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Sydney Runkle
2025-07-21 10:06:23 -04:00
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.github/ISSUE_TEMPLATE/privileged.yml
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@@ -23,3 +23,7 @@ body:
attributes:
label: Issue Content
description: Add the content of the issue here.
- type: markdown
attributes:
value: |
Community members should **NOT** work on Privileged issues unless these issues have been explicitly marked with a "help-wanted" tag.
.github/workflows/codespell.yml
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@@ -34,7 +34,7 @@
id: extract_ignore_words
- name: Codespell
uses: codespell-project/actions-codespell@v2.0
uses: codespell-project/actions-codespell@v2.1
with:
skip: '*.ambr,*.lock,*.ipynb,*.yaml,*.zlib,*.css.map,*.js.map'
ignore_words_list: ${{ steps.extract_ignore_words.outputs.ignore_words_list }}
docs/docs/cloud/deployment/egress.md
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@@ -0,0 +1,119 @@
# Egress for Subscription Metrics and Operational Metadata
> **Important: Self Hosted Only**
> This section only applies to customers who are not running in offline mode and assumes you are using a self-hosted LangGraph Platform instance.
> This does not apply to SaaS or Hybrid deployments.
Self-Hosted LangGraph Platform instances store all information locally and will never send sensitive information outside of your network. We currently only track platform usage for billing purposes according to the entitlements in your order. In order to better remotely support our customers, we do require egress to `https://beacon.langchain.com`.
In the future, we will be introducing support diagnostics to help us ensure that the LangGraph Platform is running at an optimal level within your environment.
> **Warning**
> **This will require egress to `https://beacon.langchain.com` from your network.**
> **If using an API key, you will also need to allow egress to `https://api.smith.langchain.com` or `https://eu.api.smith.langchain.com` for API key verification.**
Generally, data that we send to Beacon can be categorized as follows:
- **Subscription Metrics**
- Subscription metrics are used to determine level of access and utilization of LangSmith. This includes, but are not limited to:
- Nodes Executed
- Runs Executed
- License Key Verification
- **Operational Metadata**
- This metadata will contain and collect the above subscription metrics to assist with remote support, allowing the LangChain team to diagnose and troubleshoot performance issues more effectively and proactively.
## Example Payloads
In an effort to maximize transparency, we provide sample payloads here:
### License Verification (If using an Enterprise License)
**Endpoint:**
`POST beacon.langchain.com/v1/beacon/verify`
**Request:**
```json
{
"license": "<YOUR_LICENSE_KEY>"
}
```
**Response:**
```json
{
"token": "Valid JWT" // Short-lived JWT token to avoid repeated license checks
}
```
### Api Key Verification (If using a LangSmith API Key)
**Endpoint:**
`POST api.smith.langchain.com/auth`
**Request:**
```json
"Headers": {
X-Api-Key: <YOUR_API_KEY>
}
```
**Response:**
```json
{
"org_config": {
"org_id": "3a1c2b6f-4430-4b92-8a5b-79b8b567bbc1",
... // Additional organization details
}
}
```
### Usage Reporting
**Endpoint:**
`POST beacon.langchain.com/v1/metadata/submit`
**Request:**
```json
{
"license": "<YOUR_LICENSE_KEY>",
"from_timestamp": "2025-01-06T09:00:00Z",
"to_timestamp": "2025-01-06T10:00:00Z",
"tags": {
"langgraph.python.version": "0.1.0",
"langgraph_api.version": "0.2.0",
"langgraph.platform.revision": "abc123",
"langgraph.platform.variant": "standard",
"langgraph.platform.host": "host-1",
"langgraph.platform.tenant_id": "3a1c2b6f-4430-4b92-8a5b-79b8b567bbc1",
"langgraph.platform.project_id": "c5b5f53a-4716-4326-8967-d4f7f7799735",
"langgraph.platform.plan": "enterprise",
"user_app.uses_indexing": "true",
"user_app.uses_custom_app": "false",
"user_app.uses_custom_auth": "true",
"user_app.uses_thread_ttl": "true",
"user_app.uses_store_ttl": "false"
},
"measures": {
"langgraph.platform.runs": 150,
"langgraph.platform.nodes": 450
},
"logs": []
}
```
**Response:**
```json
"204 No Content"
```
## Our Commitment
LangChain will not store any sensitive information in the Subscription Metrics or Operational Metadata. Any data collected will not be shared with a third party. If you have any concerns about the data being sent, please reach out to your account team.
docs/docs/cloud/deployment/self_hosted_control_plane.md
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@@ -23,6 +23,8 @@ Before deploying, review the [conceptual guide for the Self-Hosted Control Plane
kubectl get storageclass
1. Egress to `https://beacon.langchain.com` from your network. This is required for license verification and usage reporting if not running in air-gapped mode. See the [Egress documentation](../../cloud/deployment/egress.md) for more details.
## Setup
1. As part of configuring your Self-Hosted LangSmith instance, you enable the `langgraphPlatform` option. This will provision a few key resources.
docs/docs/cloud/deployment/standalone_container.md
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@@ -24,6 +24,7 @@ Before deploying, review the [conceptual guide for the Standalone Container](../
1. `LANGSMITH_API_KEY`: (if using [Lite](../../concepts/langgraph_server.md#server-versions)) LangSmith API key. This will be used to authenticate ONCE at server start up.
