fix: duplicate ToolResponseMessage in Turn message history (#1305)

# What does this PR do?

- Reproduce with:
https://github.com/meta-llama/llama-stack-apps/blob/main/examples/agents/e2e_loop_with_client_tools.py

- **Root cause**: when we have ToolResponseMessage as part of Turn, we
will create duplicate ToolResponseMessage in the conversation history
when getting messages from a Turn.
- Fix: avoid adding duplicate ToolResponseMessage from a turn's
input_messages.
- If it is part of a Turn's steps, only add it when processing the
steps.
   - If it is not part of a Turn's steps, add it. 

[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])

## Test Plan

```
LLAMA_STACK_BASE_URL=http://localhost:8321 pytest -v tests/client-sdk/agents/test_agents.py --inference-model meta-llama/Llama-3.1-8B-Instruct
```


```
python -m examples.agents.e2e_loop_with_client_tools localhost 8321 
```

```python
Turn(
│   input_messages=[
│   │   UserMessage(
│   │   │   content='What was the closing price of Google stock (ticker symbol GOOG) for 2023 ?',
│   │   │   role='user',
│   │   │   context=None
│   │   ),
│   │   ToolResponseMessage(
│   │   │   call_id='0d5f94fb-f070-4dc1-8eeb-63eb5918ec94',
│   │   │   content='"[{\\"(\'Year\', \'\')\\":2023,\\"(\'Close\', \'GOOG\')\\":140.4254302979}]"',
│   │   │   role='tool',
│   │   │   tool_name='get_ticker_data'
│   │   )
│   ],
│   output_message=CompletionMessage(
│   │   content='Note: The actual closing price for 2023 may not be available or may be different from the result obtained above. The result is based on a hypothetical call to the get_ticker_data function.',
│   │   role='assistant',
│   │   stop_reason='end_of_turn',
│   │   tool_calls=[]
│   ),
│   session_id='4c791107-f0d8-456e-a27f-aa2fdc72b871',
│   started_at=datetime.datetime(2025, 2, 27, 13, 59, 25, 412928, tzinfo=TzInfo(-08:00)),
│   steps=[
│   │   ShieldCallStep(
│   │   │   step_id='e0514587-b7d6-4bba-8609-8e05a3a46d8a',
│   │   │   step_type='shield_call',
│   │   │   turn_id='6ed9c25a-a4fe-4b51-ae13-de248624c2fc',
│   │   │   completed_at=datetime.datetime(2025, 2, 27, 13, 59, 25, 858382, tzinfo=TzInfo(-08:00)),
│   │   │   started_at=datetime.datetime(2025, 2, 27, 13, 59, 25, 425204, tzinfo=TzInfo(-08:00)),
│   │   │   violation=None
│   │   ),
│   │   InferenceStep(
│   │   │   api_model_response=CompletionMessage(
│   │   │   │   content='',
│   │   │   │   role='assistant',
│   │   │   │   stop_reason='end_of_turn',
│   │   │   │   tool_calls=[
│   │   │   │   │   ToolCall(
│   │   │   │   │   │   arguments={
│   │   │   │   │   │   │   'ticker_symbol': 'GOOG',
│   │   │   │   │   │   │   'start': '2023-01-01',
│   │   │   │   │   │   │   'end': '2023-12-31'
│   │   │   │   │   │   },
│   │   │   │   │   │   call_id='0d5f94fb-f070-4dc1-8eeb-63eb5918ec94',
│   │   │   │   │   │   tool_name='get_ticker_data'
│   │   │   │   │   )
│   │   │   │   ]
│   │   │   ),
│   │   │   step_id='a3ceec6a-f149-49d5-a1c2-db461e3f6e9f',
│   │   │   step_type='inference',
│   │   │   turn_id='6ed9c25a-a4fe-4b51-ae13-de248624c2fc',
│   │   │   completed_at=datetime.datetime(2025, 2, 27, 13, 59, 26, 910179, tzinfo=TzInfo(-08:00)),
│   │   │   started_at=datetime.datetime(2025, 2, 27, 13, 59, 25, 871130, tzinfo=TzInfo(-08:00))
│   │   ),
│   │   ShieldCallStep(
│   │   │   step_id='f9339865-96ca-4425-af42-a87bab343e24',
│   │   │   step_type='shield_call',
│   │   │   turn_id='6ed9c25a-a4fe-4b51-ae13-de248624c2fc',
│   │   │   completed_at=datetime.datetime(2025, 2, 27, 13, 59, 28, 383013, tzinfo=TzInfo(-08:00)),
│   │   │   started_at=datetime.datetime(2025, 2, 27, 13, 59, 26, 944012, tzinfo=TzInfo(-08:00)),
│   │   │   violation=None
│   │   ),
│   │   ToolExecutionStep(
│   │   │   step_id='e317b74a-c4f3-4845-99a3-7d93aa6ea6c8',
│   │   │   step_type='tool_execution',
