The emergence of ChatGPT and LLM technology has swept the world with these state-of-the-art language models, attracting not only AI developers but also enthusiasts and organizations exploring innovative ways to integrate and build with these models. Various platforms have emerged rapidly, integrating and facilitating the development of new applications.
The popularity of AutoGPT has led to an increasing number of autonomous tasks and agents utilizing the GPT-4 API. These developments not only enhance the ability to handle complex tasks that integrate different systems but also push the boundaries of what we can achieve through autonomous AI.
Here we will summarize some open-source tools similar to AutoGPT, which can be broadly categorized into command-line interfaces (CLI) and browser-based solutions, with HuggingGPT supporting both.
Command Line: AutoGPT, BabyAGI
Browser: AgentGPT, CAMEL, Web LLM
Auto-GPT
Although Auto-GPT is an experimental open-source application, its growth has been rapid. This program, powered by GPT-4, can autonomously achieve any set goal.
GitHub: https://github.com/Significant-Gravitas/Auto-GPT
Looking at the growth of its GitHub Stars shows its recent popularity.
AgentGPT
AgentGPT is a web-based solution that allows for the configuration and deployment of autonomous AI agents to achieve any goal. It attempts to reach its objectives by thinking through tasks, executing them, and learning from the results.
The platform is currently in testing, developing the following features:
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Long-term memory via vector DB
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Web browsing through LangChain (a library for building applications based on large language models)
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Interactions with websites and people
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User and identity verification
GitHub: https://github.com/reworkd/AgentGPT
Website: https://agentgpt.reworkd.ai/
BabyAGI
BabyAGI is a streamlined version of a task-driven autonomous agent.
Its main idea is to create tasks based on previous task results and predefined goals. The script then uses OpenAI’s language model capabilities to create new tasks based on those goals and uses Pinecone to store and retrieve contextual task results. This can be considered the most streamlined autonomous AI architecture; if you are interested in this direction, you can check out its code.
GitHub: https://github.com/yoheinakajima/babyagi
Website: http://babyagi.org/
HuggingGPT
Microsoft’s HuggingGPT, also known as JARVIS, includes an LLM as a controller and many expert models as collaborative executors (from HuggingFace Hub). Its workflow includes four stages:
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Task Planning: Using ChatGPT to analyze requests to understand intent and break them down into solvable tasks.
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Model Selection: Using ChatGPT to select expert models based on descriptions.
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Task Execution: Calling and executing each selected model and returning results to ChatGPT.
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Response Generation: Finally, integrating all model predictions using ChatGPT to generate a response.
GitHub: https://github.com/microsoft/JARVIS
HF: https://huggingface.co/spaces/microsoft/HuggingGPT
Web LLM
Web LLM is a lightweight LLM-based chatbot that runs in the browser without server support and is accelerated by WebGPU. Technically, Web LLM is not an autonomous AI solution but a lightweight web chatbot.
GitHub:https://github.com/mlc-ai/web-llm
CAMEL
CAMEL stands for “Communicative Agents for ‘Mind’ Exploration of Large Scale Language Models” and proposes a novel agent framework, role-playing, as an alternative to AutoGPT and AgentGPT.
GitHub: https://github.com/lightaime/camel
Website: http://agents.camel-ai.org/
GPTRPG
This system combines gaming and large language models, primarily consisting of two parts:
A simple RPG-like environment supporting LLM AI agents.
Embedding AI agents into game characters via the OpenAI API.
This is based on a recently published paper where multiple agents autonomously participated in online games.
GitHub: https://github.com/dzoba/gptrpg
Arxiv:https://arxiv.org/abs/2304.03442
Conclusion
Integrating ChatGPT and LLM into various applications is just a part of the potential of using language models. These models are designed to handle natural language tasks, including text generation, translation, summarization, question answering, and more. Future language models will be even more advanced and intelligent, capable of providing assistance in a wider range of application areas.
For instance, future language models could be used for more accurate machine translation, making cross-cultural communication between humans more convenient. They could also be used for automatic summarization and content generation, helping authors and media organizations create and publish content faster. Additionally, language models could be used for speech recognition and natural language processing, enabling better human-computer interaction.
In summary, as language model technology continues to advance, we can expect to see more innovations and progress. These models will become core technologies in the field of artificial intelligence, providing us with better solutions and broader application scenarios.
Author: Tristan Wolff
Translation: Deephub Imba
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