🚀 OllaRuntime

Persistent Execution Layer for Ollama

OllaRuntime adds "hands" to Ollama's "brain." It provides a persistent, stateful local execution environment (REPL) that allows Ollama-generated code to maintain variables, functions, and context across multiple independent prompts.

🧠 The Problem

Ollama is a powerful inference engine, but it is natively stateless. Each request to an LLM like Llama 3 or DeepSeek is an isolated event. If you ask it to "Define a variable x = 10" and then "Multiply x by 2," the model loses the context of x between calls unless you manually manage complex history and shell environments.

✨ The Solution: OllaRuntime

OllaRuntime bridges this gap by providing a persistent Python InteractiveConsole wrapped in a FastAPI backend.

• Persistent State: Variables, imported modules, and defined functions stay in memory.
• FastAPI Bridge: Easy integration for any application wanting to add execution capabilities to Ollama.
• Premium UI: A high-contrast, glassmorphic dashboard for real-time monitoring of the execution state and memory.
• Docker-Less: Lightweight and easy to run locally on Windows/Linux/Mac without container overhead.

🛠️ Use Cases

1. Iterative Data Science: Ask Ollama to load a dataset, then perform multiple follow-up analysis steps without re-loading the data.
2. Autonomous Coding Agents: Build agents that can verify their own code by running it and seeing output/errors persistently.
3. Persistent Tooling: Create custom REPL-based tools where the LLM can "learn" and store utility functions over a long conversation.
4. Educational Sandboxes: Provide a safe, visual way for users to see how LLMs interact with real code execution.

🚀 Getting Started

Prerequisites

• Python 3.10+
• Ollama installed and running.

Installation

1. Clone the repository:

   git clone https://github.com/LebToki/OllaRuntime.git
   cd OllaRuntime

2. Install dependencies:

   pip install fastapi uvicorn

Running OllaRuntime

1. Start the backend:

   python main.py

2. Open your browser to http://localhost:8000.

🏗️ Technical Architecture

• Backend: FastAPI (Python)
• Runtime: Persistent code.InteractiveConsole
• Frontend: Vanilla JS/CSS (Premium Glassmorphism)
• Bridging: Regex-based markdown code extraction

    subgraph User_Interface ["Frontend (Vanilla JS/CSS)"]
        UI[Glassmorphic Dashboard]
    end

    subgraph Backend_Layer ["FastAPI Backend"]
        API[FastAPI Server]
        Extract[Regex Code Extractor]
    end

    subgraph Execution_Layer ["Stateful Runtime"]
        REPL[Persistent Python InteractiveConsole]
        Memory[(In-Memory State: Vars/Funcs)]
    end

    subgraph External
        Ollama[Ollama Local LLM]
    end

    UI <--> API
    API <--> Extract
    Extract <--> REPL
    REPL <--> Memory
    Ollama -- Generates Code --> API

🤝 Contributing

Contributions are welcome! Whether it's adding support for Node.js runtimes, improving the UI, or adding more robust sandboxing, feel free to open a PR.

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Built with ❤️ for the Ollama Community. "CASHFLOW OVER CLOUT" - 2T Interactive.