1. `LANGGRAPH_CLOUD_LICENSE_KEY`: (if using [Enterprise](../../concepts/langgraph_data_plane.md#licensing)) LangGraph Platform license key. This will be used to authenticate ONCE at server start up.
1. `LANGSMITH_ENDPOINT`: To send traces to a [self-hosted LangSmith](https://docs.smith.langchain.com/self_hosting) instance, set `LANGSMITH_ENDPOINT` to the hostname of the self-hosted LangSmith instance.
1. Egress to `https://beacon.langchain.com` from your network. This is required for license verification and usage reporting if not running in air-gapped mode. See the [Egress documentation](../../cloud/deployment/egress.md) for more details.
## Kubernetes (Helm)
docs/docs/cloud/how-tos/generative_ui_react.md
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@@ -30,17 +30,33 @@ export default {
Next, define your UI components in your `langgraph.json` configuration:
```json
{
"node_version": "20",
"graphs": {
"agent": "./src/agent/index.ts:graph"
},
"ui": {
"agent": "./src/agent/ui.tsx"
}
}
```
=== "Python agent"
```json title="langgraph.json"
{
"node_version": "20",
"graphs": {
"agent": "./src/agent.py:graph"
},
"ui": {
"agent": "./src/agent/ui.tsx"
}
}
```
=== "JS agent"
```json title="langgraph.json"
{
"node_version": "20",
"graphs": {
"agent": "./src/agent/index.ts:graph"
},
"ui": {
"agent": "./src/agent/ui.tsx"
}
}
```
The `ui` section points to the UI components that will be used by graphs. By default, we recommend using the same key as the graph name, but you can split out the components however you like, see [Customise the namespace of UI components](#customise-the-namespace-of-ui-components) for more details.
docs/docs/cloud/how-tos/webhooks.md
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@@ -140,6 +140,22 @@ https://my-server.app/my-webhook-endpoint?token=YOUR_SECRET_TOKEN
Your server should extract and validate this token before processing requests.
## Disable webhooks
As of `langgraph-api>=0.2.78`, developers can disable webhooks in the `langgraph.json` file:
```json
{
"http": {
"disable_webhooks": true
}
}
```
This feature is primarily intended for self-hosted deployments, where platform administrators or developers may prefer to disable webhooks to simplify their security posture—especially if they are not configuring firewall rules or other network controls. Disabling webhooks helps prevent untrusted payloads from being sent to internal endpoints.
For full configuration details, refer to the [configuration file reference](https://langchain-ai.github.io/langgraph/cloud/reference/cli/?h=disable_webhooks#configuration-file).
## Test webhooks
You can test your webhook using online services like:
docs/docs/cloud/reference/cli.md
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@@ -409,8 +409,8 @@ The LangGraph CLI requires a JSON configuration file that follows this [schema](
| Option | Default | Description |
| ---------------------------- | ------------------------- | ----------------------------------------------------------------------------------------------------------------------- |
| `--wait` | | Wait for services to start before returning. Implies --detach |
| `--base-image TEXT` | `langchain/langgraph-api` | Base image to use for the LangGraph API server. Pin to specific versions using version tags. |
| `--image TEXT` | | Docker image to use for the langgraph-api service. If specified, skips building and uses this image directly. |
| `--base-image TEXT` | `langchain/langgraph-api` | Base image to use for the LangGraph API server. Pin to specific versions using version tags. |
| `--image TEXT` | | Docker image to use for the langgraph-api service. If specified, skips building and uses this image directly. |
| `--postgres-uri TEXT` | Local database | Postgres URI to use for the database. |
| `--watch` | | Restart on file changes |
| `--debugger-base-url TEXT` | `http://127.0.0.1:[PORT]` | URL used by the debugger to access LangGraph API. |
@@ -438,8 +438,8 @@ The LangGraph CLI requires a JSON configuration file that follows this [schema](
| Option | Default | Description |
| ---------------------------------------------------------------------- | ------------------------- | ----------------------------------------------------------------------------------------------------------------------- |
| <span style="white-space: nowrap;">`--wait`</span> | | Wait for services to start before returning. Implies --detach |
| <span style="white-space: nowrap;">`--base-image TEXT`</span> | <span style="white-space: nowrap;">`langchain/langgraph-api`</span> | Base image to use for the LangGraph API server. Pin to specific versions using version tags. |
| <span style="white-space: nowrap;">`--image TEXT`</span> | | Docker image to use for the langgraph-api service. If specified, skips building and uses this image directly. |
| <span style="white-space: nowrap;">`--base-image TEXT`</span> | <span style="white-space: nowrap;">`langchain/langgraph-api`</span> | Base image to use for the LangGraph API server. Pin to specific versions using version tags. |
| <span style="white-space: nowrap;">`--image TEXT`</span> | | Docker image to use for the langgraph-api service. If specified, skips building and uses this image directly. |
| <span style="white-space: nowrap;">`--postgres-uri TEXT`</span> | Local database | Postgres URI to use for the database. |
| <span style="white-space: nowrap;">`--watch`</span> | | Restart on file changes |
| <span style="white-space: nowrap;">`-c, --config FILE`</span> | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables. |
docs/docs/cloud/reference/env_var.md
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@@ -28,6 +28,9 @@ Specify `DD_API_KEY` (your [Datadog API Key](https://docs.datadoghq.com/account_
If `DD_API_KEY` is specified, the application process is wrapped in the [`ddtrace-run` command](https://ddtrace.readthedocs.io/en/stable/installation_quickstart.html). Other `DD_*` environment variables (e.g. `DD_SITE`, `DD_ENV`, `DD_SERVICE`, `DD_TRACE_ENABLED`) are typically needed to properly configure the tracing instrumentation. See [`DD_*` environment variables](https://ddtrace.readthedocs.io/en/stable/configuration.html) for more details.
