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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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>
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<details>
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<li><a
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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
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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
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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
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<li><a
href="https://github.com/pallets/jinja/commit/8a8eafc6b992ba177f1d3dd483f8465f18a11116"><code>8a8eafc</code></a>
edit block assignment section</li>
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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
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Fix tests with Twisted 24.7.0</li>
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httputil: Fix quadratic performance of cookie parsing</li>
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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
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Fix tests with Twisted 24.7.0</li>
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httputil: Fix quadratic performance of cookie parsing</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
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Bumps [tornado](https://github.com/tornadoweb/tornado) from 6.4.1 to
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httputil: Fix quadratic performance of cookie parsing</li>
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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
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
@@ -45,14 +39,6 @@ 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 langgraph.graph import StateGraph
@@ -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 langgraph"
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`).
@@ -49,7 +49,7 @@ gain understanding of concepts and how they interact by showing one way to achie
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 the goal that you clearly stated in the tutorial's introduction.
be completely production-ready, it should be useful and practically satisfy the goal that you clearly stated in the tutorial's introduction.
> Looking for the JS version? Click [here](https://github.com/langchain-ai/langgraphjs) ([JS docs](https://langchain-ai.github.io/langgraphjs/)).
> 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/).
* [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/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.
* [Cloud (beta)](https://langchain-ai.github.io/langgraph/cloud/): With one click, deploy LangGraph applications to LangGraph Cloud.
* [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.
This page describes the high-level concepts of the LangGraph Cloud API. The conceptual guide of LangGraph (Python library) is [here](../../concepts/high_level.md).
## Data Models
The LangGraph Cloud API consists of a few core data models: [Assistants](#assistants), [Threads](#threads), [Runs](#runs), and [Cron Jobs](#cron-jobs).
### Assistants
When building agents, it is fairly common to make rapid changes that *do not* alter the graph logic. For example, simply changing prompts or the LLM selection can have significant impacts on the behavior of the agents. Assistants offer an easy way to make and save these types of changes to agent configuration. This can have at least two use-cases:
* Assistants give developers a quick and easy way to modify and version graph version for experimentation.
* Assistants can be modified via LangGraph Studio, offering a no-code way to configure agents (e.g., for business users).
#### Configuring Assistants
In practice, an assistant is just an *instance* of a graph with a specific configuration. Because of this, multiple assistants can reference the same graph but can contain different configurations, such as prompts, models, and other graph configuration options. The LangGraph Cloud API provides several endpoints for creating and managing assistants. See the [API reference](../reference/api/api_ref.html#tag/assistantscreate) and [this how-to](../how-tos/configuration_cloud.md) for more details on how to create assistants.
#### Versioning Assistants
Once you've created an assistant, you can save and version it to track changes to the configuration over time. You can think about this at three levels:
1) The graph lays out the general agent application logic
2) The agent configuration options represent parameters that can be changed
3) Assistant versions save and track specific settings of the agent configuration options
For example, if you have an agent that helps for planning trips, you can create a new assistant *for each user* that passes specific user preferences (e.g., desired airline and car service). As each user interacts with their own assistant, assistant versions can be saved that track the specific desires of the user. Read [this how-to](../how-tos/assistant_versioning.md) to learn how you can use assistant versioning through both the [Studio](../how-tos/index.md/#langgraph-studio) and the SDK.
### Threads
A thread contains the accumulated state of a group of runs. If a run is executed on a thread, then the [state][state] of the underlying graph of the assistant will be persisted to the thread. A thread's current and historical state can be retrieved. To persist state, a thread must be created prior to executing a run.
The state of a thread at a particular point in time is called a checkpoint.
For more on threads and checkpoints, see this section of the [LangGraph conceptual guide](../../concepts/low_level.md#persistence).
The LangGraph Cloud API provides several endpoints for creating and managing threads and thread state. See the [API reference](../reference/api/api_ref.html#tag/threadscreate) for more details.
### Runs
A run is an invocation of an assistant. Each run may have its own input, configuration, and metadata, which may affect execution and output of the underlying graph. A run can optionally be executed on a thread.
The LangGraph Cloud API provides several endpoints for creating and managing runs. See the [API reference](../reference/api/api_ref.html#tag/runscreate) for more details.
### Cron Jobs
It's often useful to run graphs on some schedule. LangGraph Cloud supports cron jobs, which run on a user defined schedule. The user specifies a schedule, an assistant, and some input. After than, on the specified schedule LangGraph cloud will:
- Create a new thread with the specified assistant
- Send the specified input to that thread
Note that this sends the same input to the thread every time. See the [how-to guide](../how-tos/cron_jobs.md) for creating cron jobs.
The LangGraph Cloud API provides several endpoints for creating and managing cron jobs. See the [API reference](../reference/api/api_ref.html#tag/runscreate/POST/threads/{thread_id}/runs/crons) for more details.
## Features
The LangGraph Cloud API offers several features to support complex agent architectures.
### Streaming
Streaming is critical for making LLM applications feel responsive to end users. When creating a streaming run, the streaming mode determines what data is streamed back to the API client. The LangGraph Cloud API supports five streaming modes.
-`values`: Stream the full state of the graph after each [super-step](https://langchain-ai.github.io/langgraph/concepts/low_level/#graphs) is executed. See the [how-to guide](../how-tos/stream_values.md) for streaming values.
