Unexpected State Update

[Sarthak-ONS]

Checked other resources

• I added a very descriptive title to this issue.
• I searched the LangChain documentation with the integrated search.
• I used the GitHub search to find a similar question and didn't find it.
• I am sure that this is a bug in LangChain rather than my code.
• The bug is not resolved by updating to the latest stable version of LangChain (or the specific integration package).

Example Code

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.tools import tool
from langchain_core.messages import  HumanMessage , AIMessage
from langchain_core.runnables import RunnableConfig

from langgraph.checkpoint.memory import MemorySaver
from langgraph.graph import StateGraph, START, add_messages
from langgraph.prebuilt import ToolNode, tools_condition

from typing import Annotated, List, TypedDict
from rich.console import Console
from rich.markdown import Markdown
from rich import print as rprint
from environs import Env

from prompts import system_prompt

from moviepy import *
import uuid

env = Env()
env.read_env()

console = Console()

# Memory configuration
memory = MemorySaver()

video_path = "video.mp4"

class VideoAppStateGraph(TypedDict):
    messages : Annotated[list , add_messages]
    video_path : str

@tool
def trim_video(start_time: str, end_time: str, state : VideoAppStateGraph ,config: RunnableConfig):
    """
    Trim a video between specified start and end times.
    
    Args:
        start_time (str): Start time in format HH:MM:SS
        end_time (str): End time in format HH:MM:SS
    
    Returns:
        str: Confirmation message
    """
    
    print("-"*10)
    print("trim_video")
    # print(state)
    # print("-"*10)
    print(config)
    print("-"*10)
    print(start_time)
    print(end_time)
    print("-"*10)

    
    video_path_trimmed = f"{uuid.uuid4().hex}.mp4"
    ffmpeg_tools.ffmpeg_extract_subclip(state["video_path"] , start_time , end_time ,video_path_trimmed)
    
    return {
        "video_path" : video_path_trimmed,
        "messages" : [AIMessage(content=f"Video has been trimmed from {start_time} to {end_time}")]
    }

@tool
def get_video_duration(state : VideoAppStateGraph):
    """
    Get Duration of the Video
    
    
    Returns:
        str: Duration of the video
    """
    
    print("-"*10)
    print("get_video_duration")
    print(state["video_path"])
    print("-"*10)
    video = VideoFileClip(state["video_path"])
    
    duration =  video.duration
    
    return {
        "video_path" : state["video_path"],
        "messages" : [AIMessage(content=f"Duration is {int(duration)} seconds")]
    }
    
    
@tool
def get_video_url(state : VideoAppStateGraph):
    """
    Get the video url of updated actions of video
    
    return str: video_url
    """
    
    base = env.str("HOSTED_BACKEND_URL")
    
    return {
        "video_path" : state["video_path"],
        "messages" : [AIMessage(content=f"{base}/{state["video_path"]}")]
    }

# Define tools list
tools = [trim_video, get_video_duration , get_video_url]

# Create tool node
tool_node = ToolNode(tools=tools)

prompt = ChatPromptTemplate.from_messages([
    ("system", system_prompt),
    MessagesPlaceholder(variable_name="messages"),
])

# Initialize the model with tools
model = ChatOpenAI(
    temperature=0.5,
    api_key=env.str("OPENAI_API_KEY"),
).bind_tools(tools)


def call_model(state):
    """
    Process the current state and generate a response.
    """
    print("*"*10)
    print("call_model")
    print(state["video_path"])
    print("*"*10)
    response = model.invoke(prompt.invoke({"messages": state["messages"]}))
    return {"messages": [response]}    

# Create the workflow graph
workflow = StateGraph(VideoAppStateGraph)

# Add nodes
workflow.add_node("assistant", call_model)
workflow.add_node("tools", tool_node)

# Add Edges
workflow.add_edge(START, "assistant")
workflow.add_conditional_edges("assistant", tools_condition)
workflow.add_edge("tools", "assistant")