!!! note
Enabling `DD_API_KEY` (and thus `ddtrace-run`) can override or interfere with other auto-instrumentation solutions (such as OpenTelemetry) that you may have instrumented into your application code.
## `LANGCHAIN_TRACING_SAMPLING_RATE`
Sampling rate for traces sent to LangSmith. Valid values: Any float between `0` and `1`.
docs/docs/cloud/reference/langgraph_server_changelog.md
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@@ -4,6 +4,17 @@
---
## v0.2.96 (2025-07-17)
- Added a fallback mechanism for configurable header patterns to handle exclude/include settings more effectively.
## v0.2.95 (2025-07-17)
- Avoided setting the future if it is already done to prevent redundant operations.
- Resolved compatibility errors in CI by switching from `typing.TypedDict` to `typing_extensions.TypedDict` for Python versions below 3.12.
## v0.2.94 (2025-07-16)
- Improved performance by omitting pending sends for langgraph versions 0.5 and above.
- Improved server startup logs to provide clearer warnings when the DD_API_KEY environment variable is set.
## v0.2.93 (2025-07-16)
- Removed the GIN index for run metadata to improve performance.
docs/docs/concepts/deployment_options.md
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@@ -10,7 +10,7 @@ search:
There are two free options for deploying LangGraph applications via the LangGraph Server:
1. [Local](../tutorials/langgraph-platform/local-server.md): Deploy for local testing and development.
1. [Standalone Container (Lite)](../concepts/langgraph_standalone_container.md): A limited version of Standalone Container for deployments unlikely to see more that 1 million node executions per year and that do not need crons and other enterprise features. Standalone Container (Lite) deployment option is free with a LangSmith API key.
1. [Standalone Container (Lite)](../concepts/langgraph_standalone_container.md): A limited version of Standalone Container for deployments unlikely to see more than 1 million node executions per year and that do not need crons and other enterprise features. Standalone Container (Lite) deployment option is free with a LangSmith API key.
## Production deployment
docs/docs/concepts/low_level.md
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@@ -45,7 +45,7 @@ The first thing you do when you define a graph is define the `State` of the grap
### Schema
The main documented way to specify the schema of a graph is by using `TypedDict`. However, we also support [using a Pydantic BaseModel](../how-tos/graph-api.md#use-pydantic-models-for-graph-state) as your graph state to add **default values** and additional data validation.
The main documented way to specify the schema of a graph is by using a [`TypedDict`](https://docs.python.org/3/library/typing.html#typing.TypedDict). If you want to provide default values in your state, use a [`dataclass`](https://docs.python.org/3/library/dataclasses.html). We also support using a Pydantic [BaseModel](../how-tos/graph-api.md#use-pydantic-models-for-graph-state) as your graph state if you want recursive data validation (though note that pydantic is less performant than a `TypedDict` or `dataclass`).
By default, the graph will have the same input and output schemas. If you want to change this, you can also specify explicit input and output schemas directly. This is useful when you have a lot of keys, and some are explicitly for input and others for output. See the [guide here](../how-tos/graph-api.md#define-input-and-output-schemas) for how to use.
docs/docs/concepts/tracing.md
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@@ -0,0 +1,17 @@
# Tracing
Traces are a series of steps that your application takes to go from input to output. Each of these individual steps is represented by a run. You can use [LangSmith](https://smith.langchain.com/) to visualize these execution steps. To use it, [enable tracing for your application](../how-tos/enable-tracing.md). This enables you to do the following:
- [Debug a locally running application](../cloud/how-tos/clone_traces_studio.md).
- [Evaluate the application performance](../agents/evals.md).
- [Monitor the application](https://docs.smith.langchain.com/observability/how_to_guides/dashboards).
To get started, sign up for a free account at [LangSmith](https://smith.langchain.com/).
## Learn more
- [Graph runs in LangSmith](../how-tos/run-id-langsmith.md)
- [LangSmith Observability quickstart](https://docs.smith.langchain.com/observability)
- [Trace with LangGraph](https://docs.smith.langchain.com/observability/how_to_guides/trace_with_langgraph)
- [Tracing conceptual guide](https://docs.smith.langchain.com/observability/concepts#traces)
docs/docs/guides/index.md
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@@ -2,6 +2,11 @@
The pages in this section provide a conceptual overview and how-tos for the following topics:
## Agent development
- [Overview](../agents/overview.md): Use prebuilt components to build an agent.
- [Run an agent](../agents/run_agents.md): Run an agent by providing input, interpreting output, enabling streaming, and controlling execution limits.
## LangGraph APIs
- [Graph API](../concepts/low_level.md): Use the Graph API to define workflows using a graph paradigm.
docs/docs/how-tos/autogen-integration.md
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@@ -290,7 +290,7 @@ my-autogen-agent/
```
langgraph>=0.1.0
pyautogen>=0.2.0
ag2>=0.2.0
langchain-core>=0.1.0
langchain-openai>=0.0.5
```
docs/docs/how-tos/enable-tracing.md
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@@ -0,0 +1,16 @@
# Enable tracing for your application
To enable [tracing](../concepts/tracing.md) for your application, set the following environment variables:
```python
export LANGSMITH_TRACING=true
export LANGSMITH_API_KEY=<your-api-key>
```
For more information, see [Trace with LangGraph](https://docs.smith.langchain.com/observability/how_to_guides/trace_with_langgraph).
