* Adds a conversion script from ipython notebook to markdown.
* Replaces one ipython notebook (create react agent) with a markdown file for testing.
---------
Co-authored-by: Ben Burns <803016+benjamincburns@users.noreply.github.com>
Adding open source web researcher agent to the third party page
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
StateType, UpdateType is set on the Client rather than on
`RunsClient.stream` because of lack of partial type arguments
application.
This PR also describes the message serialization format emitted by
LangGraph Server (which may change ie. converting `type` to `role`).
Avoiding direct import of `@langchain/core` for the core LangGraph SDK
client, thus these types were copied from `@langchain/core` (a script is
used to aid with keeping track with core)
(Keep MemorySaver around for backwards compatibility)
"MemorySaver" is ambiguous: is it saving memories? Where is it saving
memories to?
InMemorySaver aligns naming InMemoryStore as well as similar LangChain
objects (InMemoryVectorStore, etc.)
Was trying to learn the Multi Agent Workflow examples and encountered
some errors, which I fixed by editing these:
* Added missing state for Team1, and importing `Command`
* `ValueError: Node `LangGraph` already present.`: Seems to happen we
add the `team_1_graph` node without giving it a name, it will default to
the name `LangGraph`. Solved by giving the sub-graph a name when
building the top-level supervisor.
* Added the edges for the graph to feedback to the top level supervisor
to decide whether it still needs to relegate the task to other nodes or
end from there
---------
Co-authored-by: Vadym Barda <vadim.barda@gmail.com>
Alternative to https://github.com/langchain-ai/langgraph/pull/3124
Currently if a tool interrupts, the entire tool node executes again
after resuming. So tools can get executed twice if parallel tool calls
are generated. Here we allow ToolNode to accept tool calls, so we can
use the `Send` API to distribute the tool calls to multiple instances of
the tool node.
```python
from langchain_anthropic import ChatAnthropic
from langchain_core.tools import tool
from langgraph.checkpoint.memory import MemorySaver
from langgraph.prebuilt import create_react_agent
from langgraph.types import Command, Send, interrupt
@tool
def human_assistance(query: str) -> str:
"""Request assistance from a human."""
human_response = interrupt({"query": query})
return human_response["data"]
@tool
def get_weather(location: str) -> str:
"""Use this tool to get the weather."""
return "It's sunny!"
tools = [get_weather, human_assistance]
llm = ChatAnthropic(model="claude-3-5-sonnet-20240620")
agent = create_react_agent(
llm,
tools,
checkpointer=MemorySaver(),
tool_call_parallelism="parallel_tool_nodes",
)
user_input = (
"Could you please (1) request assistance for building an AI agent "
"from a human, and (2) search for the weather in Boston, MA? "
"Generate two tool calls at once."
)
config = {"configurable": {"thread_id": "1"}}
for event in agent.stream(
{"messages": [{"role": "user", "content": user_input}]},
config,
stream_mode="values",
):
event["messages"][-1].pretty_print()
```
```
...
```
```python
human_response = "You should check out LangGraph to build your agent."
human_command = Command(resume={"data": human_response})
for event in agent.stream(human_command, config, stream_mode="values"):
event["messages"][-1].pretty_print()
```
---------
Co-authored-by: Vadym Barda <vadym@langchain.dev>
- The result of these doesnt change once a node is created, and it's
fairly expensive to run, so great thing to cache
- There's a variety of errors that can come from inspecting the source
code of a function (part of what this does) so adding a catch-all
try-except block as this should be best-effort, not crash your graph
- The result of these doesnt change once a node is created, and it's fairly expensive to run, so great thing to cache
- There's a variety of errors that can come from inspecting the source code of a function (part of what this does) so adding a catch-all try-except block as this should be best-effort, not crash your graph
* Concepts page for the functional API
* How-to guides that show functional API implementations
* API reference for entrypoint, task, entrypoint.final
* Add functional API version to the workflows
---------
Co-authored-by: Vadym Barda <vadym@langchain.dev>
Co-authored-by: ccurme <chester.curme@gmail.com>
When I cloned langgraph example, I was not able to run the code because
of the requirements.txt file. The path was set to posix expression which
was not working in windows OS.
I have updated the path to posix expression so that it can work in
windows OS as well.
Try running `langgraph-example` in windows using the langgraph-cli in
windows OS. It was working for linux not in windows.
- async tests are placed in test_pregel_async, not in test_pregel
- to avoid tests placed in wrong file being accidentally skipped i've
added the auto-async mark to sync test file
- this was not possible in async where all done callbacks are called in
next tick
- in sync case this would manifest as the first task done callback
seeing counter == 1 and thus setting event
- the fix is to unset the event whenever a task is scheduled
- When using an async entrypoint you can now freely mix and match sync
and async tasks with a uniform api (ie all tasks return a sync or async
future depending on context)
- Fix issues with scheduling deeply nested tasks (use threadsafe methods
to schedule coroutines and create futures)
So that you can call agent.nodes['agent'].invoke({'messages': []})
without needing to specify is_last_step. very helpful for evaluating
just the model node of the agent
- async tests are placed in test_pregel_async, not in test_pregel
- to avoid tests placed in wrong file being accidentally skipped i've added the auto-async mark to sync test file
- this was not possible in async where all done callbacks are called in next tick
- in sync case this would manifest as the first task done callback seeing counter == 1 and thus setting event
- the fix is to unset the event whenever a task is scheduled
- When using an async entrypoint you can now freely mix and match sync and async tasks with a uniform api (ie all tasks return a sync or async future depending on context)
- Fix issues with scheduling deeply nested tasks (use threadsafe methods to schedule coroutines and create futures)
- both issues are related to the fact that waiters for futures are
notified of completion before "done" callbacks are called
- 1st issue manifested as interrupt stream event being emitted before
the result of a task that logically finished first (it's in the line
above in body of the entrypoint function) -> this is solved by always
returning to use code a fresh future chained on the original future,
because chaining is done via done callbacks (therefore the chained
future will only resolve after done callbacks of the original feature
are called)
- 2nd issue mainfested as sometimes (very rarely) the last stream event
not being printed before stream() finishes. this is solved by ensuring
we only return out of PregelRunner.tick() once all "done" callbacks are
called, previously we were approximating this through use of
asyncio.sleep(0) / time.sleep(0). The new solution instead waits on a
threading/asyncio.Event which will only be set by the last "done"
callback to fire
- this PR also disables incomplete support for calling sync tasks from
async entrypoints
- both issues are related to the fact that waiters for futures are notified of completion before "done" callbacks are called
- 1st issue manifested as interrupt stream event being emitted before the result of a task that logically finished first (it's in the line above in body of the entrypoint function) -> this is solved by always returning to use code a fresh future chained on the original future, because chaining is done via done callbacks (therefore the chained future will only resolve after done callbacks of the original feature are called)
- 2nd issue mainfested as sometimes (very rarely) the last stream event not being printed before stream() finishes. this is solved by ensuring we only return out of PregelRunner.tick() once all "done" callbacks are called, previously we were approximating this through use of asyncio.sleep(0) / time.sleep(0). The new solution instead waits on a threading/asyncio.Event which will only be set by the last "done" callback to fire
1. The inputs into foo do not affect any state behavior
2. `previous` always reflects the previous return value from the
function
3. Anything can be returned and that will be the new state for the
function on the next iteration
4. This API is not meant to support reducers in the inputs/state
```python
from langgraph.func import entrypoint
states = []
# In this version reducers do not work
@entrypoint(checkpointer=MemorySaver())
def foo(inputs, *, previous: Any) -> Any:
states.append(previous)
return {"previous": previous, "current": inputs}
config = {"configurable": {"thread_id": "1"}}
foo.invoke({"a": "1"}, config)
foo.invoke({"a": "2"}, config)
foo.invoke({"a": "3"}, config)
assert states == [
None,
{"current": {"a": "1"}, "previous": None},
{"current": {"a": "2"}, "previous": {"current": {"a": "1"}, "previous": None}},
]
```
Currently, if you're viewing a how-to guide and you click "How-to
Guides" in the sidebar, you aren't navigated back to the index page (it
will work if you click on a different guides section). To get back to
the index page, you need to scroll up and click the breadcrumbs.
After this change, clicking the link in the sidebar should navigate you
to the index page regardless of the page you are viewing.
Only side-effect from what I can tell is that "Home > Introduction" just
becomes "**Home**", which I think is fine (maybe preferable).
Before:
After:
- order was incorrectly based on task id, instead of the correct task
path
- this requires storing task paths on checkpointers
- addition of task_path to put_writes is made backwards compatible by
checking signature on call, and treating it as an optional arg
- order was incorrectly based on task id, instead of the correct task path
- this requires storing task paths on checkpointers
- addition of task_path to put_writes is made backwards compatible by checking signature on call, and treating it as an optinal arg
Some docs layout improvements to help guide user journey.
Currently we have `Home | Tutorials | How-tos | Concepts | Reference` in
top-level horizontal navigation bar.
Here we make these updates:
- Top-level horizontal navigation bar is just `Home | API Reference`
- Add vertical sidebar to `Home` with sections:
- Introduction
- Get started
- Guides
- Resources
`Get Started` contains quickstarts for LG and LG Platform / deployment.
These are tutorials in Diataxis terms.
`Guides` contains index pages for how-tos, concepts, tutorials.
Advantage of this organization is that users are directed naturally down
the sidebar from Intro -> Get started -> How-tos, which is roughly how
we expect them to proceed.
This also makes deployment info more accessible as it is highlighted in
the "Getting started" section.
