### Description
Prevents interrupt tasks from executing when the resume value has not
yet been specified.
Implemented for sync and async Pregel loop
If a task execution is skipped, the skipped interrupt is still included
in the graph result for consistency:
``` python
result = graph.invoke(...)
interrupts = result.get("__interrupt__", []) # [interrupt_1, interrupt_2]
partial_result = graph.invoke(Command(resume=interrupt_1_resume_map), ...)
remaining_interrupts = partial_result.get("__interrupt__", []) # [interrupt_2]
```
### Tests
- `test_interrupt_with_send_payloads`: test for a single resume map that
resumes all interrupts at once
- `test_interrupt_with_send_payloads_sequential_resume`: test for two
resume maps delivered in sequence
- `test_node_with_multiple_interrupts_requires_full_resume` test
optimization for multiple interrupts within a single node
Solves https://github.com/langchain-ai/langgraph/issues/6208
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Issue
Support for `Checkpoint.metadata.writes` was dropped in `langgraph`
v0.5.x.
In `langgraph-checkpoint-postgres` v2.0.23, metadata was serialized with
`BasePostgresSaver._dump_metadata` -> `JsonPlusSerializer.dumps` which
handles `pydantic.BaseModel`.
In v2.0.23, metadata is serialized with `psycopg.types.json.Jsonb`,
which raises `TypeError: Object of type AIMessage is not JSON
serializable` when trying to serialize `writes`.
Solution
- Add `BaseCheckpointSaver.get_serializable_checkpoint_metadata` which
pops the `writes` key.
- Log deprecation warning when strange version combinations are used
Solves https://github.com/langchain-ai/langgraph/issues/5769
---------
Co-authored-by: Alex Kondratev <56111142+soapun@users.noreply.github.com>
### Description
Fix `bulk_update_state` and `abulk_update_state` so history populates
`tasks[*].result` when creating state via supersteps.
There was a branch in these functions that I'm guessing was meant to be
triggered when a `StateUpdate.as_node` was the name of a real node (not
`"__input__"` or `"__copy__"`), but was never being triggered because of
a condition `CONFIG_KEY_CHECKPOINT_ID not in config[CONF]`:
```python
# apply pending writes, if not on specific checkpoint
if (
CONFIG_KEY_CHECKPOINT_ID not in config[CONF]
and saved is not None
and saved.pending_writes
):
next_tasks = prepare_next_tasks(...)
```
From what I can tell, in the bulk-update flow every superstep carries a
`checkpoint_id`, so the condition was always false. That skipped
`prepare_next_tasks(...)` and prevented us from discovering the task IDs
that we would need to attach the task result. So, I removed this check.
I also replaced the `pending_writes` check with a more lenient one (just
check it is not None to satisfy type checkers). I found that
`saved.pending_writes` was sometimes just `[]`, and in this case we
would skip `prepare_next_tasks(...)` and never attach the task result.
Now for each task discovered in `prepare_next_tasks(...)`, I collect the
task IDs and reuse them when running all writers of the chosen node
(applying the updates).
### Tests
- `test_supersteps_populate_task_results` for `PregelLoop` and
`AsyncPregelLoop`
These tests build a single node graph and compare history from two
threads: one uses `.invoke` and the other is build from supersteps. Both
tests fail on main and pass with this PR.
### Issue
Solves https://github.com/langchain-ai/langgraph/issues/6206
Update the redirects from the old docs to page changes in the new docs,
namely consolidating all the observability studio guides onto one page.
Dependent on: https://github.com/langchain-ai/docs/pull/681
This PR updates the dependencies in all Python packages using `uv lock
--upgrade`.
This is an automated PR created by the UV Lock Upgrade workflow.
Co-authored-by: sydney-runkle <54324534+sydney-runkle@users.noreply.github.com>
This PR ensures that even if a type has multiple annotations, we can
still detect the `BaseChannel` subclasses attached.
```py
class State(TypedDict):
# recognized as EphemeralValue(int)
foo: Annotated[int, EphemeralValue]
# now recognized as EphemeralValue(int)
bar: Annotated[int, EphemeralValue, OtherMetadata]
# now recognized as EphemeralValue(int)
baz: Annotated[int, SomeMetadata, EphemeralValue, OtherMetadata]
```
This adds `StateSnapshot` to the union type annotation of
`CheckpointTask.state`.
The annotation was previously incomplete: `map_debug_checkpoint()`
generates `CheckpointPayload` objects from `PregelTask` objects, and the
`state` field in `PregelTask` is of type `None | RunnableConfig |
StateSnapshot`.
We currently only support auth on the default routes; we'd like to be
able to support it on all (non-meta/liveness probe) routes by default.
This is the first step in that direction.
The `$contains` auth operator supports subset containment checks, but
this has previously been undocumented. This updates `FilterType` and its
associated docstring to reflect this support.
### Summary
This PR fixes an issue where `AsyncPregelLoop` could leave behind an
orphaned `stream.wait()` task, resulting in warnings like:
```
Task was destroyed but it is pending!
```
### Related Discussion
This PR is in response to:
[langchain-ai/langgraph#6163](https://github.com/langchain-ai/langgraph/discussions/6163)
### Problem
* In the async path, `get_waiter()` was creating a new `asyncio.Task`
via
```python
aioloop.create_task(stream.wait())
```
but never tracked or cleaned it up.
* On cancellation or shutdown, these tasks remained pending and produced
warnings.
### Solution
* Changed `get_waiter()` to:
* Maintain a **single waiter task** (similar to the sync path).
* Auto-clear the reference when the task finishes.
* Added `_cleanup_waiter()`:
* On exit, attempt to wake the waiter (`stream._count.release()` if
available).
* Otherwise, cancel and `await` the pending task to ensure proper
cleanup.
* Wrapped the `while loop.tick():` block in a `try/finally` to guarantee
`_cleanup_waiter()` runs on exit.
* Added missing `import contextlib`.
### Impact
* Prevents orphaned `stream.wait()` tasks.
* Removes noisy `"Task was destroyed but it is pending!"` warnings.
