langgraph/docs/docs/how-tos/persistence_mongodb.ipynb

How to use MongoDB checkpointer for persistence

Prerequisites

This guide assumes familiarity with the following:

• Persistence
• MongoDB

When creating LangGraph agents, you can also set them up so that they persist their state. This allows you to do things like interact with an agent multiple times and have it remember previous interactions.

This reference implementation shows how to use MongoDB as the backend for persisting checkpoint state using the langgraph-checkpoint-mongodb library.

For demonstration purposes we add persistence to a prebuilt ReAct agent.

In general, you can add a checkpointer to any custom graph that you build like this:

from langgraph.graph import StateGraph

builder = StateGraph(...)
# ... define the graph
checkpointer = # mongodb checkpointer (see examples below)
graph = builder.compile(checkpointer=checkpointer)
...

Setup

To use the MongoDB checkpointer, you will need a MongoDB cluster. Follow this guide to create a cluster if you don't already have one.

Next, let's install the required packages and set our API keys

In [1]:

%%capture --no-stderr
%pip install -U pymongo langgraph langgraph-checkpoint-mongodb

In [2]:

import getpass
import os


def _set_env(var: str):
    if not os.environ.get(var):
        os.environ[var] = getpass.getpass(f"{var}: ")


_set_env("OPENAI_API_KEY")

OPENAI_API_KEY:  ········

Set up LangSmith for LangGraph development

Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started here.

Define model and tools for the graph

In [3]:

from typing import Literal

from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent


@tool
def get_weather(city: Literal["nyc", "sf"]):
    """Use this to get weather information."""
    if city == "nyc":
        return "It might be cloudy in nyc"
    elif city == "sf":
        return "It's always sunny in sf"
    else:
        raise AssertionError("Unknown city")


tools = [get_weather]
model = ChatOpenAI(model_name="gpt-4o-mini", temperature=0)

MongoDB checkpointer usage

With a connection string

This creates a connection to MongoDB directly using the connection string of your cluster. This is ideal for use in scripts, one-off operations and short-lived applications.

In [4]:

from langgraph.checkpoint.mongodb import MongoDBSaver

MONGODB_URI = "localhost:27017"  # replace this with your connection string

with MongoDBSaver.from_conn_string(MONGODB_URI) as checkpointer:
    graph = create_react_agent(model, tools=tools, checkpointer=checkpointer)
    config = {"configurable": {"thread_id": "1"}}
    response = graph.invoke(
        {"messages": [("human", "what's the weather in sf")]}, config
    )

In [5]:

response

Out [5]:

{'messages': [HumanMessage(content="what's the weather in sf", additional_kwargs={}, response_metadata={}, id='729afd6a-fdc0-4192-a255-1dac065c79b2'),
  AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_YqaO8oU3BhGmIz9VHTxqGyyN', 'function': {'arguments': '{"city":"sf"}', 'name': 'get_weather'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_39a40c96a0', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-b45c0c12-c68e-4392-92dd-5d325d0a9f60-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_YqaO8oU3BhGmIz9VHTxqGyyN', 'type': 'tool_call'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}),
  ToolMessage(content="It's always sunny in sf", name='get_weather', id='0c72eb29-490b-44df-898f-8454c314eac1', tool_call_id='call_YqaO8oU3BhGmIz9VHTxqGyyN'),
  AIMessage(content='The weather in San Francisco is always sunny!', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 84, 'total_tokens': 94, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_818c284075', 'finish_reason': 'stop', 'logprobs': None}, id='run-33f54c91-0ba9-48b7-9b25-5a972bbdeea9-0', usage_metadata={'input_tokens': 84, 'output_tokens': 10, 'total_tokens': 94, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}})]}

Using the MongoDB client

This creates a connection to MongoDB using the MongoDB client. This is ideal for long-running applications since it allows you to reuse the client instance for multiple database operations without needing to reinitialize the connection each time.

In [6]:

from pymongo import MongoClient

mongodb_client = MongoClient(MONGODB_URI)

checkpointer = MongoDBSaver(mongodb_client)
graph = create_react_agent(model, tools=tools, checkpointer=checkpointer)
config = {"configurable": {"thread_id": "2"}}
response = graph.invoke({"messages": [("user", "What's the weather in sf?")]}, config)

In [7]:

response

Out [7]:

{'messages': [HumanMessage(content="What's the weather in sf?", additional_kwargs={}, response_metadata={}, id='4ce68bee-a843-4b08-9c02-7a0e3b010110'),
  AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_MvGxq9IU9wvW9mfYKSALHtGu', 'function': {'arguments': '{"city":"sf"}', 'name': 'get_weather'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_6fc10e10eb', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-9712c5a4-376c-4812-a0c4-1b522334a59d-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_MvGxq9IU9wvW9mfYKSALHtGu', 'type': 'tool_call'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}),
  ToolMessage(content="It's always sunny in sf", name='get_weather', id='b4eed38d-bcaf-4497-ad08-f21ccd6a8c30', tool_call_id='call_MvGxq9IU9wvW9mfYKSALHtGu'),
  AIMessage(content='The weather in San Francisco is always sunny!', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 84, 'total_tokens': 94, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_6fc10e10eb', 'finish_reason': 'stop', 'logprobs': None}, id='run-c6c4ad75-89ef-4b4f-9ca4-bd52ccb0729b-0', usage_metadata={'input_tokens': 84, 'output_tokens': 10, 'total_tokens': 94, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}})]}

