by mpaepper · Agent Tool · ★ 1.0k
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LLM Agents Small library to build agents which are controlled by large language models (LLMs) which is heavily inspired by langchain. The goal was to get a better grasp of how such an agent works and understand it all in very few lines of code. Langchain is great, but it already has a few more files and abstraction layers, so I thought it would be nice to build the most important parts of a simple agent from scratch. Some more infos are in this Hacker News discussion from April 5th 2023 and the related blog post. How it works The agent works like this: It gets instructed by a prompt which tells it the basic way to solve a task using tools Tools are custom build components which the agent can use So far, I've implemented the ability to execute Python code in a REPL, to use the Google search and to search on Hacker News The agent runs in a loop of Thought, Action, Observation, Thought, ...
| Stars | 1,041 |
| Forks | 84 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 47.7/100 |
| Open Issues | 3 |
| Last Updated | 2025-06-23 |
| Created | 2023-04-04 |
| Platforms | python |
| Est. Tokens | ~4k |
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llm_agents is Build agents which are controlled by LLMs. It is categorized as a Agent Tool with 1.0k GitHub stars.
llm_agents is primarily written in Python. It covers topics such as deep-learning, langchain, llms.
You can find installation instructions and usage details in the llm_agents GitHub repository at github.com/mpaepper/llm_agents. The project has 1.0k stars and 84 forks, indicating an active community.
llm_agents is released under the MIT license, making it free to use and modify according to the license terms.
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