain to plan and perform complex tasks that require multi-step reasoning in pursuit of long-term goals, with minimal human input.

Let’s say you want to start a coffee shop but you don’t know what would make for a good location. You want to identify criteria for good locations, gather evidence, narrow down candidate areas and surface tradeoffs and risks. Your agent will be a junior research analyst. But which tool to use? Both use machine learning to perform content generation, so theoretically you could use ChatGPT (or some other LLM-powered chatbot) for a project like this.

It’s worth noting that AgentGPT’s ability to perform real-time web search and access other external tools preceded ChatGPT’s similar functionality, though now both tools have this capability. That being said, AgentGPT still has its strengths.

ChatGPT excels if you want to ask follow-up questions, interrogate its assumptions and challenge and refine ideas interactively. However, if the task is multi-step, the path is unclear and you don’t want to manually steer it at every step along the way, AgentGPT might be the better tool.

ChatGPT waits to be told what to do, and you’ll have to guide it every step of the way. In contrast, AgentGPT takes your initial goal and runs with it, deciding for itself the best steps to take. It’s great for surfacing “unknown unknowns” and exploring territory you might not even have thought to ask it about.

With ChatGPT, you’d have to ask at each step:

1. “What makes a good coffee shop location?”
   
   
2. “OK, now apply that to mid-sized cities in my region.”
   
   
3. “OK, but what about commuter vs residential areas?”
   
   
4. “OK, but what are common mistakes people make in this domain?”
   
   
5. “OK, now how would I validate this in person?”

With AgentGPT, you can just say “Research what makes a good coffee shop location” and let the agent crank away at an exhaustively researched response. The agent will ask itself such questions and proactively answer them for you.

Let’s say you want to create an agent that monitors new regulations, trends and technologies affecting coffee shops and summarizes weekly risks and opportunities, delivered to you in a tidy report. You want the agent to be able to scan sources, filter for relevance to coffee shops, synthesize risks and opportunities and summarize the results.

This is a better use case, where AgentGPT makes more sense than ChatGPT, because it is more open-ended and you’ll be able to simply review the finished summaries instead of continually reprompting the agent for new information.

First, you’ll name your agent (for example, “Coffee Industry Risk & Opportunity Analyst”). Then you’ll write your goal:

“Scan the web and social media for new regulations, trends and technologies affecting independent coffee shops and summarize weekly risks, opportunities and items worth monitoring. Then edit and streamline the newsletter for a business executive reader.”

“Independent coffee shops” narrows relevance. “Risks, opportunities and items worth monitoring” forces categorization. “Edit and streamline” will reduce redundancy and produce results of a desired style.

You can add additional constraints or operating rules to prevent noisy results. Like this:

• “Focus on regional regulations unless otherwise specified”
   

• “Ignore irrelevant hospitality news (such as fine dining, hotels)”
   

• “Prefer sources from the last 7 days”
   

• “Summarize impacts clearly and concisely”
   

• “Flag uncertainty where information is incomplete”
   

• Lead with an executive summary (no more than 5 bullets)
   

• Embed all sources as hyperlinks
   

• Do not repeat results from previous reports
   

• Penalize “hype” news

You can save this initial prompt and re-run it on a weekly basis for new results.

AgentGPT excels for such simple use cases that fall under the “personal assistant” or “digital copilot” category. Such agents can be scaled horizontally by running multiple agents in parallel for different goals. This scalability makes AgentGPT attractive as a prototype for enterprise-style agent systems, such as IBM watsonX Orchestrate, where more robust guardrails, evaluation and monitoring are required.