When I tried to follow the How-to guide for [How to add semantic search
to your agent's
memory](https://langchain-ai.github.io/langgraph/how-tos/memory/semantic-search/#using-in-create_react_agent)
using `create_react_agent`, I got this error message when my agent used
the tool:
```python
1 validation error for upsert_memory
store
Field required [type=missing, input_value={'content': '@jimmy works...ny.', 'memory_id': None}, input_type=dict]
For further information visit https://errors.pydantic.dev/2.10/v/missingTraceback (most recent call last):
File "/usr/local/lib/python3.9/site-packages/langchain_core/tools/base.py", line 688, in run
tool_args, tool_kwargs = self._to_args_and_kwargs(tool_input, tool_call_id)
File "/usr/local/lib/python3.9/site-packages/langchain_core/tools/base.py", line 611, in _to_args_and_kwargs
tool_input = self._parse_input(tool_input, tool_call_id)
File "/usr/local/lib/python3.9/site-packages/langchain_core/tools/base.py", line 532, in _parse_input
result = input_args.model_validate(tool_input)
File "/usr/local/lib/python3.9/site-packages/pydantic/main.py", line 627, in model_validate
return cls.__pydantic_validator__.validate_python(
pydantic_core._pydantic_core.ValidationError: 1 validation error for upsert_memory
store
Field required [type=missing, input_value={'content': '@jimmy works...ny.', 'memory_id': None}, input_type=dict]
For further information visit https://errors.pydantic.dev/2.10/v/missing
```
I believe it’s because the graph did not inject the store into the tool
if we use `InjectedToolArg`.
When looking at the guide for [How to pass runtime values to
tools](https://langchain-ai.github.io/langgraph/how-tos/pass-run-time-values-to-tools/),
it suggests to use `InjectedStore` with `create_react_agent`. After
changing my code to use `InjectedStore`, my agent was able to save to
the store.
```python
class WeatherResponse(BaseModel):
"""Respond to the user with this"""
temperature: float = Field(description="The temperature in fahrenheit")
wind_direction: str = Field(
description="The direction of the wind in abbreviated form"
)
wind_speed: float = Field(description="The speed of the wind in mph")
@tool
def get_weather(city: Literal["nyc", "sf"]):
"""Use this to get weather information."""
if city == "nyc":
return "It is cloudy in NYC, with 5 mph winds in the North-East direction and a temperature of 70 degrees"
elif city == "sf":
return "It is 75 degrees and sunny in SF, with 3 mph winds in the South-East direction"
else:
raise AssertionError("Unknown city")
model = ChatOpenAI()
tools = [get_weather]
agent_with_structured_output = create_react_agent(model, tools, response_format=WeatherResponse)
agent_with_structured_output.invoke({"messages": [("user", "what's the weather in nyc?")]})
```
```pycon
{
'messages': [...],
'structured_response': WeatherResponse(temperature=70.0, wind_directon='NE', wind_speed=5.0)
}
```
Bumps [jinja2](https://github.com/pallets/jinja) from 3.1.4 to 3.1.5.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/pallets/jinja/releases">jinja2's
releases</a>.</em></p>
<blockquote>
<h2>3.1.5</h2>
<p>This is the Jinja 3.1.5 security fix release, which fixes security
issues and bugs but does not otherwise change behavior and should not
result in breaking changes compared to the latest feature release.</p>
<p>PyPI: <a
href="https://pypi.org/project/Jinja2/3.1.5/">https://pypi.org/project/Jinja2/3.1.5/</a>
Changes: <a
href="https://jinja.palletsprojects.com/changes/#version-3-1-5">https://jinja.palletsprojects.com/changes/#version-3-1-5</a>
Milestone: <a
href="https://github.com/pallets/jinja/milestone/16?closed=1">https://github.com/pallets/jinja/milestone/16?closed=1</a></p>
<ul>
<li>The sandboxed environment handles indirect calls to
<code>str.format</code>, such as by passing a stored reference to a
filter that calls its argument. <a
href="https://github.com/pallets/jinja/security/advisories/GHSA-q2x7-8rv6-6q7h">GHSA-q2x7-8rv6-6q7h</a></li>
<li>Escape template name before formatting it into error messages, to
avoid issues with names that contain f-string syntax. <a
href="https://redirect.github.com/pallets/jinja/issues/1792">#1792</a>,
<a
href="https://github.com/pallets/jinja/security/advisories/GHSA-gmj6-6f8f-6699">GHSA-gmj6-6f8f-6699</a></li>
<li>Sandbox does not allow <code>clear</code> and <code>pop</code> on
known mutable sequence types. <a
href="https://redirect.github.com/pallets/jinja/issues/2032">#2032</a></li>
<li>Calling sync <code>render</code> for an async template uses
<code>asyncio.run</code>. <a
href="https://redirect.github.com/pallets/jinja/issues/1952">#1952</a></li>
<li>Avoid unclosed <code>auto_aiter</code> warnings. <a
href="https://redirect.github.com/pallets/jinja/issues/1960">#1960</a></li>
<li>Return an <code>aclose</code>-able <code>AsyncGenerator</code> from
<code>Template.generate_async</code>. <a
href="https://redirect.github.com/pallets/jinja/issues/1960">#1960</a></li>
<li>Avoid leaving <code>root_render_func()</code> unclosed in
<code>Template.generate_async</code>. <a
href="https://redirect.github.com/pallets/jinja/issues/1960">#1960</a></li>
<li>Avoid leaving async generators unclosed in blocks, includes and
extends. <a
href="https://redirect.github.com/pallets/jinja/issues/1960">#1960</a></li>
<li>The runtime uses the correct <code>concat</code> function for the
current environment when calling block references. <a
href="https://redirect.github.com/pallets/jinja/issues/1701">#1701</a></li>
<li>Make <code>|unique</code> async-aware, allowing it to be used after
another async-aware filter. <a
href="https://redirect.github.com/pallets/jinja/issues/1781">#1781</a></li>
<li><code>|int</code> filter handles <code>OverflowError</code> from
scientific notation. <a
href="https://redirect.github.com/pallets/jinja/issues/1921">#1921</a></li>
<li>Make compiling deterministic for tuple unpacking in a <code>{% set
... %}</code> call. <a
href="https://redirect.github.com/pallets/jinja/issues/2021">#2021</a></li>
<li>Fix dunder protocol (<code>copy</code>/<code>pickle</code>/etc)
interaction with <code>Undefined</code> objects. <a
href="https://redirect.github.com/pallets/jinja/issues/2025">#2025</a></li>
<li>Fix <code>copy</code>/<code>pickle</code> support for the internal
<code>missing</code> object. <a
href="https://redirect.github.com/pallets/jinja/issues/2027">#2027</a></li>
<li><code>Environment.overlay(enable_async)</code> is applied correctly.
<a
href="https://redirect.github.com/pallets/jinja/issues/2061">#2061</a></li>
<li>The error message from <code>FileSystemLoader</code> includes the
paths that were searched. <a
href="https://redirect.github.com/pallets/jinja/issues/1661">#1661</a></li>
<li><code>PackageLoader</code> shows a clearer error message when the
package does not contain the templates directory. <a
href="https://redirect.github.com/pallets/jinja/issues/1705">#1705</a></li>
<li>Improve annotations for methods returning copies. <a
href="https://redirect.github.com/pallets/jinja/issues/1880">#1880</a></li>
<li><code>urlize</code> does not add <code>mailto:</code> to values like
<code>@a@b</code>. <a
href="https://redirect.github.com/pallets/jinja/issues/1870">#1870</a></li>
<li>Tests decorated with <code>@pass_context</code> can be used with the
<code>|select</code> filter. <a
href="https://redirect.github.com/pallets/jinja/issues/1624">#1624</a></li>
<li>Using <code>set</code> for multiple assignment (<code>a, b = 1,
2</code>) does not fail when the target is a namespace attribute. <a
href="https://redirect.github.com/pallets/jinja/issues/1413">#1413</a></li>
<li>Using <code>set</code> in all branches of <code>{% if %}{% elif %}{%
else %}</code> blocks does not cause the variable to be considered
initially undefined. <a
href="https://redirect.github.com/pallets/jinja/issues/1253">#1253</a></li>
</ul>
</blockquote>
</details>
<details>
<summary>Changelog</summary>
<p><em>Sourced from <a
href="https://github.com/pallets/jinja/blob/main/CHANGES.rst">jinja2's
changelog</a>.</em></p>
<blockquote>
<h2>Version 3.1.5</h2>
<p>Released 2024-12-21</p>
<ul>
<li>The sandboxed environment handles indirect calls to
<code>str.format</code>, such as
by passing a stored reference to a filter that calls its argument.
:ghsa:<code>q2x7-8rv6-6q7h</code></li>
<li>Escape template name before formatting it into error messages, to
avoid
issues with names that contain f-string syntax.
:issue:<code>1792</code>, :ghsa:<code>gmj6-6f8f-6699</code></li>
<li>Sandbox does not allow <code>clear</code> and <code>pop</code> on
known mutable sequence
types. :issue:<code>2032</code></li>
<li>Calling sync <code>render</code> for an async template uses
<code>asyncio.run</code>.
:pr:<code>1952</code></li>
<li>Avoid unclosed <code>auto_aiter</code> warnings.
:pr:<code>1960</code></li>
<li>Return an <code>aclose</code>-able <code>AsyncGenerator</code> from
<code>Template.generate_async</code>. :pr:<code>1960</code></li>
<li>Avoid leaving <code>root_render_func()</code> unclosed in
<code>Template.generate_async</code>. :pr:<code>1960</code></li>
<li>Avoid leaving async generators unclosed in blocks, includes and
extends.
