Autonomous agents have become a notable breakthrough in the field of generative AI this year. These innovative tools showcase the capability of AI to autonomously generate, prioritize, and accomplish tasks on the Internet without requiring human oversight.
When utilizing an autonomous agent, a user inputs an initial objective or task into a large language model (LLM), and the system proceeds to execute the task while generating subsequent tasks in an ongoing loop.
This empowers these assistants to independently perform a variety of tasks such as content creation, code writing, research, data analysis, to-do list generation, website creation, and even social media account management.
In summary, autonomous AI agents offer enterprises a wide array of applications. In the following sections, we will explore five of the top autonomous AI agents currently available in the market.
What are AI autonomous agents?
Tools like ChatGPT, DALL-E 3, or Midjourney use simple ways for people to talk to AI. You just write instructions in regular language, and sometimes you have to try a bunch of times to get a good answer.
This is slow and kind of confusing, considering what AI can do. Since we don’t have Neuralink yet, we need better ways to talk to AI.
Autonomous agents, or AI agents for short, act like bosses for AI. They are simple apps that keep telling AI what to do, deciding what’s important, and changing tasks until the main goal is done. The result? You can use AI without doing much yourself.
Fun Fact: The idea of autonomous AI agents started with a paper called “Task-Driven Autonomous Agent” by Yohei Nakajima in early 2023. The agent idea started in March 2023, and a few months later, people started using it in the open-source community. It might still seem like a crazy experiment, but there are already some really powerful models you can try.
The Role of Autonomous Agents in Task Management
“What can I use agents for?” That’s a good question. We’d love to say “everything,” but that’s not true with the current tech. Even though agents are still learning, they can already make your life easier by:
- Making research and data collection easier.
- Creating content in different styles and tones.
- Crawling the web and finding important information.
- Summarizing documents and spreadsheets.
- Translating content between languages.
- Acting as a virtual assistant for creative tasks.
- Automating tasks like scheduling and tracking.
And here’s the best part.
Agents move from tools that need human prompts to semi or fully automatic systems. That’s how AI tools should be — hands-free, reliable, and trustworthy. No long prompts or checking each step.
Let’s say you want to study market trends for the last ten years in the electric vehicle (EV) industry. Instead of doing all the work yourself, you can let an agent do it while you do other things.
Even with a tool like ChatGPT, you’d still have to keep checking.
An agent can find the right info, take notes, and organize everything. If you already have some data, it can quickly give you important insights.
Now, let’s talk about agents working together.
Some projects are too big for one agent. Even with tools like ChatGPT, you need to wait for the answers before asking more questions.
With many agents, each working on a part of the project, you can get things done faster. One agent gathers data, another makes a report outline, and a third creates the content. Magic. 🪄
Challenges and Considerations of AI Autonomous Agents
Open-source agents are like the Wild West of AI tools. They’re experimental and need some tech knowledge to set up, use, and keep working. That’s okay for DIY projects, but not if you just want something easy.
You can combine open-source agents with your usual work.
But it takes time, knowledge, and resources.
If you don’t have those, you can use no-code agents. They fit with existing tools and understand your work’s context.
Of course, there’s also a problem called hallucinations. Since agents use language models, they can sometimes make up strange stories. The longer an agent runs, the more likely it is to mix up facts.
This brings up some questions about productivity. Should you limit how long agents run? Narrow down the tasks they do? Keep a person checking the answers?
Using many smart agents with different skills can give you better results. That’s why multi-agent frameworks are popular. Just like agents trained on a company’s documents and working in a Taskade project.
Quick Review
| Agent | Description | Features and Highlights | Repository Link |
|---|---|---|---|
| AutoGPT | Developed by Toran Bruce Richards, founder of Significant Gravitas Ltd. video game company. Started in March 2023. | Complete toolkit for creating and running personalized AI agents. Uses GPT-4 and GPT-3.5 LLMs. | AutoGPT Repository |
| BabyAGI | Simplified version of Nakajima’s Task-Driven Autonomous Agent. Uses OpenAI and vector databases. | Python script with 140 lines of code. Expanded into various projects. | BabyAGI Repository |
| AutoGen | Microsoft’s open-source framework for developing and deploying multiple agents. | Aims to facilitate communication between agents, reduce errors, and maximize LLM performance. | AutoGen Repository |
| MetaGPT | Framework for open-source AI agents, mimicking the structure of a traditional software company. | Agents have roles like product managers, project managers, and engineers. Can handle coding tasks. | MetaGPT Repository |
| Camel | Multi-agent framework allowing agents to communicate and collaborate on tasks. | Uses a unique role-playing design. Tasks are assigned and executed based on a human-defined task. | Camel Repository |
| Loop GPT | Iteration of Toran Bruce Richards’ AutoGPT with better support for GPT-3.5 and custom agent capabilities. | Improved support, more connections, and consumes fewer API tokens. | LoopGPT Repository |
| JARVIS | Task planning, model selection, task execution, and content generation using ChatGPT as a “decision-making engine.” | Flexible tool using ChatGPT’s reasoning ability. Can handle various tasks. | JARVIS Repository |
| OpenAGI | Open-source AGI research platform combining small, expert models and Reinforcement Learning from Task Feedback (RLTF). | Uses popular tools like ChatGPT and LLaMa2. Selects tools dynamically based on task context. | OpenAGI Repository |
| SuperAGI | Flexible and user-friendly alternative to AutoGPT. Launchpad for open-source AI agents with various features and integrations. | Multiple AI models, graphical user interface, integrations, and a marketplace for toolkits. | SuperAGI Repository |
| ShortGPT | Framework for using large language models to simplify video-related tasks. | Handles tasks like video scripts, voiceovers, music selection, titles, and descriptions. | ShortGPT Repository |
| ChatDev | Virtual software company using multiple agents to play different roles in a traditional development organization. | Agents collaborate on tasks from designing software to writing code and documentation. | ChatDev Repository |
| MicroGPT | Pre-trained language model with 82 million parameters. Designed for basic tasks using GPT-3.5 and GPT-4. | Performs tasks like analyzing stock prices, network security tests, digital artwork, and more. | MicroGPT Repository |
11 Best AI Autonomous Agents You Should Try
AutoGPT
AutoGPT, made by Toran Bruce Richards, the founder of Significant Gravitas Ltd. video game company, is one of the early agents that started in March 2023 after Nakajima’s paper. It’s currently the most popular agent repository on GitHub.
