Invalid Format: Missing 'Action:' after 'Thought:Action:

[YogeshSaini85]

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Example Code

agent = create_react_agent(llm, tools, prompt)

agent_executor = AgentExecutor.from_agent_and_tools(
        agent=agent,
        tools=tools,
        verbose=False,
        handle_parsing_errors=True,
        max_iterations=4000,
        max_execution_time=60000,
        return_intermediate_steps=True
    )

agent_executor.invoke({"input": user_input},

Description

I have created a react agent and it fails frequently with this error:

Invalid Format: Missing 'Action:' after 'Thought:Action: get_prompt_tool
Action Input: None

Traceback (most recent call last):
File "C:\Users\rahukumar\AppData\Roaming\Python\Python312\site-packages\langchain\agents\agent.py", line 1224, in _iter_next_step
yield self._perform_agent_action(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\rahukumar\AppData\Roaming\Python\Python312\site-packages\langchain\agents\agent.py", line 1246, in _perform_agent_action
observation = tool.run(
^^^^^^^^^
File "C:\Users\rahukumar\AppData\Roaming\Python\Python312\site-packages\langchain_core\tools.py", line 452, in run
raise e
File "C:\Users\rahukumar\AppData\Roaming\Python\Python312\site-packages\langchain_core\tools.py", line 406, in run
parsed_input = self._parse_input(tool_input)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\rahukumar\AppData\Roaming\Python\Python312\site-packages\langchain_core\tools.py", line 299, in parse_input
key = next(iter(input_args.fields.keys()))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
StopIteration

Can someone please help figure out how we can fix it?

System Info

langchain==0.2.16
langchain-community==0.2.16
langchain-core==0.2.41
langchain-openai==0.1.7
langchain-text-splitters==0.2.4
langsmith==0.1.127

[dosubot[bot]]

To fix the "Invalid Format: Missing 'Action:' after 'Thought:" error in your LangChain react agent, ensure that the output from the language model follows the expected format. Specifically, after a "Thought:", there must be an "Action:" line followed by a JSON block. Here's the correct format:

Thought: agent thought here
Action:

{
    "action": "search",
    "action_input": "what is the temperature in SF"
}

The error occurs when the output does not match the expected pattern. The ReActOutputParser in LangChain uses regular expressions to parse the output, and it raises an OutputParserException if the "Action:" line is missing after "Thought:". Make sure your model's output includes the "Action:" line immediately following the "Thought:" line, and that the action details are enclosed in a JSON block to avoid this error ^[1][2][3][4].

Additionally, you might want to check if there are any issues with the prompt or the language model's configuration that could be causing it to generate output in an unexpected format.

To continue talking to Dosu, mention @dosu.

[YogeshSaini85]

@dosu I have this prompt, but still it sometimes doesn return in the correct format. Starting below, you should strictly follow this format: User input: the input given by user to build the process Thought: you should always think about what to do Action: the action to take, should be one of the tools [{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: Either the process is ready or I have no tools to execute. Final Answer: summary of the final process or a prompt for the user asking for input Example: User query: I want to sync Contact between salesforce and servicenow Thought: I have detected applications, let me add them using tool 'add_application_tool' Action: add_application_tool Action Input: 'Salesforce' Observation: True, Application 'Salesforce' added Thought: Application is added, and I have detected Contact object, I should add it using the tool 'add_object_to_application_tool' Action: add_object_to_application_tool Action Input: '["app1","obj1"]' ...

…

On Tue, Oct 1, 2024 at 5:44 PM dosubot[bot] ***@***.***> wrote: To fix the "Invalid Format: Missing 'Action:' after 'Thought:" error in your LangChain react agent, ensure that the output from the language model follows the expected format. Specifically, after a "Thought:", there must be an "Action:" line followed by a JSON block. Here's the correct format: Thought: agent thought here Action: { "action": "search", "action_input": "what is the temperature in SF" } The error occurs when the output does not match the expected pattern. The ReActOutputParser in LangChain uses regular expressions to parse the output, and it raises an OutputParserException if the "Action:" line is missing after "Thought:". Make sure your model's output includes the "Action:" line immediately following the "Thought:" line, and that the action details are enclosed in a JSON block to avoid this error [1] <https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/agents/output_parsers/react_single_input.py> [2] <https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/agents/mrkl/output_parser.py> [3] <https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/agents/output_parsers/react_json_single_input.py> [4] <https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/agents/react/output_parser.py> . Additionally, you might want to check if there are any issues with the prompt or the language model's configuration that could be causing it to generate output in an unexpected format. *To continue talking to Dosu <https://dosu.dev>, mention @dosu <https://github.com/dosu>.* — Reply to this email directly, view it on GitHub <#27018 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AHYB4NZTEVQVYX6EGXK3CSDZZKGZVAVCNFSM6AAAAABPFN2KQKVHI2DSMVQWIX3LMV43URDJONRXK43TNFXW4Q3PNVWWK3TUHMYTAOBQHA2DENA> . You are receiving this because you authored the thread.Message ID: ***@***.*** com>

[dosubot[bot]]

To ensure consistent output format from the language model, you can use the LMFormatEnforcer library. This tool helps enforce the output format by filtering tokens to match a specified JSON schema or regular expression.