:pr:<code>1960</code></li>
<li>The runtime uses the correct <code>concat</code> function for the
current environment
when calling block references. :issue:<code>1701</code></li>
<li>Make <code>|unique</code> async-aware, allowing it to be used after
another
async-aware filter. :issue:<code>1781</code></li>
<li><code>|int</code> filter handles <code>OverflowError</code> from
scientific notation.
:issue:<code>1921</code></li>
<li>Make compiling deterministic for tuple unpacking in a <code>{% set
... %}</code>
call. :issue:<code>2021</code></li>
<li>Fix dunder protocol (<code>copy</code>/<code>pickle</code>/etc)
interaction with <code>Undefined</code>
objects. :issue:<code>2025</code></li>
<li>Fix <code>copy</code>/<code>pickle</code> support for the internal
<code>missing</code> object.
:issue:<code>2027</code></li>
<li><code>Environment.overlay(enable_async)</code> is applied correctly.
:pr:<code>2061</code></li>
<li>The error message from <code>FileSystemLoader</code> includes the
paths that were
searched. :issue:<code>1661</code></li>
<li><code>PackageLoader</code> shows a clearer error message when the
package does not
contain the templates directory. :issue:<code>1705</code></li>
<li>Improve annotations for methods returning copies.
:pr:<code>1880</code></li>
<li><code>urlize</code> does not add <code>mailto:</code> to values like
<code>@a@b</code>. :pr:<code>1870</code></li>
<li>Tests decorated with <code>@pass_context`` can be used with the
``|select`` filter. :issue:</code>1624`</li>
<li>Using <code>set</code> for multiple assignment (<code>a, b = 1,
2</code>) does not fail when the
target is a namespace attribute. :issue:<code>1413</code></li>
<li>Using <code>set</code> in all branches of <code>{% if %}{% elif %}{%
else %}</code> blocks
does not cause the variable to be considered initially undefined.
:issue:<code>1253</code></li>
</ul>
</blockquote>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="https://github.com/pallets/jinja/commit/877f6e51be8e1765b06d911cfaa9033775f051d1"><code>877f6e5</code></a>
release version 3.1.5</li>
<li><a
href="https://github.com/pallets/jinja/commit/8d588592653b052f957b720e1fc93196e06f207f"><code>8d58859</code></a>
remove test pypi</li>
<li><a
href="https://github.com/pallets/jinja/commit/eda8fe86fd716dfce24910294e9f1fc81fbc740c"><code>eda8fe8</code></a>
update dev dependencies</li>
<li><a
href="https://github.com/pallets/jinja/commit/c8fdce1e0333f1122b244b03a48535fdd7b03d91"><code>c8fdce1</code></a>
Fix bug involving calling set on a template parameter within all
branches of ...</li>
<li><a
href="https://github.com/pallets/jinja/commit/66587ce989e5a478e0bb165371fa2b9d42b7040f"><code>66587ce</code></a>
Fix bug where set would sometimes fail within if</li>
<li><a
href="https://github.com/pallets/jinja/commit/fbc3a696c729d177340cc089531de7e2e5b6f065"><code>fbc3a69</code></a>
Add support for namespaces in tuple parsing (<a
href="https://redirect.github.com/pallets/jinja/issues/1664">#1664</a>)</li>
<li><a
href="https://github.com/pallets/jinja/commit/b8f4831d41e6a7cb5c40d42f074ffd92d2daccfc"><code>b8f4831</code></a>
more comments about nsref assignment</li>
<li><a
href="https://github.com/pallets/jinja/commit/ee832194cd9f55f75e5a51359b709d535efe957f"><code>ee83219</code></a>
Add support for namespaces in tuple assignment</li>
<li><a
href="https://github.com/pallets/jinja/commit/1d55cddbb28e433779511f28f13a2d8c4ec45826"><code>1d55cdd</code></a>
Triple quotes in docs (<a
href="https://redirect.github.com/pallets/jinja/issues/2064">#2064</a>)</li>
<li><a
href="https://github.com/pallets/jinja/commit/8a8eafc6b992ba177f1d3dd483f8465f18a11116"><code>8a8eafc</code></a>
edit block assignment section</li>
<li>Additional commits viewable in <a
href="https://github.com/pallets/jinja/compare/3.1.4...3.1.5">compare
view</a></li>
</ul>
</details>
<br />
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Dependabot will resolve any conflicts with this PR as long as you don't
alter it yourself. You can also trigger a rebase manually by commenting
`@dependabot rebase`.
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<details>
<summary>Dependabot commands and options</summary>
<br />
You can trigger Dependabot actions by commenting on this PR:
- `@dependabot rebase` will rebase this PR
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When looking at [docs](https://langchain-ai.github.io/langgraph/) this
sentence is confusing, not clear there's two separate links or why one
of them would lead to repo
Bumps [jinja2](https://github.com/pallets/jinja) from 3.1.4 to 3.1.5.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/pallets/jinja/releases">jinja2's
releases</a>.</em></p>
<blockquote>
<h2>3.1.5</h2>
<p>This is the Jinja 3.1.5 security fix release, which fixes security
issues and bugs but does not otherwise change behavior and should not
result in breaking changes compared to the latest feature release.</p>
<p>PyPI: <a
href="https://pypi.org/project/Jinja2/3.1.5/">https://pypi.org/project/Jinja2/3.1.5/</a>
Changes: <a
href="https://jinja.palletsprojects.com/changes/#version-3-1-5">https://jinja.palletsprojects.com/changes/#version-3-1-5</a>
Milestone: <a
href="https://github.com/pallets/jinja/milestone/16?closed=1">https://github.com/pallets/jinja/milestone/16?closed=1</a></p>
<ul>
<li>The sandboxed environment handles indirect calls to
<code>str.format</code>, such as by passing a stored reference to a
filter that calls its argument. <a
href="https://github.com/pallets/jinja/security/advisories/GHSA-q2x7-8rv6-6q7h">GHSA-q2x7-8rv6-6q7h</a></li>
<li>Escape template name before formatting it into error messages, to
avoid issues with names that contain f-string syntax. <a
href="https://redirect.github.com/pallets/jinja/issues/1792">#1792</a>,
<a
href="https://github.com/pallets/jinja/security/advisories/GHSA-gmj6-6f8f-6699">GHSA-gmj6-6f8f-6699</a></li>
<li>Sandbox does not allow <code>clear</code> and <code>pop</code> on
known mutable sequence types. <a
href="https://redirect.github.com/pallets/jinja/issues/2032">#2032</a></li>
<li>Calling sync <code>render</code> for an async template uses
<code>asyncio.run</code>. <a
href="https://redirect.github.com/pallets/jinja/issues/1952">#1952</a></li>
<li>Avoid unclosed <code>auto_aiter</code> warnings. <a
href="https://redirect.github.com/pallets/jinja/issues/1960">#1960</a></li>
<li>Return an <code>aclose</code>-able <code>AsyncGenerator</code> from
<code>Template.generate_async</code>. <a
href="https://redirect.github.com/pallets/jinja/issues/1960">#1960</a></li>
<li>Avoid leaving <code>root_render_func()</code> unclosed in
<code>Template.generate_async</code>. <a
href="https://redirect.github.com/pallets/jinja/issues/1960">#1960</a></li>
<li>Avoid leaving async generators unclosed in blocks, includes and
extends. <a
href="https://redirect.github.com/pallets/jinja/issues/1960">#1960</a></li>
<li>The runtime uses the correct <code>concat</code> function for the
current environment when calling block references. <a
href="https://redirect.github.com/pallets/jinja/issues/1701">#1701</a></li>
<li>Make <code>|unique</code> async-aware, allowing it to be used after
another async-aware filter. <a
href="https://redirect.github.com/pallets/jinja/issues/1781">#1781</a></li>
<li><code>|int</code> filter handles <code>OverflowError</code> from
scientific notation. <a
href="https://redirect.github.com/pallets/jinja/issues/1921">#1921</a></li>
<li>Make compiling deterministic for tuple unpacking in a <code>{% set
... %}</code> call. <a
href="https://redirect.github.com/pallets/jinja/issues/2021">#2021</a></li>
<li>Fix dunder protocol (<code>copy</code>/<code>pickle</code>/etc)
interaction with <code>Undefined</code> objects. <a
href="https://redirect.github.com/pallets/jinja/issues/2025">#2025</a></li>
<li>Fix <code>copy</code>/<code>pickle</code> support for the internal
<code>missing</code> object. <a
href="https://redirect.github.com/pallets/jinja/issues/2027">#2027</a></li>
<li><code>Environment.overlay(enable_async)</code> is applied correctly.
<a
href="https://redirect.github.com/pallets/jinja/issues/2061">#2061</a></li>
<li>The error message from <code>FileSystemLoader</code> includes the
paths that were searched. <a
href="https://redirect.github.com/pallets/jinja/issues/1661">#1661</a></li>
<li><code>PackageLoader</code> shows a clearer error message when the
package does not contain the templates directory. <a
href="https://redirect.github.com/pallets/jinja/issues/1705">#1705</a></li>
<li>Improve annotations for methods returning copies. <a
href="https://redirect.github.com/pallets/jinja/issues/1880">#1880</a></li>
<li><code>urlize</code> does not add <code>mailto:</code> to values like
<code>@a@b</code>. <a
href="https://redirect.github.com/pallets/jinja/issues/1870">#1870</a></li>
<li>Tests decorated with <code>@pass_context</code> can be used with the
<code>|select</code> filter. <a
href="https://redirect.github.com/pallets/jinja/issues/1624">#1624</a></li>
<li>Using <code>set</code> for multiple assignment (<code>a, b = 1,
2</code>) does not fail when the target is a namespace attribute. <a
href="https://redirect.github.com/pallets/jinja/issues/1413">#1413</a></li>
<li>Using <code>set</code> in all branches of <code>{% if %}{% elif %}{%
else %}</code> blocks does not cause the variable to be considered
initially undefined. <a
href="https://redirect.github.com/pallets/jinja/issues/1253">#1253</a></li>
</ul>
</blockquote>
</details>
<details>
<summary>Changelog</summary>
<p><em>Sourced from <a
href="https://github.com/pallets/jinja/blob/main/CHANGES.rst">jinja2's
changelog</a>.</em></p>
<blockquote>
<h2>Version 3.1.5</h2>
<p>Released 2024-12-21</p>
<ul>
<li>The sandboxed environment handles indirect calls to
<code>str.format</code>, such as
by passing a stored reference to a filter that calls its argument.