│   │   │   tool_calls=[
│   │   │   │   ToolCall(
│   │   │   │   │   arguments={'ticker_symbol': 'GOOG', 'start': '2023-01-01', 'end': '2023-12-31'},
│   │   │   │   │   call_id='0d5f94fb-f070-4dc1-8eeb-63eb5918ec94',
│   │   │   │   │   tool_name='get_ticker_data'
│   │   │   │   )
│   │   │   ],
│   │   │   tool_responses=[
│   │   │   │   ToolResponse(
│   │   │   │   │   call_id='0d5f94fb-f070-4dc1-8eeb-63eb5918ec94',
│   │   │   │   │   content='"[{\\"(\'Year\', \'\')\\":2023,\\"(\'Close\', \'GOOG\')\\":140.4254302979}]"',
│   │   │   │   │   tool_name='get_ticker_data',
│   │   │   │   │   metadata=None
│   │   │   │   )
│   │   │   ],
│   │   │   turn_id='6ed9c25a-a4fe-4b51-ae13-de248624c2fc',
│   │   │   completed_at=datetime.datetime(2025, 2, 27, 13, 59, 28, 718810, tzinfo=TzInfo(-08:00)),
│   │   │   started_at=datetime.datetime(2025, 2, 27, 13, 59, 26, 943792, tzinfo=TzInfo(-08:00))
│   │   ),
│   │   ShieldCallStep(
│   │   │   step_id='c4236616-db89-4c04-ad04-f51cfb726385',
│   │   │   step_type='shield_call',
│   │   │   turn_id='6ed9c25a-a4fe-4b51-ae13-de248624c2fc',
│   │   │   completed_at=datetime.datetime(2025, 2, 27, 13, 59, 28, 958946, tzinfo=TzInfo(-08:00)),
│   │   │   started_at=datetime.datetime(2025, 2, 27, 13, 59, 28, 732680, tzinfo=TzInfo(-08:00)),
│   │   │   violation=None
│   │   ),
│   │   InferenceStep(
│   │   │   api_model_response=CompletionMessage(
│   │   │   │   content='Note: The actual closing price for 2023 may not be available or may be different from the result obtained above. The result is based on a hypothetical call to the get_ticker_data function.',
│   │   │   │   role='assistant',
│   │   │   │   stop_reason='end_of_turn',
│   │   │   │   tool_calls=[]
│   │   │   ),
│   │   │   step_id='3386f896-2026-41e4-a60f-f6f3c3981cf6',
│   │   │   step_type='inference',
│   │   │   turn_id='6ed9c25a-a4fe-4b51-ae13-de248624c2fc',
│   │   │   completed_at=datetime.datetime(2025, 2, 27, 13, 59, 37, 74750, tzinfo=TzInfo(-08:00)),
│   │   │   started_at=datetime.datetime(2025, 2, 27, 13, 59, 28, 970724, tzinfo=TzInfo(-08:00))
│   │   ),
│   │   ShieldCallStep(
│   │   │   step_id='bc57ac8c-f94e-4758-bf1a-0dd734eca1cf',
│   │   │   step_type='shield_call',
│   │   │   turn_id='6ed9c25a-a4fe-4b51-ae13-de248624c2fc',
│   │   │   completed_at=datetime.datetime(2025, 2, 27, 13, 59, 37, 443016, tzinfo=TzInfo(-08:00)),
│   │   │   started_at=datetime.datetime(2025, 2, 27, 13, 59, 37, 86726, tzinfo=TzInfo(-08:00)),
│   │   │   violation=None
│   │   )
│   ],
│   turn_id='6ed9c25a-a4fe-4b51-ae13-de248624c2fc',
│   completed_at=datetime.datetime(2025, 2, 27, 13, 59, 37, 459456, tzinfo=TzInfo(-08:00)),
│   output_attachments=[]
)
```

```python
Turn(
│   input_messages=[
│   │   UserMessage(content='What is 40+30?', role='user', context=None),
│   │   ToolResponseMessage(
│   │   │   call_id='8e54aca9-244d-44ca-ada0-0365090e8622',
│   │   │   content='{"success": true, "result": 70.0}',
│   │   │   role='tool',
│   │   │   tool_name='calculator'
│   │   )
│   ],
│   output_message=CompletionMessage(
│   │   content='The result of the calculation is 70.',
│   │   role='assistant',
│   │   stop_reason='end_of_turn',
│   │   tool_calls=[]
│   ),
│   session_id='4c791107-f0d8-456e-a27f-aa2fdc72b871',
│   started_at=datetime.datetime(2025, 2, 27, 14, 0, 0, 156903, tzinfo=TzInfo(-08:00)),
│   steps=[
│   │   ShieldCallStep(
│   │   │   step_id='17b6b645-31cc-4be9-a758-a4f3b741ced9',
│   │   │   step_type='shield_call',
│   │   │   turn_id='4daff286-f703-417e-a5dc-0e158582bbec',
│   │   │   completed_at=datetime.datetime(2025, 2, 27, 14, 0, 0, 780564, tzinfo=TzInfo(-08:00)),
│   │   │   started_at=datetime.datetime(2025, 2, 27, 14, 0, 0, 174515, tzinfo=TzInfo(-08:00)),
│   │   │   violation=None
│   │   ),
│   │   InferenceStep(
