How to create CustomAgentOutputParser that uses Retry parser
Labels: name: area: agent, name: auto:bug, name: auto:question

### System Info

langchain ==0.0.287
python
fastapi


### Who can help?

_No response_

### Information

- [x] The official example notebooks/scripts
- [ ] My own modified scripts

### Related Components

- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [x] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async

### Reproduction

```
class CustomOutputParser(AgentOutputParser):

    def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:
        # Check if agent should finish
        if "Final Answer:" in llm_output:
            final_answer = llm_output.split("Final Answer:")[-1].strip()

            # Ensure the final answer doesn't exceed 160 characters
            if len(final_answer) > 160:
                final_answer = final_answer[:157] + '...'

            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": final_answer},
                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)
```

Here is the OutputParser that I got from the tutorial of [Custom LLM Agent ](https://python.langchain.com/docs/modules/agents/how_to/custom_llm_agent) However it fails sometimes to parse the output.


I only add this part:

```
            # Ensure the final answer doesn't exceed 160 characters
            if len(final_answer) > 160:
                final_answer = final_answer[:157] + '...'
                
             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": final_answer},
                log=llm_output,
            )
```
To ensure that the output is 160 char however somehow it affects the output and the system throws an error on the outputparser like this:

```
chain/error] [1:chain:AgentExecutor] [4.40s] Chain run errored with error:
"OutputParserException('Could not parse LLM output: `I found the closing price of Nvidia stock: $455.72 on 09/08/2023. It gained 0.27% compared to the opening price.`')"
Traceback (most recent call last):
  File "/Users/egehosgungor/Desktop/Side_Hustle/smsbotu/backend/app/llm/langchain_custom_agent_deneme.py", line 160, in <module>
    asyncio.run(main())
  File "/Users/egehosgungor/miniconda3/envs/fastapi/lib/python3.9/asyncio/runners.py", line 44, in run
    return loop.run_until_complete(main)
  File "/Users/egehosgungor/miniconda3/envs/fastapi/lib/python3.9/asyncio/base_events.py", line 647, in run_until_complete
    return future.result()
  File "/Users/egehosgungor/Desktop/Side_Hustle/smsbotu/backend/app/llm/langchain_custom_agent_deneme.py", line 155, in main
    response = await agent_executor.arun("Nvidia hissesi kapanis")
  File "/Users/egehosgungor/miniconda3/envs/fastapi/lib/python3.9/site-packages/langchain/chains/base.py", line 561, in arun
    await self.acall(
  File "/Users/egehosgungor/miniconda3/envs/fastapi/lib/python3.9/site-packages/langchain/chains/base.py", line 361, in acall
    raise e
  File "/Users/egehosgungor/miniconda3/envs/fastapi/lib/python3.9/site-packages/langchain/chains/base.py", line 355, in acall
    await self._acall(inputs, run_manager=run_manager)
  File "/Users/egehosgungor/miniconda3/envs/fastapi/lib/python3.9/site-packages/langchain/agents/agent.py", line 1171, in _acall
    next_step_output = await self._atake_next_step(
  File "/Users/egehosgungor/miniconda3/envs/fastapi/lib/python3.9/site-packages/langchain/agents/agent.py", line 1026, in _atake_next_step
    raise e
  File "/Users/egehosgungor/miniconda3/envs/fastapi/lib/python3.9/site-packages/langchain/agents/agent.py", line 1015, in _atake_next_step
    output = await self.agent.aplan(
  File "/Users/egehosgungor/miniconda3/envs/fastapi/lib/python3.9/site-packages/langchain/agents/agent.py", line 458, in aplan
    return self.output_parser.parse(output)
  File "/Users/egehosgungor/Desktop/Side_Hustle/smsbotu/backend/app/llm/langchain_custom_agent_deneme.py", line 123, in parse
    raise OutputParserException(
langchain.schema.output_parser.OutputParserException: Could not parse LLM output: `I found the closing price of Nvidia stock: $455.72 on 09/08/2023. It gained 0.27% compared to the opening price.`
```

Question 1: Why this happens when I only truncate the finalAnswer output ? How does it has an affect on the system?
Question 2: I would like to wrap my output parser with something like https://python.langchain.com/docs/modules/model_io/output_parsers/retry so If it fails to parse the output it should come to an llmchain and that llmchain's only job should be parsing correctly OR  if there is no answer giving the correct output as "I couldnt find the answer for {x}" etc.





### Expected behavior

I was expecting to truncating the final_answer only will have an effect on the output's character count however it does affect the whole system. 

How can I connect the outputParsers?