WEBDIENSTE/langgraph
1
0
Fork 0
mirror of https://github.com/langchain-ai/langgraph.git synced 2026-09-10 03:37:51 +02:00
Code Issues Packages Projects Releases Wiki Activity

chore(prebuilt): revert unnecessary breaking changes (#6020)

Browse Source 
* revert change of `agent` node to `model`
* revert removal of pydantic agent states + agent state consolidation
* revert removal of `version` arg
* revert removal of backwards compat `config_schema`
This commit is contained in:
Sydney Runkle
2025-08-26 16:34:13 -04:00
committed by GitHub
parent 33ae3d4a8a
commit b36b7e2730
9 changed files with 469 additions and 186 deletions
Download Patch File Download Diff File Expand all files Collapse all files
libs/langgraph/tests/__snapshots__/test_large_cases.ambr
+12 -12
View File
@@ -175,10 +175,10 @@
'''
# ---
# name: test_prebuilt_tool_chat
'{"$defs": {"BaseMessage": {"additionalProperties": true, "description": "Base abstract message class.\\n\\nMessages are the inputs and outputs of ChatModels.", "properties": {"content": {"anyOf": [{"type": "string"}, {"items": {"anyOf": [{"type": "string"}, {"additionalProperties": true, "type": "object"}]}, "type": "array"}], "title": "Content"}, "additional_kwargs": {"additionalProperties": true, "title": "Additional Kwargs", "type": "object"}, "response_metadata": {"additionalProperties": true, "title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "type": "string"}, "name": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Name"}, "id": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Id"}}, "required": ["content", "type"], "title": "BaseMessage", "type": "object"}}, "description": "The state of the agent.", "properties": {"messages": {"items": {"$ref": "#/$defs/BaseMessage"}, "title": "Messages", "type": "array"}, "remaining_steps": {"title": "Remaining Steps", "type": "integer"}, "structured_response": {"title": "Structured Response", "type": "null"}}, "required": ["messages"], "title": "AgentState", "type": "object"}'
'{"$defs": {"BaseMessage": {"additionalProperties": true, "description": "Base abstract message class.\\n\\nMessages are the inputs and outputs of ChatModels.", "properties": {"content": {"anyOf": [{"type": "string"}, {"items": {"anyOf": [{"type": "string"}, {"additionalProperties": true, "type": "object"}]}, "type": "array"}], "title": "Content"}, "additional_kwargs": {"additionalProperties": true, "title": "Additional Kwargs", "type": "object"}, "response_metadata": {"additionalProperties": true, "title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "type": "string"}, "name": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Name"}, "id": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Id"}}, "required": ["content", "type"], "title": "BaseMessage", "type": "object"}}, "description": "The state of the agent.", "properties": {"messages": {"items": {"$ref": "#/$defs/BaseMessage"}, "title": "Messages", "type": "array"}, "remaining_steps": {"title": "Remaining Steps", "type": "integer"}}, "required": ["messages"], "title": "AgentState", "type": "object"}'
# ---
# name: test_prebuilt_tool_chat.1
'{"$defs": {"BaseMessage": {"additionalProperties": true, "description": "Base abstract message class.\\n\\nMessages are the inputs and outputs of ChatModels.", "properties": {"content": {"anyOf": [{"type": "string"}, {"items": {"anyOf": [{"type": "string"}, {"additionalProperties": true, "type": "object"}]}, "type": "array"}], "title": "Content"}, "additional_kwargs": {"additionalProperties": true, "title": "Additional Kwargs", "type": "object"}, "response_metadata": {"additionalProperties": true, "title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "type": "string"}, "name": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Name"}, "id": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Id"}}, "required": ["content", "type"], "title": "BaseMessage", "type": "object"}}, "description": "The state of the agent.", "properties": {"messages": {"items": {"$ref": "#/$defs/BaseMessage"}, "title": "Messages", "type": "array"}, "remaining_steps": {"title": "Remaining Steps", "type": "integer"}, "structured_response": {"title": "Structured Response", "type": "null"}}, "required": ["messages"], "title": "AgentState", "type": "object"}'
'{"$defs": {"BaseMessage": {"additionalProperties": true, "description": "Base abstract message class.\\n\\nMessages are the inputs and outputs of ChatModels.", "properties": {"content": {"anyOf": [{"type": "string"}, {"items": {"anyOf": [{"type": "string"}, {"additionalProperties": true, "type": "object"}]}, "type": "array"}], "title": "Content"}, "additional_kwargs": {"additionalProperties": true, "title": "Additional Kwargs", "type": "object"}, "response_metadata": {"additionalProperties": true, "title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "type": "string"}, "name": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Name"}, "id": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Id"}}, "required": ["content", "type"], "title": "BaseMessage", "type": "object"}}, "description": "The state of the agent.", "properties": {"messages": {"items": {"$ref": "#/$defs/BaseMessage"}, "title": "Messages", "type": "array"}, "remaining_steps": {"title": "Remaining Steps", "type": "integer"}}, "required": ["messages"], "title": "AgentState", "type": "object"}'
# ---
# name: test_prebuilt_tool_chat.2
'''
@@ -198,7 +198,7 @@
}
},
{
"id": "model",
"id": "agent",
"type": "runnable",
"data": {
"id": [
@@ -207,7 +207,7 @@
"_runnable",
"RunnableCallable"
],
"name": "model"
"name": "agent"
}
},
{
@@ -230,21 +230,21 @@
"edges": [
{
"source": "__start__",
"target": "model"
"target": "agent"
},
{
"source": "model",
"source": "agent",
"target": "__end__",
"conditional": true
},
{
"source": "model",
"source": "agent",
"target": "tools",
"conditional": true
},
{
"source": "tools",
"target": "model"
"target": "agent"
}
]
}
@@ -253,10 +253,10 @@
# name: test_prebuilt_tool_chat.3
'''
graph TD;
__start__ --> model;
model -.-> __end__;
model -.-> tools;
tools --> model;
__start__ --> agent;
agent -.-> __end__;
agent -.-> tools;
tools --> agent;
'''
# ---
libs/langgraph/tests/conftest.py
+1 -1
View File
@@ -69,7 +69,7 @@ def cache(request: pytest.FixtureRequest) -> Iterator[BaseCache]:
elif request.param == "redis":
# Get worker ID for parallel test isolation
worker_id = getattr(request.config, "workerinput", {}).get("workerid", "master")
redis_client = redis.Redis(
host="localhost", port=6379, db=0, decode_responses=False
)
libs/langgraph/tests/test_large_cases.py
+18 -18
View File
@@ -1390,11 +1390,11 @@ def test_prebuilt_tool_chat(snapshot: SnapshotAssertion) -> None:
),
{
"langgraph_step": 1,
