# 04. Add memory to Agent
## Add memory to Agent
Currently, chatbots cannot remember their past interactions themselves, so there are limits to conducting consistent multi-turn conversations.
To solve this in this tutorial **memory** Add.
**Reference**
This time pre-built `ToolNode` Wow `tools_condition` Use it.
1. [ToolNode ](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.tool_node.ToolNode): Node for tool call
2. [tools\_condition ](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.tool_node.tools_condition): Condition branch based on whether the tool is called
Our chatbots can now use tools to answer user questions, but of previous interaction **context** I don't remember. This limits the ability to conduct multi-turn conversations.
`LangGraph` has **persistent checkpointing** Solve this problem.
When compiling graphs `checkpointer` When providing and calling the graph `thread_id` If you provide, `LangGraph` After each step **Save status automatically** To. same `thread_id` When you call the graph again using, the graph loads the saved state so that the chatbot can continue the conversation at the point where it was previously stopped.
**checkpointing** Is much more powerful than LangChain's memory function. (Maybe you can check this naturally by completing this tutorial.)
```python
# Configuration file for managing API keys as environment variables
from dotenv import load_dotenv
# Load API key information
load_dotenv()
```
```
True
```
```python
# Set up LangSmith tracking. https://smith.langchain.com
# !pip install -qU langchain-teddynote
from langchain_teddynote import logging
# Enter a project name.
logging.langsmith("CH17-LangGraph-Modules")
```
```
Start tracking LangSmith.
[Project name]
CH17-LangGraph-Modules
```
But before going too far, to enable multi-turn conversations **checkpointing** Let's add it.
`MemorySaver` Generates checkpointer.
```python
from langgraph.checkpoint.memory import MemorySaver
# Create a memory store
memory = MemorySaver()
```
**Reference**
In this tutorial `in-memory checkpointer` Use.
But in the production phase, this `SqliteSaver` or `PostgresSaver` You can change to and connect to your own DB.
```python
from typing import Annotated
from typing_extensions import TypedDict
from langchain_openai import ChatOpenAI
from langchain_teddynote.tools.tavily import TavilySearch
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages
from langgraph.prebuilt import ToolNode, tools_condition
########## 1. State Definition ##########
# State Definition
class State(TypedDict):
# Add a message list comment
messages: Annotated[list, add_messages]
########## 2. Tool definition and binding ##########
# Initialize tool
tool = TavilySearch(max_results=3)
tools = [tool]
# LLM Initialization
llm = ChatOpenAI(model="gpt-4o-mini")
# Combining tools and LLM
llm_with_tools = llm.bind_tools(tools)
########## 3. add note ##########
# Defining a chatbot function
def chatbot(state: State):
# Calling and returning messages
return {"messages": [llm_with_tools.invoke(state["messages"])]}
# Create a state graph
graph_builder = StateGraph(State)
# Add a chatbot node
graph_builder.add_node("chatbot", chatbot)
# Creating and adding tool nodes
tool_node = ToolNode(tools=[tool])
# Add tool node
graph_builder.add_node("tools", tool_node)
# Conditional Edge
graph_builder.add_conditional_edges(
"chatbot",
tools_condition,
)
########## 4. add edge ##########
# tools > chatbot
graph_builder.add_edge("tools", "chatbot")
# START > chatbot
graph_builder.add_edge(START, "chatbot")
# chatbot > END
graph_builder.add_edge("chatbot", END)
```
Finally, provided `checkpointer` Compile graphs using
```python
# Compile the graph builder
graph = graph_builder.compile(checkpointer=memory)
```
Graph connectivity `LangGraph-Agent` Same as
Just, what was added this time was that the graph handled each node `State` Just checkpointing.
```python
from langchain_teddynote.graphs import visualize_graph
# Graph visualization
visualize_graph(graph)
```
### RunnableConfig Settings
`RunnableConfig` Define `recursion_limit` and `thread_id` Set.
* `recursion_limit` : Maximum number of nodes to visit. More than that, RecursionError
* `thread_id` : Thread ID setting
`thread_id` Is used to separate conversation sessions. In other words, the storage of memory `thread_id` It is done individually according to.
```python
from langchain_core.runnables import RunnableConfig
config = RunnableConfig(
recursion_limit=10, # Visit up to 10 nodes, more than that will result in RecursionError
configurable={"thread_id": "1"}, # 스레드 ID 설정
)
```
```python
# First question
question = (
"My name is `Teddy Note`. I run a YouTube channel. Nice to meet you."
)
for event in graph.stream({"messages": [("user", question)]}, config=config):
for value in event.values():
value["messages"][-1].pretty_print()
```
```
================================== Ai Message ==================================
Hello, Teddy Note! Nice to meet you. I'm running a YouTube channel, so it's cool! What content are you mainly dealing with?
```
```python
# 이어지는 질문
question = "What did you say my name was?"
for event in graph.stream({"messages": [("user", question)]}, config=config):
for value in event.values():
value["messages"][-1].pretty_print()
```
```
================================== Ai Message ==================================
Your name is Teddy Note.
```
this time `RunnableConfig` of `thread_id` After changing, I'll ask if you remember the previous conversation.
