I've been experimenting with agent frameworks and noticed that many workflows reduce to a simple pattern:

- Send input - Wait for a pattern - Branch on the match

This is essentially the classic Unix expect model, but applied to LLM conversations.

So I built expectllm — a minimal pattern-matching conversation flow library (365 lines of code).

Example:

    from expectllm import Conversation

    c = Conversation()
    c.send("Review this code for security issues")
    c.expect(r"found (\d+) issues")

    if int(c.match.group(1)) > 0:
        c.send("Fix the top 3")

No chains, no schema definitions, no output parsers.

Features: - expect_json(), expect_number(), expect_yesno() - Regex → auto format instructions - Full conversation history for multi-turn flows - Auto-detects OpenAI / Anthropic via environment variables

The idea: treat LLM conversations as explicit state machines, where each expect() is a state transition.

Repo: https://github.com/entropyvector/expectllm PyPI: pip install expectllm

Would love feedback — especially on where this abstraction breaks down.