"langgraph_node": "model",
"langgraph_triggers": ("branch:to:model",),
"langgraph_path": (PULL, "model"),
"langgraph_checkpoint_ns": AnyStr("model:"),
"checkpoint_ns": AnyStr("model:"),
"langgraph_node": "agent",
"langgraph_triggers": ("branch:to:agent",),
"langgraph_path": (PULL, "agent"),
"langgraph_checkpoint_ns": AnyStr("agent:"),
"checkpoint_ns": AnyStr("agent:"),
"ls_provider": "fakechatmodel",
"ls_model_type": "chat",
},
@@ -1449,11 +1449,11 @@ def test_prebuilt_tool_chat(snapshot: SnapshotAssertion) -> None:
),
{
"langgraph_step": 3,
"langgraph_node": "model",
"langgraph_triggers": ("branch:to:model",),
"langgraph_path": (PULL, "model"),
"langgraph_checkpoint_ns": AnyStr("model:"),
"checkpoint_ns": AnyStr("model:"),
"langgraph_node": "agent",
"langgraph_triggers": ("branch:to:agent",),
"langgraph_path": (PULL, "agent"),
"langgraph_checkpoint_ns": AnyStr("agent:"),
"checkpoint_ns": AnyStr("agent:"),
"ls_provider": "fakechatmodel",
"ls_model_type": "chat",
},
@@ -1497,11 +1497,11 @@ def test_prebuilt_tool_chat(snapshot: SnapshotAssertion) -> None:
),
{
"langgraph_step": 5,
"langgraph_node": "model",
"langgraph_triggers": ("branch:to:model",),
"langgraph_path": (PULL, "model"),
"langgraph_checkpoint_ns": AnyStr("model:"),
"checkpoint_ns": AnyStr("model:"),
"langgraph_node": "agent",
"langgraph_triggers": ("branch:to:agent",),
"langgraph_path": (PULL, "agent"),
"langgraph_checkpoint_ns": AnyStr("agent:"),
"checkpoint_ns": AnyStr("agent:"),
"ls_provider": "fakechatmodel",
"ls_model_type": "chat",
},
@@ -1533,7 +1533,7 @@ def test_prebuilt_tool_chat(snapshot: SnapshotAssertion) -> None:
for output in (invoke_updates_events, stream_updates_events):
assert output[:3] == [
{
"model": {
"agent": {
"messages": [
_AnyIdAIMessage(
content="",
@@ -1560,7 +1560,7 @@ def test_prebuilt_tool_chat(snapshot: SnapshotAssertion) -> None:
}
},
{
"model": {
"agent": {
"messages": [
_AnyIdAIMessage(
content="",
@@ -1606,7 +1606,7 @@ def test_prebuilt_tool_chat(snapshot: SnapshotAssertion) -> None:
},
)
assert output[5:] == [
{"model": {"messages": [_AnyIdAIMessage(content="answer")]}}
{"agent": {"messages": [_AnyIdAIMessage(content="answer")]}}
]
libs/langgraph/tests/test_large_cases_async.py
+18 -18
View File
@@ -1143,11 +1143,11 @@ async def test_prebuilt_tool_chat() -> None:
),
{
"langgraph_step": 1,
"langgraph_node": "model",
"langgraph_triggers": ("branch:to:model",),
"langgraph_path": (PULL, "model"),
"langgraph_checkpoint_ns": AnyStr("model:"),
"checkpoint_ns": AnyStr("model:"),
"langgraph_node": "agent",
"langgraph_triggers": ("branch:to:agent",),
"langgraph_path": (PULL, "agent"),
"langgraph_checkpoint_ns": AnyStr("agent:"),
"checkpoint_ns": AnyStr("agent:"),
"ls_provider": "fakechatmodel",
"ls_model_type": "chat",
},
@@ -1202,11 +1202,11 @@ async def test_prebuilt_tool_chat() -> None:
),
{
"langgraph_step": 3,
"langgraph_node": "model",
"langgraph_triggers": ("branch:to:model",),
"langgraph_path": (PULL, "model"),
"langgraph_checkpoint_ns": AnyStr("model:"),
"checkpoint_ns": AnyStr("model:"),
"langgraph_node": "agent",
"langgraph_triggers": ("branch:to:agent",),
"langgraph_path": (PULL, "agent"),
"langgraph_checkpoint_ns": AnyStr("agent:"),
"checkpoint_ns": AnyStr("agent:"),
"ls_provider": "fakechatmodel",
"ls_model_type": "chat",
},
@@ -1250,11 +1250,11 @@ async def test_prebuilt_tool_chat() -> None:
),
{
"langgraph_step": 5,
"langgraph_node": "model",
"langgraph_triggers": ("branch:to:model",),
"langgraph_path": (PULL, "model"),
"langgraph_checkpoint_ns": AnyStr("model:"),
"checkpoint_ns": AnyStr("model:"),
"langgraph_node": "agent",
"langgraph_triggers": ("branch:to:agent",),
"langgraph_path": (PULL, "agent"),
"langgraph_checkpoint_ns": AnyStr("agent:"),
"checkpoint_ns": AnyStr("agent:"),
"ls_provider": "fakechatmodel",
"ls_model_type": "chat",
},
@@ -1269,7 +1269,7 @@ async def test_prebuilt_tool_chat() -> None:
]
assert stream_updates_events[:3] == [
{
"model": {
"agent": {
"messages": [
_AnyIdAIMessage(
content="",
@@ -1296,7 +1296,7 @@ async def test_prebuilt_tool_chat() -> None:
}
},
{
"model": {
"agent": {
"messages": [
_AnyIdAIMessage(
content="",
@@ -1342,7 +1342,7 @@ async def test_prebuilt_tool_chat() -> None:
},
)
assert stream_updates_events[5:] == [
{"model": {"messages": [_AnyIdAIMessage(content="answer")]}}
{"agent": {"messages": [_AnyIdAIMessage(content="answer")]}}
]
libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py
+149 -41
View File
@@ -7,12 +7,14 @@ from typing import (
Awaitable,
Callable,
Generic,
Literal,
Optional,
Sequence,
Union,
cast,
get_type_hints,
)
from warnings import warn
from langchain_core.language_models import (
BaseChatModel,
@@ -21,6 +23,7 @@ from langchain_core.language_models import (
)
from langchain_core.messages import (
AIMessage,
AnyMessage,
BaseMessage,
SystemMessage,
ToolCall,
@@ -35,6 +38,7 @@ from pydantic import BaseModel
from typing_extensions import Annotated, NotRequired, TypedDict, TypeVar
from langgraph._internal._runnable import RunnableCallable, RunnableLike
from langgraph._internal._typing import MISSING
from langgraph.errors import ErrorCode, create_error_message
from langgraph.graph import END, StateGraph
from langgraph.graph.message import add_messages
@@ -58,22 +62,39 @@ from langgraph.store.base import BaseStore
from langgraph.types import Checkpointer, Command, Send
from langgraph.typing import ContextT, StateT
StructuredResponseT = TypeVar(
"StructuredResponseT",
default=None,
)
StructuredResponseT = TypeVar("StructuredResponseT", default=None)
STRUCTURED_OUTPUT_ERROR_TEMPLATE = "Error: {error}\n Please fix your mistakes."
class AgentState(TypedDict, Generic[StructuredResponseT]):
class AgentState(TypedDict):
"""The state of the agent."""
messages: Annotated[Sequence[BaseMessage], add_messages]
remaining_steps: NotRequired[RemainingSteps]
structured_response: NotRequired[StructuredResponseT]
class AgentStatePydantic(BaseModel):
"""The state of the agent."""
messages: Annotated[Sequence[BaseMessage], add_messages]
remaining_steps: RemainingSteps = 25
class AgentStateWithStructuredResponse(AgentState, Generic[StructuredResponseT]):
"""The state of the agent with a structured response."""
structured_response: StructuredResponseT
class AgentStateWithStructuredResponsePydantic(
AgentStatePydantic, Generic[StructuredResponseT]
):
"""The state of the agent with a structured response."""
structured_response: StructuredResponseT
PROMPT_RUNNABLE_NAME = "Prompt"
@@ -161,17 +182,15 @@ def _validate_chat_history(
raise ValueError(error_message)
class _AgentBuilder(Generic[StateT, ContextT, StructuredResponseT]):
class _AgentBuilder(Generic[StructuredResponseT]):
"""Internal builder class for constructing and agent."""
