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Codeman/docs/wiki/Custom-Model-Endpoints.md
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DevvynandClaude Sonnet 5 7bbe408e44 feat(custom-model): live countdown on the loading banner; timeout is now an error
The loading banner now shows a live countdown against its own timeout
(updated every poll, so every second by default) instead of a static
"this can take a while" — e.g. "Loading qwen3.8-27b (16.4 GB, typically
~1-3 min) on llama-swap - 47s remaining".

If the countdown reaches zero and the model still isn't ready, this is now
treated as a real failure rather than a "keep waiting" shrug:
- The banner turns into a sticky error (_showCenterStatus gains a `type`
  option - 'error' drops the spinner and adds a close button, since nothing
  is "in progress" anymore and a sticky message needs a way to dismiss it),
  naming the llama-swap server's own logs as where to look for detail.
- The session that load was for is closed automatically (closeSession) -
  requested explicitly: a console left open and pointed at a model that
  never finished loading is worse than no console at all. Both apply paths
  now thread the new session's id through to _watchLlamaSwapLoading for
  this (new required 3rd parameter, after endpointId/modelId).

_watchLlamaSwapGeneration's existing stale-call guard extends naturally to
this: a superseded call's own eventual timeout recognises it no longer owns
the banner and neither shows the error nor closes a session that may by
then belong to a different, newer launch.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01RqZeHrRS6DYcGcGX2p9EwG
2026-09-16 20:22:02 +08:00