* Behavior of async streaming remains unchanged, only lifecycle
management improved.
### Test Plan
* Reproduced the issue by running async streaming with cancellation.
* Verified warnings no longer appear after the fix.
* Ran existing test suite (all passing).
### Notes
* Sync and Async implementations now follow the same principle: *only
one waiter at a time, always cleaned up on exit*.
* Backwards-compatible; no API changes.
### Repro & Verification
To confirm the issue and the fix I used the following minimal repro
snippet:
```python
# lg_repro.py
import asyncio
import os
# Enable asyncio debug logs to surface pending task warnings
os.environ.setdefault("PYTHONASYNCIODEBUG", "1")
from langgraph.graph import START, END, StateGraph
State = dict
# Slow async node: processes once, then sleeps to keep the waiter alive
async def slow_node(state: State) -> State:
await asyncio.sleep(0.2) # simulate work
state["count"] = state.get("count", 0) + 1
await asyncio.sleep(1.0) # keep stream.wait() waiter active
return state
# Build simple graph: START -> slow_node -> END
builder = StateGraph(State)
builder.add_node("slow", slow_node)
builder.add_edge(START, "slow")
builder.add_edge("slow", END)
graph = builder.compile()
async def run_and_cancel():
# astream with messages mode triggers internal stream.wait() waiter
async def consumer():
async for _ in graph.astream({"msg": "hi"}, stream_mode="messages"):
await asyncio.sleep(0.05)
t = asyncio.create_task(consumer(), name="astream-consumer")
# Allow the stream to start, then cancel the consumer
await asyncio.sleep(0.1)
t.cancel()
try:
await t
except asyncio.CancelledError:
pass
# Let loop settle to show pending waiter task if not cleaned
await asyncio.sleep(0.05)
def main():
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
loop.set_debug(True)
try:
loop.run_until_complete(run_and_cancel())
finally:
# If the internal waiter is not cleaned, closing the loop will warn
loop.close()
if __name__ == "__main__":
main()
````
**How to run**
```powershell
# Before (main branch)
git checkout main
pip install -e libs/langgraph
$env:PYTHONASYNCIODEBUG=1; python lg_repro.py
# After (patched branch)
git checkout async-waiter-cleanup
pip install -e libs/langgraph
$env:PYTHONASYNCIODEBUG=1; python lg_repro.py
```
**Observed results**
* **main branch (before fix):**
Shows warnings like:
```
Task was destroyed but it is pending!
... coro=<AsyncQueue.wait() ...>
created at langgraph/pregel/main.py:2927
```
* **patched branch (after fix):**
No warnings. The single waiter is properly cleaned up on exit via
`_cleanup_waiter()` (release semaphore if available, then cancel/await).
---
This confirms that the patch removes the orphaned `stream.wait()` task
and prevents
`"Task was destroyed but it is pending!"` warnings during
cancellation/shutdown.
---------
Co-authored-by: Caspar Broekhuizen <caspar@langchain.dev>
The original implementation for `refresh_on_read=True` in `asearch` for
AsyncSqliteStore used a CTE with an UPDATE statement, which is not
well-supported by SQLite in that specific construction, leading to a
syntax error.
This commit changes the approach:
1. `_prepare_batch_search_queries` in `BaseSqliteStore` no longer
constructs a CTE-based UPDATE. Instead, it returns a flag indicating if
TTL refresh is needed for the searched items.
2. `_batch_search_ops` in both `AsyncSqliteStore` and `SqliteStore` now
check this flag. If true, they perform a separate UPDATE statement after
fetching the search results to refresh the TTL of those items.
Additionally, a new test case `test_async_asearch_refresh_ttl` was added
and existing test logic was refined to accurately verify this behavior.
---------
Co-authored-by: google-labs-jules[bot] <161369871+google-labs-jules[bot]@users.noreply.github.com>
Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com>
Co-authored-by: Caspar Broekhuizen <caspar@langchain.dev>
This adds a configuration option in `HttpConfig` that allows LangGraph
Platform users to apply custom authentication hooks before (other)
custom middleware. Currently, the order is fixed (custom middleware is
always evaluated before custom auth).
(Apologies for the noise in
[de187a9](https://github.com/langchain-ai/langgraph/pull/6179/commits/de187a989e807c5687c22db1fc065d24030fa6b7),
apparently from the forced application of new linter rules.)
This PR updates the dependencies in all Python packages using `uv lock
--upgrade`.
This is an automated PR created by the UV Lock Upgrade workflow.
Co-authored-by: sydney-runkle <54324534+sydney-runkle@users.noreply.github.com>
### Description
Added unit tests for util.py.
Authored by @oumizx. Had to copy #6113 into this separate PR because
langgraph/libs/cli was having issues with secrets.
**Description:**
Add test for before and limit parameters for the list in SqliteSaver
which was marked as TODO.
---------
Co-authored-by: Caspar Broekhuizen <caspar@langchain.dev>
### Description
https://github.com/langchain-ai/langgraph/issues/6137 and
https://github.com/langchain-ai/langgraph/issues/5677 reported issues
where older checkpoints read by AsyncPostgresSaver/PostgresSaver from
`langgraph-checkpoint-postgres==2.0.19` fail to read channel values,
throwing `NoneType object is not a mapping`. This was due to a bug in
how `channel_values` is assembled:
```python
"channel_values": {
**value["checkpoint"].get("channel_values"), # <--- if channel_values doesn't exist (old checkpoint), **None errors
**self._load_blobs(value["channel_values"]),
},
```
This bug was observed for checkpoints generated by
`langgraph-checkpoint-postgres<=2.0.19`.
Fixed by providing a fallback to
`value["checkpoint"].get("channel_values")`:
```python
**value["checkpoint"],
"channel_values": {
**(
value["checkpoint"].get("channel_values") or {}
), # 'or {}' needed for backwards compat with v3 checkpoints and below, as v4 introduced channel_values key
**self._load_blobs(value["channel_values"]),
},
```
### Tests
Added test for AsyncPostgresSaver and test for PostgresSaver, using
monkeypatch to remove `channel_values` before CheckpointTuple is
assembled in `_load_checkpoint_tuple`.