_final_state_schema: type[StateT]
def __init__(
self,
model: Union[
str,
BaseChatModel,
SyncOrAsync[[StateT, Runtime[ContextT]], BaseChatModel],
SyncOrAsync[[StateT, Runtime[ContextT]], BaseModel],
],
tools: Union[Sequence[Union[BaseTool, Callable, dict[str, Any]]], ToolNode],
*,
@@ -181,6 +200,7 @@ class _AgentBuilder(Generic[StateT, ContextT, StructuredResponseT]):
post_model_hook: Optional[RunnableLike] = None,
state_schema: Optional[type[StateT]] = None,
context_schema: Optional[type[ContextT]] = None,
version: Literal["v1", "v2"] = "v2",
name: Optional[str] = None,
store: Optional[BaseStore] = None,
):
@@ -192,6 +212,7 @@ class _AgentBuilder(Generic[StateT, ContextT, StructuredResponseT]):
self.post_model_hook = post_model_hook
self.state_schema = state_schema
self.context_schema = context_schema
self.version = version
self.name = name
self.store = store
@@ -283,7 +304,11 @@ class _AgentBuilder(Generic[StateT, ContextT, StructuredResponseT]):
self._final_state_schema = self.state_schema
else:
self._final_state_schema = cast(type[StateT], AgentState)
self._final_state_schema = (
AgentStateWithStructuredResponse # type: ignore[assignment]
if self.response_format is not None
else AgentState
)
def _handle_structured_response_tool_calls(
self, response: AIMessage
@@ -344,7 +369,7 @@ class _AgentBuilder(Generic[StateT, ContextT, StructuredResponseT]):
return Command(
update={"messages": [response, *tool_messages]},
goto="model",
goto="agent",
)
def _handle_single_structured_output(
@@ -406,7 +431,7 @@ class _AgentBuilder(Generic[StateT, ContextT, StructuredResponseT]):
),
],
},
goto="model",
goto="agent",
)
def _handle_structured_output_error(
@@ -558,10 +583,20 @@ class _AgentBuilder(Generic[StateT, ContextT, StructuredResponseT]):
"""Create the 'agent' node that calls the LLM."""
def _get_model_input_state(state: StateT) -> StateT:
messages = _get_state_value(state, "messages")
error_msg = (
f"Expected input to call_model to have 'messages' key, but got {state}"
)
if self.pre_model_hook is not None:
messages = _get_state_value(
state, "llm_input_messages"
) or _get_state_value(state, "messages")
error_msg = (
f"Expected input to call_model to have 'llm_input_messages' "
f"or 'messages' key, but got {state}"
)
else:
messages = _get_state_value(state, "messages")
error_msg = (
f"Expected input to call_model to "
f"have 'messages' key, but got {state}"
)
if messages is None:
raise ValueError(error_msg)
@@ -681,6 +716,28 @@ class _AgentBuilder(Generic[StateT, ContextT, StructuredResponseT]):
return RunnableCallable(call_model, acall_model)
def _get_input_schema(self) -> type[StateT]:
"""Get input schema for model node."""
if self.pre_model_hook is not None:
if isinstance(self._final_state_schema, type) and issubclass(
self._final_state_schema, BaseModel
):
from pydantic import create_model
return create_model(
"CallModelInputSchema",
llm_input_messages=(list[AnyMessage], ...),
__base__=self._final_state_schema,
)
else:
class CallModelInputSchema(self._final_state_schema): # type: ignore
llm_input_messages: list[AnyMessage]
return CallModelInputSchema
else:
return self._final_state_schema
def create_model_router(self) -> Callable[[StateT], Union[str, list[Send]]]:
"""Create routing function for model node conditional edges."""
@@ -707,13 +764,16 @@ class _AgentBuilder(Generic[StateT, ContextT, StructuredResponseT]):
else:
return END
else:
if self.post_model_hook is not None:
return "post_model_hook"
tool_calls = [
self._tool_node.inject_tool_args(call, state, self.store) # type: ignore[arg-type]
for call in last_message.tool_calls
]
return [Send("tools", [tool_call]) for tool_call in tool_calls]
if self.version == "v1":
return "tools"
elif self.version == "v2":
if self.post_model_hook is not None:
return "post_model_hook"
tool_calls = [
self._tool_node.inject_tool_args(call, state, self.store) # type: ignore[arg-type]
for call in last_message.tool_calls
]
return [Send("tools", [tool_call]) for tool_call in tool_calls]
return should_continue
@@ -784,7 +844,7 @@ class _AgentBuilder(Generic[StateT, ContextT, StructuredResponseT]):
def _get_entry_point(self) -> str:
"""Get the workflow entry point."""
return "pre_model_hook" if self.pre_model_hook else "model"
return "pre_model_hook" if self.pre_model_hook else "agent"
def _get_model_paths(self) -> list[str]:
"""Get possible edge destinations from model node."""
@@ -817,7 +877,9 @@ class _AgentBuilder(Generic[StateT, ContextT, StructuredResponseT]):
workflow.set_entry_point(self._get_entry_point())
# Add nodes
workflow.add_node("model", self.create_model_node())
workflow.add_node(
"agent", self.create_model_node(), input_schema=self._get_input_schema()
)
if self._tool_calling_enabled:
workflow.add_node("tools", self._tool_node)
@@ -830,10 +892,10 @@ class _AgentBuilder(Generic[StateT, ContextT, StructuredResponseT]):
# Add edges
if self.pre_model_hook:
workflow.add_edge("pre_model_hook", "model")
workflow.add_edge("pre_model_hook", "agent")
if self.post_model_hook:
workflow.add_edge("model", "post_model_hook")
workflow.add_edge("agent", "post_model_hook")
post_hook_paths = self._get_post_model_hook_paths()
if len(post_hook_paths) == 1:
# No need for a conditional edge if there's only one path
@@ -848,10 +910,10 @@ class _AgentBuilder(Generic[StateT, ContextT, StructuredResponseT]):
model_paths = self._get_model_paths()
if len(model_paths) == 1:
# No need for a conditional edge if there's only one path
workflow.add_edge("model", model_paths[0])
workflow.add_edge("agent", model_paths[0])
else:
workflow.add_conditional_edges(
"model",
"agent",
self.create_model_router(),
path_map=model_paths,
)
@@ -903,7 +965,7 @@ def create_react_agent(
model: Union[
str,
BaseChatModel,
SyncOrAsync[[StateT, Runtime[ContextT]], BaseChatModel],
SyncOrAsync[[StateT, Runtime[ContextT]], BaseModel],
],
tools: Union[Sequence[Union[BaseTool, Callable, dict[str, Any]]], ToolNode],
*,
@@ -924,7 +986,9 @@ def create_react_agent(
interrupt_before: Optional[list[str]] = None,
interrupt_after: Optional[list[str]] = None,
debug: bool = False,
version: Literal["v1", "v2"] = "v2",
name: Optional[str] = None,
**deprecated_kwargs: Any,
) -> CompiledStateGraph:
"""Creates an agent graph that calls tools in a loop until a stopping condition is met.
