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revert: Support for SystemMessage in create_agent (#33889)

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Reverts langchain-ai/langchain#33640

Introduces lint errors into langchain-anthropic

Should incorporate into 1.1 instead of patch release.
This commit is contained in:
ccurme
2025-11-07 16:44:11 -05:00
committed by GitHub
parent 91560b6a7a
commit f2410f7ea7
6 changed files with 31 additions and 313 deletions
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20
libs/langchain_v1/langchain/agents/factory.py
View File

@@ -516,7 +516,7 @@ def create_agent( # noqa: PLR0915
model: str | BaseChatModel,
tools: Sequence[BaseTool | Callable | dict[str, Any]] | None = None,
*,
system_prompt: str | SystemMessage | None = None,
system_prompt: str | None = None,
middleware: Sequence[AgentMiddleware[StateT_co, ContextT]] = (),
response_format: ResponseFormat[ResponseT] | type[ResponseT] | None = None,
state_schema: type[AgentState[ResponseT]] | None = None,
@@ -548,9 +548,11 @@ def create_agent( # noqa: PLR0915
If `None` or an empty list, the agent will consist of a model node without a
tool calling loop.
system_prompt: An optional system prompt for the LLM or
can already be a [`SystemMessage`][langchain.messages.SystemMessage] object.
system_prompt: An optional system prompt for the LLM.
Prompts are converted to a
[`SystemMessage`][langchain.messages.SystemMessage] and added to the
beginning of the message list.
middleware: A sequence of middleware instances to apply to the agent.
Middleware can intercept and modify agent behavior at various stages. See
@@ -1038,10 +1040,8 @@ def create_agent( # noqa: PLR0915
# Get the bound model (with auto-detection if needed)
model_, effective_response_format = _get_bound_model(request)
messages = request.messages
if request.system_prompt and not isinstance(request.system_prompt, SystemMessage):
messages = [SystemMessage(content=request.system_prompt), *messages]
elif request.system_prompt and isinstance(request.system_prompt, SystemMessage):
messages = [request.system_prompt, *messages]
if request.system_prompt:
messages = [SystemMessage(request.system_prompt), *messages]
output = model_.invoke(messages)
@@ -1093,10 +1093,8 @@ def create_agent( # noqa: PLR0915
# Get the bound model (with auto-detection if needed)
model_, effective_response_format = _get_bound_model(request)
messages = request.messages
if request.system_prompt and not isinstance(request.system_prompt, SystemMessage):
messages = [SystemMessage(content=request.system_prompt), *messages]
elif request.system_prompt and isinstance(request.system_prompt, SystemMessage):
messages = [request.system_prompt, *messages]
if request.system_prompt:
messages = [SystemMessage(request.system_prompt), *messages]
output = await model_.ainvoke(messages)

17
libs/langchain_v1/langchain/agents/middleware/context_editing.py
View File

@@ -225,11 +225,9 @@ class ContextEditingMiddleware(AgentMiddleware):
def count_tokens(messages: Sequence[BaseMessage]) -> int:
return count_tokens_approximately(messages)
else:
system_msg = []
if request.system_prompt and not isinstance(request.system_prompt, SystemMessage):
system_msg = [SystemMessage(content=request.system_prompt)]
elif request.system_prompt and isinstance(request.system_prompt, SystemMessage):
system_msg = [request.system_prompt]
system_msg = (
[SystemMessage(content=request.system_prompt)] if request.system_prompt else []
)
def count_tokens(messages: Sequence[BaseMessage]) -> int:
return request.model.get_num_tokens_from_messages(
@@ -255,12 +253,9 @@ class ContextEditingMiddleware(AgentMiddleware):
def count_tokens(messages: Sequence[BaseMessage]) -> int:
return count_tokens_approximately(messages)
else:
system_msg = []
if request.system_prompt and not isinstance(request.system_prompt, SystemMessage):
system_msg = [SystemMessage(content=request.system_prompt)]
elif request.system_prompt and isinstance(request.system_prompt, SystemMessage):
system_msg = [request.system_prompt]
system_msg = (
[SystemMessage(content=request.system_prompt)] if request.system_prompt else []
)
def count_tokens(messages: Sequence[BaseMessage]) -> int:
return request.model.get_num_tokens_from_messages(

