Use A2A DeepAgents model resolver

agent.py

@@ -95,10 +95,9 @@ class ChartAgent(A2AAgent[ChartAgentConfig, NoAuth]):
     ) -> Any:
         # Lazy imports keep `a2a card` usable before local dependencies are
         # installed. `a2a deploy` installs requirements.txt during the build.
-        from deepagents import create_deep_agent
+        from a2a_pack.deepagents import create_a2a_deep_agent
         from langchain.agents.middleware import wrap_model_call
         from langchain_core.tools import tool
-        from langchain_openai import ChatOpenAI
 
         @tool
         def text_stats(text: str) -> str:
@@ -121,27 +120,12 @@ class ChartAgent(A2AAgent[ChartAgentConfig, NoAuth]):
             )
             return await handler(request)
 
-        model_kwargs: dict[str, Any] = {
-            "model": creds.model,
-            "base_url": creds.base_url,
-            # For CALLER_PROVIDED this is the caller's forwarded key. For
-            # PLATFORM this is the short-lived A2A LiteLLM grant token. Do not
-            # substitute provider keys, LiteLLM master keys, or fake fallback
-            # values here.
-            "api_key": creds.api_key,
-        }
-        if creds.temperature_mode != "omit":
-            model_kwargs["temperature"] = (
-                creds.temperature if creds.temperature is not None else 0.0
-            )
-        if creds.extra_body:
-            model_kwargs["extra_body"] = dict(creds.extra_body)
-
-        model = ChatOpenAI(**model_kwargs)
         backend = ctx.workspace_backend()
         skill_sources = _seed_runtime_skills(backend, ctx)
-        return create_deep_agent(
-            model=model,
+        return create_a2a_deep_agent(
+            ctx,
+            creds=creds,
+            default_temperature=0.0,
             backend=backend,
             skills=skill_sources or None,
             tools=[text_stats],