agentgpt

How to Use AgentGPT to Create ChatGPT-Like AI [2023]

by

ChatGPT has taken the world by storm, showcasing the power of large language models. While OpenAI has not open sourced ChatGPT, alternatives like Anthropic’s Claude provide similar capabilities.

If you want to build your own ChatGPT-style application, AgentGPT is an open source framework that makes it possible. With just a few lines of code, you can leverage large language models to create conversational AI.

What is AgentGPT?

AgentGPT is an open source Python library for running language models locally or on the cloud. It provides a simple API for text completion, streamlining the process of integrating large models into applications.

Features of AgentGPT:

  • Supports major models like GPT-3, Codex, Jurassic-1, and more
  • Local or cloud-based deployments
  • Conversational agent with dialogue history
  • Fine-tuned models for domains like finance and medicine
  • Streamlit web application for quick prototyping

AgentGPT handles the heavy lifting so developers can focus on creating the conversation flows and UI.

Installation and Setup

AgentGPT can be installed via pip:

pip install agentgpt

You will also need access keys for whichever model you want to use. API keys can be obtained from:

Save your keys in a .env file.

Creating a Conversational Agent

Here is sample code for a basic conversational agent:

from agentgpt import Agent

agent = Agent(model="gpt-3.5-turbo") 

while True:
  user_input = input("You: ")
  response = agent(user_input)
  print("AI: ", response)

This launches an interactive loop that accepts user input, queries the model, and prints the response.

We can enhance the agent by tracking dialogue history:

from agentgpt import Agent

agent = Agent(model="gpt-3.5-turbo")
context = ""

while True:
  user_input = input("You: ")

  context += f"\nYou: {user_input}\nAI: "
  response = agent(user_input, conversation_id=context)

  context += response

  print("AI: ", response)

Now the agent retains context instead of treating each query discretely. This improves coherency.

Deploying the Agent

To go beyond the console, AgentGPT integrates with Streamlit for web app creation:

from agentgpt import Agent, WebApp

agent = Agent(model="gpt-3.5-turbo")

class MyAgent(WebApp):

  def respond(self, prompt):
    response = agent(prompt)
    return response

app = MyAgent()
app.run()

This provides a GUI for the agent with text input and output. The app can be customized with CSS and components.

For production systems, the agent would need to be deployed on a cloud server like AWS, GCP, or Azure. AgentGPT includes helpers for Docker containers and Kubernetes.

Tips for Improving the AI Agent

Here are some ways to enhance the capabilities of an AgentGPT conversational application:

  • Fine-tune on a domain-specific dataset for more intelligent responses on topics like medicine, law, etc.
  • Moderate model outputs to filter out toxicity, bias, or incorrect information.
  • Personalize with memories and user context for more natural conversations.
  • Design conversation flows and logic for common tasks like bookings, searches, etc.
  • Implement an entity recognition system to better understand user intent.
  • Continuously train the agent on new data to expand its knowledge.

The framework gives us the power of LLMs – adding the right conversation design takes it to the next level.

Example Use Cases

Some examples of how conversational agents built with AgentGPT could be used:

  • Customer service bots that answer common questions and troubleshoot issues.
  • Personal assistants that schedule meetings, set reminders, integrate with other services.
  • Educational chatbots that tutor students and clarify concepts.
  • Domain experts like medical chatbots or legal counsel bots.
  • Entertainment bots for interactive fiction, dungeon mastering, jokes, etc.

The possibilities are endless! AgentGPT lowers the barrier for creating intelligent assistants tailored to any industry or vertical.

Conclusion

In summary, AgentGPT makes it feasible to develop your own ChatGPT-style AI:

  • Simple API for integrating large language models
  • Tools for building and deploying conversational agents
  • Modular framework that’s highly customizable
  • Streamlit web app for quick testing and prototyping

While not as powerful as ChatGPT itself, with some thoughtful conversation design you can create capable virtual assistants using this open source library.

Give AgentGPT a try and see what kinds of creative and useful conversational AI you can build!

FAQS

What models work with AgentGPT?

AgentGPT supports GPT-3, Codex, Claude, Jurassic, LaMDA and other major language models from providers like OpenAI, Anthropic, AI21, Google, and more.

Does AgentGPT run locally or on the cloud?

AgentGPT can run either locally on your own hardware or hosted on a cloud platform like AWS, GCP, or Azure for scalability.

What is the pricing for using AgentGPT?

AgentGPT itself is open source and free. However, you need pay-as-you-go API access to commercial models like GPT-3 and Claude. Costs vary based on model size and usage.

Can AgentGPT be used for commercial applications?

Yes, AgentGPT can power conversational AI in commercial products as long as you comply with the API terms for whichever models you integrate.

What programming language is AgentGPT built with?

AgentGPT is built in Python and designed to be used from Python code. But the API server enables integration from any language.

Can I “train” AgentGPT models on my own data?

Advanced users can fine-tune models on custom datasets related to a specific domain or task to improve performance.

Are there limits on the number of queries?

Usage limits depend on the API keys you are using. Most providers rate limit requests so high-volume applications may require multiple keys.

How can I customize and extend AgentGPT capabilities?

The modular architecture makes it easy to plug in new conversational flows, personalization, moderation, etc. See the documentation for customization guides.

Leave a Comment