Practical AI Agents provides developers with a comprehensive framework and ready-to-use examples to construct autonomous agents powered by large language models. It demonstrates how to integrate API tools (e.g., web browsers, databases, custom functions), implement RAG-style memory, manage conversation context, and perform dynamic planning. You can adapt examples for chatbots, data analysis assistants, task automation scripts, or research tools. The repository includes notebooks, Dockerfiles, and configuration files to streamline setup and deployment across environments.

AI Agent Setup provides a comprehensive framework for building intelligent agents that can understand, reason, and act on user instructions. At its core, it offers modular Python packages you can use to assemble agents with custom prompt templates, multi-step chain execution, and memory capabilities powered by vector databases like FAISS or Chroma. Developers can connect to various LLM providers including OpenAI, Hugging Face, and local Llama models, defining bespoke agent workflows for tasks such as information retrieval, automated research, customer support, or process automation. Environment configuration scripts simplify API key management and dependency installation, while example templates demonstrate best practices. Whether you’re prototyping a conversational assistant or deploying an autonomous digital worker, AI Agent Setup streamlines the process with flexible, extensible components.

demo_smolagents is a reference implementation of SmolAgents, a Python-based microframework for creating autonomous AI agents powered by large language models. This demo includes examples of how to configure individual agents with specific toolkits, establish communication channels between agents, and manage task handoffs dynamically. It showcases LLM integration, tool invocation, prompt management, and agent orchestration patterns for building multi-agent systems that can perform coordinated actions based on user input and intermediate results.