state is not being injected when using custom input schema on tool

[LokeshShelva]

Checked other resources

• This is a bug, not a usage question. For questions, please use GitHub Discussions.
• I added a clear and detailed title that summarizes the issue.
• I read what a minimal reproducible example is (https://stackoverflow.com/help/minimal-reproducible-example).
• I included a self-contained, minimal example that demonstrates the issue INCLUDING all the relevant imports. The code run AS IS to reproduce the issue.

Example Code

"""
Minimal reproduction case for LangGraph InjectedState bug.

ISSUE: InjectedState and InjectedToolCallId parameters must be explicitly
included in the tool's args_schema for injection to work, even though they
should be automatically excluded from the LLM schema.

EXPECTED BEHAVIOR:
- InjectedState and InjectedToolCallId should work without being in args_schema
- These parameters should be automatically injected by LangGraph
- The LLM should never see these parameters in the tool schema

ACTUAL BEHAVIOR:
- InjectedState and InjectedToolCallId must be included in args_schema
- Without them in args_schema, injection fails with missing parameter error
"""

from typing import Annotated, List
from pydantic import BaseModel, Field
from langgraph.graph import StateGraph, add_messages
from langgraph.constants import START, END
from langgraph.prebuilt import ToolNode, InjectedState
from langchain_core.tools import tool, InjectedToolCallId
from langchain_core.messages import BaseMessage, AIMessage
from langgraph.checkpoint.memory import MemorySaver


class SimpleState(BaseModel):
    messages: Annotated[List[BaseMessage], add_messages] = Field(default_factory=list)
    counter: int = 0


class CorrectToolSchema(BaseModel):
    """This should work but doesn't - injection fails."""

    text: str = Field(description="Text to process")
    # InjectedState and InjectedToolCallId should NOT need to be here


@tool(
    "broken_tool",
    description="Tool that should work with automatic injection",
    args_schema=CorrectToolSchema,
)
def broken_tool(
    text: str,
    state: Annotated[SimpleState, InjectedState],
    tool_call_id: Annotated[str, InjectedToolCallId],
) -> str:
    """This tool fails because injected params aren't in args_schema."""
    return f"Processed '{text}' (counter: {state.counter})"


class WorkaroundToolSchema(BaseModel):
    """Workaround: explicitly include injected params in schema."""

    text: str = Field(description="Text to process")
    # BUG: These shouldn't be needed but are required for injection to work
    state: Annotated[SimpleState, InjectedState]
    tool_call_id: Annotated[str, InjectedToolCallId]


@tool(
    "working_tool",
    description="Tool that works only because of explicit schema inclusion",
    args_schema=WorkaroundToolSchema,
)
def working_tool(
    text: str,
    state: Annotated[SimpleState, InjectedState],
    tool_call_id: Annotated[str, InjectedToolCallId],
) -> str:
    """This tool works but requires injected params in args_schema."""
    return f"Processed '{text}' (counter: {state.counter})"


def test_broken_injection():
    """Test the broken tool to demonstrate the issue."""
    print("🔴 TESTING BROKEN TOOL (Expected behavior that doesn't work)")
    print("=" * 70)

    workflow = StateGraph(SimpleState)
    workflow.add_node(
        "test",
        lambda state: {
            "messages": [
                AIMessage(
                    content="Using broken tool",
                    tool_calls=[
                        {"id": "test_123", "name": "broken_tool", "args": {"text": "test data"}}
                    ],
                )
            ]
        },
    )
    workflow.add_node("tools", ToolNode([broken_tool]))
    workflow.add_edge(START, "test")
    workflow.add_edge("test", "tools")
    workflow.add_edge("tools", END)

    graph = workflow.compile(checkpointer=MemorySaver())
    result = graph.invoke({"counter": 10}, {"configurable": {"thread_id": "test"}})

    print(f"❌ Broken tool result:\n{result}")


def test_working_injection():
    """Test the working tool to show the workaround."""
    print("\n🟢 TESTING WORKING TOOL (Workaround that shouldn't be necessary)")
    print("=" * 70)

