LangGraph (2/4): short-term memory

LangGraph (2/4): short-term memory

In the first part, we built a basic chatbot with LangGraph and added tools to it. The problem is that it doesn’t remember anything from one message to the next. In this chapter, we give it **short-term memory**: we persist the graph state within a thread using checkpointers.

⚠️ This chapter continues the code from the previous part. To run it, you need the environment and chatbot from Part 1.

Disclaimer: This post has been translated to English using a machine translation model. Please, let me know if you find any mistakes.

📚 **This entry is part of the _Complete LangGraph Guide_ series**, divided into four chapters that are read in order:

> * Part 1: Basic chatbot and tools

* 👉 **Part 2: Short-term memory**

* Part 3: Long-term memory and human-in-the-loop

* Part 4: State customization and checkpoints

Add memory to the chatbot - short-term memory, memory within the threadlink image 6

Our chatbot can now use tools to answer users' questions, but it does not remember the context of previous interactions. This limits its ability to have coherent, multi-turn conversations.

LangGraph solves this problem through persistent checkpoints or checkpoints. If we provide a checkpointer when compiling the graph and a thread_id when calling the graph, LangGraph automatically saves the state after each iteration in the conversation.

When we invoke the graph again using the same thread_id, the graph will load its saved state, allowing the chatbot to continue where it left off.

We’ll see later that this checkpointing is much more powerful than simple chat memory: it allows you to save and resume complex states at any time for error recovery, human in the loop workflows, interactions over time, and more. But before looking at all of that, let’s add checkpoints to enable multi-iteration conversations.

	

		

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import os
import dotenv
 
dotenv.load_dotenv()
 
HUGGINGFACE_TOKEN = os.getenv("HUGGINGFACE_LANGGRAPH")
TAVILY_API_KEY = os.getenv("TAVILY_LANGGRAPH_API_KEY")

	

	
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To begin, we create a checkpointer MemorySaver.

	

		

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from langgraph.checkpoint.memory import MemorySaver
 
memory = MemorySaver()

	

	
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**Warning**

> We are using an in-memory checkpointer, that is, it is stored in RAM and when the graph finishes executing it is removed. This works for our case, since it is an example for learning how to use LangGraph. In a production application, it is likely that we will need to change this to use it with SqliteSaver or PostgresSaver and connect to our own database.

Next, we define the graph.

	

		

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from typing import Annotated
 
from typing_extensions import TypedDict
 
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages
 
class State(TypedDict):
    messages: Annotated[list, add_messages]
 
graph_builder = StateGraph(State)

	

	
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We define the tool

	

		

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from langchain_community.utilities.tavily_search import TavilySearchAPIWrapper
from langchain_community.tools.tavily_search import TavilySearchResults
 
wrapper = TavilySearchAPIWrapper(tavily_api_key=TAVILY_API_KEY)
tool = TavilySearchResults(api_wrapper=wrapper, max_results=2)
tools_list = [tool]

	

	
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Next, the LLM with the bind_tools and we add it to the graph

	

		

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from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
from huggingface_hub import login
os.environ["LANGCHAIN_TRACING_V2"] = "false" # Disable LangSmith tracing
 
# Create the LLM
login(token=HUGGINGFACE_TOKEN) # Login to HuggingFace to use the model
MODEL = "Qwen/Qwen2.5-72B-Instruct"
model = HuggingFaceEndpoint(
    repo_id=MODEL,
    task="text-generation",
    max_new_tokens=512,
    do_sample=False,
    repetition_penalty=1.03,
)
# Create the chat model
llm = ChatHuggingFace(llm=model)
 
# Modification: tell the LLM which tools it can call
llm_with_tools = llm.bind_tools(tools_list)
 
# Define the chatbot function
def chatbot_function(state: State):
    return {"messages": [llm_with_tools.invoke(state["messages"])]}
 
# Add the chatbot node
graph_builder.add_node("chatbot_node", chatbot_function)

	

	
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&lt;langgraph.graph.state.StateGraph at 0x1173534d0&gt;

Earlier we built our own BasicToolNode to learn how it works; now we’ll replace it with LangGraph’s ToolNode method and tools_condition, since they do some nice things like parallel API execution. Aside from that, the rest is the same as before.

	

		

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from langgraph.prebuilt import ToolNode, tools_condition
 
tool_node = ToolNode(tools=[tool])
graph_builder.add_node("tools", tool_node)

	

	
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&lt;langgraph.graph.state.StateGraph at 0x1173534d0&gt;

We add the tools_condition node to the graph

	

		

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graph_builder.add_conditional_edges(
    "chatbot_node",
    tools_condition,
)

	

	
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&lt;langgraph.graph.state.StateGraph at 0x1173534d0&gt;

We add the tools node to the graph

	

		

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graph_builder.add_edge("tools", "chatbot_node")

	

	
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&lt;langgraph.graph.state.StateGraph at 0x1173534d0&gt;

We add the START node to the graph

	

		

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graph_builder.add_edge(START, "chatbot_node")

	

	
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&lt;langgraph.graph.state.StateGraph at 0x1173534d0&gt;

We compiled the graph by adding the checkpointer

	

		

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graph = graph_builder.compile(checkpointer=memory)

	

	
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We represent it graphically

	

		

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from IPython.display import Image, display
 
try:
    display(Image(graph.get_graph().draw_mermaid_png()))
except Exception as e:
    print(f"Error al visualizar el grafo: {e}")

	

	
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&lt;IPython.core.display.Image object&gt;

We create a configuration with a user's thread_id

	

		

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USER1_THREAD_ID = "1"
config_USER1 = {"configurable": {"thread_id": USER1_THREAD_ID}}

	

	
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user_input = "Hi there! My name is Maximo."
 
# The config is the **second positional argument** to stream() or invoke()!
events = graph.stream(
    {"messages": [{"role": "user", "content": user_input}]},
    config_USER1,
    stream_mode="values",
)
for event in events:
    event["messages"][-1].pretty_print()

	

	
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>_ Output 
			

				
================================ Human Message =================================
Hi there! My name is Maximo.
================================== Ai Message ==================================
Tool Calls:
  tavily_search_results_json (0)
Call ID: 0
  Args:
    query:  does not reside in any location,}},
================================= Tool Message =================================
Name: tavily_search_results_json
[{"title": "Determining an individual's tax residency status - IRS", "url": "https://www.irs.gov/individuals/international-taxpayers/determining-an-individuals-tax-residency-status", "content": "If you are not a U.S. citizen, you are considered a nonresident of the United States for U.S. tax purposes unless you meet one of two tests.", "score": 0.1508904}, {"title": "Fix "Location Is Not Available", C:\WINDOWS\system32 ... - YouTube", "url": "https://www.youtube.com/watch?v=QFD-Ptp0SJw", "content": "Fix Error "Location is not available" C:\WINDOWS\system32\config\systemprofile\Desktop is unavailable. If the location is on this PC,", "score": 0.07777658}]
================================== Ai Message ==================================
Invalid Tool Calls:
  tavily_search_results_json (0)
Call ID: 0
  Args:
    {"query": "Arguments["image={"}

			

		
	

		

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user_input = "Do you remember my name?"
 
# The config is the **second positional argument** to stream() or invoke()!
events = graph.stream(
    {"messages": [{"role": "user", "content": user_input}]},
    config_USER1,
    stream_mode="values",
)
for event in events:
    event["messages"][-1].pretty_print()

	

	
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>_ Output 
			

				
================================ Human Message =================================
Do you remember my name?
================================== Ai Message ==================================
Of course! You mentioned your name is Maximo.

As you can see, we have not passed a list with the messages, everything is being managed by the checkpointer.

If we now try with another user, that is, with another thread_id, we will see that the graph does not remember the previous conversation.

	

		

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USER2_THREAD_ID = "2"
config_USER2 = {"configurable": {"thread_id": USER2_THREAD_ID}}
 
user_input = "Do you remember my name?"
 
events = graph.stream(
    {"messages": [{"role": "user", "content": user_input}]},
    config_USER2,
    stream_mode="values",
)
for event in events:
    event["messages"][-1].pretty_print()

	

	
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>_ Output 
			

				
================================ Human Message =================================
Do you remember my name?
================================== Ai Message ==================================
Tool Calls:
  tavily_search_results_json (0)
Call ID: 0
  Args:
    query: Do you Remember My Name
================================= Tool Message =================================
Name: tavily_search_results_json
[{"title": "Sam Fender - Remember My Name (Official Video) - YouTube", "url": "https://www.youtube.com/watch?v=uaQm48G6IjY", "content": "Sam Fender - Remember My Name (Official Video) 
 SamFenderVEVO 
 10743 likes 
 862209 views 
 14 Feb 2025 
 Remember My Name is a love song dedicated to my late Grandparents - they were always so fiercely proud of our family so I wrote the song in honour of them, from the perspective of my Grandad who was looking after my Grandma when she was suffering from dementia. This video is a really special one for me and I want to say thank you to everyone involved in making it. I hope you like it ❤️ [...] If I was wanting of anymore
I’d be as greedy as those men on the hill
But I remain forlorn
In the memory of what once was

Chasing a cross in from the wing
Our boy’s a whippet, he’s faster than anything
Remember the pride that we felt
For the two of us made him ourselves

Humour me
Make my day
I’ll tell you stories
Kiss your face
And I’ll pray
You’ll remember
My name

I’m not sure of what awaits
Wasn’t a fan of St Peter and his gates
But by god I pray
That I’ll see you in some way [...] Oh 11 Walk Avenue
Something to behold
To them it’s a council house
To me it’s a home
And a home that you made
Where the grandkids could play
But it’s never the same without you

Humour me
Make my day
I’ll tell you stories
I’ll kiss your face
And I’ll pray
You’ll remember
My name

And I’ll pray you remember my name
And I’ll pray you remember my name

---", "score": 0.6609831}, {"title": "Do You Remember My Name? - Novel Updates", "url": "https://www.novelupdates.com/series/do-you-remember-my-name/", "content": "This is a Cute, Tender, and Heartwarming High School Romance. It's not Heavy. It's not so Emotional too, but it does have Emotional moments. It's story Full of", "score": 0.608897}]
================================== Ai Message ==================================
Tool Calls:
  tavily_search_results_json (0)
Call ID: 0
  Args:
    query: do you remember my name
================================= Tool Message =================================
Name: tavily_search_results_json
[{"title": "Sam Fender - Remember My Name (Official Video) - YouTube", "url": "https://www.youtube.com/watch?v=uaQm48G6IjY", "content": "Sam Fender - Remember My Name (Official Video) 
 SamFenderVEVO 
 10743 likes 
 862209 views 
 14 Feb 2025 
 Remember My Name is a love song dedicated to my late Grandparents - they were always so fiercely proud of our family so I wrote the song in honour of them, from the perspective of my Grandad who was looking after my Grandma when she was suffering from dementia. This video is a really special one for me and I want to say thank you to everyone involved in making it. I hope you like it ❤️ [...] Oh 11 Walk Avenue
Something to behold
To them it’s a council house
To me it’s a home
And a home that you made
Where the grandkids could play
But it’s never the same without you

