LLM Agents Example provides a hands-on codebase for building AI agents in Python. It demonstrates registering custom tools (web search, math solver via WolframAlpha, CSV analyzer, Python REPL), creating chat and retrieval-based agents, and connecting to vector stores for document question answering. The repo illustrates patterns for maintaining conversational memory, dispatching tool calls dynamically, and chaining multiple LLM prompts to solve complex tasks. Users learn how to integrate third-party APIs, structure agent workflows, and extend the framework with new capabilities—serving as a practical guide for developer experimentation and prototyping.

Agentin is an open-source Python library designed to help developers build intelligent agents that can plan, act, and learn. It provides abstractions for managing conversational memory, integrating external tools or APIs, and orchestrating multiple agents in parallel or hierarchical workflows. With configurable planner modules and support for custom tool wrappers, Agentin enables rapid prototyping of autonomous data-processing agents, customer service bots, or research assistants. The framework also offers extensible logging and monitoring hooks, making it easy to track agent decisions and troubleshoot complex multi-step interactions.