-`messages`: Stream complete messages (at the end of node execution) as well as tokens for any messages generated inside a node. This mode is primarily meant for powering chat applications. This is only an option if your graph contains a `messages` key. See the [how-to guide](../how-tos/stream_messages.md) for streaming messages.
-`updates`: Streams updates to the state of the graph after each node is executed. See the [how-to guide](../how-tos/stream_updates.md) for streaming updates.
-`events`: Stream all events (including the state of the graph) that occur during graph execution. See the [how-to guide](../how-tos/stream_events.md) for streaming events. This can be used to do token-by-token streaming for LLMs.
-`debug`: Stream debug events throughout graph execution. See the [how-to guide](../how-tos/stream_debug.md) for streaming debug events.
You can also specify multiple streaming modes at the same time. See the [how-to guide](../how-tos/stream_multiple.md) for configuring multiple streaming modes at the same time.
See the [API reference](../reference/api/api_ref.html#tag/runscreate/POST/threads/{thread_id}/runs/stream) for how to create streaming runs.
Streaming modes `values`, `updates`, and `debug` are very similar to modes available in the LangGraph library - for a deeper conceptual explanation of those, you can see the LangGraph library documentation [here](../../concepts/low_level.md#streaming).
Streaming mode `events` is the same as using `.astream_events` in the LangGraph library - for a deeper conceptual explanation of this, you can see the LangGraph library documentation [here](../../concepts/low_level.md#streaming).
#### `mode="messages"`
Streaming mode `messages` is a new streaming mode, currently only available in the API. What does this mode enable?
This mode is focused on streaming back messages. It currently assumes that you have a `messages` key in your graph that is a list of messages. Assuming we have a simple react agent deployed, what does this stream look like?
All events emitted have two attributes:
-`event`: This is the name of the event
-`data`: This is data associated with the event
Let's run it on a question that should trigger a tool call:
```python
thread=awaitclient.threads.create()
input={"messages":[{"role":"user","content":"what's the weather in sf?"}]}
events=[]
asyncforeventinclient.runs.stream(
thread["thread_id"],
assistant_id="agent",# This may need to change depending on the graph you deployed
input=input,
stream_mode="messages",
):
print(event.event)
```
```shell
metadata
messages/complete
messages/metadata
messages/partial
...
messages/partial
messages/complete
messages/complete
messages/metadata
messages/partial
...
messages/partial
messages/complete
end
```
We first get some `metadata` - this is metadata about the run.
We then get a BUNCH of `messages/partial` events - these are the individual tokens from the LLM! In the case below, we can see the START of a tool call.
After that, we get a `messages/complete` event - this is the AIMessage finishing. It's now a complete tool call:
```python
StreamPart(event='messages/complete',data=[{'content':'','additional_kwargs':{'tool_calls':[{'index':0,'id':'call_w8Hr8dHGuZCPgRfd5FqRBArs','function':{'arguments':'{"query":"current weather in San Francisco"}','name':'tavily_search_results_json'},'type':'function'}]},'response_metadata':{'finish_reason':'tool_calls','model_name':'gpt-4o-2024-05-13','system_fingerprint':'fp_157b3831f5'},'type':'ai','name':None,'id':'run-985c0f14-9f43-40d4-a505-4637fc58e333','example':False,'tool_calls':[{'name':'tavily_search_results_json','args':{'query':'current weather in San Francisco'},'id':'call_w8Hr8dHGuZCPgRfd5FqRBArs'}],'invalid_tool_calls':[],'usage_metadata':None}])
```
After that, we get ANOTHER `messages/complete` event. This is a tool message - our agent has called a tool, gotten a response, and now inserting it into the state in the form of a tool message.
After that, we see the agent doing another LLM call and streaming back a response. We then get an `end` event:
```python
StreamPart(event='end',data=None)
```
And that's it! This is more focused streaming mode specifically focused on streaming back messages. See this [how-to guide](../how-tos/stream_messages.md) for more information.
### Human-in-the-Loop
There are many occasions where the graph cannot run completely autonomously. For instance, the user might need to input some additional arguments to a function call, or select the next edge for the graph to continue on. In these instances, we need to insert some human in the loop interaction, which you can learn about in the [human in the loop how-tos](../how-tos/index.md#human-in-the-loop).
### Double Texting
Many times users might interact with your graph in unintended ways. For instance, a user may send one message and before the graph has finished running send a second message. To solve this issue of "double-texting" (i.e. prompting the graph a second time before the first run has finished), LangGraph has provided four different solutions, all of which are covered in the [Double Texting how-tos](../how-tos/index.md#double-texting). These options are:
-`reject`: This is the simplest option, this just rejects any follow up runs and does not allow double texting. See the [how-to guide](../how-tos/reject_concurrent.md) for configuring the reject double text option.
-`enqueue`: This is a relatively simple option which continues the first run until it completes the whole run, then sends the new input as a separate run. See the [how-to guide](../how-tos/enqueue_concurrent.md) for configuring the enqueue double text option.
-`interrupt`: This option interrupts the current execution but saves all the work done up until that point. It then inserts the user input and continues from there. If you enable this option, your graph should be able to handle weird edge cases that may arise. See the [how-to guide](../how-tos/interrupt_concurrent.md) for configuring the interrupt double text option.
-`rollback`: This option rolls back all work done up until that point. It then sends the user input in, basically as if it just followed the original run input. See the [how-to guide](../how-tos/rollback_concurrent.md) for configuring the rollback double text option.
