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Could you provide a simple graph and input example to reproduce this bug? |
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2 replies
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@gbaian10 an example to reproduce the above error After we try to resume the graph execution, it gives the below stacktrace (same as what is mentioned in the above question) |
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I got the same result as you. I organized your code and printed out a few points where strange things occurred. Maybe you should consider filing an issue to inquire about this? import operator
from typing import Annotated, Literal, TypedDict
from dotenv import load_dotenv
from langchain.agents import create_tool_calling_agent
from langchain.prompts import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
MessagesPlaceholder,
)
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.messages import AnyMessage, SystemMessage
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
from langgraph.checkpoint.memory import MemorySaver
from langgraph.graph import END, START, StateGraph, add_messages
from langgraph.prebuilt.tool_executor import ToolExecutor
from rich import get_console
load_dotenv()
@tool
def search(query: str) -> list[str]:
"""Call to surf the web."""
return [
"It's sunny in San Francisco, but you better look out if you're a Gemini 😈."
]
class AgentState(TypedDict):
input: str
chat_history: list[AnyMessage]
output: AgentAction | AgentFinish | None
intermediate_steps: Annotated[list[tuple[AgentAction, str]], operator.add]
messages: Annotated[list[AnyMessage], add_messages]
def should_continue(state: AgentState) -> Literal["end", "continue"]:
print("should_continue")
get_console().print(state)
if isinstance(state["output"], AgentFinish):
return "end"
return "continue"
def execute_tools(state):
print("execute_tools")
get_console().print(state)
intermediate_steps = []
for agent_action in state["output"]:
output = tool_executor.invoke(agent_action)
intermediate_steps.append((agent_action, str(output)))
return {"intermediate_steps": intermediate_steps}
def call_model(state):
output = tool_calling_agent.invoke(state)
return {"output": output}
tools = [search]
tool_executor = ToolExecutor(tools)
model = ChatOpenAI(model="gpt-4o-mini").bind_tools(tools)
prompt = ChatPromptTemplate.from_messages(
[
SystemMessage(
content="You are a weather reporter, and using the search tool you find the weather of the places"
),
HumanMessagePromptTemplate.from_template("{input}"),
MessagesPlaceholder(variable_name="agent_scratchpad"),
]
)
tool_calling_agent = create_tool_calling_agent(model, tools, prompt)
workflow = StateGraph(AgentState)
workflow.add_node("agent", call_model)
workflow.add_node("action", execute_tools)
workflow.add_edge(START, "agent")
workflow.add_conditional_edges(
"agent", should_continue, {"continue": "action", "end": END}
)
workflow.add_edge("action", "agent")
app = workflow.compile(checkpointer=MemorySaver(), interrupt_before=["action"])
thread_config = {"configurable": {"thread_id": "1"}}
app.invoke(input={"input": "search for the weather in sf now"}, config=thread_config)
print("get_state")
get_console().print(app.get_state(thread_config))
response = app.invoke(None, config=thread_config) |
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my code is raising the below ValueError exception with the message raise ValueError(f"Got unsupported message type: {m}")
I am using Langgraph + Human in the loop implementation.
I checked the state snapshot values and in that the "message_log" attribute is containing BaseMessage() type data, instead of what should be AIMessage()
Because of this, it fails all the above conditions and raises a value error exception
using below versions
langchain 0.2.16
langhain-core 0.2.41
langgraph-0.2.31
langgraph-checkpoint-1.0.14
langgraph-checkpoint-postgres-1.0.11
The above error does not appear if i use below old versions
langchain 0.2.12
langhain-core 0.2.29
langgraph-0.0.5
But I want to use updated versions as I want to also use the postgres saver checkpoint, which requires upgraded versions.
Below is the traceback
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