44
libs/langchain_v1/langchain/agents/middleware/todo.py
View File

@@ -8,7 +8,7 @@ from typing import TYPE_CHECKING, Annotated, Literal
if TYPE_CHECKING:
from collections.abc import Awaitable, Callable
from langchain_core.messages import SystemMessage, ToolMessage
from langchain_core.messages import ToolMessage
from langchain_core.tools import tool
from langgraph.types import Command
from typing_extensions import NotRequired, TypedDict
@@ -199,22 +199,11 @@ class TodoListMiddleware(AgentMiddleware):
handler: Callable[[ModelRequest], ModelResponse],
) -> ModelCallResult:
"""Update the system prompt to include the todo system prompt."""
if request.system_prompt is None:
request.system_prompt = self.system_prompt
elif isinstance(request.system_prompt, str):
request.system_prompt = request.system_prompt + "\n\n" + self.system_prompt
elif isinstance(request.system_prompt, SystemMessage) and isinstance(
request.system_prompt.content, str
):
request.system_prompt = SystemMessage(
content=request.system_prompt.content + self.system_prompt
)
elif isinstance(request.system_prompt, SystemMessage) and isinstance(
request.system_prompt.content, list
):
request.system_prompt = SystemMessage(
content=[*request.system_prompt.content, self.system_prompt]
)
request.system_prompt = (
request.system_prompt + "\n\n" + self.system_prompt
if request.system_prompt
else self.system_prompt
)
return handler(request)
async def awrap_model_call(
@@ -223,20 +212,9 @@ class TodoListMiddleware(AgentMiddleware):
handler: Callable[[ModelRequest], Awaitable[ModelResponse]],
) -> ModelCallResult:
"""Update the system prompt to include the todo system prompt (async version)."""
if request.system_prompt is None:
request.system_prompt = self.system_prompt
elif isinstance(request.system_prompt, str):
request.system_prompt = request.system_prompt + "\n\n" + self.system_prompt
elif isinstance(request.system_prompt, SystemMessage) and isinstance(
request.system_prompt.content, str
):
request.system_prompt = SystemMessage(
content=request.system_prompt.content + self.system_prompt
)
elif isinstance(request.system_prompt, SystemMessage) and isinstance(
request.system_prompt.content, list
):
request.system_prompt = SystemMessage(
content=[*request.system_prompt.content, self.system_prompt]
)
request.system_prompt = (
request.system_prompt + "\n\n" + self.system_prompt
if request.system_prompt
else self.system_prompt
)
return await handler(request)

9
libs/langchain_v1/langchain/agents/middleware/types.py
View File

@@ -26,7 +26,6 @@ from langchain_core.messages import ( # noqa: TC002
AIMessage,
AnyMessage,
BaseMessage,
SystemMessage,
ToolMessage,
)
from langgraph.channels.ephemeral_value import EphemeralValue
@@ -86,7 +85,7 @@ class ModelRequest:
"""Model request information for the agent."""
model: BaseChatModel
system_prompt: str | SystemMessage | None
system_prompt: str | None
messages: list[AnyMessage] # excluding system prompt
tool_choice: Any | None
tools: list[BaseTool | dict]
@@ -104,7 +103,7 @@ class ModelRequest:
Args:
**overrides: Keyword arguments for attributes to override. Supported keys:
- model: BaseChatModel instance
- system_prompt: Optional system prompt string or SystemMessage object
- system_prompt: Optional system prompt string
- messages: List of messages
- tool_choice: Tool choice configuration
- tools: List of available tools
@@ -1257,7 +1256,7 @@ def dynamic_prompt(
request: ModelRequest,
handler: Callable[[ModelRequest], ModelResponse],
) -> ModelCallResult:
prompt = cast("str | SystemMessage", func(request))
prompt = cast("str", func(request))
request.system_prompt = prompt
return handler(request)
@@ -1267,7 +1266,7 @@ def dynamic_prompt(
handler: Callable[[ModelRequest], Awaitable[ModelResponse]],
) -> ModelCallResult:
# Delegate to sync function
prompt = cast("str | SystemMessage", func(request))
prompt = cast("str", func(request))
request.system_prompt = prompt
return await handler(request)

124
libs/langchain_v1/tests/unit_tests/agents/test_context_editing_middleware.py
View File

