sma-begin sets up a streamlined codebase to create AI-driven agents by abstracting common components like input processing, decision logic, and output generation. At its core, it implements an agent loop that queries an LLM, interprets the response, and optionally executes integrated tools, such as HTTP clients, file handlers, or custom scripts. Memory modules allow the agent to recall previous interactions or context, while prompt chaining supports multi-step workflows. Error handling catches API failures or invalid tool outputs. Developers only need to define the prompts, tools, and desired behaviors. With minimal boilerplate, sma-begin accelerates prototyping of chatbots, automation scripts, or domain-specific assistants on any Python-supported platform.

X AI Agent is a developer-focused framework that simplifies building custom AI agents using large language models. It provides native support for function calling, memory storage, tool and plugin integration, chain-of-thought reasoning, and orchestration of multi-step tasks. Users can define custom actions, connect external APIs, and maintain conversational context across sessions. The framework’s modular design ensures extensibility and allows seamless integration with popular LLM providers, enabling robust automation and decision-making workflows.

AgentReader is a developer-friendly AI agent framework that enables you to load and index various data sources such as PDFs, text files, markdown documents, and web pages. It integrates seamlessly with major LLM providers to power interactive chat sessions and question-answering over your knowledge base. Features include real-time streaming of model responses, customizable retrieval pipelines, web scraping via headless browser, and a plugin architecture for extending ingestion and processing capabilities.