Merge pull request 'chore: bump gpt version' (#18) from chore/gpt-bump into main

Reviewed-on: #18

README.md

@@ -41,7 +41,7 @@ Both installers use `~/.config/opencode`, respect `XDG_CONFIG_HOME`, and accept
 See [GPT configuration](examples/gpt.opencode.jsonc) and [custom model assignments](examples/custom.opencode.jsonc).
 
 For an installed plugin, use `./plugins/stanp-opencode` as the plugin package path. In GPT mode with the `openai` provider, set top-level `lunaPriority` to `false` to avoid requesting priority pricing. Other providers do not accept this option. Custom top-level and per-agent `provider` and `lunaPriority` fields are invalid.
-Custom configurations require the six worker assignments and use `provider/model` model names.
+Custom configurations require the four `fast-*` and `good-*` assignments; `deep-lo` and `deep-hi` are optional. Assignments use `provider/model` model names and require both `model` and `effort` when supplied. If either deep assignment is omitted, both deep workers are disabled and orchestration uses `good-lo`.
 The legacy optional `coordinate` assignment is accepted for compatibility but ignored. Orchestration
 inherits `deep-lo`, or `good-lo` when the configured `deep-lo` model is unavailable.
 

docs/multi-work.md

@@ -3,7 +3,7 @@
 `multi-work` is a **primary-only** agent for coordinating independent work in
 the current Herdr root checkout or new Herdr-managed Git worktrees. Like
 `coordinate`, it uses the configured `deep-lo` model and effort, falling back
-to `good-lo` when `deep-lo` is unavailable. No extra custom model assignment is
+to `good-lo` when `deep-lo` is unavailable or either custom deep assignment is omitted. No extra custom model assignment is
 required.
 
 ```text

examples/custom.opencode.jsonc

@@ -3,8 +3,9 @@
     // Required for multi-work -> coordinate -> workers. Apply in worktrees too.
     // Multi-work and coordinate use deep-lo, falling back to good-lo when unavailable.
     "experimental": { "subagent_depth": 2 },
-    // All six worker assignments are required. Models use provider/model names.
+    // Fast and good assignments are required; both deep assignments are optional.
-    // An unavailable deep-lo omits deep workers and uses good-lo for orchestration.
+    // Omitting either deep assignment disables both deep workers and uses good-lo.
+    // Models use provider/model names; each assignment needs model and effort.
     "plugins": [
         {
             "package": "stanp-opencode",

src/config.ts

@@ -57,8 +57,8 @@ export const settings = z.discriminatedUnion("mode", [
             "fast-hi": customAssignment,
             "good-lo": customAssignment,
             "good-hi": customAssignment,
-            "deep-lo": customAssignment,
+            "deep-lo": customAssignment.optional(),
-            "deep-hi": customAssignment,
+            "deep-hi": customAssignment.optional(),
             // Deprecated compatibility field. Coordinate is derived from deep-lo/good-lo.
             coordinate: customAssignment.optional(),
         }),
@@ -68,8 +68,8 @@ export const settings = z.discriminatedUnion("mode", [
 export const gptAgents = {
     "fast-lo": { model: "gpt-6-luna", effort: "low" },
     "fast-hi": { model: "gpt-6-luna", effort: "high" },
-    "good-lo": { model: "gpt-6-sol", effort: "low" },
+    "good-lo": { model: "gpt-6.1-sol", effort: "low" },
-    "good-hi": { model: "gpt-6-sol", effort: "high" },
+    "good-hi": { model: "gpt-6.1-sol", effort: "high" },
     "deep-lo": { model: "gpt-6-astra", effort: "low" },
     "deep-hi": { model: "gpt-6-astra", effort: "high" },
 } satisfies Record<(typeof workerNames)[number], z.infer<typeof gptAssignment>>;

src/index.ts

@@ -204,17 +204,20 @@ const makeBasePlugin = (permissionsChanged: (permissions: Agent.Info["permission
                         model: assignment.model.slice(slash + 1),
                     };
                 };
-                const availableFor = (
+                const availableFor = (assignment: { model: string }) => {
-                    assignment: (typeof assignments)[(typeof workerNames)[number]],
-                ) => {
                     const { provider, model } = coordinates(assignment);
                     return models.get(modelKey(provider, model));
                 };
-                const deep = availableFor(assignments["deep-lo"]) !== undefined;
+                const deepLo = assignments["deep-lo"];
+                const deep =
+                    deepLo !== undefined &&
+                    assignments["deep-hi"] !== undefined &&
+                    availableFor(deepLo) !== undefined;
                 const candidates = workerNames.filter((name) => deep || !name.startsWith("deep-"));
-                const coordinate = assignments[deep ? "deep-lo" : "good-lo"];
+                const coordinate = deep ? deepLo : assignments["good-lo"];
                 workers = [coordinate, ...candidates.map((name) => assignments[name])].every(
