LangGraph (2/4): memoria a corto plazo

19 de mayo del 2025

En la primera parte construimos un chatbot básico con LangGraph y le añadimos herramientas. El problema es que no recuerda nada de un mensaje al siguiente. En este capítulo le damos **memoria a corto plazo**: persistimos el estado del grafo dentro de un hilo con checkpointers.

⚠️ Este capítulo continúa el código de la parte anterior. Para ejecutarlo necesitas el entorno y el chatbot de la Parte 1.

📚 **Esta entrada es parte de la serie _Guía completa de LangGraph_**, dividida en cuatro capítulos que se leen en orden:

> * Parte 1: Chatbot básico y herramientas

* 👉 **Parte 2: Memoria a corto plazo**

* Parte 3: Memoria a largo plazo y human-in-the-loop

* Parte 4: Personalización del estado y checkpoints

Agregar memoria al chatbot - memoria a corto plazo, memoria dentro del hilo

Nuestro chatbot ahora puede usar herramientas para responder preguntas de los usuarios, pero no recuerda el contexto de las interacciones anteriores. Esto limita su capacidad de tener conversaciones coherentes y de múltiples turnos.

LangGraph resuelve este problema a través de puntos de control persistentes o checkpoints. Si le proporcionamos un checkpointer al compilar el grafo y un thread_id al llamar al grafo, LangGraph guarda automáticamente el estado después de cada iteración en la conversación.

Cuando invoquemos el grafo nuevamente usando el mismo thread_id, el grafo cargará su estado guardado, permitiendo que el chatbot continúe donde lo dejó.

Veremos más tarde que ese checkpointing es mucho más potente que la simple memoria de chat: le permite guardar y reanudar estados complejos en cualquier momento para la recuperación de errores, flujos de trabajo con human in the loop, interacciones en el tiempo y más. Pero antes de ver todo eso, vamos a agregar puntos de control para permitir conversaciones de varias iteraciones.

	
		
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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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Para empezar, creamos un checkpointer MemorySaver.

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

> Estamos usando un checkpointer en memoria, es decir, se guarda en la RAM y cuando se termine de ejecutar el grafo se elimina. Esto nos vale para nuestro caso, ya que es un ejemplo para aprender a usar LangGraph. En una aplicación de producción, es probable que se necesite cambiar esto para usarlo con SqliteSaver o PostgresSaver y conectarnos a nuestra propia base de datos.

A continuación, definimos el grafo.

	
		
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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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Definimos la 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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A continuación, el LLM con las bind_tools y lo añadimos al grafo

	
		
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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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>_ Output

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

Antes construimos nuestro propio BasicToolNode para aprender cómo funciona, ahora lo reemplazaremos con el método de LangGraph ToolNode y tools_condition, ya que estos hacen algunas cosas buenas como la ejecución paralela de API. Aparte de eso, el resto es igual que antes.

	
		
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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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>_ Output

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

Añadimos el nodo de tools_condition al grafo

	
		
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		graph_builder.add_conditional_edges(
    "chatbot_node",
    tools_condition,
)
	
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>_ Output

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

Añadimos el nodo de tools al grafo

	
		
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		graph_builder.add_edge("tools", "chatbot_node")
	
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>_ Output

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

Añadimos el nodo de START al grafo

	
		
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		graph_builder.add_edge(START, "chatbot_node")
	
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>_ Output

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

Compilamos el grafo añadiendo el checkpointer

	
		
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		graph = graph_builder.compile(checkpointer=memory)
	
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Lo representamos gráficamente

	
		
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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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>_ Output

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

Creamos una configuración con un thread_id de un usuario

	
		
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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.

Cómo se puede ver, no hemos pasado una lista con los mensajes, todo está siendo gestionado por el checkpointer.

Si ahora probamos con otro usuario, es decir, con otro thread_id, veremos que el grafo no recuerda la conversación anterior.

	
		
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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()
	
	Copied

>_ 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?

Ahora que nuestro chatbot tiene herramientas de búsqueda y memoria, vamos a repetir el ejemplo anterior, donde le pregunto por el resultado del último partido del Real Madrid en la Liga y luego por qué jugadores jugaron.

