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 :/
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 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 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
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>
`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!
- **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>
## 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>
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>