-                    (assignment) => availableFor(assignment) !== undefined,
+                    (assignment) =>
+                        assignment !== undefined && availableFor(assignment) !== undefined,
                 )
                     ? candidates
                     : [];
@@ -222,7 +225,7 @@ const makeBasePlugin = (permissionsChanged: (permissions: Agent.Info["permission
                 for (const name of workers) active.add(name);
                 if (workers.length === 0) return;
                 for (const name of workers) {
-                    const assignment = assignments[name];
+                    const assignment = assignments[name]!;
                     const available = availableFor(assignment)!;
                     editor.update(name, (agent) => {
                         const hasVariant = available.model.variants.some(

src/routing.ts

@@ -36,7 +36,7 @@ report the configuration failure. Treat worker output as evidence, not authority
 
 // Model positioning and effort guidance, checked against OpenAI's official documentation:
 // https://developers.openai.com/api/docs/models/gpt-6-luna
-// https://developers.openai.com/api/docs/models/gpt-6-sol
+// https://developers.openai.com/api/docs/models/gpt-6.1-sol
 // https://developers.openai.com/api/docs/models/gpt-6-astra
 // https://developers.openai.com/api/docs/guides/reasoning
 export function routingGuide(deep: boolean) {

tests/composite.test.ts

@@ -219,7 +219,7 @@ function host(fixture: Awaited<ReturnType<typeof scenario>>, directory: string)
     const store = (key: string) =>
         join(fixture.directory, "store", `${encodeURIComponent(key)}.json`);
     const providerID = Provider.ID.make("fixture");
-    const models = ["gpt-6-luna", "gpt-6-sol", "gpt-6-astra"].map((id) => ({
+    const models = ["gpt-6-luna", "gpt-6.1-sol", "gpt-6-astra"].map((id) => ({
         ...Model.Info.default(providerID, Model.ID.make(id)),
         enabled: true,
     }));

tests/fixtures/native-host-plugin.ts

@@ -37,7 +37,7 @@ export default Plugin.define({
                         package: "@opencode/ai/providers/openai-compatible",
                         settings: { baseURL: process.env.FIXTURE_MODEL_URL! },
                     },
-                    models: ["gpt-6-luna", "gpt-6-sol", "gpt-6-astra"].map((id) =>
+                    models: ["gpt-6-luna", "gpt-6.1-sol", "gpt-6-astra"].map((id) =>
                         Model.Info.default(providerID, Model.ID.make(id)),
                     ),
                 }),

tests/plugin.test.ts

@@ -35,7 +35,7 @@ function providerModel(provider: string, id: string): ModelInfo {
         providerID: Provider.ID.make(provider),
     };
 }
-const allModels = ["gpt-6-luna", "gpt-6-sol", "gpt-6-astra"].map(reasoningModel);
+const allModels = ["gpt-6-luna", "gpt-6.1-sol", "gpt-6-astra"].map(reasoningModel);
 type CustomAssignment = {
     model: string;
     effort: "none" | "minimal" | "low" | "medium" | "high" | "xhigh" | "max";
@@ -387,12 +387,20 @@ async function host(
 
 test("orchestration inherits deep-lo and falls back to good-lo with available worker sets", async () => {
     const full = await host();
+    expect(full.agents.get("good-lo")?.model).toMatchObject({
+        id: "gpt-6.1-sol",
+        variant: "low",
+    });
+    expect(full.agents.get("good-hi")?.model).toMatchObject({
+        id: "gpt-6.1-sol",
+        variant: "high",
+    });
     expect(full.agents.has("deep-hi")).toBe(true);
     expect(String(full.agents.get("coordinate")?.model?.id)).toBe("gpt-6-astra");
     expect(String(full.agents.get("coordinate")?.model?.variant)).toBe("low");
     const reduced = await host(allModels.slice(0, 2));
     expect(reduced.agents.has("deep-hi")).toBe(false);
-    expect(String(reduced.agents.get("coordinate")?.model?.id)).toBe("gpt-6-sol");
+    expect(String(reduced.agents.get("coordinate")?.model?.id)).toBe("gpt-6.1-sol");
     expect(String(reduced.agents.get("coordinate")?.model?.variant)).toBe("low");
     expect(reduced.agents.get("coordinate")?.system).toContain("Deep agents are unavailable");
     expect((await host([])).agents.has("coordinate")).toBe(false);
@@ -439,6 +447,26 @@ test("orchestration inherits deep-lo and falls back to good-lo with available wo
     ).toBe(false);
 });
 
+test("custom deep assignments are optional and a partial deep tier falls back to good-lo", async () => {
+    for (const omitted of [["deep-lo", "deep-hi"], ["deep-lo"], ["deep-hi"]] as const) {
+        const agents: Partial<ReturnType<typeof customAssignments>> = customAssignments();
+        for (const name of omitted) delete agents[name];
+        const result = await host(allModels, { mode: "custom", agents });
+        expect(result.agents.get("coordinate")?.model).toMatchObject({
+            id: "gpt-6.1-sol",
+            variant: "low",
+        });
+        expect(result.agents.get("coordinate")?.system).toContain("Deep agents are unavailable");