    workflow = StateGraph(SimpleState)
    workflow.add_node(
        "test",
        lambda state: {
            "messages": [
                AIMessage(
                    content="Using working tool",
                    tool_calls=[
                        {
                            "id": "test_456",
                            "name": "working_tool",
                            "args": {"text": "test data"},
                        }
                    ],
                )
            ]
        },
    )
    workflow.add_node("tools", ToolNode([working_tool]))
    workflow.add_edge(START, "test")
    workflow.add_edge("test", "tools")
    workflow.add_edge("tools", END)

    graph = workflow.compile(checkpointer=MemorySaver())
    result = graph.invoke({"counter": 10}, {"configurable": {"thread_id": "test"}})

    print(f"✅ Working tool result:\n{result}")

if __name__ == "__main__":
    test_broken_injection()
    test_working_injection()

Error Message and Stack Trace (if applicable)

🔴 TESTING BROKEN TOOL (Expected behavior that doesn't work)
======================================================================
❌ Broken tool result:
{'messages': [AIMessage(content='Using broken tool', additional_kwargs={}, response_metadata={}, id='011abcbf-f246-47f1-9b18-b0620412221d', tool_calls=[{'name': 'broken_tool', 'args': {'text': 'test data'}, 'id': 'test_123', 'type': 'tool_call'}]), ToolMessage(content='Error: TypeError("broken_tool() missing 2 required positional arguments: \'state\' and \'tool_call_id\'")\n Please fix your mistakes.', name='broken_tool', id='4b4a067a-c13e-4437-b929-0210379403a7', tool_call_id='test_123', status='error')], 'counter': 10}

🟢 TESTING WORKING TOOL (Workaround that shouldn't be necessary)
======================================================================
✅ Working tool result:
{'messages': [AIMessage(content='Using working tool', additional_kwargs={}, response_metadata={}, id='e988d222-eadd-443e-9869-41fadfaea54e', tool_calls=[{'name': 'working_tool', 'args': {'text': 'test data'}, 'id': 'test_456', 'type': 'tool_call'}]), ToolMessage(content="Processed 'test data' (counter: 10)", name='working_tool', id='a99fa4b6-1c0d-4319-be75-de82f38762b3', tool_call_id='test_456')], 'counter': 10}

Description

When tool is created using the @tool decorated and input schema is provided, the state is not being injected into the tool.

The state needs to be added as part of the input schema, which does not makes sense, as the idea of the input schema is to provide the LLM with context on the inputs to the tools and adding the state and other injectable data into input schema does not look good and causes confusion.

System Info

System Information

OS: Linux
OS Version: langchain-ai/langgraph#1 SMP PREEMPT_DYNAMIC Mon Apr 21 17:08:54 UTC 2025
Python Version: 3.12.10 (main, Apr 29 2025, 00:24:54) [GCC 12.2.0]

Package Information

langchain_core: 0.3.63
langchain: 0.3.25
langchain_community: 0.3.24
langsmith: 0.3.44
langchain_openai: 0.2.14
langchain_text_splitters: 0.3.8
langgraph_injection_bug: Installed. No version info available.
langgraph_sdk: 0.1.70