Humour me
Make my day
I’ll tell you stories
I’ll kiss your face
And I’ll pray
You’ll remember
My name

And I’ll pray you remember my name
And I’ll pray you remember my name

--- [...] If I was wanting of anymore
I’d be as greedy as those men on the hill
But I remain forlorn
In the memory of what once was

Chasing a cross in from the wing
Our boy’s a whippet, he’s faster than anything
Remember the pride that we felt
For the two of us made him ourselves

Humour me
Make my day
I’ll tell you stories
Kiss your face
And I’ll pray
You’ll remember
My name

I’m not sure of what awaits
Wasn’t a fan of St Peter and his gates
But by god I pray
That I’ll see you in some way", "score": 0.7123327}, {"title": "Do you remember my name? - song and lyrics by Alea, Mama Marjas", "url": "https://open.spotify.com/track/3GVBn3rEQLxZl4zJ4dG8UJ", "content": "Listen to Do you remember my name? on Spotify. Song · Alea, Mama Marjas · 2023.", "score": 0.6506676}]
================================== Ai Message ==================================
Tool Calls:
  tavily_search_results_json (0)
Call ID: 0
  Args:
    query: do you remember my name
================================= Tool Message =================================
Name: tavily_search_results_json
[{"title": "Sam Fender - Remember My Name (Official Video) - YouTube", "url": "https://www.youtube.com/watch?v=uaQm48G6IjY", "content": "Sam Fender - Remember My Name (Official Video) 
 SamFenderVEVO 
 10743 likes 
 862209 views 
 14 Feb 2025 
 Remember My Name is a love song dedicated to my late Grandparents - they were always so fiercely proud of our family so I wrote the song in honour of them, from the perspective of my Grandad who was looking after my Grandma when she was suffering from dementia. This video is a really special one for me and I want to say thank you to everyone involved in making it. I hope you like it ❤️ [...] Oh 11 Walk Avenue
Something to behold
To them it’s a council house
To me it’s a home
And a home that you made
Where the grandkids could play
But it’s never the same without you

Humour me
Make my day
I’ll tell you stories
I’ll kiss your face
And I’ll pray
You’ll remember
My name

And I’ll pray you remember my name
And I’ll pray you remember my name

--- [...] If I was wanting of anymore
I’d be as greedy as those men on the hill
But I remain forlorn
In the memory of what once was

Chasing a cross in from the wing
Our boy’s a whippet, he’s faster than anything
Remember the pride that we felt
For the two of us made him ourselves

Humour me
Make my day
I’ll tell you stories
Kiss your face
And I’ll pray
You’ll remember
My name

I’m not sure of what awaits
Wasn’t a fan of St Peter and his gates
But by god I pray
That I’ll see you in some way", "score": 0.7123327}, {"title": "Do you remember my name? - song and lyrics by Alea, Mama Marjas", "url": "https://open.spotify.com/track/3GVBn3rEQLxZl4zJ4dG8UJ", "content": "Listen to Do you remember my name? on Spotify. Song · Alea, Mama Marjas · 2023.", "score": 0.6506676}]
================================== Ai Message ==================================
I'm here to assist you, but I don't actually have the ability to remember names or personal information from previous conversations. How can I assist you today?

Now that our chatbot has search and memory tools, let's repeat the previous example, where I ask it about the result of Real Madrid's last match in the league and then which players played.

	

		

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USER3_THREAD_ID = "3"
config_USER3 = {"configurable": {"thread_id": USER3_THREAD_ID}}
 
user_input = "How did Real Madrid fare this weekend against Leganes in La Liga?"
 
events = graph.stream(
    {"messages": [{"role": "user", "content": user_input}]},
    config_USER3,
    stream_mode="values",
)
for event in events:
    event["messages"][-1].pretty_print()

	

	
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>_ Output 
			

				
================================ Human Message =================================
How did Real Madrid fare this weekend against Leganes in La Liga?
================================== Ai Message ==================================
Tool Calls:
  tavily_search_results_json (0)
Call ID: 0
  Args:
    query: Real Madrid vs Leganes La Liga this weekend
================================= Tool Message =================================
Name: tavily_search_results_json
[{"title": "Real Madrid 3-2 Leganes: Goals and highlights - LaLiga 24/25 | Marca", "url": "https://www.marca.com/en/soccer/laliga/r-madrid-leganes/2025/03/29/01_0101_20250329_186_957-live.html", "content": "While their form has varied throughout the campaign there is no denying Real Madrid are a force at home in LaLiga this season, as they head into Saturday's match having picked up 34 points from 13 matches.

As for Leganes they currently sit 18th in the table, though they are level with Alaves for 17th as both teams look to stay in the top flight. [...] The two teams have already played twice this season, with Real Madrid securing a 3-0 win in the reverse league fixture. They also met in the quarter-finals of the Copa del Rey, a game Real won 3-2.

Real Madrid vs Leganes LIVE - Latest Updates

Match ends, Real Madrid 3, Leganes 2.

Second Half ends, Real Madrid 3, Leganes 2.

Foul by Vinícius Júnior (Real Madrid).

Seydouba Cissé (Leganes) wins a free kick in the defensive half. [...] Goal! Real Madrid 1, Leganes 1. Diego García (Leganes) left footed shot from very close range.

Attempt missed. Óscar Rodríguez (Leganes) left footed shot from the centre of the box.

Goal! Real Madrid 1, Leganes 0. Kylian Mbappé (Real Madrid) converts the penalty with a right footed shot.

Penalty Real Madrid. Arda Güler draws a foul in the penalty area.

Penalty conceded by Óscar Rodríguez (Leganes) after a foul in the penalty area.

Delay over. They are ready to continue.", "score": 0.8548001}, {"title": "Real Madrid 3-2 Leganés (Mar 29, 2025) Game Analysis - ESPN", "url": "https://www.espn.com/soccer/report/_/gameId/704946", "content": "Real Madrid

Leganés

Mbappé nets twice to keep Real Madrid's title hopes alive

Real Madrid vs. Leganés - Game Highlights

Watch the Game Highlights from Real Madrid vs. Leganés, 03/30/2025

Real Madrid's Kylian Mbappé struck twice to help his side come from behind to claim a hard-fought 3-2 home win over relegation-threatened Leganes on Saturday to move the second-placed reigning champions level on points with leaders Barcelona. [...] Leganes pushed for an equaliser but fell to a third consecutive defeat to sit 18th on 27 points, level with Alaves who are one place higher in the safety zone on goal difference.

"We have done a tremendous job. We leave with our heads held high because we were fighting until the end to score here," Leganes striker Garcia said.

"Ultimately, it was down to the details that they took it. We played a very serious game and now we have to think about next week."

Game Information", "score": 0.82220376}]
================================== Ai Message ==================================
Real Madrid secured a 3-2 victory against Leganes this weekend in their La Liga match. Kylian Mbappé scored twice, including a penalty, to help his team come from behind and claim the win, keeping Real Madrid's title hopes alive. Leganes, now sitting 18th in the table, continues to face challenges in their fight against relegation.

Now we ask him about the players who played in the match.

	

		

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user_input = "Which players played the match?"
 
events = graph.stream(
    {"messages": [{"role": "user", "content": user_input}]},
    config_USER3,
    stream_mode="values",
)
for event in events:
    event["messages"][-1].pretty_print()

	

	
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>_ Output 
			

				
================================ Human Message =================================
Which players played the match?
================================== Ai Message ==================================
Tool Calls:
  tavily_search_results_json (0)
Call ID: 0
  Args:
    query: Real Madrid vs Leganes match report players lineup
================================= Tool Message =================================
Name: tavily_search_results_json
[{"title": "Real Madrid vs. Leganes final score: La Liga result, updates, stats ...", "url": "https://www.sportingnews.com/us/soccer/news/real-madrid-leganes-score-result-updates-stats-la-liga/8ecf730cfcb9b6c5f6693a0d", "content": "Real Madrid came through a topsy-turvy game with Leganes to claim a 3-2 victory and put pressure back on Barcelona in La Liga's title race. Kylian Mbappe scored in each half either side of a Jude Bellingham goal — his first in the league since January 3 — to seal all three points for the champions after Leganes had come from behind to lead at the interval. Rodrygo won back the ball in the Leganes half and earned a free-kick on the edge of the box, and Mbappe found the bottom corner after rolling the ball short to Fran Garcia to work an angle. Leganes lead Real Madrid at the Bernabeu for the very first time! *Real Madrid starting lineup (4-3-3, right to left):* Lunin (GK) — Vazquez, Rudiger, Asencio, Garcia — Modric, Bellingham, Camavinga — B.", "score": 0.88372874}, {"title": "CONFIRMED lineups: Real Madrid vs Leganés, 2025 La Liga", "url": "https://www.managingmadrid.com/2025/3/29/24396638/real-madrid-vs-leganes-2025-la-liga-live-online-stream", "content": "Real Madrid starting XI: Lunin, Vazquez, Rudiger, Asencio, Fran Garcia, Camavinga, Guler, Modric, Bellingham, Brahim, Mbappe. Leganes starting", "score": 0.83452857}]
================================== Ai Message ==================================
Tool Calls:
  tavily_search_results_json (0)
Call ID: 0
  Args:
    query: Real Madrid vs Leganes players 2025
================================= Tool Message =================================
Name: tavily_search_results_json
[{"title": "Player Ratings: Real Madrid 3-2 Leganes; 2025 La Liga", "url": "https://www.managingmadrid.com/2025/3/30/24396688/player-ratings-real-madrid-3-2-leganes-2025-la-liga", "content": "Raúl Asencio—7: Applauded by the Bernabeu on multiple occasions with good sweeping up defensively.

Fran García—6: Better on the offensive end, getting into the final third and playing some dagger crosses.

