fix(lmstudio): bound model load error bodies

vincentkoc · vincentkoc · commit aff6e221a7b5 · 2026-06-19T20:43:17.000+02:00
diff --git a/extensions/lmstudio/src/models.fetch.ts b/extensions/lmstudio/src/models.fetch.ts
@@ -1,7 +1,10 @@
 // Lmstudio plugin module implements models.fetch behavior.
 import { createSubsystemLogger } from "openclaw/plugin-sdk/logging-core";
 import { resolveTimerTimeoutMs } from "openclaw/plugin-sdk/number-runtime";
-import { readProviderJsonArrayFieldResponse } from "openclaw/plugin-sdk/provider-http";
+import {
+  readProviderJsonArrayFieldResponse,
+  readResponseTextLimited,
+} from "openclaw/plugin-sdk/provider-http";
 import type { ModelDefinitionConfig } from "openclaw/plugin-sdk/provider-model-shared";
 import { SELF_HOSTED_DEFAULT_COST } from "openclaw/plugin-sdk/provider-setup";
 import { fetchWithSsrFGuard, type SsrFPolicy } from "openclaw/plugin-sdk/ssrf-runtime";
@@ -17,6 +20,7 @@ import {
 import { buildLmstudioAuthHeaders } from "./runtime.js";
 
 const log = createSubsystemLogger("extensions/lmstudio/models");
+const LMSTUDIO_ERROR_BODY_LIMIT_BYTES = 8 * 1024;
 
 type LmstudioLoadResponse = {
   status?: string;
@@ -253,7 +257,7 @@ export async function ensureLmstudioModelLoaded(params: {
   });
   try {
     if (!response.ok) {
-      const body = await response.text();
+      const body = await readResponseTextLimited(response, LMSTUDIO_ERROR_BODY_LIMIT_BYTES);
       throw new Error(`LM Studio model load failed (${response.status})${body ? `: ${body}` : ""}`);
     }
     let payload: LmstudioLoadResponse;
diff --git a/extensions/lmstudio/src/models.test.ts b/extensions/lmstudio/src/models.test.ts
@@ -44,6 +44,27 @@ describe("lmstudio-models", () => {
     }
     return JSON.parse(init.body) as unknown;
   };
+  const cancelTrackedResponse = (
+    text: string,
+    init: ResponseInit,
+  ): {
+    response: Response;
+    wasCanceled: () => boolean;
+  } => {
+    let canceled = false;
+    const stream = new ReadableStream<Uint8Array>({
+      start(controller) {
+        controller.enqueue(new TextEncoder().encode(text));
+      },
+      cancel() {
+        canceled = true;
+      },
+    });
+    return {
+      response: new Response(stream, init),
+      wasCanceled: () => canceled,
+    };
+  };
   const createModelLoadFetchMock = (params?: {
     loadedContextLength?: number;
     maxContextLength?: number;
@@ -486,6 +507,39 @@ describe("lmstudio-models", () => {
     ).rejects.toThrow("LM Studio model load returned malformed JSON");
   });
 
+  it("bounds model load error bodies", async () => {
+    const body = `${"lmstudio load unavailable ".repeat(512)}tail`;
+    const tracked = cancelTrackedResponse(body, { status: 503 });
+    const textSpy = vi.spyOn(tracked.response, "text").mockRejectedValue(new Error("unbounded"));
+    const fetchMock = vi.fn(async (url: string | URL) => {
+      if (String(url).endsWith("/api/v1/models")) {
+        return {
+          ok: true,
+          json: async () => ({
+            models: [{ type: "llm", key: "qwen3-8b-instruct", loaded_instances: [] }],
+          }),
+        };
+      }
+      if (String(url).endsWith("/api/v1/models/load")) {
+        return tracked.response;
+      }
+      throw new Error(`Unexpected fetch URL: ${String(url)}`);
+    });
+    vi.stubGlobal("fetch", asFetch(fetchMock));
+
+    const error = await ensureLmstudioModelLoaded({
+      baseUrl: "http://localhost:1234/v1",
+      modelKey: "qwen3-8b-instruct",
+    }).catch((caught: unknown) => caught);
+    expect(error).toBeInstanceOf(Error);
+    expect((error as Error).message).toMatch(
+      /LM Studio model load failed \(503\): lmstudio load unavailable/,
+    );
+    expect((error as Error).message).not.toContain("tail");
+    expect(tracked.wasCanceled()).toBe(true);
+    expect(textSpy).not.toHaveBeenCalled();
+  });
+
   it("reloads model to the clamped default target when already loaded below the default window", async () => {
     const fetchMock = createModelLoadFetchMock({
       loadedContextLength: 4096,