## Learn more
- [Graph runs in LangSmith](../how-tos/run-id-langsmith.md)
- [LangSmith Observability quickstart](https://docs.smith.langchain.com/observability)
- [Tracing conceptual guide](https://docs.smith.langchain.com/observability/concepts#traces)
docs/docs/how-tos/graph-api.md
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@@ -328,14 +328,15 @@ Output of graph invocation: {'a': 'set by node_3'}
A [StateGraph](https://langchain-ai.github.io/langgraph/reference/graphs.md#langgraph.graph.StateGraph) accepts a `state_schema` argument on initialization that specifies the "shape" of the state that the nodes in the graph can access and update.
In our examples, we typically use a python-native `TypedDict` for `state_schema`, but `state_schema` can be any [type](https://docs.python.org/3/library/stdtypes.html#type-objects).
In our examples, we typically use a python-native `TypedDict` or [`dataclass`](https://docs.python.org/3/library/dataclasses.html) for `state_schema`, but `state_schema` can be any [type](https://docs.python.org/3/library/stdtypes.html#type-objects).
Here, we'll see how a [Pydantic BaseModel](https://docs.pydantic.dev/latest/api/base_model/). can be used for `state_schema` to add run time validation on **inputs**.
Here, we'll see how a [Pydantic BaseModel](https://docs.pydantic.dev/latest/api/base_model/) can be used for `state_schema` to add run-time validation on **inputs**.
!!! note "Known Limitations"
- Currently, the output of the graph will **NOT** be an instance of a pydantic model.
- Run-time validation only occurs on inputs into nodes, not on the outputs.
- The validation error trace from pydantic does not show which node the error arises in.
- Pydantic's recursive validation can be slow. For performance-sensitive applications, you may want to consider using a `dataclass` instead.
```python
from langgraph.graph import StateGraph, START, END
@@ -1151,12 +1152,13 @@ LangGraph supports map-reduce and other advanced branching patterns using the Se
```python
from langgraph.graph import StateGraph, START, END
from langgraph.types import Send
from typing_extensions import TypedDict
from typing_extensions import TypedDict, Annotated
import operator
class OverallState(TypedDict):
topic: str
subjects: list[str]
jokes: list[str]
jokes: Annotated[list[str], operator.add]
best_selected_joke: str
def generate_topics(state: OverallState):
@@ -1565,9 +1567,9 @@ class State(TypedDict):
def node_a(state: State) -> Command[Literal["node_b", "node_c"]]:
print("Called A")
value = random.choice(["a", "b"])
value = random.choice(["b", "c"])
# this is a replacement for a conditional edge function
if value == "a":
if value == "b":
goto = "node_b"
else:
goto = "node_c"
docs/docs/how-tos/observability.md
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@@ -1,21 +0,0 @@
# Observability
Use [LangSmith](https://smith.langchain.com/) to visualize the execution of a LangGraph application and do the following:
- [Enable tracing for your application](#enable-tracing-for-your-application).
- [Debug a locally running application](../cloud/how-tos/clone_traces_studio.md).
- [Evaluate the application performance](../agents/evals.md).
- [Monitor the application](https://docs.smith.langchain.com/observability/how_to_guides/dashboards).
To get started, sign up for a free account at [LangSmith](https://smith.langchain.com/).
## Enable tracing for your application
To use LangSmith with your LangGraph application, enable tracing:
```python
export LANGSMITH_TRACING=true
export LANGSMITH_API_KEY=<your-api-key>
```
For more information, see [Trace with LangGraph](https://docs.smith.langchain.com/observability/how_to_guides/trace_with_langgraph).
docs/mkdocs.yml
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@@ -103,14 +103,15 @@ nav:
- 5. Customize state: tutorials/get-started/5-customize-state.md
- 6. Time travel: tutorials/get-started/6-time-travel.md
- Run a local server: tutorials/langgraph-platform/local-server.md
- Agent development:
- General concepts:
- Workflows & agents: tutorials/workflows.md
- Prebuilt components: agents/overview.md
- Run an agent: agents/run_agents.md
- Agent architectures: concepts/agentic_concepts.md
- Guides:
- guides/index.md
- Agent development:
- Overview: agents/overview.md
- Run an agent: agents/run_agents.md
- LangGraph APIs:
- Graph API:
- Overview: concepts/low_level.md
@@ -157,8 +158,9 @@ nav:
- Overview: concepts/mcp.md
- Use MCP: agents/mcp.md
- Server API: concepts/server-mcp.md
- Observability:
- Overview: how-tos/observability.md
- Tracing:
- Overview: concepts/tracing.md
- Enable tracing: how-tos/enable-tracing.md
- Evaluate performance: agents/evals.md
- Platform-only capabilities:
- LangGraph Platform:
libs/checkpoint-postgres/uv.lock
Generated
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@@ -304,7 +304,7 @@ wheels = [
[[package]]
name = "langgraph-checkpoint"
version = "2.1.0"
version = "2.1.1"
source = { editable = "../checkpoint" }
dependencies = [
{ name = "langchain-core" },
libs/checkpoint-sqlite/langgraph/checkpoint/sqlite/__init__.py
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@@ -29,7 +29,7 @@ _AIO_ERROR_MSG = (
"from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver\n"
"Note: AsyncSqliteSaver requires the aiosqlite package to use.\n"
"Install with:\n`pip install aiosqlite`\n"
"See https://langchain-ai.github.io/langgraph/reference/checkpoints/asyncsqlitesaver"
"See https://langchain-ai.github.io/langgraph/reference/checkpoints/#langgraph.checkpoint.sqlite.aio.AsyncSqliteSaver"
"for more information."
