LLM / Agent Workflows

This is a natural fit if you already use terminal-first agent tooling. The important part is to stay explicit about where message content goes.

Low-risk pattern: local summarization

openkakao-rs read <chat_id> -n 50 --json | \
  jq -r '.[] | "\(.author): \(.message)"' | \
  llm "Summarize this conversation in 3 bullet points"

This is the cleanest starting point when the LLM runs locally or inside a trusted environment.

Routing pattern

A common operator flow looks like this:

1. watch or scheduled read gathers new messages
2. a model labels urgency, topic, or owner
3. the result is stored locally or sent to another system
4. a human decides whether anything should be sent back to KakaoTalk

OpenClaw-style tooling

If you already use agent tools that ingest JSON from the shell, OpenKakao fits as another source.

Useful inputs:

• unread chats as triage items
• recent conversation slices as context windows
• message events as triggers
• contact and chat metadata for routing

Privacy boundary

The moment you pass message text to a remote model API, you have expanded the trust boundary beyond your machine and Kakao. Document that decision in your own workflow.

Recommended pattern

Use agents to:

• summarize
• classify
• extract action items
• draft responses

Do not default to autonomous sending. Keep the final send step explicit unless your risk tolerance is unusually high and you have accepted the consequences.