They were missing `Args` sections, and IDEs don't pick these up from the
canonical.
Each overload now includes the full `Args:` documentation, examples
tailored to that specific signature, and `Returns:` section.
Addresses some comments in #6682
- Update links in notebooks to point to the new documentation location.
- Add archival notices indicating that the examples are no longer
updated.
- Remove some obsolete notebooks that have been moved to the new
documentation.
Please comment here if you encounter any issues
`aiosqlite` changed it's Connection type to no longer subclass
`threading.Thread`. This removed the is_alive method, which is called
proactively in setup().
This PR handles this in a backwards compat way.
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
Adds docstring clarification that cron schedules are interpreted in UTC
for `CronClient.create`, `CronClient.create_for_thread`, and their sync
variants.
**Description:**
This PR fixes an issue where injection types (like `ToolRuntime`) were
not recognized by `ToolNode` when used with generic type arguments
(e.g., `ToolRuntime[MyContext]`).
Previously, the `_is_injection` check relied solely on `isinstance` and
`issubclass`, which fail for `typing._GenericAlias` objects. This update
adds a check using `typing.get_origin()` to correctly identify the base
class of generic types, ensuring the runtime is injected correctly even
when type hints are present.
**Issue:** Fixes#6465
**Dependencies:** None
**Twitter handle:** @SidharthRajmoh2
---------
Co-authored-by: Sydney Runkle <54324534+sydney-runkle@users.noreply.github.com>
25 is pretty unreasonable for most applications, bumping up to 1000 by
default, burden really should be on the user to enforce this based on
their application
**Description:** The crons create endpoint supports an end-time field
that is not currently supported in the client. Adding that parameter
here.
**Issue:** N/A
**Dependencies:** N/A
**Twitter handle:** N/A
Changed "BaseMessge" to "BaseMessage" in test comments.
This critical 2-character fix prevents mass confusion among developers
who might have spent milliseconds wondering what a "Messge" is.
The world is now a safer place.
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
bumping sdk-py for custom encryption cleanup changes (#6595) - these are
breaking changes on this beta, unreleased and unused feature.
Signed-off-by: Connor Braa <cwlbraa@langchain.dev>
In langgraph-api, custom-encrypted JSONs need to continue to be
SQL-json-mergable after encryption. Previous WIP docs
advocated for custom encryption impls where all encrypted kv pairs were
shoved into a `__encrypted__: $encrypted_kvs` meta-key. Turns out that
pattern causes data loss when running PATCH-style partial updates or in
the many places langgraph-api json-SQL-merges across model types.
This PR contains 2 SDK fixes:
1. remove model-type specific custom json encryption annotations - these
cause surprising behavior as config and context data propagates across
model types, specifically because today we can't guarantee that data
encrypted as one model-type will be decrypted as the same model-type
because kv pairs move across model-types in pure SQL
2. document limitations and validation around "key preservation" in
custom json encryption functions. langgraph-api now validates that
custom JSON encryption fns don't change keys. That validation prevents
customizers from writing custom encryption functions that cause data
loss through patch endpoints and x-model merge propagation.
---------
Signed-off-by: Connor Braa <cwlbraa@langchain.dev>
Description: Catch invalid checkpointer objects early by validating any
checkpointer argument before compilation/execution, raising a clear
TypeError that instructs users to pass a proper BaseCheckpointSaver
(e.g., AsyncPostgresSaver) instead of stores like AsyncPostgresStore.
Includes shared validation logic and a regression test so we don’t see
AttributeError: 'AsyncPostgresStore' object has no attribute
'get_next_version' again.
Issue: Fixes#6585
Dependencies: None
Twitter handle: none
- **Description:** Bumping to 1.0.5 for compatibility with the latest
changes to the python langgraph-sdk.
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** N/A
- **Description:** Provide the id of the event for routes that use SSE
streams. This will allow for more custom retry logic when streams
disconnect if needed.
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** N/A
---------
Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com>
- Delete `security.md` as it will be inherited from
`langchain-ai/.github`
- Move `CONTRIBUTING.md` to `.github` (will still appear on homepage),
cleaning up top level
- Copy contents of `AGENTS.md` to `CLAUDE.md` since CC still doesn't
take `AGENTS.md`
**Description**: The Agent Server API now supports counting assistants
by name.
This is similar to adding the `name` parameter to the Assistants search
API: https://github.com/langchain-ai/langgraph/pull/6483
**Description:**
This PR adds the Python SDK types necessary for langgraph platform users
to inject their own custom encryption-at-rest functions. See [docs
PR](https://github.com/langchain-ai/docs/pull/1715) for more details.
note: this PR adds a starlette dev dependency so that custom encryption
can access BaseUser information.
**Issue:**
required for LSD-172
**Dependencies:**
- [depended upon by associated langgraph-api
changes](https://github.com/langchain-ai/langgraph-api/pull/1773)(this
PR must merge before that one)
- [docs PR](https://github.com/langchain-ai/docs/pull/1715)
**TODO:**
- [x] move docs to docs repo
- [x] bump package versions before merge
---------
Signed-off-by: Connor Braa <cwlbraa@langchain.dev>
Co-authored-by: Claude <noreply@anthropic.com>
**Description:** There are times a user might want to create the client,
but conditionally set the API key. For example, consider a complex auth
situation where the system has user callers using jwts and system
callers using API keys. This allows explicitly disabling the
auto-loading behavior of API keys in the client today, so no key is set.
**Issue:** N/A
**Dependencies:** None
**Twitter handle:** N/A
Otherwise, you cannot use `context` with stateful runs, because the
server throws if you provide both configurable and context in a single
call (due to ambiguous parameters)
This PR improves the consistency of interrupt streaming.
- when streaming with stream_mode values, the stream chunk now contains
the entire state alongside the interrupt:
```python
class State(TypedDict):
robot_input: str
# at this point in time robot_input is already set to "beep boop i am a robot"
app.stream(..., stream_mode="values")
# before
{"__interrupt__": (Interrupt(value="interrupt",))}}
# after
{"robot_input": "beep boop i am a robot", "__interrupt__": (Interrupt(value="interrupt"))}
```
- when streaming with stream_mode=["values", "updates"], interrupts are
surfaced in both an update stream chunk and the value stream chunk, when
previously we keep interrupt in values only if we request values mode
only
```python
class State(TypedDict):
robot_input: str
# at this point in time robot_input is already set to "beep boop i am a robot"
app.stream(..., stream_mode=["values", "updates"])
# before (interrupt would only emit on update chunk, there would be no values chunk)
("updates", {"__interrupt__": (Interrupt(value="interrupt",))}})
# after
("updates", {"__interrupt__": (Interrupt(value="interrupt",))}})
("values", {"robot_input": "beep boop i am a robot", "__interrupt__": (Interrupt(value="interrupt"))})
```
For housekeeping: this PR improves on this revert:
https://github.com/langchain-ai/langgraph/pull/6141
## overview
The main purpose of this is to respect tool signatures that request
injected args (like `ToolRuntime`) even when the explicitly specified
`args_schema` does not.
Ex in the following example, we should still inject `runtime` despite
its absence in `ArgsSchema`
```py
class ArgsSchema(BaseModel):
some_arg: int = Field(...)
@tool(args_schema=ArgsSchema)
def my_tool(some_arg: int, runtime: ToolRuntime): ...
```
This is accompanied by
https://github.com/langchain-ai/langchain/pull/34051 which has tests
that pass w/ this change. This tests injection w/ `create_agent` (more
end to end than tests added in
https://github.com/langchain-ai/langchain/pull/33999.
This unblocks the injection of `ToolRuntime` into MCP tools which is
exciting bc that exposes tool call id and state, which we previously
were unable to do.
## other benefits
* Cleaner code structure w/ more helpful docs about injected args.
* Nice perf boost, we're no longer inspecting the annotations of a
tool's schema 3 different times to detect store, state, and runtime
injections.
## additional notes
1. I could see a world where we want more of this logic to reside on the
tools themselves, but tools don't now about LG specific injection types
(like `ToolRuntime`, hence having this logic here for now).
2. We could separately add validation for the case where something is
specified in `args_schema` and not in the function signature (probably
at the tool level though).
Extract two common cases from the big switch statement of
`prepare_single_task` since it's a tad more composable.