)
libs/checkpoint-sqlite/uv.lock
Generated
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@@ -316,7 +316,7 @@ wheels = [
[[package]]
name = "langgraph-checkpoint"
version = "2.1.0"
version = "2.1.1"
source = { editable = "../checkpoint" }
dependencies = [
{ name = "langchain-core" },
libs/checkpoint/langgraph/store/base/batch.py
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@@ -343,10 +343,14 @@ async def _run(
# set the results of each operation
for fut, result in zip(futs, results):
fut.set_result(result)
# guard against future being done (e.g. cancelled)
if not fut.done():
fut.set_result(result)
except Exception as e:
for fut in futs:
fut.set_exception(e)
# guard against future being done (e.g. cancelled)
if not fut.done():
fut.set_exception(e)
finally:
# remove strong ref to store
del s
libs/checkpoint/pyproject.toml
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@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
[project]
name = "langgraph-checkpoint"
version = "2.1.0"
version = "2.1.1"
description = "Library with base interfaces for LangGraph checkpoint savers."
authors = []
requires-python = ">=3.9"
libs/checkpoint/tests/test_store.py
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@@ -155,6 +155,43 @@ async def test_async_batch_store(mocker: MockerFixture) -> None:
]
async def test_async_batch_store_handles_cancellation() -> None:
class MockStore(AsyncBatchedBaseStore):
def batch(self, ops: Iterable[Op]) -> list[Result]:
raise NotImplementedError
async def abatch(self, ops: Iterable[Op]) -> list[Result]:
assert all(isinstance(op, GetOp) for op in ops)
return [
Item(
value={},
key=getattr(op, "key", ""),
namespace=getattr(op, "namespace", ()),
created_at=datetime(2024, 9, 24, 17, 29, 10, 128397),
updated_at=datetime(2024, 9, 24, 17, 29, 10, 128397),
)
for op in ops
]
store = MockStore()
# Simulate cancellation
task = asyncio.create_task(store.aget(namespace=("a",), key="b"))
await asyncio.sleep(0)
task.cancel()
await asyncio.sleep(0)
# Cancelling individual queries against the store should not break the store
result = await store.aget(namespace=("c",), key="d")
assert result == Item(
value={},
key="d",
namespace=("c",),
created_at=datetime(2024, 9, 24, 17, 29, 10, 128397),
updated_at=datetime(2024, 9, 24, 17, 29, 10, 128397),
)
def test_list_namespaces_basic() -> None:
store = InMemoryStore()
libs/checkpoint/uv.lock
Generated
+1 -1
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[[package]]
libs/langgraph/langgraph/graph/state.py
+12 -11
View File
@@ -1240,17 +1240,18 @@ def _control_branch(value: Any) -> Sequence[tuple[str, Any]]:
for command in commands:
if command.graph == Command.PARENT:
raise ParentCommand(command)
if isinstance(command.goto, Send):
rtn.append((TASKS, command.goto))
elif isinstance(command.goto, str):
rtn.append((_CHANNEL_BRANCH_TO.format(command.goto), None))
else:
rtn.extend(
(TASKS, go)
if isinstance(go, Send)
else (_CHANNEL_BRANCH_TO.format(go), None)
for go in command.goto
)
goto_targets = (
[command.goto] if isinstance(command.goto, (Send, str)) else command.goto
)
for go in goto_targets:
if isinstance(go, Send):
rtn.append((TASKS, go))
elif isinstance(go, str) and go != END:
# END is a special case, it's not actually a node in a practical sense
# but rather a special terminal node that we don't need to branch to
rtn.append((CHANNEL_BRANCH_TO.format(go), None))
return rtn
libs/langgraph/tests/test_large_cases.py
+27
View File
@@ -6932,3 +6932,30 @@ def test_weather_subgraph(
},
),
]
def test_subgraph_to_end_does_not_warn() -> None:
"""Regression test for https://github.com/langchain-ai/langgraph/issues/5572."""
class State(TypedDict):
x: str
def update_x(state: State):
return Command(goto=END, update={"x": state["x"] + "!"})
# Subgraph
subgraph_builder = StateGraph(State)
subgraph_builder.add_node("update_x", update_x)
subgraph_builder.add_edge(START, "update_x")
subgraph_builder.add_edge("update_x", END)
subgraph = subgraph_builder.compile()
# Parent graph
builder = StateGraph(State)
builder.add_node("subgraph_node", subgraph)
builder.add_edge(START, "subgraph_node")
builder.add_edge("subgraph_node", END)
graph = builder.compile()
response = graph.invoke({"x": "hello"})
print(response)
libs/langgraph/uv.lock
Generated
+1 -1
View File
@@ -1301,7 +1301,7 @@ wheels = [
[[package]]
name = "langgraph-checkpoint"
version = "2.1.0"
version = "2.1.1"
source = { editable = "../checkpoint" }
dependencies = [
{ name = "langchain-core" },
libs/prebuilt/Makefile
+1 -1
View File
@@ -52,7 +52,7 @@ lint lint_diff lint_package lint_tests:
format format_diff:
uv run ruff format $(PYTHON_FILES)
uv run ruff check --select I --fix $(PYTHON_FILES)
uv run ruff check --fix $(PYTHON_FILES)
spell_check:
uv run codespell --toml pyproject.toml
libs/prebuilt/langgraph/prebuilt/_internal.py
+26
View File
@@ -0,0 +1,26 @@
from typing import Any, Literal, TypedDict
from langchain_core.messages import ToolCall
class ToolCallWithContext(TypedDict):
"""ToolCall with additional context for graph state.
