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Integrating ChatGPT and LLM into various applications is just part of the potential of using language models.
The emergence of ChatGPT and LLM technology has swept the world with these state-of-the-art language models, prompting not only AI developers, enthusiasts, and some organizations to explore innovative ways to integrate and build these models. Various platforms have emerged rapidly, integrating and promoting the development of new applications.
The popularity of AutoGPT allows us to see more and more autonomous tasks and agents utilizing the GPT-4 API. These developments not only enhance the ability to handle complex tasks integrating different systems but also push the boundaries of what we can achieve through autonomous AI.
Here, we will compile some open-source tools similar to AutoGPT, which can be roughly divided into command-line interface (CLI) and browser-based solutions, with HuggingGPT supporting both solutions.
Browser: AgentGPT, CAMEL, Web LLM
Auto-GPT
Although Auto-GPT is an experimental open-source application, its growth is rapid. The program is powered by GPT-4 and can autonomously achieve any set goals.
GitHub: https://github.com/Significant-Gravitas/Auto-GPT
Looking at the growth of its GitHub Stars indicates its recent popularity.
AgentGPT
AgentGPT is a web-based solution. It allows for the configuration and deployment of autonomous AI agents to achieve any goal. It will attempt to reach its objectives by thinking through tasks, executing them, and learning from the results.
The platform is currently in testing phase and is developing the following features:
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Long-term memory via vector DB; -
Web browsing through LangChain (a library for building applications based on large language models); -
Interaction with websites and people; -
User and identity verification.
GitHub: https://github.com/reworkd/AgentGPT
BabyAGI
BabyAGI is a streamlined version of task-driven autonomous agents.
Its main idea is to create tasks based on previous task results and predefined goals. Then, the script uses OpenAI’s language model capabilities to create new tasks based on the goals, using Pinecone to store and retrieve contextual task results, which 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 consists of four stages:
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Task Planning: Using ChatGPT to analyze requests to understand intent and break it down into possible solvable tasks. -
Model Selection: Using ChatGPT to select expert models based on descriptions. -
Task Execution: Calling and executing each selected model and returning the results to ChatGPT. -
Response Generation: Finally, using ChatGPT to integrate all models’ predictions and generate responses.
GitHub: https://github.com/microsoft/JARVIS
HF: https://huggingface.co/spaces/microsoft/HuggingGPT
Web LLM
Web LLM is a browser-based LLM and LLM-based chatbot that runs 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,” proposing a novel agent framework, acting 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, mainly consisting of two parts.
A simple RPG-like environment that supports LLM-based AI agents, embedding AI agents into game characters through the OpenAI API.
This is based on a recently published paper that deployed multiple agents to autonomously participate 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 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 more advanced and intelligent, able to assist in a wider range of application areas.
For example, future language models could be used for more accurate machine translation, facilitating cross-cultural communication between humans. 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 interaction between people and computers.
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
Editor: Huang Jiyan
