MoonshotAI / MoonshotAI/kimi-cli
feat(acp): advertise available models and support mid-session model switching
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 11.4k
- Forks
- 1.3k
- Avg merge
- 9h 47m
- Merged PRs (30d)
- 2
Description
What feature would you like to see?
When driving kimi acp from an ACP client (e.g. Happy Coder mobile app, Zed), the client cannot discover which models are available, nor switch the model mid-session:
session/newdoes not advertise a model list, and nocurrent_model_update/models-related session updates are emitted (onlyavailable_commands_updateis observed).- Client-initiated model change requests are therefore no-ops — the session keeps running whatever
-m/default_modelwas set at spawn.
Requested: advertise the available models over ACP (per the ACP spec's session models support) and implement mid-session model switching, so ACP clients can list and change models without respawning the session.
Additional information
Real-world scenario: Happy Coder spawns kimi --yolo acp on a desktop and the user interacts from a phone. Model selection in the mobile UI (k3 / k3-256k / highspeed aliases from config.toml) can only be honored at spawn time today; changing it requires killing the session and starting a new one.
Related: #2364 (ACP permission mode switching) — same class of gap for modes.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start at the kimi acp entry point and trace ACP session/new, session update handling, and client-initiated model-change requests. Review the ACP session models support and the config.toml model aliases, then verify behavior with an ACP client such as Happy Coder or Zed. Done means clients can discover available models and switch the active model without respawning the session.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- api, cli
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 48/100