Eduardo Camavinga—6: Modric and Camavinga struggled to deal with Leganes counter attacks and Diego, playing as a #10 for Leganes, got the better of both of them. [...] Follow Managing Madrid online:

Site search

Managing Madrid main menu

Filed under:

Player Ratings: Real Madrid 3-2 Leganes; 2025 La Liga

Kylian Mbappe scores a brace to help Madrid secure a nervy 3-2 victory.

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Player Ratings: Real Madrid 3-2 Leganes; 2025 La Liga

Full match player ratings below:

Andriy Lunin—7: Not at fault for the goals, was left with the opposition taking a shot from near the six yard box. [...] Lucas Vázquez—4: Exposed in transition and lacking the speed and athleticism to cover the gaps he leaves when venturing forward. Needs a more “pessimistic” attitude when the ball is on the opposite flank, occupying better spots in ““rest defense”.

Antonio Rudiger—5: Several unnecessary long distance shots to hurt Madrid’s rhythm and reinforce Leganes game plan. Playing with too many matches in his legs and it’s beginning to show.", "score": 0.8832463}, {"title": "Real Madrid vs. Leganés (Mar 29, 2025) Live Score - ESPN", "url": "https://www.espn.com/soccer/match/_/gameId/704946", "content": "Match Formations · 13. Lunin · 20. García · 22. Rüdiger · 35. Asencio · 17. Vázquez · 5. Bellingham · 10. Modric · 6. Camavinga.", "score": 0.86413884}]
================================== Ai Message ==================================
Tool Calls:
  tavily_search_results_json (0)
Call ID: 0
  Args:
    query: Real Madrid vs Leganes starting lineup
================================= Tool Message =================================
Name: tavily_search_results_json
[{"title": "Starting lineups of Real Madrid and Leganés", "url": "https://www.realmadrid.com/en-US/news/football/first-team/latest-news/once-inicial-del-real-madrid-contra-el-leganes-29-03-2025", "content": "Starting lineups of Real Madrid and Leganés



The Whites’ team is: Lunin, Lucas V., Asencio, Rüdiger, Fran García, Arda Güler, Modrić, Camavinga, Bellingham, Brahim and Mbappé.





Real Madrid have named their starting line-up for the game against Leganés on matchday 29 of LaLiga, which will be played at the Santiago Bernabéu (9 pm CET). [...] Real Madrid starting line-up:
13. Lunin
17. Lucas V.
35. Asencio
22. Rüdiger
20. Fran García
15. Arda Güler
10. Modrić
6. Camavinga
5. Bellingham
21. Brahim
9. Mbappé.

Substitutes:
26. Fran González
34. Sergio Mestre
4. Alaba
7. Vini Jr.
8. Valverde
11. Rodrygo
14. Tchouameni
16. Endrick
18. Vallejo
43. Diego Aguado.

Leganés starting line-up:
13. Dmitrovic
5. Tapia
6. Sergio G.
7. Óscar
10. Raba
11. Cruz
12. V. Rosier
17. Neyou
19. Diego G.
20. Javi Hernández
22. Nastasic. [...] Suplentes:
1. Juan Soriano
36. Abajas
2. A. Alti
3. Jorge Sáenz
8. Cisse
9. Miguel
14. Darko
18. Duk
21. R. López
23. Munir
24. Chicco
30. I. Diomande.



Download Now

Official App Fan

Real Madrid © 2025 All rights reserved", "score": 0.9465623}, {"title": "Real Madrid vs. Leganes lineups, confirmed starting 11, team news ...", "url": "https://www.sportingnews.com/us/soccer/news/real-madrid-leganes-lineups-starting-11-team-news-injuries/aac757d10cc7b9a084995b4d", "content": "Real Madrid starting lineup (4-3-3, right to left): Lunin (GK) — Vazquez, Rudiger, Asencio, Garcia — Modric, Bellingham, Camavinga — B. Diaz,", "score": 0.9224337}]
================================== Ai Message ==================================
The starting lineup for Real Madrid in their match against Leganés was: Lunin (GK), Vázquez, Rüdiger, Asencio, Fran García, Modric, Bellingham, Camavinga, Brahim, Arda Güler, and Mbappé. Notable players like Vini Jr., Rodrygo, and Valverde were on the bench.

After a lot of searching, it finally finds it. So now we have a chatbot with tools and memory.

For now, we have created some checkpoints in three different threads. But what goes into each checkpoint? To inspect the state of a graph for a given configuration, we can use the get_state(config) method.

	

		

			< >
			Input
		

		

			Python
			
		

	

	

		
snapshot = graph.get_state(config_USER3)
snapshot

	

	
Copied
>_ Output 
			

				
StateSnapshot(values={'messages': [HumanMessage(content='How did Real Madrid fare this weekend against Leganes in La Liga?', additional_kwargs={}, response_metadata={}, id='a33f5825-1ae4-4717-ad17-8e306f35b027'), AIMessage(content='', additional_kwargs={'tool_calls': [{'function': {'arguments': {'query': 'Real Madrid vs Leganes La Liga this weekend'}, 'name': 'tavily_search_results_json', 'description': None}, 'id': '0', 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 25, 'prompt_tokens': 296, 'total_tokens': 321}, 'model': '', 'finish_reason': 'stop'}, id='run-7905b5ae-5dee-4641-b012-396affde984c-0', tool_calls=[{'name': 'tavily_search_results_json', 'args': {'query': 'Real Madrid vs Leganes La Liga this weekend'}, 'id': '0', 'type': 'tool_call'}]), ToolMessage(content='[{"title": "Real Madrid 3-2 Leganes: Goals and highlights - LaLiga 24/25 | Marca", "url": "https://www.marca.com/en/soccer/laliga/r-madrid-leganes/2025/03/29/01_0101_20250329_186_957-live.html", "content": "While their form has varied throughout the campaign there is no denying Real Madrid are a force at home in LaLiga this season, as they head into Saturday's match having picked up 34 points from 13 matches.\n\nAs for Leganes they currently sit 18th in the table, though they are level with Alaves for 17th as both teams look to stay in the top flight. [...] The two teams have already played twice this season, with Real Madrid securing a 3-0 win in the reverse league fixture. They also met in the quarter-finals of the Copa del Rey, a game Real won 3-2.\n\nReal Madrid vs Leganes LIVE - Latest Updates\n\nMatch ends, Real Madrid 3, Leganes 2.\n\nSecond Half ends, Real Madrid 3, Leganes 2.\n\nFoul by Vinícius Júnior (Real Madrid).\n\nSeydouba Cissé (Leganes) wins a free kick in the defensive half. [...] Goal! Real Madrid 1, Leganes 1. Diego García (Leganes) left footed shot from very close range.\n\nAttempt missed. Óscar Rodríguez (Leganes) left footed shot from the centre of the box.\n\nGoal! Real Madrid 1, Leganes 0. Kylian Mbappé (Real Madrid) converts the penalty with a right footed shot.\n\nPenalty Real Madrid. Arda Güler draws a foul in the penalty area.\n\nPenalty conceded by Óscar Rodríguez (Leganes) after a foul in the penalty area.\n\nDelay over. They are ready to continue.", "score": 0.8548001}, {"title": "Real Madrid 3-2 Leganés (Mar 29, 2025) Game Analysis - ESPN", "url": "https://www.espn.com/soccer/report/_/gameId/704946", "content": "Real Madrid\n\nLeganés\n\nMbappé nets twice to keep Real Madrid's title hopes alive\n\nReal Madrid vs. Leganés - Game Highlights\n\nWatch the Game Highlights from Real Madrid vs. Leganés, 03/30/2025\n\nReal Madrid's Kylian Mbappé struck twice to help his side come from behind to claim a hard-fought 3-2 home win over relegation-threatened Leganes on Saturday to move the second-placed reigning champions level on points with leaders Barcelona. [...] Leganes pushed for an equaliser but fell to a third consecutive defeat to sit 18th on 27 points, level with Alaves who are one place higher in the safety zone on goal difference.\n\n\"We have done a tremendous job. We leave with our heads held high because we were fighting until the end to score here,\" Leganes striker Garcia said.\n\n\"Ultimately, it was down to the details that they took it. We played a very serious game and now we have to think about next week.\"\n\nGame Information", "score": 0.82220376}]', name='tavily_search_results_json', id='0e02fce3-a6f0-4cce-9217-04c8c3219265', tool_call_id='0', artifact={'query': 'Real Madrid vs Leganes La Liga this weekend', 'follow_up_questions': None, 'answer': None, 'images': [], 'results': [{'url': 'https://www.marca.com/en/soccer/laliga/r-madrid-leganes/2025/03/29/01_0101_20250329_186_957-live.html', 'title': 'Real Madrid 3-2 Leganes: Goals and highlights - LaLiga 24/25 | Marca', 'content': "While their form has varied throughout the campaign there is no denying Real Madrid are a force at home in LaLiga this season, as they head into Saturday's match having picked up 34 points from 13 matches.

As for Leganes they currently sit 18th in the table, though they are level with Alaves for 17th as both teams look to stay in the top flight. [...] The two teams have already played twice this season, with Real Madrid securing a 3-0 win in the reverse league fixture. They also met in the quarter-finals of the Copa del Rey, a game Real won 3-2.

Real Madrid vs Leganes LIVE - Latest Updates

Match ends, Real Madrid 3, Leganes 2.

Second Half ends, Real Madrid 3, Leganes 2.

Foul by Vinícius Júnior (Real Madrid).

Seydouba Cissé (Leganes) wins a free kick in the defensive half. [...] Goal! Real Madrid 1, Leganes 1. Diego García (Leganes) left footed shot from very close range.

Attempt missed. Óscar Rodríguez (Leganes) left footed shot from the centre of the box.

Goal! Real Madrid 1, Leganes 0. Kylian Mbappé (Real Madrid) converts the penalty with a right footed shot.

Penalty Real Madrid. Arda Güler draws a foul in the penalty area.

Penalty conceded by Óscar Rodríguez (Leganes) after a foul in the penalty area.

Delay over. They are ready to continue.", 'score': 0.8548001, 'raw_content': None}, {'url': 'https://www.espn.com/soccer/report/_/gameId/704946', 'title': 'Real Madrid 3-2 Leganés (Mar 29, 2025) Game Analysis - ESPN', 'content': 'Real Madrid

Leganés

Mbappé nets twice to keep Real Madrid's title hopes alive

Real Madrid vs. Leganés - Game Highlights

Watch the Game Highlights from Real Madrid vs. Leganés, 03/30/2025

Real Madrid's Kylian Mbappé struck twice to help his side come from behind to claim a hard-fought 3-2 home win over relegation-threatened Leganes on Saturday to move the second-placed reigning champions level on points with leaders Barcelona. [...] Leganes pushed for an equaliser but fell to a third consecutive defeat to sit 18th on 27 points, level with Alaves who are one place higher in the safety zone on goal difference.

