[CLI] Update Inmem Version (#2500)

langgraph: fix error message on invalid update

.github/scripts/run_langgraph_cli_test.py

@@ -0,0 +1,115 @@
+import asyncio
+import json
+import os
+import pathlib
+import sys
+import langgraph_cli
+import langgraph_cli.docker
+import langgraph_cli.config
+
+from langgraph_cli.exec import Runner, subp_exec
+from langgraph_cli.progress import Progress
+from langgraph_cli.constants import DEFAULT_PORT
+
+
+def test(
+    config: pathlib.Path,
+    port: int,
+    tag: str,
+    verbose: bool,
+):
+    with Runner() as runner, Progress(message="Pulling...") as set:
+        # check docker available
+        capabilities = langgraph_cli.docker.check_capabilities(runner)
+        # open config
+        config_json = langgraph_cli.config.validate_config_file(config)
+
+        set("Running...")
+        args = [
+            "run",
+            "--rm",
+            "-p",
+            f"{port}:8000",
+        ]
+        if isinstance(config_json["env"], str):
+            args.extend(
+                [
+                    "--env-file",
+                    str(config.parent / config_json["env"]),
+                ]
+            )
+        else:
+            for k, v in config_json["env"].items():
+                args.extend(
+                    [
+                        "-e",
+                        f"{k}={v}",
+                    ]
+                )
+        if capabilities.healthcheck_start_interval:
+            args.extend(
+                [
+                    "--health-interval",
+                    "5s",
+                    "--health-retries",
+                    "1",
+                    "--health-start-period",
+                    "10s",
+                    "--health-start-interval",
+                    "1s",
+                ]
+            )
+        else:
+            args.extend(
+                [
+                    "--health-interval",
+                    "5s",
+                    "--health-retries",
+                    "2",
+                ]
+            )
+
+        _task = None
+
+        def on_stdout(line: str):
+            nonlocal _task
+            if "GET /ok" in line or "Uvicorn running on" in line:
+                set("")
+                sys.stdout.write(
+                    f"""Ready!
+- API: http://localhost:{port}
+"""
+                )
+                sys.stdout.flush()
+                _task.cancel()
+                return True
+            return False
+
+        async def subp_exec_task(*args, **kwargs):
+            nonlocal _task
+            _task = asyncio.create_task(subp_exec(*args, **kwargs))
+            await _task
+
+        try:
+            runner.run(
+                subp_exec_task(
+                    "docker",
+                    *args,
+                    tag,
+                    verbose=verbose,
+                    on_stdout=on_stdout,
+                )
+            )
+        except asyncio.CancelledError:
+            pass
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-t", "--tag", type=str)
+    parser.add_argument("-c", "--config", type=str, default="./langgraph.json")
+    parser.add_argument("-p", "--port", default=DEFAULT_PORT)
+    args = parser.parse_args()
+    test(pathlib.Path(args.config), args.port, args.tag, verbose=True)

.github/workflows/_integration_test.yml

@@ -39,22 +39,36 @@ jobs:
       - name: Install cli globally
         if: steps.changed-files.outputs.all
         run: pip install -e .
-      - name: Start service A
+      - name: Build and test service A
         if: steps.changed-files.outputs.all
+        working-directory: libs/cli/examples
         run: |
-          timeout 60 langgraph test -c examples/langgraph.json --verbose || (exit "$(($? == 124 ? 0 : $?))")
-      - name: Start service B
+          # The build-arg isn't used; just testing that we accept other args
+          langgraph build -t langgraph-test-a  --base-image "langchain/langgraph-trial"
+          cp .env.example .envg
+          timeout 60 python ../../../.github/scripts/run_langgraph_cli_test.py -c langgraph.json -t langgraph-test-a
+      - name: Build and test service B
         if: steps.changed-files.outputs.all
         working-directory: libs/cli/examples/graphs
         run: |
-          timeout 60 langgraph test --verbose || (exit "$(($? == 124 ? 0 : $?))")
-      - name: Start service C
+          langgraph build -t langgraph-test-b  --base-image "langchain/langgraph-trial"
+          timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-b
+      - name: Build and test service C
         if: steps.changed-files.outputs.all
         working-directory: libs/cli/examples/graphs_reqs_a
         run: |
-          timeout 60 langgraph test --verbose || (exit "$(($? == 124 ? 0 : $?))")
-      - name: Start service D
+          langgraph build -t langgraph-test-c  --base-image "langchain/langgraph-trial"
+          timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-c
+      - name: Build and test service D
         if: steps.changed-files.outputs.all
         working-directory: libs/cli/examples/graphs_reqs_b
         run: |
-          timeout 60 langgraph test --verbose || (exit "$(($? == 124 ? 0 : $?))")
+          langgraph build -t langgraph-test-d  --base-image "langchain/langgraph-trial"
+          timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-d
+
+      - name: Build JS service
+        if: steps.changed-files.outputs.all
+        working-directory: libs/cli/js-examples
+        run: |
+          langgraph build -t langgraph-test-e
+  

.github/workflows/_test_langgraph.yml

@@ -19,14 +19,19 @@ jobs:
           - "3.13"
         core-version:
           - "latest"
+        ff-send-v2:
+          - "false"
         include:
           - python-version: "3.11"
-            core-version: ">=0.2.39,<0.3.0"
+            core-version: ">=0.2.42,<0.3.0"
+          - python-version: "3.11"
+            core-version: "latest"
+            ff-send-v2: "true"
 
     defaults:
       run:
         working-directory: libs/langgraph
-    name: "test #${{ matrix.python-version }} (langchain-core: ${{ matrix.core-version }})"
+    name: "test #${{ matrix.python-version }} (langchain-core: ${{ matrix.core-version }}, ff-send-v2: ${{ matrix.ff-send-v2 }})"
     steps:
       - uses: actions/checkout@v4
       - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
@@ -52,6 +57,8 @@ jobs:
 
       - name: Run tests
         shell: bash
+        env:
+          LANGGRAPH_FF_SEND_V2: ${{ matrix.ff-send-v2 }}
         run: |
           make test
 

.github/workflows/_test_release.yml

@@ -93,3 +93,5 @@ jobs:
           # This is *only for CI use* and is *extremely dangerous* otherwise!
           # https://github.com/pypa/gh-action-pypi-publish#tolerating-release-package-file-duplicates
           skip-existing: true
+          # Temp workaround since attestations are on by default as of gh-action-pypi-publish v1.11.0
+          attestations: false

.github/workflows/deploy_docs.yml

@@ -44,6 +44,8 @@ jobs:
   deploy:
     # needs: run-changed-notebooks
     runs-on: ubuntu-latest
+    env:
+      GITHUB_TOKEN: ${{ secrets.MKDOCS_GITHUB_TOKEN }}
     steps:
       - uses: actions/checkout@v4
         with:
@@ -58,8 +60,14 @@ jobs:
 
       - name: Install dependencies
         run: |
-          poetry install --with test
-          poetry run pip install -U pytest pytest-check-links langsmith langchain GitPython
+          poetry install --with test --no-root
+          poetry run pip install -U \
+            pytest \
+            pytest-check-links \
+            langsmith \
+            langchain \
+            GitPython \
+            "git+https://${GITHUB_TOKEN}@github.com/langchain-ai/mkdocs-material-insiders.git"
 
       - name: Lint Docs
         # This step lints the docs using the existing linting set up.
@@ -82,7 +90,10 @@ jobs:
               --check-links-ignore "https://github\.com/.*" \
               --check-links-ignore "/.*\.(ipynb|html)$" \
               --check-links-ignore "https://python\.langchain\.com/.*" \
-              --check-links $(find docs/site -name "index.html" | grep -v 'storm/index.html')
+              --check-links-ignore "https://openai\.com/.*" \
+              --check-links-ignore "https://pepy\.tech/.*" \
+              --check-links $(find docs/site -name "index.html" | grep -v 'storm/index.html') 
+              
           else
             echo "Fetching changes from origin/main..."
             git fetch origin main

.github/workflows/release.yml

@@ -270,6 +270,8 @@ jobs:
           packages-dir: ${{ inputs.working-directory }}/dist/
           verbose: true
           print-hash: true
+          # Temp workaround since attestations are on by default as of gh-action-pypi-publish v1.11.0
+          attestations: false
 
   mark-release:
     needs:

README.md

@@ -16,6 +16,8 @@
 
 LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.
 
+[LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform) is infrastructure for deploying LangGraph agents. It is a commercial solution for deploying agentic applications to production, built on the open-source LangGraph framework. The LangGraph Platform consists of several components that work together to support the development, deployment, debugging, and monitoring of LangGraph applications: [LangGraph Server](https://langchain-ai.github.io/langgraph/concepts/langgraph_server) (APIs), [LangGraph SDKs](https://langchain-ai.github.io/langgraph/concepts/sdk) (clients for the APIs), [LangGraph CLI](https://langchain-ai.github.io/langgraph/concepts/langgraph_cli) (command line tool for building the server), [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio) (UI/debugger),
+
 To learn more about LangGraph, check out our first LangChain Academy course, *Introduction to LangGraph*, available for free [here](https://academy.langchain.com/courses/intro-to-langgraph).
 
 ### Key Features
@@ -26,6 +28,16 @@ To learn more about LangGraph, check out our first LangChain Academy course, *In
 - **Streaming Support**: Stream outputs as they are produced by each node (including token streaming).
 - **Integration with LangChain**: LangGraph integrates seamlessly with [LangChain](https://github.com/langchain-ai/langchain/) and [LangSmith](https://docs.smith.langchain.com/) (but does not require them).
 
+### LangGraph Platform
+
+LangGraph Platform is a commercial solution for deploying agentic applications to production, built on the open-source LangGraph framework.
+Here are some common issues that arise in complex deployments, which LangGraph Platform addresses:
+
+- **Streaming support**: LangGraph Server provides [multiple streaming modes](https://langchain-ai.github.io/langgraph/concepts/streaming) optimized for various application needs
+- **Background runs**: Runs agents asynchronously in the background
+- **Support for long running agents**: Infrastructure that can handle long running processes
+- **[Double texting](https://langchain-ai.github.io/langgraph/concepts/double_texting)**: Handle the case where you get two messages from the user before the agent can respond
+- **Handle burstiness**: Task queue for ensuring requests are handled consistently without loss, even under heavy loads
 
 ## Installation
 

docs/_scripts/prepare_notebooks_for_ci.py

@@ -36,10 +36,11 @@ NOTEBOOKS_NO_EXECUTION = [
     "docs/docs/tutorials/rag/langgraph_self_rag_local.ipynb",
     # this loads a massive dataset from gcp
     "docs/docs/tutorials/usaco/usaco.ipynb",
+    # TODO: figure out why autogen notebook is not runnable (they are just hanging. possible due to code execution?)
+    "docs/docs/how-tos/autogen-integration.ipynb",
     # TODO: need to update these notebooks to make sure they are runnable in CI
     "docs/docs/tutorials/storm/storm.ipynb",  # issues only when running with VCR
     "docs/docs/tutorials/lats/lats.ipynb",  # issues only when running with VCR
-    "docs/docs/tutorials/multi_agent/hierarchical_agent_teams.ipynb",  # taking a very long time to run
     "docs/docs/tutorials/rag/langgraph_crag.ipynb",  # flakiness from tavily
     "docs/docs/tutorials/rag/langgraph_adaptive_rag.ipynb",  # Cannot create a consistent method resolution error from VCR
     "docs/docs/how-tos/map-reduce.ipynb"  # flakiness from structured output, only when running with VCR

docs/docs/cloud/concepts/api.md

@@ -1,216 +0,0 @@
-# API Concepts
-
-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
-
-![assistant versions](./assistant_version.png)
-
-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 = await client.threads.create()
-input = {"messages": [{"role": "user", "content": "what's the weather in sf?"}]}
-
-events = []
-async for event in client.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. 
-
-```python
-StreamPart(event='metadata', data={'run_id': '1ef657cf-ae55-6f65-97d4-f4ed1dbdabc6'})
-```
-
-We then get a `messages/complete` event - this a fully formed message getting emitted. In this case,
-this was the just the input message we sent in. 
-
-```python
-StreamPart(event='messages/complete', data=[{'content': 'hi!', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '833c09a3-bb19-46c9-81d9-1e5954ec5f92', 'example': False}])
-```
-
-We then get a `messages/metadata` - this is just letting us know that a new message is starting.
-
-```python
-StreamPart(event='messages/metadata', data={'run-985c0f14-9f43-40d4-a505-4637fc58e333': {'metadata': {'created_by': 'system', 'run_id': '1ef657de-7594-66df-8eb2-31518e4a1ee2', 'graph_id': 'agent', 'thread_id': 'c178eab5-e293-423c-8e7d-1d113ffe7cd9', 'model_name': 'openai', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 1, 'langgraph_node': 'agent', 'langgraph_triggers': ['start:agent'], 'langgraph_task_idx': 0, 'ls_provider': 'openai', 'ls_model_name': 'gpt-4o', 'ls_model_type': 'chat', 'ls_temperature': 0.0}}})
-```
-
-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.
-
-```python
-StreamPart(event='messages/partial', data=[{'content': '', 'additional_kwargs': {'tool_calls': [{'index': 0, 'id': 'call_w8Hr8dHGuZCPgRfd5FqRBArs', 'function': {'arguments': '', 'name': 'tavily_search_results_json'}, 'type': 'function'}]}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-985c0f14-9f43-40d4-a505-4637fc58e333', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [{'name': 'tavily_search_results_json', 'args': '', 'id': 'call_w8Hr8dHGuZCPgRfd5FqRBArs', 'error': None}], 'usage_metadata': None}])
-```
-
-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.
-
-```python
-StreamPart(event='messages/complete', data=[{'content': '[{"url": "https://www.weatherapi.com/", "content": "{\'location\': {\'name\': \'San Francisco\', \'region\': \'California\', \'country\': \'United States of America\', \'lat\': 37.78, \'lon\': -122.42, \'tz_id\': \'America/Los_Angeles\', \'localtime_epoch\': 1724877689, \'localtime\': \'2024-08-28 13:41\'}, \'current\': {\'last_updated_epoch\': 1724877000, \'last_updated\': \'2024-08-28 13:30\', \'temp_c\': 23.3, \'temp_f\': 73.9, \'is_day\': 1, \'condition\': {\'text\': \'Partly cloudy\', \'icon\': \'//cdn.weatherapi.com/weather/64x64/day/116.png\', \'code\': 1003}, \'wind_mph\': 15.0, \'wind_kph\': 24.1, \'wind_degree\': 310, \'wind_dir\': \'NW\', \'pressure_mb\': 1014.0, \'pressure_in\': 29.93, \'precip_mm\': 0.0, \'precip_in\': 0.0, \'humidity\': 57, \'cloud\': 25, \'feelslike_c\': 25.0, \'feelslike_f\': 77.1, \'windchill_c\': 20.9, \'windchill_f\': 69.6, \'heatindex_c\': 23.3, \'heatindex_f\': 74.0, \'dewpoint_c\': 12.9, \'dewpoint_f\': 55.2, \'vis_km\': 16.0, \'vis_miles\': 9.0, \'uv\': 6.0, \'gust_mph\': 19.5, \'gust_kph\': 31.3}}"}]', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'tavily_search_results_json', 'id': '0112eba5-7660-4375-9f24-c7a1d6777b97', 'tool_call_id': 'call_w8Hr8dHGuZCPgRfd5FqRBArs'}])
-```
-
-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.

docs/docs/cloud/concepts/cloud.md

@@ -1,28 +0,0 @@
-# Cloud Concepts
-
-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.
-
-![diagram](langgraph_cloud_architecture.png)

docs/docs/cloud/deployment/cloud.md

@@ -11,7 +11,7 @@ LangGraph Cloud is available within <a href="https://www.langchain.com/langsmith
 
 Starting from the <a href="https://smith.langchain.com/" target="_blank">LangSmith UI</a>...
 
-1. In the left-hand navigation panel, select `Deployments`. The `Deployments` view contains a list of existing LangGraph Cloud deployments.
+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 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`
@@ -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 `Deployments`. The `Deployments` view contains a list of existing LangGraph Cloud deployments.
+1. In the left-hand navigation panel, select `LangGraph Cloud`. The `LangGraph Cloud` 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.
@@ -56,7 +56,7 @@ Starting from the <a href="https://smith.langchain.com/" target="_blank">LangSmi
 
 Build and deployment logs are available for each revision.
 
-Starting from the `Deployment` view...
+Starting from the `LangGraph Cloud` 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.
@@ -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 `Deployment` view...
+Starting from the `LangGraph Cloud` 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 `Deployment` view...
 
 Starting from the <a href="https://smith.langchain.com/" target="_blank">LangSmith UI</a>...
 
-1. In the left-hand navigation panel, select `Deployments`. The `Deployments` view contains a list of existing LangGraph Cloud deployments.
+1. In the left-hand navigation panel, select `LangGraph Cloud`. The `LangGraph Cloud` 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 `Deployment` view...
+Starting from the `LangGraph Cloud` view...
 
 1. In the top-right corner, select the gear icon (`Deployment Settings`).
 1. Update the `Git Branch` to the desired branch.

docs/docs/cloud/deployment/test_locally.md

@@ -8,19 +8,25 @@ Testing locally ensures that there are no errors or conflicts with Python depend
 
 Install the proper packages:
 
-```shell
-pip install langgraph-cli
-```
 
-Ensure you have an API key, which you can create from the LangSmith UI (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:
+=== "pip" 
+    ```bash
+    pip install -U langgraph-cli
+    ```
+=== "Homebrew (macOS only)"
+    ```bash
+    brew install langgraph-cli
+    ```
+
+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
 
