Error "variable agent_scratchpad should be a list of base messages", do not know how to debug

Hi,
I’m new to here, with chatgpt, i’m trying to write an agent to help to analyze log files. When adding memory function, I got this error. Chatgpt cannot give me good answer, could somebody help? Thanks in advance.

My file is like:

import os
import traceback
import re
import pandas as pd
from langchain_ollama import OllamaLLM
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.schema.document import Document
from langchain.agents import Tool, AgentExecutor, create_react_agent
from langchain.chains import RetrievalQA
from langchain.memory import ConversationBufferMemory
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.runnables import RunnablePassthrough
from langchain_core.messages import AIMessage, HumanMessage

# === CONFIGURATION ===
log_path = "/env_test/tftpboot/Book2.csv"
base_url = "http://10.59.19.3:11434"
model_name = "llama3"

# === 1. LOAD CSV LOG FILE ===
df = pd.read_csv(log_path, header=None).dropna(axis=1, how='all').fillna("").astype(str)

# === 2. Build FAISS Index ===
docs = [Document(page_content="\n".join(f"{k}={v}" for k, v in row.items() if v.strip()), metadata={"row_index": i}) for i, row in df.iterrows()]
embedding_model = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
db = FAISS.from_documents(docs, embedding_model)

# === 3. Setup LLM and Memory ===
llm = OllamaLLM(model=model_name, base_url=base_url)
memory = ConversationBufferMemory(
        memory_key="chat_history", 
        return_messages=True,
        input_key="input"    #new required field in LangChain >0.1.0
        )

# === 4. Shared cache to store last result row indexes ===
last_indexes = []

# === 5. Tool Helpers ===
def parse_keyword(q):
    match = re.search(r"(?:how many|count)?\s*(\w+)(?:\s+logs?)?", q, re.IGNORECASE)
    return match.group(1) if match else q.strip()

def get_row_indexes_by_keyword(keyword: str):
    global last_indexes
    matches = df[df.apply(lambda row: row.astype(str).str.contains(keyword, case=False).any(), axis=1)]
    last_indexes = matches.index.tolist()
    return_str = ", ".join(map(str, last_indexes)) if last_indexes else "No matching rows found"
    print("return_str is "+return_str)
    return return_str

def get_row_indexes_by_field(field: str, value: str):
    global last_indexes
    matches = df[df[field].astype(str).str.contains(value, case=False, na=False)]
    last_indexes = matches.index.tolist()
    return_str = ", ".join(map(str, last_indexes)) if last_indexes else "No matching rows found"
    print("return_str is "+return_str)
    return return_str

def print_rows_by_index(indexes: list[int], limit=5) -> str:
    output = []
    for idx in indexes[:limit]:
        if 0 <= idx < len(df):
            row = df.iloc[idx]
            clean = row[row != ""]
            output.append(f"\n--- Row {idx} ---\n" + clean.to_string())
    return "\n".join(output) if output else "No rows to show."

# === 6. Tools ===
tools = [
    Tool(
        name="FindRowIndexesByKeyword",
        func=lambda q: get_row_indexes_by_keyword(parse_keyword(q)),
        description="Return a list of row indexes where any field contains the given keyword"
    ),
    Tool(
        name="FindRowIndexesByField",
        func=lambda q: get_row_indexes_by_field(*re.search(r"(\w+)=['\"]?(.+?)['\"]?", q).groups()),
        description="Return a list of row indexes where a specific field equals a value, e.g., subtype='ha'"
    ),
    Tool(
        name="PrintRowsByIndex",
        func=lambda q: print_rows_by_index(last_indexes),
        description="Print the rows found from the last search using saved row indexes"
    )
]
#def format_log_to_messages(intermediate_steps):
#    """Construct the scratchpad that lets the agent continue its thought process."""
#    thoughts = []
#    for action, observation in intermediate_steps:
#        thoughts.append(AIMessage(content=action.log))
#        human_message = HumanMessage(content=f"Observation: {observation}")
#        thoughts.append(human_message)
#    return thoughts
#
## Ensure agent_scratchpad is formatted correctly
#agent_scratchpad = format_log_to_messages([])  # Initialize with an empty list
agent_scratchpad = []

