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first pass, things are looking ugly

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This commit is contained in:
Sydney Runkle
2025-08-06 11:22:12 -04:00
parent 9d4c86eccc
commit af5de1a7cb
1 changed files with 82 additions and 38 deletions
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libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py
+82 -38
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@@ -20,11 +20,17 @@ from langchain_core.language_models import (
LanguageModelLike,
)
from langchain_core.messages import (
AIMessage,
AnyMessage,
BaseMessage,
SystemMessage,
ToolMessage,
AIMessage as AIMessageV0,
AnyMessage as MessageV0,
BaseMessage as BaseMessageV0,
SystemMessage as SystemMessageV0,
ToolMessage as ToolMessageV0,
)
from langchain_core.v1.messages import(
AIMessage as AIMessageV1,
MessageV1,
SystemMessage as SystemMessageV1,
ToolMessage as ToolMessageV1,
)
from langchain_core.runnables import (
Runnable,
@@ -62,7 +68,7 @@ F = TypeVar("F", bound=Callable[..., Any])
class AgentState(TypedDict):
"""The state of the agent."""
messages: Annotated[Sequence[BaseMessage], add_messages]
messages: Annotated[Sequence[Union[BaseMessageV0, MessageV1]], add_messages]
remaining_steps: NotRequired[RemainingSteps]
@@ -70,7 +76,7 @@ class AgentState(TypedDict):
class AgentStatePydantic(BaseModel):
"""The state of the agent."""
messages: Annotated[Sequence[BaseMessage], add_messages]
messages: Annotated[Sequence[Union[BaseMessageV0, MessageV1]], add_messages]
remaining_steps: RemainingSteps = 25
@@ -93,7 +99,8 @@ StateSchemaType = Type[StateSchema]
PROMPT_RUNNABLE_NAME = "Prompt"
Prompt = Union[
SystemMessage,
SystemMessageV0,
SystemMessageV1,
str,
Callable[[StateSchema], LanguageModelInput],
Runnable[StateSchema, LanguageModelInput],
@@ -108,19 +115,22 @@ def _get_state_value(state: StateSchema, key: str, default: Any = None) -> Any:
)
def _get_prompt_runnable(prompt: Optional[Prompt]) -> Runnable:
def _get_prompt_runnable(prompt: Optional[Prompt], message_version: Literal["v0", "v1"]) -> Runnable:
prompt_runnable: Runnable
if prompt is None:
prompt_runnable = RunnableCallable(
lambda state: _get_state_value(state, "messages"), name=PROMPT_RUNNABLE_NAME
)
elif isinstance(prompt, str):
_system_message: BaseMessage = SystemMessage(content=prompt)
if message_version == "v0":
_system_message = SystemMessageV0(content=prompt)
else:
_system_message = SystemMessageV1(content=prompt)
prompt_runnable = RunnableCallable(
lambda state: [_system_message] + _get_state_value(state, "messages"),
name=PROMPT_RUNNABLE_NAME,
)
elif isinstance(prompt, SystemMessage):
elif isinstance(prompt, (SystemMessageV0, SystemMessageV1)):
prompt_runnable = RunnableCallable(
lambda state: [prompt] + _get_state_value(state, "messages"),
name=PROMPT_RUNNABLE_NAME,
@@ -215,17 +225,17 @@ def _get_model(model: LanguageModelLike) -> BaseChatModel:
def _validate_chat_history(
messages: Sequence[BaseMessage],
messages: Sequence[Union[BaseMessageV0, MessageV1]],
) -> None:
"""Validate that all tool calls in AIMessages have a corresponding ToolMessage."""