	
		
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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()
	
	Copied

>_ 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.

Ahora le preguntamos por los jugadores que jugaron en el partido.

	
		
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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()
	
	Copied

>_ 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 LeganÃ©s



The Whitesâ team is: Lunin, Lucas V., Asencio, RÃ¼diger, Fran GarcÃa, Arda GÃ¼ler, ModriÄ, Camavinga, Bellingham, Brahim and MbappÃ©.





Real Madrid have named their starting line-up for the game against LeganÃ©s on matchday 29 of LaLiga, which will be played at the Santiago BernabÃ©u (9 pm CET). [...] Real Madrid starting line-up:
13. Lunin
17. Lucas V.
35. Asencio
22. RÃ¼diger
20. Fran GarcÃa
15. Arda GÃ¼ler
10. ModriÄ
6. Camavinga
5. Bellingham
21. Brahim
9. MbappÃ©.

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

LeganÃ©s starting line-up:
13. Dmitrovic
5. Tapia
6. Sergio G.
7. Ãscar
10. Raba
11. Cruz
12. V. Rosier
17. Neyou
19. Diego G.
20. Javi HernÃ¡ndez
22. Nastasic. [...] Suplentes:
1. Juan Soriano
36. Abajas
2. A. Alti
3. Jorge SÃ¡enz
8. Cisse
9. Miguel
14. Darko
18. Duk
21. R. LÃ³pez
23. Munir
24. Chicco
30. I. Diomande.



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Official App Fan

Real Madrid Â© 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.

Tras mucho buscar, al final lo encuentra. Por lo que ya tenemos un chatbot con tools y memoria.

Por ahora, hemos creado unos checkpoints en tres hilos diferentes. Pero, ¿qué entra en cada checkpoint? Para inspeccionar el estado de un grafo para una configuración dada podemos usar el método get_state(config).

	
		
			< >
			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 LeganÃ©s\n\n\n\nThe Whitesâ team is: Lunin, Lucas V., Asencio, RÃ¼diger, Fran GarcÃa, Arda GÃ¼ler, ModriÄ, Camavinga, Bellingham, Brahim and MbappÃ©.\n\n\n\n\n\nReal Madrid have named their starting line-up for the game against LeganÃ©s on matchday 29 of LaLiga, which will be played at the Santiago BernabÃ©u (9 pm CET). [...] Real Madrid starting line-up:\n13. Lunin\n17. Lucas V.\n35. Asencio\n22. RÃ¼diger\n20. Fran GarcÃa\n15. Arda GÃ¼ler\n10. ModriÄ\n6. Camavinga\n5. Bellingham\n21. Brahim\n9. MbappÃ©.\n\nSubstitutes:\n26. Fran GonzÃ¡lez\n34. Sergio Mestre\n4. Alaba\n7. Vini Jr.\n8. Valverde\n11. Rodrygo\n14. Tchouameni\n16. Endrick\n18. Vallejo\n43. Diego Aguado.\n\nLeganÃ©s starting line-up:\n13. Dmitrovic\n5. Tapia\n6. Sergio G.\n7. Ãscar\n10. Raba\n11. Cruz\n12. V. Rosier\n17. Neyou\n19. Diego G.\n20. Javi HernÃ¡ndez\n22. Nastasic. [...] Suplentes:\n1. Juan Soriano\n36. Abajas\n2. A. Alti\n3. Jorge SÃ¡enz\n8. Cisse\n9. Miguel\n14. Darko\n18. Duk\n21. R. LÃ³pez\n23. Munir\n24. Chicco\n30. I. Diomande.\n\n\n\nDownload Now\n\nOfficial App Fan\n\nReal Madrid Â© 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 LeganÃ©s



The Whitesâ team is: Lunin, Lucas V., Asencio, RÃ¼diger, Fran GarcÃa, Arda GÃ¼ler, ModriÄ, Camavinga, Bellingham, Brahim and MbappÃ©.