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# Custom Model Endpoints
Point a harness at your own OpenAI-compatible server instead of its native cloud backend, for
one session at a time. "Custom endpoint" covers **local** hardware (llama.cpp, Ollama, vLLM,
a home GPU rig, DGX Spark, Strix Halo) and **cloud** services (Azure AI Foundry's
OpenAI-compatible endpoint, OpenRouter, a company gateway) alike, anything answering
`GET /v1/models` and `POST /v1/chat/completions` in the standard shape.
**Off by default.** Turn it on in App Settings → Models → **Custom model endpoints**.
## Adding an endpoint
Still in App Settings → Models → Custom model endpoints:
1. **+ Add endpoint** — give it an id, a label, and the base URL (`http://192.168.1.50:8080`,
say). An API key is optional; most local servers don't check one.
2. **Discover** — fetches the endpoint's own model list over `GET /v1/models` and stores it.
3. Pick a **default model** from what was discovered. This is the model the Run-menu entry
applies directly when only one model is discovered; with two or more, it's just the one
pre-marked in the picker dialog described below, not a silent default.
Endpoint management is admin-only in multi-user mode, the same as remote hosts and Docker
hosts — these are machine-level infra, not a per-user setting.
**Model lists refresh themselves.** Every saved endpoint is re-discovered automatically every
5 minutes in the background, so a model the server starts serving later — or stops serving —
shows up without another manual click of **Discover**. One endpoint being unreachable on a
given cycle (powered off, wrong network) never blocks the others from refreshing.
**Context length is picked up automatically where it can be, safely.** Against a
llama.cpp/llama-swap server, discovery also learns each *currently loaded* model's real
context window and applies it to the launched session (Claude Code today — see below), so
the harness stops assuming a large default window for a model name it doesn't recognise and
overflowing a much smaller real one. It's deliberately never probed for a model that isn't
already loaded, since asking a llama-swap server about an unloaded model can trigger an
actual, slow model swap as a side effect — a model just not currently loaded keeps whatever
context length an earlier cycle already learned for it instead.
## Running a session against one
With the setting on and at least one endpoint carrying a discovered model, the **Run**
dropdown grows a **Custom Endpoints** section: one entry per harness that can redirect to a
custom endpoint, per saved endpoint, e.g. "Claude Code (llama.cpp)". Picking one starts a
session on that harness exactly the way its own entry would. It is a one-off "try this
endpoint" action, not a sticky mode — the plain **Run** button still means "this harness,
native cloud" afterward, and a fresh session never inherits whatever the last one was
pointed at.
**Which model it uses depends on how many the endpoint has discovered.** With exactly one,
the session launches straight away on that model — nothing to choose. With two or more, a
small dialog asks which one to use for this launch before starting the session; the
endpoint's default model, if set, is marked but not auto-picked, so a launch can deliberately
use a different one without changing the saved default.
**For opencode, Codex, Gemini, Pi, Grok, DeepSeek and OMP, picking an entry launches
straight onto the endpoint** — no restart, because the endpoint is applied before the
session's process ever starts. **Claude still restarts the harness's process in place** —
same tab, same conversation (`--resume`) — after a normal native launch, since that restart
is far less jarring for Claude than for the other seven, whose own TUI can fully
reinitialize on a restart. Either way, every supported harness reads its endpoint config at
process start, never per turn, so there is no live hot-swap while a turn is running.
Picking an entry that launches a **brand-new** Claude session waits (up to 20 seconds) for it to
finish its own startup before applying — a freshly started CLI reports itself as busy for its
boot sequence, and applying to a genuinely busy session is refused so a real, in-progress
turn is never interrupted out from under you. A session that is still busy after that wait
(a very slow-starting CLI, or one you started typing into right away) surfaces that refusal
as an ordinary error, which now stays on screen with a close button instead of vanishing
after a few seconds — read it, it names the actual reason rather than a generic failure.
Entries are hidden entirely for a session in a **remote (SSH) or Docker case** — support for
redirecting those hasn't landed yet, see below. The picker also only appears in the desktop
**Run** dropdown; the phone home screen builds its own run picker separately and does not
currently offer these entries.
**Against llama-swap, applying a selection also starts the actual model load, rather than
waiting on your first prompt to do it.** llama-swap has no "switch model" button of its own
— the only thing that starts a swap is a real request naming the model, and confirmed live:
just applying a selection never reached llama-swap's own logs at all until something asked
it to load. Picking an entry now also sends the smallest real request that will trigger
that load, in the background, the moment the target model isn't already loaded and ready.
**The centred loading banner shows a live countdown, and a real timeout is an error, not a
shrug.** When it knows the model's discovered file size (its GB figure, when llama-swap
states one), it shows both a rough expected-time estimate and a live countdown against it —
e.g. "Loading qwen3.8-27b (16.4 GB, typically ~1–3 min) on llama-swap — 47s remaining". If
the countdown reaches zero and the model still isn't ready, the banner turns into a sticky
error telling you to check the llama-swap server's own logs, and **the session that load was
for is closed automatically** — a console left open and pointed at a model that never
finished loading would just be confusing to leave sitting there.
**Claude Code specifically gets two extra fixes applied automatically:**
- Its discovered context length (see above) is passed through as
`CLAUDE_CODE_MAX_CONTEXT_TOKENS`, so it doesn't send a full-size prompt against a much
smaller real local context and overflow it.
- Its session runs with an isolated `CLAUDE_CONFIG_DIR`, so the injected API key never sits
in the same directory as a stored claude.ai login — that combination is harmless for actual
requests (the API key wins) but the CLI still prints a "both claude.ai and
ANTHROPIC_API_KEY set" warning about it, which this avoids entirely. The isolated directory
keeps a link back to your real session history so the response viewer and similar features
still work for that session. That isolated directory starts with no prior approvals of its
own, so Codeman also pre-approves the injected key the same way answering Claude Code's own
"Detected a custom API key" prompt once would — without it, that prompt would otherwise
reappear on every single launch with nobody there to answer it.
## Which harnesses actually work
| Harness | Status |
| ------- | ------ |
| **Claude Code, opencode, Pi, Grok, OMP** | Verified end-to-end against a real local server. |
| **Codex** | Config is correct, but Codex only speaks the Responses API, which llama.cpp-style servers don't implement. A protocol gap, not a Codeman bug. |
| **Gemini** | Fails with an auth error gemini-cli raises once redirected. Unresolved; don't rely on it yet. |
| **DeepSeek** | Reaches the server but gets a consistent 404. Root cause not identified. |
| **Antigravity** | No known custom-endpoint mechanism at all. Not offered. |
Which harnesses show up in the Run-menu picker is read live off Codeman's own CLI registry,
not a fixed list here, so this table can go stale before this page does — a greyed-out or
missing entry is the more current answer.
## What it does not do
- **No remote or Docker sessions yet.** Both restart their agent differently under the hood
(reattaching a durable tmux session rather than relaunching the process), so redirecting
them needs its own plumbing that hasn't been built.
- **No live hot-swap mid-conversation.** Applying a selection always restarts the process.
- **No button to un-point a session from the UI yet.** Clearing back to native cloud is an
HTTP call (`POST .../custom-model {"clear": true}`) or deleting the session; the settings
panel manages saved endpoints, not what a running session is currently pointed at.
- **Nothing is shared with your real cloud credentials.** The endpoint's own key, if any,
never touches your Anthropic/OpenAI/Google login — a custom endpoint is a separate,
explicit choice per session.
## Security
An endpoint's base URL can't point at a link-local or cloud-metadata address (both at save
time and against the address it actually resolves to), the same guard Web Tabs uses for
saved dashboards. Endpoint records and any per-session config files a harness needs are
written with owner-only permissions. See
[custom-model-endpoints-plan.md](https://github.com/Ark0N/Codeman/blob/master/docs/custom-model-endpoints-plan.md)
in the repository for the full design reasoning, including why this feature closed a
pre-existing gap in how session environment overrides were guarded rather than opening a new
one.