Hornet: Building Durable Workflows for Local LLMs (github.com)

Hornet, a new command-line tool for developers working with local large language models (LLMs), has been released, offering a robust way to manage coding tasks and workflows. It allows users to define explicit goals for their models, breaking tasks into manageable “cells” that operate under bounded contexts. This means that even after tasks are stopped, the context, file scopes, and evidence of attempts remain accessible, enabling a seamless recovery and inspection process. Users can install Hornet via PyPI and set it up easily within a Python 3.9+ virtual environment. The significance of Hornet lies in its ability to enhance productivity and durability in developing local LLM applications by providing structured workflows. It supports various operations like delegating tasks, managing context, and handling failures, which can lead to more efficient coding practices. In its first user trial, Hornet demonstrated its capabilities by successfully executing a coding task with an OpenAI-compatible local model using 16 requests and 5 tool calls. This innovative approach not only preserves the original work environment but also allows developers to save and recover from attempts, making it a valuable tool for anyone leveraging local models for software development.