:ghsa:<code>q2x7-8rv6-6q7h</code></li>
<li>Escape template name before formatting it into error messages, to
avoid
issues with names that contain f-string syntax.
:issue:<code>1792</code>, :ghsa:<code>gmj6-6f8f-6699</code></li>
<li>Sandbox does not allow <code>clear</code> and <code>pop</code> on
known mutable sequence
types. :issue:<code>2032</code></li>
<li>Calling sync <code>render</code> for an async template uses
<code>asyncio.run</code>.
:pr:<code>1952</code></li>
<li>Avoid unclosed <code>auto_aiter</code> warnings.
:pr:<code>1960</code></li>
<li>Return an <code>aclose</code>-able <code>AsyncGenerator</code> from
<code>Template.generate_async</code>. :pr:<code>1960</code></li>
<li>Avoid leaving <code>root_render_func()</code> unclosed in
<code>Template.generate_async</code>. :pr:<code>1960</code></li>
<li>Avoid leaving async generators unclosed in blocks, includes and
extends.
:pr:<code>1960</code></li>
<li>The runtime uses the correct <code>concat</code> function for the
current environment
when calling block references. :issue:<code>1701</code></li>
<li>Make <code>|unique</code> async-aware, allowing it to be used after
another
async-aware filter. :issue:<code>1781</code></li>
<li><code>|int</code> filter handles <code>OverflowError</code> from
scientific notation.
:issue:<code>1921</code></li>
<li>Make compiling deterministic for tuple unpacking in a <code>{% set
... %}</code>
call. :issue:<code>2021</code></li>
<li>Fix dunder protocol (<code>copy</code>/<code>pickle</code>/etc)
interaction with <code>Undefined</code>
objects. :issue:<code>2025</code></li>
<li>Fix <code>copy</code>/<code>pickle</code> support for the internal
<code>missing</code> object.
:issue:<code>2027</code></li>
<li><code>Environment.overlay(enable_async)</code> is applied correctly.
:pr:<code>2061</code></li>
<li>The error message from <code>FileSystemLoader</code> includes the
paths that were
searched. :issue:<code>1661</code></li>
<li><code>PackageLoader</code> shows a clearer error message when the
package does not
contain the templates directory. :issue:<code>1705</code></li>
<li>Improve annotations for methods returning copies.
:pr:<code>1880</code></li>
<li><code>urlize</code> does not add <code>mailto:</code> to values like
<code>@a@b</code>. :pr:<code>1870</code></li>
<li>Tests decorated with <code>@pass_context`` can be used with the
``|select`` filter. :issue:</code>1624`</li>
<li>Using <code>set</code> for multiple assignment (<code>a, b = 1,
2</code>) does not fail when the
target is a namespace attribute. :issue:<code>1413</code></li>
<li>Using <code>set</code> in all branches of <code>{% if %}{% elif %}{%
else %}</code> blocks
does not cause the variable to be considered initially undefined.
:issue:<code>1253</code></li>
</ul>
</blockquote>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="https://github.com/pallets/jinja/commit/877f6e51be8e1765b06d911cfaa9033775f051d1"><code>877f6e5</code></a>
release version 3.1.5</li>
<li><a
href="https://github.com/pallets/jinja/commit/8d588592653b052f957b720e1fc93196e06f207f"><code>8d58859</code></a>
remove test pypi</li>
<li><a
href="https://github.com/pallets/jinja/commit/eda8fe86fd716dfce24910294e9f1fc81fbc740c"><code>eda8fe8</code></a>
update dev dependencies</li>
<li><a
href="https://github.com/pallets/jinja/commit/c8fdce1e0333f1122b244b03a48535fdd7b03d91"><code>c8fdce1</code></a>
Fix bug involving calling set on a template parameter within all
branches of ...</li>
<li><a
href="https://github.com/pallets/jinja/commit/66587ce989e5a478e0bb165371fa2b9d42b7040f"><code>66587ce</code></a>
Fix bug where set would sometimes fail within if</li>
<li><a
href="https://github.com/pallets/jinja/commit/fbc3a696c729d177340cc089531de7e2e5b6f065"><code>fbc3a69</code></a>
Add support for namespaces in tuple parsing (<a
href="https://redirect.github.com/pallets/jinja/issues/1664">#1664</a>)</li>
<li><a
href="https://github.com/pallets/jinja/commit/b8f4831d41e6a7cb5c40d42f074ffd92d2daccfc"><code>b8f4831</code></a>
more comments about nsref assignment</li>
<li><a
href="https://github.com/pallets/jinja/commit/ee832194cd9f55f75e5a51359b709d535efe957f"><code>ee83219</code></a>
Add support for namespaces in tuple assignment</li>
<li><a
href="https://github.com/pallets/jinja/commit/1d55cddbb28e433779511f28f13a2d8c4ec45826"><code>1d55cdd</code></a>
Triple quotes in docs (<a
href="https://redirect.github.com/pallets/jinja/issues/2064">#2064</a>)</li>
<li><a
href="https://github.com/pallets/jinja/commit/8a8eafc6b992ba177f1d3dd483f8465f18a11116"><code>8a8eafc</code></a>
edit block assignment section</li>
<li>Additional commits viewable in <a
href="https://github.com/pallets/jinja/compare/3.1.4...3.1.5">compare
view</a></li>
</ul>
</details>
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Within `libs/langgraph`, change all `TypedDict` imports to come from
`typing_extensions` rather than `typing`, as `pydantic` doesn't like the
latter.
Additionally, add a ruff rule to ban these imports too (so this doesn't
regress).
Solves #2909.
This PR adds a "shallow" version of `PostgresSaver` checkpointer that
ONLY stores the most recent checkpoint and does NOT retain any history.
It is meant to be a light-weight drop-in replacement for the
PostgresSaver that supports most of the LangGraph persistence
functionality with the exception of time travel.
Made some of the explanations more clear by rephrasing certain parts of
the sentence.
Fixed minor grammar mistakes also.
---------
Co-authored-by: Vadym Barda <vadim.barda@gmail.com>
Bumps [tornado](https://github.com/tornadoweb/tornado) from 6.4.1 to
6.4.2.
<details>
<summary>Changelog</summary>
<p><em>Sourced from <a
href="https://github.com/tornadoweb/tornado/blob/v6.4.2/docs/releases.rst">tornado's
changelog</a>.</em></p>
<blockquote>
<h1>Release notes</h1>
<p>.. toctree::
:maxdepth: 2</p>
<p>releases/v6.4.2
releases/v6.4.1
releases/v6.4.0
releases/v6.3.3
releases/v6.3.2
releases/v6.3.1
releases/v6.3.0
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releases/v4.3.0
releases/v4.2.1
releases/v4.2.0
releases/v4.1.0
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<li><a
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Bump version to 6.4.2</li>
<li><a
href="https://github.com/tornadoweb/tornado/commit/bc7df6bafdec61155e7bf385081feb205463857d"><code>bc7df6b</code></a>
Fix tests with Twisted 24.7.0</li>
<li><a
href="https://github.com/tornadoweb/tornado/commit/d5ba4a1695fbf7c6a3e54313262639b198291533"><code>d5ba4a1</code></a>
httputil: Fix quadratic performance of cookie parsing</li>
<li>See full diff in <a
href="https://github.com/tornadoweb/tornado/compare/v6.4.1...v6.4.2">compare
view</a></li>
</ul>
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Config parameter in `dev` was typed as pathlib.Path, but it is actually
a string. We need to manually create a Path from the string when parsing
the config.
FIxes#2647
Add `format` flag to `add_messages` which allows you to specify if the
contents of messages in state should be formatted in a particular way.
PR only adds support for OpenAI style contents. Helpful if you're using
different models at different nodes and want a unified messages format
to interact with when you manually update messages.
Bumps [tornado](https://github.com/tornadoweb/tornado) from 6.4.1 to
6.4.2.
<details>
<summary>Changelog</summary>
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<blockquote>
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<p>.. toctree::
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<p>releases/v6.4.2
releases/v6.4.1
releases/v6.4.0
releases/v6.3.3
releases/v6.3.2
releases/v6.3.1
releases/v6.3.0
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<li><a
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Bump version to 6.4.2</li>
<li><a
href="https://github.com/tornadoweb/tornado/commit/bc7df6bafdec61155e7bf385081feb205463857d"><code>bc7df6b</code></a>
Fix tests with Twisted 24.7.0</li>
<li><a
href="https://github.com/tornadoweb/tornado/commit/d5ba4a1695fbf7c6a3e54313262639b198291533"><code>d5ba4a1</code></a>
httputil: Fix quadratic performance of cookie parsing</li>
<li>See full diff in <a
href="https://github.com/tornadoweb/tornado/compare/v6.4.1...v6.4.2">compare
view</a></li>
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Add `format` flag to `add_messages` which allows you to specify if the
contents of messages in state should be formatted in a particular way.
PR only adds support for OpenAI style contents. Helpful if you're using
different models at different nodes and want a unified messages format
to interact with when you manually update messages.