@@ -13,7 +13,6 @@ from langchain_core.language_models.fake_chat_models import FakeChatModel
from langchain_core.messages import (
AIMessage,
MessageLikeRepresentation,
SystemMessage,
ToolMessage,
)
from langgraph.runtime import Runtime
@@ -400,126 +399,3 @@ async def test_exclude_tools_prevents_clearing_async() -> None:
assert isinstance(calc_tool, ToolMessage)
assert calc_tool.content == "[cleared]"
# ==============================================================================
# SystemMessage Tests
# ==============================================================================
def test_handles_system_message_prompt() -> None:
"""Test that middleware handles SystemMessage as system_prompt correctly."""
tool_call_id = "call-1"
ai_message = AIMessage(
content="",
tool_calls=[{"id": tool_call_id, "name": "search", "args": {}}],
)
tool_message = ToolMessage(content="12345", tool_call_id=tool_call_id)
system_prompt = SystemMessage(content="You are a helpful assistant.")
state, request = _make_state_and_request([ai_message, tool_message], system_prompt=None)
# Manually set SystemMessage as system_prompt
request.system_prompt = system_prompt
middleware = ContextEditingMiddleware(
edits=[ClearToolUsesEdit(trigger=50)],
token_count_method="model",
)
def mock_handler(req: ModelRequest) -> AIMessage:
return AIMessage(content="mock response")
# Call wrap_model_call - should not fail with SystemMessage
middleware.wrap_model_call(request, mock_handler)
# Request should have processed without errors
assert request.system_prompt == system_prompt
assert isinstance(request.system_prompt, SystemMessage)
def test_does_not_double_wrap_system_message() -> None:
"""Test that middleware doesn't wrap SystemMessage in another SystemMessage."""
tool_call_id = "call-1"
ai_message = AIMessage(
content="",
tool_calls=[{"id": tool_call_id, "name": "search", "args": {}}],
)
tool_message = ToolMessage(content="x" * 100, tool_call_id=tool_call_id)
system_prompt = SystemMessage(content="Original system prompt")
state, request = _make_state_and_request([ai_message, tool_message], system_prompt=None)
request.system_prompt = system_prompt
middleware = ContextEditingMiddleware(
edits=[ClearToolUsesEdit(trigger=50)],
token_count_method="model",
)
def mock_handler(req: ModelRequest) -> AIMessage:
return AIMessage(content="mock response")
middleware.wrap_model_call(request, mock_handler)
# System prompt should still be the same SystemMessage, not wrapped
assert request.system_prompt == system_prompt
assert isinstance(request.system_prompt, SystemMessage)
assert request.system_prompt.content == "Original system prompt"
async def test_handles_system_message_prompt_async() -> None:
"""Test async version - middleware handles SystemMessage as system_prompt correctly."""
tool_call_id = "call-1"
ai_message = AIMessage(
content="",
tool_calls=[{"id": tool_call_id, "name": "search", "args": {}}],
)
tool_message = ToolMessage(content="12345", tool_call_id=tool_call_id)
system_prompt = SystemMessage(content="You are a helpful assistant.")
state, request = _make_state_and_request([ai_message, tool_message], system_prompt=None)
# Manually set SystemMessage as system_prompt
request.system_prompt = system_prompt
middleware = ContextEditingMiddleware(
edits=[ClearToolUsesEdit(trigger=50)],
token_count_method="model",
)
async def mock_handler(req: ModelRequest) -> AIMessage:
return AIMessage(content="mock response")
# Call awrap_model_call - should not fail with SystemMessage
await middleware.awrap_model_call(request, mock_handler)
# Request should have processed without errors
assert request.system_prompt == system_prompt
assert isinstance(request.system_prompt, SystemMessage)
async def test_does_not_double_wrap_system_message_async() -> None:
"""Test async version - middleware doesn't wrap SystemMessage in another SystemMessage."""
tool_call_id = "call-1"
ai_message = AIMessage(
content="",
tool_calls=[{"id": tool_call_id, "name": "search", "args": {}}],
)
tool_message = ToolMessage(content="x" * 100, tool_call_id=tool_call_id)
system_prompt = SystemMessage(content="Original system prompt")
state, request = _make_state_and_request([ai_message, tool_message], system_prompt=None)
request.system_prompt = system_prompt
middleware = ContextEditingMiddleware(
edits=[ClearToolUsesEdit(trigger=50)],
token_count_method="model",
)
async def mock_handler(req: ModelRequest) -> AIMessage:
return AIMessage(content="mock response")
await middleware.awrap_model_call(request, mock_handler)
# System prompt should still be the same SystemMessage, not wrapped
assert request.system_prompt == system_prompt
assert isinstance(request.system_prompt, SystemMessage)
assert request.system_prompt.content == "Original system prompt"

