add type ignore

docs/docs/pages/agent-integration.mdx

@@ -93,7 +93,7 @@ class LiteLLMAgent(scenario.AgentAdapter):
             model=self.model,
             messages=input.messages
         )
-        return response.choices[0].message
+        return response.choices[0].message  # type: ignore
 ```
 
 ### 3. Stateful Agents
@@ -157,4 +157,4 @@ Now that you understand agent integration, explore specific integration guides:
 Or dive deeper into advanced topics:
 
 - [Scenario Basics](/scenario) - Master scenario creation and control
-- Check the [AgentAdapter API Reference](/reference/python/scenario/agent_adapter.html) for detailed documentation */}
+- Check the [AgentAdapter API Reference](/reference/python/scenario/agent_adapter.html) for detailed documentation */}

docs/docs/pages/basics/cache.mdx

@@ -69,7 +69,7 @@ def my_agent_function(messages) -> scenario.AgentReturnTypes:
         model="openai/gpt-4o-mini",
         messages=messages
     )
-    return response.choices[0].message
+    return response.choices[0].message  # type: ignore
 ```
 
 ### 3. Run Tests

docs/docs/pages/introduction/getting-started.mdx

@@ -83,7 +83,7 @@ def vegetarian_recipe_agent(messages) -> scenario.AgentReturnTypes:
             *messages,
         ],
     )
-    return response.choices[0].message
+    return response.choices[0].message  # type: ignore
 ```
 
 ### 2. Set up your environment
@@ -102,7 +102,10 @@ uv run pytest -s test_my_agent.py
 
 You should see output showing the conversation between the simulated user and your agent, followed by the judge's evaluation:
 
-<EmbeddedScript id="asciicast-nvO5GWGzqKTTCd8gtNSezQw11" src="https://asciinema.org/a/nvO5GWGzqKTTCd8gtNSezQw11.js" />
+<EmbeddedScript
+  id="asciicast-nvO5GWGzqKTTCd8gtNSezQw11"
+  src="https://asciinema.org/a/nvO5GWGzqKTTCd8gtNSezQw11.js"
+/>
 
 ## What happened?
 
@@ -125,4 +128,4 @@ In this example:
 
 - Learn about [Scenario Basics](../basics/concepts) to understand the framework deeply
 - Explore [Agent Integration](../agent-integration) to connect your existing agents
-- Check out more [examples](https://github.com/langwatch/scenario/tree/main/examples) on GitHub
+- Check out more [examples](https://github.com/langwatch/scenario/tree/main/examples) on GitHub

python/scenario/cache.py

@@ -86,7 +86,7 @@ def invoke(self, message: str, context: dict) -> str:
                     model="gpt-4",
                     messages=[{"role": "user", "content": message}]
                 )
-                return response.choices[0].message.content
+                return response.choices[0].message.content  # type: ignore
 
         # Usage in tests
         scenario.configure(cache_key="my-test-suite-v1")