Optional packages not installed

langserve

Other Dependencies

aiohttp<4.0.0,>=3.8.3: Installed. No version info available.
async-timeout<5.0.0,>=4.0.0;: Installed. No version info available.
dataclasses-json<0.7,>=0.5.7: Installed. No version info available.
httpx: 0.28.1
httpx-sse<1.0.0,>=0.4.0: Installed. No version info available.
httpx>=0.25.2: Installed. No version info available.
jsonpatch<2.0,>=1.33: Installed. No version info available.
langchain-anthropic;: Installed. No version info available.
langchain-aws;: Installed. No version info available.
langchain-azure-ai;: Installed. No version info available.
langchain-cohere;: Installed. No version info available.
langchain-community;: Installed. No version info available.
langchain-core<1.0.0,>=0.3.51: Installed. No version info available.
langchain-core<1.0.0,>=0.3.58: Installed. No version info available.
langchain-core<1.0.0,>=0.3.59: Installed. No version info available.
langchain-deepseek;: Installed. No version info available.
langchain-fireworks;: Installed. No version info available.
langchain-google-genai;: Installed. No version info available.
langchain-google-vertexai;: Installed. No version info available.
langchain-groq;: Installed. No version info available.
langchain-huggingface;: Installed. No version info available.
langchain-mistralai;: Installed. No version info available.
langchain-ollama;: Installed. No version info available.
langchain-openai;: Installed. No version info available.
langchain-perplexity;: Installed. No version info available.
langchain-text-splitters<1.0.0,>=0.3.8: Installed. No version info available.
langchain-together;: Installed. No version info available.
langchain-xai;: Installed. No version info available.
langchain<1.0.0,>=0.3.25: Installed. No version info available.
langsmith-pyo3: Installed. No version info available.
langsmith<0.4,>=0.1.125: Installed. No version info available.
langsmith<0.4,>=0.1.126: Installed. No version info available.
langsmith<0.4,>=0.1.17: Installed. No version info available.
numpy>=1.26.2;: Installed. No version info available.
numpy>=2.1.0;: Installed. No version info available.
openai: 1.83.0
openai-agents: Installed. No version info available.
opentelemetry-api: 1.28.2
opentelemetry-exporter-otlp-proto-http: Installed. No version info available.
opentelemetry-sdk: 1.28.2
orjson: 3.10.18
orjson>=3.10.1: Installed. No version info available.
packaging: 24.2
packaging<25,>=23.2: Installed. No version info available.
pydantic: 2.11.5
pydantic-settings<3.0.0,>=2.4.0: Installed. No version info available.
pydantic<3.0.0,>=2.7.4: Installed. No version info available.
pydantic>=2.7.4: Installed. No version info available.
pytest: 8.4.0
PyYAML>=5.3: Installed. No version info available.
requests: 2.32.3
requests-toolbelt: 1.0.0
requests<3,>=2: Installed. No version info available.
rich: Installed. No version info available.
SQLAlchemy<3,>=1.4: Installed. No version info available.
tenacity!=8.4.0,<10,>=8.1.0: Installed. No version info available.
tenacity!=8.4.0,<10.0.0,>=8.1.0: Installed. No version info available.
tiktoken: 0.9.0
typing-extensions>=4.7: Installed. No version info available.
zstandard: 0.23.0

[Fly-Playgroud]

Yes, I have the same problem:

@tool(args_schema=Assistant,
      description="It is used to ask for help from humans in certain scenarios, such as not being able to solve the CAPTCHA。")
def assistance(content: str, state: Annotated[BrowserAgentState, InjectedState]):
    result = interrupt(
        {
            "content": content,
            "task": state.task,
        }
    )
    return result

`
Error: TypeError("done() missing 1 required positional argument: 'state'")

`

[Fly-Playgroud]

I found that problem is tool if is setted custom ArgsSchema. It can't parse func's signature for injected arguments.

[Fly-Playgroud]

But the tool decorator currently allows this and does not do such a check, which causes such a problem. I have two solutions:

1. When checking for a custom argSchema, also check if the function has injected arguments, and merge the injected arguments into the customSchema.
2. When checking for a custom argSchema, also check if the function has injected arguments, if so, throw an exception.
   @nfcampos @sydney-runkle

[KodyKendall]

I'm running into similar issues where the full state isn't showing up using InjectedState and ToolNode.

from langgraph.prebuilt import tools_condition
from langgraph.prebuilt import ToolNode, InjectedState

from typing import Annotated

class CustomState(MessagesState):
    additional_state: str

# Tools
@tool
def my_great_tool_call(arg_for_llm: str, state: Annotated[dict, InjectedState]) -> str:
    """
    Run a Rails console command.
    """

    print (state.get("additional_state")) # empty. only messages is getting passed through.

UPDATE: My issue was due to my own mistake: I was using the wrong state class definition when building the graph in the the StateGraph(MessagesState) call, I had to change it to StateGraph(CustomState) and also in the actual function signature input was the wrong state class type. I had to change from def my_node(state: MessagesState) to def my_node(state: CustomState). After changing those, the full state was being passed into the tool call correctly after adding state: Annotated[CustomState, InjectedState].