All this does is shift/extract code to separate functions
`stream` and `astream` docstrings listed different available
`stream_mode` options.
Both methods support the same seven stream modes as defined in
`StreamMode`
Fixed for consistency
Hi all,
I found out that the sync and async code examples of the `task` function
in `libs/langgraph/langgraph/func/__init__.py` have a typo:
```
Example: Sync Task
```python
from langgraph.func import entrypoint, task
@task
def add_one(a: int) -> int:
return a + 1
@entrypoint()
def add_one(numbers: list[int]) -> list[int]:
futures = [add_one(n) for n in numbers]
results = [f.result() for f in futures]
return results
# Call the entrypoint
add_one.invoke([1, 2, 3]) # Returns [2, 3, 4]
```
```
Both task and entrypoint functions have the same name which gives an
error.
This is a small PR to fix this
Thank you for contributing to LangGraph! Follow these steps to mark your
pull request as ready for review. **If any of these steps are not
completed, your PR will not be considered for review.**
- [x] **PR title**: Follows the format: {TYPE}({SCOPE}): {DESCRIPTION}
- Examples:
- feat(core): add multi-tenant support
- fix(cli): resolve flag parsing error
- docs(openai): update API usage examples
- Allowed `{TYPE}` values:
- feat, fix, docs, style, refactor, perf, test, build, ci, chore,
revert, release
- Allowed `{SCOPE}` values (optional):
- langgraph, docs, cli, checkpoint, checkpoint-postgres,
checkpoint-sqlite, prebuilt, scheduler-kafka, sdk-py
- Once you've written the title, please delete this checklist item; do
not include it in the PR.
- **Description:** Azure Postgres SQL server has a limitation when doing
create extension vector is not exists, even though it manually created
before on a schema.
- **Issue:** Even though `CREATE EXTENSION vector` is executed manually
before, the permission issue arises. Putting it in an if else block
solves the issue and its not a breaking change.
```
Because vector isn't a trusted extension, only members of "azure_pg_admin" are allowed to use CREATE EXTENSION vector
HINT: to learn how to allow an extension or see the list of allowed extensions, please refer to https://go.microsoft.com/fwlink/?linkid=2301063
```
Co-authored-by: Josh Rogers <josh@langchain.dev>
## Issue
The `stream_mode` argument type includes `Sequence`, but it doesn't
correctly support non-list sequences. On the other hand, the
`print_mode` argument works as expected.
### Example
```python
from langgraph.pregel.main import Pregel
pregel = Pregel(nodes={}, channels=None, input_channels=[], output_channels=[], auto_validate=False)
stream_modes, *_ = pregel._defaults(
config={"recursion_limit": 1},
stream_mode=("values", "messages"),
print_mode=("values"),
output_keys=None,
interrupt_before=None,
interrupt_after=None,
durability=None,
)
print(stream_modes) # Expected `{'values', 'messages'}`, got `{('values', 'messages'), 'values'}`
```
## Summary
Replace f-string SQL formatting with parameterized queries to prevent
potential SQL injection in checkpoint migration code.
## Changes
Updated the migration version tracking INSERT statements in all
checkpoint saver classes to use parameterized queries instead of
f-string formatting:
- `PostgresSaver`
(libs/checkpoint-postgres/langgraph/checkpoint/postgres/__init__.py:100)
- `AsyncPostgresSaver`
(libs/checkpoint-postgres/langgraph/checkpoint/postgres/aio.py:104-106)
- `ShallowPostgresSaver`
(libs/checkpoint-postgres/langgraph/checkpoint/postgres/shallow.py:255)
- `AsyncShallowPostgresSaver`
(libs/checkpoint-postgres/langgraph/checkpoint/postgres/shallow.py:617-619)
**Before (vulnerable to SQL injection):**
```python
cur.execute(f"INSERT INTO checkpoint_migrations (v) VALUES ({v})")
```
**After (using parameterized query):**
```python
cur.execute("INSERT INTO checkpoint_migrations (v) VALUES (%s)", (v,))
```
## Risk Assessment
The practical risk is low since `v` is an integer loop variable
controlled by the codebase. However, using string formatting in SQL
queries is a well-known anti-pattern that can lead to SQL injection
vulnerabilities, especially if the code is later refactored or copied to
other contexts.
## Testing
- ✅ All 216 tests passing on PostgreSQL 15 and 16
- ✅ Linting and type checking passing
- ✅ No functional changes to behavior
Thank you for contributing to LangGraph! Follow these steps to mark your
pull request as ready for review. **If any of these steps are not
completed, your PR will not be considered for review.**
- [x] **PR title**: Follows the format: {TYPE}({SCOPE}): {DESCRIPTION}
- Examples:
- feat(core): add multi-tenant support
- fix(cli): resolve flag parsing error
- docs(openai): update API usage examples
- Allowed `{TYPE}` values:
- feat, fix, docs, style, refactor, perf, test, build, ci, chore,
revert, release
- Allowed `{SCOPE}` values (optional):
- langgraph, docs, cli, checkpoint, checkpoint-postgres,
checkpoint-sqlite, prebuilt, scheduler-kafka, sdk-py
- Once you've written the title, please delete this checklist item; do
not include it in the PR.
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change. Include a [closing
keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/using-issues/linking-a-pull-request-to-an-issue#linking-a-pull-request-to-an-issue-using-a-keyword)
if applicable.
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [x] **Add tests and docs**: If you're adding a new integration, you
must include:
1. A test for the integration, preferably unit tests that do not rely on
network access,
2. An example notebook showing its use. It lives in
`docs/docs/integrations` directory.
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. We will not consider a
PR unless these three are passing in CI. See [contribution
guidelines](https://github.com/langchain-ai/langgraph/blob/main/CONTRIBUTING.md)
for more.
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to `pyproject.toml` files (even
optional ones) unless they are **required** for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
`REMOVE_ALL_MESSAGES` is a public constant used with `RemoveMessage` to
clear all messages from the state:
```python
from langchain_core.messages import RemoveMessage
from langgraph.graph.message import REMOVE_ALL_MESSAGES
# Clear all messages
[RemoveMessage(id=REMOVE_ALL_MESSAGES)]
```
However, it is not exported in __all__, causing:
Linting errors in IDEs (PyCharm)
no-member warnings from Pylint
Confusion for users
This PR:
Adds REMOVE_ALL_MESSAGES to __all__
Adds inline docstring with usage example
No runtime behavior changes — only improves IDE support and API clarity.
Thank you for contributing to LangGraph! Follow these steps to mark your
pull request as ready for review. **If any of these steps are not
completed, your PR will not be considered for review.**
---
Local verification:
```bash
# Before
from langgraph.graph.message import REMOVE_ALL_MESSAGES # Pylint: no-member
# After: no error
```
CI Note: This is a pure export/docs fix. `make lint` and `make test`
pass unchanged.
Thank you for contributing to LangGraph! Follow these steps to mark your
pull request as ready for review. **If any of these steps are not
completed, your PR will not be considered for review.**
## Description
- Fixed a bug in `libs/sdk-py/langgraph_sdk/auth/__init__.py` where the
error message for an already-set authentication handler did not properly
render the handler value.
- Updated the error string from a static `{self._authenticate_handler}`
to a correctly interpolated f-string.
```python
"Authentication handler already set as {self._authenticate_handler}."
```
```python
f"Authentication handler already set as {self._authenticate_handler}."
```
- Error messages now correctly display the actual handler instance,
improving debugging clarity.
- **Issue:** Fixes https://github.com/langchain-ai/langgraph/issues/6387
- **Dependencies:** -
- **Twitter handle:** -
- [x] **Add tests and docs**: If you're adding a new integration, you
must include:
1. A test for the integration, preferably unit tests that do not rely on
network access,
2. An example notebook showing its use. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. We will not consider a
PR unless these three are passing in CI. See [contribution
guidelines](https://github.com/langchain-ai/langgraph/blob/main/CONTRIBUTING.md)
for more.
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to `pyproject.toml` files (even
optional ones) unless they are **required** for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
In this PR:
- Add missing LICENSE files for checkpoint-sqlite and
checkpoint-postgres libraries.
Both libraries specify the MIT License in their pyproject.toml files,
but the actual LICENSE files were missing.
