Quick Tips: smolagents - Build an AI Agent in 4 Lines of Code
Plus the Claude Code /goal command
Grab your coffee. Here are this week’s highlights.
📅 Today’s Picks
Claude Code: Run Tasks to Completion with /goal
Problem
Starting a task in Claude Code often means spending multiple turns going back and forth before it finally reaches the result you wanted.
Solution
Claude Code’s /goal command lets you define a completion condition once, then continues working until that condition is met.
After each turn, a lightweight model evaluates the conversation against your condition. If the goal is not satisfied, Claude gets sent back for another turn with a short explanation of what still needs to be done.
To get the most out of /goal, write a condition the evaluator can actually verify.
Name a measurable end state instead of a vague target (e.g. “all tests in
test/authpass” instead of “auth is working”)List constraints that must hold along the way (e.g. “no other test file is modified” or “no tests are skipped or deleted”)
Add a safety limit like “stop after 20 turns” to avoid endless iterations.
smolagents: Build an AI Agent in 4 Lines of Code
Problem
LangChain and LangGraph are powerful, but they also come with concepts like chains, runnables, and state graphs.
That is useful for complex workflows, but overkill when you just want to give a model a tool and let it solve a task.
Solution
smolagents from Hugging Face is a barebones agent library designed for simplicity.
In roughly 1,000 lines of code, it gives you the core pieces needed to build a working agent with a single import and three lines of setup.
Key features:
Agents act by writing Python in a sandbox, the same way you’d script a task in a notebook
Model-agnostic design, swap between OpenAI, Anthropic, local Ollama, or Hugging Face Inference
Push agents to the Hugging Face Hub to share with the community
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