[apoorvkk]

+1 having the same issue

[LokeshShelva]

But the tool decorator currently allows this and does not do such a check, which causes such a problem. I have two solutions:

1. When checking for a custom argSchema, also check if the function has injected arguments, and merge the injected arguments into the customSchema.
2. When checking for a custom argSchema, also check if the function has injected arguments, if so, throw an exception.
   @nfcampos @sydney-runkle

Well i would prefer the first approach. That is how i expect argSchema and injection to work. My understanding was argSchema was the explicit context to LLM on the arguments to the tool and it does not make sense to add the injection argument too to the argSchema. It should be merged in the tool.

Would love others' take on this.

[L9qmzn]

But the tool decorator currently allows this and does not do such a check, which causes such a problem. I have two solutions:

1. When checking for a custom argSchema, also check if the function has injected arguments, and merge the injected arguments into the customSchema.
2. When checking for a custom argSchema, also check if the function has injected arguments, if so, throw an exception.
   @nfcampos @sydney-runkle

Well i would prefer the first approach. That is how i expect argSchema and injection to work. My understanding was argSchema was the explicit context to LLM on the arguments to the tool and it does not make sense to add the injection argument too to the argSchema. It should be merged in the tool.

Would love others' take on this.

Agree! And, is there any way to temporarily work around this bug for now?

[LokeshShelva]

@L9qmzn, for now you can add the state and tool_call_id in the argSchema like so

class WorkaroundToolSchema(BaseModel):
    """Workaround: explicitly include injected params in schema."""

    text: str = Field(description="Text to process")
    # BUG: These shouldn't be needed but are required for injection to work
    state: Annotated[SimpleState, InjectedState]
    tool_call_id: Annotated[str, InjectedToolCallId]

@tool(
    "working_tool",
    description="Tool that works only because of explicit schema inclusion",
    args_schema=WorkaroundToolSchema,
)
def working_tool(
    text: str,
    state: Annotated[SimpleState, InjectedState],
    tool_call_id: Annotated[str, InjectedToolCallId],
) -> str:

As far as I have tested, the state or the tool_call_id are not added as part of the prompt to the llm. But I do suggest you test it yourself to be sure.

[LokeshShelva]

Update. I am now working with langchain 1.0.2 and i am using the new ToolRuntime to inject tool_call_id, state, context etc, and to make that work i had to do this workaround:

class FetchODataToolArgsSchema(BaseModel):
    data: RTDBToolInput

    # Similar to InjectedToolCallId
    runtime: Any

def _fetch_odata(data: RTDBToolInput, runtime: ToolRuntime[SessionContext]):
...

Should it be considered a bug for 1.0.2 too?

For this the error is occurring in langchain_core/tools/base.py, where

if issubclass(input_args, BaseModel):
  for k, v in get_all_basemodel_annotations(input_args).items():
      if _is_injected_arg_type(v, injected_type=InjectedToolCallId):
          if tool_call_id is None:
              msg = (
                  "When tool includes an InjectedToolCallId "
                  "argument, tool must always be invoked with a full "
                  "model ToolCall of the form: {'args': {...}, "
                  "'name': '...', 'type': 'tool_call', "
                  "'tool_call_id': '...'}"
              )
              raise ValueError(msg)
          tool_input[k] = tool_call_id
  result = input_args.model_validate(tool_input) # Error

When args_schema is present, it is trying to validate the tool input to the given args_schema. When the injected values are not part of the args_schema, error is thrown

Suggested Fix:

if issubclass(input_args, BaseModel):
  for k, v in get_all_basemodel_annotations(input_args).items():
      if _is_injected_arg_type(v, injected_type=InjectedToolCallId):
          if tool_call_id is None:
              msg = (
                  "When tool includes an InjectedToolCallId "
                  "argument, tool must always be invoked with a full "
                  "model ToolCall of the form: {'args': {...}, "
                  "'name': '...', 'type': 'tool_call', "
                  "'tool_call_id': '...'}"
              )
              raise ValueError(msg)
          tool_input[k] = tool_call_id
  # before validation, create a new input_args model using 
  # pydantic create_model (https://docs.pydantic.dev/latest/api/base_model/#pydantic.create_model)
  # that include injectable attributes such as InjectdToolCallId, ToolRuntime etc
  result = input_args.model_validate(tool_input)