Lisp Development with AI Agents: High Costs and Technical Challenges

AI Agent Performance with Lisp vs. Other Languages

A DevOps engineer using OpenRouter with Goose CLI for AI agentic development discovered significant differences in AI performance between programming languages. While working on an RSS reader format conversion tool in Lisp, he encountered multiple challenges that made development expensive and inefficient compared to Python or Go.

Technical Implementation Details

The engineer initially tried to have AI agents interact with the Lisp REPL using tmux commands: tmux capture-pane -t 0.0 -p | tail -n 1. This approach consumed excessive tokens, required sleep commands, and involved parsing tmux output. Claude showed some progress but still struggled, while cheaper models like DeepSeek and Qwen performed poorly despite working adequately for other tasks.

To improve the situation, he created tmux-repl-mcp, a Python tool that provides a more straightforward REPL interface. Instead of complex tmux interactions, agents could simply run execute_command in the REPL and receive output directly. He chose Python because his existing Goose configuration used uvx for tool installation and management.

Cost and Performance Comparison

The difference between Lisp and Python development with AI was dramatic. With Python, he could write all code and tests in a day or two using cheap models, requiring only semi-manual debugging. With Lisp, even after implementing tmux-repl-mcp, he spent $10 in 30 minutes with Claude, and the signal-to-noise ratio remained poor compared to Python.

Key observations from the experience:

• AI generates code on a path of least resistance, defaulting to common patterns like QuickLisp even when instructed to use alternatives like OCICL
• The high-latency request-response nature of AI APIs conflicts with REPL development workflows
• Languages with high internet volume (Go, Python) are orders of magnitude easier and cheaper for AI-assisted development
• AI has converted language popularity into real cost savings per million tokens

The engineer noted that regardless of language, his role remained similar: acting as an opinionated product owner managing the AI. However, the experience lacked the usual enjoyment of writing Lisp directly, leading him to consider rewriting the project in Go for better AI compatibility.