llm_agents
Basic Information
A small Python library for building agents controlled by large language models. It is inspired by langchain and aims to implement the core parts of an LLM-driven agent in very few lines of code so developers can learn and prototype. The repository provides an Agent class and a ChatLLM wrapper to interact with an LLM via the OpenAI API. It includes example tools such as a Python REPL executor, a Google search tool using SerpAPI, and a Hacker News search tool. The agent operates in a Thought, Action, Observation loop and appends observations to the LLM prompt until a final answer is produced. The repo includes a runnable script and installation instructions.
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App Details
Features
Lightweight minimal implementation of an LLM agent runtime. Agent loop with Thought, Action and Observation phases that iteratively append LLM outputs and tool observations to the prompt. Built-in tools include PythonREPLTool for executing Python, SerpAPITool for Google searches via SerpAPI, and HackerNewsSearchTool for Hacker News lookups. Simple API surface with Agent and ChatLLM classes and an example usage snippet showing how to assemble tools and run the agent. Installation via requirements.txt and editable pip install is documented. Environment variables OPENAI_API_KEY and SERPAPI_API_KEY are required for the corresponding integrations. Designed to be extended with custom tools.
Use Cases
Helpful for developers and learners who want to understand, prototype or debug LLM-guided agents by exposing a compact, readable implementation of the control loop and tool integration patterns. The repo provides runnable examples and a script to ask questions of the agent, enabling quick experimentation with prompt design, tool behavior and LLM responses. Built-in tools demonstrate combining local code execution and remote retrieval to solve tasks. Because the codebase is intentionally minimal, it is convenient for teaching, exploring agent internals, adding custom tools, and iterating on prompts without the complexity of larger frameworks.
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