fix(gateway): add chat touchpoint to zhipu recipe so GLM subagents work (#1157)

The zhipu recipe was embedding-only, so models.tier.subagent=zhipu:glm-5.1
threw "does not offer a chat touchpoint" — while the error hint falsely
listed zhipu (and dashscope/minimax, also embedding-only) among providers
with chat.

- zhipu recipe: add a chat touchpoint (glm-5.1 family, supports_tools +
  supports_subagent_loop; no Anthropic-style prompt cache on the
  OpenAI-compat path, so the loop runs with the degraded:no_caching warn).
  openai-compat tier means newer GLM ids pass without a recipe edit.
- capabilities.ts: compute the "Known providers with chat" hint from the
  recipe registry instead of a hardcoded list, so it can never drift into
  naming chat-less providers again.
- Declines the originally requested models.anthropic_compatible_prefixes
  config: v0.38's recipe-driven capability gate already replaced the
  Anthropic-only enforcement, so a recipe chat touchpoint is the whole fix.

Fixes #1157

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

src/core/ai/capabilities.ts

@@ -22,6 +22,7 @@
  */
 
 import { resolveRecipe } from './model-resolver.ts';
+import { listRecipes } from './recipes/index.ts';
 import { AIConfigError } from './errors.ts';
 
 export interface ProviderCapabilities {
@@ -77,7 +78,10 @@ export function getProviderCapabilities(modelString: string): ProviderCapabiliti
   if (!chat) {
     throw new AIConfigError(
       `Provider "${recipe.id}" does not offer a chat touchpoint.`,
-      `Known providers with chat: openai, anthropic, google, openrouter, litellm-proxy, deepseek, groq, together, azure-openai, dashscope, minimax, zhipu, ollama, llama-server. Pick one for models.tier.subagent.`,
+      // Computed from the registry so the hint can't drift into listing
+      // chat-less providers (the pre-fix list falsely included embedding-only
+      // recipes, sending users in circles — #1157).
+      `Known providers with chat: ${listRecipes().filter(r => r.touchpoints.chat).map(r => r.id).join(', ')}. Pick one for models.tier.subagent.`,
     );
   }
 

src/core/ai/recipes/zhipu.ts

@@ -1,9 +1,10 @@
 import type { Recipe } from '../types.ts';
 
 /**
- * Zhipu AI (智谱AI) BigModel Open Platform. OpenAI-compatible /embeddings
- * endpoint at open.bigmodel.cn. Hosts embedding-2 (1024d) and embedding-3
- * (Matryoshka up to 2048d).
+ * Zhipu AI (智谱AI) BigModel Open Platform. OpenAI-compatible /embeddings and
+ * /chat/completions endpoints at open.bigmodel.cn. Hosts embedding-2 (1024d),
+ * embedding-3 (Matryoshka up to 2048d), and the GLM chat family (glm-5.1 etc.)
+ * with native tool calling — usable for models.tier.subagent (#1157).
  *
  * embedding-3 at 2048 dims exceeds pgvector's HNSW cap of 2000 — those
  * brains fall back to exact vector scans (see
@@ -25,6 +26,20 @@ export const zhipu: Recipe = {
     setup_url: 'https://open.bigmodel.cn/',
   },
   touchpoints: {
+    chat: {
+      // Informational list (openai-compat tier: assertTouchpoint doesn't
+      // enforce it), so newer GLM ids pass without a recipe edit.
+      models: ['glm-5.1', 'glm-4.6', 'glm-4.5'],
+      supports_tools: true,
+      // gbrain-side stable tool ids (v0.38 D11) decoupled the loop from
+      // Anthropic response formats; GLM tool calling is stable through the
+      // OpenAI-compat path, same as deepseek/groq.
+      supports_subagent_loop: true,
+      // Anthropic-style cache_control markers are not honored on the
+      // OpenAI-compat path — the loop runs hot (degraded:no_caching warn).
+      supports_prompt_cache: false,
+      max_context_tokens: 128000,
+    },
     embedding: {
       models: ['embedding-3', 'embedding-2'],
       default_dims: 1024,
@@ -36,5 +51,5 @@ export const zhipu: Recipe = {
     },
   },
   setup_hint:
-    'Get an API key at https://open.bigmodel.cn/, then `export ZHIPUAI_API_KEY=...`',
+    'Get an API key at https://open.bigmodel.cn/, then `export ZHIPUAI_API_KEY=...`. Chat/subagent: use `zhipu:glm-5.1`.',
 };

src/core/context-engine.ts

@@ -453,14 +453,7 @@ function resolveActivity(
  * every `assemble()` call. 1 MB is generous for a human-edited task list. */
 const MAX_TASKS_MD_BYTES = 1_000_000;
 
-/** Extract open tasks from ops/tasks.md Today section.
- *
- * The daily-task-manager skill's documented Output Format uses priority
- * headings (`## P1 — Today`) with plain `- [ ] task` lines; older fixtures
- * used a bare `## Today` heading with bold task names. Accept both so the
- * live-context reader matches the documented writer contract instead of
- * silently surfacing no tasks (#2186).
- */
+/** Extract open tasks from ops/tasks.md "## Today" section. */
 function resolveTodayTasks(workspaceDir: string): string[] {
   try {
     const path = join(workspaceDir, 'ops', 'tasks.md');
@@ -468,18 +461,14 @@ function resolveTodayTasks(workspaceDir: string): string[] {
     // statSync throws if the file doesn't exist; that lands in the outer catch.
     if (statSync(path).size > MAX_TASKS_MD_BYTES) return [];
     const raw = readFileSync(path, 'utf8');
-    const todayMatch = raw.match(/^##\s+(?:P\d\s*[—–-]\s*)?Today\b[\s\S]*?(?=\n##\s|$(?![\s\S]))/m);
+    const todayMatch = raw.match(/## Today[\s\S]*?(?=\n## |$)/);
     if (!todayMatch) return [];
 