"We have done a tremendous job. We leave with our heads held high because we were fighting until the end to score here," Leganes striker Garcia said.

"Ultimately, it was down to the details that they took it. We played a very serious game and now we have to think about next week."

Game Information', 'score': 0.82220376, 'raw_content': None}], 'response_time': 1.47}), AIMessage(content="Real Madrid secured a 3-2 victory against Leganes this weekend in their La Liga match. Kylian Mbappé scored twice, including a penalty, to help his team come from behind and claim the win, keeping Real Madrid's title hopes alive. Leganes, now sitting 18th in the table, continues to face challenges in their fight against relegation.", additional_kwargs={}, response_metadata={'token_usage': {'completion_tokens': 92, 'prompt_tokens': 1086, 'total_tokens': 1178}, 'model': '', 'finish_reason': 'stop'}, id='run-22226dda-0475-49b7-882f-fe7bd63ef025-0'), HumanMessage(content='Which players played the match?', additional_kwargs={}, response_metadata={}, id='3e6d9f84-06a2-4148-8f2b-d8ef42c3bea1'), AIMessage(content='', additional_kwargs={'tool_calls': [{'function': {'arguments': {'query': 'Real Madrid vs Leganes match report players lineup'}, 'name': 'tavily_search_results_json', 'description': None}, 'id': '0', 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 29, 'prompt_tokens': 1178, 'total_tokens': 1207}, 'model': '', 'finish_reason': 'stop'}, id='run-025d3235-61b9-4add-8e1b-5b1bc795a9d3-0', tool_calls=[{'name': 'tavily_search_results_json', 'args': {'query': 'Real Madrid vs Leganes match report players lineup'}, 'id': '0', 'type': 'tool_call'}]), ToolMessage(content='[{"title": "Real Madrid vs. Leganes final score: La Liga result, updates, stats ...", "url": "https://www.sportingnews.com/us/soccer/news/real-madrid-leganes-score-result-updates-stats-la-liga/8ecf730cfcb9b6c5f6693a0d", "content": "Real Madrid came through a topsy-turvy game with Leganes to claim a 3-2 victory and put pressure back on Barcelona in La Liga's title race. Kylian Mbappe scored in each half either side of a Jude Bellingham goal — his first in the league since January 3 — to seal all three points for the champions after Leganes had come from behind to lead at the interval. Rodrygo won back the ball in the Leganes half and earned a free-kick on the edge of the box, and Mbappe found the bottom corner after rolling the ball short to Fran Garcia to work an angle. Leganes lead Real Madrid at the Bernabeu for the very first time! *Real Madrid starting lineup (4-3-3, right to left):* Lunin (GK) — Vazquez, Rudiger, Asencio, Garcia — Modric, Bellingham, Camavinga — B.", "score": 0.88372874}, {"title": "CONFIRMED lineups: Real Madrid vs Leganés, 2025 La Liga", "url": "https://www.managingmadrid.com/2025/3/29/24396638/real-madrid-vs-leganes-2025-la-liga-live-online-stream", "content": "Real Madrid starting XI: Lunin, Vazquez, Rudiger, Asencio, Fran Garcia, Camavinga, Guler, Modric, Bellingham, Brahim, Mbappe. Leganes starting", "score": 0.83452857}]', name='tavily_search_results_json', id='2dbc1324-2c20-406a-b2d7-a3d6fc609537', tool_call_id='0', artifact={'query': 'Real Madrid vs Leganes match report players lineup', 'follow_up_questions': None, 'answer': None, 'images': [], 'results': [{'url': 'https://www.sportingnews.com/us/soccer/news/real-madrid-leganes-score-result-updates-stats-la-liga/8ecf730cfcb9b6c5f6693a0d', 'title': 'Real Madrid vs. Leganes final score: La Liga result, updates, stats ...', 'content': "Real Madrid came through a topsy-turvy game with Leganes to claim a 3-2 victory and put pressure back on Barcelona in La Liga's title race. Kylian Mbappe scored in each half either side of a Jude Bellingham goal — his first in the league since January 3 — to seal all three points for the champions after Leganes had come from behind to lead at the interval. Rodrygo won back the ball in the Leganes half and earned a free-kick on the edge of the box, and Mbappe found the bottom corner after rolling the ball short to Fran Garcia to work an angle. Leganes lead Real Madrid at the Bernabeu for the very first time! *Real Madrid starting lineup (4-3-3, right to left):* Lunin (GK) — Vazquez, Rudiger, Asencio, Garcia — Modric, Bellingham, Camavinga — B.", 'score': 0.88372874, 'raw_content': None}, {'url': 'https://www.managingmadrid.com/2025/3/29/24396638/real-madrid-vs-leganes-2025-la-liga-live-online-stream', 'title': 'CONFIRMED lineups: Real Madrid vs Leganés, 2025 La Liga', 'content': 'Real Madrid starting XI: Lunin, Vazquez, Rudiger, Asencio, Fran Garcia, Camavinga, Guler, Modric, Bellingham, Brahim, Mbappe. Leganes starting', 'score': 0.83452857, 'raw_content': None}], 'response_time': 3.36}), AIMessage(content='', additional_kwargs={'tool_calls': [{'function': {'arguments': {'query': 'Real Madrid vs Leganes players 2025'}, 'name': 'tavily_search_results_json', 'description': None}, 'id': '0', 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 31, 'prompt_tokens': 1630, 'total_tokens': 1661}, 'model': '', 'finish_reason': 'stop'}, id='run-d6b4c4ff-0923-4082-9dea-7c51b2a4fc60-0', tool_calls=[{'name': 'tavily_search_results_json', 'args': {'query': 'Real Madrid vs Leganes players 2025'}, 'id': '0', 'type': 'tool_call'}]), ToolMessage(content='[{"title": "Player Ratings: Real Madrid 3-2 Leganes; 2025 La Liga", "url": "https://www.managingmadrid.com/2025/3/30/24396688/player-ratings-real-madrid-3-2-leganes-2025-la-liga", "content": "Raúl Asencio—7: Applauded by the Bernabeu on multiple occasions with good sweeping up defensively.\n\nFran García—6: Better on the offensive end, getting into the final third and playing some dagger crosses.\n\nEduardo Camavinga—6: Modric and Camavinga struggled to deal with Leganes counter attacks and Diego, playing as a #10 for Leganes, got the better of both of them. [...] Follow Managing Madrid online:\n\nSite search\n\nManaging Madrid main menu\n\nFiled under:\n\nPlayer Ratings: Real Madrid 3-2 Leganes; 2025 La Liga\n\nKylian Mbappe scores a brace to help Madrid secure a nervy 3-2 victory.\n\nShare this story\n\nShare\nAll sharing options for:\nPlayer Ratings: Real Madrid 3-2 Leganes; 2025 La Liga\n\nFull match player ratings below:\n\nAndriy Lunin—7: Not at fault for the goals, was left with the opposition taking a shot from near the six yard box. [...] Lucas Vázquez—4: Exposed in transition and lacking the speed and athleticism to cover the gaps he leaves when venturing forward. Needs a more “pessimistic” attitude when the ball is on the opposite flank, occupying better spots in ““rest defense”.\n\nAntonio Rudiger—5: Several unnecessary long distance shots to hurt Madrid’s rhythm and reinforce Leganes game plan. Playing with too many matches in his legs and it’s beginning to show.", "score": 0.8832463}, {"title": "Real Madrid vs. Leganés (Mar 29, 2025) Live Score - ESPN", "url": "https://www.espn.com/soccer/match/_/gameId/704946", "content": "Match Formations · 13. Lunin · 20. García · 22. Rüdiger · 35. Asencio · 17. Vázquez · 5. Bellingham · 10. Modric · 6. Camavinga.", "score": 0.86413884}]', name='tavily_search_results_json', id='ac15dd6e-09b1-4075-834e-d869f4079285', tool_call_id='0', artifact={'query': 'Real Madrid vs Leganes players 2025', 'follow_up_questions': None, 'answer': None, 'images': [], 'results': [{'url': 'https://www.managingmadrid.com/2025/3/30/24396688/player-ratings-real-madrid-3-2-leganes-2025-la-liga', 'title': 'Player Ratings: Real Madrid 3-2 Leganes; 2025 La Liga', 'content': 'Raúl Asencio—7: Applauded by the Bernabeu on multiple occasions with good sweeping up defensively.

Fran García—6: Better on the offensive end, getting into the final third and playing some dagger crosses.

Eduardo Camavinga—6: Modric and Camavinga struggled to deal with Leganes counter attacks and Diego, playing as a #10 for Leganes, got the better of both of them. [...] Follow Managing Madrid online:

Site search

Managing Madrid main menu

Filed under:

Player Ratings: Real Madrid 3-2 Leganes; 2025 La Liga

Kylian Mbappe scores a brace to help Madrid secure a nervy 3-2 victory.

Share this story

Share
All sharing options for:
Player Ratings: Real Madrid 3-2 Leganes; 2025 La Liga

Full match player ratings below:

Andriy Lunin—7: Not at fault for the goals, was left with the opposition taking a shot from near the six yard box. [...] Lucas Vázquez—4: Exposed in transition and lacking the speed and athleticism to cover the gaps he leaves when venturing forward. Needs a more “pessimistic” attitude when the ball is on the opposite flank, occupying better spots in ““rest defense”.

