deploy

.a2a/agent.dsl.json

@@ -3,7 +3,7 @@
   "language": "python",
   "name": "seleniumbase-website-scraper",
   "description": "SeleniumBase-style browser agent that turns observed website traffic into OpenAPI specs, coverage reports, samples, and a replay client.",
-  "version": "0.2.7",
+  "version": "0.2.8",
   "entrypoint": {
     "module": "agent",
     "class_name": "BrowserToApiAgent",
@@ -381,7 +381,7 @@
     "source": "python-a2a-pack",
     "project_manifest": {
       "name": "seleniumbase-website-scraper",
-      "version": "0.2.7",
+      "version": "0.2.8",
       "entrypoint": "agent:BrowserToApiAgent"
     }
   }

a2a.yaml

@@ -1,5 +1,5 @@
 name: seleniumbase-website-scraper
-version: 0.2.7
+version: 0.2.8
 entrypoint: agent:BrowserToApiAgent
 expose:
   public: true

agent.py

@@ -129,7 +129,7 @@ class BrowserToApiAgent(A2AAgent[BrowserToApiConfig, NoAuth]):
         "SeleniumBase-style browser agent that turns observed website traffic "
         "into OpenAPI specs, coverage reports, samples, and a replay client."
     )
-    version = "0.2.7"
+    version = "0.2.8"
 
     config_model = BrowserToApiConfig
     auth_model = NoAuth
@@ -383,6 +383,11 @@ def capture_browser_traffic(
 
         page.wait_for_timeout(wait_seconds * 1000)
         if ai_explore and ai_steps > 0 and llm_config:
+            log.append(
+                "ai: using ctx.llm "
+                f"model={llm_config.get('model', 'unknown')} "
+                f"source={llm_config.get('source', 'unknown')}"
+            )
             _ai_explore_page(
                 page,
                 llm_config=llm_config,
@@ -414,7 +419,13 @@ def _llm_config_from_ctx(ctx: RunContext[NoAuth]) -> dict[str, Any] | None:
     model = str(getattr(creds, "model", "") or "")
     if not (api_key and base_url and model):
         return None
-    return {"api_key": api_key, "base_url": base_url, "model": model}
+    return {
+        "api_key": api_key,
+        "base_url": base_url,
+        "model": model,
+        "source": str(getattr(creds, "source", "") or ""),
+        "extra_body": dict(getattr(creds, "extra_body", None) or {}),
+    }
 
 
 def _default_ai_goal(url: str) -> str:
@@ -558,15 +569,48 @@ def _llm_choose_action(
 
 def _chat_completion_json(llm_config: dict[str, Any], *, system: str, user: str) -> str:
     url = f"{llm_config['base_url'].rstrip('/')}/chat/completions"
-    payload = {
+    base_payload = {
         "model": llm_config["model"],
         "messages": [
             {"role": "system", "content": system},
             {"role": "user", "content": user},
         ],
-        "max_tokens": 320,
-        "response_format": {"type": "json_object"},
     }
+    extra_body = dict(llm_config.get("extra_body") or {})
+    attempts = [
+        {
+            **base_payload,
+            "max_completion_tokens": 1200,
+            "reasoning_effort": "minimal",
+            **extra_body,
+        },
+        {
+            **base_payload,
+            "max_tokens": 800,
+            **extra_body,
+        },
+        {
+            **base_payload,
+            "max_completion_tokens": 1200,
+            "response_format": {"type": "json_object"},
+            **extra_body,
+        },
+    ]
+    last_error: Exception | None = None
+    for payload in attempts:
+        try:
+            content = _post_chat_completion(url, llm_config["api_key"], payload)
+        except Exception as exc:  # noqa: BLE001
+            last_error = exc
+            continue
+        if content.strip():
+            return content
+    if last_error is not None:
+        raise last_error
+    return ""
+
+
+def _post_chat_completion(url: str, api_key: str, payload: dict[str, Any]) -> str:
     req = urllib.request.Request(
         url,
         data=json.dumps(payload).encode("utf-8"),
@@ -586,7 +630,15 @@ def _chat_completion_json(llm_config: dict[str, Any], *, system: str, user: str)
     if not choices:
         return ""
     message = choices[0].get("message") if isinstance(choices[0], dict) else {}
-    return str(message.get("content") or "")
+    content = message.get("content")
+    if isinstance(content, list):
+        parts = [
+            str(item.get("text") or item.get("content") or "")
+            for item in content
+            if isinstance(item, dict)
+        ]
+        return "\n".join(part for part in parts if part)
+    return str(content or "")
 
 
 def _apply_ai_action(