### Solves
https://github.com/langchain-ai/langgraph/issues/6137 and
https://github.com/langchain-ai/langgraph/issues/5677
---------
Co-authored-by: Shahrukh Shaik <144558473+shahrukh-shaik@users.noreply.github.com>
**Description**: fix#6050.
Root cause: In nested graphs, the first tick after resume often included
a checkpoint_id, which set skip_done_tasks=False. This skipped matching
pending writes and re-executed already-completed helper @task on
subsequent resumes.
Change: Initialize skip_done_tasks=True when resuming inside a nested
graph. Use original config[CONF] for checkpoint_id presence, and
self.config[CONF] for resuming (current loop state). Added a concise
comment clarifying the different config sources.
**Issue**: #6050
**Tests**:
Add regression test `test_nested_graph_resume_reuses_cached_task_writes`
---------
Signed-off-by: jitokim <pigberger70@gmail.com>
Co-authored-by: Caspar Broekhuizen <casparbroekhuizen@gmail.com>
## Summary
- add a public accessor for the last received SSE event id
- retry async and sync SSE streams using the Location reconnect path and
Last-Event-ID while skipping empty events
- add regression tests that simulate interrupted SSE streams for both
async and sync clients
## Testing
- make format
- make lint
- make test
------
https://chatgpt.com/codex/tasks/task_e_68ca8bfa26cc832d98bcb359884962ec
### Description
`test_embed_with_path` was failing on x86_64 architecture due to numeric
precision differences. `pytest.approx` was already used later on in this
test for float comparison, so this PR just updates a missed assertion.
Fixes https://github.com/langchain-ai/langgraph/issues/5845
## Summary
- ensure both async and sync HTTP clients flush the SSE decoder after
streaming
- add regression tests covering trailing SSE events without a
terminating blank line
## Testing
- make format
- make lint
- make test
------
https://chatgpt.com/codex/tasks/task_e_68c9727ca9f8832d9f207323c5e02a72
### Description
Revert change in #5201 that prevented the surfacing of interrupts when
`stream_mode="values"`. [Comment highlighting affected
lines](https://github.com/langchain-ai/langgraph/pull/5201#discussion_r2344884841)
Resolves#5409
### Test
Add test to verify interrupts are properly surfaced when
`stream_mode="values"` (`test_interrupt_stream_mode_values`)
This PR updates the dependencies in all Python packages using `uv lock
--upgrade`.
This is an automated PR created by the UV Lock Upgrade workflow.
To make tests pass:
* linting fixes
* whitespace fixes in snapshots
---------
Co-authored-by: sydney-runkle <54324534+sydney-runkle@users.noreply.github.com>
Co-authored-by: Sydney Runkle <sydneymarierunkle@gmail.com>
**Description:**
This PR adds missing documentation for the `merge` parameter in the
`push_ui_message` function. The parameter was present in the function
signature but lacked documentation in the docstring, which could confuse
API users.
Changes made:
- Added clear documentation for the `merge` parameter
- Explains the behavior difference between `merge=True`
(merges props) and `merge=False` (replaces props)
- Includes default value information
**Issue:**
N/A - Documentation improvement
**Dependencies:**
None
* Updated the relocation notice in `examples/persistence_postgres.ipynb`
to reference the correct new documentation at `add-memory.md` instead of
the previous notebook link.
Description:
Corrects _parse_version to support Docker versions with SemVer build
metadata (e.g., 28.1.1+1), resolving #5965. Adds comprehensive unit
tests for version parsing, including normal, v-prefixed, prerelease,
build metadata, combined prerelease/build metadata, and edge cases with
missing components.
Issue:
Closes#5965
Dependencies:
None
---------
Co-authored-by: Sydney Runkle <54324534+sydney-runkle@users.noreply.github.com>
### Description
* Fix `get_graph()` generating an unexpected conditional edge to
`__end__` when the last step has a single (non-terminal) source and the
graph is cyclic.
### Issue
* There was a fallback path that was triggered in `draw_graph()` when,
for a Pregel instance; no termini exist and there is only a single step
source (the last one). In this case an edge was added: (last source) ->
`__end__`, even when another node already had a valid edge: (node) ->
`__end__`.
See this example:
<details>
<summary>code</summary>
```python
from langgraph.graph import END, START, StateGraph
from pydantic import BaseModel
class State(TypedDict):
messages: list[str]
def chatbot_node(state: State) -> State:
return {"messages": state["messages"] + ["chatbot"]}
def tools_node(state: State) -> State:
return {"messages": state["messages"] + ["tools"]}
def human_node(state: State) -> State:
return {"messages": state["messages"] + ["human"]}
def tools_condition(_: State) -> str:
return "tools"
def end_condition(_: State) -> str:
return "chatbot"
workflow = StateGraph(State)
workflow.add_node("chatbot", chatbot_node)
workflow.add_node("tools", tools_node)
workflow.add_node("human", human_node)
workflow.add_edge(START, "human")
workflow.add_edge("tools", "chatbot")
# graph_builder.add_edge("chatbot", "human") !!!
workflow.add_conditional_edges(
"chatbot", tools_condition, {"tools": "tools", "human": "human"}
)
workflow.add_conditional_edges(
"human", end_condition, {"chatbot": "chatbot", END: END}
)
app = workflow.compile()
mermaid = app.get_graph().draw_mermaid()
```
</details>
The code above, as-is, generates the graph on the left. There is an
unexpected conditional edge: chatbot -> `__end__`. If you uncomment the
commented line and introduce a static edge: chatbot -> human,
`get_graph()` returns the correct representation:
1 Without `graph_builder.add_edge("chatbot", "human")` | 2 With
`graph_builder.add_edge("chatbot", "human")`
:-------------------------:|:-------------------------:
|
* In case 1), the graph is cyclic so termini is empty, and the last
`step_sources` set during the static walk contains only the chatbot
node, so an edge is added: chatbot -> `__end__`.
* In case 2), the graph is cyclic so termini is empty, and the last
`step_sources` set during the static walk contains only the human node,
so an edge is added: human -> `__end__`, but `add_edge()` dedups (the
edge already exists) so the graph appears correct.
