chore: import upstream snapshot with attribution
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This commit is contained in:
wehub-resource-sync
2026-07-13 12:37:18 +08:00
commit a7d6d88f6f
667 changed files with 232986 additions and 0 deletions
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from collections.abc import Sequence
from typing import Annotated, Literal, TypedDict
from langchain_core.messages import BaseMessage
from langchain_openai import ChatOpenAI
from langgraph.graph import END, StateGraph, add_messages
from langgraph.prebuilt import ToolNode
tools = []
model_oai = ChatOpenAI(temperature=0)
model_oai = model_oai.bind_tools(tools)
class AgentState(TypedDict):
messages: Annotated[Sequence[BaseMessage], add_messages]
# Define the function that determines whether to continue or not
def should_continue(state):
messages = state["messages"]
last_message = messages[-1]
# If there are no tool calls, then we finish
if not last_message.tool_calls:
return "end"
# Otherwise if there is, we continue
else:
return "continue"
# Define the function that calls the model
def call_model(state, config):
model = model_oai
messages = state["messages"]
response = model.invoke(messages)
# We return a list, because this will get added to the existing list
return {"messages": [response]}
# Define the function to execute tools
tool_node = ToolNode(tools)
class ContextSchema(TypedDict):
model: Literal["anthropic", "openai"]
# Define a new graph
workflow = StateGraph(AgentState, context_schema=ContextSchema)
# Define the two nodes we will cycle between
workflow.add_node("agent", call_model)
workflow.add_node("action", tool_node)
# Set the entrypoint as `agent`
# This means that this node is the first one called
workflow.set_entry_point("agent")
# We now add a conditional edge
workflow.add_conditional_edges(
# First, we define the start node. We use `agent`.
# This means these are the edges taken after the `agent` node is called.
"agent",
# Next, we pass in the function that will determine which node is called next.
should_continue,
# Finally we pass in a mapping.
# The keys are strings, and the values are other nodes.
# END is a special node marking that the graph should finish.
# What will happen is we will call `should_continue`, and then the output of that
# will be matched against the keys in this mapping.
# Based on which one it matches, that node will then be called.
{
# If `tools`, then we call the tool node.
"continue": "action",
# Otherwise we finish.
"end": END,
},
)
# We now add a normal edge from `tools` to `agent`.
# This means that after `tools` is called, `agent` node is called next.
workflow.add_edge("action", "agent")
# Finally, we compile it!
# This compiles it into a LangChain Runnable,
# meaning you can use it as you would any other runnable
graph = workflow.compile()
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[project]
name = "graph-prerelease-reqs-additional-deps"
version = "0.1.0"
description = "Test for prerelease stuff"
readme = "README.md"
requires-python = ">=3.10"
dependencies = [
"langgraph==1.1.5"
]
@@ -0,0 +1,9 @@
[project]
name = "graph-prerelease-reqs-zuper-deps"
version = "0.1.0"
description = "Test for prerelease stuff"
readme = "README.md"
requires-python = ">=3.10"
dependencies = [
"langchain-openai==1.1.14"
]
@@ -0,0 +1,13 @@
{
"python_version": "3.12",
"dependencies": [
".",
"./deps/additional_deps",
"./deps/zuper_deps"
],
"graphs": {
"agent": "./agent.py:graph"
},
"env": "../.env"
}
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[project]
name = "graph-prerelease-reqs"
version = "0.1.0"
description = "Test for prerelease stuff"
readme = "README.md"
requires-python = ">=3.10"
dependencies = [
"langchain-openai==1.1.14",
"langchain-anthropic==1.4.6",
"langgraph==1.1.5"
]
[tool.uv]
prerelease = "allow"