This is an internal data-structure meant to help the ToolNode accept
tools calls with additional context (e.g. state) when dispatched using the
`Send` API.
The Send API is used in create_react_agent to be able to distribute the tool
calls in parallel and support human-in-the-loop workflows where graph execution
may be paused for an indefinite time.
"""
tool_call: ToolCall
__type: Literal["tool_call_with_context"]
"""Type to parameterize the payload.
Using "__" as a prefix to be defensive against potential name collisions with
regular user state.
"""
state: Any
"""The state is provided as additional context."""
libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py
+21 -8
View File
@@ -42,6 +42,7 @@ from langgraph.graph import END, StateGraph
from langgraph.graph.message import add_messages
from langgraph.graph.state import CompiledStateGraph
from langgraph.managed import IsLastStep, RemainingSteps
from langgraph.prebuilt._internal import ToolCallWithContext
from langgraph.prebuilt.tool_node import ToolNode
from langgraph.store.base import BaseStore
from langgraph.types import Checkpointer, Send
@@ -664,11 +665,17 @@ def create_react_agent(
elif version == "v2":
if post_model_hook is not None:
return "post_model_hook"
tool_calls = [
tool_node.inject_tool_args(call, state, store) # type: ignore[arg-type]
for call in last_message.tool_calls
return [
Send(
"tools",
ToolCallWithContext(
__type="tool_call_with_context",
tool_call=tool_call,
state=state,
),
)
for tool_call in last_message.tool_calls
]
return [Send("tools", [tool_call]) for tool_call in tool_calls]
# Define a new graph
workflow = StateGraph(
@@ -749,11 +756,17 @@ def create_react_agent(
]
if pending_tool_calls:
pending_tool_calls = [
tool_node.inject_tool_args(call, state, store) # type: ignore[arg-type]
for call in pending_tool_calls
return [
Send(
"tools",
ToolCallWithContext(
__type="tool_call_with_context",
tool_call=tool_call,
state=state,
),
)
for tool_call in pending_tool_calls
]
return [Send("tools", [tool_call]) for tool_call in pending_tool_calls]
elif isinstance(messages[-1], ToolMessage):
return entrypoint
elif response_format is not None:
libs/prebuilt/langgraph/prebuilt/tool_node.py
+30 -15
View File
@@ -72,6 +72,7 @@ from typing_extensions import Annotated, get_args, get_origin
from langgraph._internal._runnable import RunnableCallable
from langgraph.errors import GraphBubbleUp
from langgraph.prebuilt._internal import ToolCallWithContext
from langgraph.store.base import BaseStore
from langgraph.types import Command, Send
@@ -358,7 +359,8 @@ class ToolNode(RunnableCallable):
*,
store: Optional[BaseStore],
) -> Any:
tool_calls, input_type = self._parse_input(input, store)
tool_calls, input_type = self._parse_input(input)
tool_calls = [self.inject_tool_args(call, input, store) for call in tool_calls]
config_list = get_config_list(config, len(tool_calls))
input_types = [input_type] * len(tool_calls)
with get_executor_for_config(config) as executor:
@@ -379,7 +381,8 @@ class ToolNode(RunnableCallable):
*,
store: Optional[BaseStore],
) -> Any:
tool_calls, input_type = self._parse_input(input, store)
tool_calls, input_type = self._parse_input(input)
tool_calls = [self.inject_tool_args(call, input, store) for call in tool_calls]
outputs = await asyncio.gather(
*(self._arun_one(call, input_type, config) for call in tool_calls)
)
@@ -436,12 +439,12 @@ class ToolNode(RunnableCallable):
input_type: Literal["list", "dict", "tool_calls"],
config: RunnableConfig,
) -> ToolMessage:
"""Run a single tool call synchronously."""
if invalid_tool_message := self._validate_tool_call(call):
return invalid_tool_message
try:
input = {**call, **{"type": "tool_call"}}
response = self.tools_by_name[call["name"]].invoke(input, config)
call_args = {**call, **{"type": "tool_call"}}
response = self.tools_by_name[call["name"]].invoke(call_args, config)
# GraphInterrupt is a special exception that will always be raised.
# It can be triggered in the following scenarios,
@@ -492,6 +495,7 @@ class ToolNode(RunnableCallable):
input_type: Literal["list", "dict", "tool_calls"],
config: RunnableConfig,
) -> ToolMessage:
"""Run a single tool call asynchronously."""
