AgentGPT: A Browser-Based AI Agent

AgentGPT is an autonomous AI agent platform where users only need to specify a name and goal for the agent to create and deploy an agent platform in the browser by connecting to large language models (like GPT-4).

PS: Currently, AgentGPT only supports the ChatGPT model and does not support local LLM models. However, you can refer to the code in model_factory.py#L37 and agent_service_provider.py#L18 to modify this and add the local model’s calling interface.

1. AgentGPT Function List

Code Assistance: AgentGPT can act as a programming assistant, helping to debug code, generate code snippets, and even provide coding tutorials.
Research and Content Generation: From writing blog posts and articles to creating study guides and summaries, AgentGPT can simplify content creation across multiple domains.
Email and Communication: Writing an email or crafting the perfect message can be time-consuming. With AgentGPT, you can automate this process. It can help generate emails, draft messages, and assist with other forms of communication.
Marketing and Advertising: AgentGPT can be used to generate innovative marketing ideas, create compelling ad copy, and assist in developing SEO strategies.
Budgeting and Financial Planning: AgentGPT can provide budgeting advice, financial management tips, and even create personal financial plans based on user-defined criteria.

2. Installing AgentGPT

2.1 Prerequisites:

Git (https://git-scm.com/downloads)
Node.js (https://nodejs.org/en/download)
OpenAI API key
Editor (such as VS Code)

2.2 Installing AgentGPT Using Docker (Recommended)

PS: Docker needs to be installed in advance.

For Mac and Linux systems, install as follows:

git clone https://github.com/reworkd/AgentGPT.git
cd AgentGPT
./setup.sh

For Windows systems, install as follows:

git clone https://github.com/reworkd/AgentGPT.git
cd AgentGPT
./setup.bat

After all services are started, you can enter http://localhost:3000 in the browser.

2.3 Installing AgentGPT Without Docker

If not using Docker, users need to use setup.sh to configure ENV and also need to update the Prisma configuration file to point to the local SQLite instance.

Next, configure Next.js, as shown in the code below:

// Frontend
cd ./next
npm install
npm run dev

In another window, run the following program to start the backend:

// Backend. Make sure you are at the root of the project
cd ./platform
poetry install
poetry run python -m reworkd_platform

After all services are started, you can enter http://localhost:3000 in the browser.

3. Testing AgentGPT Effectiveness

We will test the effectiveness of AgentGPT by retrieving the latest SOTA algorithms for object detection from the Paperwithcode website.

3.1 Open the AgentGPT website and enter the task

Enter the task “Get the latest SOTA models on the Paperwithcode website about object detection on COCO dataset”.

Available default tools are shown in the figure below:

3.2 Task Decomposition

Visit the website -> Navigate to the object detection section -> Filter models related to the COCO dataset -> Sort these models

3.3 Navigate to the Object Detection Section

3.4 Filter Models Related to the COCO Dataset

3.5 Sort These Models

3.6 Summary

References:

[1] https://agentgpt.reworkd.ai/zh

[2] https://github.com/reworkd/AgentGPT

[3] https://docs.reworkd.ai/introduction

[4] https://mp.weixin.qq.com/s/YkwI01yLydO2QjGGIzvBHQ

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