feat(custom-model): Custom Model Endpoint Profiles (local or cloud, all harnesses)

Point any Codeman-supported harness (Claude, opencode, Codex, Gemini, Pi,
Grok, DeepSeek, OMP) at a custom OpenAI-compatible endpoint instead of its
native cloud backend, for a given session. Covers local hardware (llama.cpp,
Ollama, vLLM, DGX Spark, Strix Halo) and cloud (Azure AI Foundry, OpenRouter).
Off by default (customModelEndpointsEnabled, synced, default OFF).

- Registry: capabilities.customModelInjection per CLI entry (env /
  configContentEnv / configDir / unsupported kinds)
- Pure injection builder (custom-model-injection.ts) turning an endpoint +
  model id into the real env vars / config content per CLI
- Endpoint store + CRUD routes (custom-model-hosts.ts,
  custom-model-routes.ts), discovery via GET /v1/models, SSRF-guarded
- Session integration: Session.setCustomModel()/restartCli()
  (POST /api/sessions/:id/custom-model), reusing the existing
  respawn-pane -k primitive to restart the CLI process with new env
- Multi-user hardening: every new redirect-capable env var added to its
  CLI's privilegedEnvKeys, closing a pre-existing gap where several were
  already reachable via the generic envOverrides field's prefix allowlist
- Standalone scripts/test-local-llm-harnesses.mjs: spawns real CLI binaries
  against a real endpoint outside the web UI, independent of tmux/sessions
- Mock-server contract tests (test/fixtures/mock-openai-server.ts) replaying
  every CLI's injected values through a real HTTP shape

Real end-to-end validation against a live llama-swap server (inside a
codeman/agent:llm-test Docker image with all 9 CLI binaries) found and
fixed three real bugs before they shipped:
- Codex's config.toml schema was wrong ([model].default table instead of
  a top-level model string + [model_providers.custom]); fixing it then
  surfaced a genuine, documented protocol incompatibility (Codex only
  speaks the Responses API since Feb 2026, which llama.cpp/llama-swap
  don't implement)
- Claude Code's async session-title-generation call validates
  ANTHROPIC_DEFAULT_HAIKU_MODEL against its own internal model list and
  hangs the whole -p invocation on an unrecognized name; documented for
  chunk 6, worked around in the standalone script only (--bare is NOT
  safe for a real interactive session, which needs hooks)
- The discovery route's authStyle: 'both' option (send both Authorization
  and api-key headers) reliably hung a real server; removed the option
  entirely rather than just changing the default

Status: draft. Chunk 6 (frontend toolbar/settings UI) not yet built — see
PR.md and deployment_plan.md for the full chunk breakdown and confidence
table.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017HqNWfmtBU2KN29SvSVWB3
This commit is contained in:
Devvyn
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co-authored by Claude Sonnet 5
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# Custom Model Endpoint Profiles
Point any Codeman-supported harness — Claude, opencode, Codex, Gemini, Pi,
Grok, DeepSeek, or OMP — at a custom OpenAI-compatible endpoint instead of
its native cloud backend, for a given session. "Custom endpoint" covers both
**local** hardware (llama.cpp, Ollama, vLLM, a home GPU rig, or purpose-built
boxes like NVIDIA DGX Spark or AMD Strix Halo mini-PCs) and **cloud**
services (Azure AI Foundry's OpenAI-compatible endpoint, OpenRouter, a
company gateway) — anything answering `GET /v1/models` and
`POST /v1/chat/completions` in the standard shape. Design doc, per-CLI
recipe confidence table, and security reasoning:
[`deployment_plan.md`](../deployment_plan.md).
> **Status**: backend is implemented and tested (registry capability, the
> injection engine, the endpoint store + discovery route, the session
> restart route). The toolbar picker / settings UI described below as the
> intended surface is **not yet built** — until it lands, use the HTTP API
> directly (examples below). Antigravity has no known custom-endpoint
> mechanism and is not supported.
## Turning it on
App Settings → Agents & CLIs → **Custom Model Endpoints** (synced setting
`customModelEndpointsEnabled`, default **OFF**). The API equivalent:
```bash
curl -sk -X PUT https://localhost:3000/api/settings \
-H 'Content-Type: application/json' \
-d '{"customModelEndpointsEnabled": true}'
```
## Adding an endpoint
```bash
curl -sk -X POST https://localhost:3000/api/model-endpoints \
-H 'Content-Type: application/json' \
-d '{"id": "llama-box", "label": "Home llama.cpp", "baseUrl": "http://192.168.1.50:8080"}'
```
`apiKey` is optional (most local servers don't check it). `authStyle`
(`bearer` | `api-key` | `both`, default `both`) controls which auth header
convention discovery uses — `both` works whether the endpoint is llama.cpp
(ignores the header) or a cloud gateway like Azure (wants `api-key`).
Discover its available models:
```bash
curl -sk -X POST https://localhost:3000/api/model-endpoints/llama-box/discover-models
```
This calls the endpoint's own `GET /v1/models` and stores the returned list
on the endpoint record; `GET /api/model-endpoints` lists everything
configured, `PUT`/`DELETE /api/model-endpoints/:id` update or remove one.
Endpoint management is admin-only in multi-user mode, same as remote/docker
hosts — these are machine-level infra, not per-user settings.
## Applying a model to a session
```bash
curl -sk -X POST https://localhost:3000/api/sessions/<sessionId>/custom-model \
-H 'Content-Type: application/json' \
-d '{"endpointId": "llama-box", "modelId": "qwen3"}'
```
This computes the CLI-specific env vars / config for that session's mode
(see the recipe table in `deployment_plan.md`) and **restarts the session's
CLI process in place** — same pane, same tmux session, fresh env. That
restart is necessary, not incidental: every supported harness reads its
endpoint config at process start, not per-turn, so there is no live
hot-swap. Clear back to the harness's native cloud default with:
```bash
curl -sk -X POST https://localhost:3000/api/sessions/<sessionId>/custom-model \
-H 'Content-Type: application/json' -d '{"clear": true}'
```
**New sessions always default back to the harness's native backend.** A
custom-endpoint selection is a per-session choice, never a sticky global
default — starting a fresh session doesn't inherit whatever the last one was
pointed at.
## Confidence per harness
Only Claude, opencode, and Codex have been verified against a real
llama.cpp server by hand. Gemini, Pi, Grok, DeepSeek, and OMP's recipes are
correct on their one-shot invocation flags (confirmed against real
installed binaries' own `--help` output) but their env-var/config
conventions for a _custom_ endpoint are still web-researched, not verified
end-to-end — see the confidence table in `deployment_plan.md` before relying
on one of those five in production. `scripts/test-local-llm-harnesses.mjs`
is the standalone script used to check a harness against a real endpoint
outside the web UI entirely; see its own `--help` for usage.
## Security note
Every env var this feature can set that redirects a session's traffic
(`ANTHROPIC_BASE_URL`, `GOOGLE_GEMINI_BASE_URL`, `CODEX_HOME`, etc.) is
listed in that CLI's `privilegedEnvKeys` in the CLI registry, so a
non-granted multi-user owner cannot set one directly via the generic
`envOverrides` API field — only through this feature's own route, which
computes the value from an admin-configured, SSRF-guarded endpoint rather
than trusting arbitrary client input. See the "Multi-user security
hardening" section of `deployment_plan.md` for the full reasoning; several
of these were reachable via the generic `envOverrides` field even before
this feature existed, and building this surfaced and closed that gap.