fix: Fix "text" Agent exit condition (#11665)

Author: sjrl

haystack/components/agents/agent.py

@@ -857,8 +857,17 @@ def run(  # noqa: PLR0915
                     llm_messages = result["replies"]
                     exe_context.state.set("messages", llm_messages)
 
-                # Check if any of the LLM responses contain a tool call or if the LLM is not using tools
-                if not any(msg.tool_call for msg in llm_messages) or self._tool_invoker is None:
+                # Exit for `exit_conditions=["text"]` behavior: the agent stops when there is no tool invoker, or when
+                # the model returns a plain text response (no tool calls). We require the last message to be a non-empty
+                # assistant text message so that an invalid response (e.g. a message with no tool calls or text) won't
+                # trigger an exit.
+                last_message = llm_messages[-1] if llm_messages else None
+                if self._tool_invoker is None or (
+                    last_message is not None
+                    and not any(msg.tool_call for msg in llm_messages)
+                    and last_message.is_from(ChatRole.ASSISTANT)
+                    and last_message.text
+                ):
                     exe_context.counter += 1
                     break
 
@@ -1092,8 +1101,17 @@ async def run_async(  # noqa: PLR0915
                     llm_messages = result["replies"]
                     exe_context.state.set("messages", llm_messages)
 
-                # Check if any of the LLM responses contain a tool call or if the LLM is not using tools
-                if not any(msg.tool_call for msg in llm_messages) or self._tool_invoker is None:
+                # Exit for `exit_conditions=["text"]` behavior: the agent stops when there is no tool invoker, or when
+                # the model returns a plain text response (no tool calls). We require the last message to be a non-empty
+                # assistant text message so that an invalid response (e.g. a message with no tool calls or text) won't
+                # trigger an exit.
+                last_message = llm_messages[-1] if llm_messages else None
+                if self._tool_invoker is None or (
+                    last_message is not None
+                    and not any(msg.tool_call for msg in llm_messages)
+                    and last_message.is_from(ChatRole.ASSISTANT)
+                    and last_message.text
+                ):
                     exe_context.counter += 1
                     break
 

releasenotes/notes/agent-text-exit-condition-empty-message-d1eac980a4036306.yaml

@@ -0,0 +1,9 @@
+---
+fixes:
+  - |
+    Fixed the ``Agent`` exiting prematurely under the default ``exit_conditions=["text"]``.
+    The agent now only stops when the last message is an assistant message with non-empty text
+    (or when no tool invoker is configured). Previously, if the LLM produced an invalid tool call
+    that was discarded, the resulting assistant message with empty text and no tool calls would
+    trigger an exit, preventing the agent from recovering. The agent now continues looping so the
+    model can recover on the next iteration.

test/components/agents/test_agent.py

@@ -792,6 +792,22 @@ def test_exit_condition_exits(self, monkeypatch, weather_tool):
         assert isinstance(result["last_message"], ChatMessage)
         assert result["messages"][-1] == result["last_message"]
 
+    def test_does_not_exit_on_empty_assistant_message(self, monkeypatch, weather_tool):
+        monkeypatch.setenv("OPENAI_API_KEY", "fake-key")
+        agent = Agent(chat_generator=OpenAIChatGenerator(), tools=[weather_tool], exit_conditions=["text"])
+
+        # The first reply simulates the LLM producing an invalid tool call that our code discards,leaving an assistant
+        # message with empty text and no tool calls. This must not be treated as a "text" exit condition, so the agent
+        # keeps looping and recovers on the second reply.
+        empty_reply = {"replies": [ChatMessage.from_assistant(text="")]}
+        recovered_reply = {"replies": [ChatMessage.from_assistant(text="The weather is sunny.")]}
+        agent.chat_generator.run = MagicMock(side_effect=[empty_reply, recovered_reply])
+
+        result = agent.run([ChatMessage.from_user("What's the weather?")])
+
+        assert agent.chat_generator.run.call_count == 2
+        assert result["last_message"].text == "The weather is sunny."
+
     def test_check_exit_conditions_parallel_tool_calls(self, monkeypatch, weather_tool):
         monkeypatch.setenv("OPENAI_API_KEY", "fake-key")
         agent = Agent(chat_generator=OpenAIChatGenerator(), tools=[weather_tool], exit_conditions=["weather_tool"])
@@ -1077,6 +1093,23 @@ def streaming_callback(chunk: StreamingChunk) -> None:
         assert result["last_message"] is not None
         assert streaming_callback_called
 
+    @pytest.mark.asyncio
+    async def test_does_not_exit_on_empty_assistant_message_async(self, monkeypatch, weather_tool):
+        monkeypatch.setenv("OPENAI_API_KEY", "fake-key")
+        agent = Agent(chat_generator=OpenAIChatGenerator(), tools=[weather_tool], exit_conditions=["text"])
+
+        # The first reply simulates the LLM producing an invalid tool call that our code discards,leaving an assistant
+        # message with empty text and no tool calls. This must not be treated as a "text" exit condition, so the agent
+        # keeps looping and recovers on the second reply.
+        empty_reply = {"replies": [ChatMessage.from_assistant(text="")]}
+        recovered_reply = {"replies": [ChatMessage.from_assistant(text="The weather is sunny.")]}
+        agent.chat_generator.run_async = AsyncMock(side_effect=[empty_reply, recovered_reply])
+
+        result = await agent.run_async([ChatMessage.from_user("What's the weather?")])
+
+        assert agent.chat_generator.run_async.call_count == 2
+        assert result["last_message"].text == "The weather is sunny."
+
     @pytest.mark.asyncio
     async def test_run_async_with_async_streaming_callback(self, weather_tool):
         chat_generator = MockChatGenerator()