@@ -1006,17 +1070,24 @@ def create_react_agent(
Useful for managing long message histories (e.g., message trimming, summarization, etc.).
Pre-model hook must be a callable or a runnable that takes in current graph state and returns a state update in the form of
```python
# Where `messages` MUST be provided
# At least one of `messages` or `llm_input_messages` MUST be provided
{
# will UPDATE the `messages` in the state
# If provided, will UPDATE the `messages` in the state
"messages": [RemoveMessage(id=REMOVE_ALL_MESSAGES), ...],
# If provided, will be used as the input to the LLM,
# and will NOT UPDATE `messages` in the state
"llm_input_messages": [...],
# Any other state keys that need to be propagated
...
}
```
!!! Important
At least one of `messages` or `llm_input_messages` MUST be provided and will be used as an input to the `agent` node.
The rest of the keys will be added to the graph state.
!!! Warning
you should OVERWRITE the `messages` key by doing the following:
If you are returning `messages` in the pre-model hook, you should OVERWRITE the `messages` key by doing the following:
```python
{
@@ -1027,6 +1098,9 @@ def create_react_agent(
post_model_hook: An optional node to add after the `agent` node (i.e., the node that calls the LLM).
Useful for implementing human-in-the-loop, guardrails, validation, or other post-processing.
Post-model hook must be a callable or a runnable that takes in current graph state and returns a state update.
!!! Note
Only available with `version="v2"`.
state_schema: An optional state schema that defines graph state.
Must have `messages` and `remaining_steps` keys.
Defaults to `AgentState` that defines those two keys.
@@ -1036,20 +1110,34 @@ def create_react_agent(
store: An optional store object. This is used for persisting data
across multiple threads (e.g., multiple conversations / users).
interrupt_before: An optional list of node names to interrupt before.
Should be one of the following: "model", "tools".
Should be one of the following: "agent", "tools".
This is useful if you want to add a user confirmation or other interrupt before taking an action.
interrupt_after: An optional list of node names to interrupt after.
Should be one of the following: "model", "tools".
Should be one of the following: "agent", "tools".
This is useful if you want to return directly or run additional processing on an output.
debug: A flag indicating whether to enable debug mode.
version: Determines the version of the graph to create.
Can be one of:
- `"v1"`: The tool node processes a single message. All tool
calls in the message are executed in parallel within the tool node.
- `"v2"`: The tool node processes a tool call.
Tool calls are distributed across multiple instances of the tool
node using the [Send](https://langchain-ai.github.io/langgraph/concepts/low_level/#send)
API.
name: An optional name for the CompiledStateGraph.
This name will be automatically used when adding ReAct agent graph to another graph as a subgraph node -
particularly useful for building multi-agent systems.
Returns:
A CompiledStateGraph that can be used for chat interactions.
!!! warning "`config_schema` Deprecated"
The `config_schema` parameter is deprecated in v0.6.0 and support will be removed in v2.0.0.
Please use `context_schema` instead to specify the schema for run-scoped context.
The "model" node calls the language model with the messages list (after applying the prompt).
Returns:
A compiled LangChain runnable that can be used for chat interactions.
The "agent" node calls the language model with the messages list (after applying the prompt).
If the resulting AIMessage contains `tool_calls`, the graph will then call the ["tools"][langgraph.prebuilt.tool_node.ToolNode].
The "tools" node executes the tools (1 tool per `tool_call`) and adds the responses to the messages list
as `ToolMessage` objects. The agent node then calls the language model again.
@@ -1088,8 +1176,24 @@ def create_react_agent(
print(chunk)
```
"""
# Handle deprecated config_schema parameter
if (
config_schema := deprecated_kwargs.pop("config_schema", MISSING)
) is not MISSING:
warn(
"`config_schema` is deprecated and will be removed. Please use `context_schema` instead.",
category=DeprecationWarning,
)
if context_schema is None:
context_schema = config_schema
if len(deprecated_kwargs) > 0:
raise TypeError(
f"create_react_agent() got unexpected keyword arguments: {deprecated_kwargs}"
)
if response_format and not isinstance(response_format, (ToolOutput, NativeOutput)):
if _supports_native_structured_output(model):
if _supports_native_structured_output(model): # type: ignore[arg-type]
response_format = NativeOutput(
schema=response_format,
)
@@ -1104,7 +1208,7 @@ def create_react_agent(
)
# Create and configure the agent builder
builder = _AgentBuilder[StateT, ContextT, StructuredResponseT](
builder = _AgentBuilder(
model=model,
tools=tools,
prompt=prompt,
@@ -1115,6 +1219,7 @@ def create_react_agent(
post_model_hook=post_model_hook,
state_schema=state_schema,
context_schema=context_schema,
version=version,
name=name,
store=store,
)
@@ -1134,4 +1239,7 @@ def create_react_agent(
__all__ = [
"create_react_agent",
"AgentState",
"AgentStatePydantic",
"AgentStateWithStructuredResponse",
"AgentStateWithStructuredResponsePydantic",
]
libs/prebuilt/tests/__snapshots__/test_react_agent_graph.ambr
+21 -21
View File
@@ -2,18 +2,18 @@
# name: test_react_agent_graph_structure[None-None-tools0]
'''
graph TD;
__start__ --> model;
model --> __end__;
__start__ --> agent;
agent --> __end__;
'''
# ---
# name: test_react_agent_graph_structure[None-None-tools1]
'''
graph TD;
__start__ --> model;
model -.-> __end__;
model -.-> tools;
tools --> model;
__start__ --> agent;
agent -.-> __end__;
agent -.-> tools;
tools --> agent;
'''
# ---
@@ -21,8 +21,8 @@
'''
graph TD;
__start__ --> pre_model_hook;
pre_model_hook --> model;
model --> __end__;
pre_model_hook --> agent;
agent --> __end__;
'''
# ---
@@ -30,9 +30,9 @@
'''
graph TD;
__start__ --> pre_model_hook;
model -.-> __end__;
model -.-> tools;
pre_model_hook --> model;
agent -.-> __end__;
agent -.-> tools;
pre_model_hook --> agent;
tools --> pre_model_hook;
'''
@@ -40,8 +40,8 @@
# name: test_react_agent_graph_structure[post_model_hook-None-tools0]
'''
graph TD;
__start__ --> model;
model --> post_model_hook;
__start__ --> agent;
agent --> post_model_hook;
post_model_hook --> __end__;
'''
@@ -49,12 +49,12 @@
# name: test_react_agent_graph_structure[post_model_hook-None-tools1]
'''
graph TD;
__start__ --> model;
model --> post_model_hook;
__start__ --> agent;
agent --> post_model_hook;
post_model_hook -.-> __end__;
post_model_hook -.-> model;
