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langgraph: add store to the prebuilt agent (#1981)

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Vadym Barda
2024-10-02 21:38:15 +00:00
committed by GitHub
parent 7f95d7de42
commit 93c22fbdde
1 changed files with 8 additions and 3 deletions
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libs/langgraph/langgraph/prebuilt/chat_agent_executor.py
+8 -3
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@@ -18,6 +18,7 @@ from langgraph.graph.message import add_messages
from langgraph.managed import IsLastStep
from langgraph.prebuilt.tool_executor import ToolExecutor
from langgraph.prebuilt.tool_node import ToolNode
from langgraph.store.base import BaseStore
from langgraph.types import Checkpointer
@@ -162,7 +163,8 @@ def create_react_agent(
state_schema: Optional[StateSchemaType] = None,
messages_modifier: Optional[MessagesModifier] = None,
state_modifier: Optional[StateModifier] = None,
checkpointer: Checkpointer = None,
checkpointer: Optional[Checkpointer] = None,
store: Optional[BaseStore] = None,
interrupt_before: Optional[list[str]] = None,
interrupt_after: Optional[list[str]] = None,
debug: bool = False,
@@ -195,8 +197,10 @@ def create_react_agent(
- str: This is converted to a SystemMessage and added to the beginning of the list of messages in state["messages"].
- Callable: This function should take in full graph state and the output is then passed to the language model.
- Runnable: This runnable should take in full graph state and the output is then passed to the language model.
checkpointer: An optional checkpoint saver object. This is useful for persisting
the state of the graph (e.g., as chat memory).
checkpointer: An optional checkpoint saver object. This is used for persisting
the state of the graph (e.g., as chat memory) for a single thread (e.g., a single conversation).
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: "agent", "tools".
This is useful if you want to add a user confirmation or other interrupt before taking an action.
@@ -551,6 +555,7 @@ def create_react_agent(
# meaning you can use it as you would any other runnable
return workflow.compile(
checkpointer=checkpointer,
store=store,
interrupt_before=interrupt_before,
interrupt_after=interrupt_after,
debug=debug,