This update adds the corresponding LICENSE files to ensure proper
license documentation and compliance.
This is a very small PR to correct a typo in the docstring of the
`PregelLoop.tick()` method.
```python
def tick(self) -> bool:
"""Execute a single iteration of the Pregel loop.
Args:
input_keys: The key(s) to read input from.
Returns:
True if more iterations are needed.
"""
```
Corrected to :
```python
def tick(self) -> bool:
"""Execute a single iteration of the Pregel loop.
Returns:
True if more iterations are needed.
"""
```
The docstring was written in #2946 when the signature of tick was
```python
def tick(
self,
*,
input_keys: Union[str, Sequence[str]],
) -> bool:
```
but it was simplified to
```python
def tick(self) -> bool:
```
in #5080
**Description:** Bumping the checkpoint-postgres package to version
3.0.1 to release an update to migrations
(https://github.com/langchain-ai/langgraph/pull/6400).
**Issue:** N/A
**Dependencies:** N/A
**Twitter handle:** N/A
- **Description:** The final migration for the postgres checkpointer is
not currently idempotent. That presents problems when migrating from one
checkpointer to another or if migrations otherwise get applied twice.
This makes the final migration idempotent to avoid this problem.
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** N/A
Renaming LangGraph Server to Agent Server, this updates the redirects
from the old site to the new site's renamed files.
PR also includes some hosting --> platform setup redirects
PR #6195 fixed `bulk_update_state` to populate `task.result` by calling
`prepare_next_tasks` to discover task IDs. Before #6195,
prepare_next_tasks was gated by the condition `CONFIG_KEY_CHECKPOINT_ID
not in config[CONF]` - so it only ran if we were resuming from an empty
checkpoint. This check was removed in order to properly populate task
results. However, the removal of this check inadvertently applied
pending writes during manual state updates which caused issues when
forking:
- When you fork from a checkpoint by calling `update_state(config,
new_values, as_node="mynode")`, pending writes from the original
execution were being applied
- This caused stale data to leak into forked threads (eg. old tool call
results appearing in forked execution)
Changes
Removed pending writes application from `bulk_update_state` and
`abulk_update_state`:
- Still call `prepare_next_tasks` to discover task IDs, but skip the
code that applies null writes and regular pending writes
Tests
- Added `test_fork_does_not_apply_pending_writes` for sync and async
which verifies forking doesn't include stale pending writes from
original execution
This syncs the checkpoint interface specification with the base class
(`BaseCheckpointSaver`) in
`langgraph/libs/checkpoint/langgraph/checkpoint/base /__init__.py`.
**Description:** Adds the syntax directive to generated dockerfile for
langgraph builds if we have additional contexts
**Issue:** fixes issue with python monorepo builds failing
**Dependencies:** N/A
namespace decisions
```
langgraph.prebuilt
├── ToolRuntime # new
# all of the other stuff that was already there
langgraph.prebuilt.tool_node
├── ToolNode
├── ToolCallRequest # new
├── ToolRuntime # new
├── InjectedState
├── InjectedStore
├── ToolCallWrapper
├── AsyncToolCallWrapper
├── tools_condition
```
```
langchain.tools
├── ToolRuntime # now from langgraph.prebuilt
├── InjectedState # now from langgraph.prebuilt
├── InjectedStore # now from langgraph.prebuilt
├── ToolException
├── tool
├── BaseTool
├── InjectedToolArg
├── InjectedToolCallId
```
bumping core dependency for `langgraph-prebuilt` to `>1.0.0` so that we
can take advantage of internal utils that allow `ToolRuntime` injection.
We were previously bumping the version in lock step with prebuilt (prev
version was 0.3.67), so this pattern is in line with that.
Also updating snapshots accordingly:
* New mermaid syntax for a few graphs
* Removal of `examples` from `AIMessage`
UntrackedValue is a special channel type where the values in it are not
persisted to memory. Our v1 create_agent middleware used UntrackedValue
in middleware (e.g. ShellToolMiddleware) for some cool features like
temp files.
If a user has elected to use a checkpointer, we normally enforce that
the values they write to channels are serializable. However, this
doesn't make sense to enforce for UntrackedValues because the contract
is they're never written to checkpoint - so the user should not be
forced to make the contents of the channel serializable
However when using a checkpointer and durability sync/async, we found
that writes would still be persisted that contained UntrackedValue
contents in two forms:
a) UntrackedValue channel objects
b) Send objects - in the state passed to another node
Patched this in put_writes by a) skipping persisting writes to
UntrackedValue channels altogether and b) popping all UntrackedValue kv
pairs nested within Send packets. We also need to sanitize in
_put_checkpoint which is called when durability=="exit".
Added a basic test for UntrackedValue in test_channel.py and added more
comprehensive tests using Send under some different scenarios in
test_pregel.py
See https://github.com/langchain-ai/langgraph/pull/6277
Adds langgraph.types.Overwrite, a deterministic way to bypass a reducer.
When encountering a value wrapped with Overwrite,
BinaryOperatorAggregate overwrites the channel value.
<img width="227" height="329" alt="image"
src="https://github.com/user-attachments/assets/f2136117-9aa3-4246-863d-d5df0e7d1df1"
/>
If either node_b or node_c overwrite (but not both), then at END the
channel is equal to the value node_b or node_c wrote. Order of execution
doesn't matter because once an Overwrite value is encountered, regular
values are ignored (self.operator is not called for the rest of the
update)
If multiple nodes overwrite in the same superstep then
InvalidUpdateError is thrown
Usage
```python
from langgraph.types import Overwrite
def node_b(state:State):
return {"messages": Overwrite(["b"])}
```
or
``` python
def node_b(state:State):
return {"messages": {"__overwrite__": ["b"]}}
```
Requests are not guaranteed to contain a body, and a request's body is
not guaranteed to be valid JSON.
This updates the type signature for authentication handlers
to account for these scenarios.
In this PR:
- Bump `langgraph-checkpoint` to 3.0
- Bump `langgraph-checkpoint-sqlite` to 3.0; Update
`langgraph-checkpoint` deps to >=3,<4
- Bump `langgraph-checkpoint-postgres` to 3.0; Update
`langgraph-checkpoint` max to <4 (keep prior min since the deprecated
functionality wasn't explicitly used)
- Bump `langgraph` to 1.0.1; update `langgraph-checkpoint` max bound to
4
- Bump `prebuilt` to 1.0.1; update `langgraph-checkpoint` max bound to 4
* catching error thrown by asyncio
* using 2nd check for annotations given Pydantic 2.12 changes
* skipping tests for remote graph bc langgraph-api is dependent on
`jsonschema-rs`
* skipping tests w/ pydantic v1 models
```bash
hint: This usually indicates a problem with the package or the build environment.
help: `jsonschema-rs` (v0.29.1) was included because `langgraph:dev` (v1.0.0rc1) depends on `langgraph-cli[inmem]` which
depends on `langgraph-api` (v0.4.29) which depends on `jsonschema-rs`
```
not yet testing for free threaded python, that'll be much more involved!
ended up separating lint / testing deps during this process bc I was
getting a ton of not required deps while testing that were complicating
things :/
**Description**
As part of this PR #6156, local deps are no longer installed in editable
mode. This change reverts that behaviour and ensures local packages are
installed in editable mode.
**Issue:** fixes#6288
This PR updates the OpenAPI specification with changes detected from the
LangGraph API server.
**Changes detected as of LangGraph API version 0.4.42**
This update was automatically generated by the sync workflow in the
langgraph-api repository.
Co-authored-by: hinthornw <hinthornw@users.noreply.github.com>
some of these changes were obvious, and some were less obvious. In a few
spots, it felt like a judgement call if we should be saying LangSmith
Deployment of LangGraph Server. But hopefully either works.
### 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>
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href="https://github.com/actions/download-artifact/compare/v4...v5">compare
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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>
This PR updates the OpenAPI specification with changes detected from the
LangGraph API server.
**Changes detected as of LangGraph API version 0.2.137**
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 fixes a minor formatting inconsistency in the StateSnapshot
examples within persistence.md.
Specifically, the next=('node_b',) value was inline with values={...},
which is inconsistent with other snapshots.
It has been moved to a new line for better readability and consistency
across examples.
This PR adds an aclose method to the LangGraphClient.