### Solution
* Check that no valid edges: (node) -> `__end__` exist before triggering
the fallback path and creating an edge.
Before | After
:-------------------------:|:-------------------------:
|
After: The graph is cyclic so termini is empty, and the last
`step_sources` contains the chatbot node, but an edge already exists:
human -> `__end__`, so no more edges are added.
### Tests
* `test_get_graph_nonterminal_last_step_source()` which asserts no
unexpected edge to `__end__` is produced from the last nonterminal step
source.
### Issue
Closes#4394
---------
Co-authored-by: Sydney Runkle <sydneymarierunkle@gmail.com>
Co-authored-by: Sydney Runkle <54324534+sydney-runkle@users.noreply.github.com>
The idea here is that we don't want to allow resuming a graph w/ an
arbitrary resume value if there are multiple interrupts in the queue,
because the order in which interrupts enter the queue is not
deterministic. We want to instead enforce that each resume value is
mapped to an interrupt id.
Instead, when multiple interrupts are present, a user should invoke w/ a
resume map, mapping interrupt id -> resume value.
The logic was more complex than expected because there are 2 copies of
an interrupt in `checkpoint_pending_writes` for the cases w/ the
functional API, because an interrupt in a task interrupts the task and
entrypoint.
This is technically breaking (users resuming multiple hanging interrupts
w/ multiple resume calls can no longer do this... but the behavior for
this case was non-deterministic in the first place so we can sell this
as a fix).
### Description
Export PoolConfig from langgraph.store.postgres.__init__ so the
documented import from langgraph.store.postgres import
AsyncPostgresStore, PoolConfig works as shown in the AsyncPostgresStore
examples. This resolves a docs vs. code inconsistency without changing
behavior.
### Issue
N/A
### Dependencies:
None
---------
Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com>
Co-authored-by: Sydney Runkle <54324534+sydney-runkle@users.noreply.github.com>
### Description
* Set `ensure_ascii=False` for all `json.dumps` calls in
`get_text_at_path`. Preserves non-ASCII text instead of embedding
`\uXXXX` escapes.
**Before**
```python
store.put(("user_123", "memories"), "1", {"text": "这是中文"})
# embeds {"text": "\\u8fd9\\u662f\\u4e2d\\u6587"}
```
**After**
```python
store.put(("user_123", "memories"), "1", {"text": "这是中文"})
# embeds {"text": "这是中文"}
```
### Tests & Docs
* Add unit test `test_non_ascii` that writes three records (Chinese,
Japanese, Korean) to an `InMemoryStore`, searches with the same strings,
and asserts the correct top hit with a score >= 0.15 for each.
### Issue
Fixes#5946
docs (graphapi) : Handle Missing Context in LLM Invocation - Invoking
the LLM without explicitly passing a context parameter resulted in the
following error:
`AttributeError: 'NoneType' object has no attribute 'model_provider'`
This occurred because the context was None, and the system attempted to
access model_provider. This PR ensures that when context is not
provided, an empty context is passed explicitly. This allows the system
to correctly fall back to the default value defined in the
ContextSchema.model_provider attribute.
Very small update to docs, I think there is an add_edge that shouldn't
be there and a bug.
Adding the Annotated, resolves this error I received running the
example.
Traceback (most recent call last):
File "/Users/toddchaney/repos/work/importal-apps/python-worker/main.py",
line 52, in <module>
for step in graph.stream({"topic": "animals"}, stream_mode = ["updates",
"values"]):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File
"/Users/toddchaney/repos/work/importal-apps/python-worker/.venv/lib/python3.12/site-packages/langgraph/pregel/__init__.py",
line 2544, in stream
loop.after_tick()
File
"/Users/toddchaney/repos/work/importal-apps/python-worker/.venv/lib/python3.12/site-packages/langgraph/pregel/loop.py",
line 526, in after_tick
self.updated_channels = apply_writes(
^^^^^^^^^^^^^
File
"/Users/toddchaney/repos/work/importal-apps/python-worker/.venv/lib/python3.12/site-packages/langgraph/pregel/algo.py",
line 299, in apply_writes
if channels[chan].update(vals) and next_version is not None:
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File
"/Users/toddchaney/repos/work/importal-apps/python-worker/.venv/lib/python3.12/site-packages/langgraph/channels/last_value.py",
line 58, in update
raise InvalidUpdateError(msg)
langgraph.errors.InvalidUpdateError: At key 'jokes': Can receive only
one value per step. Use an Annotated key to handle multiple values.