-Once you have downloaded the CLI, you can run the following command to start the API server for local testing:
+Once you have installed the CLI, you can run the following command to start the API server for local testing:
 
 ```shell
 langgraph up
@@ -48,7 +54,7 @@ You can either initialize by passing authentication or by setting an environment
     from langgraph_sdk import get_client
 
     # only pass the url argument to get_client() if you changed the default port when calling langgraph up
-    client = get_client(url=<DEPLOYMENT_URL>,api_key=<LANGCHAIN_API_KEY>)
+    client = get_client(url=<DEPLOYMENT_URL>,api_key=<LANGSMITH_API_KEY>)
     # Using the graph deployed with the name "agent"
     assistant_id = "agent"
     thread = await client.threads.create()
@@ -60,7 +66,7 @@ You can either initialize by passing authentication or by setting an environment
     import { Client } from "@langchain/langgraph-sdk";
 
     // only set the apiUrl if you changed the default port when calling langgraph up
-    const client = new Client({ apiUrl: <DEPLOYMENT_URL>, apiKey: <LANGCHAIN_API_KEY> });
+    const client = new Client({ apiUrl: <DEPLOYMENT_URL>, apiKey: <LANGSMITH_API_KEY> });
     // Using the graph deployed with the name "agent"
     const assistantId = "agent";
     const thread = await client.threads.create();
@@ -72,13 +78,13 @@ You can either initialize by passing authentication or by setting an environment
     curl --request POST \
       --url <DEPLOYMENT_URL>/threads \
       --header 'Content-Type: application/json'
-      --header 'x-api-key: <LANGCHAIN_API_KEY>'
+      --header 'x-api-key: <LANGSMITH_API_KEY>'
     ```
   
 
 #### Initialize with environment variables
 
-If you have a `LANGCHAIN_API_KEY` set in your environment, you do not need to explicitly pass authentication to the client
+If you have a `LANGSMITH_API_KEY` set in your environment, you do not need to explicitly pass authentication to the client
 
 === "Python"
 
@@ -148,7 +154,7 @@ Now we can invoke our graph to ensure it is working. Make sure to change the inp
     }
     ```
 
-  === "CURL"
+=== "CURL"
 
     ```bash
     curl --request POST \
@@ -183,4 +189,4 @@ Now we can invoke our graph to ensure it is working. Make sure to change the inp
      ' 
     ```
 
-If your graph works correctly, you should see your graph output displayed in the console. Of course, there are many more ways you might need to test your graph, for a full list of commands you can send with the SDK, see the [Python](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/) and [JS/TS](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/) references.
+If your graph works correctly, you should see your graph output displayed in the console. Of course, there are many more ways you might need to test your graph, for a full list of commands you can send with the SDK, see the [Python](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/) and [JS/TS](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/) references.

docs/docs/cloud/how-tos/assistant_versioning.md

@@ -1,6 +1,6 @@
 # How to version assistants
 
-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:
 
-![click create](./img/click_create_assistant.png)
+    ![click create](./img/click_create_assistant.png)
 
-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:
 
-![create](./img/create_assistant.png)
+    ![create](./img/create_assistant.png)
 
-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
 
-![view create](./img/create_assistant_view.png)
+    ![view create](./img/create_assistant_view.png)
 
-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 edit](./img/edit_created_assistant.png)
+    ![create edit](./img/edit_created_assistant.png)
 
 ## 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":
 
-![create new version](./img/create_new_version.png)
+    ![create new version](./img/create_new_version.png)
 
-2. Then you can see it is selected in the assistant dropdown:
+1. Then you can see it is selected in the assistant dropdown:
 
-![see version dropdown](./img/see_new_version.png)
+    ![see version dropdown](./img/see_new_version.png)
 
-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:
 
-![see versions](./img/see_version_history.png)
+    ![see versions](./img/see_version_history.png)
 
 ## Point your assistant to a different version
 

docs/docs/cloud/how-tos/copy_threads.md

@@ -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).
 
 ### SDK initialization
 

docs/docs/cloud/how-tos/enqueue_concurrent.md

@@ -1,6 +1,6 @@
 # Enqueue
 
-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.
 

docs/docs/cloud/how-tos/index.md

@@ -1,84 +0,0 @@
----
-hide:
-  - toc
----
-
-# How-to Guides
-
-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)
-
-
-## 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)

docs/docs/cloud/how-tos/interrupt_concurrent.md

@@ -1,6 +1,6 @@
 # Interrupt
 
-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.
 
@@ -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)); 
 
     let run = await client.runs.create(

docs/docs/cloud/how-tos/reject_concurrent.md

@@ -1,6 +1,6 @@
 # Reject
 
-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.
 

docs/docs/cloud/how-tos/rollback_concurrent.md

@@ -1,6 +1,6 @@
 # Rollback
 
-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?\"}]},
-    }" && sleep 2 && curl --request POST \
+    }" && curl --request POST \
     --url <DEPLOY<ENT_URL>>/threads/<THREAD_ID>/runs \
     --header 'Content-Type: application/json' \
     --data "{

docs/docs/cloud/how-tos/stream_debug.md

@@ -1,5 +1,8 @@
 # How to stream debug events
 
+!!! info "Prerequisites"
+    * [Streaming](../../concepts/streaming.md)
+
 This guide covers how to stream debug events from your graph (`stream_mode="debug"`). Streaming debug events produces responses containing `type` and `timestamp` keys. Debug events correspond to different steps in the graph's execution, and there are three different types of steps that will get streamed back to you:
 
 - `checkpoint`: These events will get streamed anytime the graph saves its state, which occurs after every super-step. Read more about checkpoints [here](https://langchain-ai.github.io/langgraph/concepts/low_level/#checkpointer)

docs/docs/cloud/how-tos/stream_events.md

@@ -1,6 +1,9 @@
 # How to stream events
 
-This guide covers how to stream events from your graph (`stream_mode="events"`). Depending on the use case and user experience of your LangGraph application, your application may process event types differently. Read more about events in this [conceptual guide](https://langchain-ai.github.io/langgraph/concepts/low_level/#astream_events-for-streaming-tokens-of-llm-calls).
+!!! info "Prerequisites"
+    * [Streaming](../../concepts/streaming.md#streaming-llm-tokens-and-events-astream_events)
+
+This guide covers how to stream events from your graph (`stream_mode="events"`). Depending on the use case and user experience of your LangGraph application, your application may process event types differently.
 
 ## Setup
 
@@ -289,131 +292,4 @@ Output:
     
     
     Receiving new event of type: end...
-    None
-
-
-## Token-by-Token Streaming
-
-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]
-        ):
-            llm_response += chunk.data["data"]["chunk"]["content"][0]['text']
-            print(llm_response)
-    ```
-
-=== "Javascript"
-
-    ```js
-    const llmResponse = "";
-    // stream events
-    const streamResponse = client.runs.stream(
-      thread["thread_id"],
-      assistantID,
-      {
-        input,
-        streamMode: "events"
-      }
-    );
-    for await (const chunk of streamResponse) {
-      if (chunk.event === "events" && chunk.data.event === "on_chat_model_stream" && chunk.data.chunk.content.length > 0 && 'text' in chunk.data.chunk.content[0]) {
-        llmResponse += chunk.data.data.chunk.content[0].text;
-        console.log(llmResponse);
-      }
-    }
-    ```
-
-=== "CURL"
-
-    ```bash
-    curl --request POST \
-     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
-     --header 'Content-Type: application/json' \
-     --data "{
-       \"assistant_id\": \"agent\",
-       \"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.
-
-
+    None

docs/docs/cloud/how-tos/stream_messages.md

@@ -1,43 +1,9 @@
 # How to stream messages from your graph
 
-This guide covers how to stream messages from your graph. In order to use this mode, the state of the graph you are interacting with MUST have a `messages` key that is a list of messages.
+!!! info "Prerequisites"
+    * [Streaming](../../concepts/streaming.md)
 
-E.g., the state should look something like:
-
-=== "Python"
-
-    ```python
-    from typing import Annotated
-    from typing_extensions import TypedDict
-    from langgraph.graph import add_messages
-    from langchain_core.messages import AnyMessage
-
-    class State(TypedDict):
-        messages: Annotated[list[AnyMessage], add_messages]
-    ```
-
-=== "Javascript"
-
-    ```js
-    import { type BaseMessage } from "@langchain/core/messages";
-    import { Annotation, messagesStateReducer } from "@langchain/langgraph";
-
-    export const StateAnnotation = Annotation.Root({
-      messages: Annotation<BaseMessage[]>({
-        reducer: messagesStateReducer,
-        default: () => [],
-      }),
-    });
-    ```
-
-Alternatively, you can use an instance or subclass of `from langgraph.graph import MessagesState` (`MessagesState` is equivalent to the implementation above). Or in Javascript: `import { MessagesAnnotation } from "@langchain/langgraph";`.
-
-With `stream_mode="messages"` two things will be streamed back:
-
-- It outputs messages produced by any chat model called inside (unless tagged in a special way)
-- It outputs messages returned from nodes (to allow for nodes to return `ToolMessages` and the like)
-
-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
 
@@ -90,101 +56,9 @@ Output:
         'values': None
     }
 
-Let's also define a helper function for better formatting of the tool calls in messages (for CURL we will define a helper script called `process_stream.sh`)
-
-=== "Python"
-
-    ```python
-    def format_tool_calls(tool_calls):
-        if tool_calls:
-            formatted_calls = []
-            for call in tool_calls:
-                formatted_calls.append(
-                    f"Tool Call ID: {call['id']}, Function: {call['name']}, Arguments: {call['args']}"
-                )
-            return "\n".join(formatted_calls)
-        return "No tool calls"
-    ```
-
-=== "Javascript"
-
-    ```js
-    function formatToolCalls(toolCalls) {
-      if (toolCalls && toolCalls.length > 0) {
-        const formattedCalls = toolCalls.map(call => {
-          return `Tool Call ID: ${call.id}, Function: ${call.name}, Arguments: ${call.args}`;
-        });
-        return formattedCalls.join("\n");
-      }
-      return "No tool calls";
-    }
-    ```
-
-=== "CURL"
-
-    ```bash
-    # process_stream.sh
-
-    format_tool_calls() {
-        echo "$1" | jq -r 'map("Tool Call ID: \(.id), Function: \(.name), Arguments: \(.args)") | join("\n")'
-    }
-
-    process_data_item() {
-        local data_item="$1"
-
-        if echo "$data_item" | jq -e '.role == "user"' > /dev/null; then
-            echo "Human: $(echo "$data_item" | jq -r '.content')"
-        else
-            local tool_calls=$(echo "$data_item" | jq -r '.tool_calls // []')
-            local invalid_tool_calls=$(echo "$data_item" | jq -r '.invalid_tool_calls // []')
-            local content=$(echo "$data_item" | jq -r '.content // ""')
-            local response_metadata=$(echo "$data_item" | jq -r '.response_metadata // {}')
-
-            if [ -n "$content" ] && [ "$content" != "null" ]; then
-                echo "AI: $content"
-            fi
-
-            if [ "$tool_calls" != "[]" ]; then
-                echo "Tool Calls:"
-                format_tool_calls "$tool_calls"
-            fi
-
-            if [ "$invalid_tool_calls" != "[]" ]; then
-                echo "Invalid Tool Calls:"
-                format_tool_calls "$invalid_tool_calls"
-            fi
-
-            if [ "$response_metadata" != "{}" ]; then
-                local finish_reason=$(echo "$response_metadata" | jq -r '.finish_reason // "N/A"')
-                echo "Response Metadata: Finish Reason - $finish_reason"
-            fi
-        fi
-    }
-
-    while IFS=': ' read -r key value; do
-        case "$key" in
-            event)
-                event="$value"
-                ;;
-            data)
-                if [ "$event" = "metadata" ]; then
-                    run_id=$(echo "$value" | jq -r '.run_id')
-                    echo "Metadata: Run ID - $run_id"
-                    echo "------------------------------------------------"
-                elif [ "$event" = "messages/partial" ]; then
-                    echo "$value" | jq -c '.[]' | while read -r data_item; do
-                        process_data_item "$data_item"
-                    done
-                    echo "------------------------------------------------"
-                fi
-                ;;
-        esac
-    done
-    ```
-
 ## 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"
 
@@ -192,41 +66,16 @@ Now we can stream by messages, which will return complete messages (at the end o
     input = {"messages": [{"role": "user", "content": "what's the weather in sf"}]}
     config = {"configurable": {"model_name": "openai"}}
 
-    async for event in client.runs.stream(
+    async for chunk in client.runs.stream(
         thread["thread_id"],
         assistant_id=assistant_id,
         input=input,
         config=config,
-        stream_mode="messages",
+        stream_mode="messages-tuple",
     ):
-        if event.event == "metadata":
-            print(f"Metadata: Run ID - {event.data['run_id']}")
-            print("-" * 50)
-        elif event.event == "messages/partial":
-            for data_item in event.data:
-                if "role" in data_item and data_item["role"] == "user":
-                    print(f"Human: {data_item['content']}")
-                else:
-                    tool_calls = data_item.get("tool_calls", [])
-                    invalid_tool_calls = data_item.get("invalid_tool_calls", [])
-                    content = data_item.get("content", "")
-                    response_metadata = data_item.get("response_metadata", {})
-
-                    if content:
-                        print(f"AI: {content}")
-
-                    if tool_calls:
-                        print("Tool Calls:")
-                        print(format_tool_calls(tool_calls))
-
-                    if invalid_tool_calls:
-                        print("Invalid Tool Calls:")
-                        print(format_tool_calls(invalid_tool_calls))
-
-                    if response_metadata:
-                        finish_reason = response_metadata.get("finish_reason", "N/A")
-                        print(f"Response Metadata: Finish Reason - {finish_reason}")
-            print("-" * 50)
+        print(f"Receiving new event of type: {chunk.event}...")
+        print(chunk.data)
+        print("\n\n")
     ```
 
 === "Javascript"
@@ -248,46 +97,13 @@ 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 event of streamResponse) {
-      if (event.event === "metadata") {
-        console.log(`Metadata: Run ID - ${event.data.run_id}`);
-        console.log("-".repeat(50));
-      } else if (event.event === "messages/partial") {
-        event.data.forEach(dataItem => {
-          if (dataItem.role && dataItem.role === "user") {
-            console.log(`Human: ${dataItem.content}`);
-          } else {
-            const toolCalls = dataItem.tool_calls || [];
-            const invalidToolCalls = dataItem.invalid_tool_calls || [];
-            const content = dataItem.content || "";
-            const responseMetadata = dataItem.response_metadata || {};
-
-            if (content) {
-              console.log(`AI: ${content}`);
-            }
-
-            if (toolCalls.length > 0) {
-              console.log("Tool Calls:");
-              console.log(formatToolCalls(toolCalls));
-            }
-
-            if (invalidToolCalls.length > 0) {
-              console.log("Invalid Tool Calls:");
-              console.log(formatToolCalls(invalidToolCalls));
-            }
-
-            if (responseMetadata) {
-              const finishReason = responseMetadata.finish_reason || "N/A";
-              console.log(`Response Metadata: Finish Reason - ${finishReason}`);
-            }
-          }
-        });
-        console.log("-".repeat(50));
-      }
+    for await (const chunk of streamResponse) {
+      console.log(`Receiving new event of type: ${chunk.event}...`);
+      console.log(chunk.data);
+      console.log("\n\n");
     }
     ```
 
@@ -295,203 +111,221 @@ Now we can stream by messages, which will return complete messages (at the end o
 
     ```bash
     curl --request POST \
-    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
-    --header 'Content-Type: application/json' \
-    --data "{
-    \"assistant_id\": \"agent\",
-    \"config\":{\"configurable\":{\"model_name\":\"openai\"}},
-    \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"What's the weather in sf\"}]},
-    \"stream_mode\": [
-    \"messages\"
-    ]
-    }" | sed 's/\r$//' | ./process_stream.sh
+     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
+     --header 'Content-Type: application/json' \
+     --data "{
+       \"assistant_id\": \"agent\",
+       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what's the weather in la\"}]},
+       \"stream_mode\": [
+         \"messages-tuple\"
+       ]
+     }" | \
+     sed 's/\r$//' | \
+     awk '
+     /^event:/ {
+         if (data_content != "") {
+             print data_content "\n"
+         }
+         sub(/^event: /, "Receiving event of type: ", $0)
+         printf "%s...\n", $0
+         data_content = ""
+     }
+     /^data:/ {
+         sub(/^data: /, "", $0)
+         data_content = $0
+     }
+     END {
+         if (data_content != "") {
+             print data_content "\n"
+         }
+     }
+     '
     ```
 
 
 Output:
 
-    Metadata: Run ID - 1ef2fe5c-6a1d-6575-bc09-d7832711c17e
-    --------------------------------------------------
-    Invalid Tool Calls:
-    Tool Call ID: call_cg14F20jMBqWYrNgEkdWHwB3, Function: tavily_search_results_json, Arguments: 
-    --------------------------------------------------
-    Tool Calls:
-    Tool Call ID: call_cg14F20jMBqWYrNgEkdWHwB3, Function: tavily_search_results_json, Arguments: {}
-    --------------------------------------------------
-    Tool Calls:
-    Tool Call ID: call_cg14F20jMBqWYrNgEkdWHwB3, Function: tavily_search_results_json, Arguments: {}
-    --------------------------------------------------
-    Tool Calls:
-    Tool Call ID: call_cg14F20jMBqWYrNgEkdWHwB3, Function: tavily_search_results_json, Arguments: {'query': ''}
-    --------------------------------------------------
-    Tool Calls:
-    Tool Call ID: call_cg14F20jMBqWYrNgEkdWHwB3, Function: tavily_search_results_json, Arguments: {'query': 'current'}
-    --------------------------------------------------
-    Tool Calls:
-    Tool Call ID: call_cg14F20jMBqWYrNgEkdWHwB3, Function: tavily_search_results_json, Arguments: {'query': 'current weather'}
-    --------------------------------------------------
-    Tool Calls:
-    Tool Call ID: call_cg14F20jMBqWYrNgEkdWHwB3, Function: tavily_search_results_json, Arguments: {'query': 'current weather in'}
-    --------------------------------------------------
-    Tool Calls:
-    Tool Call ID: call_cg14F20jMBqWYrNgEkdWHwB3, Function: tavily_search_results_json, Arguments: {'query': 'current weather in San'}
-    --------------------------------------------------
-    Tool Calls:
-    Tool Call ID: call_cg14F20jMBqWYrNgEkdWHwB3, Function: tavily_search_results_json, Arguments: {'query': 'current weather in San Francisco'}
-    --------------------------------------------------
-    Tool Calls:
-    Tool Call ID: call_cg14F20jMBqWYrNgEkdWHwB3, Function: tavily_search_results_json, Arguments: {'query': 'current weather in San Francisco'}
-    --------------------------------------------------
-    Tool Calls:
-    Tool Call ID: call_cg14F20jMBqWYrNgEkdWHwB3, Function: tavily_search_results_json, Arguments: {'query': 'current weather in San Francisco'}
-    Response Metadata: Finish Reason - tool_calls
-    --------------------------------------------------
-    --------------------------------------------------
-    AI: The
-    --------------------------------------------------
-    AI: The current
-    --------------------------------------------------
-    AI: The current weather
-    --------------------------------------------------
-    AI: The current weather in
-    --------------------------------------------------
-    AI: The current weather in San
-    --------------------------------------------------
-    AI: The current weather in San Francisco
-    --------------------------------------------------
-    AI: The current weather in San Francisco is
-    --------------------------------------------------
-    AI: The current weather in San Francisco is over
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F).
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-s
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-south
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 k
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph).
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%,
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%, and
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%, and the
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%, and the visibility
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%, and the visibility is
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%, and the visibility is 
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%, and the visibility is 16
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%, and the visibility is 16 km
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%, and the visibility is 16 km (
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%, and the visibility is 16 km (9
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%, and the visibility is 16 km (9 miles
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%, and the visibility is 16 km (9 miles).
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%, and the visibility is 16 km (9 miles). The
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%, and the visibility is 16 km (9 miles). The UV
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%, and the visibility is 16 km (9 miles). The UV index
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%, and the visibility is 16 km (9 miles). The UV index is
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%, and the visibility is 16 km (9 miles). The UV index is 
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%, and the visibility is 16 km (9 miles). The UV index is 3
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%, and the visibility is 16 km (9 miles). The UV index is 3.
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%, and the visibility is 16 km (9 miles). The UV index is 3.
-    Response Metadata: Finish Reason - stop
-    --------------------------------------------------
+    Receiving new event of type: metadata...
+    {"run_id": "1ef971e0-9a84-6154-9047-247b4ce89c4d", "attempt": 1}
 
+    ...
+
+    Receiving new event of type: messages...
+    [
+      {
+        "type": "AIMessageChunk",
+        "tool_calls": [
+          {
+            "name": "tavily_search_results_json",
+            "args": {
+              "query": "weat"
+            },
+            "id": "toolu_0114XKXdNtHQEa3ozmY1uDdM",
+            "type": "tool_call"
+          }
+        ],
+        ...
+      },
+      {
+        "graph_id": "agent",
+        "langgraph_node": "agent",
+        ...
+      }
+    ]
+
+
+
+    Receiving new event of type: messages...
+    [
+      {
+        "type": "AIMessageChunk",
+        "tool_calls": [
+          {
+            "name": "tavily_search_results_json",
+            "args": {
+              "query": "her in san "
+            },
+            "id": "toolu_0114XKXdNtHQEa3ozmY1uDdM",
+            "type": "tool_call"
+          }
+        ],
+        ...
+      },
+      {
+        "graph_id": "agent",
+        "langgraph_node": "agent",
+        ...
+      }
+    ]
+
+    ...
+
+    Receiving new event of type: messages...
+    [
+      {
+        "type": "AIMessageChunk",
+        "tool_calls": [
+          {
+            "name": "tavily_search_results_json",
+            "args": {
+              "query": "francisco"
+            },
+            "id": "toolu_0114XKXdNtHQEa3ozmY1uDdM",
+            "type": "tool_call"
+          }
+        ],
+        ...
+      },
+      {
+        "graph_id": "agent",
+        "langgraph_node": "agent",
+        ...
+      }
+    ]
+
+    ...
+
+    Receiving new event of type: messages...
+    [
+      {
+        "content": "[{\"url\": \"https://www.weatherapi.com/\", \"content\": \"{'location': {'name': 'San Francisco', 'region': 'California', 'country': 'United States of America', 'lat': 37.775, 'lon': -122.4183, 'tz_id': 'America/Los_Angeles', 'localtime_epoch': 1730475777, 'localtime': '2024-11-01 08:42'}, 'current': {'last_updated_epoch': 1730475000, 'last_updated': '2024-11-01 08:30', 'temp_c': 11.1, 'temp_f': 52.0, 'is_day': 1, 'condition': {'text': 'Partly cloudy', 'icon': '//cdn.weatherapi.com/weather/64x64/day/116.png', 'code': 1003}, 'wind_mph': 2.2, 'wind_kph': 3.6, 'wind_degree': 192, 'wind_dir': 'SSW', 'pressure_mb': 1018.0, 'pressure_in': 30.07, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 89, 'cloud': 75, 'feelslike_c': 11.5, 'feelslike_f': 52.6, 'windchill_c': 10.0, 'windchill_f': 50.1, 'heatindex_c': 10.4, 'heatindex_f': 50.7, 'dewpoint_c': 9.1, 'dewpoint_f': 48.5, 'vis_km': 16.0, 'vis_miles': 9.0, 'uv': 3.0, 'gust_mph': 6.7, 'gust_kph': 10.8}}\"}]",
+        "type": "tool",
+        "tool_call_id": "toolu_0114XKXdNtHQEa3ozmY1uDdM",
+        ...
+      },
+      {
+        "graph_id": "agent",
+        "langgraph_node": "action",
+        ...
+      }
+    ]
+
+    ...
+
+    Receiving new event of type: messages...
+    [
+      {
+        "content": [
+          {
+            "text": "\n\nThe search",
+            "type": "text",
+            "index": 0
+          }
+        ],
+        "type": "AIMessageChunk",
+        ...
+      },
+      {
+        "graph_id": "agent",
+        "langgraph_node": "agent",
+        ...
+      }
+    ]
+
+
+
+    Receiving new event of type: messages...
+    [
+      {
+        "content": [
+          {
+            "text": " results provide",
+            "type": "text",
+            "index": 0
+          }
+        ],
+        "type": "AIMessageChunk",
+        ...
+      },
+      {
+        "graph_id": "agent",
+        "langgraph_node": "agent",
+        ...
+      }
+    ]
+
+
+
+    Receiving new event of type: messages...
+    [
+      {
+        "content": [
+          {
+            "text": " the current weather conditions",
+            "type": "text",
+            "index": 0
+          }
+        ],
+        "type": "AIMessageChunk",
+        ...
+      },
+      {
+        "graph_id": "agent",
+        "langgraph_node": "agent",
+        ...
+      }
+    ]
+
+
+
+    Receiving new event of type: messages...
+    [
+      {
+        "content": [
+          {
+            "text": " in San Francisco.",
+            "type": "text",
+            "index": 0
+          }
+        ],
+        "type": "AIMessageChunk",
+        ...
+      },
+      {
+        "graph_id": "agent",
+        "langgraph_node": "agent",
+        ...
+      }
+    ]
+
+    ...

docs/docs/cloud/how-tos/stream_multiple.md

@@ -1,5 +1,8 @@
 # How to configure multiple streaming modes at the same time
 
+!!! info "Prerequisites"
+    * [Streaming](../../concepts/streaming.md)
+
 This guide covers how to configure multiple streaming modes at the same time.
 
 ## Setup
@@ -175,11 +178,6 @@ Output:
     
     
     
-    Receiving new event of type: messages/complete...
-    [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}]
-    
-    
-    
     Receiving new event of type: debug...
     {'type': 'checkpoint', 'timestamp': '2024-06-24T21:34:06.117924+00:00', 'step': 0, 'payload': {'config': {'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'callbacks': [None], 'recursion_limit': 25, 'configurable': {'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'thread_ts': '1ef32717-bc81-68c8-8000-4e18ae7d67a5', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25'}, 'values': {'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}]}, 'metadata': {'source': 'loop', 'step': 0, 'writes': None}}}
     
@@ -305,11 +303,6 @@ Output:
     
     
     
-    Receiving new event of type: messages/complete...
-    [{'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]
-    
-    
-    
     Receiving new event of type: debug...
     {'type': 'checkpoint', 'timestamp': '2024-06-24T21:34:06.124510+00:00', 'step': 1, 'payload': {'config': {'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'callbacks': [None], 'recursion_limit': 25, 'configurable': {'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'thread_ts': '1ef32717-bc91-6a34-8001-26353c117c25', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25'}, 'values': {'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}, 'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}, 'metadata': {'source': 'loop', 'step': 1, 'writes': {'agent': {'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}, 'messages': [{'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}}}}}
     
@@ -469,12 +462,7 @@ Output:
     {'event': 'on_chain_stream', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'name': 'LangGraph', 'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'data': {'chunk': ['values', {'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}, 'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '639ca779-403d-4915-a066-327e1f634c8b', 'tool_call_id': 'tool_call_id'}, {'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-0f2ef0a1-0fc7-445c-9df4-55e8bb284575', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}]}, 'parent_ids': []}
     
     
-    
-    Receiving new event of type: messages/complete...
-    [{'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-0f2ef0a1-0fc7-445c-9df4-55e8bb284575', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]
-    
-    
-    
+
     Receiving new event of type: debug...
     {'type': 'checkpoint', 'timestamp': '2024-06-24T21:34:06.134190+00:00', 'step': 3, 'payload': {'config': {'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'callbacks': [None], 'recursion_limit': 25, 'configurable': {'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'thread_ts': '1ef32717-bca9-6418-8003-8d0d0b06845c', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25'}, 'values': {'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}, 'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '639ca779-403d-4915-a066-327e1f634c8b', 'tool_call_id': 'tool_call_id'}, {'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-0f2ef0a1-0fc7-445c-9df4-55e8bb284575', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}, 'metadata': {'source': 'loop', 'step': 3, 'writes': {'agent': {'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}, 'messages': [{'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-0f2ef0a1-0fc7-445c-9df4-55e8bb284575', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}}}}}
     

docs/docs/cloud/how-tos/stream_updates.md

@@ -1,6 +1,9 @@
 # How to stream state updates of your graph
 
-This guide covers how to use `stream_mode="updates"` for your graph, which will stream the updates to the graph state that are made after each node is executed. This differs from using `stream_mode="values"`: instead of streaming the entire value of the state at each superstep, it only streams the updates from each of the nodes that made an update to the state at that superstep. Read [this conceptual guide](https://langchain-ai.github.io/langgraph/concepts/low_level/#stream-and-astream) to learn more.
+!!! info "Prerequisites"
+    * [Streaming](../../concepts/streaming.md)
+
+This guide covers how to use `stream_mode="updates"` for your graph, which will stream the updates to the graph state that are made after each node is executed. This differs from using `stream_mode="values"`: instead of streaming the entire value of the state at each superstep, it only streams the updates from each of the nodes that made an update to the state at that superstep.
 
 ## Setup
 
@@ -146,24 +149,69 @@ Now we can stream by updates, which outputs updates made to the state by each no
 Output:
 
     Receiving new event of type: metadata...
-    {'run_id': 'cfc96c16-ed9a-44bd-b5bb-c30e3c0725f0'}
-    
-    
-    
-    Receiving new event of type: data...
-    {'agent': {'messages': [{'content': [{'id': 'toolu_0148tMmDK51iLQfG1yaNwRHM', 'input': {'query': 'weather in los angeles'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}], 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-1a9d32b0-7007-4a36-abde-8df812a0ed94', 'example': False, 'tool_calls': [{'name': 'tavily_search_results_json', 'args': {'query': 'weather in los angeles'}, 'id': 'toolu_0148tMmDK51iLQfG1yaNwRHM'}], 'invalid_tool_calls': []}]}}
-    
-    
-    
-    Receiving new event of type: data...
-    {'action': {'messages': [{'content': '[{"url": "https://www.weatherapi.com/", "content": "{\'location\': {\'name\': \'Los Angeles\', \'region\': \'California\', \'country\': \'United States of America\', \'lat\': 34.05, \'lon\': -118.24, \'tz_id\': \'America/Los_Angeles\', \'localtime_epoch\': 1716062239, \'localtime\': \'2024-05-18 12:57\'}, \'current\': {\'last_updated_epoch\': 1716061500, \'last_updated\': \'2024-05-18 12:45\', \'temp_c\': 18.9, \'temp_f\': 66.0, \'is_day\': 1, \'condition\': {\'text\': \'Overcast\', \'icon\': \'//cdn.weatherapi.com/weather/64x64/day/122.png\', \'code\': 1009}, \'wind_mph\': 2.2, \'wind_kph\': 3.6, \'wind_degree\': 10, \'wind_dir\': \'N\', \'pressure_mb\': 1017.0, \'pressure_in\': 30.02, \'precip_mm\': 0.0, \'precip_in\': 0.0, \'humidity\': 65, \'cloud\': 100, \'feelslike_c\': 18.9, \'feelslike_f\': 66.0, \'vis_km\': 16.0, \'vis_miles\': 9.0, \'uv\': 6.0, \'gust_mph\': 7.5, \'gust_kph\': 12.0}}"}]', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'tavily_search_results_json', 'id': 'a36e8cd1-0e96-4417-9c15-f10a945d2b42', 'tool_call_id': 'toolu_0148tMmDK51iLQfG1yaNwRHM'}]}}
-    
-    
-    
-    Receiving new event of type: data...
-    {'agent': {'messages': [{'content': 'The weather in Los Angeles is currently overcast with a temperature of around 66°F (18.9°C). There are light winds from the north at around 2-3 mph. The humidity is 65% and visibility is good at 9 miles. Overall, mild spring weather conditions in LA.', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-d5c1c2f0-b12d-41ce-990b-f36570e7483d', 'example': False, 'tool_calls': [], 'invalid_tool_calls': []}]}}
-    
-    
-    
+    {"run_id": "cfc96c16-ed9a-44bd-b5bb-c30e3c0725f0"}
+
+
+
+    Receiving new event of type: updates...
+    {
+      "agent": {
+        "messages": [
+          {
+            "type": "ai",
+            "tool_calls": [
+              {
+                "name": "tavily_search_results_json",
+                "args": {
+                  "query": "weather in los angeles"
+                },
+                "id": "toolu_0148tMmDK51iLQfG1yaNwRHM"
+              }
+            ],
+            ...
+          }
+        ]
+      }
+    }
+
+
+
+    Receiving new event of type: updates...
+    {
+      "action": {
+        "messages": [
+          {
+            "content": [
+              {
+                "url": "https://www.weatherapi.com/",
+                "content": "{\"location\": {\"name\": \"Los Angeles\", \"region\": \"California\", \"country\": \"United States of America\", \"lat\": 34.05, \"lon\": -118.24, \"tz_id\": \"America/Los_Angeles\", \"localtime_epoch\": 1716062239, \"localtime\": \"2024-05-18 12:57\"}, \"current\": {\"last_updated_epoch\": 1716061500, \"last_updated\": \"2024-05-18 12:45\", \"temp_c\": 18.9, \"temp_f\": 66.0, \"is_day\": 1, \"condition\": {\"text\": \"Overcast\", \"icon\": \"//cdn.weatherapi.com/weather/64x64/day/122.png\", \"code\": 1009}, \"wind_mph\": 2.2, \"wind_kph\": 3.6, \"wind_degree\": 10, \"wind_dir\": \"N\", \"pressure_mb\": 1017.0, \"pressure_in\": 30.02, \"precip_mm\": 0.0, \"precip_in\": 0.0, \"humidity\": 65, \"cloud\": 100, \"feelslike_c\": 18.9, \"feelslike_f\": 66.0, \"vis_km\": 16.0, \"vis_miles\": 9.0, \"uv\": 6.0, \"gust_mph\": 7.5, \"gust_kph\": 12.0}}"
+              }
+            ],
+            "type": "tool",
+            "name": "tavily_search_results_json",
+            "tool_call_id": "toolu_0148tMmDK51iLQfG1yaNwRHM",
+            ...
+          }
+        ]
+      }
+    }
+
+
+
+    Receiving new event of type: updates...
+    {
+      "agent": {
+        "messages": [
+          {
+            "content": "The weather in Los Angeles is currently overcast with a temperature of around 66°F (18.9°C). There are light winds from the north at around 2-3 mph. The humidity is 65% and visibility is good at 9 miles. Overall, mild spring weather conditions in LA.",
+            "type": "ai",
+            ...
+          }
+        ]
+      }
+    }
+
+
+
     Receiving new event of type: end...
     None

docs/docs/cloud/how-tos/stream_values.md

@@ -1,6 +1,9 @@
 # How to stream full state of your graph
 
-This guide covers how to use `stream_mode="values"`, which streams the value of the state at each superstep. This differs from using `stream_mode="updates"`: instead of streaming just the updates to the state from each node, it streams the entire graph state at that superstep. Read [this conceptual guide](https://langchain-ai.github.io/langgraph/concepts/low_level/#stream-and-astream) to learn more.
+!!! info "Prerequisites"
+    * [Streaming](../../concepts/streaming.md)
+
+This guide covers how to use `stream_mode="values"`, which streams the value of the state at each superstep. This differs from using `stream_mode="updates"`: instead of streaming just the updates to the state from each node, it streams the entire graph state at that superstep.
 
 ## Setup
 
@@ -133,30 +136,93 @@ Now we can stream by values, which streams the full state of the graph after eac
 Output:
 
     Receiving new event of type: metadata...
-    {'run_id': 'f08791ce-0a3d-44e0-836c-ff62cd2e2786'}
-    
-    
-    
+    {"run_id": "f08791ce-0a3d-44e0-836c-ff62cd2e2786"}
+
+
+
     Receiving new event of type: values...
-    {'messages': [{'role': 'human', 'content': 'what's the weather in la'}]}
-    
-    
-    
+    {
+      "messages": [
+        {
+          "role": "human",
+          "content": "what's the weather in la"
+        }
+      ]
+    }
+
+
+
     Receiving new event of type: values...
-    {'messages': [{'content': 'what's the weather in la', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'faa15565-8823-4aa1-87af-e21b40526fae', 'example': False}, {'content': [{'id': 'toolu_01E5mSaZWm5rWJnCqmt63v4g', 'input': {'query': 'weather in los angeles'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}], 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-3fe1db7a-6b8d-4d83-ba07-8657190ad811', 'example': False, 'tool_calls': [{'name': 'tavily_search_results_json', 'args': {'query': 'weather in los angeles'}, 'id': 'toolu_01E5mSaZWm5rWJnCqmt63v4g'}], 'invalid_tool_calls': []}]}
-    
-    
-    
+    {
+      "messages": [
+        {
+          "content": "what's the weather in la",
+          "type": "human",
+          ...
+        },
+        {
+          "content": "",
+          "type": "ai",
+          "tool_calls": [
+            {
+              "name": "tavily_search_results_json",
+              "args": {
+                "query": "weather in los angeles"
+              },
+              "id": "toolu_01E5mSaZWm5rWJnCqmt63v4g"
+            }
+          ],
+          ...
+        }
+      ]
+    }
+
+    ...
+
     Receiving new event of type: values...
-    {'messages': [{'content': 'what's the weather in la', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'faa15565-8823-4aa1-87af-e21b40526fae', 'example': False}, {'content': [{'id': 'toolu_01E5mSaZWm5rWJnCqmt63v4g', 'input': {'query': 'weather in los angeles'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}], 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-3fe1db7a-6b8d-4d83-ba07-8657190ad811', 'example': False, 'tool_calls': [{'name': 'tavily_search_results_json', 'args': {'query': 'weather in los angeles'}, 'id': 'toolu_01E5mSaZWm5rWJnCqmt63v4g'}], 'invalid_tool_calls': []}, {'content': '[{"url": "https://www.weatherapi.com/", "content": "{\'location\': {\'name\': \'Los Angeles\', \'region\': \'California\', \'country\': \'United States of America\', \'lat\': 34.05, \'lon\': -118.24, \'tz_id\': \'America/Los_Angeles\', \'localtime_epoch\': 1716310320, \'localtime\': \'2024-05-21 9:52\'}, \'current\': {\'last_updated_epoch\': 1716309900, \'last_updated\': \'2024-05-21 09:45\', \'temp_c\': 16.7, \'temp_f\': 62.1, \'is_day\': 1, \'condition\': {\'text\': \'Overcast\', \'icon\': \'//cdn.weatherapi.com/weather/64x64/day/122.png\', \'code\': 1009}, \'wind_mph\': 8.1, \'wind_kph\': 13.0, \'wind_degree\': 250, \'wind_dir\': \'WSW\', \'pressure_mb\': 1015.0, \'pressure_in\': 29.97, \'precip_mm\': 0.0, \'precip_in\': 0.0, \'humidity\': 65, \'cloud\': 100, \'feelslike_c\': 16.7, \'feelslike_f\': 62.1, \'vis_km\': 16.0, \'vis_miles\': 9.0, \'uv\': 5.0, \'gust_mph\': 12.5, \'gust_kph\': 20.2}}"}]', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'tavily_search_results_json', 'id': '0d5dab31-5ff8-4ae2-a560-bc4bcba7c9d7', 'tool_call_id': 'toolu_01E5mSaZWm5rWJnCqmt63v4g'}]}
-    
-    
-    
-    Receiving new event of type: values...
-    {'messages': [{'content': 'what's the weather in la', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'faa15565-8823-4aa1-87af-e21b40526fae', 'example': False}, {'content': [{'id': 'toolu_01E5mSaZWm5rWJnCqmt63v4g', 'input': {'query': 'weather in los angeles'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}], 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-3fe1db7a-6b8d-4d83-ba07-8657190ad811', 'example': False, 'tool_calls': [{'name': 'tavily_search_results_json', 'args': {'query': 'weather in los angeles'}, 'id': 'toolu_01E5mSaZWm5rWJnCqmt63v4g'}], 'invalid_tool_calls': []}, {'content': '[{"url": "https://www.weatherapi.com/", "content": "{\'location\': {\'name\': \'Los Angeles\', \'region\': \'California\', \'country\': \'United States of America\', \'lat\': 34.05, \'lon\': -118.24, \'tz_id\': \'America/Los_Angeles\', \'localtime_epoch\': 1716310320, \'localtime\': \'2024-05-21 9:52\'}, \'current\': {\'last_updated_epoch\': 1716309900, \'last_updated\': \'2024-05-21 09:45\', \'temp_c\': 16.7, \'temp_f\': 62.1, \'is_day\': 1, \'condition\': {\'text\': \'Overcast\', \'icon\': \'//cdn.weatherapi.com/weather/64x64/day/122.png\', \'code\': 1009}, \'wind_mph\': 8.1, \'wind_kph\': 13.0, \'wind_degree\': 250, \'wind_dir\': \'WSW\', \'pressure_mb\': 1015.0, \'pressure_in\': 29.97, \'precip_mm\': 0.0, \'precip_in\': 0.0, \'humidity\': 65, \'cloud\': 100, \'feelslike_c\': 16.7, \'feelslike_f\': 62.1, \'vis_km\': 16.0, \'vis_miles\': 9.0, \'uv\': 5.0, \'gust_mph\': 12.5, \'gust_kph\': 20.2}}"}]', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'tavily_search_results_json', 'id': '0d5dab31-5ff8-4ae2-a560-bc4bcba7c9d7', 'tool_call_id': 'toolu_01E5mSaZWm5rWJnCqmt63v4g'}, {'content': 'Based on the weather API results, the current weather in Los Angeles is overcast with a temperature of around 62°F (17°C). There are light winds from the west-southwest around 8-13 mph. The humidity is 65% and visibility is good at 9 miles. Overall, mild spring weather conditions in LA.', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-4d6d4c23-5aad-4042-b0d9-19407a9e08e3', 'example': False, 'tool_calls': [], 'invalid_tool_calls': []}]}
-    
-    
-    
+    {
+      "messages": [
+        {
+          "content": "what's the weather in la",
+          "type": "human",
+          ...
+        },
+        {
+          "content": "",
+          "type": "ai",
+          "tool_calls": [
+            {
+              "name": "tavily_search_results_json",
+              "args": {
+                "query": "weather in los angeles"
+              },
+              "id": "toolu_01E5mSaZWm5rWJnCqmt63v4g"
+            }
+          ],
+          ...
+        }
+        {
+          "content": [
+            {
+              "url": "https://www.weatherapi.com/",
+              "content": "{\"location\": {\"name\": \"Los Angeles\", \"region\": \"California\", \"country\": \"United States of America\", \"lat\": 34.05, \"lon\": -118.24, \"tz_id\": \"America/Los_Angeles\", \"localtime_epoch\": 1716310320, \"localtime\": \"2024-05-21 9:52\"}, \"current\": {\"last_updated_epoch\": 1716309900, \"last_updated\": \"2024-05-21 09:45\", \"temp_c\": 16.7, \"temp_f\": 62.1, \"is_day\": 1, \"condition\": {\"text\": \"Overcast\", \"icon\": \"//cdn.weatherapi.com/weather/64x64/day/122.png\", \"code\": 1009}, \"wind_mph\": 8.1, \"wind_kph\": 13.0, \"wind_degree\": 250, \"wind_dir\": \"WSW\", \"pressure_mb\": 1015.0, \"pressure_in\": 29.97, \"precip_mm\": 0.0, \"precip_in\": 0.0, \"humidity\": 65, \"cloud\": 100, \"feelslike_c\": 16.7, \"feelslike_f\": 62.1, \"vis_km\": 16.0, \"vis_miles\": 9.0, \"uv\": 5.0, \"gust_mph\": 12.5, \"gust_kph\": 20.2}}"
+            }
+          ],
+          "type": "tool",
+          "name": "tavily_search_results_json",
+          "tool_call_id": "toolu_01E5mSaZWm5rWJnCqmt63v4g"
+          ...
+        },
+        {
+          "content": "Based on the weather API results, the current weather in Los Angeles is overcast with a temperature of around 62°F (17°C). There are light winds from the west-southwest around 8-13 mph. The humidity is 65% and visibility is good at 9 miles. Overall, mild spring weather conditions in LA.",
+          "type": "ai",
+          ...
+        }
+      ]
+    }
+
+
+
     Receiving new event of type: end...
     None
     
@@ -228,40 +294,42 @@ If we want to just get the final result, we can use this endpoint and just keep
 
 Output:
 
-    {'messages': [{'content': 'what's the weather in la',
-       'additional_kwargs': {},
-       'response_metadata': {},
-       'type': 'human',
-       'name': None,
-       'id': 'e78c2f94-d810-42fc-a399-11f6bb1b1092',
-       'example': False},
-      {'content': [{'id': 'toolu_01SBMoAGr4U9x3ibztm2UUom',
-         'input': {'query': 'weather in los angeles'},
-         'name': 'tavily_search_results_json',
-         'type': 'tool_use'}],
-       'additional_kwargs': {},
-       'response_metadata': {},
-       'type': 'ai',
-       'name': None,
-       'id': 'run-80767ab8-09fc-40ec-9e45-657ddef5e0b1',
-       'example': False,
-       'tool_calls': [{'name': 'tavily_search_results_json',
-         'args': {'query': 'weather in los angeles'},
-         'id': 'toolu_01SBMoAGr4U9x3ibztm2UUom'}],
-       'invalid_tool_calls': []},
-      {'content': '[{"url": "https://www.weatherapi.com/", "content": "{\'location\': {\'name\': \'Los Angeles\', \'region\': \'California\', \'country\': \'United States of America\', \'lat\': 34.05, \'lon\': -118.24, \'tz_id\': \'America/Los_Angeles\', \'localtime_epoch\': 1716310320, \'localtime\': \'2024-05-21 9:52\'}, \'current\': {\'last_updated_epoch\': 1716309900, \'last_updated\': \'2024-05-21 09:45\', \'temp_c\': 16.7, \'temp_f\': 62.1, \'is_day\': 1, \'condition\': {\'text\': \'Overcast\', \'icon\': \'//cdn.weatherapi.com/weather/64x64/day/122.png\', \'code\': 1009}, \'wind_mph\': 8.1, \'wind_kph\': 13.0, \'wind_degree\': 250, \'wind_dir\': \'WSW\', \'pressure_mb\': 1015.0, \'pressure_in\': 29.97, \'precip_mm\': 0.0, \'precip_in\': 0.0, \'humidity\': 65, \'cloud\': 100, \'feelslike_c\': 16.7, \'feelslike_f\': 62.1, \'vis_km\': 16.0, \'vis_miles\': 9.0, \'uv\': 5.0, \'gust_mph\': 12.5, \'gust_kph\': 20.2}}"}]',
-       'additional_kwargs': {},
-       'response_metadata': {},
-       'type': 'tool',
-       'name': 'tavily_search_results_json',
-       'id': 'af25e94a-c119-48c3-bbd3-096e42f472ac',
-       'tool_call_id': 'toolu_01SBMoAGr4U9x3ibztm2UUom'},
-      {'content': 'Based on the weather API results, the current weather in Los Angeles is overcast with a temperature of around 62°F (17°C). There are light winds from the west-southwest around 8-13 mph. The humidity is 65% and visibility is good at 9 miles. Overall, mild spring weather conditions in LA.',
-       'additional_kwargs': {},
-       'response_metadata': {},
-       'type': 'ai',
-       'name': None,
-       'id': 'run-b90f0037-e56a-4f3b-ad92-00d10d079a9e',
-       'example': False,
-       'tool_calls': [],
-       'invalid_tool_calls': []}]}
+    {
+      "messages": [
+        {
+          "content": "what's the weather in la",
+          "type": "human",
+          ...
+        },
+        {
+          "type": "ai",
+          "tool_calls": [
+            {
+              "name": "tavily_search_results_json",
+              "args": {
+                "query": "weather in los angeles"
+              },
+              "id": "toolu_01E5mSaZWm5rWJnCqmt63v4g"
+            }
+          ],
+          ...
+        }
+        {
+          "content": [
+            {
+              "url": "https://www.weatherapi.com/",
+              "content": "{\"location\": {\"name\": \"Los Angeles\", \"region\": \"California\", \"country\": \"United States of America\", \"lat\": 34.05, \"lon\": -118.24, \"tz_id\": \"America/Los_Angeles\", \"localtime_epoch\": 1716310320, \"localtime\": \"2024-05-21 9:52\"}, \"current\": {\"last_updated_epoch\": 1716309900, \"last_updated\": \"2024-05-21 09:45\", \"temp_c\": 16.7, \"temp_f\": 62.1, \"is_day\": 1, \"condition\": {\"text\": \"Overcast\", \"icon\": \"//cdn.weatherapi.com/weather/64x64/day/122.png\", \"code\": 1009}, \"wind_mph\": 8.1, \"wind_kph\": 13.0, \"wind_degree\": 250, \"wind_dir\": \"WSW\", \"pressure_mb\": 1015.0, \"pressure_in\": 29.97, \"precip_mm\": 0.0, \"precip_in\": 0.0, \"humidity\": 65, \"cloud\": 100, \"feelslike_c\": 16.7, \"feelslike_f\": 62.1, \"vis_km\": 16.0, \"vis_miles\": 9.0, \"uv\": 5.0, \"gust_mph\": 12.5, \"gust_kph\": 20.2}}"
+            }
+          ],
+          "type": "tool",
+          "name": "tavily_search_results_json",
+          "tool_call_id": "toolu_01E5mSaZWm5rWJnCqmt63v4g"
+          ...
+        },
+        {
+          "content": "Based on the weather API results, the current weather in Los Angeles is overcast with a temperature of around 62°F (17°C). There are light winds from the west-southwest around 8-13 mph. The humidity is 65% and visibility is good at 9 miles. Overall, mild spring weather conditions in LA.",
+          "type": "ai",
+          ...
+        }
+      ]
+    }

docs/docs/cloud/how-tos/test_deployment.md

@@ -4,7 +4,7 @@ The LangGraph Studio UI connects directly to LangGraph Cloud deployments.
 
 Starting from the <a href="https://smith.langchain.com/" target="_blank">LangSmith UI</a>...
 
-1. In the left-hand navigation panel, select `Deployments`. The `Deployments` view contains a list of existing LangGraph Cloud deployments.
+1. In the left-hand navigation panel, select `LangGraph Cloud`. The `LangGraph Cloud` view contains a list of existing LangGraph Cloud deployments.
 1. Select an existing deployment to test with LangGraph Studio.
 1. In the top-right corner, select `Open LangGraph Studio`.
 1. [Invoke an assistant](./invoke_studio.md) or [view an existing thread](./threads_studio.md).

docs/docs/cloud/how-tos/webhooks.md

@@ -76,7 +76,9 @@ Output:
 
 ## Use graph with a webhook
 
-Now we can invoke a run with a webhook:
+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:
         assistant_id=assistant_id,
         input=input,
         stream_mode="events",
-        webhook="your-webhook"
+        webhook="https://my-server.app/my-webhook-endpoint"
     ):
         # Do something with the stream output
         pass
@@ -107,7 +109,7 @@ Now we can invoke a run with a webhook:
       assistantID,
       {
         input: input,
-        webhook: "your-webhook"
+        webhook: "https://my-server.app/my-webhook-endpoint"
       }
     );
     for await (const chunk of streamResponse) {
@@ -124,8 +126,17 @@ Now we can invoke a run with a webhook:
         --data '{
             "assistant_id": <ASSISTANT_ID>,
             "input" : {"messages":[{"role": "user", "content": "Hello!"}]},
-            "webhook": <YOUR_WEBHOOK_URL>
+            "webhook": "https://my-server.app/my-webhook-endpoint"
         }'
     ```
 
-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.,
+```
+https://my-server.app/my-webhook-endpoint?token=...
+```
+
+The server should then extract the token from the request's parameters and validate it before processing the payload.

docs/docs/cloud/index.md

@@ -1,44 +0,0 @@
-# LangGraph Cloud (beta)
-
-!!! tip
-    - LangGraph is an MIT-licensed open-source library, which we are committed to maintaining and growing for the community.
-    - LangGraph Cloud is an optional managed hosting service for LangGraph, which provides additional features geared towards production deployments.
-    - We are actively contributing improvements back to LangGraph informed by our work on LangGraph Cloud.
-    - You can always deploy LangGraph applications on your own infrastructure using the open-source LangGraph project.
-
-!!! warning "Under Construction"
-    LangGraph Cloud documentation is under construction. Contents may change until general availability.
-
-
-<video controls preload="auto" allowfullscreen="true" poster="how-tos/img/studio_forks_poster.png">
-    <source src="how-tos/img/studio_forks.mp4" type="video/mp4">
-</video>
-
-
-## Overview
-
-LangGraph Cloud is a managed service for deploying and hosting LangGraph applications. Deploying applications with LangGraph Cloud shortens the time-to-market for developers. With one click, deploy a production-ready API with built-in persistence for your LangGraph application. LangGraph Cloud APIs are horizontally scalable and deployed with durable storage.
-
-The LangGraph Cloud API exposes functionality of your LangGraph application through [Assistants](./concepts/api.md#assistants). An assistant abstracts the cognitive architecture of your graph. Invoke an assistant by calling the pre-built [API endpoints](./reference/api/api_ref.md).
-
-LangGraph Cloud is seamlessly integrated with [LangSmith](https://www.langchain.com/langsmith) and is accessible from within the LangSmith UI.
-
-LangGraph Cloud applications can be tested and debugged using the [LangGraph Studio Desktop](https://github.com/langchain-ai/langgraph-studio). 
-
-## Key Features
-
-The LangGraph Cloud API supports key LangGraph features in addition to new functionality for enabling complex, agentic workflows.
-
-- **Assistants and Threads**: Assistants abstract the cognitive architecture of graphs and threads track the state/history of graphs.
-- **Streaming**: API support for [LangGraph streaming modes](../concepts/low_level.md#streaming) including setting multiple streaming modes at the same time.
-- **Human-in-the-Loop**: API support for [LangGraph human-in-the-loop features](../concepts/agentic_concepts.md#human-in-the-loop).
-- **Double Texting**: Configure how assistants respond when new input is received while processing a previous input. Interrupt, rollback, reject, or enqueue.
-- **Background Runs/Cron Jobs**: A built-in task queue enables background runs and scheduled cron jobs.
-- **Stateless Runs**: For simpler use cases, invoke an assistant without needing to create a thread.
-
-## Documentation
-
-- [Tutorials](./quick_start.md): Learn to build and deploy applications for LangGraph Cloud.
-- [How-to Guides](./how-tos/index.md): Learn how to set up a LangGraph application for deployment and implement features of the LangGraph Cloud API such as streaming tokens, configuring double texting, and creating cron jobs. Go here if you want to copy and run a specific code snippet.
-- [Conceptual Guides](./concepts/api.md): In-depth explanations of the core data models (e.g. assistants), key features of the LangGraph Cloud API (e.g. double texting), and the architecture of a LangGraph Cloud deployment.
-- [Reference](./reference/api/api_ref.md): References for the LangGraph Cloud API, the corresponding Python and JS/TS SDKs, the LangGraph CLI, and deployment environment variables.

docs/docs/cloud/quick_start.md

@@ -1,6 +1,8 @@
-# Quick Start
+# LangGraph Cloud Quick Start
 
-This quick start guide will cover how to build a simple agent that can look up things on the internet. We will then deploy it to LangGraph Cloud, use the LangGraph Studio to visualize and test it out, and use the LangGraph SDK to interact with it.
+In this tutorial you will build and deploy a simple chatbot agent that can look things up on the internet. You will be using [LangGraph Cloud](../concepts/langgraph_cloud.md), [LangGraph Studio](../concepts/langgraph_studio.md) to visualize and test it out, and [LangGraph SDK](./reference/sdk/python_sdk_ref.md) to interact with the deployed agent.
+
+If you want to learn how to build an agent like this from scratch, take a look at the [LangGraph Quick Start tutorial](../tutorials/introduction.ipynb).
 
 ## Set up requirements
 
@@ -10,146 +12,183 @@ This tutorial will use:
 - Tavily for the search engine - sign up and get an API key [here](https://app.tavily.com/)
 - LangSmith for hosting - sign up and get an API key [here](https://smith.langchain.com/)
 
-## Set up local files
+## Create and configure your app
 
-1.  Create a new application with the following directory and files:
+First, let's set create all of the necessary files for our LangGraph application.
 
-=== "Python"
+1. __Create application directory and files__
 
-        <my-app>/
-        |-- agent.py            # code for your LangGraph agent
-        |-- requirements.txt    # Python packages required for your graph
-        |-- langgraph.json      # configuration file for LangGraph
-        |-- .env                # environment files with API keys
+    Create a new application `my-app` with the following file structure:
 
-=== "Javascript"
-
-        <my-app>/
-        |-- agent.ts            # code for your LangGraph agent
-        |-- package.json        # Javascript packages required for your graph
-        |-- langgraph.json      # configuration file for LangGraph
-        |-- .env                # environment files with API keys
-
-2.  The `agent.py`/`agent.ts` file should contain code for defining your graph. The following code is a simple example, the important thing is that at some point in your file you compile your graph and assign the compiled graph to a variable (in this case the `graph` variable). This example code uses `create_react_agent`, a prebuilt agent. You can read more about it [here](../concepts/agentic_concepts.md#react-implementation).
-
-=== "Python"
-
-    ```python
-    from langchain_anthropic import ChatAnthropic
-    from langchain_community.tools.tavily_search import TavilySearchResults
-    from langgraph.prebuilt import create_react_agent
-
-    model = ChatAnthropic(model="claude-3-5-sonnet-20240620")
-
-    tools = [TavilySearchResults(max_results=2)]
-
-    graph = create_react_agent(model, tools)
+    ```shell
+    mkdir my-app
     ```
 
-=== "Javascript"
+    === "Python"
 
