Codeman/docs/wiki/Custom-Model-Endpoints.md

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 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.