Issue: Request: Need Help with CustomAgentExecutor for Accessing Dynamically Created Prompts
Labels: name: area: agent, name: auto:question

### Issue you'd like to raise.

I am currently piecing together some of the tutorials on the langchain documentation page to use a CustomOutputParser and a CustomPrompt Template similar to the ZeroShotReact Template.

While parsing the actions, I have a scenario where, the model returns Action as None & Action Input as None. In that case, I would like access to the dynamically created prompt with in the CustomOutputParser to call another LLM and return the action as call to another LLM to complete the action.


**Current Approach:**
```python
# Set up a prompt template
class CustomPromptTemplatePirate(StringPromptTemplate):
    # The template to use
    template: str
    # The list of tools available
    tools: List[Tool]

    def format(self, **kwargs) -> str:
        # Get the intermediate steps (AgentAction, Observation tuples)
        # Format them in a particular way
        intermediate_steps = kwargs.pop("intermediate_steps")
        thoughts = ""
        for action, observation in intermediate_steps:
            thoughts += action.log
            thoughts += f"\nObservation: {observation}\nThought: "
        # Set the agent_scratchpad variable to that value
        kwargs["agent_scratchpad"] = thoughts
        # Create a tools variable from the list of tools provided
        kwargs["tools"] = "\n".join([f"{tool.name}: {tool.description}" for tool in self.tools])
        # Create a list of tool names for the tools provided
        kwargs["tool_names"] = ", ".join([tool.name for tool in self.tools])
        return self.template.format(**kwargs)

class CustomOutputParserPirate(AgentOutputParser):

    def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:
        # Check if agent should finish
        if "Final Answer:" in llm_output:
            return AgentFinish(
                # Return values is generally always a dictionary with a single `output` key
                # It is not recommended to try anything else at the moment :)
                return_values={"output": llm_output.split("Final Answer:")[-1].strip()},
                log=llm_output,
            )
        # Parse out the action and action input
        regex = r"Action\s*\d*\s*:(.*?)\nAction\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)"
        match = re.search(regex, llm_output, re.DOTALL)
        if not match:
            raise OutputParserException(f"Could not parse LLM output: `{llm_output}`")
        action = match.group(1).strip()
        action_input = match.group(2)
        # Return the action and action input
        return AgentAction(tool=action, tool_input=action_input.strip(" ").strip('"'), log=llm_output)

# Set up the base template
template = """Complete the objective as best you can. You have access to the following tools:

{tools}

Use the following format:

Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question

These were previous tasks you completed:



Begin!

Question: {input}
{agent_scratchpad}"""

search = SerpAPIWrapper()
tools= [Tool(
    name="Search",
    func=search.run,
    description="useful for when you need to answer questions about current events",
)]
prompt = CustomPromptTemplatePirate(
    template=template,
    tools=tools,
    # This omits the `agent_scratchpad`, `tools`, and `tool_names` variables because those are generated dynamically
    # This includes the `intermediate_steps` variable because that is needed
    input_variables=["input", "intermediate_steps"]
)

output_parser = CustomOutputParserPirate()
# LLM chain consisting of the LLM and a prompt
llm_chain = LLMChain(llm=llm, prompt=prompt)

tool_names = [tool.name for tool in tools]
agent = LLMSingleActionAgent(
    llm_chain=llm_chain,
    output_parser=output_parser,
    stop=["\nObservation:"],
    allowed_tools=tool_names
)
agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
agent_executor.run("How many people live in canada as of 2023?")
```

**Challenges Faced:**
I am unable to figure out how to access the callback or any other similar approach to use the complete Prompt/User Input with in the context of the CustomOutputParser 

**Desired Outcome:**
```python
 def parse(self, llm_output: str, prompt:start) -> Union[AgentAction, AgentFinish]:
        # Check if agent should finish
        .....
		if  not 'Action' & not  'Action Input' :
			call  AgentAction with another llm fine_tuned defined as tool with the user question as input ex: Write a summary on  Canada. I need a way to access the user question here.
```

I would greatly appreciate any advice, documentation, or examples that could assist me in accomplishing this task. Thank you very much for your time and support.

### Suggestion:

_No response_