post_model_hook -.-> agent;
post_model_hook -.-> tools;
tools --> model;
tools --> agent;
'''
# ---
@@ -62,8 +62,8 @@
'''
graph TD;
__start__ --> pre_model_hook;
model --> post_model_hook;
pre_model_hook --> model;
agent --> post_model_hook;
pre_model_hook --> agent;
post_model_hook --> __end__;
'''
@@ -72,11 +72,11 @@
'''
graph TD;
__start__ --> pre_model_hook;
model --> post_model_hook;
agent --> post_model_hook;
post_model_hook -.-> __end__;
post_model_hook -.-> pre_model_hook;
post_model_hook -.-> tools;
pre_model_hook --> model;
pre_model_hook --> agent;
tools --> pre_model_hook;
'''
libs/prebuilt/tests/test_deprecation.py
+41
View File
@@ -0,0 +1,41 @@
import pytest
from typing_extensions import TypedDict
from langgraph.prebuilt import create_react_agent
from langgraph.warnings import LangGraphDeprecatedSinceV10
from tests.model import FakeToolCallingModel
class Config(TypedDict):
model: str
@pytest.mark.filterwarnings("ignore:`config_schema` is deprecated")
@pytest.mark.filterwarnings("ignore:`get_config_jsonschema` is deprecated")
def test_config_schema_deprecation() -> None:
with pytest.warns(
DeprecationWarning,
match="`config_schema` is deprecated and will be removed. Please use `context_schema` instead.",
):
agent = create_react_agent(FakeToolCallingModel(), [], config_schema=Config)
assert agent.context_schema == Config
with pytest.warns(
LangGraphDeprecatedSinceV10,
match="`config_schema` is deprecated. Use `get_context_jsonschema` for the relevant schema instead.",
):
assert agent.config_schema() is not None
with pytest.warns(
LangGraphDeprecatedSinceV10,
match="`get_config_jsonschema` is deprecated. Use `get_context_jsonschema` instead.",
):
assert agent.get_config_jsonschema() is not None
def test_extra_kwargs_deprecation() -> None:
with pytest.raises(
TypeError,
match="create_react_agent\(\) got unexpected keyword arguments: \{'extra': 'extra'\}",
):
create_react_agent(FakeToolCallingModel(), [], extra="extra")
libs/prebuilt/tests/test_react_agent.py
+197 -71
View File
@@ -2,6 +2,8 @@ import dataclasses
import inspect
from typing import (
Annotated,
Literal,
Optional,
Union,
)
@@ -31,6 +33,8 @@ from langgraph.prebuilt import (
)
from langgraph.prebuilt.chat_agent_executor import (
AgentState,
AgentStatePydantic,
StateT,
_validate_chat_history,
)
from langgraph.prebuilt.tool_node import (
@@ -49,14 +53,18 @@ from tests.model import FakeToolCallingModel
pytestmark = pytest.mark.anyio
REACT_TOOL_CALL_VERSIONS = ["v1", "v2"]
def test_no_prompt(sync_checkpointer: BaseCheckpointSaver) -> None:
@pytest.mark.parametrize("version", REACT_TOOL_CALL_VERSIONS)
def test_no_prompt(sync_checkpointer: BaseCheckpointSaver, version: str) -> None:
model = FakeToolCallingModel()
agent = create_react_agent(
model,
[],
checkpointer=sync_checkpointer,
version=version,
)
inputs = [HumanMessage("hi?")]
thread = {"configurable": {"thread_id": "123"}}
@@ -164,7 +172,8 @@ def test_runnable_prompt():
assert response == expected_response
def test_prompt_with_store():
@pytest.mark.parametrize("version", REACT_TOOL_CALL_VERSIONS)
def test_prompt_with_store(version: Literal["v1", "v2"]):
def add(a: int, b: int):
"""Adds a and b"""
return a + b
@@ -189,6 +198,7 @@ def test_prompt_with_store():
[add],
prompt=prompt,
store=in_memory_store,
version=version,
)
response = agent.invoke(
{"messages": [("user", "hi")]}, {"configurable": {"user_id": "1"}}
@@ -201,6 +211,7 @@ def test_prompt_with_store():
[add],
prompt=prompt_no_store,
store=in_memory_store,
version=version,
)
response = agent.invoke(
{"messages": [("user", "hi")]}, {"configurable": {"user_id": "2"}}
@@ -241,7 +252,9 @@ async def test_prompt_with_store_async():
assert response["messages"][-1].content == "User name is Alice-hi"
# test state modifier that doesn't use store works
agent = create_react_agent(model, [add], prompt=prompt_no_store, store=in_memory_store)
agent = create_react_agent(
model, [add], prompt=prompt_no_store, store=in_memory_store
)
response = await agent.ainvoke(
{"messages": [("user", "hi")]}, {"configurable": {"user_id": "2"}}
)
@@ -249,8 +262,9 @@ async def test_prompt_with_store_async():
@pytest.mark.parametrize("tool_style", ["openai", "anthropic"])
@pytest.mark.parametrize("version", REACT_TOOL_CALL_VERSIONS)
@pytest.mark.parametrize("include_builtin", [True, False])
def test_model_with_tools(tool_style: str, include_builtin: bool) -> None:
def test_model_with_tools(tool_style: str, version: str, include_builtin: bool) -> None:
model = FakeToolCallingModel(tool_style=tool_style)
@dec_tool
@@ -285,6 +299,7 @@ def test_model_with_tools(tool_style: str, include_builtin: bool) -> None:
create_react_agent(
model.bind_tools(tools),
tools,
version=version,
)
@@ -414,7 +429,8 @@ def test__infer_handled_types() -> None:
_infer_handled_types(handler)
def test_react_agent_with_structured_response() -> None:
@pytest.mark.parametrize("version", REACT_TOOL_CALL_VERSIONS)
def test_react_agent_with_structured_response(version: str) -> None:
class WeatherResponse(BaseModel):
temperature: float = Field(description="The temperature in fahrenheit")
@@ -435,6 +451,7 @@ def test_react_agent_with_structured_response() -> None:
model,
[get_weather],
response_format=WeatherResponse,
version=version,
)
response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
assert response["structured_response"] == expected_structured_response
@@ -463,8 +480,16 @@ class CustomState(AgentState):
user_name: str
class CustomStatePydantic(AgentStatePydantic):
user_name: Optional[str] = None
@pytest.mark.parametrize("version", REACT_TOOL_CALL_VERSIONS)
@pytest.mark.parametrize("state_schema", [CustomState, CustomStatePydantic])
def test_react_agent_update_state(
sync_checkpointer: BaseCheckpointSaver,
version: Literal["v1", "v2"],
state_schema: StateT,
) -> None:
@dec_tool
def get_user_name(tool_call_id: Annotated[str, InjectedToolCallId]):
@@ -481,22 +506,34 @@ def test_react_agent_update_state(
}
)
def prompt(state: CustomState):
user_name = state.get("user_name")
if user_name is None:
return state["messages"]
if issubclass(state_schema, AgentStatePydantic):
system_msg = f"User name is {user_name}"
return [{"role": "system", "content": system_msg}] + state["messages"]
def prompt(state: CustomStatePydantic):
user_name = state.user_name
if user_name is None:
return state.messages
system_msg = f"User name is {user_name}"
return [{"role": "system", "content": system_msg}] + state.messages
else:
def prompt(state: CustomState):
user_name = state.get("user_name")
if user_name is None:
return state["messages"]
system_msg = f"User name is {user_name}"
return [{"role": "system", "content": system_msg}] + state["messages"]