130
libs/langchain_v1/tests/unit_tests/agents/test_todo_middleware.py
View File

@@ -5,7 +5,7 @@ from __future__ import annotations
from typing import cast
from langchain_core.language_models.fake_chat_models import GenericFakeChatModel
from langchain_core.messages import AIMessage, SystemMessage
from langchain_core.messages import AIMessage
from langchain.agents.middleware.todo import TodoListMiddleware
from langchain.agents.middleware.types import ModelRequest, ModelResponse
@@ -170,131 +170,3 @@ async def test_handler_called_with_modified_request_async() -> None:
assert received_prompt["value"] is not None
assert "Original" in received_prompt["value"]
assert "write_todos" in received_prompt["value"]
# ==============================================================================
# SystemMessage Tests
# ==============================================================================
def test_appends_to_system_message_with_list_content() -> None:
"""Test that middleware appends to SystemMessage with list content."""
existing_prompt = SystemMessage(content=["You are helpful.", "Be concise."])
middleware = TodoListMiddleware()
model = GenericFakeChatModel(messages=iter([AIMessage(content="response")]))
request = ModelRequest(
model=model,
system_prompt=existing_prompt,
messages=[],
tool_choice=None,
tools=[],
response_format=None,
state=cast("AgentState", {}), # type: ignore[name-defined]
runtime=_fake_runtime(),
model_settings={},
)
def mock_handler(req: ModelRequest) -> ModelResponse:
return ModelResponse(result=[AIMessage(content="response")])
middleware.wrap_model_call(request, mock_handler)
# System prompt should be a SystemMessage with combined content
assert isinstance(request.system_prompt, SystemMessage)
assert isinstance(request.system_prompt.content, list)
assert len(request.system_prompt.content) == 3
assert request.system_prompt.content[0] == "You are helpful."
assert request.system_prompt.content[1] == "Be concise."
assert "write_todos" in request.system_prompt.content[2]
async def test_appends_to_system_message_with_string_content_async() -> None:
"""Test async version - middleware appends to SystemMessage with string content."""
existing_prompt = SystemMessage(content="You are a helpful assistant.")
middleware = TodoListMiddleware()
model = GenericFakeChatModel(messages=iter([AIMessage(content="response")]))
request = ModelRequest(
model=model,
system_prompt=existing_prompt,
messages=[],
tool_choice=None,
tools=[],
response_format=None,
state=cast("AgentState", {}), # type: ignore[name-defined]
runtime=_fake_runtime(),
model_settings={},
)
async def mock_handler(req: ModelRequest) -> ModelResponse:
return ModelResponse(result=[AIMessage(content="response")])
await middleware.awrap_model_call(request, mock_handler)
# System prompt should be a SystemMessage with combined content
assert isinstance(request.system_prompt, SystemMessage)
assert isinstance(request.system_prompt.content, str)
assert "You are a helpful assistant." in request.system_prompt.content
assert middleware.system_prompt in request.system_prompt.content
async def test_appends_to_system_message_with_list_content_async() -> None:
"""Test async version - middleware appends to SystemMessage with list content."""
existing_prompt = SystemMessage(content=["You are helpful.", "Be concise."])
middleware = TodoListMiddleware()
model = GenericFakeChatModel(messages=iter([AIMessage(content="response")]))
request = ModelRequest(
model=model,
system_prompt=existing_prompt,
messages=[],
tool_choice=None,
tools=[],
response_format=None,
state=cast("AgentState", {}), # type: ignore[name-defined]
runtime=_fake_runtime(),
model_settings={},
)
async def mock_handler(req: ModelRequest) -> ModelResponse:
return ModelResponse(result=[AIMessage(content="response")])
await middleware.awrap_model_call(request, mock_handler)
# System prompt should be a SystemMessage with combined content
assert isinstance(request.system_prompt, SystemMessage)
assert isinstance(request.system_prompt.content, list)
assert len(request.system_prompt.content) == 3
assert request.system_prompt.content[0] == "You are helpful."
assert request.system_prompt.content[1] == "Be concise."
assert "write_todos" in request.system_prompt.content[2]
def test_creates_system_message_when_prompt_is_system_message() -> None:
"""Test that middleware preserves SystemMessage type."""
existing_prompt = SystemMessage(content="Original instructions")
middleware = TodoListMiddleware(system_prompt="Todo instructions")
model = GenericFakeChatModel(messages=iter([AIMessage(content="response")]))
request = ModelRequest(
model=model,
system_prompt=existing_prompt,
messages=[],
tool_choice=None,
tools=[],
response_format=None,
state=cast("AgentState", {}), # type: ignore[name-defined]
runtime=_fake_runtime(),
model_settings={},
)
def mock_handler(req: ModelRequest) -> ModelResponse:
return ModelResponse(result=[AIMessage(content="response")])
result = middleware.wrap_model_call(request, mock_handler)
# Result should be returned from handler
assert isinstance(result, ModelResponse)
# System prompt should still be a SystemMessage
assert isinstance(request.system_prompt, SystemMessage)