Antonio Rudiger—5: Several unnecessary long distance shots to hurt Madrid’s rhythm and reinforce Leganes game plan. Playing with too many matches in his legs and it’s beginning to show.', 'score': 0.8832463, 'raw_content': None}, {'url': 'https://www.espn.com/soccer/match/_/gameId/704946', 'title': 'Real Madrid vs. Leganés (Mar 29, 2025) Live Score - ESPN', 'content': 'Match Formations · 13. Lunin · 20. García · 22. Rüdiger · 35. Asencio · 17. Vázquez · 5. Bellingham · 10. Modric · 6. Camavinga.', 'score': 0.86413884, 'raw_content': None}], 'response_time': 0.89}), AIMessage(content='', additional_kwargs={'tool_calls': [{'function': {'arguments': {'query': 'Real Madrid vs Leganes starting lineup'}, 'name': 'tavily_search_results_json', 'description': None}, 'id': '0', 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 27, 'prompt_tokens': 2212, 'total_tokens': 2239}, 'model': '', 'finish_reason': 'stop'}, id='run-68867df1-2012-47ac-9f01-42b071ef3a1f-0', tool_calls=[{'name': 'tavily_search_results_json', 'args': {'query': 'Real Madrid vs Leganes starting lineup'}, 'id': '0', 'type': 'tool_call'}]), ToolMessage(content='[{"title": "Starting lineups of Real Madrid and Leganés", "url": "https://www.realmadrid.com/en-US/news/football/first-team/latest-news/once-inicial-del-real-madrid-contra-el-leganes-29-03-2025", "content": "Starting lineups of Real Madrid and Leganés\n\n\n\nThe Whites’ team is: Lunin, Lucas V., Asencio, Rüdiger, Fran García, Arda Güler, Modrić, Camavinga, Bellingham, Brahim and Mbappé.\n\n\n\n\n\nReal Madrid have named their starting line-up for the game against Leganés on matchday 29 of LaLiga, which will be played at the Santiago Bernabéu (9 pm CET). [...] Real Madrid starting line-up:\n13. Lunin\n17. Lucas V.\n35. Asencio\n22. Rüdiger\n20. Fran García\n15. Arda Güler\n10. Modrić\n6. Camavinga\n5. Bellingham\n21. Brahim\n9. Mbappé.\n\nSubstitutes:\n26. Fran González\n34. Sergio Mestre\n4. Alaba\n7. Vini Jr.\n8. Valverde\n11. Rodrygo\n14. Tchouameni\n16. Endrick\n18. Vallejo\n43. Diego Aguado.\n\nLeganés starting line-up:\n13. Dmitrovic\n5. Tapia\n6. Sergio G.\n7. Óscar\n10. Raba\n11. Cruz\n12. V. Rosier\n17. Neyou\n19. Diego G.\n20. Javi Hernández\n22. Nastasic. [...] Suplentes:\n1. Juan Soriano\n36. Abajas\n2. A. Alti\n3. Jorge Sáenz\n8. Cisse\n9. Miguel\n14. Darko\n18. Duk\n21. R. López\n23. Munir\n24. Chicco\n30. I. Diomande.\n\n\n\nDownload Now\n\nOfficial App Fan\n\nReal Madrid © 2025 All rights reserved", "score": 0.9465623}, {"title": "Real Madrid vs. Leganes lineups, confirmed starting 11, team news ...", "url": "https://www.sportingnews.com/us/soccer/news/real-madrid-leganes-lineups-starting-11-team-news-injuries/aac757d10cc7b9a084995b4d", "content": "Real Madrid starting lineup (4-3-3, right to left): Lunin (GK) — Vazquez, Rudiger, Asencio, Garcia — Modric, Bellingham, Camavinga — B. Diaz,", "score": 0.9224337}]', name='tavily_search_results_json', id='46721f2b-2df2-4da2-831a-ce94f6b4ff8f', tool_call_id='0', artifact={'query': 'Real Madrid vs Leganes starting lineup', 'follow_up_questions': None, 'answer': None, 'images': [], 'results': [{'url': 'https://www.realmadrid.com/en-US/news/football/first-team/latest-news/once-inicial-del-real-madrid-contra-el-leganes-29-03-2025', 'title': 'Starting lineups of Real Madrid and Leganés', 'content': 'Starting lineups of Real Madrid and Leganés



The Whites’ team is: Lunin, Lucas V., Asencio, Rüdiger, Fran García, Arda Güler, Modrić, Camavinga, Bellingham, Brahim and Mbappé.





Real Madrid have named their starting line-up for the game against Leganés on matchday 29 of LaLiga, which will be played at the Santiago Bernabéu (9 pm CET). [...] Real Madrid starting line-up:
13. Lunin
17. Lucas V.
35. Asencio
22. Rüdiger
20. Fran García
15. Arda Güler
10. Modrić
6. Camavinga
5. Bellingham
21. Brahim
9. Mbappé.

Substitutes:
26. Fran González
34. Sergio Mestre
4. Alaba
7. Vini Jr.
8. Valverde
11. Rodrygo
14. Tchouameni
16. Endrick
18. Vallejo
43. Diego Aguado.

Leganés starting line-up:
13. Dmitrovic
5. Tapia
6. Sergio G.
7. Óscar
10. Raba
11. Cruz
12. V. Rosier
17. Neyou
19. Diego G.
20. Javi Hernández
22. Nastasic. [...] Suplentes:
1. Juan Soriano
36. Abajas
2. A. Alti
3. Jorge Sáenz
8. Cisse
9. Miguel
14. Darko
18. Duk
21. R. López
23. Munir
24. Chicco
30. I. Diomande.



Download Now

Official App Fan

Real Madrid © 2025 All rights reserved', 'score': 0.9465623, 'raw_content': None}, {'url': 'https://www.sportingnews.com/us/soccer/news/real-madrid-leganes-lineups-starting-11-team-news-injuries/aac757d10cc7b9a084995b4d', 'title': 'Real Madrid vs. Leganes lineups, confirmed starting 11, team news ...', 'content': 'Real Madrid starting lineup (4-3-3, right to left): Lunin (GK) — Vazquez, Rudiger, Asencio, Garcia — Modric, Bellingham, Camavinga — B. Diaz,', 'score': 0.9224337, 'raw_content': None}], 'response_time': 2.3}), AIMessage(content='The starting lineup for Real Madrid in their match against Leganés was: Lunin (GK), Vázquez, Rüdiger, Asencio, Fran García, Modric, Bellingham, Camavinga, Brahim, Arda Güler, and Mbappé. Notable players like Vini Jr., Rodrygo, and Valverde were on the bench.', additional_kwargs={}, response_metadata={'token_usage': {'completion_tokens': 98, 'prompt_tokens': 2954, 'total_tokens': 3052}, 'model': '', 'finish_reason': 'stop'}, id='run-0bd921c6-1d94-4a4c-9d9c-d255d301e2d5-0')]}, next=(), config={'configurable': {'thread_id': '3', 'checkpoint_ns': '', 'checkpoint_id': '1f010a50-49f2-6904-800c-ec8d67fe5b92'}}, metadata={'source': 'loop', 'writes': {'chatbot_node': {'messages': [AIMessage(content='The starting lineup for Real Madrid in their match against Leganés was: Lunin (GK), Vázquez, Rüdiger, Asencio, Fran García, Modric, Bellingham, Camavinga, Brahim, Arda Güler, and Mbappé. Notable players like Vini Jr., Rodrygo, and Valverde were on the bench.', additional_kwargs={}, response_metadata={'token_usage': {'completion_tokens': 98, 'prompt_tokens': 2954, 'total_tokens': 3052}, 'model': '', 'finish_reason': 'stop'}, id='run-0bd921c6-1d94-4a4c-9d9c-d255d301e2d5-0')]}}, 'thread_id': '3', 'step': 12, 'parents': {}}, created_at='2025-04-03T16:02:18.167222+00:00', parent_config={'configurable': {'thread_id': '3', 'checkpoint_ns': '', 'checkpoint_id': '1f010a50-1feb-6534-800b-079c102aaa71'}}, tasks=())

If we want to see the next node to process, we can use the next attribute

	

		

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			Input
		

		

			Python
			
		

	

	

		
snapshot.next

	

	
Copied
>_ Output 
			

				
()

Since the graph has finished, next is empty. If you obtain a state from within a graph invocation, next indicates which node will execute next.

The previous snapshot (snapshot) contains the current state values, the corresponding configuration, and the next node (next) to process. In our case, the graph has reached the END state, which is why next is empty.

We are going to rewrite all the code to make it more readable.

	

		

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			Input
		

		

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from typing import Annotated
from typing_extensions import TypedDict
 
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages
 
from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
from huggingface_hub import login
 
from langchain_community.utilities.tavily_search import TavilySearchAPIWrapper
from langchain_community.tools.tavily_search import TavilySearchResults
 
from langchain_core.messages import ToolMessage
from langgraph.prebuilt import ToolNode, tools_condition
 
from langgraph.checkpoint.memory import MemorySaver
 
from IPython.display import Image, display
import json
 
import os
os.environ["LANGCHAIN_TRACING_V2"] = "false" # Disable LangSmith tracing
 
import dotenv
dotenv.load_dotenv()
HUGGINGFACE_TOKEN = os.getenv("HUGGINGFACE_LANGGRAPH")
TAVILY_API_KEY = os.getenv("TAVILY_LANGGRAPH_API_KEY")
 
# State
class State(TypedDict):
    messages: Annotated[list, add_messages]
 
# Tools
wrapper = TavilySearchAPIWrapper(tavily_api_key=TAVILY_API_KEY)
tool = TavilySearchResults(api_wrapper=wrapper, max_results=2)
tools_list = [tool]
 
# Create the LLM model
login(token=HUGGINGFACE_TOKEN) # Login to HuggingFace to use the model
MODEL = "Qwen/Qwen2.5-72B-Instruct"
model = HuggingFaceEndpoint(
    repo_id=MODEL,
    task="text-generation",
    max_new_tokens=512,
    do_sample=False,
    repetition_penalty=1.03,
)
# Create the chat model
llm = ChatHuggingFace(llm=model)
# Create the LLM with tools
llm_with_tools = llm.bind_tools(tools_list)
 
# Tool node
tool_node = ToolNode(tools=tools_list)
 
# Functions
def chatbot_function(state: State):
    return {"messages": [llm_with_tools.invoke(state["messages"])]}
 
# Start to build the graph
graph_builder = StateGraph(State)
 
# Add nodes to the graph
graph_builder.add_node("chatbot_node", chatbot_function)
graph_builder.add_node("tools", tool_node)
 
# Add edges
graph_builder.add_edge(START, "chatbot_node")
graph_builder.add_conditional_edges( "chatbot_node", tools_condition)
graph_builder.add_edge("tools", "chatbot_node")
 
# Compile the graph
memory = MemorySaver()
graph = graph_builder.compile(checkpointer=memory)
 
# Display the graph
try:
    display(Image(graph.get_graph().draw_mermaid_png()))
except Exception as e:
    print(f"Error al visualizar el grafo: {e}")

	

	
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>_ Output 
			

				
Error al visualizar el grafo: Failed to reach https://mermaid.ink/ API while trying to render your graph after 1 retries. To resolve this issue:
1. Check your internet connection and try again
2. Try with higher retry settings: `draw_mermaid_png(..., max_retries=5, retry_delay=2.0)`
3. Use the Pyppeteer rendering method which will render your graph locally in a browser: `draw_mermaid_png(..., draw_method=MermaidDrawMethod.PYPPETEER)`

			

		
	

		

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			Python
			
		

	

	

		
USER1_THREAD_ID = "1"
config_USER1 = {"configurable": {"thread_id": USER1_THREAD_ID}}
 
user_input = "Hi there! My name is Maximo."
 
