Use tiered low-cost AI Gateway fallbacks

.github/workflows/cloud-agent.yml

@@ -61,7 +61,8 @@ jobs:
       INCLUDE_MAINTENANCE_CONTEXT: ${{ vars.INCLUDE_MAINTENANCE_CONTEXT || 'false' }}
       INCLUDE_RUNBOOK_CONTEXT: ${{ vars.INCLUDE_RUNBOOK_CONTEXT || 'false' }}
       DRY_RUN_ON_BUDGET_EXCEEDED: ${{ vars.DRY_RUN_ON_BUDGET_EXCEEDED || 'true' }}
-      AI_GATEWAY_FALLBACK_MODELS: ${{ vars.AI_GATEWAY_FALLBACK_MODELS || 'google/gemini-2.5-flash-lite' }}
+      AI_GATEWAY_FALLBACK_MODELS: ${{ vars.AI_GATEWAY_FALLBACK_MODELS || 'openai/gpt-5-nano' }}
+      AI_GATEWAY_SCREEN_FALLBACK_MODELS: ${{ vars.AI_GATEWAY_SCREEN_FALLBACK_MODELS || 'google/gemini-2.5-flash-lite' }}
       AI_GATEWAY_MAX_OUTPUT_TOKENS: ${{ vars.AI_GATEWAY_MAX_OUTPUT_TOKENS || '32768' }}
       # Tuned in-repo (repo Actions variables held stale lower values and
       # silently clamped collection breadth; see docs/release-v0.19.0.md).

CHANGELOG.md

@@ -9,6 +9,7 @@
 - AI Gateway requests rely on the existing JSON-only prompt and strict parser instead of `response_format=json_object`, which DeepSeek V4 rejects through the Gateway.
 - Added an explicit 32,768-token output ceiling and low-cost fallback recovery for HTTP 200 responses whose model content is truncated or otherwise invalid JSON.
 - Normalize pure Markdown JSON fences before strict parsing so a valid fenced response does not spend a fallback call.
+- Split fallback by workload: Flash Lite backs up Nano screening, while Nano backs up GPT-OSS synthesis after Flash Lite failed the weekly bilingual-content quality gate.
 - A zero AI Gateway call budget now completes as a real dry run instead of failing the empty-update guard.
 
 ## v0.19.5 - 2026-07-18

README.md

@@ -90,7 +90,7 @@ Daily notes are append-oriented. Use them to record high-signal items without fo
 
 For fully Cloud Agent-driven operation, use `automation/daily.md`.
 
-For true 24/7 cloud execution, `.github/workflows/cloud-agent.yml` uses GitHub Models by default with the built-in GitHub Actions `GITHUB_TOKEN`. The recommended cost-controlled mode uses Vercel AI Gateway directly with GPT-5 Nano for screening, GPT-OSS 120B for research and final synthesis, Gemini 2.5 Flash Lite only as a transient-failure fallback, and free public sources only; see `docs/cloud-agent.md`.
+For true 24/7 cloud execution, `.github/workflows/cloud-agent.yml` uses GitHub Models by default with the built-in GitHub Actions `GITHUB_TOKEN`. The recommended cost-controlled mode uses Vercel AI Gateway directly with GPT-5 Nano for screening, GPT-OSS 120B for research and final synthesis, Nano again as the synthesis fallback, Gemini 2.5 Flash Lite only as the screening fallback, and free public sources only; see `docs/cloud-agent.md`.
 
 Cloud mode uses source lanes, source-cache novelty tracking, scoring, source health, lane health, and structured telemetry. Daily, weekly, and monthly reports use nested bilingual pairs: each substantive item is a label bullet with `中文：` first and `English:` second as sub-bullets; short metadata fields stay on one line as `中文值（English value）`; URLs and product names are written once. At least 60% of substantive English lines must have a real Chinese counterpart.
 

docs/cloud-agent.md

@@ -53,7 +53,8 @@ REDDIT_SUBREDDITS=LocalLLaMA,MachineLearning,ClaudeAI,GithubCopilot,Cursor,ChatG
 MAX_AI_GATEWAY_CALLS_PER_TASK=
 MAX_PROMPT_CHARS=120000
 DRY_RUN_ON_BUDGET_EXCEEDED=true
-AI_GATEWAY_FALLBACK_MODELS=google/gemini-2.5-flash-lite
+AI_GATEWAY_FALLBACK_MODELS=openai/gpt-5-nano
+AI_GATEWAY_SCREEN_FALLBACK_MODELS=google/gemini-2.5-flash-lite
 AI_GATEWAY_MAX_OUTPUT_TOKENS=32768
 MAX_RELEASE_REPOS=20
 MAX_RELEASES_PER_REPO=3
@@ -82,7 +83,7 @@ Model routing stays bounded but discovery-oriented:
 - `promote-candidates`: GPT-OSS 120B automatically promotes at most 3 high-quality candidates from `research-log.md`.
 - `weekly` and `monthly`: GPT-5 Nano screens public signals, then GPT-OSS 120B performs final synthesis (default `MAX_AI_GATEWAY_CALLS_PER_TASK=2`).
 