all_tool_calls = [
tool_call
for message in messages
if isinstance(message, AIMessage)
if isinstance(message, (AIMessageV0, AIMessageV1))
for tool_call in message.tool_calls
]
tool_call_ids_with_results = {
message.tool_call_id for message in messages if isinstance(message, ToolMessage)
message.tool_call_id for message in messages if isinstance(message, (ToolMessageV0, ToolMessageV1))
}
tool_calls_without_results = [
tool_call
@@ -252,11 +262,11 @@ def create_react_agent(
Callable[[StateSchema, Runtime[ContextT]], BaseChatModel],
Callable[[StateSchema, Runtime[ContextT]], Awaitable[BaseChatModel]],
Callable[
[StateSchema, Runtime[ContextT]], Runnable[LanguageModelInput, BaseMessage]
[StateSchema, Runtime[ContextT]], Runnable[LanguageModelInput, Union[BaseMessageV0, MessageV1]]
],
Callable[
[StateSchema, Runtime[ContextT]],
Awaitable[Runnable[LanguageModelInput, BaseMessage]],
Awaitable[Runnable[LanguageModelInput, Union[BaseMessageV0, MessageV1]]],
],
],
tools: Union[Sequence[Union[BaseTool, Callable, dict[str, Any]]], ToolNode],
@@ -276,6 +286,7 @@ def create_react_agent(
debug: bool = False,
version: Literal["v1", "v2"] = "v2",
name: Optional[str] = None,
message_version: Literal["v0", "v1"] = "v0",
**deprecated_kwargs: Any,
) -> CompiledStateGraph:
"""Creates an agent graph that calls tools in a loop until a stopping condition is met.
@@ -477,6 +488,11 @@ def create_react_agent(
raise ValueError(
f"Invalid version {version}. Supported versions are 'v1' and 'v2'."
)
if message_version == "v0":
message_constructor = AIMessageV0
else:
message_constructor = AIMessageV1
if state_schema is not None:
required_keys = {"messages", "remaining_steps"}
@@ -530,7 +546,7 @@ def create_react_agent(
tool_classes + llm_builtin_tools # type: ignore[operator]
)
static_model: Optional[Runnable] = _get_prompt_runnable(prompt) | model # type: ignore[operator]
static_model: Optional[Runnable] = _get_prompt_runnable(prompt, message_version) | model # type: ignore[operator]
else:
# For dynamic models, we'll create the runnable at runtime
static_model = None
@@ -560,11 +576,11 @@ def create_react_agent(
else:
return static_model
def _are_more_steps_needed(state: StateSchema, response: BaseMessage) -> bool:
has_tool_calls = isinstance(response, AIMessage) and response.tool_calls
def _are_more_steps_needed(state: StateSchema, response: Union[BaseMessageV0, MessageV1]) -> bool:
has_tool_calls = isinstance(response, (AIMessageV0, AIMessageV1)) and response.tool_calls
all_tools_return_direct = (
all(call["name"] in should_return_direct for call in response.tool_calls)
if isinstance(response, AIMessage)
if isinstance(response, (AIMessageV0, AIMessageV1))
else False
)
remaining_steps = _get_state_value(state, "remaining_steps", None)
@@ -617,9 +633,14 @@ def create_react_agent(
if is_dynamic_model:
# Resolve dynamic model at runtime and apply prompt
dynamic_model = _resolve_model(state, runtime)
response = cast(AIMessage, dynamic_model.invoke(model_input, config)) # type: ignore[arg-type]
response = dynamic_model.invoke(model_input, config) # type: ignore[arg-type]
else:
response = cast(AIMessage, static_model.invoke(model_input, config)) # type: ignore[union-attr]
response = static_model.invoke(model_input, config) # type: ignore[union-attr]
if message_version == "v0":
response = cast(AIMessageV0, response)
else:
response = cast(AIMessageV1, response)
# add agent name to the AIMessage
response.name = name
@@ -627,7 +648,7 @@ def create_react_agent(
if _are_more_steps_needed(state, response):
return {
"messages": [
AIMessage(
message_constructor(
id=response.id,
content="Sorry, need more steps to process this request.",
)
@@ -645,16 +666,21 @@ def create_react_agent(
# Resolve dynamic model at runtime and apply prompt
# (supports both sync and async)
dynamic_model = await _aresolve_model(state, runtime)
response = cast(AIMessage, await dynamic_model.ainvoke(model_input, config)) # type: ignore[arg-type]
response = await dynamic_model.ainvoke(model_input, config) # type: ignore[arg-type]
else:
response = cast(AIMessage, await static_model.ainvoke(model_input, config)) # type: ignore[union-attr]
