Github

⚡ Everything you need to create an LLM Agent is here. Access a comprehensive suite of tools, prompts, frameworks, and a knowledge hub of LLM models—all in one place to streamline your agent development.
📑 Table of Contents
- Introduction
- Architecture
- Installation
- Usage
- Package Version
- Python Interface
- Source Code Version
- DemoGPT AgentHub
- Installation
- Creating Tools
- Available Tools
- Creating an Agent
- Using an Agent
- Available Agent Types
- Using ReactAgent
- Using RAG
- Combining RAG with Agents
- To-Do
- Contribute
- Citations
- License
🤖 DemoGPT AgentHub
DemoGPT AgentHub is a powerful library that allows you to create, customize, and use AI agents with various tools.
🛠 Installation
To use DemoGPT AgentHub, simply install the main package:
🔧 Creating Tools
Creating custom tools is easy:
**Example Usage:**
**Example Output:**
🧰 Available Tools
DemoGPT AgentHub comes with several built-in tools:
- 🔍 TavilySearchTool
- 🌦 WeatherTool
- 📚 WikipediaTool
- 🐚 BashTool
- 🐍 PythonTool
- 📄 ArxivTool
- 🎥 YouTubeSearchTool
- 💻 StackOverFlowTool
- 🌐 RequestUrlTool
- 🗃 WikiDataTool
- 🏥 PubmedTool
🤖 Creating an Agent
To create an agent:
🎮 Using an Agent
Once you've created an agent, use it to ask questions or perform tasks:
**Example Output:**
This example demonstrates how an agent can:
1. Access document information through RAG
2. Use Python for calculations
3. Combine multiple tools to answer complex questions
The agent will:
1. Use RAG to find information about the current weather in New York
2. Use the Python tool to calculate the current weather conditions
3. Provide a comprehensive answer using both pieces of information
🧰 Example: Creating a Simple Math Tool
Here's an example of creating a custom power calculation tool:
**Example Output:**
🧠 Using ReactAgent
The ReactAgent provides a detailed reasoning process:
Example 1: Weather and Math Calculations
**Example Output:**
Example 2: Object Detection in Images
**Example Output:**
These examples demonstrate how ReactAgent can:
1. Process complex multi-step queries using multiple tools
2. Perform computer vision tasks with YOLO object detection
3. Show detailed reasoning and decision-making process
4. Provide structured and informative responses
🧮 Using RAG
BaseRAG provides an easy way to implement Retrieval Augmented Generation with various vector stores:
**Example Output:**
**Another Query Example:**
**Output:**
These examples provide users with a clear understanding of:
1. What to expect when running these code blocks
2. The format of the output
3. How different agents and tools behave
4. How the reasoning process works in the ReactAgent
5. How RAG responds to questions
🔄 Combining RAG with Agents
You can also use RAG within agents to enable document-based reasoning. Here's an example:
**Example Output:**
This example demonstrates how an agent can:
1. Access document information through RAG
2. Use Python for calculations
3. Combine multiple tools to answer complex questions
The agent will:
1. Use RAG to find information about the GitHub repository stars
2. Use the Python tool to calculate the square root
3. Provide a comprehensive answer using both pieces of information
🔥 Demo
📚 Documentation
See our documentation site here for full how-to docs and guidelines
⚡ With DemoGPT v1.3, API usage will be possible with the power of **Gorilla** within 2 weeks.
After this release, you will be able use external APIs autonomously. ⚡
📦 Using DemoGPT Package
The DemoGPT package is now available and can be installed using pip. Run the following command to install the package:
To use the DemoGPT application, simply type "demogpt" into your terminal:
📌 Introduction
Welcome to DemoGPT, a revolutionary open-source initiative that is reshaping the landscape of Large Language Model (LLM) based application development.