# The config is the **second positional argument** to stream() or invoke()!
events = graph.stream(
    {"messages": [{"role": "user", "content": user_input}]},
    config_USER1,
    stream_mode="values",
)
for event in events:
    event["messages"][-1].pretty_print()

	

	
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>_ Output 
			

				
================================ Human Message =================================
Hi there! My name is Maximo.
================================== Ai Message ==================================
Hello Maximo! It's nice to meet you. How can I assist you today? Feel free to ask me any questions or let me know if you need help with anything specific.

			

		
	

		

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user_input = "Do you remember my name?"
 
# The config is the **second positional argument** to stream() or invoke()!
events = graph.stream(
    {"messages": [{"role": "user", "content": user_input}]},
    config_USER1,
    stream_mode="values",
)
for event in events:
    event["messages"][-1].pretty_print()

	

	
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>_ Output 
			

				
================================ Human Message =================================
Do you remember my name?
================================== Ai Message ==================================
Yes, I remember your name! You mentioned it's Maximo. It's nice to chat with you, Maximo. How can I assist you today?

**Congratulations!** Our chatbot can now maintain conversation state across all sessions thanks to LangGraph's checkpoint system (checkpoints). This opens up possibilities for more natural and contextual interactions. LangGraph's control even handles complex graph states.

Morelink image 7

Chatbot with summary messagelink image 8

If we are going to manage the conversation context so as not to spend too many tokens, one thing we can do to improve the conversation is add a message with the summary of the conversation. This can be useful for the previous example, in which we have filtered the state so much that the LLM does not have enough context.

	

		

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			Python
			
		

	

	

		
from typing import Annotated
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages
from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
from langchain_core.messages import RemoveMessage, trim_messages, SystemMessage, HumanMessage, AIMessage, RemoveMessage
from langgraph.checkpoint.memory import MemorySaver
from huggingface_hub import login
from IPython.display import Image, display
import os
import dotenv
 
dotenv.load_dotenv()
HUGGINGFACE_TOKEN = os.getenv("HUGGINGFACE_LANGGRAPH")
 
memory_saver = MemorySaver()
 
class State(TypedDict):
    messages: Annotated[list, add_messages]
    summary: str
 
os.environ["LANGCHAIN_TRACING_V2"] = "false" # Disable LangSmith tracing
 
# Create the LLM model
login(token=HUGGINGFACE_TOKEN) # Login to HuggingFace to use the model
MODEL = "Qwen/Qwen2.5-72B-Instruct"
model = HuggingFaceEndpoint(
    repo_id=MODEL,
    task="text-generation",
    max_new_tokens=512,
    do_sample=False,
    repetition_penalty=1.03,
)
# Create the chat model
llm = ChatHuggingFace(llm=model)
 
# Print functions
def print_message(m):
    if isinstance(m, HumanMessage):
        message_content = m.content
        message_lines = message_content.split("
")
        for i, line in enumerate(message_lines):
            if i == 0:
                print(f"		[HumanMessage]: {line}")
            else:
                print(f"		{line}")
    elif isinstance(m, SystemMessage):
        message_content = m.content
        message_lines = message_content.split("
")
        for i, line in enumerate(message_lines):
            if i == 0:
                print(f"		[SystemMessage]: {line}")
            else:
                print(f"		{line}")
    elif isinstance(m, AIMessage):
        message_content = m.content
        message_lines = message_content.split("
")
        for i, line in enumerate(message_lines):
            if i == 0:
                print(f"		[AIMessage]: {line}")
            else:
                print(f"		{line}")
    elif isinstance(m, RemoveMessage):
        message_content = m.content
        message_lines = message_content.split("
")
        for i, line in enumerate(message_lines):
            if i == 0:
                print(f"		[RemoveMessage]: {line}")
            else:
                print(f"		{line}")
    else:
        message_content = m.content
        message_lines = message_content.split("
")
        for i, line in enumerate(message_lines):
            if i == 0:
                print(f"		[{type(m)}]: {line}")
            else:
                print(f"		{line}")
 
def print_state_summary(state: State):
    if state.get("summary"):
        summary_lines = state["summary"].split("
")
        for i, line in enumerate(summary_lines):
            if i == 0:
                print(f"		Summary of the conversation: {line}")
            else:
                print(f"		{line}")
    else:
        print("		No summary of the conversation")
 
def print_summary(summary: str):
    if summary:
        summary_lines = summary.split("
")
        for i, line in enumerate(summary_lines):
            if i == 0:
                print(f"		Summary of the conversation: {line}")
            else:
                print(f"		{line}")
    else:
        print("		No summary of the conversation")
 
# Nodes
def filter_messages(state: State):
    print("	--- 1 messages (input to filter_messages) ---")
    for m in state["messages"]:
        print_message(m)
    print_state_summary(state)
    print("	------------------------------------------------")
 
    # Delete all but the 2 most recent messages if there are more than 2
    if len(state["messages"]) &gt; 2:
        delete_messages = [RemoveMessage(id=m.id) for m in state["messages"][:-2]]
    else:
        delete_messages = []
 
    print("	--- 1 messages (output of filter_messages) ---")
    for m in delete_messages:
        print_message(m)
    print_state_summary(state)
    print("	------------------------------------------------")
 
    return {"messages": delete_messages}
 
def trim_messages_node(state: State):
    # print the messages received from filter_messages_node
    print("

	--- 2 messages (input to trim_messages) ---")
    for m in state["messages"]:
        print_message(m)
    print_state_summary(state)
    print("	------------------------------------------------")
 
    # Trim the messages based on the specified parameters
    trimmed_messages = trim_messages(
        state["messages"],
        max_tokens=100, # Maximum tokens allowed in the trimmed list
        strategy="last", # Keep the latest messages
        token_counter=llm, # Use the LLM's tokenizer to count tokens
        allow_partial=True, # Allow cutting messages mid-way if needed
    )
 
    # Identify the messages that must be removed
    # This is crucial: determine which messages are in 'state["messages"]' but not in 'trimmed_messages'
    original_ids = {m.id for m in state["messages"]}
    trimmed_ids = {m.id for m in trimmed_messages}
    ids_to_remove = original_ids - trimmed_ids
    
    # Create a RemoveMessage for each message that must be removed
    messages_to_remove = [RemoveMessage(id=msg_id) for msg_id in ids_to_remove]
 
    # Print the result of the trimming
    print("	--- 2 messages (output of trim_messages - after trimming) ---")
    if trimmed_messages:
        for m in trimmed_messages:
            print_message(m)
    else:
        print("[Empty list - No messages after trimming]")
    print_state_summary(state)
    print("	------------------------------------------------")
 
    return {"messages": messages_to_remove}
 
def chat_model_node(state: State):
    # Get summary of the conversation if it exists
    summary = state.get("summary", "")
 
    print("

	--- 3 messages (input to chat_model_node) ---")
    for m in state["messages"]:
        print_message(m)
    print_state_summary(state)
    print("	------------------------------------------------")
 
    # If there is a summary, add it to the system message
    if summary:
        # Add the summary to the system message
        system_message = f"Summary of the conversation earlier: {summary}"
 
        # Add the system message to the messages at the beginning
        messages = [SystemMessage(content=system_message)] + state["messages"]
    
    # If there is no summary, just return the messages
    else:
        messages = state["messages"]
    print(f"	--- 3 messages (input to chat_model_node) ---")
    for m in messages:
        print_message(m)
    print_summary(summary)
    print("	------------------------------------------------")
 
    # Invoke the LLM with the messages
    response = llm.invoke(messages)
 
    print("	--- 3 messages (output of chat_model_node) ---")
    print_message(response)
    print_summary(summary)
    print("	------------------------------------------------")
 
    # Return the LLM's response in the correct state format
    return {"messages": [response]}
 
def summarize_conversation(state: State):
    # Get summary of the conversation if it exists
    summary = state.get("summary", "")
 
    print("

	--- 4 messages (input to summarize_conversation) ---")
    for m in state["messages"]:
        print_message(m)
    print_summary(summary)
    print("	------------------------------------------------")
 
    # If there is a summary, add it to the system message
    if summary:
        summary_message = (
            f"This is a summary of the conversation to date: {summary}

"
            "Extend the summary by taking into account the new messages above."
        )
    
    # If there is no summary, create a new one
    else:
        summary_message = "Create a summary of the conversation above."
    print(f"	--- 4 summary message ---")
    summary_lines = summary_message.split("
")
    for i, line in enumerate(summary_lines):
        if i == 0:
            print(f"		{line}")
        else:
            print(f"		{line}")
    print_summary(summary)
    print("	------------------------------------------------")
 
    # Add prompt to the messages
    messages = state["messages"] + [HumanMessage(summary_message)]
 
    print("	--- 4 messages (input to summarize_conversation with summary) ---")
    for m in messages:
        print_message(m)
    print("	------------------------------------------------")
 
    # Invoke the LLM with the messages
    response = llm.invoke(messages)
 
    print("	--- 4 messages (output of summarize_conversation) ---")
    print_message(response)
    print("	------------------------------------------------")
 
    # Return the summary message in the correct state format
    return {"summary": response.content}
 
# Create graph builder
graph_builder = StateGraph(State)
 
# Add nodes
graph_builder.add_node("filter_messages_node", filter_messages)
graph_builder.add_node("trim_messages_node", trim_messages_node)
graph_builder.add_node("chatbot_node", chat_model_node)
graph_builder.add_node("summarize_conversation_node", summarize_conversation)
 
# Connecto nodes
graph_builder.add_edge(START, "filter_messages_node")
graph_builder.add_edge("filter_messages_node", "trim_messages_node")
graph_builder.add_edge("trim_messages_node", "chatbot_node")
graph_builder.add_edge("chatbot_node", "summarize_conversation_node")
graph_builder.add_edge("summarize_conversation_node", END)
 
# Compile the graph
graph = graph_builder.compile(checkpointer=memory_saver)
 
display(Image(graph.get_graph().draw_mermaid_png()))

	

	
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>_ Output 
			

				
&lt;IPython.core.display.Image object&gt;

As we can see, we have:

  • Message filtering function: If the state contains more than 2 messages, all messages except the last 2 are removed.
  • Message trimming function: Messages exceeding 100 tokens are removed.* Chatbot function: Runs the model with the filtered and trimmed messages. Additionally, if a summary exists, it is added to the system message.
  • Summary function: A summary of the conversation is created.