-Gemini 2.5 Flash Lite is not part of the normal route. It is tried once only after a 408/429/5xx response, a transport timeout, an empty response, or malformed/truncated JSON. The 32,768-token ceiling is an output cap rather than prepaid usage; tokens are billed only when generated.
+Fallback is tiered by workload and is not part of the normal route. A failed Nano screening call tries Gemini 2.5 Flash Lite; a failed GPT-OSS synthesis call tries Nano, which is more reliable for the long bilingual schema. Fallback is limited to a 408/429/5xx response, transport timeout, empty response, or malformed/truncated JSON. The 32,768-token ceiling is an output cap rather than prepaid usage; tokens are billed only when generated.
 
 This keeps paid search calls at zero. Model usage is bounded by the fixed task route, `MAX_PUBLIC_SOURCE_ITEMS`, `MAX_AI_GATEWAY_CALLS_PER_TASK`, and `MAX_PROMPT_CHARS`.
 

docs/subscription-mode.md

@@ -93,7 +93,8 @@ MAX_PUBLIC_SOURCE_ITEMS=
 PUBLIC_SOURCE_COLLECTION=true
 MAX_PROMPT_CHARS=120000
 DRY_RUN_ON_BUDGET_EXCEEDED=true
-AI_GATEWAY_FALLBACK_MODELS=google/gemini-2.5-flash-lite
+AI_GATEWAY_FALLBACK_MODELS=openai/gpt-5-nano
+AI_GATEWAY_SCREEN_FALLBACK_MODELS=google/gemini-2.5-flash-lite
 AI_GATEWAY_MAX_OUTPUT_TOKENS=32768
 MAX_RELEASE_REPOS=12
 MAX_RELEASES_PER_REPO=2
@@ -108,4 +109,4 @@ Behavior:
 - Promote-candidates runs automatically promote at most 3 high-quality candidates.
 - Daily runs use GPT-5 Nano for screening and GPT-OSS 120B for final updates.
 - Weekly/monthly runs use GPT-5 Nano for screening, then GPT-OSS 120B for final synthesis (default `MAX_AI_GATEWAY_CALLS_PER_TASK=2`).
-- Gemini 2.5 Flash Lite is tried only for transient HTTP/transport failures or invalid/truncated JSON, keeping fallback output at the same low price tier as the primary route.
+- Fallback remains workload-aware and low-cost: Flash Lite backs up Nano screening, while Nano backs up GPT-OSS long-form synthesis after transient HTTP/transport failures or invalid/truncated JSON.

scripts/cloud_agent_runner.py

@@ -5445,9 +5445,16 @@ def ai_gateway_headers() -> dict[str, str]:
 
 
 def ai_gateway_fallback_models(model: str) -> list[str]:
-    fallback = split_env_list(
-        "AI_GATEWAY_FALLBACK_MODELS", [DEFAULT_AI_GATEWAY_FALLBACK_MODEL]
-    )
+    cheap = os.environ.get("CHEAP_SCREEN_MODEL", DEFAULT_CHEAP_SCREEN_MODEL)
+    if model == cheap:
+        fallback = split_env_list(
+            "AI_GATEWAY_SCREEN_FALLBACK_MODELS", [DEFAULT_AI_GATEWAY_FALLBACK_MODEL]
+        )
+    else:
+        # Reuse Nano for synthesis recovery: it is cheaper than Mini, retains a
+        # long output window, and avoids asking Flash Lite to write the long
+        # bilingual reports that it failed the production quality gate on.
+        fallback = split_env_list("AI_GATEWAY_FALLBACK_MODELS", [DEFAULT_CHEAP_SCREEN_MODEL])
     models = [model]
     for item in fallback:
         if item not in models:
@@ -5495,6 +5502,8 @@ def call_ai_gateway_model(prompt: str, model: str) -> dict[str, Any]:
         audit_model(candidate_model)
         if candidate_model != model:
             RUN_AUDIT["fallbacks"].append(f"{model}->{candidate_model}")
+            reason = last_error.split(":", 1)[0] if last_error else "transient failure"
+            print(f"AI Gateway fallback {model}->{candidate_model}: {reason}")
         if attempt > 0:
             # Brief backoff before retrying a transient failure.
             time.sleep(min(8, 2 ** attempt))

tests/test_cloud_agent_runner.py

@@ -55,6 +55,17 @@ def test_ai_gateway_default_model_route_uses_nano_and_gpt_oss(self) -> None:
                 ["openai/gpt-oss-120b"],
             )
 
+    def test_ai_gateway_default_fallbacks_are_tiered_by_workload(self) -> None:
+        with mock.patch.dict(os.environ, {}, clear=True):
+            self.assertEqual(
+                cloud_agent_runner.ai_gateway_fallback_models("openai/gpt-5-nano"),
+                ["openai/gpt-5-nano", "google/gemini-2.5-flash-lite"],
+            )
+            self.assertEqual(
+                cloud_agent_runner.ai_gateway_fallback_models("openai/gpt-oss-120b"),
+                ["openai/gpt-oss-120b", "openai/gpt-5-nano"],
+            )
+
     def test_auto_tasks_include_candidate_promotion_on_sunday(self) -> None:
         tasks = cloud_agent_runner.auto_tasks(cloud_agent_runner.parse_date("2026-07-05"))
         self.assertIn("daily", tasks)