OpenAgents-EN | chenoiLab

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📄Data Agent 🔌Plugins Agent 🌐Web Agent 💡Design Philosophy 🪄Getting Started

As @ AI Daily Guy mentioned, the product features include:

Easy Deployment: Simplifies the installation and getting started process.

Full-Stack Support: Provides comprehensive tools and functionalities for developers.

Chat Interface: Offers an intuitive user interface for seamless interaction.

Agent Methods: Employs advanced technologies and methods to enhance functionality.

OpenAgents is an open-source platform that develops LLM-powered agents, integrating three powerful agents in XLang.

Project link: https://github.com/xlang-ai/OpenAgents

Paper link: https://arxiv.org/abs/2310.10634

User manual: https://docs.xlang.ai/category/user-manual

📄Data Agent

Highly available for structured data like tables, and directly displays data. The main feature of the Data Agent is to write and execute code (Python and SQL) and present the results to users.

Before: Download data as xlsx or csv → Import to Excel → Process data.

Now: One step! For instance, simply enter your topic of interest on the platform, and the Data Agent can automatically search datasets on Kaggle, find useful datasets that meet user needs, and directly generate Python or SQL code to automatically clean the data.

It can also automatically run the pyecharts package code, generating a JSON object and displaying it as an Echarts chart on the frontend.

Additionally, users can also input images, allowing the Data Agent to perform image processing, such as greyscale.

🔌Plugins Agent

Integrated with over 200 third-party plugins, users can find information on a range of plugins (including Wolfram, NewsAPI, RapidAPI, etc.) in XLang's user manual, and interact with popular applications like Spotify, Twelve Data, Youtube, and Alpha Vantage.

The use is divided into categories requiring authentication and those that do not.

No authentication needed, such as shopping, weather, creating flowcharts.

For example, tell the Klarna Shopping plugin what you want to buy, and it will return user link cards to navigate to the product page.

Tools that require authentication, such as Wolfram for scientific calculations, a very popular tool for science subjects.

When users are unsure which plugin they need, there is an auto plugin selection feature, where the Plugins Agent will automatically match plugins based on user needs.

🌐Web Agent

Leveraging the browser Sidebar plugin, the Web Agent can automatically operate webpages, simplifying web browsing. Users can directly search for "XLang Web Agent" in the Chrome extensions web store, download, and configure it according to the prompts.

When using it, users input commands into the Web Agent, jump to the corresponding webpage, and need to activate the console [shortcut: "Cmd + Option + I" (on Mac) or "Ctrl + Shift + I" (on Windows)] to manually select to start the search. The Web Agent will automatically analyze the webpage and import the webpage information into the chat on the platform.

Although this Agent requires an extra step of manual activation in the console, it is undeniably powerful. For instance, based on user queries for flight information, it can automatically input flight origin, time, round trip conditions into the webpage, continue to compare ticket prices after the webpage jumps, and return results to the user.

Additionally, it can automatically browse movies on IMDb and write reviews; provide time and distance information based on Google Maps data; automatically send and forward tweets; and automatically fill out Google forms. The functionality is truly powerful!

💡Design Philosophy

XLang Agents are tailored for real-world scenarios, designed to solve specific human challenges.

In Reinforcement Learning (RL), an "agent" is a decision-maker that interacts with its environment to achieve goals. It learns through a feedback loop, adjusting strategies based on rewards or punishments to maximize accumulated rewards over time.

In the fields of Natural Language Processing (NLP) and robotics, an "agent" refers to a system or model that interacts with its environment directly or through tools.

The team believes that agents are large language models (LLMs) that follow language instructions and predict actions by activating API functions with specific parameters through prediction. Specifically, they accept user intentions as input, with LLM-based strategies to predict the next action, and consist of the action space for selection.