response = await static_model.ainvoke(model_input, config) # type: ignore[union-attr]
if message_version == "v0":
response = cast(AIMessageV0, response)
else:
response = cast(AIMessageV1, response)
# add agent name to the AIMessage
response.name = name
if _are_more_steps_needed(state, response):
return {
"messages": [
AIMessage(
message_constructor(
id=response.id,
content="Sorry, need more steps to process this request.",
)
@@ -665,20 +691,32 @@ def create_react_agent(
input_schema: StateSchemaType
if pre_model_hook is not None:
# Dynamically create a schema that inherits from state_schema and adds 'llm_input_messages'
if isinstance(state_schema, type) and issubclass(state_schema, BaseModel):
# For Pydantic schemas
from pydantic import create_model
input_schema = create_model(
if message_version == "v0":
input_schema = create_model(
"CallModelInputSchema",
llm_input_messages=(list[AnyMessage], ...),
llm_input_messages=(list[MessageV0], ...),
__base__=state_schema,
)
else:
input_schema = create_model(
"CallModelInputSchema",
llm_input_messages=(list[MessageV1], ...),
__base__=state_schema,
)
else:
# For TypedDict schemas
class CallModelInputSchema(state_schema): # type: ignore
llm_input_messages: list[AnyMessage]
if message_version == "v0":
class CallModelInputSchema(state_schema): # type: ignore
llm_input_messages: list[MessageV0]
else:
class CallModelInputSchema(state_schema): # type: ignore
llm_input_messages: list[MessageV1]
input_schema = CallModelInputSchema
else:
@@ -698,7 +736,10 @@ def create_react_agent(
structured_response_schema = response_format
if isinstance(response_format, tuple):
system_prompt, structured_response_schema = response_format
messages = [SystemMessage(content=system_prompt)] + list(messages)
if message_version == "v0":
messages = [SystemMessageV0(content=system_prompt)] + list(messages)
else:
messages = [SystemMessageV1(content=system_prompt)] + list(messages)
resolved_model = _resolve_model(state, runtime)
model_with_structured_output = _get_model(
@@ -716,7 +757,10 @@ def create_react_agent(
structured_response_schema = response_format
if isinstance(response_format, tuple):
system_prompt, structured_response_schema = response_format
messages = [SystemMessage(content=system_prompt)] + list(messages)
if message_version == "v0":
messages = [SystemMessageV0(content=system_prompt)] + list(messages)
else:
messages = [SystemMessageV1(content=system_prompt)] + list(messages)
resolved_model = await _aresolve_model(state, runtime)
model_with_structured_output = _get_model(
@@ -775,7 +819,7 @@ def create_react_agent(
messages = _get_state_value(state, "messages")
last_message = messages[-1]
# If there is no function call, then we finish
if not isinstance(last_message, AIMessage) or not last_message.tool_calls:
if not isinstance(last_message, (AIMessageV0, AIMessageV1)) or not last_message.tool_calls:
if post_model_hook is not None:
return "post_model_hook"
elif response_format is not None:
@@ -864,10 +908,10 @@ def create_react_agent(
messages = _get_state_value(state, "messages")
tool_messages = [
m.tool_call_id for m in messages if isinstance(m, ToolMessage)
m.tool_call_id for m in messages if isinstance(m, (ToolMessageV0, ToolMessageV1)):
]
last_ai_message = next(
m for m in reversed(messages) if isinstance(m, AIMessage)
m for m in reversed(messages) if isinstance(m, (AIMessageV0, AIMessageV1))
)
pending_tool_calls = [
c for c in last_ai_message.tool_calls if c["id"] not in tool_messages
@@ -879,7 +923,7 @@ def create_react_agent(
for call in pending_tool_calls
]
return [Send("tools", [tool_call]) for tool_call in pending_tool_calls]
elif isinstance(messages[-1], ToolMessage):
elif isinstance(messages[-1], (ToolMessageV0, ToolMessageV1)):
return entrypoint
elif response_format is not None:
return "generate_structured_response"
@@ -907,7 +951,7 @@ def create_react_agent(
# handle a case of parallel tool calls where
# the tool w/ `return_direct` was executed in a different `Send`
if isinstance(m, AIMessage) and m.tool_calls:
if isinstance(m, (AIMessageV0, AIMessageV1)) and m.tool_calls:
if any(call["name"] in should_return_direct for call in m.tool_calls):
return END