### Stateless Runs
All runs use the built-in checkpointer to store checkpoints for runs. However, it can often be useful to just kick off a run without worrying about explicitly creating a thread and without wanting to keep those checkpointers around. Stateless runs allow you to do this by exposing an endpoint that:
- Takes in user input
- Under the hood, creates a thread
- Runs the agent but skips all checkpointing steps
- Cleans up the thread afterwards
Stateless runs are still retried as regular retries are per node, while everything still in memory, so doesn't use checkpoints.
The only difference is in stateless background runs, if the task worker dies halfway (not because the run itself failed, for some external reason) then the whole run will be retried like any background run, but
- whereas a stateful background run would retry from the last successful checkpoint
- a stateless background run would retry from the beginning
See the [how-to guide](../how-tos/stateless_runs.md) for creating stateless runs.
### Webhooks
For all types of runs, langgraph cloud supports completion webhooks. When you create the run you can pass a webhook URL to be called when the completes (successfully or not). This is especially useful for background runs and cron jobs, as the webhook can give you an indication the run has completed and you can perform further actions for your appilcation.
See this [how-to guide](../how-tos/webhooks.md) to learn about how to use webhooks with LangGraph Cloud.
## Deployment
The LangGraph Cloud offers several features to support secure and robost deployments.
### Authentication
LangGraph applications deployed to LangGraph Cloud are automatically configured with LangSmith authentication. In order to call the API, a valid <a href="https://docs.smith.langchain.com/how_to_guides/setup/create_account_api_key#api-keys" target="_blank">LangSmith API key</a> is required.
### Local Testing
Before deploying your app in production to LangGraph Cloud, you may wish to test out your graph locally in order to ensure that everything is running as expected. Luckily, LangGraph makes this easy for you through use of the LangGraph CLI. Read more in this [how-to guide](../deployment/test_locally.md) or look at the [CLI reference](../reference/cli.md) to learn more.
This page describes the high-level concepts of the LangGraph Cloud deployment.
## Deployment
A deployment is an instance of a LangGraph API. A single deployment can have many [revisions](#revision). When a deployment is created, all of the necessary infrastructure (e.g. database, containers, secrets store) are automatically provisioned. See the [architecture diagram](#architecture) below for more details.
See the [how-to guide](../deployment/cloud.md#create-new-deployment) for creating a new deployment.
## Revision
A revision is an iteration of a [deployment](#deployment). When a new deployment is created, an initial revision is automatically created. To deploy new code changes or update environment variable configurations for a deployment, a new revision must be created. When a revision is created, a new container image is built automatically.
See the [how-to guide](../deployment/cloud.md#create-new-revision) for creating a new revision.
## Asynchronous Deployment
Infrastructure for [deployments](#deployment) and [revisions](#revision) are provisioned and deployed asynchronously. They are not deployed immediately after submission. Currently, deployment can take up to several minutes.
## Architecture
!!! warning "Subject to Change"
The LangGraph Cloud deployment architecture may change in the future.
A high-level diagram of a LangGraph Cloud deployment.
@@ -5,17 +5,17 @@ LangGraph Cloud is available within <a href="https://www.langchain.com/langsmith
## Prerequisites
1. LangGraph Cloud applications are deployed from GitHub repositories. Configure and upload a LangGraph Cloud application to a GitHub repository in order to deploy it to LangGraph Cloud.
1. [Verify that the LangGraph API runs locally](test_locally.md). If the API does not build and run successfully (i.e. `langgraph up`), deploying to LangGraph Cloud will fail as well.
1. [Verify that the LangGraph API runs locally](test_locally.md). If the API does not run successfully (i.e. `langgraph dev`), deploying to LangGraph Cloud will fail as well.
## Create New Deployment
Starting from the <a href="https://smith.langchain.com/" target="_blank">LangSmith UI</a>...
1. In the left-hand navigation panel, select `LangGraph Cloud`. The `LangGraph Cloud` view contains a list of existing LangGraph Cloud deployments.
1. In the left-hand navigation panel, select `LangGraph Platform`. The `LangGraph Platform` view contains a list of existing LangGraph Cloud deployments.
1. In the top-right corner, select `+ New Deployment` to create a new deployment.
1. In the `Create New Deployment` panel, fill out the required fields.
1.`Deployment details`
1. Select `Import from GitHub` and follow the GitHub OAuth workflow to install and authorize LangChain's `hosted-langserve` GitHub app to access the selected repositories. After installation is complete, return to the `Create New Deployment` panel and select the GitHub repository to deploy from the dropdown menu.
1. Select `Import from GitHub` and follow the GitHub OAuth workflow to install and authorize LangChain's `hosted-langserve` GitHub app to access the selected repositories. After installation is complete, return to the `Create New Deployment` panel and select the GitHub repository to deploy from the dropdown menu.**Note**: The GitHub user installing LangChain's `hosted-langserve` GitHub app must be an [owner](https://docs.github.com/en/organizations/managing-peoples-access-to-your-organization-with-roles/roles-in-an-organization#organization-owners) of the organization or account.
1. Specify a name for the deployment.
1. Specify the desired `Git Branch`. A deployment is linked to a branch. When a new revision is created, code for the linked branch will be deployed. The branch can be updated later in the [Deployment Settings](#deployment-settings).
1. Specify the full path to the [LangGraph API config file](../reference/cli.md#configuration-file) including the file name. For example, if the file `langgraph.json` is in the root of the repository, simply specify `langgraph.json`.