In [8]:

# Retrieve the latest checkpoint for the given thread ID
# To retrieve a specific checkpoint, pass the checkpoint_id in the config
checkpointer.get_tuple(config)

Out [8]:

CheckpointTuple(config={'configurable': {'thread_id': '2', 'checkpoint_ns': '', 'checkpoint_id': '1efb8c75-9262-68b4-8003-1ac1ef198757'}}, checkpoint={'v': 1, 'ts': '2024-12-12T20:26:20.545003+00:00', 'id': '1efb8c75-9262-68b4-8003-1ac1ef198757', 'channel_values': {'messages': [HumanMessage(content="What's the weather in sf?", additional_kwargs={}, response_metadata={}, id='4ce68bee-a843-4b08-9c02-7a0e3b010110'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_MvGxq9IU9wvW9mfYKSALHtGu', 'function': {'arguments': '{"city":"sf"}', 'name': 'get_weather'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_6fc10e10eb', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-9712c5a4-376c-4812-a0c4-1b522334a59d-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_MvGxq9IU9wvW9mfYKSALHtGu', 'type': 'tool_call'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}), ToolMessage(content="It's always sunny in sf", name='get_weather', id='b4eed38d-bcaf-4497-ad08-f21ccd6a8c30', tool_call_id='call_MvGxq9IU9wvW9mfYKSALHtGu'), AIMessage(content='The weather in San Francisco is always sunny!', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 84, 'total_tokens': 94, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_6fc10e10eb', 'finish_reason': 'stop', 'logprobs': None}, id='run-c6c4ad75-89ef-4b4f-9ca4-bd52ccb0729b-0', usage_metadata={'input_tokens': 84, 'output_tokens': 10, 'total_tokens': 94, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}})], 'agent': 'agent'}, 'channel_versions': {'__start__': 2, 'messages': 5, 'start:agent': 3, 'agent': 5, 'branch:agent:should_continue:tools': 4, 'tools': 5}, 'versions_seen': {'__input__': {}, '__start__': {'__start__': 1}, 'agent': {'start:agent': 2, 'tools': 4}, 'tools': {'branch:agent:should_continue:tools': 3}}, 'pending_sends': []}, metadata={'source': 'loop', 'writes': {'agent': {'messages': [AIMessage(content='The weather in San Francisco is always sunny!', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 84, 'total_tokens': 94, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_6fc10e10eb', 'finish_reason': 'stop', 'logprobs': None}, id='run-c6c4ad75-89ef-4b4f-9ca4-bd52ccb0729b-0', usage_metadata={'input_tokens': 84, 'output_tokens': 10, 'total_tokens': 94, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}})]}}, 'thread_id': '2', 'step': 3, 'parents': {}}, parent_config={'configurable': {'thread_id': '2', 'checkpoint_ns': '', 'checkpoint_id': '1efb8c75-8d89-6ffe-8002-84a4312c4fed'}}, pending_writes=[])

In [9]:

# Remember to close the connection after you're done
mongodb_client.close()

Using an async connection

This creates a short-lived asynchronous connection to MongoDB.

Async connections allow non-blocking database operations. This means other parts of your application can continue running while waiting for database operations to complete. It's particularly useful in high-concurrency scenarios or when dealing with I/O-bound operations.

In [10]:

from langgraph.checkpoint.mongodb.aio import AsyncMongoDBSaver

async with AsyncMongoDBSaver.from_conn_string(MONGODB_URI) as checkpointer:
    graph = create_react_agent(model, tools=tools, checkpointer=checkpointer)
    config = {"configurable": {"thread_id": "3"}}
    response = await graph.ainvoke(
        {"messages": [("user", "What's the weather in sf?")]}, config
    )

In [11]:

response

Out [11]:

{'messages': [HumanMessage(content="What's the weather in sf?", additional_kwargs={}, response_metadata={}, id='fed70fe6-1b2e-4481-9bfc-063df3b587dc'),
  AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_miRiF3vPQv98wlDHl6CeRxBy', 'function': {'arguments': '{"city":"sf"}', 'name': 'get_weather'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_6fc10e10eb', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-7f2d5153-973e-4a9e-8b71-a77625c342cf-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_miRiF3vPQv98wlDHl6CeRxBy', 'type': 'tool_call'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}),
  ToolMessage(content="It's always sunny in sf", name='get_weather', id='49035e8e-8aee-4d9d-88ab-9a1bc10ecbd3', tool_call_id='call_miRiF3vPQv98wlDHl6CeRxBy'),
  AIMessage(content='The weather in San Francisco is always sunny!', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 84, 'total_tokens': 94, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_6fc10e10eb', 'finish_reason': 'stop', 'logprobs': None}, id='run-9403d502-391e-4407-99fd-eec8ed184e50-0', usage_metadata={'input_tokens': 84, 'output_tokens': 10, 'total_tokens': 94, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}})]}

Using the async MongoDB client

This routes connections to MongoDB through an asynchronous MongoDB client.