Bumps [tornado](https://github.com/tornadoweb/tornado) from 6.4.1 to
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Config parameter in `dev` was typed as pathlib.Path, but it is actually
a string. We need to manually create a Path from the string when parsing
the config.
Hi,
While reading the [update state from
tools](https://langchain-ai.github.io/langgraph/how-tos/update-state-from-tools/)
tutorial. I noticed that this code snippet contains a syntax error:
```python
def call_tools(state):
...
commands = [tools_by_name[call["name"].invoke(call, config={"coerce_tool_content": False}) for tool_call in tool_calls]
return commands
```
There is a missing closing bracket `]` in the list comprehension.
Additionally, the variable `call` inside the list comprehension is
undefined, it should be `tool_call`.
Here is a corrected version of the code:
```python
def call_tools(state):
...
commands = [tools_by_name[tool_call["name"]].invoke(tool_call, config={"coerce_tool_content": False}) for tool_call in tool_calls]
return commands
```
---------
Co-authored-by: Vadym Barda <vadim.barda@gmail.com>
This PR updates the [How-to
guide](https://langchain-ai.github.io/langgraph/how-tos/persistence_mongodb/)
on using the MongoDB checkpointer.
The guide currently explains how to create a custom MongoDB
checkpointer, but we now have a checkpointer implementation available
via the `langgraph-checkpoint-mongodb` library. This PR updates the
current resource to guide users on how to use this implementation.
---------
Co-authored-by: ajosh0504 <apoorva.joshi@mongodb.com>
Co-authored-by: vbarda <vadym@langchain.dev>
- Document interrupt reference
- Update conceptual guides for HIL
- Split time-travel conceptual guide
- Split breakpoints into separate conceptual guide
- Update relevant how-tos
- Update how-to index page for HIL with more information and recommendations
- New how-to for multi turn conversation
- don't create contextvars.Context/asyncio.Task in RunnableSeq (not needed as each step creates it if necessary)
- don't run in-memory-saver methods in background threads (no point as they hold the gil)
- avoid calling should_interrupt when no interrupts set
- Whereas Send is for fire-and-forget type of calls, new `call` and `acall` functions are for flows where you want to wait for the node to finish before doing something else
- Because we return regular python future objects (concurrent.futures.Future or asyncio.Future) all the python primitives for working with futures work, eg. wait, gather, etc
Replace hardcoded database saver class names with `cls` in
`from_conn_string` factory methods to improve subclassing support
## Changes
* Replaced direct class instantiations with `cls(conn)` in
`from_conn_string` classmethods across all database implementations
* Updated both synchronous and asynchronous variants for DuckDB,
PostgreSQL, and SQLite savers
## Why
This refactor makes the database saver classes more extensible by
following Python's convention of using `cls` in class methods. This
enables proper inheritance patterns where subclasses can reuse the
factory methods without needing to override them. Previously, the
hardcoded class names would always instantiate the parent class, even
when called from a subclass.
## Testing
The change is backward compatible and doesn't alter existing
functionality. All existing tests should continue to pass as this is
purely a structural refactoring that preserves the current behavior
while improving extensibility.
## Notes
This PR addresses follow up on comments from #2518 - AsyncPostgresSaver
didn't need to be fixed but many of the other DB saver classes did.
It seems that actually once i moved the operators & other things out,
the query planner does do reasonable things and do sequential scanning
if filtered N < some size but the index otherwise, even with namespace
filtering.
Small change to install the dependencies with `edit` mode so that users
or freshman can see the effect immediately when they change the template
code. As below,
`pip install -e .`
It's very good to evaluate how agent works and easy to test &
re-develop!
---------
Signed-off-by: Mingqi Hu <mingqi.hu@intel.com>
Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com>
Adds a few of preliminaries:
1. Makes the returned "score" actually the result of the requested
operation (cosine, inner_product, l2)
2. Sorts asc, etc. so that if you were to add an HNSW index (and not
have any WHERE filters), it would be used
3. Drop the inner WHERE statement if no namespace or other filters are
provided. See (2) for why.
I don't yet add an index to the migrations since I think we need to
agree on the right balance to ensure it's actually used in common query
patterns.
- Initializing the store with an 'embedding config' -> this contains the
'dims' (used to create the table) and the encoder object (rn langchain
embeddings object, though that is ......)
- Call setup() -> creates the vector table.
Each document has 1 or more vectors associated with it for each json
path in the embedding config.
Would welcome critique and requests!
Leaving the params as the defaults for pgvector but open to feedback if
you think it's important to be able to more transparently configure that
in setup()
```python
from typing import TypedDict, List, Dict, Any, Optional
from langchain_openai import OpenAIEmbeddings
from langgraph.graph import StateGraph
from langgraph.store.postgres import PostgresStore
emb_config = {
"dims": 1536, # OpenAI embedding dimensions
"embed": OpenAIEmbeddings(model="text-embedding-3-small"),
"distance_type": "cosine",
}
with PostgresStore.from_conn_string(
"postgres://postgres:postgres@localhost:5441",
embedding=emb_config,
) as store:
store.setup()
# Define the state type for our graph
class State(TypedDict):
query: str
results: Optional[List[Dict[str, Any]]]
def put_stuff(state: State) -> State:
docs = [
("doc1", {"text": "red apple in kitchen"}),
("doc2", {"text": "blue car in garage"}),
("doc3", {"text": "green apple on table"}),
]
for key, value in docs:
store.put(("docs",), key, value)
def search_stuff(state: State) -> State:
"""Search for documents using vector similarity."""
results = store.search(("docs",), query=state["query"])
return {"results": results}
builder = StateGraph(State)
builder.add_node(put_stuff)
builder.add_node(search_stuff)
builder.add_edge("__start__", "put_stuff")
builder.add_edge("put_stuff", "search_stuff")
# Compile
with PostgresStore.from_conn_string(
"postgres://postgres:postgres@localhost:5441",
embedding=emb_config,
) as store:
chain = builder.compile(store=store)
result = chain.invoke({"query": "sour apple"})
# Print results
for doc in result["results"]:
print(doc.key)
print(doc.value)
print(doc.response_metadata)
```
- This makes the command bubble up out of the current graph and be handled by the calling graph (the immediate parent)
- This could be extended to support eg. ROOT graph, or some other level
- This is asynchronous, so we shouldn't use for regular writes to the output stream (ie those from PregelLoop)
- For writes from subgraphs / nodes this is fine to use, as we make no guarantees about when those show up anyway
- This should only be used in very specific circunstances, sqlite or
postgres adapters much more appropriate in most circunstances
---------
Co-authored-by: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com>
- This works similarly to the input() function from stdlib
- calling it in a node interrupts execution
- invoking the graph with Command(resume=...) will set ... as the return value of interrupt() so that the node can access the "answer" to the "question"
- This PR also starts the work to control the graph on invoke/stream with Command() input, to be continued in a future PR
- Keep old code path for compatibility with existing checkpoints
- Keep a similar order of application of updates, in some cases there will be no visible change
- Update task path for Sends to contain the path of all the parent tasks (multiple parents when a Send task creates another Send)
- That lineage path is used to ensure order of application of updates respects their logical lineage (ie updates from parents always applied before their child tasks)
- Move Interrupt writes to use negative indexes, which allow replacing/shadowing (when task is re-run it may interrupt again, or succeed)
- Runner will now attempt to schedule new Send tasks as soon as the write is received (ie while the originating node is still running)
- Update kafka scheduler to support new Send behavior
- Previously order was enforced in prepare_next_tasks, but that's not a good fit for future features
- This changes order between PULL and PUSH tasks, updates from PUSH tasks will now be applied after updates from PULL tasks
- updates from inside Send tasks are applied in the order the Sends were created, if when you fan out, and have each task write results to a list with reducer, the final list is in the order you used when triggering
- Return Control(update_state=, trigger=, send=) from your nodes instead
- Annotate nodes with Control[Literal["destination"]] to see your graph connections drawn
Added a js-example to show it builds
Adapted integration tests after removing the test CLI command
---------
Co-authored-by: Nuno Campos <nuno@langchain.dev>
* Remove land hand sidebar on most pages
* Cleans up some headings
* Adds error reference information to index (it was already on the
sidebar for the how-to page) -- should probably be its own tab?
* Adds an index page for the reference (so it's easier to link to a main
reference page), alternatively we can set up a redirect from index to
graph
This change expands error-handling functionality of the `ToolNode` by
introducing more options for `handle_tool_errors`. Default behavior of
the `ToolNode` is unchanged -- all errors are handled and wrapped in a
`ToolMessage` to be sent back to LLM.
