Build a Reproducible Local Agent Tool with ArcLM (No GPU Required)
Build a Reproducible Local Agent Tool with ArcLM (No GPU Required) An AI agent becomes useful when it can do more than generate text. It needs a way to call functions, validate their inputs, execute operations, and return structured results. But giving an agent access to tools introduces engineering questions: What arguments can a tool accept? How do you prevent an agent from running…
Build a Reproducible Local Agent Tool with ArcLM (No GPU Required)
In this tutorial, a small calculator agent is built using ArcLM without requiring a GPU or external LLM API. The process involves several key steps:
1. Install ArcLM by creating a Python virtual environment and installing the lightweight ArcLM package. No PyTorch, GPU, downloaded language model, or API key is needed.
2. Define a typed Python function that adds two integers. This function is then registered as a tool with ArcLM, which exposes it through the @tool decorator. The function's type hints, docstring, category, permission, and side-effect level are used to describe its expected arguments, return type, description, tool category, permissions, and side effects, respectively.
3. Register the defined tool in ArcLM's ToolRegistry, which allows for consistent retrieval and inspection of registered tools' metadata, such as names, descriptions, and input schemas. The input schema describes the expected arguments for the tool, providing explicit information about its expected input.
4. Build a deterministic fake model adapter to remove variable behavior from the agent. This adapter will request a call to the add_numbers tool with arguments 2 and 5, read the structured tool result, and return a final response. A DeterministicFakeModel class is created to fulfill these requirements, tracking the number of calls to the tool.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