When using the client in a FastAPI application, it's common to share a
single instance across the application's lifespan. The absence of an
aclose method makes it difficult to gracefully close the underlying HTTP
session on application shutdown. This change enables proper resource
management by allowing the client to be closed cleanly.
Thank you for contributing to LangGraph! Follow these steps to mark your
pull request as ready for review. **If any of these steps are not
completed, your PR will not be considered for review.**
- [ ] **PR title**: Follows the format: {TYPE}({SCOPE}): {DESCRIPTION}
- Examples:
- feat(core): add multi-tenant support
- fix(cli): resolve flag parsing error
- docs(openai): update API usage examples
- Allowed `{TYPE}` values:
- feat, fix, docs, style, refactor, perf, test, build, ci, chore,
revert, release
- Allowed `{SCOPE}` values (optional):
- langgraph, docs, cli, checkpoint, checkpoint-postgres,
checkpoint-sqlite, prebuilt, scheduler-kafka, sdk-py
- Once you've written the title, please delete this checklist item; do
not include it in the PR.
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change. Include a [closing
keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/using-issues/linking-a-pull-request-to-an-issue#linking-a-pull-request-to-an-issue-using-a-keyword)
if applicable.
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: If you're adding a new integration, you
must include:
1. A test for the integration, preferably unit tests that do not rely on
network access,
2. An example notebook showing its use. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. We will not consider a
PR unless these three are passing in CI. See [contribution
guidelines](https://github.com/langchain-ai/langgraph/blob/main/CONTRIBUTING.md)
for more.
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to `pyproject.toml` files (even
optional ones) unless they are **required** for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
### Description
Adds Redis as a supported cache backend for LangGraph node-level
caching, enabling distributed caching across multiple processes/servers.
This implementation follows the same patterns as existing InMemoryCache
and SqliteCache.
### Key changes
- New RedisCache class implementing the BaseCache interface
- Support for TTL-based expiration and batch operations
- Worker-specific cache prefixes for parallel test isolation
### Dependencies
- redis package (already included in dev dependencies)
### Test Plan
- Unit tests: Added Redis cache tests covering basic operations, TTL,
batch operations, and error handling
- Integration tests: Redis cache integrated into existing LangGraph test
suite, tested with all checkpointer combinations
Reproduces:
https://github.com/langchain-ai/langgraph/issues/5249#issuecomment-3156519635
Caused after this change:
https://github.com/langchain-ai/langgraph/pull/4843
Fix to allow emitting messages from subgraphs if the subgraphs
explicitly used a stream mode "messages".
```python
def node_in_parent(...):
# subgraph was called as a function.
# messages are explicitly requested.
for event in subgraph.stream(..., stream_mode="messages"):
# something is done with `event`
return ...
# subgraphs = False!
parent_graph.invoke(..., subgraphs=False)
```
The code above should continue to work correctly regardless of the value
of subgraphs as streaming messages was requested explicitly in the
parent node!
---------
Co-authored-by: Sydney Runkle <54324534+sydney-runkle@users.noreply.github.com>
- This makes prepare_next_tasks constant on number of nodes in all
cases, whereas before we were falling back to node iteration when
resuming from an existing checkpoint
`MessageGraph` is deprecated, to be removed in v2.
A `StateGraph` with a `messages` key should be used instead.
Alternatively, folks can use `Annotated[list[AnyMessage], add_messages]` as their state schema.
Reverts https://github.com/langchain-ai/langgraph/pull/5562
I anticipate that we want to do another pass at a refactor here in the
short term, but this makes it easier to adapt to new langchain core
message types for v0.4 support in the short term.
Fixes https://github.com/langchain-ai/langgraph/issues/5784
* Removes usage of `is_last_step`, no longer needed with
`remaining_steps`
* Make `remaining_steps` `NotRequired` so that json schema doesn't
suggest need for user input
* Move `PregelScratchpad` to shared utils file to prevent circular
import issue (it's used from `channels/managed` and other pregel files).
* Ensures that managed values wrapped in `NotRequired` or `Required` are
still recognized!
Fixes: #5787
Ensures that if `config` is not typed as one of `RunanbleConfig` or
`Optional[RunnableConfig]` a warning is raised to help developers avoid
unexpected results at invocation time.
---------
Co-authored-by: open-swe[bot] <open-swe@users.noreply.github.com>
Co-authored-by: Sydney Runkle <sydneymarierunkle@gmail.com>
Fixes#5781
Fixes the incorrect import statement in the Python documentation
tutorial.
- Changed import from `MemorySaver` to `InMemorySaver`
- Ensures consistency between import statement and class instantiation
- Verified through formatting and linting checks
The documentation now correctly reflects the proper import for the
InMemorySaver class.
---------
Co-authored-by: open-swe[bot] <open-swe@users.noreply.github.com>
Fixes https://github.com/langchain-ai/langgraph/issues/5795
* Must use `category=None` on decorator so that we get type checking
support but no dupe warning
* Fixed tuple on `confix_type` warning causing false warning
I suppose that the code snippets are intended to run each on its own. To
guarantee this the snippet for the example:
"Write long-temr memory from tools"
needs to include `RunnableConfig`
Same also for the second commit of this pull request.
The other commits are about similar issues, where imports are missing to
make a snippet executable on its own.
---------
Signed-off-by: Kai Wendel <kai.wendel@iws.uni-stuttgart.de>
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
Co-authored-by: Lauren Hirata Singh <lauren@langchain.dev>
**Description:**
Replaced the outdated image in the "Run graph nodes in parallel" section
of the N Graph API how-to guide to correctly show parallel node
execution.
**Twitter handle:** @MichaelLoukeris
Co-authored-by: Lauren Hirata Singh <lauren@langchain.dev>
Nit, without comments I thought image would somehow show in terminal,
but that's not true you'd only get image to show if in jupyter notebook.
Don't have to merge, i'm just seeing this particular as not a pleasure
DevX.
<img width="848" height="440" alt="Screenshot 2025-07-21 at 12 26 04 PM"
src="https://github.com/user-attachments/assets/50261b35-f09d-4516-86f4-14e8bf53f8e2"
/>
**Description:**
Closes #___
Removed the redundant `pretty_print_message`/`pretty_print_messages`
helper snippet from
`docs/docs/tutorials/multi_agent/agent_supervisor.md`. Now there is a
single, authoritative definition of these functions, which:
- Simplifies the tutorial
- Avoids reader confusion over which helper to use
- Prevents future drift between duplicate code blocks
**Issue:** Closes #___
**Dependencies:** None
Co-authored-by: Lauren Hirata Singh <lauren@langchain.dev>
The example had the node names as - node_a, node_b & node_c. But the
graph shows the image of generate_topics. This change includes the
addition of complete & accurate graph.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
## docs: Removed repetition of block pretty_print_messages
- **Description:** The documentation had the same cell duplicated, I
fixed it by deleting one example
- **Issue:** #5616
- **Description:** Update the type annotations in create_react_agent to
allow one to provide a callable for the model that uses bind_tools and
returns a Runnable[LanguageModelInput, BaseMessage]
- **Issue:** #5739
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Fixes#5735
Implement context coercion functionality for LangGraph runtime to
improve API usability.
Key changes:
- Added `_coerce_context` function in `pregel/main.py`
- Supports coercion for:
- Pydantic BaseModel
- Dataclasses
- TypedDict
- Comprehensive test coverage added in `tests/test_runtime.py`
- Handles edge cases like None context and missing fields
The implementation allows users to pass dictionaries as context, which
will be automatically converted to the expected schema type, making the
API more flexible and user-friendly.
---------
Co-authored-by: open-swe[bot] <open-swe@users.noreply.github.com>
Co-authored-by: Sydney Runkle <sydneymarierunkle@gmail.com>
Co-authored-by: Sydney Runkle <54324534+sydney-runkle@users.noreply.github.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
**docs: The notebook redirects to a page that does not exist**
- **Description:** The current documentation redirects to a file that
does not exist. I have removed the doc to avoid confusion.
- **Issue:** Fixes#5637
**Description:**
Removed 4 broken notebooks in `examples/human_in_the_loop/` that
referenced missing files (list below). These notebooks displayed
redirect messages but the new paths are either invalid or don’t contain
content.
