v1.35.1 — Output Length menu, the Agents window & remote presets

v1.35.1 — Output Length menu, the Agents window & remote presets

Everything since v1.32.2 in one release: an Output Length dropdown in the model picker, first-class support for the new VS Code Agents window ("Open in Agents"), remote model presets that update without a VSIX release, plus new model presets (DeepSeek-V4-Pro, Kimi K3, MiniMax M3, Qwen3.8-Flash-Next, GLM-5.3) and server renaming.

1.35.1 changes only the Marketplace listing text (the Agents window pitch is baked into the VSIX description, and the Marketplace rejects duplicate versions). No code changes since 1.35.0.

Install: download the .vsix below, then in VS Code: Extensions ⋯ Deploy Extension from VSIX Package. A full VS Code restart after updating is recommended.

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v1.35.0 — Output Length, picker banners & the Agents window

Added

• Works in the new Agents window ("Open in Agents") — your vLLM models now show up in Agent Host sessions; the required VS Code settings are enabled automatically (values you set yourself are never overwritten). Fully restart VS Code once after updating.
• Output-length picker — models whose maxOutputTokens is an array of response lengths get a second model-picker dropdown ("Output Length"), independent of Model Mode. First entry = default; your pick overrides per-mode / defaultParams max_tokens. Picking a shorter length hands the freed tokens back to your prompt; a longer pick applies once the context re-resolves (one request later). All 14 bundled presets now ship a length ladder. VS Code remembers your choice per model.
• Model-picker banners — models whose output budget is clamped by the context window or a provider cap now say so, with the reason, right in the model picker.
• One-time menu update for existing models — offered on startup, built from the matching preset or your own configured max_tokens values; previewable; nothing changes unless you accept.

Changed

• Minimum VS Code raised to 1.128 — required by the model-picker APIs the new dropdown and banners use.

• GLM-5.3 preset split — the full model is not Flash. Full GLM-5.3 reuses the GLM-5.2 base (text-only); GLM-5.3-Flash is a separate multimodal model with its own preset. Served live via remote preset lookup — no update needed.

v1.34.0 — Remote model presets

Added

• Live preset lookup — when you Add Server / Auto-Configure, the extension checks a tiny preset index on the vLLM-Copilot GitHub repo and, if your model matches, downloads that one preset file. New presets pushed to main now reach every user the same day — no VSIX release needed. The lookup runs parallel to the normal discovery, is bounded by a 2-second timeout, and any failure (offline, firewalled, bad data) silently falls back to bundled presets. Remote files pass the same schema guard as bundled ones: only client request-shaping fields are allowed — a preset can never touch your server URL, headers or backend type; a single unknown field rejects the whole file.
• Provenance in the preset confirm dialog — the dialog now shows what a preset configures (notes), its source and its verification date. Shown for bundled and remote presets alike. A View Preset File button opens the exact preset file on GitHub, so you can read the real JSON yourself.
• One dialog, not two — choosing Use Preset now saves the model immediately; a toast confirms and repeats the link to the preset file. The preset dialog already asked an informed question, so the old second "Save to Settings" confirm was a rubber stamp. HuggingFace auto-discovered configs still get the full review-before-save dialog.

Changed

• Preset format v2 — all bundled presets use a versioned envelope (presetVersion / match / meta / config). Matching and merge behavior are byte-for-byte unchanged. model-configs/index.json is generated from the presets (GitHub Action on push, npm run gen:presets manually) and a drift test fails the build on a stale list.

v1.33.2 — New model presets: DeepSeek-V4-Pro, Kimi K3 & MiniMax M3

Added

• DeepSeek-V4-Pro preset — 1M-context text flagship (1.6T/49B). Think (Max) and Think (High) drive its custom template via chat_template_kwargs (thinking + reasoning_effort), No Think sends a plain request. Official sampling (temp 1.0; top_p 0.95 thinking / 1.0 plain), budgets 64K/32K/16K. Matches -0813, preview and quantized served ids by substring.
• Kimi K3 preset — first open 3T-class MoE (2.8T/104B), 1M context, native vision. Thinking is always on; modes are Think (Max / High / Low) via top-level reasoning_effort. Sampling is fixed per Moonshot's API (temp 1.0, top_p 0.95, no penalties). Budgets 64K/32K/16K.
• MiniMax M3 preset — 428B-A23B, 1M context, multimodal. Modes Think (Always) / Think (Adaptive) / No Think via chat_template_kwargs.thinking_mode; official sampling (temp 1.0, top_p 0.95, top_k 40). Budgets 64K/32K/16K.

Changed

• GLM-5.3-Flash preset renamed to GLM-5.3 — the generic id covers GLM-5.3 and GLM-5.3-Flash alike via substring matching; a dedicated Flash preset will only return if the full model turns out to need a different config.
• Preset headers are client config only — hosting tips (serve flags, hardware, docker recipes) removed from all presets; headers keep mode mechanics, sampling rationale and wire caveats, plus bare vLLM recipe links.

Removed

• DeepSeek-V4-Flash-0731 preset — identical to the generic DeepSeek-V4-Flash preset, which already matches that id by substring.

v1.33.1 — New model presets: Qwen3.8-Flash-Next & GLM-5.3-Flash

Added

• Qwen3.8-Flash-Next preset — new qwen4_exp family (125B-A6B MoE, multimodal, 262K native context). Three modes: Think (Deep) (reasoning_effort: xhigh), Think (Balanced) (medium) and No Think, each with the official sampling recipe and its own output budget (32K/32K/16K) so large-codebase input is never starved. YaRN context extension is deliberately not assumed — static YaRN can degrade short-context quality, so the preset budgets for native context.
• GLM-5.3-Flash preset — new glm5_next family (320B-A18B, natively multimodal, 1M native context). Thinking is always on, so modes are Think (Max / High / Low) via reasoning_effort (no No-Think mode, unlike GLM-5.2). Output budgets 64K/32K/16K.
• Both presets match any quantization variant (FP8/NVFP4/GGUF) via substring matching.

v1.33.0 — Rename servers & V symbol in the model picker

Added

• Rename Server — right-click any server in the Dashboard and give it a friendly name (e.g. IT Server for GLM5.2) instead of the bare host. The name is used everywhere the server is shown — Dashboard tree, Deep-Dive panel title, and the Model Settings server dropdown. Clear it any time to show the URL again.
• Rename applies to the whole server — the name is written to every model sharing that server URL, so all its entries stay in sync.
• Not for OpenRouter — the fixed openrouter.ai relay endpoint can't be renamed (menu hidden there).
• Your V in the model picker — every model (vLLM, LM Studio, llama.cpp, Ollama, and OpenRouter) now shows the project's own V icon instead of a generic fallback. The glyph is derived from resources/vllm-icon.svg with the left arm as a hollow outline, and is built from the tracked scripts/build-vllm-icon-font.py.

Fixed

• Server names survive reconfigure — re-running Auto-Configure or applying a preset no longer wipes a custom server name.
• Deep-Dive panels retitle live — renaming while a Deep-Dive panel is open updates its title immediately, instead of showing the old name until reopen.

Changed

• Whitespace-safe names — a hand-edited whitespace-only name is treated as unset and falls back to the URL.