This PR does a few things:
1. Surfaces interrupts when `stream_mode='values'` (particularly
relevant for `invoke`, where this is the default behavior)
2. Adds an `interrupt_id` property to the `Interrupt` dataclass so that
interrupts can effectively be mapped to resumes
3. Minor docs updates to reflect the new pattern (no need for a special
section on interrupts with `invoke` and `ainvoke`)
* In a different PR (the one with the multiple resume values), as it's
more relevant there: add an `interrupts` property to `StateSnapshot` so
that `interrupts` can easily be iterated over if users are attempting to
map interrupts to resumes.
I **don't** recommend we release this until we have multi-resumes
working.
## Example
We have the following setup where we're sending multiple prompts to the
child graph, which uses `interrupt`:
```py
def child_graph(state):
human_input = interrupt(state["prompt"])
return {
"human_inputs": [human_input],
}
```
<img width="142" alt="Screenshot 2025-04-23 at 10 01 12 AM"
src="https://github.com/user-attachments/assets/c6238bf1-54ad-4e48-ab0b-60a0bfc18485"
/>
Old behavior:
```py
initial_input = {"prompts": ["a", "b"]}
print(parent_graph.invoke(input=initial_input,config=thread_config,stream_mode="values"))
#> {'prompts': ['a', 'b'], 'human_inputs': []}
print(parent_graph.invoke(Command(resume="hello 1"),config=thread_config,stream_mode="values"))
#> {'prompts': ['a', 'b'], 'human_inputs': ['hello 1']}
print(parent_graph.invoke(Command(resume="hello 2"),config=thread_config,stream_mode="values"))
#> {'prompts': ['a', 'b'], 'human_inputs': ['hello 1', 'hello 2']}
```
New behavior:
```py
initial_input = {"prompts": ["a", "b"]}
print(parent_graph.invoke(input=initial_input,config=thread_config,stream_mode="values"))
"""
{
"prompts": ["a", "b"],
"human_inputs": [],
"__interrupt__": [
Interrupt(
value="a",
resumable=True,
ns=["child_graph:38d43a18-a5e7-8ab2-ca83-9d80f6e9ca83"]
),
Interrupt(
value="b",
resumable=True,
ns=["child_graph:dad810e8-738e-9f90-41cd-30c0091eb79b"]
)
]
}
"""
print(parent_graph.invoke(Command(resume="hello 1"),config=thread_config,stream_mode="values"))
"""
{
"prompts": ["a", "b"],
"human_inputs": ["hello 1"],
"__interrupt__": [
Interrupt(
value="b",
resumable=True,
ns=["child_graph:dad810e8-738e-9f90-41cd-30c0091eb79b"]
)
]
}
"""
print(parent_graph.invoke(Command(resume="hello 2"),config=thread_config,stream_mode="values"))
#> {'prompts': ['a', 'b'], 'human_inputs': ['hello 1', 'hello 2']}
```
If you're deploying with langgraph API, you don't need to manually
define a checkpointer. For folks who already know they'll be developing
with the api server, I'd like to save everyone time by making this more
clear in the docs on checkpointing.
…entation.
This commit fixes a syntax error in the "langchain-ai.github"
documentation. The function called "some_node_inside_alice" was missing
a colon (:) after the function, which is required for valid Python
syntax.