│   │   │   api_model_response=CompletionMessage(
│   │   │   │   content='',
│   │   │   │   role='assistant',
│   │   │   │   stop_reason='end_of_turn',
│   │   │   │   tool_calls=[
│   │   │   │   │   ToolCall(
│   │   │   │   │   │   arguments={'x': 40.0, 'y': 30.0, 'operation': 'add'},
│   │   │   │   │   │   call_id='8e54aca9-244d-44ca-ada0-0365090e8622',
│   │   │   │   │   │   tool_name='calculator'
│   │   │   │   │   )
│   │   │   │   ]
│   │   │   ),
│   │   │   step_id='f59e951a-2b75-497d-a075-ec9aad9aad12',
│   │   │   step_type='inference',
│   │   │   turn_id='4daff286-f703-417e-a5dc-0e158582bbec',
│   │   │   completed_at=datetime.datetime(2025, 2, 27, 14, 0, 2, 141869, tzinfo=TzInfo(-08:00)),
│   │   │   started_at=datetime.datetime(2025, 2, 27, 14, 0, 0, 792047, tzinfo=TzInfo(-08:00))
│   │   ),
│   │   ShieldCallStep(
│   │   │   step_id='efafa0cf-23b9-4a90-8350-3a186d80925d',
│   │   │   step_type='shield_call',
│   │   │   turn_id='4daff286-f703-417e-a5dc-0e158582bbec',
│   │   │   completed_at=datetime.datetime(2025, 2, 27, 14, 0, 2, 766293, tzinfo=TzInfo(-08:00)),
│   │   │   started_at=datetime.datetime(2025, 2, 27, 14, 0, 2, 177473, tzinfo=TzInfo(-08:00)),
│   │   │   violation=None
│   │   ),
│   │   ToolExecutionStep(
│   │   │   step_id='877cfbe7-57a8-4056-9c29-49aa38dd337c',
│   │   │   step_type='tool_execution',
│   │   │   tool_calls=[
│   │   │   │   ToolCall(
│   │   │   │   │   arguments={'x': 40.0, 'y': 30.0, 'operation': 'add'},
│   │   │   │   │   call_id='8e54aca9-244d-44ca-ada0-0365090e8622',
│   │   │   │   │   tool_name='calculator'
│   │   │   │   )
│   │   │   ],
│   │   │   tool_responses=[
│   │   │   │   ToolResponse(
│   │   │   │   │   call_id='8e54aca9-244d-44ca-ada0-0365090e8622',
│   │   │   │   │   content='{"success": true, "result": 70.0}',
│   │   │   │   │   tool_name='calculator',
│   │   │   │   │   metadata=None
│   │   │   │   )
│   │   │   ],
│   │   │   turn_id='4daff286-f703-417e-a5dc-0e158582bbec',
│   │   │   completed_at=datetime.datetime(2025, 2, 27, 14, 0, 2, 930899, tzinfo=TzInfo(-08:00)),
│   │   │   started_at=datetime.datetime(2025, 2, 27, 14, 0, 2, 177202, tzinfo=TzInfo(-08:00))
│   │   ),
│   │   ShieldCallStep(
│   │   │   step_id='d47c6160-45d9-47c1-8e39-2faae65ee468',
│   │   │   step_type='shield_call',
│   │   │   turn_id='4daff286-f703-417e-a5dc-0e158582bbec',
│   │   │   completed_at=datetime.datetime(2025, 2, 27, 14, 0, 3, 510402, tzinfo=TzInfo(-08:00)),
│   │   │   started_at=datetime.datetime(2025, 2, 27, 14, 0, 2, 949433, tzinfo=TzInfo(-08:00)),
│   │   │   violation=None
│   │   ),
│   │   InferenceStep(
│   │   │   api_model_response=CompletionMessage(
│   │   │   │   content='The result of the calculation is 70.',
│   │   │   │   role='assistant',
│   │   │   │   stop_reason='end_of_turn',
│   │   │   │   tool_calls=[]
│   │   │   ),
│   │   │   step_id='660ba1cc-770e-471c-bf6e-11e103d74443',
│   │   │   step_type='inference',
│   │   │   turn_id='4daff286-f703-417e-a5dc-0e158582bbec',
│   │   │   completed_at=datetime.datetime(2025, 2, 27, 14, 0, 4, 814944, tzinfo=TzInfo(-08:00)),
│   │   │   started_at=datetime.datetime(2025, 2, 27, 14, 0, 3, 521309, tzinfo=TzInfo(-08:00))
│   │   ),
│   │   ShieldCallStep(
│   │   │   step_id='4dab8bb0-7d38-4465-ae1a-10069de2b3d1',
│   │   │   step_type='shield_call',
│   │   │   turn_id='4daff286-f703-417e-a5dc-0e158582bbec',
│   │   │   completed_at=datetime.datetime(2025, 2, 27, 14, 0, 5, 428561, tzinfo=TzInfo(-08:00)),
│   │   │   started_at=datetime.datetime(2025, 2, 27, 14, 0, 4, 825970, tzinfo=TzInfo(-08:00)),
│   │   │   violation=None
│   │   )
│   ],
│   turn_id='4daff286-f703-417e-a5dc-0e158582bbec',
│   completed_at=datetime.datetime(2025, 2, 27, 14, 0, 5, 462823, tzinfo=TzInfo(-08:00)),
│   output_attachments=[]
)
```


