GitHub - ranjanprj/agentollama: Simple and easy to use UI Based Agentic System

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Agentollama Agentollama UI

🚀 Overview

The Agentollama Framework is a UI-driven framework that enables the creation, execution, and monitoring of intelligent Agents without writing business logic. It leverages AI to dynamically invoke API calls, integrate with legacy applications, and automate complex workflows—all within an intuitive user interface.

Agentollama UI

✨ Features

1️⃣ Dynamic Tool Invocation

  • AI determines which functions to call dynamically, without hardcoding.
  • Example: A simple calculator POC that invokes arithmetic tools purely based on AI reasoning.

2️⃣ Automated API Integration

  • Generates code dynamically for API calls using AI.
  • Tools are loaded dynamically when an Agent is called to perform a task.
  • Can be used to integrate with legacy applications for productivity enhancement.

3️⃣ Execution Logs & Debugging

  • Real-time execution step tracking.
  • Displays logs of task execution and decision-making by Agents.
  • Helps debug why a particular Agent took a specific action.

4️⃣ Structured Output Enforcement

  • Ensures Agents produce outputs in a predefined format.
  • Prevents deviation in reasoning and decision-making.
  • Essential for enterprise use cases where multiple API interactions must follow strict formats.

5️⃣ Knowledge Repository & RAG Integration

  • Vectorized file storage for intelligent querying.
  • Retrieval-Augmented Generation (RAG) Agents to enhance decision-making with external knowledge.
  • Loop Agents to iterate over datasets and execute workflows dynamically.

6️⃣ Enterprise Workflow Automation

  • Enables seamless multi-agent orchestration for business processes.
  • Example: Stock inventory management using AI and APIs—without writing business logic.
  • Example: Automated stock market sentiment analysis:
    • Queries a knowledge repository for stock symbols.
    • Fetches news articles for each symbol.
    • Analyzes sentiment via a RAG Agent.
    • Stores insights in a structured output.

🔥 Recent Enhancements

  • DeepSeek R1 (8B Model) Integration for on-device tool code generation.
  • UI-based Agent Execution—no need to touch an IDE!
  • Performance Metrics & Testing (Upcoming Feature) to analyze Agent efficiency and decision-making accuracy.

📌 Getting Started

Quick Setup Instructions

Prerequisites

  • Python 3.x
  • Ollama (AI Model Server)
  • Django
  • Vector Database (for Knowledge Repository)

Installation

# Clone the repository
git clone https://github.com/your-repo/agentollama.git
cd agentollama

# Create and activate virtual environment
python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

# Install dependencies
pip install django ollama

# Run Ollama server in the background with Llama3.1 8B Model
ollama run llama3.1 8b &

# Run the Django server
python manage.py runserver

Usage

  1. Define your Agents through the UI.
  2. Load API Tools dynamically (generated by AI).
  3. Monitor execution logs in real-time.
  4. Automate workflows effortlessly!

🎯 Roadmap

  • Dynamic Business Testing & Performance Metrics
  • Advanced Agent Collaboration for multi-step AI-driven decision-making
  • Enhanced RAG Capabilities for deeper contextual understanding

🤝 Contributing

Pull requests are welcome! For major changes, please open an issue first to discuss your ideas.

📜 License

Apache 2.0. See LICENSE for details.

💡 Connect

For updates and discussions, follow me on LinkedIn.

✨ AI-driven automation is the future. Let’s build it together! 🚀