@@ -38,7 +38,7 @@ When [creating a new deployment](#create-new-deployment), a new revision is crea
Starting from the <a href="https://smith.langchain.com/" target="_blank">LangSmith UI</a>...
1. In the left-hand navigation panel, select `LangGraph Cloud`. The `LangGraph Cloud` view contains a list of existing LangGraph Cloud deployments.
1. In the left-hand navigation panel, select `LangGraph Platform`. The `LangGraph Platform` view contains a list of existing LangGraph Cloud deployments.
1. Select an existing deployment to create a new revision for.
1. In the `Deployment` view, in the top-right corner, select `+ New Revision`.
1. In the `New Revision` modal, fill out the required fields.
@@ -52,15 +52,15 @@ Starting from the <a href="https://smith.langchain.com/" target="_blank">LangSmi
1. Update the value of existing secrets or environment variables.
1. Select `Submit`. After a few seconds, the `New Revision` modal will close and the new revision will be queued for deployment.
## View Build and Deployment Logs
## View Build and Server Logs
Build and deployment logs are available for each revision.
Build and server logs are available for each revision.
Starting from the `LangGraph Cloud` view...
Starting from the `LangGraph Platform` view...
1. Select the desired revision from the `Revisions` table. A panel slides open from the right-hand side and the `Build` tab is selected by default, which displays build logs for the revision.
1. In the panel, select the `Deploy` tab to view deployment logs for the revision.
1. Within the `Deploy` tab, adjust the date/time range picker as needed. By default, the date/time range picker is set to the `Last 15 minutes`.
1. In the panel, select the `Server` tab to view server logs for the revision. Server logs are only available after a revision has been deployed.
1. Within the `Server` tab, adjust the date/time range picker as needed. By default, the date/time range picker is set to the `Last 7 days`.
## Interrupt Revision
@@ -69,7 +69,7 @@ Interrupting a revision will stop deployment of the revision.
!!! warning "Undefined Behavior"
Interrupted revisions have undefined behavior. This is only useful if you need to deploy a new revision and you already have a revision "stuck" in progress. In the future, this feature may be removed.
Starting from the `LangGraph Cloud` view...
Starting from the `LangGraph Platform` view...
1. Select the menu icon (three dots) on the right-hand side of the row for the desired revision from the `Revisions` table.
1. Select `Interrupt` from the menu.
@@ -79,13 +79,13 @@ Starting from the `LangGraph Cloud` view...
Starting from the <a href="https://smith.langchain.com/" target="_blank">LangSmith UI</a>...
1. In the left-hand navigation panel, select `LangGraph Cloud`. The `LangGraph Cloud` view contains a list of existing LangGraph Cloud deployments.
1. In the left-hand navigation panel, select `LangGraph Platform`. The `LangGraph Platform` view contains a list of existing LangGraph Cloud deployments.
1. Select the menu icon (three dots) on the right-hand side of the row for the desired deployment and select `Delete`.
1. A `Confirmation` modal will appear. Select `Delete`.
## Deployment Settings
Starting from the `LangGraph Cloud` view...
Starting from the `LangGraph Platform` view...
1. In the top-right corner, select the gear icon (`Deployment Settings`).
# How to add semantic search to your LangGraph deployment
This guide explains how to add semantic search to your LangGraph deployment's cross-thread [store](../../concepts/persistence.md#memory-store), so that your agent can search for memories and other documents by semantic similarity.
## Prerequisites
- A LangGraph deployment (see [how to deploy](setup_pyproject.md))
- API keys for your embedding provider (in this case, OpenAI)
-`langchain >= 0.3.8` (if you specify using the string format below)
## Steps
1. Update your `langgraph.json` configuration file to include the store configuration:
```json
{
...
"store":{
"index":{
"embed":"openai:text-embeddings-3-small",
"dims":1536,
"fields":["$"]
}
}
}
```
This configuration:
- Uses OpenAI's text-embeddings-3-small model for generating embeddings
- Sets the embedding dimension to 1536 (matching the model's output)
- Indexes all fields in your stored data (`["$"]` means index everything, or specify specific fields like `["text", "metadata.title"]`)
2. To use the string embedding format above, make sure your dependencies include `langchain >= 0.3.8`:
```toml
# In pyproject.toml
[project]
dependencies=[
"langchain>=0.3.8"
]
```
Or if using requirements.txt:
```
langchain>=0.3.8
```
## Usage
Once configured, you can use semantic search in your LangGraph nodes. The store requires a namespace tuple to organize memories:
```python
defsearch_memory(state:State,*,store:BaseStore):
# Search the store using semantic similarity
# The namespace tuple helps organize different types of memories
# e.g., ("user_facts", "preferences") or ("conversation", "summaries")
results=store.search(
namespace=("memory","facts"),# Organize memories by type
query="your search query",
limit=3# number of results to return
)
returnresults
```
## Custom Embeddings
If you want to use custom embeddings, you can pass a path to a custom embedding function:
```json
{
...
