Llm Agents

Intelligent agents guided by LLMs is a tiny, open-source Python library that demonstrates how to build an LLM-driven agent with a simple Thought→Action→Observation loop. Created by Marc Päpper as a blog post on paepper.com, the project strips away the abstraction layers of larger frameworks like LangChain to show exactly how agent reasoning works in under 200 lines of code. Agents use a pluggable tool interface: each tool is a Python class with a name, description, and a `use()` method. Included tools cover a Python REPL, Google Search via SerpAPI, and Hacker News search. The prompt template feeds the LLM the current date, tool descriptions, the question, and prior reasoning turns. To avoid tool-use hallucinations, the code stops LLM generation at the `Observation:` token, executes the real tool, and appends the actual observation before the next loop. The project is purely educational – it is not meant for production. Developers can clone the GitHub repo, run examples, and easily add custom tools. It works with the OpenAI API (any model) and requires minimal dependencies. Compared to LangChain or AutoGen, this library sacrifices robustness and breadth for transparency. A developer can read the entire agent loop in five minutes and truly understand each component. It is best for learners and tinkerers who want to grasp agent internals without fighting through layers of framework code.