tool_calls = [[{"args": {}, "id": "1", "name": "get_user_name"}]]
model = FakeToolCallingModel(tool_calls=tool_calls)
agent = create_react_agent(
model,
[get_user_name],
state_schema=CustomState,
state_schema=state_schema,
prompt=prompt,
checkpointer=sync_checkpointer,
version=version,
)
config = {"configurable": {"thread_id": "1"}}
# Run until interrupted
@@ -512,8 +549,9 @@ def test_react_agent_update_state(
assert tool_message.name == "get_user_name"
@pytest.mark.parametrize("version", REACT_TOOL_CALL_VERSIONS)
def test_react_agent_parallel_tool_calls(
sync_checkpointer: BaseCheckpointSaver,
sync_checkpointer: BaseCheckpointSaver, version: str
) -> None:
human_assistance_execution_count = 0
@@ -546,6 +584,7 @@ def test_react_agent_parallel_tool_calls(
model,
[human_assistance, get_weather],
checkpointer=sync_checkpointer,
version=version,
)
config = {"configurable": {"thread_id": "1"}}
query = "Get user assistance and also check the weather"
@@ -556,11 +595,17 @@ def test_react_agent_parallel_tool_calls(
if messages := event.get("messages"):
message_types.append([m.type for m in messages])
assert message_types == [
["human"],
["human", "ai"],
["human", "ai", "tool"],
]
if version == "v1":
assert message_types == [
["human"],
["human", "ai"],
]
elif version == "v2":
assert message_types == [
["human"],
["human", "ai"],
["human", "ai", "tool"],
]
# Resume
message_types = []
@@ -576,28 +621,54 @@ def test_react_agent_parallel_tool_calls(
["human", "ai", "tool", "tool", "ai"],
]
assert human_assistance_execution_count == 1
assert get_weather_execution_count == 1
if version == "v1":
assert human_assistance_execution_count == 1
assert get_weather_execution_count == 2
elif version == "v2":
assert human_assistance_execution_count == 1
assert get_weather_execution_count == 1
class AgentStateExtraKey(AgentState):
foo: int
def test_create_react_agent_inject_vars() -> None:
class AgentStateExtraKeyPydantic(AgentStatePydantic):
foo: int
@pytest.mark.parametrize("version", REACT_TOOL_CALL_VERSIONS)
@pytest.mark.parametrize(
"state_schema", [AgentStateExtraKey, AgentStateExtraKeyPydantic]
)
def test_create_react_agent_inject_vars(
version: Literal["v1", "v2"], state_schema: StateT
) -> None:
"""Test that the agent can inject state and store into tool functions."""
store = InMemoryStore()
namespace = ("test",)
store.put(namespace, "test_key", {"bar": 3})
def tool1(
some_val: int,
state: Annotated[dict, InjectedState],
store: Annotated[BaseStore, InjectedStore()],
) -> str:
"""Tool 1 docstring."""
store_val = store.get(namespace, "test_key").value["bar"]
return some_val + state["foo"] + store_val
if issubclass(state_schema, AgentStatePydantic):
def tool1(
some_val: int,
state: Annotated[AgentStateExtraKeyPydantic, InjectedState],
store: Annotated[BaseStore, InjectedStore()],
) -> str:
"""Tool 1 docstring."""
store_val = store.get(namespace, "test_key").value["bar"]
return some_val + state.foo + store_val
else:
def tool1(
some_val: int,
state: Annotated[dict, InjectedState],
store: Annotated[BaseStore, InjectedStore()],
) -> str:
"""Tool 1 docstring."""
store_val = store.get(namespace, "test_key").value["bar"]
return some_val + state["foo"] + store_val
tool_call = {
"name": "tool1",
@@ -609,8 +680,9 @@ def test_create_react_agent_inject_vars() -> None:
agent = create_react_agent(
model,
ToolNode([tool1], handle_tool_errors=False),
state_schema=AgentStateExtraKey,
state_schema=state_schema,
store=store,
version=version,
)
result = agent.invoke({"messages": [{"role": "user", "content": "hi"}], "foo": 2})
assert result["messages"] == [
@@ -622,7 +694,8 @@ def test_create_react_agent_inject_vars() -> None:
assert result["foo"] == 2
async def test_return_direct() -> None:
@pytest.mark.parametrize("version", REACT_TOOL_CALL_VERSIONS)
async def test_return_direct(version: str) -> None:
@dec_tool(return_direct=True)
def tool_return_direct(input: str) -> str:
"""A tool that returns directly."""
@@ -649,6 +722,7 @@ async def test_return_direct() -> None:
agent = create_react_agent(
model,
[tool_return_direct, tool_normal],
version=version,
)
# Test direct return for tool_return_direct
@@ -673,7 +747,9 @@ async def test_return_direct() -> None:
),
]
model = FakeToolCallingModel(tool_calls=[second_tool_call, []])
agent = create_react_agent(model, [tool_return_direct, tool_normal])
agent = create_react_agent(
model, [tool_return_direct, tool_normal], version=version
)
result = agent.invoke(
{"messages": [HumanMessage(content="Test normal", id="hum1")]}
)
@@ -702,7 +778,9 @@ async def test_return_direct() -> None:
),
]
model = FakeToolCallingModel(tool_calls=[both_tool_calls, []])
agent = create_react_agent(model, [tool_return_direct, tool_normal])
agent = create_react_agent(
model, [tool_return_direct, tool_normal], version=version
)
result = agent.invoke({"messages": [HumanMessage(content="Test both", id="hum2")]})
assert result["messages"] == [
HumanMessage(content="Test both", id="hum2"),
@@ -740,11 +818,12 @@ def test__get_state_args() -> None:
def test_inspect_react() -> None:
model = FakeToolCallingModel(tool_calls=[])
agent = create_react_agent(model, [])
inspect.getclosurevars(agent.nodes["model"].bound.func)
inspect.getclosurevars(agent.nodes["agent"].bound.func)
@pytest.mark.parametrize("version", REACT_TOOL_CALL_VERSIONS)
def test_react_with_subgraph_tools(
sync_checkpointer: BaseCheckpointSaver,
sync_checkpointer: BaseCheckpointSaver, version: Literal["v1", "v2"]
) -> None:
class State(TypedDict):
a: int
@@ -800,6 +879,7 @@ def test_react_with_subgraph_tools(
model,
tool_node,
checkpointer=sync_checkpointer,
version=version,
)
result = agent.invoke(
{"messages": [HumanMessage(content="What's 2 + 3 and 2 * 3?")]},
@@ -830,7 +910,8 @@ def test_react_with_subgraph_tools(
]
def test_react_agent_subgraph_streaming_sync() -> None:
@pytest.mark.parametrize("version", REACT_TOOL_CALL_VERSIONS)
def test_react_agent_subgraph_streaming_sync(version: Literal["v1", "v2"]) -> None:
"""Test React agent streaming when used as a subgraph node sync version"""
@dec_tool
@@ -850,6 +931,7 @@ def test_react_agent_subgraph_streaming_sync() -> None:
model,
tools=[get_weather],
prompt="You are a helpful travel assistant.",
version=version,
)
# Create a subgraph that uses the React agent as a node
@@ -919,7 +1001,8 @@ def test_react_agent_subgraph_streaming_sync() -> None:
assert msg.content.startswith("The weather of Tokyo is sunny.")
async def test_react_agent_subgraph_streaming() -> None:
@pytest.mark.parametrize("version", REACT_TOOL_CALL_VERSIONS)
async def test_react_agent_subgraph_streaming(version: Literal["v1", "v2"]) -> None:
"""Test React agent streaming when used as a subgraph node."""