With this change, users have flexibility to only handle the exceptions
that they need to pass back to the LLM:
* they can specify exceptions to handle by passing a tuple of exceptions
in `handle_tool_errors`
* specify `handle_tool_errors=True/str/callable`
* when `handle_tool_errors` is a callable, the signature will be
inspected and exceptions from the signature will be handled
---------
Co-authored-by: vbarda <vadym@langchain.dev>
- Share step/stop logic with PregelLoop
- Add RemainingSteps value which contains the number of remaining steps
- Switch create_react_agent to use RemainingSteps, so that it behave correctly for return_direct tools
Updated the following guides:
docs/docs/how-tos/streaming-content.ipynb
docs/docs/how-tos/streaming-events-from-within-tools-without-langchain.ipynb
docs/docs/how-tos/streaming-events-from-within-tools.ipynb
docs/docs/how-tos/streaming-tokens-without-langchain.ipynb
Updates the following how to guides
docs/docs/how-tos/disable-streaming.ipynb
docs/docs/how-tos/input_output_schema.ipynb
docs/docs/how-tos/many-tools.ipynb
docs/docs/how-tos/map-reduce.ipynb
docs/docs/how-tos/node-retries.ipynb
docs/docs/how-tos/pass-config-to-tools.ipynb
docs/docs/how-tos/pass_private_state.ipynb
Updates the following how to guides:
docs/docs/how-tos/persistence.ipynb
docs/docs/how-tos/persistence_mongodb.ipynb
docs/docs/how-tos/persistence_postgres.ipynb
docs/docs/how-tos/persistence_redis.ipynb
docs/docs/how-tos/react-agent-from-scratch.ipynb
docs/docs/how-tos/react-agent-structured-output.ipynb
docs/docs/how-tos/recursion-limit.ipynb
docs/docs/how-tos/return-when-recursion-limit-hits.ipynb
docs/docs/how-tos/run-id-langsmith.ipynb
docs/docs/how-tos/state-model.ipynb
Add links to the following how-to guides:
docs/docs/how-tos/async.ipynb
docs/docs/how-tos/branching.ipynb
docs/docs/how-tos/configuration.ipynb
docs/docs/how-tos/create-react-agent-hitl.ipynb
docs/docs/how-tos/create-react-agent-memory.ipynb
docs/docs/how-tos/create-react-agent-system-prompt.ipynb
docs/docs/how-tos/create-react-agent.ipynb
Identified two missing concepts:
1) RunnableConfig in LangChain
2) Unclear where ReAct should link in langgraph
- running callback handler in background thread could potentially lead to ordering issues
- deduping on `id()` could lead to messages being dropped if they reused memory address of a previous chunk
* Update HIL conceptual docs
* Update images
* Update figures and text per feedback
* Update links for how-tos
* Embed img directly in ntbks
* Add dynamic breakpoints
* Update figure in ntbk
* Fix link to dynamic breakpoints
* wip
* actually catch errors
* fixing tool-calling-errors and persistence-redis
* poetry changes
* poetry update
* run tutorials
* only tutorials (testing)
* print errors
* rewoo fixes
* remove customer support because of user input
* skip notebooks programatically
* add back how-tos
* remove redundant if
* add cassetes for tutorials
* remove no execution since it is generated by CI,
* remove multi-agent/usaco
* try to run in parallel
* skip notebooks fix
* remove magic/non-magic cells
* msgpack instead of yaml
* prepare_notebooks change
* ignore msgpack for spelling
* use compression for cassettes
* update spell check
* reset poetry changes
* upgrade packages for latest
* no update
* poetry changes
- adds the ability for nodes (including in subgraphs) to emit chunks directly to the output stream, emitted chunks can have any type
- when stream_mode=custom isnt requested by the caller emitted chunks are ignored
* docs: Clarify exceptions retried by default_retry_on in retry policy tutorial
- Added a remark in the tutorial explaining that the `default_retry_on` function retries on any exception except for the following:
- ValueError
- TypeError
- ArithmeticError
- ImportError
- LookupError
- NameError
- SyntaxError
- RuntimeError
- ReferenceError
- StopIteration
- StopAsyncIteration
- OSError
* http status codes
---------
Co-authored-by: Itay Etelis <itay.etelis@ibm.com>
Co-authored-by: isaac hershenson <ihershenson@hmc.edu>
- Correctly distinguish between exception classes, lists/tuples of exception classes, and callables.
- Add support for lists in `retry_on`, alongside tuples.
- Prevent exception classes from being incorrectly treated as callables.
- Raise a `TypeError` if `retry_on` is of an unsupported type.
Previously, `retry_on` in `RetryPolicy` accepted only exception classes, tuples of exception classes, or callables.
Update the `retry_on` type annotation to include `List[Type[Exception]]`
Changes:
- Updated `retry_on` in `RetryPolicy` to accept `List[Type[Exception]]`.
* Implement serialization with msgpack library
- encode custom python objects with a msgpack extension type, with constructor path string, and args encoded as nested msgpack doc
* Smaller msgpack extension types
* Update lock files
* lock
* Don't delegate to pydantic json
* Fix kafka serde
- should use our serializer to load, as inputs to subgraphs are serialized using it
* Failing test
* Ensure retried subgraphs resume from current point (if any)
* Lint
* Cleanup Test
---------
Co-authored-by: Nuno Campos <nuno@boringbits.io>
* Performance improvements in checkpointer libs
- Use sha1 instead of md5 for hashing (faster in python 3.x)
- Use orjson instead of json for json dumping (sadly can't use for json loading)
* Update tests
* Update
* Use random number instead of hash for get_version_number
* Avoid saving writes for the last task to complete in each step
- only when possible, exceptions for ERROR, INTERRUPT, SEND
* Make Channel.from_checkpoint a regular function
- context manager no longer needed since Context became a managed value
* Use __slots__ for Channels
* Fix for kafka
* Remove unused fil;e
* Add benchmark-fast command for running locally
* Small improvements to jsonplus serializer
* Don't use PregelNode.mapper when schema is a typed dict
- All it would do is create a new copy of same dict
* Avoid copying checkpoint when fetching at beginning of loop
* Fix needs array
* Update tests
* Performance improvements in core library
- Avoid creating new callback manager when received one as arg
- Avoid looking for config when already received one as arg
- Avoid copies of values in ensure_config/merge_configs
- Implement version of ensure_config that accepts multiple configs (avoids calling merge_configs first)
- Avoid calling merge_configs when we only need to attach extra tags/metadata
* Fix
* Fix
* Try again
* Debug ci job
* Fix
* Try again
* Try again
* Try again
* Some more variations
* Attach annotation to first changed file
* Fix
* Re-enable benchmarks
- Define protocol for sync and async producer and consumer
- Accept consumer/producer as init args in Orchestrator/Executor
- If not passed in, create default consumer/producer as before
- await future returned by send() instead of flush()
- use consumer groups by default
- process tasks in batches by default, configurable
- manually commit offsets when batch is processed
- Orchestrator and Executor classes to run LangGraph in a distributed fashion using Kafka as a message bus for communication
- Orchestrator and Executor run on-demand when a new message is published to the topic they listen to
- Orchestrator is responsible for running the Pregel algorithm (deciding next tasks to run) and sending messages to the executor topic
- Executor is responsible for executing each task (node), and sending messages to the orchestrator topic when done
- Use a simpler version of RunnableSequence without tracing serialization
- Remove accepts_run_manager check in RunnableCallable
- Remove creation of ChannelWrite dynamically every time conditional edge runs
- This more closely remembers the environment they're used in, so it's what we should be testing
- Remove unnecessary pytest-asyncio dependency, use anyio pytest plugin instead
- Convert remaining async tests using only memory checkpointer to use all existing ones
- Sends are stored through put_writes, so we don't need to also store them inside checkpoint object
- On reading checkpoint, reconstruct pending_sends from the stored writes
- Convert Context to a ManagedValue
- Add shim for old Context constructor
- Add `runtime` flag for managed values, which, prior to serialization, replaces the value with a placeholder, and replaces it back with the actual value on resuming from checkpoint
* Fix semantics of put_writes/list
- put_writes(error) should not prevent saving future successful if task is retried successfully
- put_writes(writes) should be a no-op if non-error writes already exist for that task (this prevents tasks executed more than once from modifying writes previously saved / acted on)
- checkpoints should not include channel default values (ie those without a version)
- list() should fetch and return writes for each checkpoint
* Lint
* Rm print
* Fix import
* Lint
- Save errors produced by tasks, under pending_writes
- Re-work logic to cancel other tasks when one fails, ready to change for interrupt exception
- Update serializer to handle exceptions
- Update get_state/get_state_history with new return value property "tasks" which contains a richer description of the next tasks, currently with id, name and error (if already ran and errored)
Updated the documentation to describe the chatbot node function's return value in a more Pythonic and enthusiastic way. The description now emphasizes that the function returns a dictionary with the updated messages list neatly tucked under the messages key.
- while the inner graph makes progress it overwrites the partial progress checkpoints, eventually keeping only one for each outer step
- implement parent_config in MemorySaver
- fix edge cases in PregelLoop
This fixes a small mistake in the manage-conversation-history notebook
where the model bound with tools was not used, instead, the original model
was used when invocations occur.
* small changes
* harrison comments
* Update docs/docs/cloud/deployment/test_locally.md
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
* Fix example
CC @vbarda
* Progress on tool calling errors
* Update
* Rename
* Format
* Clean up outputs
* Revert
* Use stream instead of invoke for final example
* Fix bug in add_conditional_edges when no path_map is provided
When an instance of a callable class is passed as the path arg to
add_conditional_edges but no path_map is provided, get_type_hints(path) is
called, which raises a TypeError (since get_type_hints only accepts a module,
class, method, or function).
This patch fixes the error by trying to get type hints from path.__call__ first,
which should work for instances of callable classes.
Tested: Added a test that raises TypeError without the fix in this patch but
passes with the fix.
* More defensive, additional test
---------
Co-authored-by: Nuno Campos <nuno@langchain.dev>
Plus:
1. Improve docstrings of add_node
2. Update crosslinking of sqlite and aiosqlite docstrings
3. Fix a bunch of links so we can turn on strict validation
* Change LangGraph Deploy to LangGraph Cloud in CLI reference.
* Update main README to link to Cloud docs. Update How-to Guide link in Cloud index page.
* Create Environments Variable reference page.
* Add Authentication to Conceptual Guide.
* Update setup how-to to refer back to CompiledGraph variable name.
* Update how-to notebooks for double texting.
