Localgpt

What is Localgpt

localGPT is an Ollama interface that provides models with MCPs, designed to facilitate the integration of machine learning models into applications.

Use cases

Use cases include building interactive web applications, developing production-ready applications with strict type checking, and enhancing developer productivity through efficient linting.

How to use

To use localGPT, set up a React application with TypeScript and Vite, and integrate the provided plugins for optimal performance and development experience.

Key features

Key features include support for Fast Refresh using Babel or SWC, customizable ESLint configurations for type-aware linting, and the ability to use React-specific lint rules.

Where to use

localGPT can be used in web development, particularly in projects that require real-time updates and type safety in React applications.

evoAI: LLMs + evolution

evo-ai is a local runner for Ollama that enhances any LLM with agentic capabilities — enabling models not just to use tools, but to create new ones autonomously and invoke them when needed.

💡 Think of it as a LLM which has real-time, self-expanding toolchains built on-demand by the LLM itself.

Features:
🛠️ Tool Creation On the Fly
When the LLM encounters a missing tool or function, it can generate and register new code snippets or scripts to accomplish the task.

Modular & Local:
No cloud dependencies. Everything runs locally via Ollama, ensuring privacy and full control.

Supports Any LLM:
Works with any LLM accessible via Ollama (e.g., LLaMA, Mistral, Code LLMs, etc).

⚠️ Note: The effectiveness of this application heavily depends on the capabilities of the LLM you choose. For best results, use models that excel at coding and analytical reasoning.

📦 Installation

Prerequisites:

• Ollama installed

git clone https://github.com/pahulgogna/evoAI.git

cd evoAI

npm install

🧪 Usage

Run the agent:

Give it a goal and let the model break it down, create tools, and execute steps autonomously.

🧠 How It Works

Introspective Planning
The LLM receives your high-level goal and plans a multi-step solution.

Tool Audit
Checks available tools. If something’s missing, it builds it.

Execution
Executes tools in sequence, handling input/output passing and error catching.

Self-Extension
If new needs arise during execution, the agent can loop back and generate new capabilities on the fly.

🔒 Security Note
Since the LLM can generate and execute arbitrary code, it’s strongly recommended to run in a sandboxed or containerized environment if you’re testing unfamiliar prompts.

🛠️ Project Structure

evoAI/
     ├── src/
            ├── electron/
            ├── ui/

🤝 Contributing
Pull requests are welcome! For major changes, please open an issue first to discuss what you’d like to change.