-    ```ts
-    import { ChatAnthropic } from "@langchain/anthropic";
-    import { TavilySearchResults } from "@langchain/community/tools/tavily_search";
-    import { createReactAgent } from "@langchain/langgraph/prebuilt";
+            my-app/
+            |-- agent.py            # code for your LangGraph agent
+            |-- requirements.txt    # Python packages required for your graph
+            |-- langgraph.json      # configuration file for LangGraph
+            |-- .env                # environment files with API keys
 
-    const model = new ChatAnthropic({
-      model: "claude-3-5-sonnet-20240620",
-    });
+    === "Javascript"
 
-    const tools = [
-      new TavilySearchResults({ maxResults: 3, }),
-    ];
+            my-app/
+            |-- agent.ts            # code for your LangGraph agent
+            |-- package.json        # Javascript packages required for your graph
+            |-- langgraph.json      # configuration file for LangGraph
+            |-- .env                # environment files with API keys
 
-    export const graph = createReactAgent({ llm: model, tools });
-    ```
 
-3.  The `requirements.txt`/`package.json` file should contain any dependencies for your graph(s). In this case we only require four packages for our graph to run:
+1. __Define your graph__
 
-=== "Python"
+    === "Python"
+        The `agent.py` file should contain code with your graph.
 
-      ```python
-      langgraph
-      langchain_anthropic
-      tavily-python
-      langchain_community
-      ```
+    === "Javascript"
+        The `agent.ts` file should contain code with your graph.
 
-=== "Javascript"
+    The following code example is a simple chatbot agent (similar to the one in the [previous tutorial](../tutorials/introduction.ipynb)). Specifically, it uses [create_react_agent][langgraph.prebuilt.chat_agent_executor.create_react_agent], a prebuilt [ReAct](../concepts/agentic_concepts.md#react-implementation)-style agent.
 
-      ```js
-      {
-        "name": "my-app",
-        "packageManager": "yarn@1.22.22",
-        "dependencies": {
-          "@langchain/community": "^0.2.31",
-          "@langchain/core": "^0.2.31",
-          "@langchain/langgraph": "0.2.0",
-          "@langchain/openai": "^0.2.8"
+    The `agent` file needs to have a variable with a [CompiledGraph][langgraph.graph.graph.CompiledGraph] (in this case the `graph` variable).
+
+    === "Python"
+
+        ```python
+        # agent.py
+        from langchain_anthropic import ChatAnthropic
+        from langchain_community.tools.tavily_search import TavilySearchResults
+        from langgraph.prebuilt import create_react_agent
+
+        model = ChatAnthropic(model="claude-3-5-sonnet-20240620")
+
+        tools = [TavilySearchResults(max_results=2)]
+
+        # compiled graph
+        graph = create_react_agent(model, tools)
+        ```
+
+    === "Javascript"
+
+        ```ts
+        // agent.ts
+        import { ChatAnthropic } from "@langchain/anthropic";
+        import { TavilySearchResults } from "@langchain/community/tools/tavily_search";
+        import { createReactAgent } from "@langchain/langgraph/prebuilt";
+
+        const model = new ChatAnthropic({
+          model: "claude-3-5-sonnet-20240620",
+        });
+
+        const tools = [
+          new TavilySearchResults({ maxResults: 3, }),
+        ];
+
+        // compiled graph
+        export const graph = createReactAgent({ llm: model, tools });
+        ```
+
+1. __Specify dependencies__
+
+    === "Python"
+        You should add dependencies for your graph(s) to `requirements.txt`.
+
+    === "Javascript"
+        You should add dependencies for your graph(s) to `package.json`.
+
+    In this case we only require four packages for our graph to run:
+
+    === "Python"
+
+          ```python
+          langgraph
+          langchain_anthropic
+          tavily-python
+          langchain_community
+          ```
+
+    === "Javascript"
+
+          ```js
+          {
+            "name": "my-app",
+            "packageManager": "yarn@1.22.22",
+            "dependencies": {
+              "@langchain/community": "^0.3.11",
+              "@langchain/core": "^0.3.16",
+              "@langchain/langgraph": "0.2.18",
+              "@langchain/anthropic": "^0.3.7"
+            }
+          }
+          ```
+
+1. __Create LangGraph configuration file__
+
+    The [`langgraph.json`][langgraph.json] file is a configuration file that describes what graph(s) you are going to deploy. In this case we only have one graph: the compiled `graph` object from `agent.py` / `agent.ts`.
+
+    === "Python"
+
+        ```json
+        {
+          "dependencies": ["."],
+          "graphs": {
+            "agent": "./agent.py:graph"
+          },
+          "env": ".env"
         }
-      }
-      ```
+        ```
 
-4.  The [`langgraph.json`][langgraph.json] file is a configuration file that describes what graph(s) you are going to host. In this case we only have one graph to host: the compiled `graph` object from `agent.py`/`agent.ts`.
+    === "Javascript"
 
-=== "Python"
+        ```json
+        {
+          "node_version": "20",
+          "dockerfile_lines": [],
+          "dependencies": ["."],
+          "graphs": {
+            "agent": "./src/agent.ts:graph"
+          },
+          "env": ".env"
+        }
+        ```
 
-    ```json
-    {
-      "dependencies": ["."],
-      "graphs": {
-        "agent": "./agent.py:graph"
-      },
-      "env": ".env"
-    }
-    ```
+    Learn more about the LangGraph CLI configuration file [here](./reference/cli.md#configuration-file).
 
-=== "Javascript"
+1. __Specify environment variables__
 
-    ```json
-    {
-      "node_version": "20",
-      "dockerfile_lines": [],
-      "dependencies": ["."],
-      "graphs": {
-        "agent": "./src/agent.ts:graph"
-      },
-      "env": ".env"
-    }
-    ```
+    The `.env` file should have any environment variables needed to run your graph. This will only be used for local testing, so if you are not testing locally you can skip this step. 
 
-Learn more about the LangGraph CLI configuration file [here](./reference/cli.md#configuration-file).
+    !!! warning
+        The `.env` file should NOT be included with the rest of source code in your Github repository. When creating a deployment using LangGraph Cloud, you will be able to specify the environment variables manually.
 
-5.  The `.env` file should have any environment variables needed to run your graph. This will only be used for local testing, so if you are not testing locally you can skip this step. NOTE: if you do add this, you should NOT check this into git. For this graph, we need two environment variables:
+    For this graph, we need two environment variables:
 
     ```shell
     ANTHROPIC_API_KEY=...
     TAVILY_API_KEY=...
     ```
 
-Now that we have set everything up on our local file system, we are ready to host our graph.
+!!! tip
+    Learn more about different application structure options [here](../how-tos/index.md#application-structure).
 
-## Test the graph build locally
+Now that we have set everything up on our local file system, we are ready to test our graph locally.
 
-### Using LangGraph Studio Desktop (recommended)
+## Test the app locally
 
-![LangGraph Studio Desktop](./img/graph_video_poster.png)
+To test the LangGraph app before deploying it using LangGraph Cloud, you can start the [LangGraph server](../concepts/langgraph_server.md) locally or use [LangGraph Studio](../concepts/langgraph_studio.md).
 
-Testing your graph locally is easy with LangGraph Studio Desktop. LangGraph Studio offers a new way to develop LLM applications by providing a specialized agent IDE that enables visualization, interaction, and debugging of complex agentic applications
+## Using local server
 
-With visual graphs and the ability to edit state, you can better understand agent workflows and iterate faster. LangGraph Studio integrates with [LangSmith](https://smith.langchain.com) so you can collaborate with teammates to debug failure modes.
+You can test your app by running [LangGraph server](../concepts/langgraph_server.md) locally. This is useful to make sure you have configured our [CLI configuration file][langgraph.json] correctly and can interact with your graph.
 
-### Using the LangGraph CLI
-
-Before deploying to the cloud, we probably want to test the building of our graph locally. This is useful to make sure we have configured our [CLI configuration file][langgraph.json] correctly and our graph runs.
-
-In order to do this we can first install the LangGraph CLI
+To run the server locally, you need to first install the LangGraph CLI:
 
 ```shell
 pip install langgraph-cli
 ```
 
-We can then test our API server locally. This requires access to LangGraph closed beta. In order to run the server locally, you will need to add your `LANGSMITH_API_KEY` to the .env file so we can validate you have access to LangGraph closed beta.
+You can then test our API server locally. In order to run the server locally, you will need to add your `LANGSMITH_API_KEY` to the `.env` file.
 
 ```shell
 langgraph up
@@ -160,10 +199,21 @@ This will start up the LangGraph API server locally. If this runs successfully,
 ```shell
 Ready!
 - API: http://localhost:8123
-2024-06-26 19:20:41,056:INFO:uvicorn.access 127.0.0.1:44138 - "GET /ok HTTP/1.1" 200
 ```
 
-You can now test this out! **Note: this local server is intended SOLELY for local testing purposes and is not performant enough for production applications, so please do not use it as such.** To test it out, you can go to another terminal window and run:
+First, let's verify that the server is running correctly by calling `/ok` endpoint:
+
+```shell
+curl --request GET --url http://localhost:8123/ok
+```
+
+Output:
+
+```
+{"ok": "true"}
+```
+
+Now we're ready to test the app with the real inputs!
 
 ```shell
 curl --request POST \
@@ -175,36 +225,57 @@ curl --request POST \
         "messages": [
             {
                 "role": "user",
-                "content": "How are you?"
+                "content": "What is the weather in NYC?"
             }
         ]
     },
-    "metadata": {},
-    "config": {
-        "configurable": {}
-    },
-    "multitask_strategy": "reject",
-    "stream_mode": [
-        "values"
-    ]
+    "stream_mode": "updates"
 }'
 ```
 
-If you get back a valid response, then all is functioning properly!
+Output:
 
-## Deploy to Cloud
+```
+...
 
-### Push your code to GitHub
+data: {
+  "agent": {
+    "messages": [
+      {
+        "content": "The search results from Tavily provide the current weather conditions in New York City, including temperature, wind speed, precipitation, humidity, and cloud cover. According to the results, as of 3:00pm on October 30th, 2024, it is overcast in NYC with a temperature of around 66°F (19°C), light winds from the southwest around 8 mph (13 km/h), and 66% humidity.\n\nSo in summary, the current weather in NYC is overcast with mild temperatures in the mid 60sF and light winds, based on the search results. Let me know if you need any other details!",
+        "type": "ai",
+        ...
+      }
+    ]
+  }
+}
+```
 
-Turn the `<my-app>` directory into a GitHub repo. You can use the GitHub CLI if you like, or just create a repo manually (if unfamiliar, instructions [here](https://docs.github.com/en/migrations/importing-source-code/using-the-command-line-to-import-source-code/adding-locally-hosted-code-to-github)).
+You can see that our agent responds with the up-to-date search results!
 
-### Deploy from GitHub with LangGraph Cloud
+###  Using LangGraph Studio Desktop
 
-Once you have created your github repository with a Python file containing your compiled graph as well as a `langgraph.json` file containing the configuration for hosting your graph, you can head over to LangSmith and click on the 🚀 icon on the left navbar to create a new deployment. Then click the `+ New Deployment` button.
+You can also test your app locally with [LangGraph Studio](../concepts/langgraph_studio.md). LangGraph Studio offers a new way to develop LLM applications by providing a specialized agent IDE that enables visualization, interaction, and debugging of complex agentic applications.
 
-![Langsmith Workflow](./img/cloud_deployment.png)
+With visual graphs and the ability to edit state, you can better understand agent workflows and iterate faster. LangGraph Studio integrates with LangSmith allowing you to collaborate with teammates to debug failure modes.
 
-**_If you have not deployed to LangGraph Cloud before:_** there will be a button that shows up saying Import from GitHub. You’ll need to follow that flow to connect LangGraph Cloud to GitHub.
+LangGraph Studio is available as a [desktop app](https://studio.langchain.com/) for MacOS users. Once you have installed the app, you can select `my-app` directory, which will automatically start the server locally and load the graph in the UI.
+
+To interact with your chatbot agent in LangGraph Studio, you can add a new message in the `Input` section and press `Submit`.
+
+![LangGraph Studio Desktop](./deployment/img/quick_start_studio.png)
+
+## Deploy to LangGraph Cloud
+
+Once you've tested your graph locally and verified that it works as expected, you can deploy it to the LangGraph Cloud.
+
+First, you'll need to turn the `my-app` directory into a GitHub repo and [push it to GitHub](https://docs.github.com/en/migrations/importing-source-code/using-the-command-line-to-import-source-code/adding-locally-hosted-code-to-github).
+
+Once you have created your GitHub repository with a Python file containing your compiled graph as well as a `langgraph.json` with the configuration, you can head over to [LangSmith](https://smith.langchain.com/) and click on the graph icon (`LangGraph Cloud`) on the bottom of the left navbar. This will open the LangGraph deployments page. On this page, click the `+ New Deployment` button in the top right corner.
+
+![Langsmith Workflow](./deployment/img/cloud_deployment.png)
+
+**_If you have not deployed to LangGraph Cloud before:_** there will be a button that shows up saying `Import from GitHub`. You’ll need to follow that flow to connect LangGraph Cloud to GitHub.
 
 **_Once you have set up your GitHub connection:_** the new deployment page will look as follows:
 
@@ -213,53 +284,43 @@ Once you have created your github repository with a Python file containing your
 To deploy your application, you should do the following:
 
 1. Select your GitHub username or organization from the selector
-2. Search for your repo to deploy in the search bar and select it
-3. Choose any name
-4. In the `LangGraph API config file` field, enter the path to your `langgraph.json` file (which in this case is just `langgraph.json`)
-5. For Git Reference, you can select either the git branch for the code you want to deploy, or the exact commit SHA.
-6. If your chain relies on environment variables, add those in. They will be propagated to the underlying server so your code can access them. In this case, we need `ANTHROPIC_API_KEY` and `TAVILY_API_KEY`.
-
-Putting this all together, you should have something as follows for your deployment details:
-
-![Deployment filled out](./deployment/img/deploy_filled_out.png)
+1. Search for your repo to deploy in the search bar and select it
+1. Choose a name for your deployment
+1. In the `Git Branch` field, you can specify either the branch for the code you want to deploy, or the exact commit SHA.
+1. In the `LangGraph API config file` field, enter the path to your `langgraph.json` file (which in this case is just `langgraph.json`)
+1. If your application needs environment variables, add those in the `Environment Variables` section. They will be propagated to the underlying server so your code can access them. In this case, we will need `ANTHROPIC_API_KEY` and `TAVILY_API_KEY`.
 
 Hit `Submit` and your application will start deploying!
 
-## Inspect Traces + Monitor Service
-
-### Deployments View
-
 After your deployment is complete, your deployments page should look as follows:
 
 ![Deployed page](./deployment/img/deployed_page.png)
 
-You can see that by default, you get access to the `Trace Count` monitoring chart and `Recent Traces` run view. These are powered by LangSmith.
+## Interact with your deployment
 
-You can click on `All Charts` to view all monitoring info for your server, or click on `See tracing project` to get more information on an individual trace.
+### Using LangGraph Studio (Cloud)
 
-### Access the Docs
-
-You can access the docs by clicking on the API docs link, which should send you to a page that looks like this:
-
-![API Docs page](./deployment/img/api_page.png)
-
-You won’t actually be able to test any of the API endpoints without authorizing first. To do so, grab your Langsmith API key and add it at the top where it says `API KEY (X-API-KEY)`. You should now be able to select any of the API endpoints, click `Test Request`, enter the parameters you would like to pass, and then click `Send` to view the results of the API call.
-
-## Interact with your deployment via LangGraph Studio
-
-If you click on your deployment you should see a blue button in the top right that says `LangGraph Studio`. Clicking on this button will take you to a page that looks like this:
-
-![Studio UI before being run](./deployment/img/graph_visualization.png)
-
-On this page you can test out your graph by passing in starting states and clicking `Start Run` (this should behave identically to calling `.invoke`). You will then be able to look into the execution thread for each run and explore the steps your graph is taking to produce its output.
+On the deployment page for your application,, you should see a button in the top right corner that says `LangGraph Studio`. Clicking on this button will take you to the web version of LangGraph Studio. This is the same UI that you interacted with when [testing the app locally](#using-langgraph-studio-recommended), but instead of using a local LangGraph server, it uses the one from your LangGraph Cloud deployment.
 
 ![Studio UI once being run](./deployment/img/graph_run.png)
 
-## Use with the SDK
+### Using LangGraph SDK
 
-Once you have tested that your hosted graph works as expected using LangGraph Studio, you can start using your hosted graph all over your organization by using the LangGraph SDK. Let's see how we can access our hosted graph and execute our run from a python file.
+You can also interact with your deployed LangGraph application programmatically, using [LangGraph SDK](./reference/sdk/python_sdk_ref.md).
 
-First, make sure you have the SDK installed by calling `pip install langgraph_sdk`.
+First, make sure you have the SDK installed:
+
+=== "Python"
+
+    ```shell
+    pip install langgraph_sdk
+    ```
+
+=== "Javascript"
+
+    ```shell
+    yarn add @langchain/langgraph-sdk
+    ```
 
 Before using, you need to get the URL of your LangGraph deployment. You can find this in the `Deployment` view. Click the URL to copy it to the clipboard.
 
@@ -278,8 +339,8 @@ The first thing to do when using the SDK is to setup our client, access our assi
 
      client = get_client(url=<DEPLOYMENT_URL>)
      # get default assistant
-     assistants = await client.assistants.search()
-     assistant = [a for a in assistants if not a["config"]][0]
+     assistants = await client.assistants.search(metadata={"created_by": "system"})
+     assistant = assistants[0]
      # create thread
      thread = await client.threads.create()
      print(thread)
@@ -292,8 +353,8 @@ The first thing to do when using the SDK is to setup our client, access our assi
 
      const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
      // get default assistant
-     const assistants = await client.assistants.search();
-     const assistant = assistants.find(a => !a.config);
+     const assistants = await client.assistants.search({ metadata: {"created_by": "system"} })
+     const assistant = assistants[0];
      // create thread
      const thread = await client.threads.create();
      console.log(thread)
@@ -307,8 +368,9 @@ The first thing to do when using the SDK is to setup our client, access our assi
          --header 'Content-Type: application/json' \
          --data '{
              "limit": 10,
-             "offset": 0
-         }' | jq -c 'map(select(.config == null or .config == {})) | .[0]' && \
+             "offset": 0,
+             "metadata": {"created_by": "system"}
+         }' &&
      curl --request POST \
          --url <DEPLOYMENT_URL>/threads \
          --header 'Content-Type: application/json' \
@@ -320,32 +382,35 @@ We can then execute a run on the thread:
 === "Python"
 
     ```python
-    input = {"messages":[{"role": "user", "content": "Hello! My name is Bagatur and I am 26 years old."}]}
+    input = {
+        "messages": [{"role": "user", "content": "What is the weather in NYC?"}]
+    }
 
     async for chunk in client.runs.stream(
-            thread['thread_id'],
-            assistant["assistant_id"],
-            input=input,
-            stream_mode="updates",
-        ):
-        if chunk.data and chunk.event != "metadata":
+        thread["thread_id"],
+        assistant["assistant_id"],
+        input=input,
+        stream_mode="updates",
+    ):
+        if chunk.data:
             print(chunk.data)
     ```
 
 === "Javascript"
 
     ```js
-    const input = { "messages":[{ "role": "user", "content": "Hello! My name is Bagatur and I am 26 years old." }] };
+    const input = { "messages": [{ "role": "user", "content": "What is the weather in NYC?" }] };
 
     const streamResponse = client.runs.stream(
       thread["thread_id"],
       assistant["assistant_id"],
       {
         input,
+        streamMode: "updates"
       }
     );
     for await (const chunk of streamResponse) {
-      if (chunk.data && chunk.event !== "metadata" ) {
+      if (chunk.data) {
         console.log(chunk.data);
       }
     }
@@ -357,43 +422,41 @@ We can then execute a run on the thread:
     curl --request POST \
       --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
       --header 'Content-Type: application/json' \
-      --data "{
-        \"assistant_id\": <ASSISTANT_ID>,
-        \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"Hello! My name is Bagatur and I am 26 years old.\"}]},
-      }" | sed 's/\r$//' | awk '
-      /^event:/ { event = $2 }
-      /^data:/ {
-          json_data = substr($0, index($0, $2))
-
-          if (event != "metadata") {
-          print json_data
-          }
+      --data '{
+        "assistant_id": <ASSISTANT_ID>,
+        "input": {
+        "messages": [
+            {
+                "role": "user",
+                "content": "What is the weather in NYC?"
+            }
+          ]
+        },
+        "stream_mode": "updates"
       }'
     ```
 
-
 Output:
 
-    {'agent': {'messages': [{'content': "Hi Bagatur! It's nice to meet you. How can I assist you today?", 'additional_kwargs': {}, 'response_metadata': {'finish_reason': 'stop', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_9cb5d38cf7'}, 'type': 'ai', 'name': None, 'id': 'run-c89118b7-1b1e-42b9-a85d-c43fe99881cd', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}}
+```
+...
 
-## What's Next
+data: {
+  "agent": {
+    "messages": [
+      {
+        "content": "The search results from Tavily provide the current weather conditions in New York City, including temperature, wind speed, precipitation, humidity, and cloud cover. According to the results, as of 3:00pm on October 30th, 2024, it is overcast in NYC with a temperature of around 66°F (19°C), light winds from the southwest around 8 mph (13 km/h), and 66% humidity.\n\nSo in summary, the current weather in NYC is overcast with mild temperatures in the mid 60sF and light winds, based on the search results. Let me know if you need any other details!",
+        "type": "ai",
+        ...
+      }
+    ]
+  }
+}
+```
+
+## Next steps
 
 Congratulations! If you've worked your way through this tutorial you are well on your way to becoming a LangGraph Cloud expert. Here are some other resources to check out to help you out on the path to expertise:
 
-### LangGraph Cloud How-tos
-
-If you want to learn more about streaming from hosted graphs, check out the Streaming [how-to guides](how-tos/index.md#streaming).
-
-To learn more about double-texting and all the ways you can handle it in your application, read up on these [how-to guides](how-tos/index.md#double-texting).
-
-To learn about how to include different human-in-the-loop behavior in your graph, take a look at [these how-tos](how-tos/index.md#human-in-the-loop).
-
-### LangGraph Tutorials
-
-Before hosting, you have to write a graph to host. Here are some tutorials to get you more comfortable with writing LangGraph graphs and give you inspiration for the types of graphs you want to host.
-
-[This tutorial](../tutorials/customer-support/customer-support.ipynb) walks you through how to write a customer support bot using LangGraph.
-
-If you are interested in writing a SQL agent, check out [this tutorial](../tutorials/sql-agent.ipynb).
-
-Check out the [LangGraph tutorials](../tutorials/index.md) page to read about more exciting use cases.
+* [LangGraph How-to guides](../how-tos/index.md)
+* [LangGraph Tutorials](../tutorials/index.md)

docs/docs/cloud/reference/cli.md

@@ -1,23 +1,38 @@
 # LangGraph CLI
-The LangGraph CLI includes commands to build and run a LangGraph Cloud API server locally in [Docker](https://www.docker.com/). For development and testing, use the CLI to deploy a local API server.
+
+The LangGraph command line interface includes commands to build and run a LangGraph Cloud API server locally in [Docker](https://www.docker.com/). For development and testing, you can use the CLI to deploy a local API server as an alternative to the [Studio desktop app](../../concepts/langgraph_studio.md).
 
