* Add support for ToolOutput response format.
* I don't love the name -- it's confusing unless you know that it's parameterizing a strategy.
We should determine if we want to support our old strategy for doing
things -- it has a higher latency (one extra LLM call), but it's a
reasonable built-in strategy as it doesn't do anything awkward with
conversation history. (Wouldn't surprising if it has overall better
performance than tool choice for longer conversations)
Do not support prebound tools on the model. There's reason users should be prebinding tools to the model!
This is a breaking change that might affect some users, but the work-around is simple -- provide tools into the create_react_agent api.
* Add structured output tools to ToolNode
* Fix default tool node name to match the actual default ('tools')
* Update doc-strings to explain what inputs/outputs are for the ToolNode.
* Mark internal attributes as private (potentially breaking -- although hopefully users aren't accessing these)
## Decisions points
* OK with two properties? Done since users may be relying on
`tools_by_name` and expanding the return type will break user code.
## Changes in public/private interface
### Marked as public
* Make `tools_by_name` an official public property
* Make `structured_output_tools` a public property
### Marked as private
There should be no reason why users are accessing these attributes
```python
_tool_to_state_args
_tool_to_store_arg
_handle_tool_errors
_messages_key
```
### Usage
```python
class OutputSchema(BaseModel):
name: str
age: int
location: str
tool_node = ToolNode([OutputSchema])
# Test that the structured output tool is registered correctly
assert "OutputSchema" in tool_node.structured_output_tools
# Create a tool call that matches the schema
tool_call = {
"name": "OutputSchema",
"args": {"name": "Alice", "age": 30, "location": "NYC"},
"id": "call_123",
"type": "tool_call",
}
# Test sync execution
result = tool_node.invoke(
{"messages": [AIMessage(content="", tool_calls=[tool_call])]}
)
# Should return a Command with structured response
assert isinstance(result, list)
assert len(result) == 1
command = result[0]
assert isinstance(command, Command)
# Check the update structure
assert "messages" in command.update
assert "structured_response" in command.update
# Check the tool message
tool_message = command.update["messages"][0]
assert isinstance(tool_message, ToolMessage)
assert tool_message.name == "OutputSchema"
assert tool_message.tool_call_id == "call_123"
# Check the structured response
structured_response = command.update["structured_response"]
assert isinstance(structured_response, OutputSchema)
assert structured_response.name == "Alice"
assert structured_response.age == 30
assert structured_response.location == "NYC"
```
Add option to split tool node to individual nodes.
Summary:
* User code (specifically streaming) may break if it's relying on the
name of the `tools` node
* The boolean flag in the interface is likely **temporary** (especially
if there are no major breaking changes)
* We'll need to decide if we can get rid of the version in create react
agent. "v1" is not consistent conceptually with a node per tool.
### 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>
