如何使用 MongoDB 检查点程序进行持久化¶
在创建 LangGraph 代理时，您还可以将其设置为持久化其状态。这允许您执行诸如多次与代理交互并使其记住以前的交互之类的操作。
此参考实现展示了如何使用 MongoDB 作为后端，使用 langgraph-checkpoint-mongodb 库来持久化检查点状态。
出于演示目的，我们向 预构建的 ReAct 代理 添加持久化功能。
通常，您可以像这样向您构建的任何自定义图添加检查点程序
from langgraph.graph import StateGraph
builder = StateGraph(...)
# ... define the graph
checkpointer = # mongodb checkpointer (see examples below)
graph = builder.compile(checkpointer=checkpointer)
...
设置¶
要使用 MongoDB 检查点程序，您将需要一个 MongoDB 集群。如果您还没有集群，请按照本指南创建一个。
接下来，让我们安装所需的软件包并设置我们的 API 密钥
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")
设置 LangSmith 以进行 LangGraph 开发
注册 LangSmith 以快速发现问题并提高 LangGraph 项目的性能。LangSmith 允许您使用跟踪数据来调试、测试和监控使用 LangGraph 构建的 LLM 应用程序 — 阅读此处以了解更多关于如何开始的信息。
为图定义模型和工具¶
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)
API 参考：tool | ChatOpenAI | create_react_agent
MongoDB 检查点程序用法¶
使用连接字符串¶
这将直接使用您的集群的连接字符串创建到 MongoDB 的连接。这非常适合在脚本、一次性操作和短生命周期的应用程序中使用。
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
)
{'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}})]}
使用 MongoDB 客户端¶
这将使用 MongoDB 客户端创建到 MongoDB 的连接。这非常适合长期运行的应用程序，因为它允许您为多个数据库操作重用客户端实例，而无需每次都重新初始化连接。
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)
{'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}})]}
# 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)
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=[])
使用异步连接¶
这将创建一个到 MongoDB 的短生命周期异步连接。
异步连接允许非阻塞数据库操作。这意味着应用程序的其他部分可以在等待数据库操作完成时继续运行。这在高并发场景或处理 I/O 密集型操作时尤其有用。
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
)
{'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}})]}
使用异步 MongoDB 客户端¶
这通过异步 MongoDB 客户端路由到 MongoDB 的连接。
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
)
{'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}})]}
# 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=[])