We create a function to print the graph messages.

	

		

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# Colors for the terminal
COLOR_GREEN = "\033[32m"
COLOR_YELLOW = "\033[33m"
COLOR_RESET = "\033[0m"
 
def stream_graph_updates(user_input: str, config: dict):
    # Initialize a flag to track if an assistant response has been printed
    assistant_response_printed = False
 
    # Print the user's input immediately
    print(f"

{COLOR_GREEN}User: {COLOR_RESET}{user_input}")
    
    # Create the user's message with the HumanMessage class
    user_message = HumanMessage(content=user_input)
    
    # Stream events from the graph execution
    for event in graph.stream({"messages": [user_message]}, config, stream_mode="values"):
        # event is a dictionary mapping node names to their output
        # Example: {'chatbot_node': {'messages': [...]}} or {'summarize_conversation_node': {'summary': '...'}}
 
        # Iterate through node name and its output
        for node_name, value in event.items():
            # Check if this event is from the chatbot node which should contain the assistant's reply
            if node_name == 'messages':
                # Ensure the output format is as expected (list of messages)
                if isinstance(value, list):
                    # Get the messages from the event
                    messages = value
                    # Ensure 'messages' is a non-empty list
                    if isinstance(messages, list) and messages:
                        # Get the last message (presumably the assistant's reply)
                        last_message = messages[-1]
                        # Ensure the message is an instance of AIMessage
                        if isinstance(last_message, AIMessage):
                            # Ensure the message has content to display
                            if hasattr(last_message, 'content'):
                                # Print the assistant's message content
                                print(f"{COLOR_YELLOW}Assistant: {COLOR_RESET}{last_message.content}")
                                assistant_response_printed = True # Mark that we've printed the response
    
    # Fallback if no assistant response was printed (e.g., graph error before chatbot_node)
    if not assistant_response_printed:
        print(f"{COLOR_YELLOW}Assistant: {COLOR_RESET}[No response generated or error occurred]")

	

	
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Now we execute the graph

	

		

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USER1_THREAD_ID = "1"
config_USER1 = {"configurable": {"thread_id": USER1_THREAD_ID}}
while True:
    user_input = input(f"

User: ")
    if user_input.lower() in ["quit", "exit", "q"]:
        print(f"{COLOR_GREEN}User: {COLOR_RESET}Exiting...")
        print(f"{COLOR_YELLOW}Assistant: {COLOR_RESET}Goodbye!")
        break
    
    events = stream_graph_updates(user_input, config_USER1)

	

	
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>_ Output 
			

				
User: Hello
--- 1 messages (input to filter_messages) ---
[HumanMessage]: Hello
No summary of the conversation
------------------------------------------------
--- 1 messages (output of filter_messages) ---
No summary of the conversation
------------------------------------------------
--- 2 messages (input to trim_messages) ---
[HumanMessage]: Hello
No summary of the conversation
------------------------------------------------
--- 2 messages (output of trim_messages - after trimming) ---
[HumanMessage]: Hello
No summary of the conversation
------------------------------------------------
--- 3 messages (input to chat_model_node) ---
[HumanMessage]: Hello
No summary of the conversation
------------------------------------------------
...
   - Use LangSmith’s web interface to visualize the evaluation results.
   - Compare different models and versions to identify the best performing model.
### Additional Tips
- **Model Tuning**: Use Hugging Face’s `Trainer` class to fine-tune models on your datasets and then evaluate them using LangSmith.
- **Custom Metrics**: Define custom evaluation metrics and use them to assess model performance.
- **Collaboration**: Share datasets and models with team members using the Hugging Face Hub and LangSmith.
By following these steps, you can effectively integrate Hugging Face models with LangSmith, leveraging the strengths of both platforms to build and evaluate robust NLP applications.
If you have any specific questions or need further assistance, feel free to ask!
User: Exiting...
Assistant: Goodbye!

If we go to the end of the conversation, we can see

--- 2 messages (input to trim_messages) ---
		[AIMessage]: Yes, I can help with information about LangGraph! LangGraph is a language model graph that represents the relationships and connections between different language models and their components. It can be used to visualize and understand the architecture, training processes, and performance characteristics of various language models.
		
		LangGraph puede ser especialmente útil para investigadores y desarrolladores que trabajan en tareas de procesamiento de lenguaje natural (NLP). Ayuda en:
		
		1. **Visualizing Model Architecture**: Provides a clear and detailed view of how different components of a language model are connected.
		2. **Comparing Models**: Permite comparar fácilmente diferentes modelos de lenguaje en términos de su estructura, datos de entrenamiento y métricas de rendimiento.
3. **Understanding Training Processes**: Helps in understanding the training dynamics and the flow of data through the model.
		4. **Identifying Bottlenecks**: Can help in identifying potential bottlenecks or areas for improvement in the model.
		
		If you have specific questions or aspects of LangGraph you're interested in, feel free to let me know!
		[HumanMessage]: I would like to know about using langsmith with huggingface LLMs, the integration of huggingface
		Summary of the conversation: Sure! Here's an extended summary of the conversation:
		
---
		
**Usuario:** Hola
		
		**Qwen:** Hello! How can I assist you today? Whether you need help with information, a specific task, or just want to chat, I'm here to help.
		
**User:** Estoy estudiando sobre LangGraph, ¿lo conoces?
		
		**Qwen:** Sí, ¡puedo ayudar con información sobre LangGraph! LangGraph es un grafo de modelos de lenguaje que representa las relaciones y conexiones entre distintos modelos de lenguaje y sus componentes. Puede usarse para visualizar y comprender la arquitectura, los procesos de entrenamiento y las características de rendimiento de varios modelos de lenguaje. LangGraph puede ser especialmente útil para investigadores y desarrolladores que trabajan en tareas de procesamiento del lenguaje natural (NLP). Ayuda en:
		1. Visualizing Model Architecture
		2. Comparing Models
		3. Understanding Training Processes
		4. Identifying Bottlenecks
		
		If you have specific questions or aspects of LangGraph you're interested in, feel free to let me know!
		
		**User:** This is a summary of the conversation to date: Sure! Here's a summary of the conversation above:
	User: Hello
Qwen: ¡Hola! ¿Cómo puedo ayudarte hoy? Ya sea que necesites ayuda con información, una tarea específica o solo quieras chatear, estoy aquí para ayudarte.
User: Create a summary of the conversation above.
Qwen: [Provided the summary you are now reading.]
		
		Is there anything else you need assistance with?		
		**Qwen:** [Extended the summary you are now reading.]
		
---
		
Is there anything else you need assistance with?
------------------------------------------------

We see that in the status messages only the are preserved

		[AIMessage]: Sí, puedo ayudar con información sobre LangGraph. LangGraph es un grafo de modelos de lenguaje que representa las relaciones y conexiones entre diferentes modelos de lenguaje y sus componentes. Puede usarse para visualizar y comprender la arquitectura, los procesos de entrenamiento y las características de rendimiento de varios modelos de lenguaje.
		
		LangGraph puede ser particularmente útil para investigadores y desarrolladores que trabajan en tareas de procesamiento del lenguaje natural (NLP). Ayuda en:
		
		1. **Visualizing Model Architecture**: Proporciona una vista clara y detallada de cómo están conectados los diferentes componentes de un modelo de lenguaje.
		2. **Comparing Models**: Allows for easy comparison of different language models in terms of their structure, training data, and performance metrics.
		3. **Entendiendo los procesos de entrenamiento**: Ayuda a comprender la dinámica del entrenamiento y el flujo de datos a través del modelo.
		4. **Identifying Bottlenecks**: Can help in identifying potential bottlenecks or areas for improvement in the model.
		
		Si tienes preguntas específicas o aspectos de LangGraph que te interesen, ¡no dudes en hacérmelo saber!
		[HumanMessage]: I would like to know about using langsmith with huggingface llms, the integration of huggingface

In other words, the filtering function only keeps the last 2 messages.

But afterwards we can see

--- 2 messages (output of trim_messages - after trimming) ---
		[HumanMessage]: I would like to know about using langsmith with huggingface llms, the integration of huggingface
		Summary of the conversation: Sure! Here's an extended summary of the conversation:
		
---
		
		**User:** Hello
		
**Qwen:** Hello! How can I assist you today? Whether you need help with information, a specific task, or just want to chat, I'm here to help.
		
**Usuario:** Estoy estudiando langgraph, ¿lo conoces?
		
		**Qwen:** Sí, puedo ayudar con información sobre LangGraph. LangGraph es un grafo de modelos de lenguaje que representa las relaciones y conexiones entre diferentes modelos de lenguaje y sus componentes. Puede usarse para visualizar y comprender la arquitectura, los procesos de entrenamiento y las características de rendimiento de varios modelos de lenguaje. LangGraph puede ser particularmente útil para investigadores y desarrolladores que trabajan en tareas de procesamiento de lenguaje natural (NLP). Ayuda en:
		1. Visualizing Model Architecture
		2. Comparing Models
		3. Entendiendo los procesos de entrenamiento
		4. Identifying Bottlenecks
		
		If you have specific questions or aspects of LangGraph you're interested in, feel free to let me know!
		
		**User:** This is a summary of the conversation to date: Sure! Here's a summary of the conversation above:
		User: Hello		Qwen: Hello! How can I assist you today? Whether you need help with information, a specific task, or just want to chat, I'm here to help.
User: Create a summary of the conversation above.
Qwen: [Provided the summary you are now reading.]
		
	Is there anything else you need assistance with?
		
		**Qwen:** [Extended the summary you are now reading.]
		
---
		
Is there anything else you need assistance with?
------------------------------------------------

That is, the trimming function removes the assistant’s message because it exceeds 100 tokens.

Even after deleting messages, so that the LLM no longer has them as context, we can still have a conversation thanks to the conversation summary that we keep generating.