For troubleshooting, visit:
https://python.langchain.com/docs/troubleshooting/errors/INVALID_CONCURRENT_GRAPH_UPDATE
Co-authored-by: Sydney Runkle <54324534+sydney-runkle@users.noreply.github.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Bumps
[actions/download-artifact](https://github.com/actions/download-artifact)
from 4 to 5.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/actions/download-artifact/releases">actions/download-artifact's
releases</a>.</em></p>
<blockquote>
<h2>v5.0.0</h2>
<h2>What's Changed</h2>
<ul>
<li>Update README.md by <a
href="https://github.com/nebuk89"><code>@nebuk89</code></a> in <a
href="https://redirect.github.com/actions/download-artifact/pull/407">actions/download-artifact#407</a></li>
<li>BREAKING fix: inconsistent path behavior for single artifact
downloads by ID by <a
href="https://github.com/GrantBirki"><code>@GrantBirki</code></a> in <a
href="https://redirect.github.com/actions/download-artifact/pull/416">actions/download-artifact#416</a></li>
</ul>
<h2>v5.0.0</h2>
<h3>🚨 Breaking Change</h3>
<p>This release fixes an inconsistency in path behavior for single
artifact downloads by ID. <strong>If you're downloading single artifacts
by ID, the output path may change.</strong></p>
<h4>What Changed</h4>
<p>Previously, <strong>single artifact downloads</strong> behaved
differently depending on how you specified the artifact:</p>
<ul>
<li><strong>By name</strong>: <code>name: my-artifact</code> → extracted
to <code>path/</code> (direct)</li>
<li><strong>By ID</strong>: <code>artifact-ids: 12345</code> → extracted
to <code>path/my-artifact/</code> (nested)</li>
</ul>
<p>Now both methods are consistent:</p>
<ul>
<li><strong>By name</strong>: <code>name: my-artifact</code> → extracted
to <code>path/</code> (unchanged)</li>
<li><strong>By ID</strong>: <code>artifact-ids: 12345</code> → extracted
to <code>path/</code> (fixed - now direct)</li>
</ul>
<h4>Migration Guide</h4>
<h5>✅ No Action Needed If:</h5>
<ul>
<li>You download artifacts by <strong>name</strong></li>
<li>You download <strong>multiple</strong> artifacts by ID</li>
<li>You already use <code>merge-multiple: true</code> as a
workaround</li>
</ul>
<h5>⚠️ Action Required If:</h5>
<p>You download <strong>single artifacts by ID</strong> and your
workflows expect the nested directory structure.</p>
<p><strong>Before v5 (nested structure):</strong></p>
<pre lang="yaml"><code>- uses: actions/download-artifact@v4
with:
artifact-ids: 12345
path: dist
# Files were in: dist/my-artifact/
</code></pre>
<blockquote>
<p>Where <code>my-artifact</code> is the name of the artifact you
previously uploaded</p>
</blockquote>
<p><strong>To maintain old behavior (if needed):</strong></p>
<pre lang="yaml"><code></tr></table>
</code></pre>
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="https://github.com/actions/download-artifact/commit/634f93cb2916e3fdff6788551b99b062d0335ce0"><code>634f93c</code></a>
Merge pull request <a
href="https://redirect.github.com/actions/download-artifact/issues/416">#416</a>
from actions/single-artifact-id-download-path</li>
<li><a
href="https://github.com/actions/download-artifact/commit/b19ff4302770b82aa4694b63703b547756dacce6"><code>b19ff43</code></a>
refactor: resolve download path correctly in artifact download tests
(mainly ...</li>
<li><a
href="https://github.com/actions/download-artifact/commit/e262cbee4ab8c473c61c59a81ad8e9dc760e90db"><code>e262cbe</code></a>
bundle dist</li>
<li><a
href="https://github.com/actions/download-artifact/commit/bff23f9308ceb2f06d673043ea6311519be6a87b"><code>bff23f9</code></a>
update docs</li>
<li><a
href="https://github.com/actions/download-artifact/commit/fff8c148a8fdd56aa81fcb019f0b5f6c65700c4d"><code>fff8c14</code></a>
fix download path logic when downloading a single artifact by id</li>
<li><a
href="https://github.com/actions/download-artifact/commit/448e3f862ab3ef47aa50ff917776823c9946035b"><code>448e3f8</code></a>
Merge pull request <a
href="https://redirect.github.com/actions/download-artifact/issues/407">#407</a>
from actions/nebuk89-patch-1</li>
<li><a
href="https://github.com/actions/download-artifact/commit/47225c44b359a5155efdbbbc352041b3e249fb1b"><code>47225c4</code></a>
Update README.md</li>
<li>See full diff in <a
href="https://github.com/actions/download-artifact/compare/v4...v5">compare
view</a></li>
</ul>
</details>
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`@dependabot rebase`.
[//]: # (dependabot-automerge-start)
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You can trigger Dependabot actions by commenting on this PR:
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</details>
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Sydney Runkle <54324534+sydney-runkle@users.noreply.github.com>
Description:
The documentation for the should_continue function contained an
incorrect return type annotation.
It was shown as Literal["environment", END], but the actual logic
returns "Action" or END.
This PR updates the example to use Literal["Action", END] so that the
documentation matches the intended behavior of the function.
Issue:
N/A
Dependencies:
None
docs (graphapi.md) : With the new langgraph version, the RetryPolicy
needs to be imported from `langgraph.types` to avoid `ImportError:
cannot import name 'RetryPolicy' from 'langgraph.pregel'`
**Description:** Fix a typo where "runtie" was incorrectly used instead
of "runtime" in line 158 of the StateGraph class in state.py. This
resolves the example error caused by the misspelled variable name.
**Dependencies:** None
basically - for conditional edges, we use this to merge the updates from
state with the state object (before the actual update really occurs in
the tick.after)
otherwise - an emphemeral value will actually last through the logic in
the conditional edge of the node after
We previously errored when a user had prerelease dependencies, this PR
passes the `--prereleases=allow` flag to our `uv pip install` call.
This PR also adds a test to verify that said deployments will build and
run as expected.
This PR introduces the `--build-command` and `--install-command`
arguments to `langgraph build`.
`--install-command` is a custom install command. If passed, it will be
run from wherever the `langgraph build` call was made, i.e. NOT where
the langgraph.json file lives (except if these are the same place). This
will override the detected install command that we previously used.
`--build-command` is a custom build command. This will run from wherever
the langgraph.json file lives, and will be done after the install has
been run.
You don't need to provide both. Just providing one will make the install
(detected or supplied) run in the directory from where `langgraph build
was called` and then have the build command (if one exists) run in the
directory where langgraph.json exists.
I think we should probably allow configuring the directories from which
these commands get run, but I don't think this needs to be part of the
MVP.
---------
Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com>
This PR updates the OpenAPI specification with changes detected from the
LangGraph API server.
**Changes detected as of LangGraph API version 0.4.11**
This update was automatically generated by the sync workflow in the
langgraph-api repository.
Co-authored-by: hinthornw <hinthornw@users.noreply.github.com>
This PR updates the OpenAPI specification with changes detected from the
LangGraph API server.
**Changes detected as of LangGraph API version 0.4.9**
This update was automatically generated by the sync workflow in the
langgraph-api repository.
Co-authored-by: hinthornw <hinthornw@users.noreply.github.com>
This PR updates the OpenAPI specification with changes detected from the
LangGraph API server.
**Changes detected as of LangGraph API version 0.4.8**
This update was automatically generated by the sync workflow in the
langgraph-api repository.