# === 7. Agent Setup ===
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful log analysis assistant.\n\n"
               "You have access to the following tools:\n\n{tools}\n\n"
               "Use them if they help answer the question.\n"
               "Tools available: {tool_names}"),
    ("human", "{input}"),
    MessagesPlaceholder(variable_name="agent_scratchpad"),
])
print("prompt is "+str(prompt))
agent = create_react_agent(llm=llm, tools=tools, prompt=prompt)
agent_executor = AgentExecutor(
    agent=agent,
    tools=tools,
    memory=memory,
    verbose=True,
    handle_parsing_errors=True,
    return_intermediate_steps=True,
    agent_scratchpad_key="agent_scratchpad",   # ? match placeholder
    input_key="input",                         # ? match prompt
)


# === 8. Main Loop ===
print("\n?? You can now ask questions about the logs (type 'exit' to quit).")
while True:
    query = input("\n?? Ask: ").strip()
    if query.lower() == "exit":
        break
    if isinstance(agent_scratchpad, list):
       print("agent_scratchpad is a list")
    #result = agent_executor.invoke({"input": query})
    try:
        result = agent_executor.invoke({"input": query})
    except Exception as e:
        print(f"? Exception: {e}")
        traceback.print_exc()
        result = None
    print("\n? Answer:\n", result["answer"])

root@cenubu81:/env_test/my_codes/document_analyzer# 

When I run it, i got:

(env) root@cenubu81:/env_test/my_codes/document_analyzer# python3 agent2.py 
/env_test/my_codes/document_analyzer/agent2.py:26: LangChainDeprecationWarning: The class `HuggingFaceEmbeddings` was deprecated in LangChain 0.2.2 and will be removed in 1.0. An updated version of the class exists in the :class:`~langchain-huggingface package and should be used instead. To use it run `pip install -U :class:`~langchain-huggingface` and import as `from :class:`~langchain_huggingface import HuggingFaceEmbeddings``.
  embedding_model = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
/env_test/my_codes/document_analyzer/agent2.py:31: LangChainDeprecationWarning: Please see the migration guide at: https://python.langchain.com/docs/versions/migrating_memory/
  memory = ConversationBufferMemory(
prompt is input_variables=['agent_scratchpad', 'input', 'tool_names', 'tools'] input_types={'agent_scratchpad': list[typing.Annotated[typing.Union[typing.Annotated[langchain_core.messages.ai.AIMessage, Tag(tag='ai')], typing.Annotated[langchain_core.messages.human.HumanMessage, Tag(tag='human')], typing.Annotated[langchain_core.messages.chat.ChatMessage, Tag(tag='chat')], typing.Annotated[langchain_core.messages.system.SystemMessage, Tag(tag='system')], typing.Annotated[langchain_core.messages.function.FunctionMessage, Tag(tag='function')], typing.Annotated[langchain_core.messages.tool.ToolMessage, Tag(tag='tool')], typing.Annotated[langchain_core.messages.ai.AIMessageChunk, Tag(tag='AIMessageChunk')], typing.Annotated[langchain_core.messages.human.HumanMessageChunk, Tag(tag='HumanMessageChunk')], typing.Annotated[langchain_core.messages.chat.ChatMessageChunk, Tag(tag='ChatMessageChunk')], typing.Annotated[langchain_core.messages.system.SystemMessageChunk, Tag(tag='SystemMessageChunk')], typing.Annotated[langchain_core.messages.function.FunctionMessageChunk, Tag(tag='FunctionMessageChunk')], typing.Annotated[langchain_core.messages.tool.ToolMessageChunk, Tag(tag='ToolMessageChunk')]], FieldInfo(annotation=NoneType, required=True, discriminator=Discriminator(discriminator=<function _get_type at 0x79b96f3b6b60>, custom_error_type=None, custom_error_message=None, custom_error_context=None))]]} partial_variables={} messages=[SystemMessagePromptTemplate(prompt=PromptTemplate(input_variables=['tool_names', 'tools'], input_types={}, partial_variables={}, template='You are a helpful log analysis assistant.\n\nYou have access to the following tools:\n\n{tools}\n\nUse them if they help answer the question.\nTools available: {tool_names}'), additional_kwargs={}), HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['input'], input_types={}, partial_variables={}, template='{input}'), additional_kwargs={}), MessagesPlaceholder(variable_name='agent_scratchpad')]

?? You can now ask questions about the logs (type 'exit' to quit).