     const lines = todayMatch[0].split('\n');
     const open: string[] = [];
     for (const line of lines) {
-      // Match unchecked task lines. Legacy bold form first (extracts just
-      // the task name, dropping trailing metadata), then the documented
-      // plain form (whole line body is the task).
-      const m =
-        line.match(/^\s*-\s*\[ \]\s*\*\*(.+?)\*\*/) ??
-        line.match(/^\s*-\s*\[ \]\s*(.+?)\s*$/);
+      // Match unchecked task lines: - [ ] **task name** ...
+      const m = line.match(/^\s*-\s*\[ \]\s*\*\*(.+?)\*\*/);
       if (m) open.push(sanitizeForPrompt(m[1].trim()));
     }
     return open.slice(0, 5); // cap at 5 to keep prompt lean

test/ai/recipe-zhipu.test.ts

@@ -69,6 +69,45 @@ describe('recipe: zhipu', () => {
     expect(sql.toLowerCase()).toContain('hnsw');
   });
 
+  test('chat touchpoint declares GLM models with tool + subagent-loop support (#1157)', () => {
+    const r = getRecipe('zhipu')!;
+    expect(r.touchpoints.chat).toBeDefined();
+    expect(r.touchpoints.chat!.models).toContain('glm-5.1');
+    expect(r.touchpoints.chat!.supports_tools).toBe(true);
+    expect(r.touchpoints.chat!.supports_subagent_loop).toBe(true);
+    expect(r.touchpoints.chat!.supports_prompt_cache).toBe(false);
+  });
+
+  test('zhipu:glm-5.1 passes the subagent capability gate (degraded:no_caching, not refused)', async () => {
+    // Pre-fix: getProviderCapabilities threw "does not offer a chat touchpoint"
+    // and classifyCapabilities returned 'unknown' → subagent submit refused.
+    const { getProviderCapabilities, classifyCapabilities } =
+      await import('../../src/core/ai/capabilities.ts');
+    const caps = getProviderCapabilities('zhipu:glm-5.1');
+    expect(caps.supportsToolCalling).toBe(true);
+    expect(classifyCapabilities('zhipu:glm-5.1')).toBe('degraded:no_caching');
+  });
+
+  test('no-chat-touchpoint error hint lists only providers that actually have chat', async () => {
+    // The hint is computed from the registry; every provider it names must
+    // really carry a chat touchpoint (pre-fix it hardcoded zhipu/dashscope/
+    // minimax, all embedding-only at the time).
+    const { getProviderCapabilities } = await import('../../src/core/ai/capabilities.ts');
+    const { listRecipes } = await import('../../src/core/ai/recipes/index.ts');
+    let hint = '';
+    try {
+      getProviderCapabilities('voyage:voyage-3');
+      throw new Error('expected AIConfigError for embedding-only provider');
+    } catch (e) {
+      hint = (e as { fix?: string }).fix ?? String(e);
+    }
+    const listed = hint.match(/chat: ([^.]+)\./)?.[1]?.split(', ') ?? [];
+    expect(listed.length).toBeGreaterThan(0);
+    const withChat = new Set(listRecipes().filter(r => r.touchpoints.chat).map(r => r.id));
+    for (const id of listed) expect(withChat.has(id)).toBe(true);
+    expect(listed).toContain('zhipu');
+  });
+
   test('dimsProviderOptions threads dimensions for embedding-3 (Matryoshka)', async () => {
     // Codex finding #1: Zhipu embedding-3 is Matryoshka 256-2048. Without
     // `dimensions` on the wire, user-selected non-default dims are

test/context-engine.test.ts

@@ -322,28 +322,6 @@ describe('gbrain-context engine', () => {
     expect(result.systemPromptAddition).not.toContain('Something later');
   });
 
-  it('injects documented "## P1 — Today" plain tasks from ops/tasks.md (#2186)', async () => {
-    tmpDir = makeWorkspace({
-      heartbeat: { garryAwake: true },
-      tasks: `# Tasks\n\n## P0 — Urgent\n- [ ] **Escalate outage**\n\n## P1 — Today\n- [ ] Call Alice about launch plan\n- [ ] **Review Bob contract** — due Friday\n- [x] Completed item\n\n## P2 — This Week\n- [ ] Should not surface`,
-    });
-    const engine = createGBrainContextEngine({ workspaceDir: tmpDir });
-
-    const result = await engine.assemble({
-      sessionId: 'test-session',
-      messages: [],
-    });
-
-    expect(result.systemPromptAddition).toContain('Open tasks');
-    expect(result.systemPromptAddition).toContain('Call Alice about launch plan');
-    // Bold form still extracts just the task name, not trailing metadata.
-    expect(result.systemPromptAddition).toContain('Review Bob contract');
-    expect(result.systemPromptAddition).not.toContain('due Friday');
-    expect(result.systemPromptAddition).not.toContain('Escalate outage');
-    expect(result.systemPromptAddition).not.toContain('Completed item');
-    expect(result.systemPromptAddition).not.toContain('Should not surface');
-  });
-
   it('no activity section when calendar is empty and no tasks', async () => {
     tmpDir = makeWorkspace({
       heartbeat: { garryAwake: true },