# Compile the app
app = workflow.compile(checkpointer=memory)

class VideoEditor:
    def __init__(self):
        self.messages: List[HumanMessage] = []
        self.video_path : str = video_path
        
    def process_message(self, user_input: str) -> str:
        """Process a single message and return the response"""
        self.messages.append(HumanMessage(content=user_input))
        
        response = app.invoke({
            "messages": self.messages,
            "video_path" : self.video_path
        } , config={ "configurable" : {"thread_id": "1312312312"}})
        
        # Extract the last assistant message
        last_message = response["messages"][-1]
        self.messages.extend(response["messages"])
        
        return last_message.content

def main():
    """Main CLI interface for the video editing agent"""
    editor = VideoEditor()
    
    # Print welcome message
    console.print("[bold blue]Welcome to the Video Editing Assistant![/bold blue]")
    console.print("Type 'quit' or 'exit' to end the conversation.\n")
    
    while True:
        try:
            # Get user input
            user_input = console.input("[bold green]You:[/bold green] ")
            
            # Check for exit command
            if user_input.lower() in ['quit', 'exit', 'bye', 'goodbye']:
                console.print("\n[bold blue]Goodbye! Thank you for using the Video Editing Assistant.[/bold blue]")
                break
            
            # Process the message
            response = editor.process_message(user_input)
            
            # Print the response with markdown formatting
            console.print("\n[bold purple]Assistant:[/bold purple]")
            console.print(Markdown(response))
            console.print()  # Empty line for better readability
            
        except KeyboardInterrupt:
            console.print("\n[bold red]Session terminated by user.[/bold red]")
            break
        except Exception as e:
            console.print(f"\n[bold red]An error occurred: {str(e)}[/bold red]")
            console.print("Please try again or type 'exit' to quit.")

if __name__ == "__main__":
    main()

Error Message and Stack Trace (if applicable)

No response

Description

Unexpected State Change,
Initially, When, call_model is called, value of state["video_path"] is correct("video.mp4"), but when there is a tool call, unexpectedly, state is being updated, and print inside the trim_video tool says state["video_path"] is "sample_video.mp4"

System Info

System Information

OS: Windows
OS Version: 10.0.22631
Python Version: 3.13.0 (tags/v3.13.0:60403a5, Oct 7 2024, 09:38:07) [MSC v.1941 64 bit (AMD64)]

Package Information

langchain_core: 0.3.33
langsmith: 0.3.3
langchain_openai: 0.3.3
langgraph_sdk: 0.1.51

Optional packages not installed

langserve

Other Dependencies

httpx: 0.28.1
jsonpatch: 1.33
langsmith-pyo3: Installed. No version info available.
openai: 1.60.2
orjson: 3.10.15
packaging: 24.2
pydantic: 2.10.6
pytest: Installed. No version info available.
PyYAML: 6.0.2
requests: 2.32.3
requests-toolbelt: 1.0.0
rich: 13.9.4
tenacity: 9.0.0
tiktoken: 0.8.0
typing-extensions: 4.12.2
zstandard: 0.23.0

[vbarda]

@Sarthak-ONS that's because in your example the LLM is populating the state field (and it's hallucinating the video path). to propagate actual graph state you need to use InjectedState https://langchain-ai.github.io/langgraph/how-tos/pass-run-time-values-to-tools/#pass-graph-state-to-tools

[Sarthak-ONS]

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.tools import tool , InjectedToolCallId
from langchain_core.messages import  HumanMessage , AIMessage , ToolMessage
from langgraph.types import Command

from langgraph.checkpoint.memory import MemorySaver
from langgraph.graph import StateGraph, START, add_messages
from langgraph.prebuilt import ToolNode, tools_condition, InjectedState , create_react_agent
from langgraph.prebuilt.chat_agent_executor import AgentState

from typing import Annotated, List
from rich.console import Console
from rich.markdown import Markdown
from rich import print as rprint
from environs import Env

from prompts import system_prompt

from moviepy import *
import uuid

env = Env()
env.read_env()

console = Console()

# Memory configuration
memory = MemorySaver()

video_path = "video.mp4"

class VideoAppStateGraph(AgentState):
    messages : Annotated[list , add_messages]
    video_path : str

@tool
def trim_video(start_time: str, end_time: str, state: Annotated[dict, InjectedState] , tool_call_id: Annotated[str, InjectedToolCallId],):
    """
    Trim a video between specified start and end times.
    