 ## Installation
+
 1. Ensure that Docker is installed (e.g. `docker --version`).
-2. Install the `langgraph-cli` Python package (e.g. `pip install langgraph-cli`).
+2. Install the `langgraph-cli` package:
+ 
+    === "pip"
+        ```bash    
+        pip install langgraph-cli
+        ```
+
+    === "Homebrew (MacOS only)"
+        ```bash
+        brew install langgraph-cli
+        ```
+ 
 3. Run the command `langgraph --help` to confirm that the CLI is installed.
 
 [](){#langgraph.json}
+
 ## Configuration File
+
 The LangGraph CLI requires a JSON configuration file with the following keys:
 
-| Key | Description |
-| --- | ----------- |
-| `dependencies` | **Required**. Array of dependencies for LangGraph Cloud API server. Dependencies can be one of the following: (1) `"."`, which will look for local Python packages, (2) `pyproject.toml`, `setup.py` or `requirements.txt` in the app directory `"./local_package"`, or (3) a package name. |
-| `graphs` | **Required**. Mapping from graph ID to path where the compiled graph or a function that makes a graph is defined. Example: <ul><li>`./your_package/your_file.py:variable`, where `variable` is an instance of `langgraph.graph.state.CompiledStateGraph`</li><li>`./your_package/your_file.py:make_graph`, where `make_graph` is a function that takes a config dictionary (`langchain_core.runnables.RunnableConfig`) and creates an instance of `langgraph.graph.state.StateGraph` / `langgraph.graph.state.CompiledStateGraph`.</li></ul> |
-| `env` | Path to `.env` file or a mapping from environment variable to its value. |
-| `python_version` | `3.11` or `3.12`. Defaults to `3.11`. |
-| `pip_config_file`| Path to `pip` config file. |
-| `dockerfile_lines` | Array of additional lines to add to Dockerfile following the import from parent image. |
+| Key                | Description                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
+|--------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| `dependencies`     | **Required**. Array of dependencies for LangGraph Cloud API server. Dependencies can be one of the following: (1) `"."`, which will look for local Python packages, (2) `pyproject.toml`, `setup.py` or `requirements.txt` in the app directory `"./local_package"`, or (3) a package name.                                                                                                                                                                                                                                                  |
+| `graphs`           | **Required**. Mapping from graph ID to path where the compiled graph or a function that makes a graph is defined. Example: <ul><li>`./your_package/your_file.py:variable`, where `variable` is an instance of `langgraph.graph.state.CompiledStateGraph`</li><li>`./your_package/your_file.py:make_graph`, where `make_graph` is a function that takes a config dictionary (`langchain_core.runnables.RunnableConfig`) and creates an instance of `langgraph.graph.state.StateGraph` / `langgraph.graph.state.CompiledStateGraph`.</li></ul> |
+| `env`              | Path to `.env` file or a mapping from environment variable to its value.                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
+| `python_version`   | `3.11` or `3.12`. Defaults to `3.11`.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+| `pip_config_file`  | Path to `pip` config file.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
+| `dockerfile_lines` | Array of additional lines to add to Dockerfile following the import from parent image.                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
 
 <div class="admonition tip">
     <p class="admonition-title">Note</p>
@@ -27,101 +42,162 @@ The LangGraph CLI requires a JSON configuration file with the following keys:
 </div>
 
 Example:
+
 ```json
 {
-    "dependencies": [
-        "langchain_openai",
-        "./your_package"
-    ],
-    "graphs": {
-        "my_graph_id": "./your_package/your_file.py:variable"
-    },
-    "env": "./.env"
+  "dependencies": ["langchain_openai", "./your_package"],
+  "graphs": {
+    "my_graph_id": "./your_package/your_file.py:variable"
+  },
+  "env": "./.env"
 }
 ```
 
-Example:
+Example with environment variables:
+
 ```json
 {
-    "python_version": "3.11",
-    "dependencies": [
-        "langchain_openai",
-        "."
-    ],
-    "graphs": {
-        "my_graph_id": "./your_package/your_file.py:make_graph"
-    },
-    "env": {
-        "OPENAI_API_KEY": "secret-key"
-    }
+  "python_version": "3.11",
+  "dependencies": ["langchain_openai", "."],
+  "graphs": {
+    "my_graph_id": "./your_package/your_file.py:make_graph"
+  },
+  "env": {
+    "OPENAI_API_KEY": "secret-key"
+  }
 }
 ```
 
 ## Commands
+
 The base command for the LangGraph CLI is `langgraph`.
 
 **Usage**
+
 ```
 langgraph [OPTIONS] COMMAND [ARGS]
 ```
 
+### `dev`
+
+Run LangGraph API server in development mode with hot reloading and debugging capabilities. This lightweight server requires no Docker installation and is suitable for development and testing. State is persisted to a local directory.
+
+**Installation**
+
+This command requires the "inmem" extra to be installed:
+
+```bash
+pip install -U "langgraph-cli[inmem]"
+```
+
+**Usage**
+
+```
+langgraph dev [OPTIONS]
+```
+
+**Options**
+
+| Option                       | Default          | Description                                                                                |
+|----------------------------|------------------|--------------------------------------------------------------------------------------------|
+| `-c, --config FILE`        | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables       |
+| `--host TEXT`              | `127.0.0.1`      | Host to bind the server to                                                                |
+| `--port INTEGER`           | `2024`           | Port to bind the server to                                                                |
+| `--no-reload`              |                  | Disable auto-reload                                                                        |
+| `--n-jobs-per-worker INTEGER` |               | Number of jobs per worker. Default is 10                                                  |
+| `--no-browser`             |                  | Disable automatic browser opening                                                          |
+| `--debug-port INTEGER`     |                  | Port for debugger to listen on                                                            |
+| `--help`                   |                  | Display command documentation                                                              |
+
 ### `build`
+
 Build LangGraph Cloud API server Docker image.
 
 **Usage**
+
 ```
 langgraph build [OPTIONS]
 ```
 
 **Options**
 
-| Option | Default | Description |
-| ------ | ------- | ----------- |
-| `--platform TEXT` | | Target platform(s) to build the Docker image for. Example: `langgraph build --platform linux/amd64,linux/arm64` |
-| `-t, --tag TEXT` | | **Required**. Tag for the Docker image. Example: `langgraph build -t my-image` |
-| `--pull / --no-pull` | `--pull` | Build with latest remote Docker image. Use `--no-pull` for running the LangGraph Cloud API server with locally built images. |
-| `-c, --config FILE` | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables. |
-| `--help` | | Display command documentation. |
+| Option               | Default          | Description                                                                                                                  |
+|----------------------|------------------|------------------------------------------------------------------------------------------------------------------------------|
+| `--platform TEXT`    |                  | Target platform(s) to build the Docker image for. Example: `langgraph build --platform linux/amd64,linux/arm64`              |
+| `-t, --tag TEXT`     |                  | **Required**. Tag for the Docker image. Example: `langgraph build -t my-image`                                               |
+| `--pull / --no-pull` | `--pull`         | Build with latest remote Docker image. Use `--no-pull` for running the LangGraph Cloud API server with locally built images. |
+| `-c, --config FILE`  | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables.                                         |
+| `--help`             |                  | Display command documentation.                                                                                               |
 
 ### `up`
-Start langgraph API server. For local testing, requires a LangSmith API key with access to LangGraph Cloud closed beta. Requires a license key for production use.
+
+Start LangGraph API server. For local testing, requires a LangSmith API key with access to LangGraph Cloud closed beta. Requires a license key for production use.
 
 **Usage**
+
 ```
 langgraph up [OPTIONS]
 ```
 
 **Options**
 
-| Option | Default | Description |
-| ------ | ------- | ----------- |
-| `--wait` | | Wait for services to start before returning. Implies --detach |
-| `--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. |
-| `--debugger-port INTEGER` | | Pull the debugger image locally and serve the UI on specified port |
-| `--verbose` | | Show more output from the server logs. |
-| `-c, --config FILE` | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables. |
-| `-d, --docker-compose FILE` | | Path to docker-compose.yml file with additional services to launch. |
-| `-p, --port INTEGER` | `8123` | Port to expose. Example: `langgraph test --port 8000` |
-| `--pull / --no-pull` | `pull` | Pull latest images. Use --no-pull for running the server with locally-built images. Example: `langgraph up --no-pull` |
-| `--recreate / --no-recreate` | `no-recreate` | Recreate containers even if their configuration and image haven't changed |
-| `--help` | | Display command documentation. | 
+| Option                       | Default                   | Description                                                                                                           |
+|------------------------------|---------------------------|-----------------------------------------------------------------------------------------------------------------------|
+| `--wait`                     |                           | Wait for services to start before returning. Implies --detach                                                         |
+| `--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.                                                                     |
+| `--debugger-port INTEGER`    |                           | Pull the debugger image locally and serve the UI on specified port                                                    |
+| `--verbose`                  |                           | Show more output from the server logs.                                                                                |
+| `-c, --config FILE`          | `langgraph.json`          | Path to configuration file declaring dependencies, graphs and environment variables.                                  |
+| `-d, --docker-compose FILE`  |                           | Path to docker-compose.yml file with additional services to launch.                                                   |
+| `-p, --port INTEGER`         | `8123`                    | Port to expose. Example: `langgraph up --port 8000`                                                                 |
+| `--pull / --no-pull`         | `pull`                    | Pull latest images. Use `--no-pull` for running the server with locally-built images. Example: `langgraph up --no-pull` |
+| `--recreate / --no-recreate` | `no-recreate`             | Recreate containers even if their configuration and image haven't changed                                             |
+| `--help`                     |                           | Display command documentation.                                                                                        |
 
-### `test`
-Test your LangGraph in the cloud. The only function you can call from the SDK after testing your graph is `client.runs.stream(thread_id=None, ...)`
+### `dockerfile`
+
+Generate a Dockerfile for building a LangGraph Cloud API server Docker image.
 
 **Usage**
+
 ```
-langgraph test [OPTIONS]
+langgraph dockerfile [OPTIONS] SAVE_PATH
 ```
 
 **Options**
 
-| Option | Default | Description |
-| ------ | ------- | ----------- |
-| `--verbose` | | Show more output from the server logs. |
-| `-c, --config FILE` | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables. |
-| `-p, --port INTEGER` | `8123` | Port to expose. Example: `langgraph test --port 8000` |
-| `--pull / --no-pull` | `pull` | Pull latest images. Use --no-pull for running the server with locally-built images. Example: `langgraph up --no-pull` |
-| `--help` | | Display command documentation. |
+| Option              | Default          | Description                                                                                                     |
+|---------------------|------------------|-----------------------------------------------------------------------------------------------------------------|
+| `-c, --config FILE` | `langgraph.json` | Path to the [configuration file](#configuration-file) declaring dependencies, graphs and environment variables. |
+| `--help`            |                  | Show this message and exit.                                                                                     |
+
+Example:
+
+```bash
+langgraph dockerfile -c langgraph.json Dockerfile
+```
+
+This generates a Dockerfile that looks similar to:
+
+```dockerfile
+FROM langchain/langgraph-api:3.11
+
+ADD ./pipconf.txt /pipconfig.txt
+
+RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt langchain_community langchain_anthropic langchain_openai wikipedia scikit-learn
+
+ADD ./graphs /deps/__outer_graphs/src
+RUN set -ex && \
+    for line in '[project]' \
+                'name = "graphs"' \
+                'version = "0.1"' \
+                '[tool.setuptools.package-data]' \
+                '"*" = ["**/*"]'; do \
+        echo "$line" >> /deps/__outer_graphs/pyproject.toml; \
+    done
+
+RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*
+
+ENV LANGSERVE_GRAPHS='{"agent": "/deps/__outer_graphs/src/agent.py:graph", "storm": "/deps/__outer_graphs/src/storm.py:graph"}'

docs/docs/concepts/application_structure.md

@@ -0,0 +1,167 @@
+# Application Structure
+
+!!! info "Prerequisites"
+
+    - [LangGraph Server](./langgraph_server.md)
+    - [LangGraph Glossary](./low_level.md)
+
+## Overview
+
+A LangGraph application consists of one or more graphs, a LangGraph API Configuration file (`langgraph.json`), a file that specifies dependencies, and an optional .env file that specifies environment variables.
+
+This guide shows a typical structure for a LangGraph application and shows how the required information to deploy a LangGraph application using the LangGraph Platform is specified.
+
+## Key Concepts
+
+To deploy using the LangGraph Platform, the following information should be provided:
+
+1. A [LangGraph API Configuration file](#configuration-file) (`langgraph.json`) that specifies the dependencies, graphs, environment variables to use for the application.
+2. The [graphs](#graphs) that implement the logic of the application.
+3. A file that specifies [dependencies](#dependencies) required to run the application.
+4. [Environment variable](#environment-variables) that are required for the application to run.
+
+## File Structure
+
+Below are examples of directory structures for Python and JavaScript applications:
+
+=== "Python (requirements.txt)"
+
+    ```plaintext
+    my-app/
+    ├── my_agent # all project code lies within here
+    │   ├── utils # utilities for your graph
+    │   │   ├── __init__.py
+    │   │   ├── tools.py # tools for your graph
+    │   │   ├── nodes.py # node functions for you graph
+    │   │   └── state.py # state definition of your graph
+    │   ├── requirements.txt # package dependencies
+    │   ├── __init__.py
+    │   └── agent.py # code for constructing your graph
+    ├── .env # environment variables
+    └── langgraph.json # configuration file for LangGraph
+    ```
+=== "Python (pyproject.toml)"
+
+    ```plaintext
+    my-app/
+    ├── my_agent # all project code lies within here
+    │   ├── utils # utilities for your graph
+    │   │   ├── __init__.py
+    │   │   ├── tools.py # tools for your graph
+    │   │   ├── nodes.py # node functions for you graph
+    │   │   └── state.py # state definition of your graph
+    │   ├── __init__.py
+    │   └── agent.py # code for constructing your graph
+    ├── .env # environment variables
+    ├── langgraph.json  # configuration file for LangGraph
+    └── pyproject.toml # dependencies for your project
+    ```
+
+=== "JS (package.json)"
+
+    ```plaintext
+    my-app/
+    ├── src # all project code lies within here
+    │   ├── utils # optional utilities for your graph
+    │   │   ├── tools.ts # tools for your graph
+    │   │   ├── nodes.ts # node functions for you graph
+    │   │   └── state.ts # state definition of your graph
+    │   └── agent.ts # code for constructing your graph
+    ├── package.json # package dependencies
+    ├── .env # environment variables
+    └── langgraph.json # configuration file for LangGraph
+    ```
+
+!!! note
+
+    The directory structure of a LangGraph application can vary depending on the programming language and the package manager used.
+
+
+## Configuration File
+
+The `langgraph.json` file is a JSON file that specifies the dependencies, graphs, environment variables, and other settings required to deploy a LangGraph application.
+
+The file supports specification of the following information:
+
+
+| Key                | Description                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
+|--------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| `dependencies`     | **Required**. Array of dependencies for LangGraph API server. Dependencies can be one of the following: (1) `"."`, which will look for local Python packages, (2) `pyproject.toml`, `setup.py` or `requirements.txt` in the app directory `"./local_package"`, or (3) a package name.                                                                                                                                                                                                                                                        |
+| `graphs`           | **Required**. Mapping from graph ID to path where the compiled graph or a function that makes a graph is defined. Example: <ul><li>`./your_package/your_file.py:variable`, where `variable` is an instance of `langgraph.graph.state.CompiledStateGraph`</li><li>`./your_package/your_file.py:make_graph`, where `make_graph` is a function that takes a config dictionary (`langchain_core.runnables.RunnableConfig`) and creates an instance of `langgraph.graph.state.StateGraph` / `langgraph.graph.state.CompiledStateGraph`.</li></ul> |
+| `env`              | Path to `.env` file or a mapping from environment variable to its value.                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
+| `python_version`   | `3.11` or `3.12`. Defaults to `3.11`.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+| `pip_config_file`  | Path to `pip` config file.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
+| `dockerfile_lines` | Array of additional lines to add to Dockerfile following the import from parent image.                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
+!!! tip
+
+    The LangGraph CLI defaults to using the configuration file **langgraph.json** in the current directory.
+
+
+### Examples
+
+=== "Python"
+
+    * The dependencies involve a custom local package and the `langchain_openai` package.
+    * A single graph will be loaded from the file `./your_package/your_file.py` with the variable `variable`.
+    * The environment variables are loaded from the `.env` file.
+
+    ```json
+    {
+        "dependencies": [
+            "langchain_openai",
+            "./your_package"
+        ],
+        "graphs": {
+            "my_agent": "./your_package/your_file.py:agent"
+        },
+        "env": "./.env"
+    }
+    ```
+
+=== "JavaScript"
+
+    * The dependencies will be loaded from a dependency file in the local directory (e.g., `package.json`).
+    * A single graph will be loaded from the file `./your_package/your_file.js` with the function `agent`.
+    * The environment variable `OPENAI_API_KEY` is set inline.
+
+    ```json
+    {
+        "dependencies": [
+            "."
+        ],
+        "graphs": {
+            "my_agent": "./your_package/your_file.js:agent"
+        },
+        "env": {
+            "OPENAI_API_KEY": "secret-key"
+        }
+    }
+    ```
+
+## Dependencies
+
+A LangGraph application may depend on other Python packages or JavaScript libraries (depending on the programming language in which the application is written).
+
+You will generally need to specify the following information for dependencies to be set up correctly:
+
+1. A file in the directory that specifies the dependencies (e.g., `requirements.txt`, `pyproject.toml`, or `package.json`).
+2. A `dependencies` key in the [LangGraph configuration file](#configuration-file) that specifies the dependencies required to run the LangGraph application.
+3. Any additional binaries or system libraries can be specified using `dockerfile_lines` key in the [LangGraph configuration file](#configuration-file).
+
+## Graphs
+
+Use the `graphs` key in the [LangGraph configuration file](#configuration-file) to specify which graphs will be available in the deployed LangGraph application.
+
+You can specify one or more graphs in the configuration file. Each graph is identified by a name (which should be unique) and a path for either: (1) the compiled graph or (2) a function that makes a graph is defined.
+
+## Environment Variables
+
+If you're working with a deployed LangGraph application locally, you can configure environment variables in the `env` key of the [LangGraph configuration file](#configuration-file).
+
+For a production deployment, you will typically want to configure the environment variables in the deployment environment.
+
+## Related
+
+Please see the following resources for more information:
+
+- How-to guides for [Application Structure](../how-tos/index.md#application-structure).

docs/docs/concepts/assistants.md

@@ -0,0 +1,37 @@
+# Assistants
+
+!!! info "Prerequisites"
+
+    - [LangGraph Server](./langgraph_server.md)
+
+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 agents for experimentation.
+* Assistants can be modified via LangGraph Studio, offering a no-code way to configure agents  (e.g., for business users). 
+
+Assistants build off the concept of ["configuration"](low_level.md#configuration). 
+While ["configuration"](low_level.md#configuration) is available in the open source LangGraph library as well,  assistants are only present in [LangGraph Platform](langgraph_platform.md).
+This is because Assistants are tightly coupled to your deployed graph, and so we can only make them available when we are also deploying the graphs.
+
+## 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](../cloud/reference/api/api_ref.html) and [this how-to](../cloud/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, let's imagine you have a general writing agent. You have created a general graph architecture that works well for writing. However, there are different types of writing, e.g. blogs vs tweets. In order to get the best performance on each use case, you need to make some minor changes to the models and prompts used. In this setup, you could create an assistant for each use case - one for blog writing and one for tweeting. These would share the same graph structure, but they may use different models and different prompts. Read [this how-to](../cloud/how-tos/assistant_versioning.md) to learn how you can use assistant versioning through both the [Studio](../concepts/langgraph_studio.md) and the SDK.
+
+![assistant versions](img/assistants.png)
+
+
+## Resources
+
+For more information on assistants, see the following resources:
+
+- [Assistants how-to guides](../how-tos/index.md#assistants)

docs/docs/concepts/bring_your_own_cloud.md

@@ -0,0 +1,54 @@
+# Bring Your Own Cloud (BYOC)
+
+!!! note Prerequisites
+
+    - [LangGraph Platform](./langgraph_platform.md)
+    - [Deployment Options](./deployment_options.md)
+
+## Architecture
+
+Split control plane (hosted by us) and data plane (hosted by you, managed by us).
+
+|                             | Control Plane                   | Data Plane                                    |
+|-----------------------------|---------------------------------|-----------------------------------------------|
+| What it does                | Manages deployments, revisions. | Runs your LangGraph graphs, stores your data. |
+| Where it is hosted          | LangChain Cloud account         | Your cloud account                            |
+| Who provisions and monitors | LangChain                       | LangChain                                     |
+
+LangChain has no direct access to the resources created in your cloud account, and can only interact with them via AWS APIs. Your data never leaves your cloud account / VPC at rest or in transit.
+
+![Architecture](img/byoc_architecture.png)
+
+## Requirements
+
+- You’re using AWS already.
+- You use `langgraph-cli` and/or [LangGraph Studio](./langgraph_studio.md) app to test graph locally.
+- You use `langgraph build` command to build image and then push it to your AWS ECR repository (`docker push`).
+
+## How it works
+
+- We provide you a [Terraform module](https://github.com/langchain-ai/terraform/tree/main/modules/langgraph_cloud_setup) which you run to set up our requirements
+    1. Creates an AWS role (which our control plane will later assume to provision and monitor resources)
+        - https://docs.aws.amazon.com/aws-managed-policy/latest/reference/AmazonVPCReadOnlyAccess.html
+            - Read VPCS to find subnets
+        - https://docs.aws.amazon.com/aws-managed-policy/latest/reference/AmazonECS_FullAccess.html
+            - Used to create/delete ECS resources for your LangGraph Cloud instances
+        - https://docs.aws.amazon.com/aws-managed-policy/latest/reference/SecretsManagerReadWrite.html
+            - Create secrets for your ECS resources
+        - https://docs.aws.amazon.com/aws-managed-policy/latest/reference/CloudWatchReadOnlyAccess.html
+            - Read CloudWatch metrics/logs to monitor your instances/push deployment logs
+        - https://docs.aws.amazon.com/aws-managed-policy/latest/reference/AmazonRDSFullAccess.html
+            - Provision `RDS` instances for your LangGraph Cloud instances
+    2. Either
+        - Tags an existing vpc / subnets as `langgraph-cloud-enabled`
+        - Creates a new vpc and subnets and tags them as `langgraph-cloud-enabled`
+- You create a LangGraph Cloud Project in `smith.langchain.com` providing
+    - the ID of the AWS role created in the step above
+    - the AWS ECR repo to pull the service image from
+- We provision the resources in your cloud account using the role above
+- We monitor those resources to ensure uptime and recovery from errors
+
+Notes for customers using [self-hosted LangSmith](https://docs.smith.langchain.com/self_hosting):
+
+- Creation of new LangGraph Cloud projects and revisions currently needs to be done on smith.langchain.com.
+- You can however set up the project to trace to your self-hosted LangSmith instance if desired

docs/docs/concepts/deployment_options.md

@@ -0,0 +1,96 @@
+# Deployment Options
+
+!!! info "Prerequisites"
+
+    - [LangGraph Platform](./langgraph_platform.md)
+    - [LangGraph Server](./langgraph_server.md)
+    - [LangGraph Platform Plans](./plans.md)
+
+## Overview
+
+There are 4 main options for deploying with the LangGraph Platform:
+
+1. **[Self-Hosted Lite](#self-hosted-lite)**: Available for all plans.
+
+2. **[Self-Hosted Enterprise](#self-hosted-enterprise)**: Available for the **Enterprise** plan.
+
+3. **[Cloud SaaS](#cloud-saas)**: Available for **Plus** and **Enterprise** plans.
+
+4. **[Bring Your Own Cloud](#bring-your-own-cloud)**: Available only for **Enterprise** plans and **only on AWS**.
+
+Please see the [LangGraph Platform Plans](./plans.md) for more information on the different plans.
+
+The guide below will explain the differences between the deployment options.
+
+## Self-Hosted Enterprise
+
+!!! important
+
+    The Self-Hosted Enterprise version is only available for the **Enterprise** plan.
+
+With a Self-Hosted Enterprise deployment, you are responsible for managing the infrastructure, including setting up and maintaining required databases and Redis instances.
+
+You’ll build a Docker image using the [LangGraph CLI](./langgraph_cli.md), which can then be deployed on your own infrastructure.
+
+For more information, please see:
+
+* [Self-Hosted conceptual guide](./self_hosted.md)
+* [Self-Hosted Deployment how-to guide](../how-tos/deploy-self-hosted.md)
+
+## Self-Hosted Lite
+
+!!! important
+
+    The Self-Hosted Lite version is available for all plans.
+
+The Self-Hosted Lite deployment option is a free (up to 1 million nodes executed), limited version of LangGraph Platform that you can run locally or in a self-hosted manner.
+
+With a Self-Hosted Lite deployment, you are responsible for managing the infrastructure, including setting up and maintaining required databases and Redis instances.
+
+You’ll build a Docker image using the [LangGraph CLI](./langgraph_cli.md), which can then be deployed on your own infrastructure.
+
+
+For more information, please see:
+
+* [Self-Hosted conceptual guide](./self_hosted.md)
+* [Self-Hosted deployment how-to guide](../how-tos/deploy-self-hosted.md)
+
+## Cloud SaaS
+
+!!! important
+
+    The Cloud SaaS version of LangGraph Platform is only available for **Plus** and **Enterprise** plans.
+
+
+The [Cloud SaaS](./langgraph_cloud.md) version of LangGraph Platform is hosted as part of [LangSmith](https://smith.langchain.com/).
+
+The Cloud SaaS version of LangGraph Platform provides a simple way to deploy and manage your LangGraph applications.
+
+This deployment option provides an integration with GitHub, allowing you to deploy code from any of your repositories on GitHub.
+
+For more information, please see:
+
+* [Cloud SaaS Conceptual Guide](./langgraph_cloud.md)
+* [How to deploy to Cloud SaaS](../cloud/deployment/cloud.md)
+
+
+## Bring Your Own Cloud
+
+!!! important
+
+    The Bring Your Own Cloud version of LangGraph Platform is only available for **Enterprise** plans.
+
+
+This combines the best of both worlds for Cloud and Self-Hosted. We manage the infrastructure, so you don't have to, but the infrastructure all runs within your cloud. This is currently only available on AWS.
+
+For more information please see:
+
+* [Bring Your Own Cloud Conceptual Guide](./bring_your_own_cloud.md)
+
+## Related
+
+For more information, please see:
+
+* [LangGraph Platform plans](./plans.md)
+* [LangGraph Platform pricing](https://www.langchain.com/langgraph-platform-pricing)
+* [Deployment how-to guides](../how-tos/index.md#deployment)

docs/docs/concepts/double_texting.md

@@ -0,0 +1,42 @@
+# Double Texting
+
+!!! info "Prerequisites"
+    - [LangGraph Server](./langgraph_server.md)
+
+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. 
+More generally, users may invoke the graph a second time before the first run has finished.
+We call this "double texting".
+
+Currently, LangGraph only addresses this as part of [LangGraph Platform](langgraph_platform.md), not in the open source.
+The reason for this is that in order to handle this we need to know how the graph is deployed, and since LangGraph Platform deals with deployment the logic needs to live there.
+If you do not want to use LangGraph Platform, we describe the options we have implemented in detail below.
+
+![](img/double_texting.png)
+
+## Reject
+
+This is the simplest option, this just rejects any follow up runs and does not allow double texting. 
+See the [how-to guide](../cloud/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](../cloud/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. 
+For example, you could have called a tool but not yet gotten back a result from running that tool.
+You may need to remove that tool call in order to not have a dangling tool call.
+
+See the [how-to guide](../cloud/how-tos/interrupt_concurrent.md) for configuring the interrupt double text option.
+
+## Rollback
+
+This option interrupts the current execution AND rolls back all work done up until that point, including the original run input. It then sends the new user input in, basically as if it was the original input.
+
+See the [how-to guide](../cloud/how-tos/rollback_concurrent.md) for configuring the rollback double text option.

docs/docs/concepts/faq.md

@@ -2,9 +2,58 @@
 
 Common questions and their answers!
 
-## Do I need to use LangChain in order to use LangGraph?
+## Do I need to use LangChain to use LangGraph? What’s the difference?
 
-No! LangGraph is a general-purpose framework - the nodes and edges are nothing more than Python functions. You can use LangChain, raw HTTP requests, or even other frameworks inside these nodes and edges.
+No. LangGraph is an orchestration framework for complex agentic systems and is more low-level and controllable than LangChain agents. LangChain provides a standard interface to interact with models and other components, useful for straight-forward chains and retrieval flows.
+
+## How is LangGraph different from other agent frameworks?
+
+Other agentic frameworks can work for simple, generic tasks but fall short for complex tasks bespoke to a company’s needs. LangGraph provides a more expressive framework to handle companies’ unique tasks without restricting users to a single black-box cognitive architecture.
+
+## Does LangGraph impact the performance of my app?
+
+LangGraph will not add any overhead to your code and is specifically designed with streaming workflows in mind.
+
+## Is LangGraph open source? Is it free?
+
+Yes. LangGraph is an MIT-licensed open-source library and is free to use.
+
+## How are LangGraph and LangGraph Platform different?
+
+LangGraph is a stateful, orchestration framework that brings added control to agent workflows. LangGraph Platform is a service for deploying and scaling LangGraph applications, with an opinionated API for building agent UXs, plus an integrated developer studio.
+
+| Features | LangGraph (open source) | LangGraph Platform |
+|----------|------------------------|-------------------|
+| Description | Stateful orchestration framework for agentic applications | Scalable infrastructure for deploying LangGraph applications |
+| SDKs | Python and JavaScript | Python and JavaScript |
+| HTTP APIs | None | Yes - useful for retrieving & updating state or long-term memory, or creating a configurable assistant |
+| Streaming | Basic | Dedicated mode for token-by-token messages |
+| Checkpointer | Community contributed | Supported out-of-the-box |
+| Persistence Layer | Self-managed | Managed Postgres with efficient storage |
+| Deployment | Self-managed | • Cloud SaaS <br> • Free self-hosted <br> • Enterprise (BYOC or paid self-hosted) |
+| Scalability | Self-managed | Auto-scaling of task queues and servers |
+| Fault-tolerance | Self-managed | Automated retries |
+| Concurrency Control | Simple threading | Supports double-texting |
+| Scheduling | None | Cron scheduling |
+| Monitoring | None | Integrated with LangSmith for observability |
+| IDE integration | LangGraph Studio for Desktop | LangGraph Studio for Desktop & Cloud |
+
+## What are my deployment options for LangGraph Platform?
+
+We currently have the following deployment options for LangGraph applications:
+
+- [‍Self-Hosted Lite](./deployment_options.md#self-hosted-lite): A free (up to 1M nodes executed), limited version of LangGraph Platform that you can run locally or in a self-hosted manner. This version requires a LangSmith API key and logs all usage to LangSmith. Fewer features are available than in paid plans.
+- [Cloud SaaS](./deployment_options.md#cloud-saas): Fully managed and hosted as part of LangSmith, with automatic updates and zero maintenance.
+- [‍Bring Your Own Cloud (BYOC)](./deployment_options.md#bring-your-own-cloud): Deploy LangGraph Platform within your VPC, provisioned and run as a service. Keep data in your environment while outsourcing the management of the service.
+- [Self-Hosted Enterprise](./deployment_options.md#self-hosted-enterprise): Deploy LangGraph entirely on your own infrastructure.
+
+## Is LangGraph Platform open source?
+
+No. LangGraph Platform is proprietary software.
+
+There is a free, self-hosted version of LangGraph Platform with access to basic features. The Cloud SaaS deployment option is free while in beta, but will eventually be a paid service. We will always give ample notice before charging for a service and reward our early adopters with preferential pricing. The Bring Your Own Cloud (BYOC) and Self-Hosted Enterprise options are also paid services. [Contact our sales team](https://www.langchain.com/contact-sales) to learn more.
+
+For more information, see our [LangGraph Platform pricing page](https://www.langchain.com/pricing-langgraph-platform).
 