* Add warning about setting top-level variable for CompiledGraph.
description:Report a bug in LangChain. To report a security issue, please instead use the security option below. For questions, please use the GitHub Discussions.
description:Report a bug in LangGraph. To report a security issue, please instead use the security option below. For questions, please use the GitHub Discussions.
labels:["02 Bug Report"]
body:
- type:markdown
@@ -7,35 +7,29 @@ body:
value:>
Thank you for taking the time to file a bug report.
Use this to report bugs in LangChain.
If you're not certain that your issue is due to a bug in LangChain, please use [GitHub Discussions](https://github.com/langchain-ai/langchain/discussions)
to ask for help with your issue.
Use this to report BUGS in LangGraph. For usage questions, feature requests and general design questions, please use [GitHub Discussions](https://github.com/langchain-ai/langgraph/discussions).
Relevant links to check before filing a bug report to see if your issue has already been reported, fixed or
description:Please confirm and check all the following options.
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.
options:
- label:I added a very descriptive title to this issue.
- label:This is a bug, not a usage question. For questions, please use GitHub Discussions.
required:true
- label:I searched the [LangGraph](https://langchain-ai.github.io/langgraph/)/LangChain documentation with the integrated search.
- label:I added a clear and detailed title that summarizes the issue.
required:true
- label:I used the GitHub search to find a similar question and didn't find it.
- label:I read what a minimal reproducible example is (https://stackoverflow.com/help/minimal-reproducible-example).
required:true
- label:I am sure that this is a bug in LangGraph/LangChain rather than my code.
required:true
- label:I am sure this is better as an issue [rather than a GitHub discussion](https://github.com/langchain-ai/langgraph/discussions/new/choose), since this is a LangGraph bug and not a design question.
- 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.
required:true
- type:textarea
id:reproduction
@@ -45,22 +39,14 @@ body:
label:Example Code
description:|
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!**
* Reduce your code to the minimum required to reproduce the issue if possible. This makes it much easier for others to help you.
* Avoid screenshots when possible, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
placeholder:|
from langchain_core.runnables import RunnableLambda
from langgraph.graph import StateGraph
def bad_code(inputs) -> int:
raise NotImplementedError('For demo purpose')
chain = RunnableLambda(bad_code)
chain.invoke('Hello!')
chain = StateGraph(list)
chain.invoke('Hello!')
render:python
- type:textarea
id:error
@@ -82,7 +68,7 @@ body:
Write a short description telling what you are doing, what you expect to happen, and what is currently happening.
placeholder:|
* I'm trying to use the `langchain` library to do X.
* I'm trying to use the `langgraph` library to do X.
* I expect to see Y.
* Instead, it does Z.
validations:
@@ -92,25 +78,8 @@ body:
attributes:
label:System Info
description:|
Please share your system info with us.
"pip freeze | grep langchain"
platform (windows / linux / mac)
python version
OR if you're on a recent version of langchain-core you can paste the output of:
python -m langchain_core.sys_info
placeholder:|
"pip freeze | grep langchain"
platform
python version
Alternatively, if you're on a recent version of langchain-core you can paste the output of:
python -m langchain_core.sys_info
These will only surface LangChain packages, don't forget to include any other relevant
packages you're using (if you're not sure what's relevant, you can paste the entire output of `pip freeze`).
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 open an issue or discussion and 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://github.com/langchain-ai/langgraph/discussions), 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-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 they 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 actue 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
LangChain documentation consists of two components:
1. Main Documentation: Hosted at [https://langchain-ai.github.io](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:
```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
The documentation is linted from the **monorepo root**. To lint it, run the following from there:
```bash
make spellcheck
```
### ️In-code Documentation
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 LangChain 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.
> Looking for the JS version? See the [JS repo](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://langchain-ai.github.io/langgraphjs/).
## Overview
[LangGraph](https://langchain-ai.github.io/langgraph/) is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures, differentiating it from DAG-based solutions. As a very low-level framework, it provides fine-grained control over both the flow and state of your application, crucial for creating reliable agents. Additionally, LangGraph includes built-in persistence, enabling advanced human-in-the-loop and memory features.
[LangGraph](https://langchain-ai.github.io/langgraph/) is a library for building
stateful, multi-actor applications with LLMs, used to create agent and multi-agent
workflows. Check out an introductory tutorial [here](https://langchain-ai.github.io/langgraph/tutorials/introduction/).
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.
### Key Features
### Why use LangGraph?
- **Cycles and Branching**: Implement loops and conditionals in your apps.
- **Persistence**: Automatically save state after each step in the graph. Pause and resume the graph execution at any point to support error recovery, human-in-the-loop workflows, time travel and more.
- **Human-in-the-Loop**: Interrupt graph execution to approve or edit next action planned by the agent.
- **Streaming Support**: Stream outputs as they are produced by each node (including token streaming).
- **Integration with LangChain**: LangGraph integrates seamlessly with [LangChain](https://github.com/langchain-ai/langchain/) and [LangSmith](https://docs.smith.langchain.com/) (but does not require them).
LangGraph powers [production-grade agents](https://www.langchain.com/built-with-langgraph), trusted by Linkedin, Uber, Klarna, GitLab, and many more. LangGraph provides fine-grained control over both the flow and state of your agent applications. It implements a central [persistence layer](https://langchain-ai.github.io/langgraph/concepts/persistence/), enabling features that are common to most agent architectures:
- **Memory**: LangGraph persists arbitrary aspects of your application's state,
supporting memory of conversations and other updates within and across user
interactions;
- **Human-in-the-loop**: Because state is checkpointed, execution can be interrupted
and resumed, allowing for decisions, validation, and corrections at key stages via
human input.
Standardizing these components allows individuals and teams to focus on the behavior
of their agent, instead of its supporting infrastructure.
Through [LangGraph Platform](#langgraph-platform), LangGraph also provides tooling for
the development, deployment, debugging, and monitoring of your applications.
LangGraph integrates seamlessly with
[LangChain](https://python.langchain.com/docs/introduction/) and
[LangSmith](https://docs.smith.langchain.com/) (but does not require them).
To learn more about LangGraph, check out our first LangChain Academy
course, *Introduction to LangGraph*, available for free
[LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform) is infrastructure for deploying LangGraph agents. It is a commercial solution for deploying agentic applications to production, built on the open-source LangGraph framework. The LangGraph Platform consists of several components that work together to support the development, deployment, debugging, and monitoring of LangGraph applications: [LangGraph Server](https://langchain-ai.github.io/langgraph/concepts/langgraph_server) (APIs), [LangGraph SDKs](https://langchain-ai.github.io/langgraph/concepts/sdk) (clients for the APIs), [LangGraph CLI](https://langchain-ai.github.io/langgraph/concepts/langgraph_cli) (command line tool for building the server), and [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio) (UI/debugger).
See deployment options [here](https://langchain-ai.github.io/langgraph/concepts/deployment_options/)
(includes a free tier).
Here are some common issues that arise in complex deployments, which LangGraph Platform addresses:
- **Streaming support**: LangGraph Server provides [multiple streaming modes](https://langchain-ai.github.io/langgraph/concepts/streaming) optimized for various application needs
- **Background runs**: Runs agents asynchronously in the background
- **Support for long running agents**: Infrastructure that can handle long running processes
- **[Double texting](https://langchain-ai.github.io/langgraph/concepts/double_texting)**: Handle the case where you get two messages from the user before the agent can respond
- **Handle burstiness**: Task queue for ensuring requests are handled consistently without loss, even under heavy loads
## Installation
@@ -31,45 +67,114 @@ pip install -U langgraph
## Example
One of the central concepts of LangGraph is state. Each graph execution creates a state that is passed between nodes in the graph as they execute, and each node updates this internal state with its return value after it executes. The way that the graph updates its internal state is defined by either the type of graph chosen or a custom function.
Let's take a look at a simple example of an agent that can search the web using [Tavily Search API](https://tavily.com/).
Let's build a tool-calling [ReAct-style](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#react-implementation) agent that uses a search tool!
```shell
pip install langchain_openai langchain_community
pip install langchain-anthropic
```
```shell
exportOPENAI_API_KEY=sk-...
exportTAVILY_API_KEY=tvly-...
exportANTHROPIC_API_KEY=sk-...
```
Optionally, we can set up [LangSmith](https://docs.smith.langchain.com/) for best-in-class observability.
```shell
exportLANGCHAIN_TRACING_V2="true"
exportLANGCHAIN_API_KEY=ls__...
exportLANGSMITH_TRACING=true
exportLANGSMITH_API_KEY=lsv2_sk_...
```
```python
fromtypingimportAnnotated,Literal,TypedDict
The simplest way to create a tool-calling agent in LangGraph is to use `create_react_agent`:
{"messages":[{"role":"user","content":"what is the weather in sf"}]},
config={"configurable":{"thread_id":42}}
)
final_state["messages"][-1].content
```
```
"Based on the search results, I can tell you that the current weather in San Francisco is:\n\nTemperature: 60 degrees Fahrenheit\nConditions: Foggy\n\nSan Francisco is known for its microclimates and frequent fog, especially during the summer months. The temperature of 60°F (about 15.5°C) is quite typical for the city, which tends to have mild temperatures year-round. The fog, often referred to as "Karl the Fog" by locals, is a characteristic feature of San Francisco\'s weather, particularly in the mornings and evenings.\n\nIs there anything else you\'d like to know about the weather in San Francisco or any other location?"
