diff --git a/src/codeinterpreterapi/planners/planners.py b/src/codeinterpreterapi/planners/planners.py
index 7433845a..ec334c5d 100644
--- a/src/codeinterpreterapi/planners/planners.py
+++ b/src/codeinterpreterapi/planners/planners.py
@@ -2,6 +2,9 @@
 
 from langchain.agents import AgentExecutor
 from langchain.agents.agent import RunnableAgent
+from langchain.output_parsers import OutputFixingParser
+from langchain.schema import AIMessage, Generation
+from langchain_core.output_parsers import PydanticOutputParser
 from langchain_core.runnables import Runnable
 from pydantic import BaseModel, Field
 
@@ -32,6 +35,43 @@ class CodeInterpreterPlanList(BaseModel):
     )
 
 
+class Metadata(BaseModel):
+    id: str = Field(description="The ID of the content")
+    name: str = Field(description="The name of the content")
+    type: str = Field(description="The type of the content")
+
+
+class ResponseMetadata(BaseModel):
+    id: str = Field(description="The ID of the response")
+    model: str = Field(description="The model used for the response")
+    stop_reason: str = Field(description="The reason for stopping")
+    stop_sequence: str | None = Field(description="The stop sequence, if any")
+    usage: dict = Field(description="The token usage information")
+
+
+class CodeInterpreterPlanOutput(BaseModel):
+    agent_task_list: List[CodeInterpreterPlan] = Field(description="List of agent tasks")
+    metadata: Metadata = Field(description="Metadata of the content")
+    response_metadata: ResponseMetadata = Field(description="Metadata of the response")
+
+
+class CustomPydanticOutputParser(PydanticOutputParser):
+    def parse(self, text) -> CodeInterpreterPlanList:
+        print("parse text=", text)
+        input_data = self.preprocess_input(text)
+        return super().parse(input_data)
+
+    def preprocess_input(self, input_data) -> str:
+        if isinstance(input_data, AIMessage):
+            return input_data.content
+        elif isinstance(input_data, Generation):
+            return input_data.text
+        elif isinstance(input_data, str):
+            return input_data
+        else:
+            raise ValueError(f"Unexpected input type: {type(input_data)}")
+
+
 class CodeInterpreterPlanner:
     @staticmethod
     def choose_planner(ci_params: CodeInterpreterParams) -> Union[Runnable, AgentExecutor]:
@@ -52,19 +92,14 @@ def choose_planner(ci_params: CodeInterpreterParams) -> Union[Runnable, AgentExe
         # structured_llm
         structured_llm = ci_params.llm.bind_tools(tools=[CodeInterpreterPlanList])
 
+        # parser
+        parser = CustomPydanticOutputParser(pydantic_object=CodeInterpreterPlanList)
+        new_parser = OutputFixingParser.from_llm(parser=parser, llm=ci_params.llm)
         # runnable
-        runnable = (
-            create_complement_input(prompt) | prompt | structured_llm
-            # | StrOutputParser()
-            # | PlannerSingleOutputParser()
-        )
-        # runnable = assign_runnable_history(runnable, ci_params.runnable_config)
+        runnable = create_complement_input(prompt) | prompt | structured_llm | new_parser
 
         # agent
-        # planner_agent = create_react_agent(ci_params.llm_fast, ci_params.tools, prompt)
-        print("choose_planner prompt.input_variables=", prompt.input_variables)
-        remapped_inputs = create_complement_input(prompt).invoke({})
-        agent = RunnableAgent(runnable=runnable, input_keys=list(remapped_inputs.keys()))
+        agent = RunnableAgent(runnable=runnable, input_keys=list(prompt.input_variables))
 
         # return executor or runnable
         return_as_executor = False
diff --git a/src/codeinterpreterapi/supervisors/supervisors.py b/src/codeinterpreterapi/supervisors/supervisors.py
index 56734250..0e80cdd6 100644
--- a/src/codeinterpreterapi/supervisors/supervisors.py
+++ b/src/codeinterpreterapi/supervisors/supervisors.py
@@ -140,7 +140,7 @@ def route(next_action: str) -> str:
         llm_with_structured_output = ci_params.llm.with_structured_output(RouteSchema)
         # supervisor_agent = prompt | ci_params.llm | output_parser
         # supervisor_agent_for_executor = prompt | ci_params.llm
-        supervisor_agent_structured_output = prompt | llm_with_structured_output
+        supervisor_agent_structured_output = planner | prompt | llm_with_structured_output
 
         ci_params.supervisor_agent = supervisor_agent_structured_output