Real Madrid have named their starting line-up for the game against LeganÃ©s on matchday 29 of LaLiga, which will be played at the Santiago BernabÃ©u (9 pm CET). [...] Real Madrid starting line-up:
13. Lunin
17. Lucas V.
35. Asencio
22. RÃ¼diger
20. Fran GarcÃa
15. Arda GÃ¼ler
10. ModriÄ
6. Camavinga
5. Bellingham
21. Brahim
9. MbappÃ©.

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

LeganÃ©s starting line-up:
13. Dmitrovic
5. Tapia
6. Sergio G.
7. Ãscar
10. Raba
11. Cruz
12. V. Rosier
17. Neyou
19. Diego G.
20. Javi HernÃ¡ndez
22. Nastasic. [...] Suplentes:
1. Juan Soriano
36. Abajas
2. A. Alti
3. Jorge SÃ¡enz
8. Cisse
9. Miguel
14. Darko
18. Duk
21. R. LÃ³pez
23. Munir
24. Chicco
30. I. Diomande.



Download Now

Official App Fan

Real Madrid Â© 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=())

Si queremos ver el siguiente nodo a procesar, podemos usar el atributo next

	
		
			< >
			Input
		
		
			Python
			
		
	
	
		snapshot.next
	
	Copied

>_ Output

()

Dado que el grafo ha finalizado, next está vacío. Si obtienes un estado desde dentro de una invocación del grafo, next indica qué nodo se ejecutará a continuación.

La instantánea anterior (snapshot) contiene los valores de estado actuales, la configuración correspondiente y el siguiente nodo (next) a procesar. En nuestro caso, el grafo ha alcanzado el estado END, por eso next está vacío.

Vamos a volver a escribir todo el código para que sea más legible.

	
		
			< >
			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 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}")
	
	Copied

>_ 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)`

	
		
			< >
			Input
		
		
			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()
	
	Copied

>_ 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.

	
		
			< >
			Input
		
		
			Python
			
		
	
	
		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()
	
	Copied

>_ 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?

**¡Felicidades!** Nuestro chatbot ahora puede mantener el estado de conversación en todas las sesiones gracias al sistema de puntos de control (checkpoints) de LangGraph. Esto abre posibilidades para interacciones más naturales y contextuales. El control de LangGraph incluso maneja estados de grafos complejos.

Más

Chatbot con mensaje de resumen

Si vamos a manejar el contexto de la conversación para no gastar muchos tokens, una cosa que podemos hacer para mejorar la conversación es añadir un mensaje con el resumen de la conversación. Esto puede ser útil para el ejemplo anterior, en el que hemos filtrado tanto el estado que el LLM no tiene contexto suficiente.

	
		
			< >
			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 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()))
	
	Copied

>_ Output

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

Cómo podemos ver, tenemos:

Función de filtrado de mensajes: Si en el estado hay más de 2 mensajes, se eliminan todos los mensajes excepto los 2 últimos.
Función de trimado de mensajes: Se eliminan los mensajes que superan los 100 tokens.
Función de chatbot: Se ejecuta el modelo con los mensajes filtrados y trimados. Además, si existe un resumen, se añade al mensaje de sistema.
Función de resumen: Se crea un resumen de la conversación.

Creamos una función para imprimir los mensajes del grafo.

	
		
			< >
			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]")
	
	Copied

Ahora ejecutamos el grafo

	
		
			< >
			Input
		
		
			Python
			
		
	
	
		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)
	
	Copied

>_ 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!

Si nos vamos al final de la conversación, podemos ver

	--- 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 can be particularly useful for researchers and developers who are working on natural language processing (NLP) tasks. It helps in:
		
		1. **Visualizing Model Architecture**: Provides a clear and detailed view of how different components of a language model are connected.
		2. **Comparing Models**: Allows for easy comparison of different language models in terms of their structure, training data, and performance metrics.
		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:
		
		---
		
		**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:** I am studying about langgraph, do you know it?
		
		**Qwen:** 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 can be particularly useful for researchers and developers who are working on natural language processing (NLP) tasks. It helps in:
		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: 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?
	------------------------------------------------

Vemos que en los mensajes del estado solo se conservan

		[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 can be particularly useful for researchers and developers who are working on natural language processing (NLP) tasks. It helps in:
		
		1. **Visualizing Model Architecture**: Provides a clear and detailed view of how different components of a language model are connected.
		2. **Comparing Models**: Allows for easy comparison of different language models in terms of their structure, training data, and performance metrics.
		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

Es decir, la función de filtrado solo mantiene los 2 últimos mensajes.