```
Now when we pass the same <code>"thread_id"</code>, the conversation context is retained via the saved state (i.e. stored list of messages)
```python
final_state=app.invoke(
{"messages":[{"role":"user","content":"what about ny"}]},
config={"configurable":{"thread_id":42}}
)
final_state["messages"][-1].content
```
```
"Based on the search results, I can tell you that the current weather in New York City is:\n\nTemperature: 90 degrees Fahrenheit (approximately 32.2 degrees Celsius)\nConditions: Sunny\n\nThis weather is quite different from what we just saw in San Francisco. New York is experiencing much warmer temperatures right now. Here are a few points to note:\n\n1. The temperature of 90°F is quite hot, typical of summer weather in New York City.\n2. The sunny conditions suggest clear skies, which is great for outdoor activities but also means it might feel even hotter due to direct sunlight.\n3. This kind of weather in New York often comes with high humidity, which can make it feel even warmer than the actual temperature suggests.\n\nIt's interesting to see the stark contrast between San Francisco's mild, foggy weather and New York's hot, sunny conditions. This difference illustrates how varied weather can be across different parts of the United States, even on the same day.\n\nIs there anything else you'd like to know about the weather in New York or any other location?"
```
</details>
> [!TIP]
> LangGraph is a **low-level** framework that allows you to implement any custom agent
architectures. Click on the low-level implementation below to see how to implement a
# Note that we're (optionally) passing the memory when compiling the graph
app=workflow.compile(checkpointer=checkpointer)
# Use the Runnable
# Use the agent
final_state=app.invoke(
{"messages":[HumanMessage(content="what is the weather in sf")]},
{"messages":[{"role":"user","content":"what is the weather in sf"}]},
config={"configurable":{"thread_id":42}}
)
final_state["messages"][-1].content
```
```
'The current weather in San Francisco is as follows:\n- Temperature: 60.1°F (15.6°C)\n- Condition: Partly cloudy\n- Wind: 5.6 mph (9.0 kph) from SSW\n- Humidity: 83%\n- Visibility: 9.0 miles (16.0 km)\n- UV Index: 4.0\n\nFor more details, you can visit [Weather API](https://www.weatherapi.com/).'
```
<b>Step-by-step Breakdown</b>:
Now when we pass the same `"thread_id"`, the conversation context is retained via the saved state (i.e. stored list of messages)
<details>
<summary>Initialize the model and tools.</summary>
<ul>
<li>
We use <code>ChatAnthropic</code> as our LLM. <strong>NOTE:</strong> we need to make sure the model knows that it has these tools available to call. We can do this by converting the LangChain tools into the format for OpenAI tool calling using the <code>.bind_tools()</code> method.
</li>
<li>
We define the tools we want to use - a search tool in our case. It is really easy to create your own tools - see documentation here on how to do that <a href="https://python.langchain.com/docs/how_to/custom_tools/">here</a>.
</li>
</ul>
</details>
```python
final_state=app.invoke(
{"messages":[HumanMessage(content="what about ny")]},
config={"configurable":{"thread_id":42}}
)
final_state["messages"][-1].content
```
<details>
<summary>Initialize graph with state.</summary>
```
'The current weather in New York is as follows:\n- Temperature: 20.3°C (68.5°F)\n- Condition: Overcast\n- Wind: 2.2 mph from the north\n- Humidity: 65%\n- Cloud Cover: 100%\n- UV Index: 5.0\n\nFor more details, you can visit [Weather API](https://www.weatherapi.com/).'
```
<ul>
<li>We initialize graph (<code>StateGraph</code>) by passing state schema (in our case <code>MessagesState</code>)</li>
<li><code>MessagesState</code> is a prebuilt state schema that has one attribute -- a list of LangChain <code>Message</code> objects, as well as logic for merging the updates from each node into the state.</li>
</ul>
</details>
### Step-by-step Breakdown:
<details>
<summary>Define graph nodes.</summary>
1. <details>
<summary>Initialize the model and tools.</summary>
There are two main nodes we need:
- we use `ChatOpenAI` as our LLM. **NOTE:** we need make sure the model knows that it has these tools available to call. We can do this by converting the LangChain tools into the format for OpenAI tool calling using the `.bind_tools()` method.
- we define the tools we want to use -- a web search tool in our case. It is really easy to create your own tools - see documentation here on how to do that [here](https://python.langchain.com/docs/modules/agents/tools/custom_tools).
</details>
2. <details>
<summary>Initialize graph with state.</summary>
<ul>
<li>The <code>agent</code> node: responsible for deciding what (if any) actions to take.</li>
<li>The <code>tools</code> node that invokes tools: if the agent decides to take an action, this node will then execute that action.</li>
</ul>
</details>
- we initialize graph (`StateGraph`) by passing state schema (in our case `MessagesState`)
-`MessagesState` is a prebuilt state schema that has one attribute -- a list of LangChain `Message` objects, as well as logic for merging the updates from each node into the state
</details>
3. <details>
<summary>Define graph nodes.</summary>
<details>
<summary>Define entry point and graph edges.</summary>
There are two main nodes we need:
- The `agent` node: responsible for deciding what (if any) actions to take.
- The `tools` node that invokes tools: if the agent decides to take an action, this node will then execute that action.
</details>
4. <details>
<summary>Define entry point and graph edges.</summary>
First, we need to set the entry point for graph execution - <code>agent</code> node.
First, we need to set the entry point for graph execution - `agent` node.
Then we define one normal and one conditional edge. Conditional edge means that the destination depends on the contents of the graph's state (<code>MessagesState</code>). In our case, the destination is not known until the agent (LLM) decides.
Then we define one normal and one conditional edge. Conditional edge means that the destination depends on the contents of the graph's state (`MessageState`). In our case, the destination is not known until the agent (LLM) decides.
<ul>
<li>Conditional edge: after the agent is called, we should either:
<ul>
<li>a. Run tools if the agent said to take an action, OR</li>
<li>b. Finish (respond to the user) if the agent did not ask to run tools</li>
</ul>
</li>
<li>Normal edge: after the tools are invoked, the graph should always return to the agent to decide what to do next</li>
</ul>
</details>
- Conditional edge: after the agent is called, we should either:
- a. Run tools if the agent said to take an action, OR
- b. Finish (respond to the user) if the agent did not ask to run tools
- Normal edge: after the tools are invoked, the graph should always return to the agent to decide what to do next
</details>
5. <details>
<summary>Compile the graph.</summary>
<details>
<summary>Compile the graph.</summary>
- When we compile the graph, we turn it into a LangChain [Runnable](https://python.langchain.com/v0.2/docs/concepts/#runnable-interface), which automatically enables calling `.invoke()`, `.stream()` and `.batch()` with your inputs
- We can also optionally pass checkpointer object for persisting state between graph runs, and enabling memory, human-in-the-loop workflows, time travel and more. In our case we use `MemorySaver` - a simple in-memory checkpointer
</details>
6. <details>
<summary>Execute the graph.</summary>
<ul>
<li>
When we compile the graph, we turn it into a LangChain
which automatically enables calling <code>.invoke()</code>, <code>.stream()</code> and <code>.batch()</code>
with your inputs
</li>
<li>
We can also optionally pass checkpointer object for persisting state between graph runs, and enabling memory,
human-in-the-loop workflows, time travel and more. In our case we use <code>MemorySaver</code> -
a simple in-memory checkpointer
</li>
</ul>
</details>
1. LangGraph adds the input message to the internal state, then passes the state to the entrypoint node, `"agent"`.
2. The `"agent"` node executes, invoking the chat model.
3. The chat model returns an `AIMessage`. LangGraph adds this to the state.
4. Graph cycles the following steps until there are no more `tool_calls` on `AIMessage`:
- If `AIMessage` has `tool_calls`, `"tools"` node executes
- The `"agent"` node executes again and returns `AIMessage`
5. Execution progresses to the special `END` value and outputs the final state.
And as a result, we get a list of all our chat messages as output.
</details>
<details>
<summary>Execute the graph.</summary>
<ol>
<li>LangGraph adds the input message to the internal state, then passes the state to the entrypoint node, <code>"agent"</code>.</li>
<li>The <code>"agent"</code> node executes, invoking the chat model.</li>
<li>The chat model returns an <code>AIMessage</code>. LangGraph adds this to the state.</li>
<li>Graph cycles the following steps until there are no more <code>tool_calls</code> on <code>AIMessage</code>:
<ul>
<li>If <code>AIMessage</code> has <code>tool_calls</code>, <code>"tools"</code> node executes</li>
<li>The <code>"agent"</code> node executes again and returns <code>AIMessage</code></li>
</ul>
</li>
<li>Execution progresses to the special <code>END</code> value and outputs the final state. And as a result, we get a list of all our chat messages as output.</li>
</ol>
</details>
</details>
## Documentation
* [Tutorials](https://langchain-ai.github.io/langgraph/tutorials/): Learn to build with LangGraph through guided examples.
* [How-to Guides](https://langchain-ai.github.io/langgraph/how-tos/): Accomplish specific things within LangGraph, from streaming, to adding memory & persistence, to common design patterns (branching, subgraphs, etc.), these are the place to go if you want to copy and run a specific code snippet.
* [Conceptual Guides](https://langchain-ai.github.io/langgraph/concepts/): In-depth explanations of the key concepts and principles behind LangGraph, such as nodes, edges, state and more.
* [API Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Review important classes and methods, simple examples of how to use the graph and checkpointing APIs, higher-level prebuilt components and more.
* [Conceptual Guides](https://langchain-ai.github.io/langgraph/concepts/high_level/): In-depth explanations of the key concepts and principles behind LangGraph, such as nodes, edges, state and more.
* [API Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Review important classes and methods, simple examples of how to use the graph and checkpointing APIs, higher-level prebuilt components and more.
* [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform): LangGraph Platform is a commercial solution for deploying agentic applications in production, built on the open-source LangGraph framework.
## Resources
* [Built with LangGraph](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship powerful, production-ready AI applications.
## Contributing
For more information on how to contribute, see [here](https://github.com/langchain-ai/langgraph/blob/main/CONTRIBUTING.md).
`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.
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