"store":{
"index":{
"embed":"path/to/embedding_function.py:embed",
"dims":1536,
"fields":["$"]
}
}
}
```
The deployment will look for the function in the specified path. The function must be async and accept a list of strings:
@@ -6,22 +6,16 @@ Testing locally ensures that there are no errors or conflicts with Python depend
## Setup
Install the proper packages:
Install the LangGraph CLI package:
=== "pip"
```bash
pip install -U langgraph-cli
```
=== "Homebrew (macOS only)"
```bash
brew install langgraph-cli
```
```bash
pip install -U "langgraph-cli[inmem]"
```
Ensure you have an API key, which you can create from the [LangSmith UI](https://smith.langchain.com) (Settings > API Keys). This is required to authenticate that you have LangGraph Cloud access. After you have saved the key to a safe place, place the following line in your `.env` file:
```python
LANGCHAIN_API_KEY = *********
LANGSMITH_API_KEY=*********
```
## Start the API server
@@ -29,16 +23,26 @@ LANGCHAIN_API_KEY = *********
Once you have installed the CLI, you can run the following command to start the API server for local testing:
```shell
langgraph up
langgraph dev
```
This will start up the LangGraph API server locally. If this runs successfully, you should see something like:
> - LangGraph Studio Web UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024
!!! note "In-Memory Mode"
The `langgraph dev` command starts LangGraph Server in an in-memory mode. This mode is suitable for development and testing purposes. For production use, you should deploy LangGraph Server with access to a persistent storage backend.
If you want to test your application with a persistent storage backend, you can use the `langgraph up` command instead of `langgraph dev`. You will
need to have `docker` installed on your machine to use this command.
### Interact with the server
@@ -53,8 +57,8 @@ You can either initialize by passing authentication or by setting an environment
```python
from langgraph_sdk import get_client
# only pass the url argument to get_client() if you changed the default port when calling langgraph up
There are a few reasons that your project might fail to start, here are some of the most common ones.
### Docker issues
LangGraph Studio requires Docker Desktop version 4.24 or higher. Please make sure you have a version of Docker installed that satisfies that requirement and also make sure you have the Docker Desktop app up and running before trying to use LangGraph Studio. In addition, make sure you have docker-compose updated to version 2.22.0 or higher.
### Configuration or environment issues
Another reason your project might fail to start is because your configuration file is defined incorrectly, or you are missing required environment variables.
## How does interrupt work?
When you select the `Interrupts` dropdown and select a node to interrupt the graph will pause execution before and after (unless the node goes straight to `END`) that node has run. This means that you will be able to both edit the state before the node is ran and the state after the node has ran. This is intended to allow developers more fine-grained control over the behavior of a node and make it easier to observe how the node is behaving. You will not be able to edit the state after the node has ran if the node is the final node in the graph.
## How do I reload the app?
If you would like to reload the app, don't use Command+R as you might normally do. Instead, close and reopen the app for a full refresh.
## How does automatic rebuilding work?
One of the key features of LangGraph Studio is that it automatically rebuilds your image when you change the source code. This allows for a super fast development and testing cycle which makes it easy to iterate on your graph. There are two different ways that LangGraph rebuilds your image: either by editing the image or completely rebuilding it.
### Rebuilds from source code changes
If you modified the source code only (no configuration or dependency changes!) then the image does not require a full rebuild, and LangGraph Studio will only update the relevant parts. The UI status in the bottom left will switch from `Online` to `Stopping` temporarily while the image gets edited. The logs will be shown as this process is happening, and after the image has been edited the status will change back to `Online` and you will be able to run your graph with the modified code!
### Rebuilds from configuration or dependency changes
If you edit your graph configuration file (`langgraph.json`) or the dependencies (either `pyproject.toml` or `requirements.txt`) then the entire image will be rebuilt. This will cause the UI to switch away from the graph view and start showing the logs of the new image building process. This can take a minute or two, and once it is done your updated image will be ready to use!
## Why is my graph taking so long to startup?
The LangGraph Studio interacts with a local LangGraph API server. To stay aligned with ongoing updates, the LangGraph API requires regular rebuilding. As a result, you may occasionally experience slight delays when starting up your project.
## Why are extra edges showing up in my graph?
If you don't define your conditional edges carefully, you might notice extra edges appearing in your graph. This is because without proper definition, LangGraph Studio assumes the conditional edge could access all other nodes. In order for this to not be the case, you need to be explicit about how you define the nodes the conditional edge routes to. There are two ways you can do this:
### Solution 1: Include a path map
The first way to solve this is to add path maps to your conditional edges. A path map is just a dictionary or array that maps the possible outputs of your router function with the names of the nodes that each output corresponds to. The path map is passed as the third argument to the `add_conditional_edges` function like so:
In this case, the routing function returns either True or False, which map to `node_b` and `node_c` respectively.