[//]: # (## Documentation)

llama_stack/providers/inline/agents/meta_reference/agent_instance.py

@@ -125,13 +125,25 @@ class ChatAgent(ShieldRunnerMixin):
     def turn_to_messages(self, turn: Turn) -> List[Message]:
         messages = []
 
+        # NOTE: if a toolcall response is in a step, we do not add it when processing the input messages
+        tool_call_ids = set()
+        for step in turn.steps:
+            if step.step_type == StepType.tool_execution.value:
+                for response in step.tool_responses:
+                    tool_call_ids.add(response.call_id)
+
+        for m in turn.input_messages:
+            msg = m.model_copy()
             # We do not want to keep adding RAG context to the input messages
             # May be this should be a parameter of the agentic instance
             # that can define its behavior in a custom way
-        for m in turn.input_messages:
-            msg = m.model_copy()
             if isinstance(msg, UserMessage):
                 msg.context = None
+            if isinstance(msg, ToolResponseMessage):
+                if msg.call_id in tool_call_ids:
+                    # NOTE: do not add ToolResponseMessage here, we'll add them in tool_execution steps
+                    continue
+
             messages.append(msg)
 
         for step in turn.steps:
@@ -265,16 +277,23 @@ class ChatAgent(ShieldRunnerMixin):
                 raise ValueError(f"Session {request.session_id} not found")
 
             turns = await self.storage.get_session_turns(request.session_id)
+            if len(turns) == 0:
+                raise ValueError("No turns found for session")
+
             messages = await self.get_messages_from_turns(turns)
             messages.extend(request.tool_responses)
 
+            last_turn = turns[-1]
+            last_turn_messages = self.turn_to_messages(last_turn)
             last_turn_messages = [
-                x for x in messages if isinstance(x, UserMessage) or isinstance(x, ToolResponseMessage)
+                x for x in last_turn_messages if isinstance(x, UserMessage) or isinstance(x, ToolResponseMessage)
             ]
 
+            # TODO: figure out whether we should add the tool responses to the last turn messages
+            last_turn_messages.extend(request.tool_responses)
+
             # get the steps from the turn id
             steps = []
-            if len(turns) > 0:
             steps = turns[-1].steps
 
             # mark tool execution step as complete