[ANN] Kaimon.jl — opening the gate between AI agents and Julia

kahliburke
Fri, 27 Feb 2026 09:07:33 +0000
Package Announcements

The AI coding landscape is moving fast. Agents that can genuinely reason, write, and iterate on
code are here, and they’re getting dramatically better every few months. The question for us
as Julia users is: how do we connect all of that to the ecosystem we’ve built?

github.com

GitHub - kahliburke/Kaimon.jl

Contribute to kahliburke/Kaimon.jl development by creating an account on GitHub.

Kaimon (開門, “opening the gate”) is my answer to that question.

It’s an MCP server that gives AI agents — Claude Code, Cursor, VS Code Copilot, Gemini CLI —
live, stateful access to Julia sessions. Not file editing and shell commands, but a real
running REPL: persistent state, loaded packages, Revise-aware, the full thing.

When an agent has a fast, reliable way to execute code and immediately introspect the results,
something shifts. It stops being cautious and speculative — hedging with “this should work” —
and starts iterating the way a good programmer does: run it, see what happened, adjust. Type
errors, wrong shapes, unexpected outputs — all surfaced instantly, in the same session, without
round-tripping through files and shell invocations. The agent gets tight feedback loops. Token
usage drops because it’s not reasoning in the dark. Things that would take many back-and-forth
exchanges collapse into a handful of direct tool calls.

I’m also excited about the Gate. Any Julia process can connect to Kaimon and register its own
tools:

Gate.serve(tools=[Gate.GateTool(my_domain_function)])

Kaimon reflects on your function signatures and docstrings to generate the schema
automatically. Your simulation engine, your data pipeline, your custom DSL — all of it becomes
agent-callable with essentially no glue code. The agent can call your domain functions right
alongside introspecting types, running tests, stepping through the debugger, and searching your
codebase semantically. It operates on your system as it actually is.

I think the possibilities here are genuinely huge. Julia’s combination of expressiveness,
performance, and deep scientific/numerical ecosystem is exactly what you want available to an
agent that can actually use it. We’re at an early moment in figuring out what that looks like,
and I wanted to have the right infrastructure in place.

Kaimon ships with a terminal dashboard for monitoring sessions and tool activity, a setup
wizard, and MCP config generation for all the major clients. It requires Julia 1.12+.

Repo: GitHub - kahliburke/Kaimon.jl
Docs: https://kahliburke.github.io/Kaimon.jl/dev/

This is an initial release. I’d love to hear how people are using AI agents with Julia, what’s
working, what’s missing.

64 posts - 16 participants