    Args:
        start_time (str): Start time in format HH:MM:SS
        end_time (str): End time in format HH:MM:SS
    
    Returns:
        str: Confirmation message
    """
    
    print("-"*10)
    print("trim_video")
    print(state["video_path"])
    print(tool_call_id)
    # print("-"*10)
    print("-"*10)
    print(start_time)
    print(end_time)
    print("-"*10)

    
    video_path_trimmed = f"{uuid.uuid4()}.mp4"
    ffmpeg_tools.ffmpeg_extract_subclip(state["video_path"] , start_time , end_time ,video_path_trimmed)
    
    return Command(
        update={
        "video_path" : video_path_trimmed,
        "messages" : [ToolMessage(content=f"Video has been trimmed from {start_time} to {end_time}" ,tool_call_id=tool_call_id)]
    })

@tool
def get_video_duration(state: Annotated[dict, InjectedState]):
    """
    Get Duration of the Video
    
    
    Returns:
        str: Duration of the video
    """
    
    print("-"*10)
    print("get_video_duration")
    print(state["video_path"])
    print("-"*10)
    video = VideoFileClip(state["video_path"])
    
    duration =  video.duration
    
    return f"Duration is {int(duration)} seconds"
    
@tool
def get_video_url(state: Annotated[dict, InjectedState]):
    """
    Get the video url of updated actions of video
    
    return str: video_url
    """
    
    base = env.str("HOSTED_BACKEND_URL")
    
    print(state["video_path"])
    
    return f"{base}/{state["video_path"]}"

# Define tools list
tools = [trim_video, get_video_duration , get_video_url]

# Initialize the model with tools
model = ChatOpenAI(
    temperature=0.5,
    api_key=env.str("OPENAI_API_KEY"),
)

agent = create_react_agent(model=model , tools=tools , prompt=system_prompt , state_schema= VideoAppStateGraph , store=MemorySaver)

class VideoEditor:
    def __init__(self):
        self.messages: List[HumanMessage] = []
        self.video_path : str = video_path
        
    def process_message(self, user_input: str) -> str:
        """Process a single message and return the response"""
        self.messages.append(HumanMessage(content=user_input))
        
        response = agent.invoke({
            "messages": self.messages,
            "video_path" : self.video_path
        } , config={ "configurable" : {"thread_id": "1312312312"}})
        
        # Extract the last assistant message
        last_message = response["messages"][-1]
        self.messages.extend(response["messages"])
        
        return last_message.content

def main():
    """Main CLI interface for the video editing agent"""
    editor = VideoEditor()
    
    # Print welcome message
    console.print("[bold blue]Welcome to the Video Editing Assistant![/bold blue]")
    console.print("Type 'quit' or 'exit' to end the conversation.\n")
    
    while True:
        try:
            # Get user input
            user_input = console.input("[bold green]You:[/bold green] ")
            
            # Check for exit command
            if user_input.lower() in ['quit', 'exit', 'bye', 'goodbye']:
                console.print("\n[bold blue]Goodbye! Thank you for using the Video Editing Assistant.[/bold blue]")
                break
            
            # Process the message
            response = editor.process_message(user_input)
            
            # Print the response with markdown formatting
            console.print("\n[bold purple]Assistant:[/bold purple]")
            console.print(Markdown(response))
            console.print()  # Empty line for better readability
            
        except KeyboardInterrupt:
            console.print("\n[bold red]Session terminated by user.[/bold red]")
            break
        except Exception as e:
            console.print(f"\n[bold red]An error occurred: {str(e)}[/bold red]")
            console.print("Please try again or type 'exit' to quit.")

if __name__ == "__main__":
    main()

After going through this documentation,
https://langchain-ai.github.io/langgraph/how-tos/pass-run-time-values-to-tools/#use-it_1
https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/update-state-from-tools.ipynb

State update isn't happening

In trim_video tool, I am trying to update the state, But state update isn't happening,

After trim I am trying to call get_video_url its returning, initial video_path only, similarly for the get_video_duration also

[vbarda]

can you provide some langsmith traces / boil the code down to a minimum reproducible example?