feat: improve action detection when agent provide multiples choices

src/crewai/agents/parser.py

@@ -89,6 +89,8 @@ class CrewAgentParser:
 
         thought = self._extract_thought(text)
         includes_answer = FINAL_ANSWER_ACTION in text
+        action_match = self._find_last_action_input_pair(cleaned_text)
+
             final_answer = cleaned_text.split(FINAL_ANSWER_ACTION)[-1].strip()
             # Check whether the final answer ends with triple backticks.
             if final_answer.endswith("```"):
@@ -100,15 +102,7 @@ class CrewAgentParser:
             return AgentFinish(thought, final_answer, text)
 
         elif action_match:
-            action = action_match.group(1)
+            return self._create_agent_action(thought, action_match, cleaned_text)
-            clean_action = self._clean_action(action)
-
-            action_input = action_match.group(2).strip()
-
-            tool_input = action_input.strip(" ").strip('"')
-            safe_tool_input = self._safe_repair_json(tool_input)
-
-            return AgentAction(thought, clean_action, safe_tool_input, cleaned_text)
 
         if not re.search(r"Action\s*\d*\s*:[\s]*(.*?)", cleaned_text, re.DOTALL):
             raise OutputParserException(
@@ -173,6 +167,54 @@ class CrewAgentParser:
 
         return str(result)
 
+    def _create_agent_action(self, thought: str, action_match: dict, cleaned_text: str) -> AgentAction:
+        action = action_match["action"]
+        clean_action = self._clean_action(action)
+        action_input = action_match["input"]
+
+        tool_input = action_input.strip(" ").strip('"')
+        safe_tool_input = self._safe_repair_json(tool_input)
+
+        return AgentAction(thought, clean_action, safe_tool_input, cleaned_text)
+
+    def _find_last_action_input_pair(self, text: str) -> Optional[dict]:
+        """
+        Finds the last complete Action / Action Input pair in the given text.
+        Useful for handling multiple action/observation cycles.
+        """
+        def _match_all_pairs(text: str) -> list[tuple[str, str]]:
+            pattern = (
+                r"Action\s*\d*\s*:\s*([^\n]+)"                            # Action content
+                r"\s*[\n]+"                                               # Optional whitespace/newline
+                r"Action\s*\d*\s*Input\s*\d*\s*:\s*"                      # Action Input label
+                r"([^\n]*(?:\n(?!Observation:|Thought:|Action\s*\d*\s*:|Final Answer:)[^\n]*)*)"
+            )
+            return re.findall(pattern, text, re.MULTILINE | re.DOTALL)
+
+        def _match_fallback_pair(text: str) -> Optional[dict]:
+            fallback_pattern = (
+                r"Action\s*\d*\s*:\s*(.*?)"
+                r"\s*Action\s*\d*\s*Input\s*\d*\s*:\s*"
+                r"(.*?)(?=\n(?:Observation:|Thought:|Action\s*\d*\s*:|Final Answer:)|$)"
+            )
+            match = re.search(fallback_pattern, text, re.DOTALL)
+            if match:
+                return {
+                    "action": match.group(1).strip(),
+                    "input": match.group(2).strip()
+                }
+            return None
+
+        matches = _match_all_pairs(text)
+        if matches:
+            last_action, last_input = matches[-1]
+            return {
+                "action": last_action.strip(),
+                "input": last_input.strip()
+            }
+
+        return _match_fallback_pair(text)
+
     def _clean_agent_observations(self, text: str) -> str:
         """
         Remove agent-written observations from the text.