Co-authored-by: hinthornw <hinthornw@users.noreply.github.com>
This PR updates the OpenAPI specification with changes detected from the
LangGraph API server.
**Changes detected as of LangGraph API version 0.4.8**
This update was automatically generated by the sync workflow in the
langgraph-api repository.
Co-authored-by: hinthornw <hinthornw@users.noreply.github.com>
This PR updates the OpenAPI specification with changes detected from the
LangGraph API server.
**Changes detected as of LangGraph API version 0.4.0**
This update was automatically generated by the sync workflow in the
langgraph-api repository.
Co-authored-by: hinthornw <hinthornw@users.noreply.github.com>
@@ -71,7 +71,7 @@ While LangGraph can be used standalone, it also integrates seamlessly with any L
## Additional resources
- [Guides](https://langchain-ai.github.io/langgraph/how-tos/): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
- [Guides](https://langchain-ai.github.io/langgraph/guides/): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
- [Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
- [Examples](https://langchain-ai.github.io/langgraph/examples/): Guided examples on getting started with LangGraph.
- [LangChain Forum](https://forum.langchain.com/): Connect with the community and share all of your technical questions, ideas, and feedback.
To setup requirements for building docs you can run:
For more information on contributing to our documentation, see the [Contributing Guide](../CONTRIBUTING.md).
```bash
uv sync --group test
## Structure
The primary documentation is located in the `docs/` directory. This directory contains both the source files for the main documentation as well as the API reference doc build process.
### Main Documentation
Main documentation files are located in `docs/docs/` and are written in Markdown format. The site uses [**MkDocs**](https://www.mkdocs.org/) with the [Material theme](https://squidfunk.github.io/mkdocs-material/) and includes:
- **Concepts**: Core LangGraph concepts and explanations
- **Tutorials**: Step-by-step learning guides
- **How-tos**: Task-focused guides for specific use cases
- **Examples**: Real-world applications and use cases
- **Jupyter Notebooks**: Interactive tutorials that are automatically converted to markdown
### API Reference
API reference documentation is defined in `docs/docs/reference/`. Each `.md` file outlines the "template" that each page is built from. Reference content is automatically generated from docstrings in the codebase using the **mkdocstrings** plugin. Once generated, the content is plugged into the corresponding markdown file where it is referenced by using manual directives to specify which classes and/or functions are documented:
```markdown
::: langgraph.graph.state.StateGraph
options:
show_if_no_docstring: true
show_root_heading: true
show_root_full_path: false
members:
- add_node
- add_edge
- add_conditional_edges
- add_sequence
- compile
```
## Serving documentation locally
## Build Process
To run the documentation server locally you can run:
Docs are built following these steps:
1.**Content Processing:**
-`_scripts/notebook_hooks.py` - Main processing pipeline that:
- Converts how-tos/tutorial Jupyter notebooks to markdown using `notebook_convert.py`
- Adds automatic API reference links to code blocks using `generate_api_reference_links.py`
- Handles conditional rendering for Python/JS versions
- Processes highlight comments and custom syntax
2.**API Reference Generation:**
- **mkdocstrings** plugin extracts docstrings from Python source code
- Manual `::: module.Class` directives in reference pages (`/docs/docs/*`) specify what to document
- Cross-references are automatically generated between docs and API
3.**Site Generation:**
- **MkDocs** processes all markdown files and generates static HTML
- Custom hooks handle redirects and inject additional functionality
4.**Deployment:**
- Site is deployed with Vercel
-`make build-docs` generates production build (also usable for local testing)
- Automatic redirects handle URL changes between versions
### Local Development
For local development, use the Makefile targets:
```bash
# Serve docs locally with hot reloading
make serve-docs
# Clean build for production testing
make build-docs
# Serve with clean build
make serve-clean-docs
```
This will start the documentation server on [http://127.0.0.1:8000/langgraph/](http://127.0.0.1:8000/langgraph/).
The `serve-docs` command:
- Watches source files for changes
- Includes dirty builds for faster iteration
- Serves on [http://127.0.0.1:8000/langgraph/](http://127.0.0.1:8000/langgraph/)
## Standards
**Docstring Format:**
The API reference uses **Google-style docstrings** with Markdown markup. The `mkdocstrings` plugin processes these to generate documentation.
@@ -33,7 +33,7 @@ LangGraph provides three ways to manage context, which combines the mutability a
**Static runtime context** represents immutable data like user metadata, tools, and database connections that are passed to an application at the start of a run via the `context` argument to `invoke`/`stream`. This data does not change during execution.
!!! version-added "New in LangGraph v0.6: `context` replaces `config['configurable']`"
!!! version-added "Added in version 0.6.0: `context` replaces `config['configurable']`"
Runtime context is now passed to the `context` argument of `invoke`/`stream`,
which replaces the previous pattern of passing application configuration to `config['configurable']`.
@@ -351,11 +351,13 @@ If your desired LLM isn't officially supported by LangChain, consider these opti
:::python
1. **Implement a custom LangChain chat model**: Create a model conforming to the [LangChain chat model interface](https://python.langchain.com/docs/how_to/custom_chat_model/). This enables full compatibility with LangGraph's agents and workflows but requires understanding of the LangChain framework.
:::
:::js
1. **Implement a custom LangChain chat model**: Create a model conforming to the [LangChain chat model interface](https://js.langchain.com/docs/how_to/custom_chat/). This enables full compatibility with LangGraph's agents and workflows but requires understanding of the LangChain framework.
:::
2. **Direct invocation with custom streaming**: Use your model directly by [adding custom streaming logic](../how-tos/streaming.md#use-with-any-llm) with `StreamWriter`.
@@ -371,6 +373,7 @@ If your desired LLM isn't officially supported by LangChain, consider these opti
- [Force model to call a specific tool](https://python.langchain.com/docs/how_to/tool_choice/)
- [All chat model how-to guides](https://python.langchain.com/docs/how_to/#chat-models)
- [Chat model integrations](https://python.langchain.com/docs/integrations/chat/)
:::
:::js
@@ -381,4 +384,5 @@ If your desired LLM isn't officially supported by LangChain, consider these opti
- [Force model to call a specific tool](https://js.langchain.com/docs/how_to/tool_choice/)
- [All chat model how-to guides](https://js.langchain.com/docs/how_to/#chat-models)
- [Chat model integrations](https://js.langchain.com/docs/integrations/chat/)
@@ -99,8 +99,8 @@ Starting from the `LangGraph Platform` view...