@dec_tool
@@ -939,6 +1022,7 @@ async def test_react_agent_subgraph_streaming() -> None:
model,
tools=[get_weather],
prompt="You are a helpful travel assistant.",
version=version,
)
# Create a subgraph that uses the React agent as a node
@@ -1010,8 +1094,9 @@ async def test_react_agent_subgraph_streaming() -> None:
assert msg.content.startswith("The weather of Tokyo is sunny.")
@pytest.mark.parametrize("version", REACT_TOOL_CALL_VERSIONS)
def test_tool_node_node_interrupt(
sync_checkpointer: BaseCheckpointSaver,
sync_checkpointer: BaseCheckpointSaver, version: str
) -> None:
def tool_normal(some_val: int) -> str:
"""Tool docstring."""
@@ -1037,6 +1122,7 @@ def test_tool_node_node_interrupt(
model,
[tool_interrupt, tool_normal],
checkpointer=sync_checkpointer,
version=version,
)
result = agent.invoke({"messages": [HumanMessage("hi?")]}, config)
expected_messages = [
@@ -1061,7 +1147,11 @@ def test_tool_node_node_interrupt(
),
_AnyIdToolMessage(content="normal", name="tool_normal", tool_call_id="2"),
]
assert result["messages"] == expected_messages
if version == "v1":
# Interrupt blocks second tool result
assert result["messages"] == expected_messages[:-1]
elif version == "v2":
assert result["messages"] == expected_messages
state = agent.get_state(config)
assert state.next == ("tools",)
@@ -1075,7 +1165,8 @@ def test_tool_node_node_interrupt(
)
def test_dynamic_model_basic() -> None:
@pytest.mark.parametrize("version", REACT_TOOL_CALL_VERSIONS)
def test_dynamic_model_basic(version: str) -> None:
"""Test basic dynamic model functionality."""
def dynamic_model(state, runtime: Runtime):
@@ -1085,7 +1176,7 @@ def test_dynamic_model_basic() -> None:
else:
return FakeToolCallingModel(tool_calls=[])
agent = create_react_agent(dynamic_model, [])
agent = create_react_agent(dynamic_model, [], version=version)
result = agent.invoke({"messages": [HumanMessage("hello")]})
assert len(result["messages"]) == 2
@@ -1096,7 +1187,8 @@ def test_dynamic_model_basic() -> None:
assert result["messages"][-1].content == "urgent help"
def test_dynamic_model_with_tools() -> None:
@pytest.mark.parametrize("version", REACT_TOOL_CALL_VERSIONS)
def test_dynamic_model_with_tools(version: Literal["v1", "v2"]) -> None:
"""Test dynamic model with tool calling."""
@dec_tool
@@ -1123,7 +1215,9 @@ def test_dynamic_model_with_tools() -> None:
tool_calls=[[{"args": {"x": 1}, "id": "1", "name": "basic_tool"}], []]
)
agent = create_react_agent(dynamic_model, [basic_tool, advanced_tool])
agent = create_react_agent(
dynamic_model, [basic_tool, advanced_tool], version=version
)
# Test basic tool usage
result = agent.invoke({"messages": [HumanMessage("basic request")]})
@@ -1145,7 +1239,8 @@ class Context:
user_id: str
def test_dynamic_model_with_context() -> None:
@pytest.mark.parametrize("version", REACT_TOOL_CALL_VERSIONS)
def test_dynamic_model_with_context(version: str) -> None:
"""Test dynamic model using config parameters."""
def dynamic_model(state, runtime: Runtime[Context]):
@@ -1156,7 +1251,9 @@ def test_dynamic_model_with_context() -> None:
else:
return FakeToolCallingModel(tool_calls=[])
agent = create_react_agent(dynamic_model, [], context_schema=Context)
agent = create_react_agent(
dynamic_model, [], context_schema=Context, version=version
)
# Test with basic user
result = agent.invoke(
@@ -1173,7 +1270,8 @@ def test_dynamic_model_with_context() -> None:
assert len(result["messages"]) == 2
def test_dynamic_model_with_state_schema() -> None:
@pytest.mark.parametrize("version", REACT_TOOL_CALL_VERSIONS)
def test_dynamic_model_with_state_schema(version: Literal["v1", "v2"]) -> None:
"""Test dynamic model with custom state schema."""
class CustomDynamicState(AgentState):
@@ -1186,7 +1284,9 @@ def test_dynamic_model_with_state_schema() -> None:
else:
return FakeToolCallingModel(tool_calls=[])
agent = create_react_agent(dynamic_model, [], state_schema=CustomDynamicState)
agent = create_react_agent(
dynamic_model, [], state_schema=CustomDynamicState, version=version
)
result = agent.invoke(
{"messages": [HumanMessage("hello")], "model_preference": "advanced"}
@@ -1195,14 +1295,15 @@ def test_dynamic_model_with_state_schema() -> None:
assert result["model_preference"] == "advanced"
def test_dynamic_model_with_prompt() -> None:
@pytest.mark.parametrize("version", REACT_TOOL_CALL_VERSIONS)
def test_dynamic_model_with_prompt(version: Literal["v1", "v2"]) -> None:
"""Test dynamic model with different prompt types."""
def dynamic_model(state: AgentState, runtime: Runtime) -> BaseChatModel:
return FakeToolCallingModel(tool_calls=[])
# Test with string prompt
agent = create_react_agent(dynamic_model, [], prompt="system_msg")
agent = create_react_agent(dynamic_model, [], prompt="system_msg", version=version)
result = agent.invoke({"messages": [HumanMessage("human_msg")]})
assert result["messages"][-1].content == "system_msg-human_msg"
@@ -1211,7 +1312,9 @@ def test_dynamic_model_with_prompt() -> None:
"""Generate a dynamic system message based on state."""
return [{"role": "system", "content": "system_msg"}] + list(state["messages"])
agent = create_react_agent(dynamic_model, [], prompt=dynamic_prompt)
agent = create_react_agent(
dynamic_model, [], prompt=dynamic_prompt, version=version
)
result = agent.invoke({"messages": [HumanMessage("human_msg")]})
assert result["messages"][-1].content == "system_msg-human_msg"
@@ -1229,7 +1332,8 @@ async def test_dynamic_model_async() -> None:
assert result["messages"][-1].content == "hello async"
def test_dynamic_model_with_structured_response() -> None:
@pytest.mark.parametrize("version", REACT_TOOL_CALL_VERSIONS)
def test_dynamic_model_with_structured_response(version: str) -> None:
"""Test dynamic model with structured response format."""
class TestResponse(BaseModel):
@@ -1250,7 +1354,9 @@ def test_dynamic_model_with_structured_response() -> None:
],
)
agent = create_react_agent(dynamic_model, [], response_format=TestResponse)
agent = create_react_agent(
dynamic_model, [], response_format=TestResponse, version=version
)
result = agent.invoke({"messages": [HumanMessage("hello")]})
assert "structured_response" in result
@@ -1289,7 +1395,8 @@ def test_dynamic_model_with_checkpointer(sync_checkpointer):
assert call_count >= 2
def test_dynamic_model_state_dependent_tools() -> None:
@pytest.mark.parametrize("version", REACT_TOOL_CALL_VERSIONS)
def test_dynamic_model_state_dependent_tools(version: Literal["v1", "v2"]) -> None:
"""Test dynamic model that changes available tools based on state."""