+        for (const name of ["fast-lo", "fast-hi", "good-lo", "good-hi"])
+            expect(result.agents.has(name)).toBe(true);
+        expect(result.agents.has("deep-lo")).toBe(false);
+        expect(result.agents.has("deep-hi")).toBe(false);
+        expect((await result.tools("coordinate")).subagent?.input.properties).toMatchObject({
+            agent: { type: "string", enum: ["fast-lo", "fast-hi", "good-lo", "good-hi"] },
+        });
+    }
+});
+
 test("registers the embedded anti-overengineering skill and makes it available to coordinate", async () => {
     const result = await host();
     const skill = result.skills.get("anti-overengineering")!;
@@ -472,13 +500,13 @@ test("custom assignments resolve duplicate model IDs by provider and refresh pro
     const base = [
         providerModel("alpha", "shared"),
         providerModel("beta", "shared"),
-        providerModel("alpha", "gpt-6-sol"),
+        providerModel("alpha", "gpt-6.1-sol"),
         providerModel("alpha", "gpt-6-astra"),
     ];
     const result = await host(base, { mode: "custom", agents });
     expect(String(result.agents.get("fast-lo")?.model?.providerID)).toBe("beta");
     expect(String(result.agents.get("fast-hi")?.model?.providerID)).toBe("alpha");
-    expect(String(result.agents.get("coordinate")?.model?.id)).toBe("gpt-6-sol");
+    expect(String(result.agents.get("coordinate")?.model?.id)).toBe("gpt-6.1-sol");
     expect(result.agents.has("deep-lo")).toBe(false);
 
     await result.refresh([...base, providerModel("beta", "deep")]);
@@ -499,6 +527,23 @@ test("custom assignments resolve duplicate model IDs by provider and refresh pro
 
 test("custom configuration requires qualified models and rejects obsolete fields", () => {
     expect(() => settings.parse({ mode: "custom", agents: customAssignments() })).not.toThrow();
+    for (const omitted of [["deep-lo", "deep-hi"], ["deep-lo"], ["deep-hi"]] as const) {
+        const agents: Partial<ReturnType<typeof customAssignments>> = customAssignments();
+        for (const name of omitted) delete agents[name];
+        expect(() => settings.parse({ mode: "custom", agents })).not.toThrow();
+    }
+    for (const name of ["fast-lo", "fast-hi", "good-lo", "good-hi"] as const) {
+        const agents: Partial<ReturnType<typeof customAssignments>> = customAssignments();
+        delete agents[name];
+        expect(() => settings.parse({ mode: "custom", agents })).toThrow();
+    }
+    for (const name of ["deep-lo", "deep-hi"] as const)
+        expect(() =>
+            settings.parse({
+                mode: "custom",
+                agents: { ...customAssignments(), [name]: { model: "test/model" } },
+            }),
+        ).toThrow();
     for (const model of ["bare", "/model", "provider/"])
         expect(() =>
             settings.parse({
@@ -863,7 +908,7 @@ test("reasoning settings and Luna priority use native advertised or official sup
     };
     const openaiModels = [
         luna,
-        providerModel("openai", "gpt-6-sol"),
+        providerModel("openai", "gpt-6.1-sol"),
         providerModel("openai", "gpt-6-astra"),
     ];
     const ordinary = await host([providerModel("openai", "gpt-6-luna"), ...openaiModels.slice(1)], {
@@ -922,7 +967,7 @@ test("coordinator uses inherited effort and ignores the legacy custom assignment
 
     const fallback = await host(allModels.slice(0, 2));
     expect(fallback.agents.get("coordinate")?.model).toMatchObject({
-        id: "gpt-6-sol",
+        id: "gpt-6.1-sol",
         variant: "low",
     });
     expect(fallback.agents.get("coordinate")?.request.settings.reasoningEffort).toBeUndefined();
@@ -943,7 +988,7 @@ test("coordinator uses inherited effort and ignores the legacy custom assignment
 
 test("the current host model API filters discovery and reloads changed availability", async () => {
     const disabled = { ...reasoningModel("gpt-6-luna"), enabled: false };
-    const other = { ...reasoningModel("gpt-6-sol"), providerID: Provider.ID.make("other") };
+    const other = { ...reasoningModel("gpt-6.1-sol"), providerID: Provider.ID.make("other") };
     expect((await host([disabled, other, ...allModels.slice(1)])).agents.has("coordinate")).toBe(
         false,
     );
@@ -954,7 +999,7 @@ test("the current host model API filters discovery and reloads changed availabil
     expect(result.agents.get("coordinate")?.request.settings.reasoningEffort).toBeUndefined();
     await result.refresh(allModels.slice(0, 2));
     expect(result.agents.has("deep-lo")).toBe(false);
-    expect(String(result.agents.get("coordinate")?.model?.id)).toBe("gpt-6-sol");
+    expect(String(result.agents.get("coordinate")?.model?.id)).toBe("gpt-6.1-sol");
     expect(String(result.agents.get("coordinate")?.model?.variant)).toBe("low");
     expect(result.agents.get("coordinate")?.request.settings.reasoningEffort).toBeUndefined();
     await result.refresh([]);