Remember before AI, LLMs and Agentic Engineering? Since going all-in agentic, I have felt that something useful was lost. I had a pretty effective and joyful workflow back then, practicing REPL Driven Development with almost instant feedback loops. I have written about this workflow before.
Today: when steering and monitoring the work my agents do, I've often wondered why it think it needs to construct these complex shell commands, and huge chunks of temporary Python scripts - sometimes entire programs - to try things out. Have you also noticed this? Sometimes, the agent copies entire folders of code into a /tmp folder just to verify something. It is very difficult to understand what I am actually approving.
That's why I have developed a way for the agent to send small pieces of Python code to a *Live running REPL*, so it can verify things step-by-step without losing context.
The Open Source **Agent REPL MCP** gives your agents a persistent Python REPL. Each agent session gets its own *Jupyter kernel* per project that stays alive across calls. Edits to real source files are picked up automatically on the next run, using the built-in features of the kernel. Having a persistent REPL will make the REPL Driven Development workflow possible for your agent. The agent can try out and run small portions of code when it need to verify or figure out things, instead of running complex bash calls.
- Use it when the behavior of a function, library call, or data shape is unclear, and running code with representative inputs answers it faster than reading.
- When you are about to edit project code and want to try a change and verify it in a live process first.
- Skip it when reading the code already answers the question and for trivial changes.
You will find the Agent REPL MCP here. With setup instructions for You, and skills for your Agents.
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