DemoGPT provides an all-in-one library for developing LLM-based agents, streamlining the process from concept to interactive application. It targets developers seeking to quickly build and deploy agent-powered tools, offering a comprehensive suite of components including tools, prompts, frameworks, and model integrations. The primary benefit is accelerated agent development through automated LangChain code generation and conversion into user-friendly Streamlit applications.

How It Works

DemoGPT employs a pipeline that translates user instructions into functional LangChain code, which is then rendered as a Streamlit application. The process involves: 1) generating a high-level plan from the user's instruction, 2) breaking the plan into discrete tasks, 3) generating code snippets for each task, and 4) assembling these snippets into a complete, executable application. This approach automates complex code generation, making LLM agent development more accessible and efficient.

Quick Start & Requirements

• Install via pip: pip install demogpt
• Usage: Run demogpt in the terminal or import as a Python library (from demogpt import DemoGPT).
• Prerequisites: Requires an OpenAI API key (can be set via environment variable OPENAI_API_KEY or passed directly).
• Documentation: https://demogpt.readthedocs.io/

Highlighted Details

• Automates LangChain code generation from natural language instructions.
• Transforms generated code into interactive Streamlit applications.
• Supports model flexibility, aiming to integrate any LLM capable of code generation.
• Future plans include Gorilla integration for autonomous API usage and a public database for accelerated generation.

Maintenance & Community

The project is actively developed, with a roadmap indicating planned features like Gorilla integration, Llama2 support, and a public database. Contributions are welcomed via pull requests.

Licensing & Compatibility

Licensed under the MIT License, permitting commercial use and integration with closed-source projects.

Limitations & Caveats

The self-refining strategy and full LangChain task implementation are still under development. Integration with Gorilla for API calls is planned for a future release.