1. In the top-right corner, select the gear icon (`Deployment Settings`).
1. Update the `Git Branch` to the desired branch.
1. Check/uncheck checkbox to `Automatically update deployment on push to branch`.
1. Branch creation/deletion and tag creation/deletion events will not trigger an update. Only pushes to an existing branch will trigger an update.
1. Pushes in quick succession to a branch will not trigger subsequent updates. In the future, this functionality may be changed/improved.
1. Branch creation/deletion and tag creation/deletion events will not trigger an update. Only pushes to an existing branch will trigger an update.
1. Pushes in quick succession to a branch will queue subsequent updates. Once a build completes, the most recent commit will begin building and the other queued builds will be skipped.
"description":"Store is an API for managing persistent key-value store (long-term memory) that is available from any thread."
},
{
"name":"A2A",
"description":"Agent-to-Agent Protocol related endpoints for exposing assistants as A2A-compliant agents."
},
{
"name":"MCP",
"description":"Model Context Protocol related endpoints for exposing an agent as an MCP server."
@@ -1520,6 +1524,96 @@
}
}
},
"/threads/{thread_id}/stream":{
"get":{
"tags":[
"Threads"
],
"summary":"Join Thread Stream",
"description":"This endpoint streams output in real-time from a thread. The stream will include the output of each run executed sequentially on the thread and will remain open indefinitely. It is the responsibility of the calling client to close the connection.",
"description":"The ID of the last event received. Used to resume streaming from a specific point. Pass '-' to resume from the beginning."
},
"name":"Last-Event-ID",
"in":"header"
},
{
"required":false,
"schema":{
"anyOf":[
{
"type":"string",
"enum":["lifecycle","run_modes","state_update"]
},
{
"type":"array",
"items":{
"type":"string",
"enum":["lifecycle","run_modes","state_update"]
}
}
],
"default":["run_modes"],
"title":"Stream Modes",
"description":"Stream modes to control which events are returned. 'lifecycle' returns only run start/end events, 'run_modes' returns all run events (default behavior), 'state_update' returns only state update events."
},
"name":"stream_modes",
"in":"query"
}
],
"responses":{
"200":{
"description":"Success",
"content":{
"text/event-stream":{
"schema":{
"type":"string",
"description":"The server will send a stream of events in SSE format.\n\n**Example event**:\n\nid: 1\n\nevent: message\n\ndata: {}"
}
}
}
},
"404":{
"description":"Not Found",
"content":{
"application/json":{
"schema":{
"$ref":"#/components/schemas/ErrorResponse"
}
}
}
},
"422":{
"description":"Validation Error",
"content":{
"application/json":{
"schema":{
"$ref":"#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/threads/{thread_id}/runs":{
"get":{
"tags":[
@@ -3092,6 +3186,195 @@
}
}
},
"/a2a/{assistant_id}":{
"post":{
"operationId":"post_a2a",
"summary":"A2A Post",
"description":"Communicate with an assistant using the Agent-to-Agent Protocol.\nSends a JSON-RPC 2.0 message to the assistant.\n\n- **Request**: Provide an object with `jsonrpc`, `id`, `method`, and optional `params`.\n- **Response**: Returns a JSON-RPC response with task information or error.\n\n**Supported Methods:**\n- `message/send`: Send a message to the assistant\n- `tasks/get`: Get the status and result of a task\n\n**Notes:**\n- Supports threaded conversations via thread context\n- Messages can contain text and data parts\n- Tasks run asynchronously and return completion status\n",
"parameters":[
{
"name":"assistant_id",
"in":"path",
"required":true,
"schema":{
"type":"string",
"format":"uuid"
},
"description":"The ID of the assistant to communicate with"
},
{
"name":"Accept",
"in":"header",
"required":true,
"schema":{
"type":"string",
"enum":["application/json"]
},
"description":"Must be application/json"
}
],
"requestBody":{
"required":true,
"content":{
"application/json":{
"schema":{
"type":"object",
"properties":{
"jsonrpc":{
"type":"string",
"enum":["2.0"],
"description":"JSON-RPC version"
},
"id":{
"type":"string",
"description":"Request identifier"
},
"method":{
"type":"string",
"enum":["message/send","tasks/get"],
"description":"The method to invoke"
},
"params":{
"type":"object",
"description":"Method parameters",
"oneOf":[
{
"title":"Message Send Parameters",
"properties":{
"message":{
"type":"object",
"properties":{
"role":{
"type":"string",
"enum":["user","assistant"],
"description":"Message role"
},
"parts":{
"type":"array",
"items":{
"oneOf":[
{
"title":"Text Part",
"type":"object",
"properties":{
"kind":{
"type":"string",
"enum":["text"]
},
"text":{
"type":"string"
}
},
"required":["kind","text"]
},
{
"title":"Data Part",
"type":"object",
"properties":{
"kind":{
"type":"string",
"enum":["data"]
},
"data":{
"type":"object"
}
},
"required":["kind","data"]
}
]
},
"description":"Message parts"
},
"messageId":{
"type":"string",
"description":"Unique message identifier"
}
},
"required":["role","parts","messageId"]
},
"thread":{
"type":"object",
"properties":{
"threadId":{
"type":"string",
"description":"Thread identifier for conversation context"
}
},
"description":"Optional thread context"
}
},
"required":["message"]
},
{
"title":"Task Get Parameters",
"properties":{
"taskId":{
"type":"string",
"description":"Task identifier to retrieve"
}
},
"required":["taskId"]
}
]
}
},
"required":["jsonrpc","id","method"]
}
}
}
},
"responses":{
"200":{
"description":"JSON-RPC response",
"content":{
"application/json":{
"schema":{
"type":"object",
"properties":{
"jsonrpc":{
"type":"string",
"enum":["2.0"]
},
"id":{
"type":"string"
},
"result":{
"type":"object",
"description":"Success result containing task information or task details"
},
"error":{
"type":"object",
"properties":{
"code":{
"type":"integer"
},
"message":{
"type":"string"
}
},
"description":"Error information if request failed"
}
},
"required":["jsonrpc","id"]
}
}
}
},
"400":{
"description":"Bad request - invalid JSON-RPC or missing Accept header"
},
"404":{
"description":"Assistant not found"
},
"500":{
"description":"Internal server error"
}
},
"tags":[
"A2A"
]
}
},
"/mcp/":{
"post":{
"operationId":"post_mcp",
@@ -4346,6 +4629,17 @@
"title":"Checkpoint During",
"description":"Whether to checkpoint during the run.",
"default":false
},
"durability":{
"type":"string",
"enum":[
"sync",
"async",
"exit"
],
"title":"Durability",
"description":"Durability level for the run. Must be one of 'sync', 'async', or 'exit'.",
"default":"async"
}
},
"type":"object",
@@ -4582,6 +4876,17 @@
"title":"Checkpoint During",
"description":"Whether to checkpoint during the run.",
"default":false
},
"durability":{
"type":"string",
"enum":[
"sync",
"async",
"exit"
],
"title":"Durability",
"description":"Durability level for the run. Must be one of 'sync', 'async', or 'exit'.",
"default":"async"
}
},
"type":"object",
@@ -4710,6 +5015,12 @@
},
"ThreadSearchRequest":{
"properties":{
"ids":{
"type":"array",
"items":{"type":"string","format":"uuid"},
"title":"Ids",
"description":"List of thread IDs to include. Others are excluded."