Save state in SQLitelink image 9

We have seen how to store the graph state in memory, but as soon as the process ends, that memory is lost, so we are going to see how to store it in SQLite

First, we need to install the sqlite package for LangGraph.

pip install langgraph-checkpoint-sqlite

We import the sqlite and langgraph-checkpoint-sqlite libraries. Previously, when we saved the state in memory, we used memory_saver; now we will use SqliteSaver to store the state in a SQLite database.

	

		

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import sqlite3
from langgraph.checkpoint.sqlite import SqliteSaver
import os
 
# Create the directory if it doesn't exist
os.makedirs("state_db", exist_ok=True)
 
db_path = "state_db/langgraph_sqlite.db"
conn = sqlite3.connect(db_path, check_same_thread=False)
memory = SqliteSaver(conn)

	

	
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Let's create a basic chatbot to avoid adding complexity beyond the functionality we want to test.

	

		

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			Input
		

		

			Python
			
		

	

	

		
from typing import Annotated
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages
from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
from langchain_core.messages import HumanMessage, AIMessage
from huggingface_hub import login
from IPython.display import Image, display
import os
import dotenv
 
dotenv.load_dotenv()
HUGGINGFACE_TOKEN = os.getenv("HUGGINGFACE_LANGGRAPH")
 
class State(TypedDict):
    messages: Annotated[list, add_messages]
 
os.environ["LANGCHAIN_TRACING_V2"] = "false" # Disable LangSmith tracing
 
# Create the LLM model
login(token=HUGGINGFACE_TOKEN) # Login to HuggingFace to use the model
MODEL = "Qwen/Qwen2.5-72B-Instruct"
model = HuggingFaceEndpoint(
    repo_id=MODEL,
    task="text-generation",
    max_new_tokens=512,
    do_sample=False,
    repetition_penalty=1.03,
)
# Create the chat model
llm = ChatHuggingFace(llm=model)
 
# Nodes
def chat_model_node(state: State):
    # Return the LLM's response in the correct state format
    return {"messages": [llm.invoke(state["messages"])]}
 
# Create graph builder
graph_builder = StateGraph(State)
 
# Add nodes
graph_builder.add_node("chatbot_node", chat_model_node)
 
# Connecto nodes
graph_builder.add_edge(START, "chatbot_node")
graph_builder.add_edge("chatbot_node", END)
 
# Compile the graph
graph = graph_builder.compile(checkpointer=memory)
 
display(Image(graph.get_graph().draw_mermaid_png()))

	

	
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>_ Output 
			

				
&lt;IPython.core.display.Image object&gt;

We define the function to print the graph messages.

	

		

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			Input
		

		

			Python
			
		

	

	

		
# Colors for the terminal
COLOR_GREEN = "\033[32m"
COLOR_YELLOW = "\033[33m"
COLOR_RESET = "\033[0m"
 
def stream_graph_updates(user_input: str, config: dict):
    # Initialize a flag to track if an assistant response has been printed
    assistant_response_printed = False
 
    # Print the user's input immediately
    print(f"

{COLOR_GREEN}User: {COLOR_RESET}{user_input}")
    
    # Create the user's message with the HumanMessage class
    user_message = HumanMessage(content=user_input)
    
    # Stream events from the graph execution
    for event in graph.stream({"messages": [user_message]}, config, stream_mode="values"):
        # event is a dictionary mapping node names to their output
        # Example: {'chatbot_node': {'messages': [...]}} or {'summarize_conversation_node': {'summary': '...'}}
 
        # Iterate through node name and its output
        for node_name, value in event.items():
            # Check if this event is from the chatbot node which should contain the assistant's reply
            if node_name == 'messages':
                # Ensure the output format is as expected (list of messages)
                if isinstance(value, list):
                    # Get the messages from the event
                    messages = value
                    # Ensure 'messages' is a non-empty list
                    if isinstance(messages, list) and messages:
                        # Get the last message (presumably the assistant's reply)
                        last_message = messages[-1]
                        # Ensure the message is an instance of AIMessage
                        if isinstance(last_message, AIMessage):
                            # Ensure the message has content to display
                            if hasattr(last_message, 'content'):
                                # Print the assistant's message content
                                print(f"{COLOR_YELLOW}Assistant: {COLOR_RESET}{last_message.content}")
                                assistant_response_printed = True # Mark that we've printed the response
    
    # Fallback if no assistant response was printed (e.g., graph error before chatbot_node)
    if not assistant_response_printed:
        print(f"{COLOR_YELLOW}Assistant: {COLOR_RESET}[No response generated or error occurred]")

	

	
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We execute the graph

	

		

			< >
			Input
		

		

			Python
			
		

	

	

		
USER1_THREAD_ID = "USER1"
config_USER1 = {"configurable": {"thread_id": USER1_THREAD_ID}}
while True:
    user_input = input(f"

User: ")
    if user_input.lower() in ["quit", "exit", "q"]:
        print(f"{COLOR_GREEN}User: {COLOR_RESET}Exiting...")
        print(f"{COLOR_YELLOW}Assistant: {COLOR_RESET}Goodbye!")
        break
    
    events = stream_graph_updates(user_input, config_USER1)

	

	
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>_ Output 
			

				
User: Hello, my name is Máximo
Assistant: Hello Máximo! It's a pleasure to meet you. How can I assist you today?
User: Exiting...
Assistant: Goodbye!

As you can see, I have only told them what my name is.

Now we restart the notebook so that all data stored in the notebook's RAM is deleted, and we run the previous code again.

We recreate the sqlite memory with SqliteSaver

	

		

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			Input
		

		

			Python
			
		

	

	

		
import sqlite3
from langgraph.checkpoint.sqlite import SqliteSaver
import os
 
# Create the directory if it doesn't exist
os.makedirs("state_db", exist_ok=True)
 
db_path = "state_db/langgraph_sqlite.db"
conn = sqlite3.connect(db_path, check_same_thread=False)
memory = SqliteSaver(conn)

	

	
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We recreate the graph

	

		

			< >
			Input
		

		

			Python
			
		

	

	

		
from typing import Annotated
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages
from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
from langchain_core.messages import HumanMessage, AIMessage
from huggingface_hub import login
from IPython.display import Image, display
import os
import dotenv
 
dotenv.load_dotenv()
HUGGINGFACE_TOKEN = os.getenv("HUGGINGFACE_LANGGRAPH")
 
class State(TypedDict):
    messages: Annotated[list, add_messages]
 
os.environ["LANGCHAIN_TRACING_V2"] = "false" # Disable LangSmith tracing
 
# Create the LLM model
login(token=HUGGINGFACE_TOKEN) # Login to HuggingFace to use the model
MODEL = "Qwen/Qwen2.5-72B-Instruct"
model = HuggingFaceEndpoint(
    repo_id=MODEL,
    task="text-generation",
    max_new_tokens=512,
    do_sample=False,
    repetition_penalty=1.03,
)
# Create the chat model
llm = ChatHuggingFace(llm=model)
 
# Nodes
def chat_model_node(state: State):
    # Return the LLM's response in the correct state format
    return {"messages": [llm.invoke(state["messages"])]}
 
# Create graph builder
graph_builder = StateGraph(State)
 
# Add nodes
graph_builder.add_node("chatbot_node", chat_model_node)
 
# Connecto nodes
graph_builder.add_edge(START, "chatbot_node")
graph_builder.add_edge("chatbot_node", END)
 
# Compile the graph
graph = graph_builder.compile(checkpointer=memory)
 
display(Image(graph.get_graph().draw_mermaid_png()))

	

	
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>_ Output 
			

				
&lt;IPython.core.display.Image object&gt;

We redefine the function to print the graph messages.

	

		

			< >
			Input
		

		

			Python
			
		

	

	

		
# Colors for the terminal
COLOR_GREEN = "\033[32m"
COLOR_YELLOW = "\033[33m"
COLOR_RESET = "\033[0m"
 
def stream_graph_updates(user_input: str, config: dict):
    # Initialize a flag to track if an assistant response has been printed
    assistant_response_printed = False
 
    # Print the user's input immediately
    print(f"

{COLOR_GREEN}User: {COLOR_RESET}{user_input}")
    
    # Create the user's message with the HumanMessage class
    user_message = HumanMessage(content=user_input)
    
    # Stream events from the graph execution
    for event in graph.stream({"messages": [user_message]}, config, stream_mode="values"):
        # event is a dictionary mapping node names to their output
        # Example: {'chatbot_node': {'messages': [...]}} or {'summarize_conversation_node': {'summary': '...'}}
 
        # Iterate through node name and its output
        for node_name, value in event.items():
            # Check if this event is from the chatbot node which should contain the assistant's reply
            if node_name == 'messages':
                # Ensure the output format is as expected (list of messages)
                if isinstance(value, list):
                    # Get the messages from the event
                    messages = value
                    # Ensure 'messages' is a non-empty list
                    if isinstance(messages, list) and messages:
                        # Get the last message (presumably the assistant's reply)
                        last_message = messages[-1]
                        # Ensure the message is an instance of AIMessage
                        if isinstance(last_message, AIMessage):
                            # Ensure the message has content to display
                            if hasattr(last_message, 'content'):
                                # Print the assistant's message content
                                print(f"{COLOR_YELLOW}Assistant: {COLOR_RESET}{last_message.content}")
                                assistant_response_printed = True # Mark that we've printed the response
    
    # Fallback if no assistant response was printed (e.g., graph error before chatbot_node)
    if not assistant_response_printed:
        print(f"{COLOR_YELLOW}Assistant: {COLOR_RESET}[No response generated or error occurred]")

	

	
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And we run it again

	

		

			< >
			Input
		

		

			Python
			
		

	

	

		
USER1_THREAD_ID = "USER1"
config_USER1 = {"configurable": {"thread_id": USER1_THREAD_ID}}
while True:
    user_input = input(f"

User: ")
    if user_input.lower() in ["quit", "exit", "q"]:
        print(f"{COLOR_GREEN}User: {COLOR_RESET}Exiting...")
        print(f"{COLOR_YELLOW}Assistant: {COLOR_RESET}Goodbye!")
        break
    
    events = stream_graph_updates(user_input, config_USER1)

	

	
Copied
>_ Output 
			

				
User: What's my name?
Assistant: Your name is Máximo. It's nice to know and use your name as we chat. How can I assist you today, Máximo?
User: Exiting...
Assistant: Goodbye!

As can be seen, we have been able to recover the state of the SQLite database graph.

---

➡️ **Continue in Part 3: long-term memory and human-in-the-loop**.

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Machine Learning and AI specialist. I develop solutions with generative AI, intelligent agents and custom models.

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