In [12]:

from pymongo import AsyncMongoClient

async_mongodb_client = AsyncMongoClient(MONGODB_URI)

checkpointer = AsyncMongoDBSaver(async_mongodb_client)
graph = create_react_agent(model, tools=tools, checkpointer=checkpointer)
config = {"configurable": {"thread_id": "4"}}
response = await graph.ainvoke(
    {"messages": [("user", "What's the weather in sf?")]}, config
)

In [13]:

response

Out [13]:

{'messages': [HumanMessage(content="What's the weather in sf?", additional_kwargs={}, response_metadata={}, id='58282e2b-4cc1-40a1-8e65-420a2177bbd6'),
  AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_SJFViVHl1tYTZDoZkNN3ePhJ', 'function': {'arguments': '{"city":"sf"}', 'name': 'get_weather'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_bba3c8e70b', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-131af8c1-d388-4d7f-9137-da59ebd5fefd-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_SJFViVHl1tYTZDoZkNN3ePhJ', 'type': 'tool_call'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}),
  ToolMessage(content="It's always sunny in sf", name='get_weather', id='6090a56f-177b-4d3f-b16a-9c05f23800e3', tool_call_id='call_SJFViVHl1tYTZDoZkNN3ePhJ'),
  AIMessage(content='The weather in San Francisco is always sunny!', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 84, 'total_tokens': 94, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_6fc10e10eb', 'finish_reason': 'stop', 'logprobs': None}, id='run-6ff5ddf5-6e13-4126-8df9-81c8638355fc-0', usage_metadata={'input_tokens': 84, 'output_tokens': 10, 'total_tokens': 94, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}})]}

In [14]:

# Retrieve the latest checkpoint for the given thread ID
# To retrieve a specific checkpoint, pass the checkpoint_id in the config
latest_checkpoint = await checkpointer.aget_tuple(config)
print(latest_checkpoint)

CheckpointTuple(config={'configurable': {'thread_id': '4', 'checkpoint_ns': '', 'checkpoint_id': '1efb8c76-21f4-6d10-8003-9496e1754e93'}}, checkpoint={'v': 1, 'ts': '2024-12-12T20:26:35.599560+00:00', 'id': '1efb8c76-21f4-6d10-8003-9496e1754e93', 'channel_values': {'messages': [HumanMessage(content="What's the weather in sf?", additional_kwargs={}, response_metadata={}, id='58282e2b-4cc1-40a1-8e65-420a2177bbd6'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_SJFViVHl1tYTZDoZkNN3ePhJ', 'function': {'arguments': '{"city":"sf"}', 'name': 'get_weather'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_bba3c8e70b', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-131af8c1-d388-4d7f-9137-da59ebd5fefd-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_SJFViVHl1tYTZDoZkNN3ePhJ', 'type': 'tool_call'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}), ToolMessage(content="It's always sunny in sf", name='get_weather', id='6090a56f-177b-4d3f-b16a-9c05f23800e3', tool_call_id='call_SJFViVHl1tYTZDoZkNN3ePhJ'), AIMessage(content='The weather in San Francisco is always sunny!', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 84, 'total_tokens': 94, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_6fc10e10eb', 'finish_reason': 'stop', 'logprobs': None}, id='run-6ff5ddf5-6e13-4126-8df9-81c8638355fc-0', usage_metadata={'input_tokens': 84, 'output_tokens': 10, 'total_tokens': 94, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}})], 'agent': 'agent'}, 'channel_versions': {'__start__': 2, 'messages': 5, 'start:agent': 3, 'agent': 5, 'branch:agent:should_continue:tools': 4, 'tools': 5}, 'versions_seen': {'__input__': {}, '__start__': {'__start__': 1}, 'agent': {'start:agent': 2, 'tools': 4}, 'tools': {'branch:agent:should_continue:tools': 3}}, 'pending_sends': []}, metadata={'source': 'loop', 'writes': {'agent': {'messages': [AIMessage(content='The weather in San Francisco is always sunny!', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 84, 'total_tokens': 94, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_6fc10e10eb', 'finish_reason': 'stop', 'logprobs': None}, id='run-6ff5ddf5-6e13-4126-8df9-81c8638355fc-0', usage_metadata={'input_tokens': 84, 'output_tokens': 10, 'total_tokens': 94, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}})]}}, 'thread_id': '4', 'step': 3, 'parents': {}}, parent_config={'configurable': {'thread_id': '4', 'checkpoint_ns': '', 'checkpoint_id': '1efb8c76-1c6c-6474-8002-9c2595cd481c'}}, pending_writes=[])

In [15]:

# Remember to close the connection after you're done
await async_mongodb_client.close()