```python
from langchain_core.runnables import RunnableConfig
question = "What did you say my name was?"
config = RunnableConfig(
recursion_limit=10, # Visit up to 10 nodes, more than that will result in RecursionError
configurable={"thread_id": "2"}, # Thread ID setting
)
for event in graph.stream({"messages": [("user", question)]}, config=config):
for value in event.values():
value["messages"][-1].pretty_print()
```
```
================================== Ai Message ==================================
Sorry, there is no function to remember your name. If you haven't mentioned your name in the previous conversation, I don't know. I hope you give me your name!
```
### Snapshot: Check the saved State
So far, I have made several checkpoints in two different threads.
`Checkpoint` Is the current status value, its configuration, and to be processed `next` Node included.
Graph at a given setting `state` Anytime to check `get_state(config)` Call
```python
from langchain_core.runnables import RunnableConfig
config = RunnableConfig(
configurable={"thread_id": "1"}, # Thread ID setting
)
# Create a graph state snapshot
snapshot = graph.get_state(config)
snapshot.values["messages"]
```
```
[HumanMessage (content=' My name is `Tedinot`. I am running a YouTube channel. Nice to meet you', additional_kwargs={}, response_metadata={}, id='0b4fb756-225d-49a0-a90b-acb39cb42d48'), AIMessage (content='Hello, Teddy Nice to meet you. I'm running a YouTube channel, so it's cool! '1':'Toks={'fusal': None}, response_metadata={'token_usage': {'completion_tokens': 45,'prompt_tokens': 105,' lun-c9143f45-843c-478c-9360-47d2bce2207e-0', usage_metadata={'input_tokens': 105,'total_tokens': 150,' Nok:'Get',None={, respons_metadata}'token_usage': ={'completion_tokens': 12,'prompt_tokens': 165,'total_tokens':1 gpt-4o-mini-2024-07-18','system_fingerprint':'fp_0ba0d124f1','finish_reason':'stop','logprobs': None}, id='run-7cc53da-982d- response_metadata={}, id='c0c1ad0a-7c51-49ba-9aa8-fa125b501ab2'), AIMessage (content=' Your name is Tedinot.', additional_kwargs={'refus None,'reasoning_tokens': 0},'prompt_tokens_details': {'audio_tokens': None,'cached_tokens':'gpt-4o-mini-2024-07 'stop','logprobs': None}, id='run-e65d423b-fb03-4237-8f51-15d8b56df860-0', usage_metadata={'input_tokens': 192,' 'stop','logprobs': None}, id='run-e65d423b-fb03-4237-8f51-15d8b56df860-0', usage_metadata={'input_tokens': 192,'
```
`snapshot.config` You can check the config information set to output.
```python
# Set config information
snapshot.config
```
```
{'configurable': {'thread_id': '1','checkpoint_ns':'','checkpoint_id': '1ef99493-62b0-6020-8007-4fb8c65ebabc'}}
```
`snapshot.value` You can check the state values stored so far by outputting.
```python
# stored values
snapshot.values
```
```
{'messages': [HumanMessage (content=' My name is `Tedinot`. I am running a YouTube channel. Nice to meet you', additional_kwargs={}, response_metadata={}, id='0b4fb756-225d-49a0-a90b-acb39cb42d48'), AIMessage (content='Hello, Teddy Nice to meet you. I'm running a YouTube channel, so it's cool! '1':'Toks={'fusal': None}, response_metadata={'token_usage': {'completion_tokens': 45,'prompt_tokens': 105,' id='run-c9143f45-843c-478c-9360-47d2bce207e-0', usage_metadata={'input_tokens': 45,'total_tokens'Your name is Tedinot.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 12,'prompt_tokens': 165,'tot '4p','fp_0ba0d124f1','fp','fin','gout','gop','logprobs': None} response_metadata={}, id='c0c1ad0a-7c51-49ba-9aa8-fa125b501ab2'), AIMessage (content=' Your name is Tedinot.', additional_kwargs={'refus {'audio_tokens': None,'reasoning_tokens': 0},'prompt_tokens_details': {'audio_tokens': None,'model_name':'gpt 0}})]} 'fp_0ba0d124f1','finish_reason':'stop','logprobs': None}, id='run-e65d423b-fb03-4237-8f51-15d8b56df860-0', usage_met 'fp_0ba0d124f1','finish_reason':'stop','logprobs': None}, id='run-e65d423b-fb03-4237-8f51-15d8b56df860-0', usage_met
```
`snapshot.next` Output to find forward at this point **Check the following nodes** You can.
**END** Since it has been reached, the next node is output with an empty value.
```python
# next node
snapshot.next
```
```
()
```
```python
snapshot.metadata["writes"]["chatbot"]["messages"][0]
```
```
AIMessage (content=' Your name is Tedinot.', additional_kwargs={'fusal': None}, response_metadata={'token_usage': 12,'promrun-e65d423b-fb03-4237-8f51-15d8b56df860-0', usage_metadata={'input_tokens': 192,'output_tokens': 12,'total_tokens': 204,' reasoning': 0}})
```
To visualize metadata of complex structures `display_message_tree` Use functions.
```python
from langchain_teddynote.messages import display_message_tree
# Metadata (output in tree format)
display_message_tree(snapshot.metadata)
```