Filenames:
1. examples/human_in_the_loop/dynamic_breakpoints.ipynb
2. examples/human_in_the_loop/edit-graph-state.ipynb
3. examples/human_in_the_loop/review-tool-calls.ipynb
4. examples/human_in_the_loop/time-travel.ipynb
**Issue:**
Closes#5642
**Dependencies:**
None
Co-authored-by: gawhaarya <gawhaneaarya@gmail.com>
## Description
Previously, when a tool returned `Command` to update the graph's state,
the `_validate_tool_command` method in `ToolNode` would raise a
`ValueError` if the `messages_update` list contained only a
`RemoveMessage(id=REMOVE_ALL_MESSAGES)` object. This was because the
validation logic expected a matching `ToolMessage` for the tool call and
did not account for this specific state-clearing scenario.
This commit modifies the validation logic to check if the
`messages_update` list contains a single
`RemoveMessage(id=REMOVE_ALL_MESSAGES)` element. If this condition is
met, the `ToolMessage` validation is bypassed, allowing a tool to clear
the entire message history without causing a validation error.
A new test case, `test_tool_node_command_remove_all_messages`, has been
added to `tests/test_tool_node.py` to verify this change and prevent
future regressions.
## Example
Here is a self-contained example that illustrates the problem and the
fix. Without this change, the code block for `Example 2` would raise a
`ValueError`.
```python
from typing import Annotated, List
from langchain_core.messages import (
AIMessage,
AnyMessage,
HumanMessage,
RemoveMessage,
ToolMessage,
)
from langchain_core.tools import InjectedToolCallId, tool
from langchain_openai import ChatOpenAI
from langgraph.graph import END, StateGraph, add_messages
from langgraph.graph.message import REMOVE_ALL_MESSAGES
from langgraph.prebuilt import InjectedState, ToolNode
from langgraph.types import Command
from pydantic import BaseModel, Field
# Agent state tracks current and all messages
class AgentState(BaseModel):
messages: Annotated[List[AnyMessage], add_messages] = Field(
default_factory=list, description="Current conversation messages."
)
all_messages: Annotated[List[AnyMessage], add_messages] = Field(
default_factory=list, description="All messages, including removed ones."
)
# Tool to clear history if long enough, otherwise returns a warning
@tool
def clear_history_tool(
state: Annotated[AgentState, InjectedState],
tool_call_id: Annotated[str, InjectedToolCallId],
):
"""Clears message history if it's long enough."""
if len(state.messages) < 3:
return Command(
update={
"messages": [
ToolMessage(
"History is not long enough to be cleared. Please try again.",
tool_call_id=tool_call_id,
)
]
}
)
else:
return Command(
update={
"messages": [RemoveMessage(id=REMOVE_ALL_MESSAGES)],
"all_messages": state.messages
+ [
ToolMessage(
"History has been successfully cleared.",
tool_call_id=tool_call_id,
)
],
}
)
# Bind the tool to the model
model = ChatOpenAI(model="gpt-4o-mini").bind_tools([clear_history_tool])
def model_node(state: AgentState):
return {"messages": [model.invoke(state.messages)]}
# Build the agent graph
graph_builder = StateGraph(AgentState)
graph_builder.add_node("model", model_node)
graph_builder.add_node("tools", ToolNode([clear_history_tool]))
graph_builder.set_entry_point("model")
graph_builder.add_edge("model", "tools")
graph_builder.add_edge("tools", END)
graph = graph_builder.compile()
def print_messages(header, messages):
print(f"\n{header}")
for message in messages:
message.pretty_print()
### Example 1: Not enough history to clear
state_1 = AgentState(
messages=[HumanMessage(content="Please clear my message history.")]
)
output_1 = graph.invoke(state_1)
print_messages("First call: State 'messages'", output_1["messages"])
print_messages("First call: State 'all_messages'", output_1["all_messages"])
### Example 2: History is cleared
state_2 = AgentState(
messages=[
HumanMessage(content="Will this PR get merged?"),
AIMessage(content="Maybe, if it's good enough."),
HumanMessage(content="Please clear my message history."),
]
)
# Without the changes in this PR, the following line will raise a ValueError
output_2 = graph.invoke(state_2)
print_messages("Second call: State 'messages'", output_2["messages"])
print_messages("Second call: State 'all_messages'", output_2["all_messages"])
```
### Outputs
*Without the changes in this PR:*
```
First call: State 'messages'
================================ Human Message =================================
Please clear my message history.
================================== Ai Message ==================================
Tool Calls:
clear_history_tool (ba421ac3-1e1a-4208-a8f6-c5500ee0abcc)
Call ID: ba421ac3-1e1a-4208-a8f6-c5500ee0abcc
Args:
================================= Tool Message =================================
Name: clear_history_tool
History is not long enough to be cleared. Please try again.
First call: State 'all_messages'
Traceback (most recent call last):
File "main.py", line 114, in <module>
output_2 = graph.invoke(state_2)
^^^^^^^^^^^^^^^^^^^^^
File ".venv/lib/python3.11/site-packages/langgraph/pregel/__init__.py", line 2844, in invoke
for chunk in self.stream(
File ".venv/lib/python3.11/site-packages/langgraph/pregel/__init__.py", line 2534, in stream
for _ in runner.tick(
File ".venv/lib/python3.11/site-packages/langgraph/prebuilt/tool_node.py", line 241, in _func
outputs = [
^
File ".venv/lib/python3.11/concurrent/futures/_base.py", line 619, in result_iterator
yield _result_or_cancel(fs.pop())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File ".venv/lib/python3.11/concurrent/futures/_base.py", line 317, in _result_or_cancel
return fut.result(timeout)
^^^^^^^^^^^^^^^^^^^
File ".venv/lib/python3.11/concurrent/futures/_base.py", line 449, in result
return self.__get_result()
^^^^^^^^^^^^^^^^^^^
File ".venv/lib/python3.11/concurrent/futures/_base.py", line 401, in __get_result
raise self._exception
File ".venv/lib/python3.11/concurrent/futures/thread.py", line 58, in run
result = self.fn(*self.args, **self.kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File ".venv/lib/python3.11/site-packages/langchain_core/runnables/config.py", line 555, in _wrapped_fn
return contexts.pop().run(fn, *args)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File ".venv/lib/python3.11/site-packages/langgraph/prebuilt/tool_node.py", line 353, in _run_one
return self._validate_tool_command(response, call, input_type)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File ".venv/lib/python3.11/site-packages/langgraph/prebuilt/tool_node.py", line 616, in _validate_tool_command
raise ValueError(
ValueError: Expected to have a matching ToolMessage in Command.update for tool 'clear_history_tool', got: [RemoveMessage(content='', additional_kwargs={}, response_metadata={}, id='__remove_all__')]. Every tool call (LLM requesting to call a tool) in the message history MUST have a corresponding ToolMessage. You can fix it by modifying the tool to return `Command(update={"messages": [ToolMessage("Success", tool_call_id=tool_call_id), ...]}, ...)`.
```
*With the changes in this PR:*
```
First call: State 'messages'
================================ Human Message =================================
Please clear my message history.
================================== Ai Message ==================================
Tool Calls:
clear_history_tool (ba421ac3-1e1a-4208-a8f6-c5500ee0abcc)
Call ID: ba421ac3-1e1a-4208-a8f6-c5500ee0abcc
Args:
================================= Tool Message =================================
Name: clear_history_tool
History is not long enough to be cleared. Please try again.
First call: State 'all_messages'
Second call: State 'messages'
Second call: State 'all_messages'
================================ Human Message =================================
Will this PR get merged?
================================== Ai Message ==================================
Maybe, if it's good enough.
================================ Human Message =================================
Please clear my message history.
================================== Ai Message ==================================
Tool Calls:
clear_history_tool (499b1be3-6df1-493f-85e5-8d7e429dead8)
Call ID: 499b1be3-6df1-493f-85e5-8d7e429dead8
Args:
================================= Tool Message =================================
Name: clear_history_tool
History has been successfully cleared.
```
## Twitter handle
[@samuelpullely](https://x.com/samuelpullely)
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Introduces a syntax for cross-reference links that work across language
and change behavior depending on which scope they appear in.