if invalid_tool_message := self._validate_tool_call(call):
return invalid_tool_message
@@ -549,16 +553,25 @@ class ToolNode(RunnableCallable):
dict[str, Any],
BaseModel,
],
store: Optional[BaseStore],
) -> Tuple[list[ToolCall], Literal["list", "dict", "tool_calls"]]:
input_type: Literal["list", "dict", "tool_calls"]
if isinstance(input, list):
if isinstance(input[-1], dict) and input[-1].get("type") == "tool_call":
input_type = "tool_calls"
tool_calls = input
tool_calls = cast(list[ToolCall], input)
return tool_calls, input_type
else:
input_type = "list"
messages = input
elif (
isinstance(input, dict) and input.get("__type") == "tool_call_with_context"
):
# mypy will not be able to type narrow correctly since the signature
# for input contains dict[str, Any]. We'd need to type dict[str, Any]
# before we can apply correct typing.
input = cast(ToolCallWithContext, input) # type: ignore[assignment]
input_type = "tool_calls"
return [input["tool_call"]], input_type
elif isinstance(input, dict) and (messages := input.get(self.messages_key, [])):
input_type = "dict"
elif messages := getattr(input, self.messages_key, []):
@@ -574,10 +587,7 @@ class ToolNode(RunnableCallable):
except StopIteration:
raise ValueError("No AIMessage found in input")
tool_calls = [
self.inject_tool_args(call, input, store)
for call in latest_ai_message.tool_calls
]
tool_calls = [call for call in latest_ai_message.tool_calls]
return tool_calls, input_type
def _validate_tool_call(self, call: ToolCall) -> Optional[ToolMessage]:
@@ -619,15 +629,20 @@ class ToolNode(RunnableCallable):
required_fields_str = ", ".join(f for f in required_fields if f)
err_msg += f" State should contain fields {required_fields_str}."
raise ValueError(err_msg)
if isinstance(input, dict):
if isinstance(input, dict) and input.get("__type") == "tool_call_with_context":
state = input["state"]
else:
state = input
if isinstance(state, dict):
tool_state_args = {
tool_arg: input[state_field] if state_field else input
tool_arg: state[state_field] if state_field else state
for tool_arg, state_field in state_args.items()
}
else:
tool_state_args = {
tool_arg: getattr(input, state_field) if state_field else input
tool_arg: getattr(state, state_field) if state_field else state
for tool_arg, state_field in state_args.items()
}
libs/prebuilt/tests/any_str.py
+1 -69
View File
@@ -1,27 +1,5 @@
import re
from typing import Any, Sequence, Union
from typing_extensions import Self
class FloatBetween(float):
def __new__(cls, min_value: float, max_value: float) -> Self:
return super().__new__(cls, min_value)
def __init__(self, min_value: float, max_value: float) -> None:
super().__init__()
self.min_value = min_value
self.max_value = max_value
def __eq__(self, other: object) -> bool:
return (
isinstance(other, float)
and other >= self.min_value
and other <= self.max_value
)
def __hash__(self) -> int:
return hash((float(self), self.min_value, self.max_value))
from typing import Union
class AnyStr(str):
@@ -38,49 +16,3 @@ class AnyStr(str):
def __hash__(self) -> int:
return hash((str(self), self.prefix))
class AnyDict(dict):
def __init__(self, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
def __eq__(self, other: object) -> bool:
if not isinstance(other, dict) or len(self) != len(other):
return False
for k, v in self.items():
if kk := next((kk for kk in other if kk == k), None):
if v == other[kk]:
continue
else:
return False
else:
return True
class AnyVersion:
def __init__(self) -> None:
super().__init__()
def __eq__(self, other: object) -> bool:
return isinstance(other, (str, int, float))
def __hash__(self) -> int:
return hash(str(self))
class UnsortedSequence:
def __init__(self, *values: Any) -> None:
self.seq = values
def __eq__(self, value: object) -> bool:
return (
isinstance(value, Sequence)
and len(self.seq) == len(value)
and all(a in value for a in self.seq)
)
def __hash__(self) -> int:
return hash(frozenset(self.seq))
def __repr__(self) -> str:
return repr(self.seq)
libs/prebuilt/tests/memory_assert.py
+1 -76
View File
@@ -1,31 +1,19 @@
import asyncio
import os
import tempfile
from collections import defaultdict
from functools import partial
from typing import Any, Optional
from langchain_core.runnables import RunnableConfig
from typing import Optional
from langgraph.checkpoint.base import (
ChannelVersions,
Checkpoint,
CheckpointMetadata,
CheckpointTuple,
SerializerProtocol,
)
from langgraph.checkpoint.memory import InMemorySaver, PersistentDict
from langgraph.pregel._checkpoint import copy_checkpoint
class NoopSerializer(SerializerProtocol):
def loads_typed(self, data: tuple[str, bytes]) -> Any:
return data[1]
def dumps_typed(self, obj: Any) -> tuple[str, bytes]:
return "type", obj
class MemorySaverAssertImmutable(InMemorySaver):
storage_for_copies: defaultdict[str, dict[str, dict[str, Checkpoint]]]
@@ -69,66 +57,3 @@ class MemorySaverAssertImmutable(InMemorySaver):
)
# call super to write checkpoint
return super().put(config, checkpoint, metadata, new_versions)
class MemorySaverAssertCheckpointMetadata(InMemorySaver):
"""This custom checkpointer is for verifying that a run's configurable
fields are merged with the previous checkpoint config for each step in
the run. This is the desired behavior. Because the checkpointer's (a)put()
method is called for each step, the implementation of this checkpointer
should produce a side effect that can be asserted.
"""
def put(
self,
config: RunnableConfig,
checkpoint: Checkpoint,
metadata: CheckpointMetadata,
new_versions: ChannelVersions,
) -> None:
"""The implementation of put() merges config["configurable"] (a run's
configurable fields) with the metadata field. The state of the
checkpoint metadata can be asserted to confirm that the run's
configurable fields were merged with the previous checkpoint config.