 ## Does LangGraph work with LLMs that don't support tool calling?
 

docs/docs/concepts/high_level.md

@@ -2,13 +2,13 @@
 
 LLMs are extremely powerful, particularly when connected to other systems such as a retriever or APIs. This is why many LLM applications use a control flow of steps before and / or after LLM calls. As an example [RAG](https://github.com/langchain-ai/rag-from-scratch) performs retrieval of relevant documents to a question, and passes those documents to an LLM in order to ground the response. Often a control flow of steps before and / or after an LLM is called a "chain." Chains are a popular paradigm for programming with LLMs and offer a high degree of reliability; the same set of steps runs with each chain invocation.
 
-However, we often want LLM systems that can pick their own control flow! This is one definition of an [agent](https://blog.langchain.dev/what-is-an-agent/): an agent is a system that uses an LLM to decide the control flow of an application. Unlike a chain, an agent given an LLM some degree of control over the sequence of steps in the application. Examples of using an LLM to decide the control of an application:
+However, we often want LLM systems that can pick their own control flow! This is one definition of an [agent](https://blog.langchain.dev/what-is-an-agent/): an agent is a system that uses an LLM to decide the control flow of an application. Unlike a chain, an agent gives an LLM some degree of control over the sequence of steps in the application. Examples of using an LLM to decide the control of an application:
 
 - Using an LLM to route between two potential paths
 - Using an LLM to decide which of many tools to call
 - Using an LLM to decide whether the generated answer is sufficient or more work is need
 
-There are many different types of [agent architectures](https://blog.langchain.dev/what-is-a-cognitive-architecture/) to consider, which given an LLM varying levels of control. On one extreme, a router allows an LLM to select a single step from a specified set of options and, on the other extreme, a fully autonomous long-running agent may have complete freedom to select any sequence of steps that it wants for a given problem. 
+There are many different types of [agent architectures](https://blog.langchain.dev/what-is-a-cognitive-architecture/) to consider, which give an LLM varying levels of control. On one extreme, a router allows an LLM to select a single step from a specified set of options and, on the other extreme, a fully autonomous long-running agent may have complete freedom to select any sequence of steps that it wants for a given problem. 
 
 ![Agent Types](img/agent_types.png)
 
@@ -55,4 +55,4 @@ Once you've built a graph, you often want to test and debug it. [LangGraph Studi
 
 ## Deployment
 
-Once you have confidence in your LangGraph application, many developers want an easy path to deployment. [LangGraph Cloud](../cloud/index.md) is an opinionated, simple way to deploy LangGraph objects from the LangChain team. Of course, you can also use services like [FastAPI](https://fastapi.tiangolo.com/) and call your graph from inside the FastAPI server as you see fit.
+Once you have confidence in your LangGraph application, many developers want an easy path to deployment. [LangGraph Platform](../concepts/index.md#langgraph-platform) offers a range of options for deploying LangGraph graphs.

docs/docs/concepts/index.md

@@ -0,0 +1,76 @@
+---
+hide:
+  - navigation
+title: Concepts
+description: Conceptual Guide for LangGraph
+---
+
+# Conceptual Guide
+
+This guide provides explanations of the key concepts behind the LangGraph framework and AI applications more broadly.
+
+We recommend that you go through at least the [Quick Start](../tutorials/introduction.ipynb) before diving into the conceptual guide. This will provide practical context that will make it easier to understand the concepts discussed here.
+
+The conceptual guide does not cover step-by-step instructions or specific implementation examples — those are found in the [Tutorials](../tutorials/index.md) and [How-to guides](../how-tos/index.md). For detailed reference material, please see the [API reference](../reference/index.md).
+
+## LangGraph
+
+**High Level**
+
+- [Why LangGraph?](high_level.md): A high-level overview of LangGraph and its goals.
+
+**Concepts**
+
+- [LangGraph Glossary](low_level.md): LangGraph workflows are designed as graphs, with nodes representing different components and edges representing the flow of information between them. This guide provides an overview of the key concepts associated with LangGraph graph primitives.
+- [Common Agentic Patterns](agentic_concepts.md): An agent uses an LLM to pick its own control flow to solve more complex problems! Agents are a key building block in many LLM applications. This guide explains the different types of agent architectures and how they can be used to control the flow of an application.
+- [Multi-Agent Systems](multi_agent.md): Complex LLM applications can often be broken down into multiple agents, each responsible for a different part of the application. This guide explains common patterns for building multi-agent systems.
+- [Human-in-the-Loop](human_in_the_loop.md): Explains different ways of integrating human feedback into a LangGraph application.
+- [Persistence](persistence.md): LangGraph has a built-in persistence layer, implemented through checkpointers. This persistence layer helps to support powerful capabilities like human-in-the-loop, memory, time travel, and fault-tolerance.
+- [Memory](memory.md): Memory in AI applications refers to the ability to process, store, and effectively recall information from past interactions. With memory, your agents can learn from feedback and adapt to users' preferences.  
+- [Streaming](streaming.md): Streaming is crucial for enhancing the responsiveness of applications built on LLMs. By displaying output progressively, even before a complete response is ready, streaming significantly improves user experience (UX), particularly when dealing with the latency of LLMs. 
+- [FAQ](faq.md): Frequently asked questions about LangGraph.
+
+## LangGraph Platform 
+
+LangGraph Platform is a commercial solution for deploying agentic applications in production, built on the open-source LangGraph framework.
+
+The LangGraph Platform offers a few different deployment options described in the [deployment options guide](./deployment_options.md).
+
+
+!!! tip
+
+    * LangGraph is an MIT-licensed open-source library, which we are committed to maintaining and growing for the community.
+    * You can always deploy LangGraph applications on your own infrastructure using the open-source LangGraph project without using LangGraph Platform.
+
+### High Level
+
+- [Why LangGraph Platform?](./langgraph_platform.md): The LangGraph platform is an opinionated way to deploy and manage LangGraph applications. This guide provides an overview of the key features and concepts behind LangGraph Platform.
+- [Deployment Options](./deployment_options.md): LangGraph Platform offers four deployment options: [Self-Hosted Lite](./self_hosted.md#self-hosted-lite), [Self-Hosted Enterprise](./self_hosted.md#self-hosted-enterprise), [bring your own cloud (BYOC)](./bring_your_own_cloud.md), and [Cloud SaaS](./langgraph_cloud.md). This guide explains the differences between these options, and which Plans they are available on.
+- [Plans](./plans.md): LangGraph Platforms offer three different plans: Developer, Plus, Enterprise. This guide explains the differences between these options, what deployment options are available for each, and how to sign up for each one.
+- [Template Applications](./template_applications.md): Reference applications designed to help you get started quickly when building with LangGraph.
+
+### Components
+
+The LangGraph Platform comprises several components that work together to support the deployment and management of LangGraph applications:
+
+- [LangGraph Server](./langgraph_server.md): The LangGraph Server is designed to support a wide range of agentic application use cases, from background processing to real-time interactions. 
+- [LangGraph Studio](./langgraph_studio.md): LangGraph Studio is a specialized IDE that can connect to a LangGraph Server to enable visualization, interaction, and debugging of the application locally.
+- [LangGraph CLI](./langgraph_cli.md): LangGraph CLI is a command-line interface that helps to interact with a local LangGraph
+- [Python/JS SDK](./sdk.md): The Python/JS SDK provides a programmatic way to interact with deployed LangGraph Applications.
+- [Remote Graph](../how-tos/use-remote-graph.md): A RemoteGraph allows you to interact with any deployed LangGraph application as though it were running locally.
+
+### LangGraph Server
+
+- [Application Structure](./application_structure.md): A LangGraph application consists of one or more graphs, a LangGraph API Configuration file (`langgraph.json`), a file that specifies dependencies, and environment variables.
+- [Assistants](./assistants.md): Assistants are a way to save and manage different configurations of your LangGraph applications.
+- [Web-hooks](./langgraph_server.md#webhooks): Webhooks allow your running LangGraph application to send data to external services on specific events.
+- [Cron Jobs](./langgraph_server.md#cron-jobs): Cron jobs are a way to schedule tasks to run at specific times in your LangGraph application.
+- [Double Texting](./double_texting.md): Double texting is a common issue in LLM applications where users may send multiple messages before the graph has finished running. This guide explains how to handle double texting with LangGraph Deploy.
+
+### Deployment Options
+
+
+- [Self-Hosted Lite](./self_hosted.md): A free (up to 1 million nodes executed), limited version of LangGraph Platform that you can run locally or in a self-hosted manner
+- [Cloud SaaS](./langgraph_cloud.md): Hosted as part of LangSmith.
+- [Bring Your Own Cloud](./bring_your_own_cloud.md): We manage the infrastructure, so you don't have to, but the infrastructure all runs within your cloud.
+- [Self-Hosted Enterprise](./self_hosted.md): Completely managed by you.

docs/docs/concepts/langgraph_cli.md

@@ -0,0 +1,62 @@
+# LangGraph CLI
+
+!!! info "Prerequisites"
+    - [LangGraph Platform](./langgraph_platform.md)
+    - [LangGraph Server](./langgraph_server.md)
+
+The LangGraph CLI is a multi-platform command-line tool for building and running the [LangGraph API server](./langgraph_server.md) locally. This offers an alternative to the [LangGraph Studio desktop app](./langgraph_studio.md) for developing and testing agents across all major operating systems (Linux, Windows, MacOS). The resulting server includes all API endpoints for your graph's runs, threads, assistants, etc. as well as the other services required to run your agent, including a managed database for checkpointing and storage.
+
+## Installation
+
+The LangGraph CLI can be installed via Homebrew (on macOS) or pip:
+
+=== "Homebrew"
+    ```bash
+    brew install langgraph-cli
+    ```
+
+=== "pip" 
+    ```bash
+    pip install langgraph-cli
+    ```
+
+## Commands
+
+The CLI provides the following core functionality:
+
+### `build`
+
+The `langgraph build` command builds a Docker image for the [LangGraph API server](./langgraph_server.md) that can be directly deployed.
+
+### `dev`
+
+!!! note "New in version 0.1.55"
+    The `langgraph dev` command was introduced in langgraph-cli version 0.1.55.
+
+The `langgraph dev` command starts a lightweight development server that requires no Docker installation. This server is ideal for rapid development and testing, with features like:
+
+- Hot reloading: Changes to your code are automatically detected and reloaded
+- Debugger support: Attach your IDE's debugger for line-by-line debugging
+- In-memory state with local persistence: Server state is stored in memory for speed but persisted locally between restarts
+
+To use this command, you need to install the CLI with the "inmem" extra:
+
+```bash
+pip install -U "langgraph-cli[inmem]"
+```
+
+**Note**: This command is intended for local development and testing only. It is not recommended for production use. Since it does not use Docker, we recommend using virtual environments to manage your project's dependencies.
+
+### `up`
+
+The `langgraph up` command starts an instance of the [LangGraph API server](./langgraph_server.md) locally in a docker container. This requires thedocker server to be running locally. It also requires a LangSmith API key for local development or a license key for production use.
+
+The server includes all API endpoints for your graph's runs, threads, assistants, etc. as well as the other services required to run your agent, including a managed database for checkpointing and storage.
+
+### `dockerfile`
+
+The `langgraph dockerfile` command generates a [Dockerfile](https://docs.docker.com/reference/dockerfile/) that can be used to build images for and deploy instances of the [LangGraph API server](./langgraph_server.md). This is useful if you want to further customize the dockerfile or deploy in a more custom way.
+
+## Related
+
+- [LangGraph CLI API Reference](../cloud/reference/cli.md)

docs/docs/concepts/langgraph_cloud.md

@@ -0,0 +1,38 @@
+# Cloud SaaS
+
+!!! info "Prerequisites" 
+    - [LangGraph Platform](./langgraph_platform.md) 
+    - [LangGraph Server](./langgraph_server.md)
+
+## Overview
+
+LangGraph's Cloud SaaS is a managed service for deploying LangGraph APIs, regardless of its definition or dependencies. The service offers managed implementations of checkpointers and stores, allowing you to focus on building the right cognitive architecture for your use case. By handling scalable & secure infrastructure, LangGraph Cloud offers the fastest path to getting your LangGraph API deployed to production.
+
+## Deployment
+
+A **deployment** is an instance of a LangGraph API. A single deployment can have many [revisions](#revision). When a deployment is created, all 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](../cloud/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](../cloud/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 Cloud SaaS deployment architecture may change in the future.
+
+A high-level diagram of a Cloud SaaS deployment.
+
+![diagram](img/langgraph_cloud_architecture.png)
+
+## Related
+
+- [Deployment Options](./deployment_options.md)

docs/docs/concepts/langgraph_platform.md

@@ -0,0 +1,62 @@
+# LangGraph Platform
+
+## Overview
+
+LangGraph Platform is a commercial solution for deploying agentic applications to production, built on the open-source [LangGraph framework](./high_level.md).
+
+The LangGraph Platform consists of several components that work together to support the development, deployment, debugging, and monitoring of LangGraph applications:
+
+- [LangGraph Server](./langgraph_server.md): The server defines an opinionated API and architecture that incorporates best practices for deploying agentic applications, allowing you to focus on building your agent logic rather than developing server infrastructure.
+- [LangGraph Studio](./langgraph_studio.md): LangGraph Studio is a specialized IDE that can connect to a LangGraph Server to enable visualization, interaction, and debugging of the application locally.
+- [LangGraph CLI](./langgraph_cli.md): LangGraph CLI is a command-line interface that helps to interact with a local LangGraph
+- [Python/JS SDK](./sdk.md): The Python/JS SDK provides a programmatic way to interact with deployed LangGraph Applications.
+- [Remote Graph](../how-tos/use-remote-graph.md): A RemoteGraph allows you to interact with any deployed LangGraph application as though it were running locally.
+
+![](img/lg_platform.png)
+
+The LangGraph Platform offers a few different deployment options described in the [deployment options guide](./deployment_options.md).
+
+## Why Use LangGraph Platform?
+
+LangGraph Platform is designed to make deploying agentic applications seamless and production-ready. 
+
+For simpler applications, deploying a LangGraph agent can be as straightforward as using your own server logic—for example, setting up a FastAPI endpoint and invoking LangGraph directly.
+
+### Option 1: Deploying with Custom Server Logic
+
+For basic LangGraph applications, you may choose to handle deployment using your custom server infrastructure. Setting up endpoints with frameworks like [FastAPI](https://fastapi.tiangolo.com/) allows you to quickly deploy and run LangGraph as you would any other Python application:
+
+```python
+from fastapi import FastAPI
+from your_agent_package import graph
+
+app = FastAPI()
+
+@app.get("/foo")
+async def foo(...):
+    return await graph.ainvoke({...})
+```
+
+This approach works well for simple applications with straightforward needs and provides you with full control over the deployment setup. For example, you might use this for a single-assistant application that doesn’t require long-running sessions or persistent memory.
+
+### Option 2: Leveraging LangGraph Platform for Complex Deployments
+
+As your applications scale or add complex features, the deployment requirements often evolve. Running an application with more nodes, longer processing times, or a need for persistent memory can introduce challenges that quickly become time-consuming and difficult to manage manually. [LangGraph Platform](./langgraph_platform.md) is built to handle these challenges seamlessly, allowing you to focus on agent logic rather than server infrastructure.
+
+Here are some common issues that arise in complex deployments, which LangGraph Platform addresses:
+
+- **[Streaming Support](streaming.md)**: As agents grow more sophisticated, they often benefit from streaming both token outputs and intermediate states back to the user. Without this, users are left waiting for potentially long operations with no feedback. LangGraph Server provides [multiple streaming modes](streaming.md) optimized for various application needs.
+
+- **Background Runs**: For agents that take longer to process (e.g., hours), maintaining an open connection can be impractical. The LangGraph Server supports launching agent runs in the background and provides both polling endpoints and webhooks to monitor run status effectively.
+ 
+- **Support for long runs**: Vanilla server setups often encounter timeouts or disruptions when handling requests that take a long time to complete. LangGraph Server’s API provides robust support for these tasks by sending regular heartbeat signals, preventing unexpected connection closures during prolonged processes.
+
+- **Handling Burstiness**: Certain applications, especially those with real-time user interaction, may experience "bursty" request loads where numerous requests hit the server simultaneously. LangGraph Server includes a task queue, ensuring requests are handled consistently without loss, even under heavy loads.
+
+- **[Double Texting](double_texting.md)**: In user-driven applications, it’s common for users to send multiple messages rapidly. This “double texting” can disrupt agent flows if not handled properly. LangGraph Server offers built-in strategies to address and manage such interactions.
+
+- **[Checkpointers and Memory Management](persistence.md#checkpoints)**: For agents needing persistence (e.g., conversation memory), deploying a robust storage solution can be complex. LangGraph Platform includes optimized [checkpointers](persistence.md#checkpoints) and a [memory store](persistence.md#memory-store), managing state across sessions without the need for custom solutions.
+
+- **[Human-in-the-loop Support](human_in_the_loop.md)**: In many applications, users require a way to intervene in agent processes. LangGraph Server provides specialized endpoints for human-in-the-loop scenarios, simplifying the integration of manual oversight into agent workflows.
+
+By using LangGraph Platform, you gain access to a robust, scalable deployment solution that mitigates these challenges, saving you the effort of implementing and maintaining them manually. This allows you to focus more on building effective agent behavior and less on solving deployment infrastructure issues.

docs/docs/concepts/langgraph_server.md

@@ -0,0 +1,130 @@
+# LangGraph Server
+
+!!! info "Prerequisites"
+    - [LangGraph Platform](./langgraph_platform.md)
+    - [LangGraph Glossary](low_level.md)
+
+## Overview
+
+LangGraph Server offers an API for creating and managing agent-based applications. It is built on the concept of [assistants](assistants.md), which are agents configured for specific tasks, and includes built-in [persistence](persistence.md#memory-store) and a **task queue**. This versatile API supports a wide range of agentic application use cases, from background processing to real-time interactions.
+
+## Key Features
+
+The LangGraph Platform incorporates best practices for agent deployment, so you can focus on building your agent logic.
+
+* **Streaming endpoints**: Endpoints that expose [multiple different streaming modes](streaming.md). We've made these work even for long-running agents that may go minutes between consecutive stream events.
+* **Background runs**: The LangGraph Server supports launching assistants in the background with endpoints for polling the status of the assistant's run and webhooks to monitor run status effectively.
+- **Support for long runs**: Our blocking endpoints for running assistants send regular heartbeat signals, preventing unexpected connection closures when handling requests that take a long time to complete.
+* **Task queue**: We've added a task queue to make sure we don't drop any requests if they arrive in a bursty nature.
+* **Horizontally scalable infrastructure**: LangGraph Server is designed to be horizontally scalable, allowing you to scale up and down your usage as needed.
+* **Double texting support**: 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. We call this ["double texting"](double_texting.md) and have added four different ways to handle this.
+* **Optimized checkpointer**: LangGraph Platform comes with a built-in [checkpointer](./persistence.md#checkpoints) optimized for LangGraph applications.
+* **Human-in-the-loop endpoints**: We've exposed all endpoints needed to support [human-in-the-loop](human_in_the_loop.md) features.
+* **Memory**: In addition to thread-level persistence (covered above by [checkpointers]l(./persistence.md#checkpoints)), LangGraph Platform also comes with a built-in [memory store](persistence.md#memory-store).
+* **Cron jobs**: Built-in support for scheduling tasks, enabling you to automate regular actions like data clean-up or batch processing within your applications.
+* **Webhooks**: Allows your application to send real-time notifications and data updates to external systems, making it easy to integrate with third-party services and trigger actions based on specific events.
+* **Monitoring**: LangGraph Server integrates seamlessly with the [LangSmith](https://docs.smith.langchain.com/) monitoring platform, providing real-time insights into your application's performance and health.
+
+## What are you deploying?
+
+When you deploy a LangGraph Server, you are deploying one or more [graphs](#graphs), a database for [persistence](persistence.md), and a task queue.
+
+### Graphs
+
+When you deploy a graph with LangGraph Server, you are deploying a "blueprint" for an [Assistant](assistants.md). 
+
+An [Assistant](assistants.md) is a graph paired with specific configuration settings. You can create multiple assistants per graph, each with unique settings to accommodate different use cases
+that can be served by the same graph.
+
+Upon deployment, LangGraph Server will automatically create a default assistant for each graph using the graph's default configuration settings.
+
+You can interact with assistants through the [LangGraph Server API](#langgraph-server-api).
+
+!!! note
+
+    We often think of a graph as implementing an [agent](agentic_concepts.md), but a graph does not necessarily need to implement an agent. For example, a graph could implement a simple
+    chatbot that only supports back-and-forth conversation, without the ability to influence any application control flow. In reality, as applications get more complex, a graph will often implement a more complex flow that may use [multiple agents](./multi_agent.md) working in tandem.
+
+### Persistence and Task Queue
+
+The LangGraph Server leverages a database for [persistence](persistence.md) and a task queue.
+
+Currently, only [Postgres](https://www.postgresql.org/) is supported as a database for LangGraph Server and [Redis](https://redis.io/) as the task queue.
+
+If you're deploying using [LangGraph Cloud](./langgraph_cloud.md), these components are managed for you. If you're deploying LangGraph Server on your own infrastructure, you'll need to set up and manage these components yourself.
+
+Please review the [deployment options](./deployment_options.md) guide for more information on how these components are set up and managed.
+
+## Application Structure
+
+To deploy a LangGraph Server application, you need to specify the graph(s) you want to deploy, as well as any relevant configuration settings, such as dependencies and environment variables.
+
+Read the [application structure](./application_structure.md) guide to learn how to structure your LangGraph application for deployment.
+
+## LangGraph Server API
+
+The LangGraph Server API allows you to create and manage [assistants](assistants.md), [threads](#threads), [runs](#runs), [cron jobs](#cron-jobs), and more.
+
+The [LangGraph Cloud API Reference](../cloud/reference/api/api_ref.html) provides detailed information on the API endpoints and data models.
+
+### Assistants
+
+An [Assistant](assistants.md) refers to a [graph](#graphs) plus specific [configuration](low_level.md#configuration) settings for that graph.
+
+You can think of an assistant as a saved configuration of an [agent](agentic_concepts.md).
+
+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.
+
+### Threads
+
+A thread contains the accumulated state of a sequence of [runs](#runs). If a run is executed on a thread, then the [state](low_level.md#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](persistence.md#checkpoints). Checkpoints can be used to restore the state of a thread at a later time.
+
+For more on threads and checkpoints, see this section of the [LangGraph conceptual guide](low_level.md#persistence).
+
+The LangGraph Cloud API provides several endpoints for creating and managing threads and thread state. See the [API reference](../cloud/reference/api/api_ref.html#tag/threads) for more details.
+
+### Runs
+
+A run is an invocation of an [assistant](#assistants). 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](#threads).
+
+The LangGraph Cloud API provides several endpoints for creating and managing runs. See the [API reference](../cloud/reference/api/api_ref.html#tag/thread-runs/) for more details.
+
+### Store
+
+Store is an API for managing persistent [key-value store](./persistence.md#memory-store) that is available from any [thread](#threads).
+
+Stores are useful for implementing [memory](./memory.md) in your LangGraph application.
+
+### Cron Jobs
+
+There are many situations in which it is useful to run an assistant on a schedule. 
+
+For example, say that you're building an assistant that runs daily and sends an email summary
+of the day's news. You could use a cron job to run the assistant every day at 8:00 PM.
+
+LangGraph Cloud supports cron jobs, which run on a user-defined schedule. The user specifies a schedule, an assistant, and some input. After that, on the specified schedule, the server 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](../cloud/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](../cloud/reference/api/api_ref.html#tag/runscreate/POST/threads/{thread_id}/runs/crons) for more details.
+
+### Webhooks
+
+Webhooks enable event-driven communication from your LangGraph Cloud application to external services. For example, you may want to issue an update to a separate service once an API call to LangGraph Cloud has finished running.
+
+Many LangGraph Cloud endpoints accept a `webhook` parameter. If this parameter is specified by a an endpoint that can accept POST requests, LangGraph Cloud will send a request at the completion of a run.
+
+See the corresponding [how-to guide](../cloud/how-tos/webhooks.md) for more detail.
+
+## Related
+
+* LangGraph [Application Structure](./application_structure.md) guide explains how to structure your LangGraph application for deployment.
+* [How-to guides for the LangGraph Platform](../how-tos/index.md).
+* The [LangGraph Cloud API Reference](../cloud/reference/api/api_ref.html) provides detailed information on the API endpoints and data models.

docs/docs/concepts/langgraph_studio.md

@@ -1,39 +1,86 @@
-# Studio FAQs
+# LangGraph Studio
 
-## Why is my project failing to start?
+!!! info "Prerequisites"
+
+    - [LangGraph Platform](./langgraph_platform.md)
+    - [LangGraph Server](./langgraph_server.md)
+
+LangGraph Studio offers a new way to develop LLM applications by providing a specialized agent IDE that enables visualization, interaction, and debugging of complex agentic applications.
+
+With visual graphs and the ability to edit state, you can better understand agent workflows and iterate faster. LangGraph Studio integrates with LangSmith allowing you to  collaborate with teammates to debug failure modes.
+
+![](img/lg_studio.png)
+
+## Features
+
+The key features of LangGraph Studio are:
+
+- Visualizes your graph
+- Test your graph by running it from the UI
+- Debug your agent by [modifying its state and rerunning](human_in_the_loop.md)
+- Create and manage [assistants](assistants.md)
+- View and manage [threads](persistence.md#threads)
+- View and manage [long term memory](memory.md)
+- Add node input/outputs to [LangSmith](https://smith.langchain.com/) datasets for testing
+
+## Types
+
+### Desktop app
+
+LangGraph Studio is available as a [desktop app](https://studio.langchain.com/) for MacOS users.
+
+While in Beta, LangGraph Studio is available for free to all [LangSmith](https://smith.langchain.com/) users on any plan tier.
+
+### Cloud studio
+
+If you have deployed your LangGraph application on LangGraph Platform (Cloud), you can access the studio as part of that
+
+### Development server
+
+LangGraph CLI also contains a command for running an in-memory development server that can be used to connect a local LangGraph app with the studio.
+See [instructions here](../cloud/reference/cli.md#dev) for more information.
+
+The way this works is that it runs inside your local environment.
+It will spin up an in-memory, development server to deploy the graph.
+You can then connect to the studio via the Cloud hosted version of LangGraph Platform.
+To be clear, the web studio will connect to your locally running server - your agent is still running locally and never leaves your device.
+
+## Studio FAQs
+
+### Why is my project failing to start?
 
 There are a few reasons that your project might fail to start, here are some of the most common ones.
 
-### Docker issues
+#### Docker issues (desktop only)
 
-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.
+LangGraph Studio (desktop) 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
+#### 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?
+### 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?
+### How do I reload the app?  (desktop only)
 
 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?
+### How does automatic rebuilding work?  (desktop only)
 
 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
+#### 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
+#### 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?
+### Why is my graph taking so long to startup?  (desktop only)
 
 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.
 
@@ -71,3 +118,9 @@ def routing_function(state: GraphState) -> Literal["node_b","node_c"]:
         return "node_c"
 ```
 
+
+## Related
+
+For more information please see the following:
+
+*  [LangGraph Studio how-to guides](../how-tos/index.md#langgraph-studio)

docs/docs/concepts/low_level.md

@@ -391,7 +391,7 @@ Read [this how-to](https://langchain-ai.github.io/langgraph/how-tos/recursion-li
 
 It can often be useful to set breakpoints before or after certain nodes execute. This can be used to wait for human approval before continuing. These can be set when you ["compile" a graph](#compiling-your-graph). You can set breakpoints either _before_ a node executes (using `interrupt_before`) or after a node executes (using `interrupt_after`.)
 
-You **MUST** use a [checkpoiner](./persistence.md) when using breakpoints. This is because your graph needs to be able to resume execution.
+You **MUST** use a [checkpointer](./persistence.md) when using breakpoints. This is because your graph needs to be able to resume execution.
 
 In order to resume execution, you can just invoke your graph with `None` as the input.
 

docs/docs/concepts/memory.md

@@ -2,7 +2,7 @@
 
 ## What is Memory?
 
-Memory in AI applications refers to the ability to process, store, and effectively recall information from past interactions. With memory, your agents can learn from feedback and adapt to users' preferences. This guide is divided into two sections based on the scope of memory recall: short-term memory and long-term memory.
+[Memory](https://pmc.ncbi.nlm.nih.gov/articles/PMC10410470/) is a cognitive function that allows people to store, retrieve, and use information to understand their present and future. Consider the frustration of working with a colleague who forgets everything you tell them, requiring constant repetition! As AI agents undertake more complex tasks involving numerous user interactions, equipping them with memory becomes equally crucial for efficiency and user satisfaction. With memory, agents can learn from feedback and adapt to users' preferences. This guide covers two types of memory based on recall scope:
 
 **Short-term memory**, or [thread](persistence.md#threads)-scoped memory, can be recalled at any time **from within** a single conversational thread with a user. LangGraph manages short-term memory as a part of your agent's [state](low_level.md#state). State is persisted to a database using a [checkpointer](persistence.md#checkpoints) so the thread can be resumed at any time. Short-term memory updates when the graph is invoked or a step is completed, and the State is read at the start of each step.
 
@@ -173,6 +173,8 @@ trim_messages(
 
 Long-term memory in LangGraph allows systems to retain information across different conversations or sessions. Unlike short-term memory, which is thread-scoped, long-term memory is saved within custom "namespaces."
 
+### Storing memories
+
 LangGraph stores long-term memories as JSON documents in a [store](persistence.md#memory-store) ([reference doc](https://langchain-ai.github.io/langgraph/reference/store/#langgraph.store.base.BaseStore)). Each memory is organized under a custom `namespace` (similar to a folder) and a distinct `key` (like a filename). Namespaces often include user or org IDs or other labels that makes it easier to organize information. This structure enables hierarchical organization of memories. Cross-namespace searching is then supported through content filters. See the example below for an example.
 