```markdown
@[interrupt]
:::python
@[StateGraph]
:::
:::js
@[create_react_agent]
:::
```
Can be compiled to
```markdown
# a link that changes based on global context or compile target
<div> ... </div> -> `interrupt` in global context
:::python
[StateGraph](link to python cross reference)
:::
:::js
[create_react_agent](link to js cross reference)
:::
```
TODO:
- [x] fix broken unit test
- [x] no f strings in logger (it's a sin)
- [x] remove cross-refs.txt (we'll instead start updating the cross link
map)
Automated changelog update created by the LangGraph Server Changelog
Bot.
Feel free to merge anytime.
---------
Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com>
This PR allows a developer to change the model configuration at run time based on context. This includes that list of tools available to the model to call.
```python
def create_react_agent(
model: Union[
str,
LanguageModelLike,
Callable[[SateLike, Runtime...], BaseChatModel], # <--- New
],
tools: Union[
Sequence[Union[BaseTool, Callable, dict[str, Any]]], ToolNode]
],
*,
....
llm = init_chat_model(...)
def prepare_model(state, runtime):
selected_tool_names = func(state, context)
return llm.bind(tools=selected_tool_names)
create_react_agent(
prepare_model,
tools=all_known_tools
)
```
## Semantics
1. `tools` = are the known tools, used to configure ToolNode and will
configure:
1. model provided as string
2. model provided as BaseChatModel (if it has no tools bound to it)
2. If a user provides a dynamic model (callable), the user is
responsible for binding tools
Alternative considered:
1. Passing `Callable[[SateLike, Config...], list[BaseTool]]` to tools
2. Passing `Callable[[SateLike, Config...], list[str]]` to a tool
selector
Both have the issue that there's non obvious interplay between tool
selection and dynamic models. (i.e., if we want to introduce dynamic
models at in the future, the API will become tricky to explain)
---------
Co-authored-by: Sydney Runkle <54324534+sydney-runkle@users.noreply.github.com>
description:Report a bug in LangGraph. To report a security issue, please instead use the security option below. For questions, please use the LangChain Forum at forum.langchain.com.
labels:[pending,bug]
description:Report a bug in LangGraph. To report a security issue, please instead use the security option (below). For questions, please use the LangChain forum (below).
labels:["bug"]
type:bug
body:
- type:markdown
attributes:
value:|
Thank you for taking the time to file a bug report.
Use this to report BUGS in LangGraph. For usage questions, feature requests and general design questions, please use the [LangChain Forum](https://forum.langchain.com/).
Relevant links to check before filing a bug report to see if your issue has already been reported, fixed or
description:Before submitting this issue, please confirm that you have completed all the steps below by checking each option. These steps help ensure your issue is well-defined, relevant, and actionable.
description:Please confirm and check all the following options.
options:
- label:This is a bug, not a usage question. For questions, please use the LangChain Forum (https://forum.langchain.com/).
- label:This is a bug, not a usage question.
required:true
- label:I added a clear and detailed title that summarizes the issue.
- label:I added a clear and descriptive title that summarizes this issue.
required:true
- label:I read what a minimal reproducible example is (https://stackoverflow.com/help/minimal-reproducible-example).
- label:I used the GitHub search to find a similar question and didn't find it.
required:true
- label:I included a self-contained, minimal example that demonstrates the issue INCLUDING all the relevant imports. The code run AS IS to reproduce the issue.
- label:I am sure that this is a bug in LangGraph rather than my code.
required:true
- label:The bug is not resolved by updating to the latest stable version of LangGraph (or the specific integration package).
required:true
- label:This is not related to the langchain-community package.
required:true
- label:I posted a self-contained, minimal, reproducible example. A maintainer can copy it and run it AS IS.
required:true
- type:textarea
id:reproduction
validations:
required:true
attributes:
label:Example Code
label:Reproduction Steps / Example Code (Python)
description:|
Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case. Replace this code with your own!
Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case.
If a maintainer can copy it, run it, and see it right away, there's a much higher chance that you'll be able to get help.
**Important!**
* Avoid screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
* Reduce your code to the minimum required to reproduce the issue if possible.
(This will be automatically formatted into code, so no need for backticks.)
render:python
placeholder:|
from langgraph.graph import StateGraph
@@ -47,17 +63,13 @@ body:
chain = StateGraph(list)
chain.invoke('Hello!')
render:python
- type:textarea
id:error
validations:
required:false
attributes:
label:Error Message and Stack Trace (if applicable)
description:|
If you are reporting an error, please include the full error message and stack trace.
placeholder:|
Exception + full stack trace
If you are reporting an error, please copy and paste the full error message and
stack trace.
(This will be automatically formatted into code, so no need for backticks.)
render:shell
- type:textarea
id:description
@@ -78,7 +90,18 @@ body:
attributes:
label:System Info
description:|
Run on your machine: `python -m langchain_core.sys_info`
Please share your system info with us.
Run the following command in your terminal and paste the output here:
`python -m langchain_core.sys_info`
or if you have an existing python interpreter running:
@@ -21,7 +21,7 @@ Thank you for contributing to LangGraph! Follow these steps to mark your pull re
1. A test for the integration, preferably unit tests that do not rely on network access,
2. An example notebook showing its use. It lives in `docs/docs/integrations` directory.
- [ ]**Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. We will not consider a PR unless these three are passing in CI. See [contribution guidelines](https://github.com/langchain-ai/langgraph/blob/main/CONTRIBUTING.md) for more.
- [ ]**Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. We will not consider a PR unless these three are passing in CI. See [contribution guidelines](https://docs.langchain.com/oss/python/contributing/overview) for more.
Thank you for being interested in contributing to LangGraph!
## General guidelines
Here are some things to keep in mind for all types of contributions:
- Follow the ["fork and pull request"](https://docs.github.com/en/get-started/exploring-projects-on-github/contributing-to-a-project) workflow.
- Fill out the checked-in pull request template when opening pull requests. Note related issues and tag relevant maintainers.
- Ensure your PR passes formatting, linting, and testing checks before requesting a review.
- If you would like comments or feedback, please tag a maintainer.
- Backwards compatibility is key. Your changes must not be breaking, except in case of critical bug and security fixes.
- Look for duplicate PRs or issues that have already been opened before opening a new one.
- Keep scope as isolated as possible. As a general rule, your changes should not affect more than one package at a time.
### Bugfixes
For bug fixes, please open up an issue before proposing a fix to ensure the proposal properly addresses the underlying problem. In general, bug fixes should all have an accompanying unit test that fails before the fix.
### New features
For new features, please start a new [discussion](https://forum.langchain.com/), where the maintainers will help with scoping out the necessary changes.
## Contribute Documentation
Documentation is a vital part of LangGraph. We welcome both new documentation for new features and
community improvements to our current documentation. Please read the resources below before getting started:
As LangGraph continues to grow, the surface area of documentation required to cover it continues to grow too.
This page provides guidelines for anyone writing documentation for LangGraph, as well as some of our philosophies around organization and structure.
## Philosophy
LangGraph's documentation follows the [Diataxis framework](https://diataxis.fr).
Under this framework, all documentation falls under one of four categories: [Tutorials](#tutorials),
[How-to guides](#how-to-guides),
[References](#references), and [Explanations (aka conceptual guides)](#conceptual-guide).
### Tutorials
Tutorials are lessons that take the reader through a practical activity. Their purpose is to help the user
gain understanding of concepts and how they interact by showing one way to achieve some goal in a hands-on way.
They should **avoid** giving
multiple permutations of ways to achieve that goal in-depth. Choice is burdensome. Instead, they should guide a new user through a recommended path to accomplishing a concrete goal. While the end result of a tutorial does not necessarily need to
be completely production-ready, it should be useful and practically satisfy the goal that you clearly stated in the tutorial's introduction.
To quote the Diataxis website:
> A tutorial serves the user’s *acquisition* of skills and knowledge - their study. Its purpose is not to help the user get something done, but to help them learn.
In LangGraph, these are often higher level guides that show off end-to-end use cases.
Some examples include:
- [Build a Customer Support Bot](https://langchain-ai.github.io/langgraph/tutorials/customer-support/customer-support/)
- [Build a SQL Agent](https://langchain-ai.github.io/langgraph/tutorials/sql/sql-agent/)
Here are some high-level tips on writing a good tutorial:
- Focus on guiding the user to get something done, but keep in mind the end-goal is more to impart principles than to create a perfect production system.