?? Ask: how many logs contain shutdown
agent_scratchpad is a list


> Entering new AgentExecutor chain...
? Exception: variable agent_scratchpad should be a list of base messages, got  of type <class 'str'>
Traceback (most recent call last):
  File "/env_test/my_codes/document_analyzer/agent2.py", line 134, in <module>
    result = agent_executor.invoke({"input": query})
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/env/lib/python3.12/site-packages/langchain/chains/base.py", line 167, in invoke
    raise e
  File "/root/env/lib/python3.12/site-packages/langchain/chains/base.py", line 157, in invoke
    self._call(inputs, run_manager=run_manager)
  File "/root/env/lib/python3.12/site-packages/langchain/agents/agent.py", line 1620, in _call
    next_step_output = self._take_next_step(
                       ^^^^^^^^^^^^^^^^^^^^^
  File "/root/env/lib/python3.12/site-packages/langchain/agents/agent.py", line 1326, in _take_next_step
    [
  File "/root/env/lib/python3.12/site-packages/langchain/agents/agent.py", line 1354, in _iter_next_step
    output = self._action_agent.plan(
             ^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/env/lib/python3.12/site-packages/langchain/agents/agent.py", line 461, in plan
    for chunk in self.runnable.stream(inputs, config={"callbacks": callbacks}):
  File "/root/env/lib/python3.12/site-packages/langchain_core/runnables/base.py", line 3438, in stream
    yield from self.transform(iter([input]), config, **kwargs)
  File "/root/env/lib/python3.12/site-packages/langchain_core/runnables/base.py", line 3424, in transform
    yield from self._transform_stream_with_config(
  File "/root/env/lib/python3.12/site-packages/langchain_core/runnables/base.py", line 2215, in _transform_stream_with_config
    chunk: Output = context.run(next, iterator)
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/env/lib/python3.12/site-packages/langchain_core/runnables/base.py", line 3386, in _transform
    yield from final_pipeline
  File "/root/env/lib/python3.12/site-packages/langchain_core/runnables/base.py", line 1429, in transform
    for ichunk in input:
  File "/root/env/lib/python3.12/site-packages/langchain_core/runnables/base.py", line 5647, in transform
    yield from self.bound.transform(
  File "/root/env/lib/python3.12/site-packages/langchain_core/runnables/base.py", line 1429, in transform
    for ichunk in input:
  File "/root/env/lib/python3.12/site-packages/langchain_core/runnables/base.py", line 1447, in transform
    yield from self.stream(final, config, **kwargs)
  File "/root/env/lib/python3.12/site-packages/langchain_core/runnables/base.py", line 1014, in stream
    yield self.invoke(input, config, **kwargs)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/env/lib/python3.12/site-packages/langchain_core/prompts/base.py", line 216, in invoke
    return self._call_with_config(
           ^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/env/lib/python3.12/site-packages/langchain_core/runnables/base.py", line 1940, in _call_with_config
    context.run(
  File "/root/env/lib/python3.12/site-packages/langchain_core/runnables/config.py", line 428, in call_func_with_variable_args
    return func(input, **kwargs)  # type: ignore[call-arg]
           ^^^^^^^^^^^^^^^^^^^^^
  File "/root/env/lib/python3.12/site-packages/langchain_core/prompts/base.py", line 190, in _format_prompt_with_error_handling
    return self.format_prompt(**_inner_input)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/env/lib/python3.12/site-packages/langchain_core/prompts/chat.py", line 726, in format_prompt
    messages = self.format_messages(**kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/env/lib/python3.12/site-packages/langchain_core/prompts/chat.py", line 1187, in format_messages
    message = message_template.format_messages(**kwargs)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/env/lib/python3.12/site-packages/langchain_core/prompts/chat.py", line 183, in format_messages
    raise ValueError(msg)  # noqa: TRY004
    ^^^^^^^^^^^^^^^^^^^^^
ValueError: variable agent_scratchpad should be a list of base messages, got  of type <class 'str'>
Traceback (most recent call last):
  File "/env_test/my_codes/document_analyzer/agent2.py", line 139, in <module>
    print("\n? Answer:\n", result["answer"])
                           ~~~~~~^^^^^^^^^^
TypeError: 'NoneType' object is not subscriptable
(env) root@cenubu81:/env_test/my_codes/document_analyzer# 


Thank you very much!!!

Hans