 ```python
@@ -190,94 +192,75 @@ item = store.get(namespace, "a-memory")
 items = store.search(namespace, filter={"my-key": "my-value"})
 ```
 
-When adding long-term memory to your agent, it's important to think about how to **write memories**, how to **store and manage memory updates**, and how to **recall & represent memories** for the LLM in your application. These questions are all interdependent: how you want to recall & format memories for the LLM dictates what you should store and how to manage it. Furthermore, each technique has tradeoffs. The right approach for you largely depends on your application's needs.
-LangGraph aims to give you the low-level primitives to directly control the long-term memory of your application, based on memory [Store](persistence.md#memory-store)'s.
+### Framework for thinking about long-term memory
 
-Long-term memory is far from a solved problem. While it is hard to provide generic advice, we have provided a few reliable patterns below for your consideration as you implement long-term memory.
+Long-term memory is a complex challenge without a one-size-fits-all solution. However, the following questions provide a structure framework to help you navigate the different techniques:
 
-**Do you want to write memories "on the hot path" or "in the background"**
+**What is the type of memory?**
 
-Memory can be updated either as part of your primary application logic (e.g. "on the hot path" of the application) or as a background task (as a separate function that generates memories based on the primary application's state). We document some tradeoffs for each approach in [the writing memories section below](#writing-memories).
+Humans use memories to remember [facts](https://en.wikipedia.org/wiki/Semantic_memory), [experiences](https://en.wikipedia.org/wiki/Episodic_memory), and [rules](https://en.wikipedia.org/wiki/Procedural_memory). AI agents can use memory in the same ways. For example, AI agents can use memory to remember specific facts about a user to accomplish a task. We expand on several types of memories in the [section below](#memory-types).
 
-**Do you want to manage memories as a single profile or as a collection of documents?**
+**When do you want to update memories?**
 
-We provide two main approaches to managing long-term memory: a single, continuously updated document (referred to as a "profile" or "schema") or a collection of documents. Each method offers its own benefits, depending on the type of information you need to store and how you intend to access it.
+Memory can be updated as part of an agent's application logic (e.g. "on the hot path"). In this case, the agent typically decides to remember facts before responding to a user. Alternatively, memory can be updated as a background task (logic that runs in the background / asynchronously and generates memories). We explain the tradeoffs between these approaches in the [section below](#writing-memories).
 
-Managing memories as a single, continuously updated "profile" or "schema" is useful when there is well-scoped, specific information you want to remember about a user, organization, or other entity (including the agent itself). You can define the schema of the profile ahead of time, and then use an LLM to update this based on interactions. Querying the "memory" is easy since it's a simple GET operation on a JSON document. We explain this in more detail in [remember a profile](#manage-individual-profiles). This technique can provide higher precision (on known information use cases) at the expense of lower recall (since you have to anticipate and model your domain, and updates to the doc tend to delete or rewrite away old information at a greater frequency).
+## Memory types
 
-Managing long-term memory as a collection of documents, on the other hand, lets you store an unbounded amount of information. This technique is useful when you want to repeatedly extract & remember items over a long time horizon but can be more complicated to query and manage over time.
-Similar to the "profile" memory, you still define schema(s) for each memory. Rather than overwriting a single document, you instead will insert new ones (and potentially update or re-contextualize existing ones in the process). We explain this approach in more detail in ["managing a collection of memories"](#manage-a-collection-of-memories).
+Different applications require various types of memory. Although the analogy isn't perfect, examining [human memory types](https://www.psychologytoday.com/us/basics/memory/types-of-memory?ref=blog.langchain.dev) can be insightful. Some research (e.g., the [CoALA paper](https://arxiv.org/pdf/2309.02427)) have even mapped these human memory types to those used in AI agents.
 
-**Do you want to present memories to your agent as updated instructions or as few-shot examples?**
+| Memory Type | What is Stored | Human Example | Agent Example |
+|-------------|----------------|---------------|---------------|
+| Semantic | Facts | Things I learned in school | Facts about a user |
+| Episodic | Experiences | Things I did | Past agent actions |
+| Procedural | Instructions | Instincts or motor skills | Agent system prompt |
 
-Memories are typically provided to the LLM as a part of the system prompt. Some common ways to "frame" memories for the LLM include providing raw information as "memories from previous interactions with user A", as system instructions or rules, or as few-shot examples.
+### Semantic Memory
 
-Framing memories as "learning rules or instructions" typically means dedicating a portion of the system prompt to instructions the LLM can manage itself. After each conversation, you can prompt the LLM to evaluate its performance and update the instructions to better handle this type of task in the future. We explain this approach in more detail in [this section](#update-own-instructions).
+[Semantic memory](https://en.wikipedia.org/wiki/Semantic_memory), both in humans and AI agents, involves the retention of specific facts and concepts. In humans, it can include information learned in school and the understanding of concepts and their relationships. For AI agents, semantic memory is often used to personalize applications by remembering facts or concepts from past interactions. 
 
-Storing memories as few-shot examples lets you store and manage instructions as cause and effect. Each memory stores an input or context and expected response. Including a reasoning trajectory (a chain-of-thought) can also help provide sufficient context so that the memory is less likely to be mis-used in the future. We elaborate on this concept more in [this section](#few-shot-examples).
+#### Profile
 
-We will expand on techniques for writing, managing, and recalling & formatting memories in the following section.
+Semantic memories can be managed in different ways. For example, memories can be a single, continuously updated "profile" of well-scoped and specific information about a user, organization, or other entity (including the agent itself). A profile is generally just a JSON document with various key-value pairs you've selected to represent your domain. 
 
-### Writing memories
-
-Humans form long-term memories when we sleep, but when and how should our agents create new memories? The two most common ways we see agents write memories are "on the hot path" and "in the background".
-
-![](img/memory/hot_path_vs_background.png)
-
-#### Writing memories in the hot path
-
-This involves creating memories while the application is running. To provide a popular production example, ChatGPT manages memories using a "save_memories" tool to upsert memories as content strings. It decides whether (and how) to use this tool every time it receives a user message and multi-tasks memory management with the rest of the user instructions.
-
-This has a few benefits. First of all, it happens "in real time". If the user starts a new thread right away that memory will be present. The user also transparently sees when memories are stored, since the bot has to explicitly decide to store information and can relate that to the user.
-
-This also has several downsides. It complicates the decisions the agent must make (what to commit to memory). This complication can degrade its tool-calling performance and reduce task completion rates. It will slow down the final response since it needs to decide what to commit to memory. It also typically leads to fewer things being saved to memory (since the assistant is multi-tasking), which will cause **lower recall** in later conversations.
-
-#### Writing memories in the background
-
-This involves updating memory as a conceptually separate task, typically as a completely separate graph or function. Since it happens in the background, it incurs no latency. It also splits up the application logic from the memory logic, making it more modular and easy to manage. It also lets you separate the timing of memory creation, letting you avoid redundant work. Your agent can focus on accomplishing its immediate task without having to consciously think about what it needs to remember.
-
-This approach is not without its downsides, however. You have to think about how often to write memories. If it doesn't run in realtime, the user's interactions on other threads won't benefit from the new context. You also have to think about when to trigger this job. We typically recommend scheduling memories after some point of time, cancelling and re-scheduling for the future if new events occur on a given thread. Other popular choices are to form memories on some cron schedule or to let the user or application logic manually trigger memory formation.
-
-### Managing memories
-
-Once you've sorted out memory scheduling, it's important to think about **how to update memory with new information**.
-
-There are two main approaches: you can either continuously update a single document (memory profile) or insert new documents each time you receive new information.
-
-We will outline some tradeoffs between these two approaches below, understanding that most people will find it most appropriate to combine approaches and to settle somewhere in the middle.
-
-#### Manage individual profiles
-
-A profile is generally just a JSON document with various key-value pairs you've selected to represent your domain. When remembering a profile, you will want to make sure that you are **updating** the profile each time. As a result, you will want to pass in the previous profile and ask the LLM to generate a new profile (or some JSON patch to apply to the old profile).
-
-The larger the document, the more error-prone this can become. If your document becomes **too** large, you may want to consider splitting up the profiles into separate sections. You will likely need to use generation with retries and/or **strict** decoding when generating documents to ensure the memory schemas remains valid.
+When remembering a profile, you will want to make sure that you are **updating** the profile each time. As a result, you will want to pass in the previous profile and [ask the model to generate a new profile](https://github.com/langchain-ai/memory-template) (or some [JSON patch](https://github.com/hinthornw/trustcall) to apply to the old profile). This can be become error-prone as the profile gets larger, and may benefit from splitting a profile into multiple documents or **strict** decoding when generating documents to ensure the memory schemas remains valid.
 
 ![](img/memory/update-profile.png)
 
-#### Manage a collection of memories
+#### Collection
 
-Saving memories as a collection of documents simplifies some things. Each individual memory can be more narrowly scoped and easier to generate. It also means you're less likely to **lose** information over time, since it's easier for an LLM to generate _new_ objects for new information than it is for it to reconcile that new information with information in a dense profile. This tends to lead to higher recall downstream.
+Alternatively, memories can be a collection of documents that are continuously updated and extended over time. Each individual memory can be more narrowly scoped and easier to generate, which means that you're less likely to **lose** information over time. It's easier for an LLM to generate _new_ objects for new information than reconcile new information with an existing profile. As a result, a document collection tends to lead to [higher recall downstream](https://en.wikipedia.org/wiki/Precision_and_recall).
 
-This approach shifts some complexity to how you prompt the LLM to apply memory updates. You now have to enable the LLM to _delete_ or _update_ existing items in the list. This can be tricky to prompt the LLM to do. Some LLMs may default to over-inserting; others may default to over-updating. Tuning the behavior here is best done through evals, something you can do with a tool like [LangSmith](https://docs.smith.langchain.com/tutorials/Developers/evaluation).
+However, this shifts some complexity memory updating. The model must now _delete_ or _update_ existing items in the list, which can be tricky. In addition, some models may default to over-inserting and others may default to over-updating. See the [Trustcall](https://github.com/hinthornw/trustcall) package for one way to manage this and consider evaluation (e.g., with a tool like [LangSmith](https://docs.smith.langchain.com/tutorials/Developers/evaluation)) to help you tune the behavior.
 
-This also shifts complexity to memory **search** (recall). You have to think about what relevant items to use. Right now we support filtering by metadata. We will be adding semantic search shortly.
+Working with document collections also shifts complexity to memory **search** over the list. The `Store` currently supports [filtering by metadata](https://langchain-ai.github.io/langgraph/reference/store/#storage) and will soon add [semantic search shortly](https://python.langchain.com/docs/concepts/vectorstores/), but selecting the most relevant documents can be tricky as the list grows.
 
-Finally, this shifts some complexity to how you represent the memories for the LLM (and by extension, the schemas you use to save each memories). It's very easy to write memories that can easily be mistaken out-of-context. It's important to prompt the LLM to include all necessary contextual information in the given memory so that when you use it in later conversations it doesn't mistakenly mis-apply that information.
+Finally, using a collection of memories can make it challenging to provide comprehensive context to the model. While individual memories may follow a specific schema, this structure might not capture the full context or relationships between memories. As a result, when using these memories to generate responses, the model may lack important contextual information that would be more readily available in a unified profile approach.
 
 ![](img/memory/update-list.png)
 
-### Representing memories
+Regardless of memory management approach, the central point is that the agent will use the semantic memories to [ground its responses](https://python.langchain.com/docs/concepts/rag/), which often leads to more personalized and relevant interactions.
 
-Once you have saved memories, the way you then retrieve and present the memory content for the LLM can play a large role in how well your LLM incorporates that information in its responses.
-The following sections present a couple of common approaches. Note that these sections also will largely inform how you write and manage memories. Everything in memory is connected!
+### Episodic Memory
 
-#### Update own instructions
+[Episodic memory](https://en.wikipedia.org/wiki/Episodic_memory), in both humans and AI agents, involves recalling past events or actions. The [CoALA paper](https://arxiv.org/pdf/2309.02427) frames this well: facts can be written to semantic memory, whereas *experiences* can be written to episodic memory. For AI agents, episodic memory is often used to help an agent remember how to accomplish a task. 
 
-While instructions are often static text written by the developer, many AI applications benefit from letting the users personalize the rules and instructions the agent should follow whenever it interacts with that user. This ideally can be inferred by its interactions with the user (so the user doesn't have to explicitly change settings in yoru app). In this sense, instructions are a form of long-form memory!
+In practice, episodic memories are often implemented through [few-shot example prompting](https://python.langchain.com/docs/concepts/few_shot_prompting/), where agents learn from past sequences to perform tasks correctly. Sometimes it's easier to "show" than "tell" and LLMs learn well from examples. Few-shot learning lets you ["program"](https://x.com/karpathy/status/1627366413840322562) your LLM by updating the prompt with input-output examples to illustrate the intended behavior. While various [best-practices](https://python.langchain.com/docs/concepts/#1-generating-examples) can be used to generate few-shot examples, often the challenge lies in selecting the most relevant examples based on user input.
 
-One way to apply this is using "reflection" or "Meta-prompting" steps. Prompt the LLM with the current instruction set (from the system prompt) and a conversation with the user, and instruct the LLM to refine its instructions. This approach allows the system to dynamically update and improve its own behavior, potentially leading to better performance on various tasks. This is particularly useful for tasks where the instructions are challenging to specify a priori.
+Note that the memory [store](persistence.md#memory-store) is just one way to store data as few-shot examples. If you want to have more developer involvement, or tie few-shots more closely to your evaluation harness, you can also use a [LangSmith Dataset](https://docs.smith.langchain.com/evaluation/how_to_guides/datasets/index_datasets_for_dynamic_few_shot_example_selection) to store your data. Then dynamic few-shot example selectors can be used out-of-the box to achieve this same goal. LangSmith will index the dataset for you and enable retrieval of few shot examples that are most relevant to the user input based upon keyword similarity ([using a BM25-like algorithm](https://docs.smith.langchain.com/how_to_guides/datasets/index_datasets_for_dynamic_few_shot_example_selection) for keyword based similarity). 
 
-Meta-prompting uses past information to refine prompts. For instance, a [Tweet generator](https://www.youtube.com/watch?v=Vn8A3BxfplE) employs meta-prompting to enhance its paper summarization prompt for Twitter. You could implement this using LangGraph's memory store to save updated instructions in a shared namespace. In this case, we will namespace the memories as "agent_instructions" and key the memory based on the agent.
+See this how-to [video](https://www.youtube.com/watch?v=37VaU7e7t5o) for example usage of dynamic few-shot example selection in LangSmith. Also, see this [blog post](https://blog.langchain.dev/few-shot-prompting-to-improve-tool-calling-performance/) showcasing few-shot prompting to improve tool calling performance and this [blog post](https://blog.langchain.dev/aligning-llm-as-a-judge-with-human-preferences/) using few-shot example to align an LLMs to human preferences.
+
+### Procedural Memory
+
+[Procedural memory](https://en.wikipedia.org/wiki/Procedural_memory), in both humans and AI agents, involves remembering the rules used to perform tasks. In humans, procedural memory is like the internalized knowledge of how to perform tasks, such as riding a bike via basic motor skills and balance. Episodic memory, on the other hand, involves recalling specific experiences, such as the first time you successfully rode a bike without training wheels or a memorable bike ride through a scenic route. For AI agents, procedural memory is a combination of model weights, agent code, and agent's prompt that collectively determine the agent's functionality. 
+
+In practice, it is fairly uncommon for agents to modify their model weights or rewrite their code. However, it is more common for agents to [modify their own prompts](https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/prompt-generator). 
+
+One effective approach to refining an agent's instructions is through ["Reflection"](https://blog.langchain.dev/reflection-agents/) or meta-prompting. This involves prompting the agent with its current instructions (e.g., the system prompt) along with recent conversations or explicit user feedback. The agent then refines its own instructions based on this input. This method is particularly useful for tasks where instructions are challenging to specify upfront, as it allows the agent to learn and adapt from its interactions.
+
+For example, we built a [Tweet generator](https://www.youtube.com/watch?v=Vn8A3BxfplE) using external feedback and prompt re-writing to produce high-quality paper summaries for Twitter. In this case, the specific summarization prompt was difficult to specify *a priori*, but it was fairly easy for a user to critique the generated Tweets and provide feedback on how to improve the summarization process. 
+
+The below pseudo-code shows how you might implement this with the LangGraph memory [store](persistence.md#memory-store), using the store to save a prompt, the `update_instructions` node to get the current prompt (as well as feedback from the conversation with the user captured in `state["messages"]`), update the prompt, and save the new prompt back to the store. Then, the `call_model` get the updated prompt from the store and uses it to generate a response.
 
 ```python
 # Node that *uses* the instructions
@@ -288,7 +271,6 @@ def call_model(state: State, store: BaseStore):
     prompt = prompt_template.format(instructions=instructions.value["instructions"])
     ...
 
-
 # Node that updates instructions
 def update_instructions(state: State, store: BaseStore):
     namespace = ("instructions",)
@@ -303,8 +285,24 @@ def update_instructions(state: State, store: BaseStore):
 
 ![](img/memory/update-instructions.png)
 
-#### Few-shot examples
+## Writing memories
 
-Sometimes it's easier to "show" than "tell." LLMs learn well from examples. Few-shot learning lets you ["program"](https://x.com/karpathy/status/1627366413840322562) your LLM by updating the prompt with input-output examples to illustrate the intended behavior. While various [best-practices](https://python.langchain.com/docs/concepts/#1-generating-examples) can be used to generate few-shot examples, often the challenge lies in selecting the most relevant examples based on user input.
+While [humans often form long-term memories during sleep](https://medicine.yale.edu/news-article/sleeps-crucial-role-in-preserving-memory/), AI agents need a different approach. When and how should agents create new memories? There are at least two primary methods for agents to write memories: "on the hot path" and "in the background".
 
-Note that the memory store is just one way to store data as few-shot examples. If you want to have more developer involvement, or tie few-shots more closely to your evaluation harness, you can also use a [LangSmith Dataset](https://docs.smith.langchain.com/how_to_guides/datasets) to store your data. Then dynamic few-shot example selectors can be used out-of-the box to achieve this same goal. LangSmith will index the dataset for you and enable retrieval of few shot examples that are most relevant to the user input based upon keyword similarity ([using a BM25-like algorithm](https://docs.smith.langchain.com/how_to_guides/datasets/index_datasets_for_dynamic_few_shot_example_selection) for keyword based similarity). See this how-to [video](https://www.youtube.com/watch?v=37VaU7e7t5o) for example usage of dynamic few-shot example selection in LangSmith. Also, see this [blog post](https://blog.langchain.dev/few-shot-prompting-to-improve-tool-calling-performance/) showcasing few-shot prompting to improve tool calling performance and this [blog post](https://blog.langchain.dev/aligning-llm-as-a-judge-with-human-preferences/) using few-shot example to align an LLMs to human preferences.
+![](img/memory/hot_path_vs_background.png)
+
+### Writing memories in the hot path
+
+Creating memories during runtime offers both advantages and challenges. On the positive side, this approach allows for real-time updates, making new memories immediately available for use in subsequent interactions. It also enables transparency, as users can be notified when memories are created and stored.
+
+However, this method also presents challenges. It may increase complexity if the agent requires a new tool to decide what to commit to memory. In addition, the process of reasoning about what to save to memory can impact agent latency. Finally, the agent must multitask between memory creation and its other responsibilities, potentially affecting the quantity and quality of memories created.
+
+As an example, ChatGPT uses a [save_memories](https://openai.com/index/memory-and-new-controls-for-chatgpt/) tool to upsert memories as content strings, deciding whether and how to use this tool with each user message. See our [memory-agent](https://github.com/langchain-ai/memory-agent) template as an reference implementation.
+
+### Writing memories in the background
+
+Creating memories as a separate background task offers several advantages. It eliminates latency in the primary application, separates application logic from memory management, and allows for more focused task completion by the agent. This approach also provides flexibility in timing memory creation to avoid redundant work.
+
+However, this method has its own challenges. Determining the frequency of memory writing becomes crucial, as infrequent updates may leave other threads without new context. Deciding when to trigger memory formation is also important. Common strategies include scheduling after a set time period (with rescheduling if new events occur), using a cron schedule, or allowing manual triggers by users or the application logic.
+
+See our [memory-service](https://github.com/langchain-ai/memory-template) template as an reference implementation.

docs/docs/concepts/persistence.md

@@ -159,7 +159,7 @@ You must pass these when invoking the graph as part of the `configurable` portio
 # {"configurable": {"thread_id": "1", "checkpoint_id": "0c62ca34-ac19-445d-bbb0-5b4984975b2a"}}  # also valid config
 
