🧠 LLM Agent
🚀 Overview
LLM Agent is a modular and extensible framework that enables building intelligent autonomous AI agents powered by Large Language Models (LLMs) such as OpenAI GPT, Claude, and local open-weight models. It provides a clean architecture to integrate tools, APIs, memory, and decision loops for creating production-grade intelligent agents.
🧩 Features
✅ Multi-LLM Support (OpenAI, Anthropic, HuggingFace, Ollama) ✅ Modular Agent Architecture ✅ Tool Integration (Search, Calculator, File I/O, API Calls) ✅ Memory & Context Management ✅ Chain-of-Thought Reasoning ✅ Configurable Prompts and Workflows ✅ Extendable Plugin System
🏗️ Project Structure llm-agent/ │ ├── src/ │ ├── agent_core/ # Core agent logic and architecture │ ├── tools/ # External tool integrations (API, search, etc.) │ ├── memory/ # Long-term and short-term memory management │ ├── workflows/ # Custom agent workflows and actions │ └── main.py # Entry point for the agent │ ├── configs/ │ └── agent_config.yaml # Model and system settings │ ├── requirements.txt # Python dependencies ├── README.md # Project documentation └── LICENSE # License file
⚙️ Installation 1️⃣ Clone the Repository git clone https://github.com/Shank312/llm-agent.git cd llm-agent
2️⃣ Create a Virtual Environment python -m venv venv venv\Scripts\activate # On Windows
source venv/bin/activate # On macOS/Linux
3️⃣ Install Dependencies pip install -r requirements.txt
🧩 Usage
To start the agent: python src/main.py
You can modify configuration (model, tools, memory) inside: configs/agent_config.yaml
🧰 Example Workflow
Example: Question Answering Agent
from src.agent_core import LLMAgent agent = LLMAgent(model="gpt-4", memory=True) response = agent.run("Summarize the main ideas from 'Designing Data-Intensive Applications'") print(response)
🔌 Tool Integrations
| Tool | Description |
|---|---|
| 🔍 Search | Uses DuckDuckGo or Bing API for live web context |
| 🧮 Calculator | Handles math operations via safe evaluation |
| 📂 FileReader | Reads and processes text or JSON files |
| 🧠 Memory | Stores conversation context using local vector DB |
📦 Dependencies
Common dependencies (add these to your requirements.txt): openai langchain huggingface_hub python-dotenv tiktoken requests pydantic
🌍 Roadmap
Add LangGraph / CrewAI support
Implement Tool Routing with LangChain Agents
Integrate Local LLM (Llama 3 / Mistral)
Web UI Dashboard for Agent Logs
Add Docker Support
🤝 Contributing
Contributions are welcome!
Fork the repo
Create your feature branch (git checkout -b feature-name)
Commit changes (git commit -m 'Add new feature')
Push to branch (git push origin feature-name)
Open a Pull Request 🎯
🧾 License
This project is licensed under the MIT License – see the LICENSE file for details.
💡 Author
👤 Shankar Kumar
💬 Building next-gen AI systems | Open Source Contributor | Machine Learning Engineer