### Solution 2: Update the typing of the router (Python only)
Instead of passing a path map, you can also be explicit about the typing of your routing function by specifying the nodes it can map to using the `Literal` python definition. Here is an example of how to define a routing function in that way:
In this how-to guide we will walk through how you can create and manage different assistant versions. If you haven't already, you can read [this](../concepts/api.md/#versioning-assistants) conceptual guide to gain a better understanding of what assistant versioning is. This how-to assumes you have a graph that is configurable, which means you have defined a config schema and passed it to your graph as follows:
In this how-to guide we will walk through how you can create and manage different assistant versions. If you haven't already, you can read [this](../../concepts/assistants.md#versioning-assistants) conceptual guide to gain a better understanding of what assistant versioning is. This how-to assumes you have a graph that is configurable, which means you have defined a config schema and passed it to your graph as follows:
=== "Python"
@@ -86,19 +86,19 @@ To create an assistant using the studio do the following steps:
1. Click on the "Create New Assistant" button:
2. Use the create assistant pane to enter info for the assistant you wish to create, and then click create:
1. Use the create assistant pane to enter info for the assistant you wish to create, and then click create:
3. See that your assistant was created and is displayed in the Studio
1. See that your assistant was created and is displayed in the Studio
4. Click on the edit button next to the selected assistant to manage your created assistant:
1. Click on the edit button next to the selected assistant to manage your created assistant:
## Create a new version for your assistant
@@ -131,15 +131,15 @@ Let's now say we wanted to add a system prompt to our assistant. We can do this
1. First, click on the edit button next to the `openai_assistant`. Then, add a system prompt and click "Save New Version":
2. Then you can see it is selected in the assistant dropdown:
1. Then you can see it is selected in the assistant dropdown:
3. And you can see all the version history in the edit pane for the assistant:
1. And you can see all the version history in the edit pane for the assistant:
@@ -4,7 +4,7 @@ You may wish to copy (i.e. "fork") an existing thread in order to keep the exist
## Setup
This code assumes you already have a thread to copy. You can read about what a thread is [here](https://langchain-ai.github.io/langgraph/cloud/concepts/api/#threads) and learn how to stream a run on a thread in [these how-to guides](https://langchain-ai.github.io/langgraph/cloud/how-tos/#streaming).
This code assumes you already have a thread to copy. You can read about what a thread is [here](../../concepts/langgraph_server.md#threads) and learn how to stream a run on a thread in [these how-to guides](../../how-tos/index.md#streaming_1).
In LangGraph Studio you can create dataset examples from the thread history in the right-hand pane. This can be especially useful when you want to evaluate intermediate steps of the agent.
1. Click on the `Add to Dataset` button to enter the dataset mode.
1. Select nodes which you want to add to dataset.
1. Select the target dataset to create the example in.
You can edit the example payload before sending it to the dataset, which is useful if you need to make changes to conform the example to the dataset schema.
Finally, you can customise the target dataset by clicking on the `Settings` button.
See [Evaluating intermediate steps](https://docs.smith.langchain.com/evaluation/how_to_guides/langgraph#evaluating-intermediate-steps) for more details on how to evaluate intermediate steps.
This guide assumes knowledge of what double-texting is, which you can learn about in the [double-texting conceptual guide](../concepts/api.md#double-texting).
This guide assumes knowledge of what double-texting is, which you can learn about in the [double-texting conceptual guide](../../concepts/double_texting.md).
The guide covers the `enqueue` option for double texting, which adds the interruptions to a queue and executes them in the order they are received by the client. Below is a quick example of using the `enqueue` option.
Welcome to the LangGraph Cloud how-to guides! These guides provide practical, step-by-step instructions for accomplishing key tasks in LangGraph Cloud.
## Setup
LangGraph Cloud gives you best in class observability, testing, and hosting services. Learn how to setup your app for deployment to LangGraph Cloud in these how-to guides
- [How to set up app for deployment (requirements.txt)](../deployment/setup.md)
- [How to set up app for deployment (pyproject.toml)](../deployment/setup_pyproject.md)
- [How to set up app for deployment (JavaScript)](../deployment/setup_javascript.md)
- [How to customize Dockerfile](../deployment/custom_docker.md)
- [How to test locally](../deployment/test_locally.md)
## Deploy
Learn how to deploy your app to LangGraph Cloud in these how to guides:
- [How to deploy to LangGraph cloud](../deployment/cloud.md)
- [How to interact with the deployment using RemoteGraph](../../how-tos/use-remote-graph.md)
## Streaming
Streaming the results of your LLM application is vital for ensuring a good user experience, especially when your graph may call multiple models and take a long time to fully complete a run. Read about how to stream values from your graph in these how to guides:
- [How to stream values](./stream_values.md)
- [How to stream updates](./stream_updates.md)
- [How to stream messages](./stream_messages.md)
- [How to stream events](./stream_events.md)
- [How to stream in debug mode](./stream_debug.md)
- [How to stream multiple modes](./stream_multiple.md)
## Double-texting
Graph execution can take a while, and sometimes users may change their mind about the input they wanted to send before their original input has finished running. For example, a user might notice a typo in their original request and will edit the prompt and resend it. Deciding what to do in these cases is important for ensuring a smooth user experience and preventing your graphs from behaving in unexpected ways. The following how-to guides provide information on the various options LangGraph Cloud gives you for dealing with double-texting:
- [How to use the interrupt option](./interrupt_concurrent.md)
- [How to use the rollback option](./rollback_concurrent.md)
- [How to use the reject option](./reject_concurrent.md)
- [How to use the enqueue option](./enqueue_concurrent.md)
## Human-in-the-loop
When creating complex graphs, leaving every decision up to the LLM can be dangerous, especially when the decisions involve invoking certain tools or accessing specific documents. To remedy this, LangGraph allows you to insert human-in-the-loop behavior to ensure your graph does not have undesired outcomes. Read more about the different ways you can add human-in-the-loop capabilities to your LangGraph Cloud projects in these how-to guides:
- [How to add a breakpoint](./human_in_the_loop_breakpoint.md)
- [How to wait for user input](./human_in_the_loop_user_input.md)
- [How to edit graph state](./human_in_the_loop_edit_state.md)
- [How to replay and branch from prior states](./human_in_the_loop_time_travel.md)
- [How to review tool calls](./human_in_the_loop_review_tool_calls.md)
## LangGraph Studio
LangGraph Studio is a built-in UI for visualizing, testing, and debugging your agents.