- Be specific, not abstract and follow one path.
- No need to go deeply into alternative approaches, but it’s ok to reference them, ideally with a link to an appropriate how-to guide.
- Get "a point on the board" as soon as possible - something the user can run that outputs something.
- You can iterate and expand afterwards.
- Try to frequently checkpoint at given steps where the user can run code and see progress.
- Focus on results, not technical explanation.
- Crosslink heavily to appropriate conceptual/reference pages
- The first time you mention a LangGraph concept, use its full name (e.g. "human-in-the-loop"), and link to its conceptual/other documentation page.
- It's also helpful to add a prerequisite callout that links to any pages with necessary background information.
- End with a recap/next steps section summarizing what the tutorial covered and future reading, such as related how-to guides.
- Use phrases like "Next we can run X & Y. We will expect Z.". Then afterwards, use language like "Notice Z" that recalls our expectations and directs the reader's attention to the topic we are trying to teach.
- Do not shy away from repetition.
### How-to guides
A how-to guide, as the name implies, demonstrates how to do something discrete and specific.
It should assume that the user is already familiar with underlying concepts, and is trying to solve an immediate problem, but
should still give some background or list the scenarios where the information contained within can be relevant.
They can and should discuss alternatives if one approach may be better than another in certain cases.
To quote the Diataxis website:
> A how-to guide serves the work of the already-competent user, whom you can assume to know what they want to do, and to be able to follow your instructions correctly.
Some examples include:
- [How to add persistence to your graph](https://langchain-ai.github.io/langgraph/how-tos/persistence/)
- [How to view and update past graph state](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/time-travel/)
Here are some high-level tips on writing a good how-to guide:
- Clearly explain what you are guiding the user through at the start
- Assume higher intent than a tutorial and show what the user needs to do to get that task done
- Assume familiarity of concepts, but explain why suggested actions are helpful
- Crosslink heavily to conceptual/reference pages
- Discuss alternatives and responses to real-world tradeoffs that may arise when solving a problem
- Use lots of example code, ideally within complete code blocks that the reader can copy and run.
- End with a recap/next steps section summarizing what the tutorial covered and future reading, such as other related how-to guides
### Conceptual guides
LangGraph's conceptual guides fall under the **Explanation** quadrant of Diataxis. They should cover LangChain terms and concepts
in a more abstract way than how-to guides or tutorials, and should be geared towards curious users interested in
gaining a deeper understanding of the framework. Try to avoid excessively large code examples. The goal here is to
impart perspective to the user rather than to finish a practical project. These guides should cover **why** things work the way they do.
To quote the Diataxis website:
> The perspective of explanation is higher and wider than that of the other types. It does not take the user’s eye-level view, as in a how-to guide, or a close-up view of the machinery, like reference material. Its scope in each case is a topic - “an area of knowledge”, that somehow has to be bounded in a reasonable, meaningful way.
Some examples include:
- [What does it mean to be agentic?](https://langchain-ai.github.io/langgraph/concepts/high_level/)
Here are some high-level tips on writing a good conceptual guide:
- Explain design decisions. Why does concept X exist and why was it designed this way?
- Use analogies and reference other concepts and alternatives
- Avoid blending in too much reference content
- You can and should reference content covered in other guides, but make sure to link to them
### References
References contain detailed, low-level information that describes exactly what functionality exists and how to use it.
In LangGraph, this is mainly our API reference pages, which are populated from docstrings within code.
References pages are generally not read end-to-end, but are consulted as necessary when a user needs to know
how to use something specific.
To quote the Diataxis website:
> The only purpose of a reference guide is to describe, as succinctly as possible, and in an orderly way. Whereas the content of tutorials and how-to guides are led by needs of the user, reference material is led by the product it describes.
Many of the reference pages in LangChain are automatically generated from code,
but here are some high-level tips on writing a good docstring:
- Be concise
- Discuss special cases and deviations from a user's expectations
- Go into detail on required inputs and outputs
- Light details on when one might use the feature are fine, but in-depth details belong in other sections.
Each category serves a distinct purpose and requires a specific approach to writing and structuring the content.
## General guidelines
Here are some other guidelines you should think about when writing and organizing documentation.
We generally do not merge new tutorials from outside contributors without an actual need.
We welcome updates as well as new integration docs, how-tos, and references.
### Avoid duplication
Multiple pages that cover the same material in depth are difficult to maintain and cause confusion. There should
be only one (very rarely two), canonical pages for a given concept or feature. Instead, you should link to other guides.
### Link to other sections
Because sections of the docs do not exist in a vacuum, it is important to link to other sections as often as possible
to allow a developer to learn more about an unfamiliar topic inline.
This includes linking to the API references as well as conceptual sections!
### Be concise
In general, take a less-is-more approach. If a section with a good explanation of a concept already exists, you should link to it rather than
re-explain it, unless the concept you are documenting presents some new wrinkle.
Be concise, including in code samples.
### General style
- Use active voice and present tense whenever possible
- Use examples and code snippets to illustrate concepts and usage
- Use appropriate header levels (`#`, `##`, `###`, etc.) to organize the content hierarchically
- Use fewer cells with more code to make copy/paste easier
- Use bullet points and numbered lists to break down information into easily digestible chunks
- Use tables (especially for **Reference** sections) and diagrams often to present information visually
- Include the table of contents for longer documentation pages to help readers navigate the content, but hide it for shorter pages
## Setup
LangGraph documentation consists of two components:
1. Main Documentation: Hosted at [https://langchain-ai.github.io/langgraph/](https://langchain-ai.github.io/langgraph/),
this comprehensive resource serves as the primary user-facing documentation.
It covers a wide array of topics, including tutorials, use cases, integrations,
and more, offering extensive guidance on building with LangGraph.
The content for this documentation lives in the `/docs` directory of the monorepo.
2. In-code Documentation: This is documentation of the codebase itself, which is also
used to generate the externally facing [API Reference](https://langchain-ai.github.io/langgraph/reference/graphs/).
The content for the API reference is autogenerated by scanning the docstrings in the codebase. For this reason we ask that developers document their code well.
We appreciate all contributions to the documentation, whether it be fixing a typo,
adding a new tutorial or example and whether it be in the main documentation or the API Reference.
### 📜 Main Documentation
The content for the main documentation is located in the `/docs` directory of the monorepo.
The documentation is written using a combination of ipython notebooks (`.ipynb` files)
and markdown (`.md` files). The notebooks are converted to markdown
and then built using [MkDocs](https://www.mkdocs.org/).
Feel free to make contributions to the main documentation! 🥰
After modifying the documentation:
1. Run the linting and formatting commands (see below) to ensure that the documentation is well-formatted and free of errors.
2. Optionally build the documentation locally to verify that the changes look good.
3. Make a pull request with the changes.
### ⚒️ Linting and Building Documentation Locally
After writing up the documentation, you may want to lint and build the documentation
locally to ensure that it looks good and is free of errors.
If you're unable to build it locally that's okay as well, as you will be able to
see a preview of the documentation on the pull request page.
From the **monorepo root**, run the following command to install the dependencies:
<!-- TODO -->
```bash
poetry install --with docs --no-root
```
#### Building
The code that builds the documentation is located in the `/docs` directory of the monorepo.
Before building the documentation, it is always a good idea to clean the build directory:
```bash
make clean-docs
```
You can build and preview the documentation as outlined below:
```bash
make serve-docs
```
#### Linting
To spell check the docs, run the following from the `docs` directory:
The in-code documentation is autogenerated from docstrings.
For the API reference to be useful, the codebase must be well-documented. This means that all functions, classes, and methods should have a docstring that explains what they do, what the arguments are, and what the return value is. This is a good practice in general, but it is especially important for LangGraph because the API reference is the primary resource for developers to understand how to use the codebase.
We generally follow the [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings) for docstrings.
Here is an example of a well-documented function:
```python
defmy_function(arg1:int,arg2:str)->float:
"""This is a short description of the function. (It should be a single sentence.)
This is a longer description of the function. It should explain what
the function does, what the arguments are, and what the return value is.
It should wrap at 88 characters.
Examples:
This is a section for examples of how to use the function.
.. code-block:: python
my_function(1, "hello")
Args:
arg1: This is a description of arg1. We do not need to specify the type since
it is already specified in the function signature.