 config = {"configurable": {"thread_id": "1"}}
-graph.invoke(inputs, config=config)
+graph.invoke(None, config=config)
 ```
 
 Importantly, LangGraph knows whether a particular checkpoint has been executed previously. If it has, LangGraph simply *re-plays* that particular step in the graph and does not re-execute the step. See this [how to guide on time-travel to learn more about replaying](../how-tos/human_in_the_loop/time-travel.ipynb).

docs/docs/concepts/plans.md

@@ -0,0 +1,39 @@
+# LangGraph Platform Plans
+
+
+## Overview
+LangGraph Platform is a commercial solution for deploying agentic applications in production.
+There are three different plans for using it.
+
+- **Developer**: All [LangSmith](https://smith.langchain.com/) users have access to this plan. You can sign up for this plan simply by creating a LangSmith account. This gives you access to the [Self-Hosted Lite](./deployment_options.md#self-hosted-lite) deployment option.
+- **Plus**: All [LangSmith](https://smith.langchain.com/) users with a [Plus account](https://docs.smith.langchain.com/administration/pricing) have access to this plan. You can sign up for this plan simply by upgrading your LangSmith account to the Plus plan type. This gives you access to the [Cloud](./deployment_options.md#cloud-saas) deployment option.
+- **Enterprise**: This is separate from LangSmith plans. You can sign up for this plan by contacting sales@langchain.dev. This gives you access to all deployment options: [Cloud](./deployment_options.md#cloud-saas), [Bring-Your-Own-Cloud](./deployment_options.md#bring-your-own-cloud), and [Self Hosted Enterprise](./deployment_options.md#self-hosted-enterprise)
+
+
+## Plan Details
+
+|                                                                  | Developer                                   | Plus                                                  | Enterprise                                          |
+|------------------------------------------------------------------|---------------------------------------------|-------------------------------------------------------|-----------------------------------------------------|
+| Deployment Options                                               | Self-Hosted Lite                            | Cloud                                                 | Self-Hosted Enterprise, Cloud, Bring-Your-Own-Cloud |
+| Usage                                                     | Free, limited to 1M nodes executed per year | Free while in Beta, will be charged per node executed | Custom                                              |
+| APIs for retrieving and updating state and conversational history | ✅                                           | ✅                                                     | ✅                                                   |
+| APIs for retrieving and updating long-term memory                | ✅                                           | ✅                                                     | ✅                                                   |
+| Horizontally scalable task queues and servers                    | ✅                                           | ✅                                                     | ✅                                                   |
+| Real-time streaming of outputs and intermediate steps            | ✅                                           | ✅                                                     | ✅                                                   |
+| Assistants API (configurable templates for LangGraph apps)       | ✅                                           | ✅                                                     | ✅                                                   |
+| Cron scheduling                                                  | --                                          | ✅                                                     | ✅                                                   |
+| LangGraph Studio for prototyping                                 | Desktop only                                | Coming Soon!                                          | Coming Soon!                                        |
+| Authentication & authorization to call the LangGraph APIs        | --                                          | Coming Soon!                                          | Coming Soon!                                        |
+| Smart caching to reduce traffic to LLM API                       | --                                          | Coming Soon!                                          | Coming Soon!                                        |
+| Publish/subscribe API for state                                  | --                                          | Coming Soon!                                          | Coming Soon!                                        |
+| Scheduling prioritization                                        | --                                          | Coming Soon!                                          | Coming Soon!                                        |
+
+Please see the [LangGraph Platform Pricing](https://www.langchain.com/langgraph-platform-pricing) for information on pricing.
+
+## Related
+
+For more information, please see:
+
+* [Deployment Options conceptual guide](./deployment_options.md)
+* [LangGraph Platform Pricing](https://www.langchain.com/langgraph-platform-pricing)
+* [LangSmith Plans](https://docs.smith.langchain.com/administration/pricing)

docs/docs/concepts/sdk.md

@@ -0,0 +1,56 @@
+# LangGraph SDK
+
+!!! info "Prerequisites"
+    - [LangGraph Platform](./langgraph_platform.md)
+    - [LangGraph Server](./langgraph_server.md)
+
+The LangGraph Platform provides both a Python and JS SDK for interacting with the [LangGraph Server API](./langgraph_server.md). 
+
+## Installation
+
+You can install the packages using the appropriate package manager for your language.
+
+=== "Python"
+    ```bash
+    pip install langgraph-sdk
+    ```
+
+=== "JS"
+    ```bash
+    yarn add @langchain/langgraph-sdk
+    ```
+
+
+## API Reference
+
+You can find the API reference for the SDKs here:
+
+- [Python SDK Reference](../cloud/reference/sdk/python_sdk_ref.md)
+- [JS/TS SDK Reference](../cloud/reference/sdk/js_ts_sdk_ref.md)
+
+## Python Sync vs. Async
+
+The Python SDK provides both synchronous (`get_sync_client`) and asynchronous (`get_client`) clients for interacting with the LangGraph Server API.
+
+=== "Async"
+    ```python
+    from langgraph_sdk import get_client
+
+    client = get_client(url=..., api_key=...)
+    await client.assistants.search()
+    ```
+
+=== "Sync"
+
+    ```python
+    from langgraph_sdk import get_sync_client
+
+    client = get_sync_client(url=..., api_key=...)
+    client.assistants.search()
+    ```
+
+## Related
+
+- [LangGraph CLI API Reference](../cloud/reference/cli.md)
+- [Python SDK Reference](../cloud/reference/sdk/python_sdk_ref.md)
+- [JS/TS SDK Reference](../cloud/reference/sdk/js_ts_sdk_ref.md)

docs/docs/concepts/self_hosted.md

@@ -0,0 +1,39 @@
+# Self-Hosted
+
+!!! note Prerequisites
+
+    - [LangGraph Platform](./langgraph_platform.md)
+    - [Deployment Options](./deployment_options.md)
+
+## Versions
+
+There are two versions of the self hosted deployment: [Self-Hosted Enterprise](./deployment_options.md#self-hosted-enterprise) and [Self-Hosted Lite](./deployment_options.md#self-hosted-lite).
+
+### Self-Hosted Lite
+
+The Self-Hosted Lite version is a limited version of LangGraph Platform that you can run locally or in a self-hosted manner (up to 1 million nodes executed).
+
+When using the Self-Hosted Lite version, you authenticate with a [LangSmith](https://smith.langchain.com/) API key.
+
+### Self-Hosted Enterprise
+
+The Self-Hosted Enterprise version is the full version of LangGraph Platform.
+
+To use the Self-Hosted Enterprise version, you must acquire a license key that you will need to pass in when running the Docker image. To acquire a license key, please email sales@langchain.dev.
+
+## Requirements
+
+- You use `langgraph-cli` and/or [LangGraph Studio](./langgraph_studio.md) app to test graph locally.
+- You use `langgraph build` command to build image.
+
+## How it works
+
+- Deploy Redis and Postgres instances on your own infrastructure.
+- Build the docker image for [LangGraph Server](./langgraph_server.md) using the [LangGraph CLI](./langgraph_cli.md).
+- Deploy a web server that will run the docker image and pass in the necessary environment variables.
+
+For step-by-step instructions, see [How to set up a self-hosted deployment of LangGraph](../how-tos/deploy-self-hosted.md).
+
+## Related
+
+- [How to set up a self-hosted deployment of LangGraph](../how-tos/deploy-self-hosted.md).

docs/docs/concepts/streaming.md

@@ -9,8 +9,23 @@ There are several different modes you can specify when calling these methods (e.
 
 - [`"values"`](../how-tos/stream-values.ipynb): This streams the full value of the state after each step of the graph.
 - [`"updates"`](../how-tos/stream-updates.ipynb): This streams the updates to the state after each step of the graph. If multiple updates are made in the same step (e.g. multiple nodes are run) then those updates are streamed separately.
+- [`"custom"`](../how-tos/streaming-content.ipynb): This streams custom data from inside your graph nodes.
+- [`"messages"`](../how-tos/streaming-tokens.ipynb): This streams LLM tokens and metadata for the graph node where LLM is invoked.
 - `"debug"`: This streams as much information as possible throughout the execution of the graph.
 
+You can also specify multiple streaming modes at the same time by passing them as a list. When you do this, the streamed outputs will be tuples `(stream_mode, data)`. For example:
+
+```python
+graph.stream(..., stream_mode=["updates", "messages"])
+```
+
+```
+...
+('messages', (AIMessageChunk(content='Hi'), {'langgraph_step': 3, 'langgraph_node': 'agent', ...}))
+...
+('updates', {'agent': {'messages': [AIMessage(content="Hi, how can I help you?")]}})
+```
+
 The below visualization shows the difference between the `values` and `updates` modes:
 
 ![values vs updates](../static/values_vs_updates.png)
@@ -130,4 +145,28 @@ guide for that [here](../how-tos/streaming-tokens.ipynb).
 
 
 !!! warning "ASYNC IN PYTHON<=3.10"
-    You may fail to see events being emitted from inside a node when using `.astream_events` in Python <= 3.10. If you're using a Langchain RunnableLambda, a RunnableGenerator, or Tool asynchronously inside your node, you will have to propagate callbacks to these objects manually. This is because LangChain cannot automatically propagate callbacks to child objects in this case. Please see examples [here](../how-tos/streaming-content.ipynb) and [here](../how-tos/streaming-events-from-within-tools.ipynb).
+    You may fail to see events being emitted from inside a node when using `.astream_events` in Python <= 3.10. If you're using a Langchain RunnableLambda, a RunnableGenerator, or Tool asynchronously inside your node, you will have to propagate callbacks to these objects manually. This is because LangChain cannot automatically propagate callbacks to child objects in this case. Please see examples [here](../how-tos/streaming-content.ipynb) and [here](../how-tos/streaming-events-from-within-tools.ipynb).
+
+
+## LangGraph Platform
+
+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. LangGraph Platform 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](../cloud/how-tos/stream_values.md) for streaming values.
+- `messages-tuple`: Stream LLM tokens for any messages generated inside a node. This mode is primarily meant for powering chat applications. See the [how-to guide](../cloud/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](../cloud/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](../cloud/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](../cloud/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](../cloud/how-tos/stream_multiple.md) for configuring multiple streaming modes at the same time.
+
+See the [API reference](../cloud/reference/api/api_ref.html#tag/threads-runs/POST/threads/{thread_id}/runs/stream) for how to create streaming runs.
+
+Streaming modes `values`, `updates`, `messages-tuple` and `debug` are very similar to modes available in the LangGraph library - for a deeper conceptual explanation of those, you can see the [previous section](#streaming-graph-outputs-stream-and-astream).
+
+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 [previous section](#streaming-graph-outputs-stream-and-astream).
+
+All events emitted have two attributes:
+
+- `event`: This is the name of the event
+- `data`: This is data associated with the event

docs/docs/concepts/template_applications.md

@@ -0,0 +1,19 @@
+# Template Applications
+
+!!! note Prerequisites
+
+    - [LangGraph Studio](./langgraph_studio.md)
+
+Templates are open source reference applications designed to help you get started quickly when building with LangGraph. They provide working examples of common agentic workflows that can be customized to your needs.
+
+Templates can be accessed via [LangGraph Studio (macOS only)](langgraph_studio.md), or cloned directly from Github. You can download LangGraph Studio and see available templates [here](https://studio.langchain.com/).
+
+## Available templates
+
+| Template                  | Description                                                                              | Python                                                           | JS/TS                                                               |
+|---------------------------|------------------------------------------------------------------------------------------|------------------------------------------------------------------|---------------------------------------------------------------------|
+| **New LangGraph Project** | A simple, minimal chatbot with memory.                                                   | [Repo](https://github.com/langchain-ai/new-langgraph-project)    | [Repo](https://github.com/langchain-ai/new-langgraphjs-project)     |
+| **ReAct Agent**           | A simple agent that can be flexibly extended to many tools.                              | [Repo](https://github.com/langchain-ai/react-agent)              | [Repo](https://github.com/langchain-ai/react-agent-js)              |
+| **Memory Agent**          | A ReAct-style agent with an additional tool to store memories for use across threads.    | [Repo](https://github.com/langchain-ai/memory-agent)             | [Repo](https://github.com/langchain-ai/memory-agent-js)             |
+| **Retrieval Agent**       | An agent that includes a retrieval-based question-answering system.                      | [Repo](https://github.com/langchain-ai/retrieval-agent-template) | [Repo](https://github.com/langchain-ai/retrieval-agent-template-js) |
+| **Data-Enrichment Agent** | An agent that performs web searches and organizes its findings into a structured format. | [Repo](https://github.com/langchain-ai/data-enrichment)          | [Repo](https://github.com/langchain-ai/data-enrichment-js)          |

docs/docs/how-tos/autogen-langgraph-platform.ipynb

@@ -0,0 +1,171 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8381b6e0-29a6-48c5-b451-5d2549351249",
+   "metadata": {},
+   "source": [
+    "# How to use LangGraph Platform to deploy CrewAI, AutoGen, and other frameworks\n",
+    "\n",
+    "[LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/) provides infrastructure for deploying agents. This integrates seamlessly with LangGraph, but can also work with other frameworks. The way to make this work is to wrap the agent in a single LangGraph node, and have that be the entire graph.\n",
+    "\n",
+    "Doing so will allow you to deploy to LangGraph Platform, and allows you to get a lot of the [benefits](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/). You get horizontally scalable infrastructure, a task queue to handle bursty operations, a persistence layer to power short term memory, and long term memory support.\n",
+    "\n",
+    "In this guide we show how to do this with an AutoGen agent, but this method should work for agents defined in other frameworks like CrewAI, LlamaIndex, and others as well."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1113cb16-b538-448c-924c-85731ce96ebd",
+   "metadata": {},
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "f05993fa-9d03-4f45-bc13-0a8d87260d86",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "%pip install autogen langgraph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4e0ca12-1714-4776-a30a-9527e519799b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "def _set_env(var: str):\n",
+    "    if not os.environ.get(var):\n",
+    "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
+    "\n",
+    "\n",
+    "_set_env(\"OPENAI_API_KEY\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1926bbc3-6b06-41e0-9604-860a2bbf8fa3",
+   "metadata": {},
+   "source": [
+    "## Define autogen agent\n",
+    "\n",
+    "Here we define our AutoGen agent. From https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_web_info.ipynb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d4a14dc7-d565-4207-8788-525f85b9fb27",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import autogen\n",
+    "import os\n",
+    "\n",
+    "config_list = [{\"model\": \"gpt-4o\", \"api_key\": os.environ[\"OPENAI_API_KEY\"]}]\n",
+    "\n",
+    "llm_config = {\n",
+    "    \"timeout\": 600,\n",
+    "    \"cache_seed\": 42,\n",
+    "    \"config_list\": config_list,\n",
+    "    \"temperature\": 0,\n",
+    "}\n",
+    "\n",
+    "autogen_agent = autogen.AssistantAgent(\n",
+    "    name=\"assistant\",\n",
+    "    llm_config=llm_config,\n",
+    ")\n",
+    "\n",
+    "user_proxy = autogen.UserProxyAgent(\n",
+    "    name=\"user_proxy\",\n",
+    "    human_input_mode=\"NEVER\",\n",
+    "    max_consecutive_auto_reply=10,\n",
+    "    is_termination_msg=lambda x: x.get(\"content\", \"\").rstrip().endswith(\"TERMINATE\"),\n",
+    "    code_execution_config={\n",
+    "        \"work_dir\": \"web\",\n",
+    "        \"use_docker\": False,\n",
+    "    },  # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.\n",
+    "    llm_config=llm_config,\n",
+    "    system_message=\"Reply TERMINATE if the task has been solved at full satisfaction. Otherwise, reply CONTINUE, or the reason why the task is not solved yet.\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1170836-f23e-4e4c-ab83-ce791cd7fbd2",
+   "metadata": {},
+   "source": [
+    "## Wrap in LangGraph\n",
+    "\n",
+    "We now wrap the AutoGen agent in a single LangGraph node, and make that the entire graph.\n",
+    "The main thing this involves is defining an Input and Output schema for the node, which you would need to do if deploying this manually, so it's no extra work"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "7b417c16-ff4e-4d5c-a9a9-0aaeeef6ede5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.graph import StateGraph, MessagesState\n",
+    "\n",
+    "\n",
+    "def call_autogen_agent(state: MessagesState):\n",
+    "    last_message = state[\"messages\"][-1]\n",
+    "    response = user_proxy.initiate_chat(autogen_agent, message=last_message.content)\n",
+    "    # get the final response from the agent\n",
+    "    content = response.chat_history[-1][\"content\"]\n",
+    "    return {\"messages\": {\"role\": \"assistant\", \"content\": content}}\n",
+    "\n",
+    "\n",
+    "graph = StateGraph(MessagesState)\n",
+    "graph.add_node(call_autogen_agent)\n",
+    "graph.set_entry_point(\"call_autogen_agent\")\n",
+    "graph = graph.compile()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6a18377-ac29-478f-a76a-b213f1a3c85d",
+   "metadata": {},
+   "source": [
+    "## Deploy with LangGraph Platform\n",
+    "\n",
+    "You can now deploy this as you normally would with LangGraph Platform. See [these instructions](https://langchain-ai.github.io/langgraph/concepts/deployment_options/) for more details."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/how-tos/configuration.ipynb

@@ -345,7 +345,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.11.1"
   }
  },
  "nbformat": 4,

docs/docs/how-tos/deploy-self-hosted.md

@@ -0,0 +1,137 @@
+# How to do a Self-hosted deployment of LangGraph
+
+!!! info "Prerequisites"
+
+    - [Application Structure](../concepts/application_structure.md)
+    - [Deployment Options](../concepts/deployment_options.md)
+
+This how-to guide will walk you through how to create a docker image from an existing LangGraph application, so you can deploy it on your own infrastructure.
+
+## How it works
+
+With the self-hosted deployment option, you are responsible for managing the infrastructure, including setting up and maintaining necessary databases, Redis instances, and other services.
+
+You will need to do the following:
+
+1. Deploy Redis and Postgres instances on your own infrastructure.
+2. Build a docker image with the [LangGraph Server](../concepts/langgraph_server.md) using the [LangGraph CLI](../concepts/langgraph_cli.md).
+3. Deploy a web server that will run the docker image and pass in the necessary environment variables.
+
+## Environment Variables
+
+You will eventually need to pass in the following environment variables to the LangGraph Deploy server:
+
+- `REDIS_URI`: Connection details to a Redis instance. Redis will be used as a pub-sub broker to enable streaming real time output from background runs.
+- `DATABASE_URI`: Postgres connection details. Postgres will be used to store assistants, threads, runs, persist thread state and long term memory, and to manage the state of the background task queue with 'exactly once' semantics.
+- `LANGSMITH_API_KEY`: (If using [Self-Hosted Lite](../concepts/deployment_options.md#self-hosted-lite)) LangSmith API key. This will be used to authenticate ONCE at server start up.
+- `LANGGRAPH_CLOUD_LICENSE_KEY`: (If using [Self-Hosted Enterprise](../concepts/deployment_options.md#self-hosted-enterprise)) LangGraph Platform license key. This will be used to authenticate ONCE at server start up.
+
+
+## Build the Docker Image
+
+Please read the [Application Structure](../concepts/application_structure.md) guide to understand how to structure your LangGraph application.
+
+If the application is structured correctly, you can build a docker image with the LangGraph Deploy server.
+
+To build the docker image, you first need to install the CLI:
+
+```shell
+pip install -U langgraph-cli
+```
+
+You can then use:
+
+```
+langgraph build -t my-image
+```
+
+This will build a docker image with the LangGraph Deploy server. The `-t my-image` is used to tag the image with a name.
+
+When running this server, you need to pass three environment variables:
+
+## Running the application locally
+
+### Using Docker
+
+```shell
+docker run \
+    --env-file .env \
+    -p 8123:8000 \
+    -e REDIS_URI="foo" \
+    -e DATABASE_URI="bar" \
+    -e LANGSMITH_API_KEY="baz" \
+    my-image
+```
+
+If you want to run this quickly without setting up a separate Redis and Postgres instance, you can use this docker compose file.
+
+!!! note
+
+    * You need to replace `my-image` with the name of the image you built in the previous step (from `langgraph build`).
+    and you should provide appropriate values for `REDIS_URI`, `DATABASE_URI`, and `LANGSMITH_API_KEY`.
+    * If your application requires additional environment variables, you can pass them in a similar way.
+    * If using [Self-Hosted Enterprise](../concepts/deployment_options.md#self-hosted-enterprise), you must provide `LANGGRAPH_CLOUD_LICENSE_KEY` as an additional environment variable.
+
+
+### Using Docker Compose
+
+```yml
+volumes:
+    langgraph-data:
+        driver: local
+services:
+    langgraph-redis:
+        image: redis:6
+        healthcheck:
+            test: redis-cli ping
+            interval: 5s
+            timeout: 1s
+            retries: 5
+    langgraph-postgres:
+        image: postgres:16
+        ports:
+            - "5433:5432"
+        environment:
+            POSTGRES_DB: postgres
+            POSTGRES_USER: postgres
+            POSTGRES_PASSWORD: postgres
+        volumes:
+            - langgraph-data:/var/lib/postgresql/data
+        healthcheck:
+            test: pg_isready -U postgres
+            start_period: 10s
+            timeout: 1s
+            retries: 5
+            interval: 5s
+    langgraph-api:
+        image: ${IMAGE_NAME}
+        ports:
+            - "8123:8000"
+        depends_on:
+            langgraph-redis:
+                condition: service_healthy
+            langgraph-postgres:
+                condition: service_healthy
+        env_file:
+            - .env
+        environment:
+            REDIS_URI: redis://langgraph-redis:6379
+            LANGSMITH_API_KEY: ${LANGSMITH_API_KEY}
+            POSTGRES_URI: postgres://postgres:postgres@langgraph-postgres:5432/postgres?sslmode=disable
+```
+
+You can then run `docker compose up` with this Docker compose file in the same folder.
+
+This will spin up LangGraph Deploy on port `8123` (if you want to change this, you can change this by changing the ports in the `langgraph-api` volume).
+
+You can test that the application is up by checking:
+
+```shell
+curl --request GET --url 0.0.0.0:8123/ok
+```
+Assuming everything is running correctly, you should see a response like:
+
+```shell
+{"ok":true}
+```
+

docs/docs/how-tos/index.md

@@ -1,24 +1,29 @@
 ---
 hide:
-  - toc
+  - navigation
+title: How-to Guides
+description: How to accomplish common tasks in LangGraph
 ---
 
-# How-to guides
+# How-to Guides
 
-Welcome to the LangGraph how-to guides! These guides provide practical, step-by-step instructions for accomplishing key tasks in LangGraph.
+Here you’ll find answers to “How do I...?” types of questions. These guides are **goal-oriented** and concrete; they're meant to help you complete a specific task. For conceptual explanations see the [Conceptual guide](../concepts/index.md). For end-to-end walk-throughs see [Tutorials](../tutorials/index.md). For comprehensive descriptions of every class and function see the [API Reference](../reference/index.md).
 
-## Controllability
+## LangGraph
+
+### Controllability
+
+LangGraph offers a high level of control over the execution of your graph.
 
-LangGraph is known for being a highly controllable agent framework.
 These how-to guides show how to achieve that controllability.
 
 - [How to create branches for parallel execution](branching.ipynb)
 - [How to create map-reduce branches for parallel execution](map-reduce.ipynb)
 - [How to control graph recursion limit](recursion-limit.ipynb)
 
-## Persistence
+### Persistence
 
-LangGraph makes it easy to persist state across graph runs (thread-level persistence) and across threads (cross-thread persistence). These how-to guides show how to add persistence to your graph.
+[LangGraph Persistence](../concepts/persistence.md) makes it easy to persist state across graph runs (thread-level persistence) and across threads (cross-thread persistence). These how-to guides show how to add persistence to your graph.
 
 - [How to add thread-level persistence to your graph](persistence.ipynb)
 - [How to add thread-level persistence to subgraphs](subgraph-persistence.ipynb)
@@ -27,7 +32,7 @@ LangGraph makes it easy to persist state across graph runs (thread-level persist
 - [How to create a custom checkpointer using MongoDB](persistence_mongodb.ipynb)
 - [How to create a custom checkpointer using Redis](persistence_redis.ipynb)
 
-## Memory
+### Memory
 
 LangGraph makes it easy to manage conversation [memory](../concepts/memory.md) in your graph. These how-to guides show how to implement different strategies for that.
 
@@ -35,22 +40,21 @@ LangGraph makes it easy to manage conversation [memory](../concepts/memory.md) i
 - [How to delete messages](memory/delete-messages.ipynb)
 - [How to add summary conversation memory](memory/add-summary-conversation-history.ipynb)
 
-## Human in the Loop
+### Human-in-the-loop
 
-One of LangGraph's main benefits is that it makes human-in-the-loop workflows easy.
-These guides cover common examples of that.
+[Human-in-the-loop](../concepts/human_in_the_loop.md) functionality allows
+you to involve humans in the decision-making process of your graph. These how-to guides show how to implement human-in-the-loop workflows in your graph.
 
 - [How to add breakpoints](human_in_the_loop/breakpoints.ipynb)
 - [How to add dynamic breakpoints](human_in_the_loop/dynamic_breakpoints.ipynb)
 - [How to edit graph state](human_in_the_loop/edit-graph-state.ipynb)
 - [How to wait for user input](human_in_the_loop/wait-user-input.ipynb)
 - [How to view and update past graph state](human_in_the_loop/time-travel.ipynb)
-- [Review tool calls](human_in_the_loop/review-tool-calls.ipynb)
+- [How to review tool calls](human_in_the_loop/review-tool-calls.ipynb)
 
-## Streaming
+### Streaming
 
-LangGraph is built to be streaming first.
-These guides show how to use different streaming modes.
+[Streaming](../concepts/streaming.md) is crucial for enhancing the responsiveness of applications built on LLMs. By displaying output progressively, even before a complete response is ready, streaming significantly improves user experience (UX), particularly when dealing with the latency of LLMs.
 
 - [How to stream full state of your graph](stream-values.ipynb)
 - [How to stream state updates of your graph](stream-updates.ipynb)
@@ -64,7 +68,11 @@ These guides show how to use different streaming modes.
 - [How to stream from subgraphs](streaming-subgraphs.ipynb)
 - [How to disable streaming for models that don't support it](disable-streaming.ipynb)
 
-## Tool calling
+### Tool calling
+
+[Tool calling](https://python.langchain.com/docs/concepts/tool_calling/) is a type of chat model API that accepts tool schemas, along with messages, as input and returns invocations of those tools as part of the output message. 
+
+These how-to guides show common patterns for tool calling with LangGraph:
 
 - [How to call tools using ToolNode](tool-calling.ipynb)
 - [How to handle tool calling errors](tool-calling-errors.ipynb)
@@ -72,33 +80,38 @@ These guides show how to use different streaming modes.
 - [How to pass config to tools](pass-config-to-tools.ipynb)
 - [How to handle large numbers of tools](many-tools.ipynb)
 
-## Subgraphs
+### Subgraphs
+
+[Subgraphs](../concepts/low_level.md#subgraphs) allow you to reuse an existing graph from another graph. These how-to guides show how to use subgraphs:
 
 - [How to add and use subgraphs](subgraph.ipynb)
 - [How to view and update state in subgraphs](subgraphs-manage-state.ipynb)
 - [How to transform inputs and outputs of a subgraph](subgraph-transform-state.ipynb)
 
-## State Management
+### State Management
 
-- [Use Pydantic model as state](state-model.ipynb)
-- [Have a separate input and output schema](input_output_schema.ipynb)
-- [Pass private state between nodes inside the graph](pass_private_state.ipynb)
+- [How to use Pydantic model as state](state-model.ipynb)
+- [How to define input/output schema for your graph](input_output_schema.ipynb)
+- [How to pass private state between nodes inside the graph](pass_private_state.ipynb)
 
-## Other
+### Other
 
 - [How to run graph asynchronously](async.ipynb)
 - [How to visualize your graph](visualization.ipynb)
 - [How to add runtime configuration to your graph](configuration.ipynb)
-- [How to use a Pydantic model as your state](state-model.ipynb)
 - [How to add node retries](node-retries.ipynb)
 - [How to force function calling agent to structure output](react-agent-structured-output.ipynb)
 - [How to pass custom LangSmith run ID for graph runs](run-id-langsmith.ipynb)
 - [How to return state before hitting recursion limit](return-when-recursion-limit-hits.ipynb)
+- [How to integrate LangGraph with AutoGen, CrewAI, and other frameworks](autogen-integration.ipynb)
 
-## Prebuilt ReAct Agent
+### Prebuilt ReAct Agent
 
-These guides show how to use the prebuilt ReAct agent.
-Please note that here will we use a **prebuilt agent**. One of the big benefits of LangGraph is that you can easily create your own agent architectures. So while it's fine to start here to build an agent quickly, we would strongly recommend learning how to build your own agent so that you can take full advantage of LangGraph.
+The LangGraph [prebuilt ReAct agent](../reference/prebuilt.md#langgraph.prebuilt.chat_agent_executor.create_react_agent) is pre-built implementation of a [tool calling agent](../concepts/agentic_concepts.md#tool-calling-agent).
+
+One of the big benefits of LangGraph is that you can easily create your own agent architectures. So while it's fine to start here to build an agent quickly, we would strongly recommend learning how to build your own agent so that you can take full advantage of LangGraph.
+
+These guides show how to use the prebuilt ReAct agent:
 
 - [How to create a ReAct agent](create-react-agent.ipynb)
 - [How to add memory to a ReAct agent](create-react-agent-memory.ipynb)
@@ -106,8 +119,115 @@ Please note that here will we use a **prebuilt agent**. One of the big benefits
 - [How to add human-in-the-loop processes to a ReAct agent](create-react-agent-hitl.ipynb)
 - [How to create prebuilt ReAct agent from scratch](react-agent-from-scratch.ipynb)
 
+## LangGraph Platform
+
+This section includes how-to guides for LangGraph Platform.
+
+LangGraph Platform is a commercial solution for deploying agentic applications in production, built on the open-source LangGraph framework. 
+
+The LangGraph Platform offers a few different deployment options described in the [deployment options guide](../concepts/deployment_options.md).
+
+!!! tip
+
+    * LangGraph is an MIT-licensed open-source library, which we are committed to maintaining and growing for the community.
+    * You can always deploy LangGraph applications on your own infrastructure using the open-source LangGraph project without using LangGraph Platform.
+
+### Application Structure
+
+Learn how to set up your app for deployment to LangGraph Platform:
+
+- [How to set up app for deployment (requirements.txt)](../cloud/deployment/setup.md)
+- [How to set up app for deployment (pyproject.toml)](../cloud/deployment/setup_pyproject.md)
+- [How to set up app for deployment (JavaScript)](../cloud/deployment/setup_javascript.md)
+- [How to customize Dockerfile](../cloud/deployment/custom_docker.md)
+- [How to test locally](../cloud/deployment/test_locally.md)
+- [How to rebuild graph at runtime](../cloud/deployment/graph_rebuild.md)
+- [How to use LangGraph Platform to deploy CrewAI, AutoGen, and other frameworks](autogen-langgraph-platform.ipynb)
+
+### Deployment
+
+LangGraph applications can be deployed using LangGraph Cloud, which provides a range of services to help you deploy, manage, and scale your applications.
+
+- [How to deploy to LangGraph cloud](../cloud/deployment/cloud.md)
+- [How to deploy to a self-hosted environment](./deploy-self-hosted.md)
+- [How to interact with the deployment using RemoteGraph](./use-remote-graph.md) 
+ 
+### Assistants
+
+[Assistants](../concepts/assistants.md) is a configured instance of a template.
+
+- [How to configure agents](../cloud/how-tos/configuration_cloud.md)
+- [How to version assistants](../cloud/how-tos/assistant_versioning.md)
+
+### Threads
+
+- [How to copy threads](../cloud/how-tos/copy_threads.md)
+- [How to check status of your threads](../cloud/how-tos/check_thread_status.md)
+
+### Runs
+
+LangGraph Platform supports multiple types of runs besides streaming runs.
+
+- [How to run an agent in the background](../cloud/how-tos/background_run.md)
+- [How to run multiple agents in the same thread](../cloud/how-tos/same-thread.md)
+- [How to create cron jobs](../cloud/how-tos/cron_jobs.md)
+- [How to create stateless runs](../cloud/how-tos/stateless_runs.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](../cloud/how-tos/stream_values.md)
+- [How to stream updates](../cloud/how-tos/stream_updates.md)
+- [How to stream messages](../cloud/how-tos/stream_messages.md)
+- [How to stream events](../cloud/how-tos/stream_events.md)
+- [How to stream in debug mode](../cloud/how-tos/stream_debug.md)
+- [How to stream multiple modes](../cloud/how-tos/stream_multiple.md)
+
+### Human-in-the-loop
+
+When designing complex graphs, relying entirely on the LLM for decision-making can be risky, particularly when it involves tools that interact with files, APIs, or databases. These interactions may lead to unintended data access or modifications, depending on the use case. To mitigate these risks, LangGraph allows you to integrate human-in-the-loop behavior, ensuring your LLM applications operate as intended without undesirable outcomes.
+
+- [How to add a breakpoint](../cloud/how-tos/human_in_the_loop_breakpoint.md)
+- [How to wait for user input](../cloud/how-tos/human_in_the_loop_user_input.md)
+- [How to edit graph state](../cloud/how-tos/human_in_the_loop_edit_state.md)
+- [How to replay and branch from prior states](../cloud/how-tos/human_in_the_loop_time_travel.md)
+- [How to review tool calls](../cloud/how-tos/human_in_the_loop_review_tool_calls.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. 
+
+- [How to use the interrupt option](../cloud/how-tos/interrupt_concurrent.md)
+- [How to use the rollback option](../cloud/how-tos/rollback_concurrent.md)
+- [How to use the reject option](../cloud/how-tos/reject_concurrent.md)
+- [How to use the enqueue option](../cloud/how-tos/enqueue_concurrent.md)
+
+### Webhooks
+
+- [How to integrate webhooks](../cloud/how-tos/webhooks.md)
+
+### Cron Jobs
+
+- [How to create cron jobs](../cloud/how-tos/cron_jobs.md)
+
+### LangGraph Studio
+
+LangGraph Studio is a built-in UI for visualizing, testing, and debugging your agents.
+
+- [How to connect to a LangGraph Cloud deployment](../cloud/how-tos/test_deployment.md)
+- [How to connect to a local deployment](../cloud/how-tos/test_local_deployment.md)
+- [How to test your graph in LangGraph Studio](../cloud/how-tos/invoke_studio.md)
+- [How to interact with threads in LangGraph Studio](../cloud/how-tos/threads_studio.md)
+
 ## Troubleshooting
 
-### Errors
+These are the guides for resolving common errors you may find while building with LangGraph. Errors referenced below will have an `lc_error_code` property corresponding to one of the below codes when they are thrown in code.
+
+- [GRAPH_RECURSION_LIMIT](../troubleshooting/errors/GRAPH_RECURSION_LIMIT.md)
+- [INVALID_CONCURRENT_GRAPH_UPDATE](../troubleshooting/errors/INVALID_CONCURRENT_GRAPH_UPDATE.md)
+- [INVALID_GRAPH_NODE_RETURN_VALUE](../troubleshooting/errors/INVALID_GRAPH_NODE_RETURN_VALUE.md)
+- [MULTIPLE_SUBGRAPHS](../troubleshooting/errors/MULTIPLE_SUBGRAPHS.md)
+- [INVALID_CHAT_HISTORY](../troubleshooting/errors/INVALID_CHAT_HISTORY.md)
+
 
-- [Error reference](../troubleshooting/errors/index.md)

docs/docs/how-tos/local-studio.md

@@ -0,0 +1,86 @@
+# How to connect a local agent to LangGraph Studio
+
+This guide shows you how to connect your local agent to [LangGraph Studio](../concepts/langgraph_studio.md) for visualization, interaction, and debugging.
+
+## Connection Options
+
+There are two ways to connect your local agent to LangGraph Studio:
+
+- [LangGraph Desktop](../concepts/langgraph_studio.md#desktop-app): Application, Mac only, requires Docker
+- [Development Server](../concepts/langgraph_studio.md#dev-server): Python package, all platforms, no Docker
+
+In this guide we will cover how to use the development server as that is generally an easier and better experience.
+
+## Setup your application
+
+First, you will need to setup your application in the proper format.
+This means defining a `langgraph.json` file which contains paths to your agent(s).
+See [this guide](../concepts/application_structure.md) for information on how to do so.
+
+## Install langgraph-cli
+
+You will need to install [`langgraph-cli`](../cloud/reference/cli.md#langgraph-cli) (version `0.1.55` or higher).
+You will need to make sure to install the `inmem` extras.
+
+```shell
+pip install "langgraph-cli[inmem]==0.1.55"
+```
+
+## Run the development server
+
+1. Navigate to your project directory (where `langgraph.json` is located)
+
+2. Start the server:
+   ```bash
+   langgraph dev
+   ```
+