@dec_tool
@@ -1313,7 +1420,7 @@ def test_dynamic_model_state_dependent_tools() -> None:
tool_calls=[[{"args": {"x": 1}, "id": "1", "name": "tool_a"}], []]
)
agent = create_react_agent(dynamic_model, [tool_a, tool_b])
agent = create_react_agent(dynamic_model, [tool_a, tool_b], version=version)
# Ask to use tool B
result = agent.invoke({"messages": [HumanMessage("use_b please")]})
@@ -1328,7 +1435,8 @@ def test_dynamic_model_state_dependent_tools() -> None:
assert last_message.content == "A: 1"
def test_dynamic_model_error_handling() -> None:
@pytest.mark.parametrize("version", REACT_TOOL_CALL_VERSIONS)
def test_dynamic_model_error_handling(version: Literal["v1", "v2"]) -> None:
"""Test error handling in dynamic model."""
def failing_dynamic_model(state, runtime: Runtime):
@@ -1336,7 +1444,7 @@ def test_dynamic_model_error_handling() -> None:
raise ValueError("Dynamic model failed")
return FakeToolCallingModel(tool_calls=[])
agent = create_react_agent(failing_dynamic_model, [])
agent = create_react_agent(failing_dynamic_model, [], version=version)
# Normal operation should work
result = agent.invoke({"messages": [HumanMessage("hello")]})
@@ -1435,7 +1543,6 @@ async def test_dynamic_model_receives_correct_state_async():
assert received_state["messages"][0].content == "hello async"
@pytest.mark.skip(reason="TODO: support with prepare call")
def test_pre_model_hook() -> None:
model = FakeToolCallingModel(tool_calls=[])
@@ -1490,7 +1597,7 @@ def test_post_model_hook() -> None:
events = list(pmh_agent.stream({"messages": [HumanMessage("hi?")], "flag": False}))
assert events == [
{
"model": {
"agent": {
"messages": [
AIMessage(
content="hi?",
@@ -1559,7 +1666,7 @@ def test_post_model_hook_with_structured_output() -> None:
)
assert events == [
{
"model": {
"agent": {
"messages": [
AIMessage(
content="What's the weather?",
@@ -1591,7 +1698,7 @@ def test_post_model_hook_with_structured_output() -> None:
}
},
{
"model": {
"agent": {
"messages": [
AIMessage(
content="What's the weather?-What's the weather?-The weather is sunny and 75°F.",
@@ -1620,19 +1727,36 @@ def test_post_model_hook_with_structured_output() -> None:
]
def test_create_react_agent_inject_vars_with_post_model_hook() -> None:
@pytest.mark.parametrize(
"state_schema", [AgentStateExtraKey, AgentStateExtraKeyPydantic]
)
def test_create_react_agent_inject_vars_with_post_model_hook(
state_schema: StateT,
) -> None:
store = InMemoryStore()
namespace = ("test",)
store.put(namespace, "test_key", {"bar": 3})
def tool1(
some_val: int,
state: Annotated[dict, InjectedState],
store: Annotated[BaseStore, InjectedStore()],
) -> str:
"""Tool 1 docstring."""
store_val = store.get(namespace, "test_key").value["bar"]
return some_val + state["foo"] + store_val
if issubclass(state_schema, AgentStatePydantic):
def tool1(
some_val: int,
state: Annotated[AgentStateExtraKeyPydantic, InjectedState],
store: Annotated[BaseStore, InjectedStore()],
) -> str:
"""Tool 1 docstring."""
store_val = store.get(namespace, "test_key").value["bar"]
return some_val + state.foo + store_val
else:
def tool1(
some_val: int,
state: Annotated[dict, InjectedState],
store: Annotated[BaseStore, InjectedStore()],
) -> str:
"""Tool 1 docstring."""
store_val = store.get(namespace, "test_key").value["bar"]
return some_val + state["foo"] + store_val
tool_call = {
"name": "tool1",
@@ -1649,7 +1773,7 @@ def test_create_react_agent_inject_vars_with_post_model_hook() -> None:
agent = create_react_agent(
model,
ToolNode([tool1], handle_tool_errors=False),
state_schema=AgentStateExtraKey,
state_schema=state_schema,
store=store,
post_model_hook=post_model_hook,
)
@@ -1664,7 +1788,8 @@ def test_create_react_agent_inject_vars_with_post_model_hook() -> None:
assert result["foo"] == 2
def test_response_format_using_tool_choice() -> None:
@pytest.mark.parametrize("version", REACT_TOOL_CALL_VERSIONS)
def test_response_format_using_tool_choice(version: Literal["v1", "v2"]) -> None:
"""Test response format using tool choice."""
class WeatherResponse(BaseModel):
@@ -1685,6 +1810,7 @@ def test_response_format_using_tool_choice() -> None:
model,
[get_weather],
response_format=WeatherResponse,
version=version,
)
response = agent.invoke(
{
libs/prebuilt/tests/test_response_format.py
+12 -4
View File
@@ -120,7 +120,9 @@ class TestResponseFormatAsModel:
model = FakeToolCallingModel(tool_calls=tool_calls)
agent = create_react_agent(model, [get_weather], response_format=WeatherBaseModel)
agent = create_react_agent(
model, [get_weather], response_format=WeatherBaseModel
)
response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
assert response["structured_response"] == EXPECTED_WEATHER_PYDANTIC
@@ -141,7 +143,9 @@ class TestResponseFormatAsModel:
model = FakeToolCallingModel(tool_calls=tool_calls)
agent = create_react_agent(model, [get_weather], response_format=WeatherDataclass)
agent = create_react_agent(
model, [get_weather], response_format=WeatherDataclass
)
response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
assert response["structured_response"] == EXPECTED_WEATHER_DATACLASS
@@ -162,7 +166,9 @@ class TestResponseFormatAsModel:
model = FakeToolCallingModel(tool_calls=tool_calls)
agent = create_react_agent(model, [get_weather], response_format=WeatherTypedDict)
agent = create_react_agent(
model, [get_weather], response_format=WeatherTypedDict
)
response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
assert response["structured_response"] == EXPECTED_WEATHER_DICT
@@ -183,7 +189,9 @@ class TestResponseFormatAsModel:
model = FakeToolCallingModel(tool_calls=tool_calls)
agent = create_react_agent(model, [get_weather], response_format=weather_json_schema)
agent = create_react_agent(
model, [get_weather], response_format=weather_json_schema
)
response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
assert response["structured_response"] == EXPECTED_WEATHER_DICT