Trusted by companies shaping the future of agents – including Klarna, Replit, Elastic, and more – LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents.
@@ -23,62 +23,69 @@ Install LangGraph:
pip install -U langgraph
```
Then, create an agent [using prebuilt components](https://langchain-ai.github.io/langgraph/agents/agents/):
Create a simple workflow:
```python
# pip install -qU "langchain[anthropic]" to call the model
fromlanggraph.graphimportSTART,StateGraph
fromtyping_extensionsimportTypedDict
fromlanggraph.prebuiltimportcreate_react_agent
defget_weather(city:str)->str:
"""Get weather for a given city."""
returnf"It's always sunny in {city}!"
classState(TypedDict):
text:str
agent=create_react_agent(
model="anthropic:claude-3-7-sonnet-latest",
tools=[get_weather],
prompt="You are a helpful assistant"
)
# Run the agent
agent.invoke(
{"messages":[{"role":"user","content":"what is the weather in sf"}]}
)
defnode_a(state:State)->dict:
return{"text":state["text"]+"a"}
defnode_b(state:State)->dict:
return{"text":state["text"]+"b"}
graph=StateGraph(State)
graph.add_node("node_a",node_a)
graph.add_node("node_b",node_b)
graph.add_edge(START,"node_a")
graph.add_edge("node_a","node_b")
print(graph.compile().invoke({"text":""}))
# {'text': 'ab'}
```
For more information, see the [Quickstart](https://langchain-ai.github.io/langgraph/agents/agents/). Or, to learn how to build an [agent workflow](https://langchain-ai.github.io/langgraph/concepts/low_level/) with a customizable architecture, long-term memory, and other complex task handling, see the [LangGraph basics tutorials](https://langchain-ai.github.io/langgraph/tutorials/get-started/1-build-basic-chatbot/).
Get started with the [LangGraph Quickstart](https://docs.langchain.com/oss/python/langgraph/quickstart).
To quickly build agents with LangChain's `create_agent` (built on LangGraph), see the [LangChain Agents documentation](https://docs.langchain.com/oss/python/langchain/agents).
## Core benefits
LangGraph provides low-level supporting infrastructure for *any* long-running, stateful workflow or agent. LangGraph does not abstract prompts or architecture, and provides the following central benefits:
- [Durable execution](https://langchain-ai.github.io/langgraph/concepts/durable_execution/): Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off.
- [Human-in-the-loop](https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/): Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution.
- [Comprehensive memory](https://langchain-ai.github.io/langgraph/concepts/memory/): Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions.
- [Durable execution](https://docs.langchain.com/oss/python/langgraph/durable-execution): Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off.
- [Human-in-the-loop](https://docs.langchain.com/oss/python/langgraph/interrupts): Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution.
- [Comprehensive memory](https://docs.langchain.com/oss/python/langgraph/memory): Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions.
- [Debugging with LangSmith](http://www.langchain.com/langsmith): Gain deep visibility into complex agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics.
- [Production-ready deployment](https://langchain-ai.github.io/langgraph/concepts/deployment_options/): Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows.
- [Production-ready deployment](https://docs.langchain.com/langsmith/app-development): Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows.
## LangGraph’s ecosystem
While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents. To improve your LLM application development, pair LangGraph with:
- [LangSmith](http://www.langchain.com/langsmith) — Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
- [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/) — Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/).
- [LangChain](https://python.langchain.com/docs/introduction/) – Provides integrations and composable components to streamline LLM application development.
- [LangSmith Deployment](https://docs.langchain.com/langsmith/deployments) — Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangGraph Studio](https://docs.langchain.com/oss/python/langgraph/studio).
- [LangChain](https://docs.langchain.com/oss/python/langchain/overview) – Provides integrations and composable components to streamline LLM application development.
> [!NOTE]
> Looking for the JS version of LangGraph? See the [JS repo](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://langchain-ai.github.io/langgraphjs/).
> Looking for the JS version of LangGraph? See the [JS repo](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://docs.langchain.com/oss/javascript/langgraph/overview).
## 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.).
- [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.
- [Guides](https://docs.langchain.com/oss/python/langgraph/overview): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
- [Reference](https://reference.langchain.com/python/langgraph/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
- [Examples](https://docs.langchain.com/oss/python/langgraph/agentic-rag): 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.
- [LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph): Learn the basics of LangGraph in our free, structured course.
- [Templates](https://langchain-ai.github.io/langgraph/concepts/template_applications/): Pre-built reference apps for common agentic workflows (e.g. ReAct agent, memory, retrieval etc.) that can be cloned and adapted.
- [Case studies](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship AI applications at scale.
## Acknowledgements
LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.
LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.
`prepare_notebooks_for_ci.py` script will add VCR cassette context manager for each cell in the notebook, so that:
* when the notebook is run for the first time, cells with network requests will be recorded to a VCR cassette file
* when the notebook is run subsequently, the cells with network requests will be replayed from the cassettes
## Adding new notebooks
If you are adding a notebook with API requests, it's **recommended** to record network requests so that they can be subsequently replayed. If this is not done, the notebook runner will make API requests every time the notebook is run, which can be costly and slow.
To record network requests, please make sure to first run `prepare_notebooks_for_ci.py` script.
Then, run
```bash
jupyter execute <path_to_notebook>
```
Once the notebook is executed, you should see the new VCR cassettes recorded in `cassettes` directory and discard the updated notebook.
## Updating existing notebooks
If you are updating an existing notebook, please make sure to remove any existing cassettes for the notebook in `cassettes` directory (each cassette is prefixed with the notebook name), and then run the steps from the "Adding new notebooks" section above.
"You are a helpful assistant that translates Python-based technical "
"documentation written in Markdown to equivalent TypeScript-based documentation. "
"The input is a Markdown file written in mkdocs format. It contains "
"Python code snippets embedded in prose. "
"Your task is to rewrite the content by translating the Python code to "
"idiomatic TypeScript, using the provided TypeScript reference snippets "
"to ensure accurate and consistent usage (e.g., correct imports, function "
"names, and patterns). "
"Remove the original Python code and replace it with the corresponding "
"TypeScript version. "
"Do not alter the surrounding prose unless a change is necessary to "
"reflect differences between Python and TypeScript. "
"Preserve the structure and formatting of the original Markdown document. "
"Do not make stylistic or structural changes unless they directly support "
"the translation. "
"Use the reference TypeScript snippets as guidance whenever possible to "
"maintain alignment with existing conventions.\n\n"
f"Here are the reference TypeScript snippets:\n\n{reference_snippets}\n\n"
)
CONSOLIDATION_PROMPT=(
"You are a helpful assistant that consolidates parallel Python and JavaScript (TypeScript) technical documentation "
"written in Markdown into a single unified Markdown document. "
"The input consists of two documents: the first is for Python users, and the second is for JavaScript/TypeScript users. "
"Your task is to merge these into one Markdown file using language-specific fenced blocks to separate the content where needed. "
"Use the following syntax to distinguish content for each language:\n\n"
":::python\n"
"# Python-specific content\n"
":::\n\n"
":::js\n"
"# JavaScript/TypeScript-specific content\n"
":::\n\n"
"Follow these consolidation rules:\n"
"- When content (prose or code) is the same or nearly identical in both versions, include it only once—outside of any fenced block.\n"
"- When content differs between the Python and JS versions, wrap each version in its corresponding fenced block.\n"
"- Prefer **paragraph-level separation** of language-specific content. Do not combine Python and JS snippets or terminology in the same sentence or paragraph using conditional phrases.\n"
" The `add_messages` function in our `State` will append the LLM's response messages to whatever messages are already in the state.\n"
" ::: \n\n"
" :::js\n"
" The `reducer` function in our `StateAnnotation` will append the LLM's response messages to whatever messages are already in the state.\n"
" :::\n\n"
"- Preserve the overall structure, ordering, and formatting of the original Markdown documents.\n"
"- Do not rephrase or unify content unless it is logically and semantically identical.\n"
"- Use the fenced blocks for both prose and code as needed, and ensure output is clean, readable Markdown suitable for tools that parse these directives.\n"
"Your goal is to produce a cleanly merged documentation file that serves both Python and JavaScript users without redundancy, while maximizing clarity and separation of language-specific details."
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