- [How to enter LangGraph Studio](./test_deployment.md)
- [How to enter LangGraph Studio for local deployment](./test_local_deployment.md)
- [How to test your graph in LangGraph Studio](./invoke_studio.md)
- [Interact with threads in LangGraph Studio](./threads_studio.md)
## Different Types of Runs:
LangGraph Cloud supports multiple types of runs besides streaming runs.
- [How to run an agent in the background](./background_run.md)
- [How to run multiple agents in the same thread](./same-thread.md)
- [How to create cron jobs](./cron_jobs.md)
- [How to create stateless runs](./stateless_runs.md)
## Other
Other guides that may prove helpful!
- [How to configure agents](./configuration_cloud.md)
- [How to version assistants](./assistant_versioning.md)
- [How to convert LangGraph calls to LangGraph cloud calls](./langgraph_to_langgraph_cloud.ipynb)
- [How to integrate webhooks](./webhooks.md)
- [How to copy threads](./copy_threads.md)
- [How to check status of your threads](./check_thread_status.md)
This guide assumes knowledge of what double-texting is, which you can learn about in the [double-texting conceptual guide](../concepts/api.md#double-texting).
This guide assumes knowledge of what double-texting is, which you can learn about in the [double-texting conceptual guide](../../concepts/double_texting.md).
The guide covers the `interrupt` option for double texting, which interrupts the prior run of the graph and starts a new one with the double-text. This option does not delete the first run, but rather keeps it in the database but sets its status to `interrupted`. Below is a quick example of using the `interrupt` option.
@@ -83,7 +83,7 @@ Now, let's import our required packages and instantiate our client, assistant, a
## Create runs
Now we can start our two runs and join the second oneuntil it has completed:
Now we can start our two runs and join the second oneuntil it has completed:
=== "Python"
@@ -94,6 +94,7 @@ Now we can start our two runs and join the second on euntil it has completed:
assistant_id,
input={"messages": [{"role": "user", "content": "what's the weather in sf?"}]},
)
# sleep a bit to get partial outputs from the first run
await asyncio.sleep(2)
run = await client.runs.create(
thread["thread_id"],
@@ -114,6 +115,7 @@ Now we can start our two runs and join the second on euntil it has completed:
assistantId,
{ input: { messages: [{ role: "human", content: "what's the weather in sf?" }] } }
);
// sleep a bit to get partial outputs from the first run
await new Promise(resolve => setTimeout(resolve, 2000));
This guide assumes knowledge of what double-texting is, which you can learn about in the [double-texting conceptual guide][double-texting].
This guide assumes knowledge of what double-texting is, which you can learn about in the [double-texting conceptual guide](../../concepts/double_texting.md).
The guide covers the `reject` option for double texting, which rejects the new run of the graph by throwing an error and continues with the original run until completion. Below is a quick example of using the `reject` option.
This guide assumes knowledge of what double-texting is, which you can learn about in the [double-texting conceptual guide][double-texting].
This guide assumes knowledge of what double-texting is, which you can learn about in the [double-texting conceptual guide](../../concepts/double_texting.md).
The guide covers the `rollback` option for double texting, which interrupts the prior run of the graph and starts a new one with the double-text. This option is very similar to the `interrupt` option, but in this case the first run is completely deleted from the database and cannot be restarted. Below is a quick example of using the `rollback` option.
@@ -95,7 +95,6 @@ Now let's run a thread with the multitask parameter set to "rollback":
assistant_id,
input={"messages": [{"role": "user", "content": "what's the weather in sf?"}]},
)
await asyncio.sleep(2)
run = await client.runs.create(
thread["thread_id"],
assistant_id,
@@ -115,7 +114,6 @@ Now let's run a thread with the multitask parameter set to "rollback":
assistantId,
{ input: { messages: [{ role: "human", content: "what's the weather in sf?" }] } }
);
await new Promise(resolve => setTimeout(resolve, 2000));
let run = await client.runs.create(
thread["thread_id"],
@@ -139,7 +137,7 @@ Now let's run a thread with the multitask parameter set to "rollback":
--data "{
\"assistant_id\": \"agent\",
\"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what\'s the weather in sf?\"}]},
Token-by-token streaming can be implemented with the `events` streaming mode. The `on_chat_model_stream` event type should be processed to stream LLM responses token-by-token.
=== "Python"
```python
llm_response = ""
# stream token-by-token
async for chunk in client.runs.stream(
thread_id=thread["thread_id"],
assistant_id=assistant_id,
input=input,
stream_mode="events",
):
if (
chunk.event == "events" and
chunk.data["event"] == "on_chat_model_stream" and
len(chunk.data["data"]["chunk"]["content"]) > 0 and
'text' in chunk.data["data"]["chunk"]["content"][0]
\"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"What's the weather in sf\"}]},
\"stream_mode\": [
\"events\"
]
}" | sed 's/\r$//' | awk '
/^event:/ { event = $2 }
/^data:/ {
json_data = substr($0, index($0, $2))
if (event == "events") {
print json_data
}
}' | jq -r '
select(.event == "on_chat_model_stream") |
.data.chunk.content[] | .text // empty
' | awk '
BEGIN { llm_response="" }
$0 != "" && $0 != "null" {
llm_response = llm_response $0
print llm_response
}'
```
Output:
The
The search
The search results provide
The search results provide the current weather conditions
The search results provide the current weather conditions in San Francisco.