"""
configurable = config["configurable"].copy()
# remove checkpoint_id to make testing simpler
checkpoint_id = configurable.pop("checkpoint_id", None)
thread_id = config["configurable"]["thread_id"]
checkpoint_ns = config["configurable"]["checkpoint_ns"]
self.storage[thread_id][checkpoint_ns].update(
{
checkpoint["id"]: (
self.serde.dumps_typed(checkpoint),
# merge configurable fields and metadata
self.serde.dumps_typed({**configurable, **metadata}),
checkpoint_id,
)
}
)
return {
"configurable": {
"thread_id": config["configurable"]["thread_id"],
"checkpoint_id": checkpoint["id"],
}
}
async def aput(
self,
config: RunnableConfig,
checkpoint: Checkpoint,
metadata: CheckpointMetadata,
new_versions: ChannelVersions,
) -> RunnableConfig:
return await asyncio.get_running_loop().run_in_executor(
None, self.put, config, checkpoint, metadata, new_versions
)
class MemorySaverNoPending(InMemorySaver):
def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
result = super().get_tuple(config)
if result:
return CheckpointTuple(result.config, result.checkpoint, result.metadata)
return result
libs/prebuilt/tests/messages.py
+1 -23
View File
@@ -9,33 +9,11 @@ subclassed strings.
from typing import Any
from langchain_core.documents import Document
from langchain_core.messages import AIMessage, AIMessageChunk, HumanMessage, ToolMessage
from langchain_core.messages import HumanMessage, ToolMessage
from tests.any_str import AnyStr
def _AnyIdDocument(**kwargs: Any) -> Document:
"""Create a document with an id field."""
message = Document(**kwargs)
message.id = AnyStr()
return message
def _AnyIdAIMessage(**kwargs: Any) -> AIMessage:
"""Create ai message with an any id field."""
message = AIMessage(**kwargs)
message.id = AnyStr()
return message
def _AnyIdAIMessageChunk(**kwargs: Any) -> AIMessageChunk:
"""Create ai message with an any id field."""
message = AIMessageChunk(**kwargs)
message.id = AnyStr()
return message
def _AnyIdHumanMessage(**kwargs: Any) -> HumanMessage:
"""Create a human message with an any id field."""
message = HumanMessage(**kwargs)
libs/prebuilt/tests/test_react_agent.py
+12 -10
View File
@@ -5,6 +5,7 @@ from functools import partial
from typing import (
Annotated,
List,
Literal,
Optional,
Type,
TypeVar,
@@ -509,7 +510,7 @@ class CustomStatePydantic(AgentStatePydantic):
@pytest.mark.parametrize("state_schema", [CustomState, CustomStatePydantic])
def test_react_agent_update_state(
sync_checkpointer: BaseCheckpointSaver,
version: str,
version: Literal["v1", "v2"],
state_schema: StateSchemaType,
) -> None:
@dec_tool
@@ -557,7 +558,7 @@ def test_react_agent_update_state(
version=version,
)
config = {"configurable": {"thread_id": "1"}}
# run until interrpupted
# Run until interrupted
agent.invoke({"messages": [("user", "what's my name")]}, config)
# supply the value for the interrupt
response = agent.invoke(Command(resume="Archibald"), config)
@@ -781,8 +782,9 @@ class AgentStateExtraKeyPydantic(AgentStatePydantic):
"state_schema", [AgentStateExtraKey, AgentStateExtraKeyPydantic]
)
def test_create_react_agent_inject_vars(
version: str, state_schema: StateSchemaType
version: Literal["v1", "v2"], state_schema: StateSchemaType
) -> None:
"""Test that the agent can inject state and store into tool functions."""
store = InMemoryStore()
namespace = ("test",)
store.put(namespace, "test_key", {"bar": 3})
@@ -817,15 +819,14 @@ def test_create_react_agent_inject_vars(
model = FakeToolCallingModel(tool_calls=[[tool_call], []])
agent = create_react_agent(
model,
[tool1],
ToolNode([tool1], handle_tool_errors=False),
state_schema=state_schema,
store=store,
version=version,
)
input_message = HumanMessage("hi")
result = agent.invoke({"messages": [input_message], "foo": 2})
result = agent.invoke({"messages": [{"role": "user", "content": "hi"}], "foo": 2})
assert result["messages"] == [
input_message,
_AnyIdHumanMessage(content="hi"),
AIMessage(content="hi", tool_calls=[tool_call], id="0"),
_AnyIdToolMessage(content="6", name="tool1", tool_call_id="some 0"),
AIMessage("hi-hi-6", id="1"),
@@ -1578,13 +1579,14 @@ def test_create_react_agent_inject_vars_with_post_model_hook(
"type": "tool_call",
}
def post_model_hook(state: dict) -> None:
return
def post_model_hook(state: dict) -> dict:
"""Post model hook is injecting a new foo key."""
return {"foo": 2}
model = FakeToolCallingModel(tool_calls=[[tool_call], []])
agent = create_react_agent(
model,
[tool1],
ToolNode([tool1], handle_tool_errors=False),
state_schema=state_schema,
store=store,
post_model_hook=post_model_hook,
libs/prebuilt/uv.lock
Generated
+1 -1
View File
@@ -367,7 +367,7 @@ dev = [
[[package]]
name = "langgraph-checkpoint"
version = "2.1.0"
version = "2.1.1"
source = { editable = "../checkpoint" }
dependencies = [
{ name = "langchain-core" },