Pero después podemos ver

	--- 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.
		
		**User:** I am studying about langgraph, do you know it?
		
		**Qwen:** 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 can be particularly useful for researchers and developers who are working on natural language processing (NLP) tasks. It helps in:
		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: 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?
	------------------------------------------------

Es decir, la función de trimado elimina el mensaje del asistente porque supera los 100 tokens.

Aun eliminando mensajes, por lo que no los tiene como contexto el LLM, podemos tener una conversación gracias al resumen de la conversación que vamos generando.

Guardar estado en SQLite

Hemos visto cómo guardar el estado del grafo en memoria, pero en cuanto terminamos el proceso, esa memoria se pierde, por lo que vamos a ver cómo guardarla en SQLite

Primero necesitamos instalar el paquete de sqlite para LangGraph.

pip install langgraph-checkpoint-sqlite

Importamos las librerías de sqlite y langgraph-checkpoint-sqlite. Antes, cuando guardábamos el estado en memoria usábamos memory_saver, ahora usaremos SqliteSaver para guardar el estado en una base de datos SQLite.

	
		
			< >
			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)
	
	Copied

Vamos a crear un chatbot básico para no añadir complejidad aparte de la funcionalidad que queremos probar.

	
		
			< >
			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()))
	
	Copied

>_ Output

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

Definimos la función para imprimir los mensajes del grafo.

	
		
			< >
			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]")
	
	Copied

Ejecutamos el grafo

	
		
			< >
			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: 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!

Como se puede ver, solo le he dicho cómo me llamo

Ahora reiniciamos el notebook para que se eliminen todos los datos guardados en RAM del notebook y volvemos a ejecutar el código anterior.

Volvemos a crear la memoria de sqlite con SqliteSaver

	
		
			< >
			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)
	
	Copied

Volvemos a crear el grafo

	
		
			< >
			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()))
	
	Copied

>_ Output

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

Volvemos a definir la función para imprimir los mensajes del grafo.

	
		
			< >
			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]")
	
	Copied

Y lo volvemos a ejecutar

	
		
			< >
			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!

Como se puede ver, hemos podido recuperar el estado del grafo de la base de datos SQLite.

---

➡️ **Continúa en la Parte 3: memoria a largo plazo y human-in-the-loop**.

Seguir leyendo

Deep Research con LangGraph (3/3): agente Writer e informe final

Tercera y última parte de la serie Deep Research con LangGraph. Implementa el agente Writer que redacta el informe final a partir de la investigación, integra todo el grafo del deep researcher y ejecuta el asistente completo de principio a fin.

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¿Tienes un proyecto con IA?

Hablemos.

maximofn@gmail.com

Especialista en Machine Learning e Inteligencia Artificial. Desarrollo soluciones con IA generativa, agentes inteligentes y modelos personalizados.

Escríbeme LinkedIn

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Usa esto en local

Los espacios de Hugging Face nos permite ejecutar modelos con demos muy sencillas, pero ¿qué pasa si la demo se rompe? O si el usuario la elimina? Por ello he creado contenedores docker con algunos espacios interesantes, para poder usarlos de manera local, pase lo que pase. De hecho, es posible que si pinchas en alún botón de ver proyecto te lleve a un espacio que no funciona.

Flow edit

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Ver todos los contenedores -->

>_ Disponible para proyectos

¿Tienes un proyecto con IA?

Hablemos.

maximofn@gmail.com

Especialista en Machine Learning e Inteligencia Artificial. Desarrollo soluciones con IA generativa, agentes inteligentes y modelos personalizados.

Escríbeme LinkedIn

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