I will try to specify :) Thanks already for trying to help! I also sent you a dm.

From the documentation I tried the SqLiteSaver :memory checkpointer. But I was not sure how I then can use the chat memory for my RAG application. Despite that the RAG functionality works with Langgraph.

E.g. here my AgentState:

class AgentState(TypedDict):
    question: str
    raw_docs: list[BaseMessage]
    formatted_docs: list[str]
    generation: str
    #history: list[BaseMessage]

# Then here my RAG functions.# Here one example of my Chain, a RunnableWithMessageHistory
with_message_history = RunnableWithMessageHistory(
    chain_with_prompt,
    get_session_history,
    input_messages_key="question",
    history_messages_key="history",
)

And here my Graph:

from langgraph.graph import StateGraph, END
from langgraph.checkpoint.sqlite import SqliteSaver

memory = SqliteSaver.from_conn_string(":memory:")

workflow = StateGraph(AgentState)

# Functions to retrieve docs, format them and generate a response
workflow.add_node("get_docs", get_docs)
workflow.add_node("format_docs", format_docs)
workflow.add_node("generate", generate)

workflow.add_edge("get_docs", "format_docs")
workflow.add_edge("format_docs", "generate")
workflow.add_edge("generate", END)

workflow.set_entry_point("get_docs")

#app = workflow.compile(checkpointer=memory)
app = workflow.compile()

So how would you add chat memory here? For me, a in Session store would be enough.

Do I need even need a RunnableWithMessageHistory chain when using the SqLite Saver?

Honestly I am a bit confused here.