},
"metadata":{
"type":"object",
"title":"Metadata",
@@ -4950,11 +5261,30 @@
"type":"object",
"title":"Metadata",
"description":"Metadata to merge with existing thread metadata."
},
"ttl":{
"type":"object",
"title":"TTL",
"description":"The time-to-live for the thread.",
"properties":{
"strategy":{
"type":"string",
"enum":[
"delete"
],
"description":"The TTL strategy. 'delete' removes the entire thread.",
"default":"delete"
},
"ttl":{
"type":"number",
"description":"The time-to-live in minutes from now until thread should be swept."
@@ -897,5 +897,5 @@ There are two high-level approaches to achieve that:
An agent might need to have a different state schema from the rest of the agents. For example, a search agent might only need to keep track of queries and retrieved documents. There are two ways to achieve this in LangGraph:
- Define [subgraph](./subgraphs.md) agents with a separate state schema. If there are no shared state keys (channels) between the subgraph and the parent graph, it's important to [add input / output transformations](../how-tos/subgraph.ipynb#different-state-schemas) so that the parent graph knows how to communicate with the subgraphs.
- Define agent node functions with a [private input state schema](../how-tos/graph-api.ipynb#pass-private-state-between-nodes) that is distinct from the overall graph state schema. This allows passing information that is only needed for executing that particular agent.
- Define [subgraph](./subgraphs.md) agents with a separate state schema. If there are no shared state keys (channels) between the subgraph and the parent graph, it's important to [add input / output transformations](../how-tos/subgraph.md#different-state-schemas) so that the parent graph knows how to communicate with the subgraphs.
- Define agent node functions with a [private input state schema](../how-tos/graph-api.md#pass-private-state-between-nodes) that is distinct from the overall graph state schema. This allows passing information that is only needed for executing that particular agent.
@@ -1205,7 +1205,7 @@ There are many use cases where you may wish for your node to have a custom retry
To configure a retry policy, pass the `retry_policy` parameter to the [add_node](../reference/graphs.md#langgraph.graph.state.StateGraph.add_node). The `retry_policy` parameter takes in a `RetryPolicy` named tuple object. Below we instantiate a `RetryPolicy` object with the default parameters and associate it with a node:
```python
from langgraph.pregel import RetryPolicy
from langgraph.types import RetryPolicy
builder.add_node(
"node_name",
@@ -1260,7 +1260,7 @@ By default, the retry policy retries on any exception except for the following:
from typing_extensions import TypedDict
from langchain.chat_models import init_chat_model
from langgraph.graph import END, MessagesState, StateGraph, START
from langgraph.pregel import RetryPolicy
from langgraph.types import RetryPolicy
from langchain_community.utilities import SQLDatabase
from langchain_core.messages import AIMessage
@@ -1422,15 +1422,15 @@ const builder = new StateGraph(State)
:::
??? info "Why split application steps into a sequence with LangGraph?"
LangGraph makes it easy to add an underlying persistence layer to your application.
This allows state to be checkpointed in between the execution of nodes, so your LangGraph nodes govern:
LangGraph makes it easy to add an underlying persistence layer to your application.
This allows state to be checkpointed in between the execution of nodes, so your LangGraph nodes govern:
- How state updates are [checkpointed](../concepts/persistence.md)
- How interruptions are resumed in [human-in-the-loop](../concepts/human_in_the_loop.md) workflows
- How we can "rewind" and branch-off executions using LangGraph's [time travel](../concepts/time-travel.md) features
- How state updates are [checkpointed](../concepts/persistence.md)
- How interruptions are resumed in [human-in-the-loop](../concepts/human_in_the_loop.md) workflows
- How we can "rewind" and branch-off executions using LangGraph's [time travel](../concepts/time-travel.md) features
They also determine how execution steps are [streamed](../concepts/streaming.md), and how your application is visualized
and debugged using [LangGraph Studio](../concepts/langgraph_studio.md).
They also determine how execution steps are [streamed](../concepts/streaming.md), and how your application is visualized
and debugged using [LangGraph Studio](../concepts/langgraph_studio.md).
Let's demonstrate an end-to-end example. We will create a sequence of three steps:
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These docs will be deprecated and removed with the release of LangGraph v1.0 in October 2025.<ahref="https://docs.langchain.com/oss/python/langgraph/overview"target="_blank">Visit the v1.0 alpha docs</a>
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