The idea behind AutoGPT is simple—it’s a complete toolkit for creating and running personalized AI agents for various projects. This tool uses OpenAI’s GPT-4 and GPT-3.5 large language models (LLM) and allows you to build agents for personal and business projects.
BabyAGI
BabyAGI is a simplified version of Nakajima’s Task-Driven Autonomous Agent. The Python script is only 140 lines of code and, according to the official GitHub repository, “uses OpenAI and vector databases such as Chroma or Weaviate to create, prioritize, and execute tasks.”
Since its launch, BabyAGI has expanded into various projects. Some, like twitter-agent or BabyAGI on Slack, bring agent capabilities to existing platforms. Others add plugins, additional features, or port BabyAGI to other languages (e.g., babyagi-perl).
AutoGen
Microsoft has put $13 billion into OpenAI and made Bing a bit smarter, making it a big player in AI. AutoGen, their open-source framework, helps in making and using many agents that can work together to do things on their own.
AutoGen tries to make it easy for agents to talk, reduce mistakes, and make large language models (LLMs) work better. You can also customize it a lot, choose your favorite models, improve the results with human feedback, and use extra tools.
MetaGPT
MetaGPT is another framework for open-source AI agents. It copies how a regular software company works, just like ChatDev. Agents in MetaGPT have roles like product managers, project managers, and engineers. They work together on coding tasks you tell them to do.
Right now, MetaGPT can do tasks that are a bit challenging, like coding a snake game or making simple utility apps. It’s a cool tool that might get even better in the future. It costs about $2 in OpenAI API fees to make a whole project.
Camel
We talked about Camel before, and it’s changed since then. In short, Camel is one of the first tools that let many agents talk and work together. It’s like they have roles in a play, and they act out tasks you give them.
First, a person tells Camel what to do. Then, the tool uses a powerful LLM to decide what roles each agent should have, figure out complex tasks, and set up scenarios for them to work together. It’s like a play for artificial intelligence.
Also read: 20+ Best AI Marketing Tools You Should Try in 2024
Loop GPT
LoopGPT is a newer version of Toran Bruce Richards’ AutoGPT. It’s got better support for GPT-3.5, more connections, and custom agent skills. Plus, it uses fewer API tokens, so it’s cheaper to use.
LoopGPT can work mostly on its own or with a person helping to avoid mistakes. The cool thing is, it doesn’t need extra databases or storage to keep data. It can save agent states to files or Python projects.
JARVIS
JARVIS isn’t as cool as Tony Stark’s assistant, but it’s got some tricks. It uses ChatGPT as its “decision-making engine.” JARVIS plans tasks, picks the best models, does the tasks, and creates content.
With lots of special models in the HuggingFace hub, JARVIS uses ChatGPT’s smarts to pick the best model for each task. It’s pretty flexible and can do many tasks, from summarizing to finding objects.
OpenAGI
OpenAGI is a platform for open-source AGI (artificial general intelligence) research. It puts together small, expert models for tasks like feeling sentiments or fixing blurry images. It also uses Reinforcement Learning from Task Feedback (RLTF) to make the results better.
Underneath, OpenAGI is like other open-source AI frameworks. It brings in popular tools like ChatGPT, LLMs like LLaMa2, and other special models. It picks the right tools depending on what the task needs.
SuperAGI
SuperAGI is a more flexible and user-friendly alternative to AutoGPT. Think of it as a launchpad for open-source AI agents that provides everything you need to build, maintain, and run your agents. This includes plugins and a cloud version for testing.
The framework includes multiple AI models, a graphical user interface, integrations with vector databases (for storing/retrieving data), and performance insights. There’s also a marketplace with toolkits to connect it to popular apps and services like Google Analytics.
ShortGPT
AI models are excelling at generating content, but video formats have been somewhat neglected until recently. ShortGPT is a framework that lets you use large language models to simplify complex tasks like video creation, voice synthesis, and editing.
ShortGPT can handle most typical video-related tasks such as writing video scripts, generating voiceovers, selecting background music, writing titles and descriptions, and even editing videos. The tool works for both short and longer video content, regardless of the platform.
ChatDev
CoPilot, Bard, ChatGPT, and others are powerful coding assistants, but projects like ChatDev might soon give them a challenge. Marketed as “a virtual software company,” ChatDev uses multiple agents that play different roles in a traditional development organization.
Each agent, with a unique role, can collaborate to handle various tasks, from designing software to writing code and documentation. It’s ambitious and more of a test bed for agent interactions, but it’s worth exploring, especially if you’re a developer.
MicroGPT
MicroGPT is a language model made by Sin Liang Lee. It’s trained on a computer called RTX4060 8GB with a 6GB dataset and has 82 million parameters. MicroGPT is designed to use GPT-3.5 and GPT-4 for simple tasks, like checking stock prices, testing network security, making digital art, or ordering pizza. The results of each task can be seen on the user’s computer.
Because it has fewer parameters, MicroGPT isn’t great for big, complicated tasks. To use the program, you need to have Python, Git, and a code editor.