Here's how you can adjust your setup:

1. Define a JSON Schema: Use a Pydantic model to define the desired output format.

   class ActionOutput(BaseModel):
       action: str
       action_input: str

2. Create a Prompt with the Schema: Format the prompt to include the JSON schema.

   def get_prompt(user_input):
       return f"""
       User input: {user_input}
       Thought: you should always think about what to do
       Action: the action to take, should be one of the tools
       Action Input: the input to the action
       Observation: the result of the action
       ...
       Final Answer: summary of the final process or a prompt for the user
       """

3. Use LMFormatEnforcer: Apply the LMFormatEnforcer with the JSON schema to ensure the output conforms to the specified format.

   from langchain_experimental.llms import LMFormatEnforcer
   
   lm_format_enforcer = LMFormatEnforcer(
       json_schema=ActionOutput.schema(), pipeline=hf_model
   )
   results = lm_format_enforcer.predict(get_prompt("I want to sync Contact between Salesforce and ServiceNow"))
   print(results)

This approach helps ensure that the output is free of parsing errors and conforms to the exact specification of the JSON schema ^[1].

To continue talking to Dosu, mention @dosu.

[YogeshSaini85]

@dosu 1. Shouldn't the schema have 'thought' as well? 2. How would it work in cases where it returns the 'Final Answer' and not Thought,Action,Action Input? 3. How would I pass LMFormatEnforcer to an agent/agentexecutor? 4. How would it ensure the llm returns in correct format? By sending it back and telling the llm that it's made a mistake?

…

On Tue, Oct 1, 2024 at 10:21 PM dosubot[bot] ***@***.***> wrote: To ensure consistent output format from the language model, you can use the LMFormatEnforcer library. This tool helps enforce the output format by filtering tokens to match a specified JSON schema or regular expression. Here's how you can adjust your setup: 1. *Define a JSON Schema*: Use a Pydantic model to define the desired output format. class ActionOutput(BaseModel): action: str action_input: str 2. *Create a Prompt with the Schema*: Format the prompt to include the JSON schema. def get_prompt(user_input): return f""" User input: {user_input} Thought: you should always think about what to do Action: the action to take, should be one of the tools Action Input: the input to the action Observation: the result of the action ... Final Answer: summary of the final process or a prompt for the user """ 3. *Use LMFormatEnforcer*: Apply the LMFormatEnforcer with the JSON schema to ensure the output conforms to the specified format. from langchain_experimental.llms import LMFormatEnforcer lm_format_enforcer = LMFormatEnforcer( json_schema=ActionOutput.schema(), pipeline=hf_model )results = lm_format_enforcer.predict(get_prompt("I want to sync Contact between Salesforce and ServiceNow"))print(results) This approach helps ensure that the output is free of parsing errors and conforms to the exact specification of the JSON schema [1] <https://github.com/langchain-ai/langchain/blob/master/docs/docs/integrations/llms/lmformatenforcer_experimental.ipynb> . *To continue talking to Dosu <https://dosu.dev>, mention @dosu <https://github.com/dosu>.* — Reply to this email directly, view it on GitHub <#27018 (reply in thread)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AHYB4NZZ5R2BUQKCXQ2ERSTZZLHJ3AVCNFSM6AAAAABPFN2KQKVHI2DSMVQWIX3LMV43URDJONRXK43TNFXW4Q3PNVWWK3TUHMYTAOBRGE2DQNI> . You are receiving this because you authored the thread.Message ID: ***@***.*** com>

[dosubot[bot]]

1. Include 'Thought' in the Schema: Yes, you should include 'thought' in your schema to ensure the output format is complete. Modify your Pydantic model to include this field.

   class ActionOutput(BaseModel):
       thought: str
       action: str
       action_input: str

2. Handling 'Final Answer': The ReActJsonSingleInputOutputParser can handle cases where the output is a 'Final Answer' by checking for this in the output and returning an AgentFinish object if found. Ensure your prompt and model are configured to output 'Final Answer' when appropriate.

3. Integrate with Agent/AgentExecutor: You can pass the LMFormatEnforcer to an agent by using it as part of the language model pipeline. This ensures that the output is validated before being processed by the agent.

4. Ensuring Correct Format: The LMFormatEnforcer uses a JSON schema or regex to validate the output. If the output doesn't match, it can raise an exception or handle it as configured. This helps ensure the LLM returns the correct format by enforcing strict compliance with the schema ^[1][2].

To continue talking to Dosu, mention @dosu.

[lz7626]

Hi, I met the same error, but when I used
Action {
"action": "search",
"action_input": "what is the temperature in SF"
}, but I got Prompt missing required variables: {'tool_names'}, really appreciate some help here, thank you

[eyurtsev]

Move to langgraph and use a chat model that supports tool calling.

https://langchain-ai.github.io/langgraph/how-tos/create-react-agent/
https://langchain-ai.github.io/langgraph/tutorials/introduction/
https://python.langchain.com/docs/concepts/#functiontool-calling