The search results provide the current weather conditions in San Francisco. According
The search results provide the current weather conditions in San Francisco. According to the data,
The search results provide the current weather conditions in San Francisco. According to the data, as
The search results provide the current weather conditions in San Francisco. According to the data, as of 3
The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19
The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August
The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12,
The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024,
The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather
The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in
The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny
The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of
The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60
The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.
The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (
The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°
The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C).
The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The win
The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is bl
The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west
The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-
The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13
The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph
The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6
The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 k
The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph).
The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph). The humidity is
The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph). The humidity is 70%
The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph). The humidity is 70% and visibility
The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph). The humidity is 70% and visibility is 6
The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph). The humidity is 70% and visibility is 6 miles
The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph). The humidity is 70% and visibility is 6 miles (10 km
The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph). The humidity is 70% and visibility is 6 miles (10 km).
The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph). The humidity is 70% and visibility is 6 miles (10 km). Overall
The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph). The humidity is 70% and visibility is 6 miles (10 km). Overall, it appears
The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph). The humidity is 70% and visibility is 6 miles (10 km). Overall, it appears to be a nice
The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph). The humidity is 70% and visibility is 6 miles (10 km). Overall, it appears to be a nice sunny day in San
The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph). The humidity is 70% and visibility is 6 miles (10 km). Overall, it appears to be a nice sunny day in San Francisco.
This guide covers how to stream messages from your graph. With `stream_mode="messages"`, messages from any chat model invocations inside your graph nodes will be streamed back.
Read more about how the `messages` streaming mode works [here](https://langchain-ai.github.io/langgraph/cloud/concepts/api/#modemessages)
This guide covers how to stream messages from your graph. With `stream_mode="messages-tuple"`, messages (i.e. individual LLM tokens) from any chat model invocations inside your graph nodes will be streamed back.
## Setup
@@ -60,7 +58,7 @@ Output:
## Stream graph in messages mode
Now we can stream by messages, which will return complete messages (at the end of node execution) as well as tokens for any messages generated inside a node:
Now we can stream LLM tokens for any messages generated inside a node in the form of tuples `(message, metadata)`. Metadata contains additional information that can be useful for filtering the streamed outputs to a specific node or LLM.
=== "Python"
@@ -73,7 +71,7 @@ Now we can stream by messages, which will return complete messages (at the end o
assistant_id=assistant_id,
input=input,
config=config,
stream_mode="messages",
stream_mode="messages-tuple",
):
print(f"Receiving new event of type: {chunk.event}...")
print(chunk.data)
@@ -99,7 +97,7 @@ Now we can stream by messages, which will return complete messages (at the end o
{
input,
config,
streamMode: "messages"
streamMode: "messages-tuple"
}
);
for await (const chunk of streamResponse) {
@@ -119,7 +117,7 @@ Now we can stream by messages, which will return complete messages (at the end o
\"assistant_id\": \"agent\",
\"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what's the weather in la\"}]},
"text": "\n\nThe search results provide the current weather conditions in San Francisco. According to the data, as of 5:20pm on October 30, 2024, the weather in San Francisco is partly cloudy with a temperature of 54\\u00b0F (12\\u00b0C). The wind is blowing from the west-southwest at around 10 mph (15 km/h). The humidity is high at 93% and visibility is 6 miles (10 km). Overall, it seems to be a cool, partly cloudy day with moderate winds in San Francisco.",
Make sure you have setup your app correctly, by creating a compiled graph, a `.env` file with any environment variables, and a `langgraph.json` config file that points to your environment file and compiled graph. See [here](https://langchain-ai.github.io/langgraph/cloud/deployment/setup/) for more detailed instructions.
After you have your app setup, head into the directory with your `langgraph.json` file and call `langgraph up -c langgraph.json --watch` to start the API server in watch mode which means it will restart on code changes, which is ideal for local testing. If the API server start correctly you should see logs that look something like this:
After you have your app setup, head into the directory with your `langgraph.json` file and call `langgraph dev` to start the API server in watch mode which means it will restart on code changes, which is ideal for local testing. If the API server start correctly you should see logs that look something like this:
> - LangGraph Studio Web UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024
Read this [reference](https://langchain-ai.github.io/langgraph/cloud/reference/cli/#up) to learn about all the options for starting the API server.
## Access Studio
Once you have successfully started the API server, you can access the studio by going to the following URL: `https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:8123` (see warning above if using Safari).
Once you have successfully started the API server, you can access the studio by going to the following URL: `https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024` (see warning above if using Safari).
If everything is working correctly you should see the studio show up looking something like this (with your graph diagram on the left hand side):
To invoke a run with a webhook, we specify the `webhook` parameter with the desired endpoint when creating a run. Webhook requests are triggered by the end of a run.
For example, if we can receive requests at `https://my-server.app/my-webhook-endpoint`, we can pass this to `stream`:
=== "Python"
@@ -89,7 +91,7 @@ Now we can invoke a run with a webhook:
And that's it! Now you can trigger your custom webhooks whenever you want in your LangGraph applications!
The schema for the payload sent to `my-webhook-endpoint` is that of a [run](../../concepts/langgraph_server.md/#runs). See [API Reference](https://langchain-ai.github.io/langgraph/cloud/reference/api/api_ref.html#model/run) for more detail. Note that the run input, configuration, etc. are included in the `kwargs` field.
### Signing webhook requests
To sign the webhook requests, we can specify a token parameter in the webhook URL, e.g.,
The server should then extract the token from the request's parameters and validate it before processing the payload.
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