+This will look for the `langgraph.json` file in your current directory. 
+In there, it will find the paths to the graph(s), and start those up.
+It will then automatically connect to the cloud-hosted studio.
+
+## Use the studio
+
+After connecting to the studio, a browser window should automatically pop up.
+This will use the cloud hosted studio UI to connect to your local development server.
+Your graph is still running locally, the UI is connecting to visualizing the agent and threads that are defined locally.
+
+The graph will always use the most up-to-date code, so you will be able to change the underlying code and have it automatically reflected in the studio.
+This is useful for debugging workflows.
+You can run your graph in the UI until it messes up, go in and change your code, and then rerun from the node that failed.
+
+# (Optional) Attach a debugger
+
+For step-by-step debugging with breakpoints and variable inspection:
+
+```bash
+# Install debugpy package
+pip install debugpy
+
+# Start server with debugging enabled
+langgraph dev --debug-port 5678
+```
+
+Then attach your preferred debugger:
+
+=== "VS Code"
+    Add this configuration to `launch.json`:
+    ```json
+    {
+      "name": "Attach to LangGraph",
+      "type": "debugpy",
+      "request": "attach",
+      "connect": {
+        "host": "0.0.0.0",
+        "port": 5678
+      }
+    }
+    ```
+    Specify the port number you chose in the previous step.
+
+=== "PyCharm"
+    1. Go to Run → Edit Configurations
+    2. Click + and select "Python Debug Server"
+    3. Set IDE host name: `localhost`
+    4. Set port: `5678` (or the port number you chose in the previous step)
+    5. Click "OK" and start debugging

docs/docs/how-tos/use-remote-graph.md

@@ -0,0 +1,256 @@
+# How to interact with the deployment using RemoteGraph
+
+!!! info "Prerequisites"
+    - [LangGraph Platform](../concepts/langgraph_platform.md)
+    - [LangGraph Server](../concepts/langgraph_server.md)
+
+`RemoteGraph` is an interface that allows you to interact with your LangGraph Platform deployment as if it were a regular, locally-defined LangGraph graph (e.g. a `CompiledGraph`). This guide shows you how you can initialize a `RemoteGraph` and interact with it.
+
+## Initializing the graph
+
+When initializing a `RemoteGraph`, you must always specify:
+
+- `name`: the name of the graph you want to interact with. This is the same graph name you use in `langgraph.json` configuration file for your deployment. 
+- `api_key`: a valid LangSmith API key. Can be set as an environment variable (`LANGSMITH_API_KEY`) or passed directly via the `api_key` argument. The API key could also be provided via the `client` / `sync_client` arguments, if `LangGraphClient` / `SyncLangGraphClient` were initialized with `api_key` argument.
+
+Additionally, you have to provide one of the following:
+
+- `url`: URL of the deployment you want to interact with. If you pass `url` argument, both sync and async clients will be created using the provided URL, headers (if provided) and default configuration values (e.g. timeout, etc).
+- `client`: a `LangGraphClient` instance for interacting with the deployment asynchronously (e.g. using `.astream()`, `.ainvoke()`, `.aget_state()`, `.aupdate_state()`, etc.)
+- `sync_client`: a `SyncLangGraphClient` instance for interacting with the deployment synchronously (e.g. using `.stream()`, `.invoke()`, `.get_state()`, `.update_state()`, etc.)
+
+!!! Note
+
+    If you pass both `client` or `sync_client` as well as `url` argument, they will take precedence over the `url` argument. If none of the `client` / `sync_client` / `url` arguments are provided, `RemoteGraph` will raise a `ValueError` at runtime.
+
+
+### Using URL
+
+=== "Python"
+
+    ```python
+    from langgraph.pregel.remote import RemoteGraph
+
+    url = <DEPLOYMENT_URL>
+    graph_name = "agent"
+    remote_graph = RemoteGraph(graph_name, url=url)
+    ```
+
+=== "JavaScript"
+
+    ```ts
+    import { RemoteGraph } from "@langchain/langgraph/remote";
+
+    const url = `<DEPLOYMENT_URL>`;
+    const graphName = "agent";
+    const remoteGraph = new RemoteGraph({ graphId: graphName, url });
+    ```
+
+### Using clients
+
+=== "Python"
+
+    ```python
+    from langgraph_sdk import get_client, get_sync_client
+    from langgraph.pregel.remote import RemoteGraph
+
+    url = <DEPLOYMENT_URL>
+    graph_name = "agent"
+    client = get_client(url=url)
+    sync_client = get_sync_client(url=url)
+    remote_graph = RemoteGraph(graph_name, client=client, sync_client=sync_client)
+    ```
+
+=== "JavaScript"
+
+    ```ts
+    import { Client } from "@langchain/langgraph-sdk";
+    import { RemoteGraph } from "@langchain/langgraph/remote";
+
+    const client = new Client({ apiUrl: `<DEPLOYMENT_URL>` });
+    const graphName = "agent";
+    const remoteGraph = new RemoteGraph({ graphId: graphName, client });
+    ```
+
+## Invoking the graph
+
+Since `RemoteGraph` is a `Runnable` that implements the same methods as `CompiledGraph`, you can interact with it the same way you normally would with a compiled graph, i.e. by calling `.invoke()`, `.stream()`, `.get_state()`, `.update_state()`, etc (as well as their async counterparts).
+
+### Asynchronously
+
+!!! Note
+
+    To use the graph asynchronously, you must provide either the `url` or `client` when initializing the `RemoteGraph`.
+
+=== "Python"
+
+    ```python
+    # invoke the graph
+    result = await remote_graph.ainvoke({
+        "messages": [{"role": "user", "content": "what's the weather in sf"}]
+    })
+
+    # stream outputs from the graph
+    async for chunk in remote_graph.astream({
+        "messages": [{"role": "user", "content": "what's the weather in la"}]
+    }):
+        print(chunk)
+    ```
+
+=== "JavaScript"
+
+    ```ts
+    // invoke the graph
+    const result = await remoteGraph.invoke({
+        messages: [{role: "user", content: "what's the weather in sf"}]
+    })
+
+    // stream outputs from the graph
+    for await (const chunk of await remoteGraph.stream({
+        messages: [{role: "user", content: "what's the weather in la"}]
+    })):
+        console.log(chunk)
+    ```
+
+### Synchronously
+
+!!! Note
+
+    To use the graph synchronously, you must provide either the `url` or `sync_client` when initializing the `RemoteGraph`.
+
+=== "Python"
+
+    ```python
+    # invoke the graph
+    result = remote_graph.invoke({
+        "messages": [{"role": "user", "content": "what's the weather in sf"}]
+    })
+
+    # stream outputs from the graph
+    for chunk in remote_graph.stream({
+        "messages": [{"role": "user", "content": "what's the weather in la"}]
+    }):
+        print(chunk)
+    ```
+
+## Thread-level persistence
+
+By default, the graph runs (i.e. `.invoke()` or `.stream()` invocations) are stateless - the checkpoints and the final state of the graph are not persisted. If you would like to persist the outputs of the graph run (for example, to enable human-in-the-loop features), you can create a thread and provide the thread ID via the `config` argument, same as you would with a regular compiled graph:
+
+=== "Python"
+
+    ```python
+    from langgraph_sdk import get_sync_client
+    url = <DEPLOYMENT_URL>
+    graph_name = "agent"
+    sync_client = get_sync_client(url=url)
+    remote_graph = RemoteGraph(graph_name, url=url)
+
+    # create a thread (or use an existing thread instead)
+    thread = sync_client.threads.create()
+
+    # invoke the graph with the thread config
+    config = {"configurable": {"thread_id": thread["thread_id"]}}
+    result = remote_graph.invoke({
+        "messages": [{"role": "user", "content": "what's the weather in sf"}]
+    }, config=config)
+
+    # verify that the state was persisted to the thread
+    thread_state = remote_graph.get_state(config)
+    print(thread_state)
+    ```
+
+=== "JavaScript"
+
+    ```ts
+    import { Client } from "@langchain/langgraph-sdk";
+    import { RemoteGraph } from "@langchain/langgraph/remote";
+
+    const url = `<DEPLOYMENT_URL>`;
+    const graphName = "agent";
+    const client = new Client({ apiUrl: url });
+    const remoteGraph = new RemoteGraph({ graphId: graphName, url });
+
+    // create a thread (or use an existing thread instead)
+    const thread = await client.threads.create();
+
+    // invoke the graph with the thread config
+    const config = { configurable: { thread_id: thread.thread_id }};
+    const result = await remoteGraph.invoke({
+      messages: [{ role: "user", content: "what's the weather in sf" }],
+    }, config);
+
+    // verify that the state was persisted to the thread
+    const threadState = await remoteGraph.getState(config);
+    console.log(threadState);
+    ```
+
+## Using as a subgraph
+
+!!! Note
+
+    If you need to use a `checkpointer` with a graph that has a `RemoteGraph` subgraph node, make sure to use UUIDs as thread IDs.
+
+
+Since the `RemoteGraph` behaves the same way as a regular `CompiledGraph`, it can be also used as a subgraph in another graph. For example:
+
+=== "Python"
+
+    ```python
+    from langgraph_sdk import get_sync_client
+    from langgraph.graph import StateGraph, MessagesState, START
+    from typing import TypedDict
+
+    url = <DEPLOYMENT_URL>
+    graph_name = "agent"
+    remote_graph = RemoteGraph(graph_name, url=url)
+
+    # define parent graph
+    builder = StateGraph(MessagesState)
+    # add remote graph directly as a node
+    builder.add_node("child", remote_graph)
+    builder.add_edge(START, "child")
+    graph = builder.compile()
+
+    # invoke the parent graph
+    result = graph.invoke({
+        "messages": [{"role": "user", "content": "what's the weather in sf"}]
+    })
+    print(result)
+
+    # stream outputs from both the parent graph and subgraph
+    for chunk in graph.stream({
+        "messages": [{"role": "user", "content": "what's the weather in sf"}]
+    }, subgraphs=True):
+        print(chunk)
+    ```
+
+=== "JavaScript"
+
+    ```ts
+    import { MessagesAnnotation, StateGraph, START } from "@langchain/langgraph";
+    import { RemoteGraph } from "@langchain/langgraph/remote";
+
+    const url = `<DEPLOYMENT_URL>`;
+    const graphName = "agent";
+    const remoteGraph = new RemoteGraph({ graphId: graphName, url });
+
+    // define parent graph and add remote graph directly as a node
+    const graph = new StateGraph(MessagesAnnotation)
+      .addNode("child", remoteGraph)
+      .addEdge(START, "child")
+      .compile()
+
+    // invoke the parent graph
+    const result = await graph.invoke({
+      messages: [{ role: "user", content: "what's the weather in sf" }]
+    });
+    console.log(result);
+
+    // stream outputs from both the parent graph and subgraph
+    for await (const chunk of await graph.stream({
+      messages: [{ role: "user", content: "what's the weather in la" }]
+    }, { subgraphs: true })) {
+      console.log(chunk);
+    }
+    ```

docs/docs/index.md

@@ -3,7 +3,6 @@ hide_comments: true
 hide:
   - navigation
 title: Home
-
 ---
 
 {!README.md!}

docs/docs/reference/index.md

@@ -0,0 +1,17 @@
+---
+title: Reference
+description: API reference for LangGraph
+---
+
+<style>
+.md-sidebar {
+  display: block !important;
+}
+</style>
+
+
+# Reference
+
+Welcome to the LangGraph API reference! This reference provides detailed information about the LangGraph API, including classes, methods, and other components.
+
+If you are new to LangGraph, we recommend starting with the [Quick Start](../tutorials/introduction.ipynb) in the Tutorials section.

docs/docs/reference/remote_graph.md

@@ -0,0 +1,6 @@
+# RemoteGraph
+
+::: langgraph.pregel.remote
+    options:
+      members:
+        - RemoteGraph

docs/docs/troubleshooting/errors/INVALID_CHAT_HISTORY.md

@@ -0,0 +1,30 @@
+# INVALID_CHAT_HISTORY
+
+This error is raised in the prebuilt [create_react_agent][langgraph.prebuilt.chat_agent_executor.create_react_agent] when the `call_model` graph node receives a malformed list of messages. Specifically, it is malformed when there are `AIMessages` with `tool_calls` (LLM requesting to call a tool) that do not have a corresponding `ToolMessage` (result of a tool invocation to return to the LLM).
+
+There could be a few reasons you're seeing this error:
+
+1. You manually passed a malformed list of messages when invoking the graph, e.g. `graph.invoke({'messages': [AIMessage(..., tool_calls=[...])]})`
+2. The graph was interrupted before receiving updates from the `tools` node (i.e. a list of ToolMessages)
+and you invoked it with a an input that is not None or a ToolMessage,
+e.g. `graph.invoke({'messages': [HumanMessage(...)]}, config)`.
+    This interrupt could have been triggered in one of the following ways:
+     - You manually set `interrupt_before = ['tools']` in `create_react_agent`
+     - One of the tools raised an error that wasn't handled by the [ToolNode][langgraph.prebuilt.tool_node.ToolNode] (`"tools"`)
+
+## Troubleshooting
+
+To resolve this, you can do one of the following:
+
+1. Don't invoke the graph with a malformed list of messages
+2. In case of an interrupt (manual or due to an error) you can:
+
+    - provide ToolMessages that match existing tool calls and call `graph.invoke({'messages': [ToolMessage(...)]})`.
+    **NOTE**: this will append the messages to the history and run the graph from the START node.
+    - manually update the state and resume the graph from the interrupt:
+
+        1. get the list of most recent messages from the graph state with `graph.get_state(config)`
+        2. modify the list of messages to either remove unanswered tool calls from AIMessages
+or add ToolMessages with tool_call_ids that match unanswered tool calls
+        3. call `graph.update_state(config, {'messages': ...})` with the modified list of messages
+        4. resume the graph, e.g. call `graph.invoke(None, config)`

docs/docs/troubleshooting/errors/index.md

@@ -7,3 +7,4 @@ Errors referenced below will have an `lc_error_code` property corresponding to o
 - [INVALID_CONCURRENT_GRAPH_UPDATE](./INVALID_CONCURRENT_GRAPH_UPDATE.md)
 - [INVALID_GRAPH_NODE_RETURN_VALUE](./INVALID_GRAPH_NODE_RETURN_VALUE.md)
 - [MULTIPLE_SUBGRAPHS](./MULTIPLE_SUBGRAPHS.md)
+- [INVALID_CHAT_HISTORY](./INVALID_CHAT_HISTORY.md)

docs/docs/tutorials/code_assistant/langgraph_code_assistant.ipynb

@@ -102,7 +102,7 @@
     "from langchain_community.document_loaders.recursive_url_loader import RecursiveUrlLoader\n",
     "\n",
     "# LCEL docs\n",
-    "url = \"https://python.langchain.com/docs/concepts/#langchain-expression-language-lcel\"\n",
+    "url = \"https://python.langchain.com/docs/concepts/lcel/\"\n",
     "loader = RecursiveUrlLoader(\n",
     "    url=url, max_depth=20, extractor=lambda x: Soup(x, \"html.parser\").text\n",
     ")\n",

docs/docs/tutorials/customer-support/customer-support.ipynb

@@ -35,7 +35,7 @@
    "outputs": [],
    "source": [
     "%%capture --no-stderr\n",
-    "% pip install -U langgraph langchain-community langchain-anthropic tavily-python pandas"
+    "%pip install -U langgraph langchain-community langchain-anthropic tavily-python pandas openai"
    ]
   },
   {
@@ -55,6 +55,7 @@
     "\n",
     "\n",
     "_set_env(\"ANTHROPIC_API_KEY\")\n",
+    "_set_env(\"OPENAI_API_KEY\")\n",
     "_set_env(\"TAVILY_API_KEY\")"
    ]
   },
@@ -85,7 +86,9 @@
    "cell_type": "code",
    "execution_count": 21,
    "id": "71638c2a-5038-439e-907a-de2bb548db34",
-   "metadata": {"hide_from_vcr": true},
+   "metadata": {
+    "hide_from_vcr": true
+   },
    "outputs": [],
    "source": [
     "import os\n",
@@ -176,7 +179,9 @@
    "cell_type": "code",
    "execution_count": 22,
    "id": "654e2f81",
-   "metadata": {"hide_from_vcr": true},
+   "metadata": {
+    "hide_from_vcr": true
+   },
    "outputs": [],
    "source": [
     "import re\n",
@@ -1077,7 +1082,7 @@
     "        ),\n",
     "        (\"placeholder\", \"{messages}\"),\n",
     "    ]\n",
-    ").partial(time=datetime.now())\n",
+    ").partial(time=datetime.now)\n",
     "\n",
     "part_1_tools = [\n",
     "    TavilySearchResults(max_results=1),\n",
@@ -1893,7 +1898,7 @@
     "        ),\n",
     "        (\"placeholder\", \"{messages}\"),\n",
     "    ]\n",
-    ").partial(time=datetime.now())\n",
+    ").partial(time=datetime.now)\n",
     "\n",
     "part_2_tools = [\n",
     "    TavilySearchResults(max_results=1),\n",
@@ -2472,7 +2477,7 @@
     "        ),\n",
     "        (\"placeholder\", \"{messages}\"),\n",
     "    ]\n",
-    ").partial(time=datetime.now())\n",
+    ").partial(time=datetime.now)\n",
     "\n",
     "\n",
     "# \"Read\"-only tools (such as retrievers) don't need a user confirmation to use\n",
@@ -3183,7 +3188,7 @@
     "        ),\n",
     "        (\"placeholder\", \"{messages}\"),\n",
     "    ]\n",
-    ").partial(time=datetime.now())\n",
+    ").partial(time=datetime.now)\n",
     "\n",
     "update_flight_safe_tools = [search_flights]\n",
     "update_flight_sensitive_tools = [update_ticket_to_new_flight, cancel_ticket]\n",
@@ -3215,7 +3220,7 @@
     "        ),\n",
     "        (\"placeholder\", \"{messages}\"),\n",
     "    ]\n",
-    ").partial(time=datetime.now())\n",
+    ").partial(time=datetime.now)\n",
     "\n",
     "book_hotel_safe_tools = [search_hotels]\n",
     "book_hotel_sensitive_tools = [book_hotel, update_hotel, cancel_hotel]\n",
@@ -3247,7 +3252,7 @@
     "        ),\n",
     "        (\"placeholder\", \"{messages}\"),\n",
     "    ]\n",
-    ").partial(time=datetime.now())\n",
+    ").partial(time=datetime.now)\n",
     "\n",
     "book_car_rental_safe_tools = [search_car_rentals]\n",
     "book_car_rental_sensitive_tools = [\n",
@@ -3282,7 +3287,7 @@
     "        ),\n",
     "        (\"placeholder\", \"{messages}\"),\n",
     "    ]\n",
-    ").partial(time=datetime.now())\n",
+    ").partial(time=datetime.now)\n",
     "\n",
     "book_excursion_safe_tools = [search_trip_recommendations]\n",
     "book_excursion_sensitive_tools = [book_excursion, update_excursion, cancel_excursion]\n",
@@ -3389,7 +3394,7 @@
     "        ),\n",
     "        (\"placeholder\", \"{messages}\"),\n",
     "    ]\n",
-    ").partial(time=datetime.now())\n",
+    ").partial(time=datetime.now)\n",
     "primary_assistant_tools = [\n",
     "    TavilySearchResults(max_results=1),\n",
     "    search_flights,\n",

docs/docs/tutorials/index.md

@@ -1,6 +1,7 @@
 ---
 hide:
-  - toc
+  - navigation
+title: Tutorials
 ---
 
 # Tutorials
@@ -11,35 +12,40 @@ Welcome to the LangGraph Tutorials! These notebooks introduce LangGraph through
 
 Learn the basics of LangGraph through a comprehensive quick start in which you will build an agent from scratch.
 
-- [Quick Start](introduction.ipynb)
+- [Quick Start](introduction.ipynb): In this tutorial, you will build a support chatbot using LangGraph.
+- [LangGraph Cloud Quick Start](../cloud/quick_start.md): In this tutorial, you will build and deploy an agent to LangGraph Cloud.
 
 ## Use cases
 
 Learn from example implementations of graphs designed for specific scenarios and that implement common design patterns.
 
-#### Chatbots
+### Chatbots
 
 - [Customer Support](customer-support/customer-support.ipynb): Build a customer support chatbot to manage flights, hotel reservations, car rentals, and other tasks
 - [Prompt Generation from User Requirements](chatbots/information-gather-prompting.ipynb): Build an information gathering chatbot
 - [Code Assistant](code_assistant/langgraph_code_assistant.ipynb): Build a code analysis and generation assistant
 
+
+### RAG
+
+- [Agentic RAG](rag/langgraph_agentic_rag.ipynb): Use an agent to figure out how to retrieve the most relevant information before using the retrieved information to answer the user's question.
+- [Adaptive RAG](rag/langgraph_adaptive_rag.ipynb): Adaptive RAG is a strategy for RAG that unites (1) query analysis with (2) active / self-corrective RAG. Implementation of: https://arxiv.org/abs/2403.14403
+    - For a version that uses a local LLM: [Adaptive RAG using local LLMs](rag/langgraph_adaptive_rag_local.ipynb)
+- [Corrective RAG](rag/langgraph_crag.ipynb): Uses an LLM to grade the quality of the retrieved information from the given source, and if the quality is low, it will try to retrieve the information from another source. Implementation of: https://arxiv.org/pdf/2401.15884.pdf 
+    - For a version that uses a local LLM: [Corrective RAG using local LLMs](rag/langgraph_crag_local.ipynb)
+- [Self-RAG](rag/langgraph_self_rag.ipynb): Self-RAG is a strategy for RAG that incorporates self-reflection / self-grading on retrieved documents and generations. Implementation of https://arxiv.org/abs/2310.11511.
+    - For a version that uses a local LLM: [Self-RAG using local LLMs](rag/langgraph_self_rag_local.ipynb) 
+- [SQL Agent](sql-agent.ipynb): Build a SQL agent that can answer questions about a SQL database.
+
+
+### Agent Architectures
+
 #### Multi-Agent Systems
 
 - [Network](multi_agent/multi-agent-collaboration.ipynb): Enable two or more agents to collaborate on a task
 - [Supervisor](multi_agent/agent_supervisor.ipynb): Use an LLM to orchestrate and delegate to individual agents
 - [Hierarchical Teams](multi_agent/hierarchical_agent_teams.ipynb): Orchestrate nested teams of agents to solve problems
-
-#### RAG
-
-- [Adaptive RAG](rag/langgraph_adaptive_rag.ipynb)
-    - [Adaptive RAG using local LLMs](rag/langgraph_adaptive_rag_local.ipynb)
-- [Agentic RAG](rag/langgraph_agentic_rag.ipynb)
-- [Corrective RAG](rag/langgraph_crag.ipynb)
-    - [Corrective RAG using local LLMs](rag/langgraph_crag_local.ipynb)
-- [Self-RAG](rag/langgraph_self_rag.ipynb)
-    - [Self-RAG using local LLMs](rag/langgraph_self_rag_local.ipynb)
-- [SQL Agent](sql-agent.ipynb)
-
+ 
 #### Planning Agents
 
 - [Plan-and-Execute](plan-and-execute/plan-and-execute.ipynb): Implement a basic planning and execution agent
@@ -50,15 +56,16 @@ Learn from example implementations of graphs designed for specific scenarios and
 
 - [Basic Reflection](reflection/reflection.ipynb): Prompt the agent to reflect on and revise its outputs
 - [Reflexion](reflexion/reflexion.ipynb): Critique missing and superfluous details to guide next steps
-- [Language Agent Tree Search](lats/lats.ipynb): Use reflection and rewards to drive a tree search over agents
+- [Tree of Thoughts](tot/tot.ipynb): Search over candidate solutions to a problem using a scored tree
+- [Language Agent Tree Search](lats/lats.ipynb): Use reflection and rewards to drive a monte-carlo tree search over agents
 - [Self-Discover Agent](self-discover/self-discover.ipynb): Analyze an agent that learns about its own capabilities
 
-#### Evaluation
+### Evaluation
 
 - [Agent-based](chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb): Evaluate chatbots via simulated user interactions
 - [In LangSmith](chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb): Evaluate chatbots in LangSmith over a dialog dataset
 
-#### Experimental
+### Experimental
 
 - [Web Research (STORM)](storm/storm.ipynb): Generate Wikipedia-like articles via research and multi-perspective QA
 - [TNT-LLM](tnt-llm/tnt-llm.ipynb): Build rich, interpretable taxonomies of user intentand using the classification system developed by Microsoft for their Bing Copilot application.

docs/docs/tutorials/introduction.ipynb

@@ -5,7 +5,7 @@
    "id": "4a1aae78-88a6-4133-b905-7e46c8e3772f",
    "metadata": {},
    "source": [
-    "# Quick Start\n",
+    "# LangGraph Quick Start\n",
     "\n",
     "In this comprehensive quick start, we will build a support chatbot in LangGraph that can:\n",
     "\n",
@@ -127,7 +127,7 @@
     "<div class=\"admonition tip\">\n",
     "    <p class=\"admonition-title\">Note</p>\n",
     "    <p>\n",
-    "    The first thing you do when you define a graph is define the <code>State</code> of the graph. The <code>State</code> consists of the schema of the graph as well as reducer functions which specify how to apply updates to the state. In our example <code>State</code> is a <code>TypedDict</code> with a single key: <code>messages</code>. The <code>messages</code> key is annotated with the <a href=\"https://langchain-ai.github.io/langgraph/reference/graphs/?h=add+messages#add_messages\"><code>add_messages</code></a> reducer function, which tells LangGraph to append new messages to the existing list, rather than overwriting it. State keys without an annotation will be overwritten by each update, storing the most recent value. Check out <a href=\"https://langchain-ai.github.io/langgraph/reference/graphs/?h=add+messages#add_messages\">this conceptual guide</a> to learn more about state, reducers and other low-level concepts.\n",
+    "    The first thing you do when you define a graph is define the <code>State</code> of the graph. The <code>State</code> consists of the schema of the graph as well as <a href=\"https://langchain-ai.github.io/langgraph/concepts/low_level/#reducers\">reducer functions</a> which specify how to apply updates to the state. In our example <code>State</code> is a <code>TypedDict</code> with a single key: <code>messages</code>. The <code>messages</code> key is annotated with the <a href=\"https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.message.add_messages\"><code>add_messages</code></a> reducer function, which tells LangGraph to append new messages to the existing list, rather than overwriting it. State keys without an annotation will be overwritten by each update, storing the most recent value. Check out <a href=\"https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.message.add_messages\">this conceptual guide</a> to learn more about state, reducers and other low-level concepts.\n",
     "    </p>\n",
     "</div>"
    ]

docs/docs/tutorials/rag/langgraph_adaptive_rag_local.ipynb

@@ -112,7 +112,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "_set_env(\"LANGCHAIN_API_KEY\")\n",
+    "_set_env(\"LANGSMITH_API_KEY\")\n",
     "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
     "os.environ[\"LANGCHAIN_PROJECT\"] = \"local-llama32-rag\""
    ]

docs/docs/tutorials/reflexion/reflexion.ipynb

@@ -46,8 +46,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install -U --quiet  langgraph langchain_anthropic\n",
-    "%pip install -U --quiet tavily-python"
+    "%pip install -U --quiet langgraph langchain_anthropic tavily-python"
    ]
   },
   {
@@ -189,7 +188,7 @@
     "        self.runnable = runnable\n",
     "        self.validator = validator\n",
     "\n",
-    "    def respond(self, state: list):\n",
+    "    def respond(self, state: dict):\n",
     "        response = []\n",
     "        for attempt in range(3):\n",
     "            response = self.runnable.invoke(\n",
@@ -622,12 +621,6 @@
     "2. The 'reflections' can be paired with additional external feedback (such as validators), to further guide the actor.\n",
     "3. In the paper, 1 environment (AlfWorld) uses external memory. It does this by storing summaries of the reflections to an external store and using them in subsequent trials/invocations."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "39e44dd6",
-   "metadata": {},
-   "source": []
   }
  ],
  "metadata": {
@@ -646,7 +639,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,

docs/mkdocs.yml

@@ -22,10 +22,13 @@ theme:
     - navigation.footer
     - navigation.indexes
     - navigation.instant
+    - navigation.sections
     - navigation.instant.prefetch
     - navigation.instant.progress
+    - navigation.path
     - navigation.prune
     - navigation.tabs
+    - navigation.tabs.sticky
     - navigation.top
     - navigation.tracking
     - search.highlight
@@ -52,6 +55,13 @@ plugins:
   - search:
       separator: '[\s\u200b\-_,:!=\[\]()"`/]+|\.(?!\d)|&[lg]t;|(?!\b)(?=[A-Z][a-z])'
   - autorefs
+  - redirects:
+      redirect_maps:
+        'cloud/index.md': 'concepts/index.md#langgraph-platform'
+        'cloud/how-tos/index.md': 'how-tos/index.md#langgraph-platform'
+        'cloud/concepts/api.md': 'concepts/langgraph_server.md'
+        'cloud/concepts/cloud.md': 'concepts/langgraph_cloud.md'
+        'cloud/faq/studio.md': 'concepts/langgraph_studio.md#studio-faqs'
   - mkdocstrings:
       handlers:
         python:
@@ -78,199 +88,260 @@ plugins:
               filters:
                 - "!^_"
 nav:
-  - "index.md"
+  - Home: index.md
   - Tutorials:
-      - "tutorials/index.md"
-      - Quick Start: tutorials/introduction.ipynb
+      - tutorials/index.md
+      - Quick Start:
+        - Quick Start: tutorials#quick-start
+        - tutorials/introduction.ipynb
+        - cloud/quick_start.md
       - Chatbots:
-          - Customer Support: tutorials/customer-support/customer-support.ipynb
-          - Prompt Generation from User Requirements: tutorials/chatbots/information-gather-prompting.ipynb
-          - Code Assistant: tutorials/code_assistant/langgraph_code_assistant.ipynb
+          - Chatbots: tutorials#chatbots
+          - tutorials/customer-support/customer-support.ipynb
+          - tutorials/chatbots/information-gather-prompting.ipynb
+          - tutorials/code_assistant/langgraph_code_assistant.ipynb
       - RAG:
-          - Adaptive RAG: tutorials/rag/langgraph_adaptive_rag.ipynb
-          - Adaptive RAG using local LLMs: tutorials/rag/langgraph_adaptive_rag_local.ipynb
-          - Agentic RAG: tutorials/rag/langgraph_agentic_rag.ipynb
-          - Corrective RAG (CRAG): tutorials/rag/langgraph_crag.ipynb
-          - Corrective RAG (CRAG) using local LLMs: tutorials/rag/langgraph_crag_local.ipynb
-          - Self-RAG: tutorials/rag/langgraph_self_rag.ipynb
-          - Self-RAG using local LLMs: tutorials/rag/langgraph_self_rag_local.ipynb
-          - SQL Agent: tutorials/sql-agent.ipynb
+          - RAG: tutorials#rag
+          - tutorials/rag/langgraph_adaptive_rag.ipynb
+          - tutorials/rag/langgraph_adaptive_rag_local.ipynb
+          - tutorials/rag/langgraph_agentic_rag.ipynb
+          - tutorials/rag/langgraph_crag.ipynb
+          - tutorials/rag/langgraph_crag_local.ipynb
+          - tutorials/rag/langgraph_self_rag.ipynb
+          - tutorials/rag/langgraph_self_rag_local.ipynb
+          - tutorials/sql-agent.ipynb
       - Agent Architectures:
+          - Agent Architectures: tutorials#agent-architectures
           - Multi-Agent Systems:
-              - Network: tutorials/multi_agent/multi-agent-collaboration.ipynb
-              - Supervisor: tutorials/multi_agent/agent_supervisor.ipynb
-              - Hierarchical Teams: tutorials/multi_agent/hierarchical_agent_teams.ipynb
+              - Multi-Agent Systems: tutorials#multi-agent-systems
+              - tutorials/multi_agent/multi-agent-collaboration.ipynb
+              - tutorials/multi_agent/agent_supervisor.ipynb
+              - tutorials/multi_agent/hierarchical_agent_teams.ipynb
           - Planning Agents:
-              - Plan-and-Execute: tutorials/plan-and-execute/plan-and-execute.ipynb
-              - Reasoning without Observation: tutorials/rewoo/rewoo.ipynb
-              - LLMCompiler: tutorials/llm-compiler/LLMCompiler.ipynb
+              - Planning Agents: tutorials#planning-agents
+              - tutorials/plan-and-execute/plan-and-execute.ipynb
+              - tutorials/rewoo/rewoo.ipynb
+              - tutorials/llm-compiler/LLMCompiler.ipynb
           - Reflection & Critique:
-              - Basic Reflection: tutorials/reflection/reflection.ipynb
-              - Reflexion: tutorials/reflexion/reflexion.ipynb
-              - Language Agent Tree Search: tutorials/lats/lats.ipynb
-              - Self-Discover Agent: tutorials/self-discover/self-discover.ipynb
+              - Reflection & Critique: tutorials#reflection-critique
+              - tutorials/reflection/reflection.ipynb
+              - tutorials/reflexion/reflexion.ipynb
+              - tutorials/tot/tot.ipynb
+              - tutorials/lats/lats.ipynb
+              - tutorials/self-discover/self-discover.ipynb
       - Evaluation & Analysis:
-          - Chatbot Evaluation via Simulation:
-              - Agent-based: tutorials/chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb
-              - In LangSmith: tutorials/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb
+          - Evaluation & Analysis: tutorials#evaluation
+          - tutorials/chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb
+          - tutorials/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb
       - Experimental:
-          - Web Research (STORM): tutorials/storm/storm.ipynb
-          - TNT-LLM: tutorials/tnt-llm/tnt-llm.ipynb
-          - Web Navigation: tutorials/web-navigation/web_voyager.ipynb
-          - Competitive Programming: tutorials/usaco/usaco.ipynb
-          - Extract structured output: tutorials/extraction/retries.ipynb
+          - Experimental: tutorials#experimental
+          - tutorials/storm/storm.ipynb
+          - tutorials/tnt-llm/tnt-llm.ipynb
+          - tutorials/web-navigation/web_voyager.ipynb
+          - tutorials/usaco/usaco.ipynb
+          - tutorials/extraction/retries.ipynb
 
-  - "How-to Guides":
-      - "how-tos/index.md"
-      - Controllability:
-          - Create branches for parallel execution: how-tos/branching.ipynb
-          - Create map-reduce branches for parallel execution: how-tos/map-reduce.ipynb
-          - Control graph recursion limit: how-tos/recursion-limit.ipynb
-      - Persistence:
-          - Add thread-level persistence: how-tos/persistence.ipynb
-          - Add thread-level persistence to subgraphs: how-tos/subgraph-persistence.ipynb
-          - Add cross-thread persistence: how-tos/cross-thread-persistence.ipynb
-          - Use Postgres checkpointer for persistence: how-tos/persistence_postgres.ipynb
-          - Create custom checkpointer using MongoDB: how-tos/persistence_mongodb.ipynb
-          - Create custom checkpointer using Redis: how-tos/persistence_redis.ipynb
-      - Memory:
-          - Manage conversation history: how-tos/memory/manage-conversation-history.ipynb
-          - Delete messages: how-tos/memory/delete-messages.ipynb
-          - Add summary of the conversation history: how-tos/memory/add-summary-conversation-history.ipynb
-      - Human-in-the-loop:
-          - Add breakpoints: how-tos/human_in_the_loop/breakpoints.ipynb
-          - Add dynamic breakpoints: how-tos/human_in_the_loop/dynamic_breakpoints.ipynb
-          - Wait for user input: how-tos/human_in_the_loop/wait-user-input.ipynb
-          - View and update past graph state: how-tos/human_in_the_loop/time-travel.ipynb
-          - Edit graph state: how-tos/human_in_the_loop/edit-graph-state.ipynb
-          - Review tool calls: how-tos/human_in_the_loop/review-tool-calls.ipynb
-      - Streaming:
-          - Stream full state: how-tos/stream-values.ipynb
-          - Stream state updates: how-tos/stream-updates.ipynb
-          - Stream LLM tokens: how-tos/streaming-tokens.ipynb
-          - Stream LLM tokens without LangChain models: how-tos/streaming-tokens-without-langchain.ipynb
-          - Stream custom data: how-tos/streaming-content.ipynb
-          - Configure multiple streaming modes: how-tos/stream-multiple.ipynb
-          - Stream events from within tools: how-tos/streaming-events-from-within-tools.ipynb
-          - Stream events from within tools without LangChain models: how-tos/streaming-events-from-within-tools-without-langchain.ipynb
-          - Stream events from the final node: how-tos/streaming-from-final-node.ipynb
-          - Stream from subgraphs: how-tos/streaming-subgraphs.ipynb
-          - Disable streaming for models that don't support it: how-tos/disable-streaming.ipynb
-      - Tool calling:
-          - Call tools using ToolNode: how-tos/tool-calling.ipynb
-          - Handle tool calling errors: how-tos/tool-calling-errors.ipynb
-          - Pass runtime values to tools: how-tos/pass-run-time-values-to-tools.ipynb
-          - Pass config to tools: how-tos/pass-config-to-tools.ipynb
-          - Handle many tools: how-tos/many-tools.ipynb
-      - Subgraphs:
-          - Add and use subgraphs: how-tos/subgraph.ipynb
-          - View and update state in subgraphs: how-tos/subgraphs-manage-state.ipynb
-          - Transform inputs and outputs of a subgraph: how-tos/subgraph-transform-state.ipynb
-      - State Management:
-          - Use Pydantic model as state: how-tos/state-model.ipynb
-          - Have a separate input and output schema: how-tos/input_output_schema.ipynb
-          - Pass private state between nodes inside the graph: how-tos/pass_private_state.ipynb
-      - Other:
-          - Run graph asynchronously: how-tos/async.ipynb
-          - Visualize your graph: how-tos/visualization.ipynb
-          - Add runtime configuration: how-tos/configuration.ipynb
-          - Add node retries: how-tos/node-retries.ipynb
-          - Return structured output from a ReAct agent: how-tos/react-agent-structured-output.ipynb
-          - Pass custom LangSmith run ID for graph runs: how-tos/run-id-langsmith.ipynb
-          - Return state before hitting recursion limit: how-tos/return-when-recursion-limit-hits.ipynb
-          - Error reference:
-              - "troubleshooting/errors/index.md"
-              - GRAPH_RECURSION_LIMIT: "troubleshooting/errors/GRAPH_RECURSION_LIMIT.md"
-              - INVALID_CONCURRENT_GRAPH_UPDATE: "troubleshooting/errors/INVALID_CONCURRENT_GRAPH_UPDATE.md"
-              - INVALID_GRAPH_NODE_RETURN_VALUE: "troubleshooting/errors/INVALID_GRAPH_NODE_RETURN_VALUE.md"
-              - MULTIPLE_SUBGRAPHS: "troubleshooting/errors/MULTIPLE_SUBGRAPHS.md"
-      - Prebuilt ReAct Agent:
-          - Create a ReAct agent: how-tos/create-react-agent.ipynb
-          - Add memory to a ReAct agent: how-tos/create-react-agent-memory.ipynb
-          - Add a system prompt to a ReAct agent: how-tos/create-react-agent-system-prompt.ipynb
-          - Add Human-in-the-loop to a ReAct agent: how-tos/create-react-agent-hitl.ipynb
-          - Create prebuilt ReAct agent from scratch: how-tos/react-agent-from-scratch.ipynb
-  - "Conceptual Guides":
-      - Why LangGraph?: concepts/high_level.md
-      - LangGraph Glossary: concepts/low_level.md
-      - Common Agentic Patterns: concepts/agentic_concepts.md
-      - Human-in-the-Loop: concepts/human_in_the_loop.md
-      - Memory: concepts/memory.md
-      - Multi-Agent Systems: concepts/multi_agent.md
-      - Persistence: concepts/persistence.md
-      - Streaming: concepts/streaming.md
-      - FAQ: concepts/faq.md
-  - Reference:
-      - Graphs: reference/graphs.md
-      - Checkpointing: reference/checkpoints.md
-      - Storage: reference/store.md
-      - Prebuilt Components: reference/prebuilt.md
-      - Channels: reference/channels.md
-      - Errors: reference/errors.md
-      - Types: reference/types.md
-      - Constants: reference/constants.md
-  - "Cloud (beta)":
-      - "cloud/index.md"
-      - Tutorials:
-          - Quick Start: "cloud/quick_start.md"
-      - How-to Guides:
-          - "cloud/how-tos/index.md"
-          - Setup:
-              - Setup App: "cloud/deployment/setup.md"
-              - Setup App (pyproject.toml): "cloud/deployment/setup_pyproject.md"
-              - Setup App (JavaScript): "cloud/deployment/setup_javascript.md"
-              - Rebuild Graph at Runtime: "cloud/deployment/graph_rebuild.md"
-              - Customize Dockerfile: "cloud/deployment/custom_docker.md"
-              - Test App Locally: "cloud/deployment/test_locally.md"
+  - How-to Guides:
+      - how-tos/index.md
+      - LangGraph:
+        - LangGraph: how-tos#langgraph
+        - Controllability:
+            - Controllability: how-tos#controllability
+            - how-tos/branching.ipynb
+            - how-tos/map-reduce.ipynb
+            - how-tos/recursion-limit.ipynb
+        - Persistence:
+            - Persistence: how-tos#persistence
+            - how-tos/persistence.ipynb
+            - how-tos/subgraph-persistence.ipynb
+            - how-tos/cross-thread-persistence.ipynb
+            - how-tos/persistence_postgres.ipynb
+            - how-tos/persistence_mongodb.ipynb
+            - how-tos/persistence_redis.ipynb
+        - Memory:
+            - Memory: how-tos#memory
+            - how-tos/memory/manage-conversation-history.ipynb
+            - how-tos/memory/delete-messages.ipynb
+            - how-tos/memory/add-summary-conversation-history.ipynb
+        - Human-in-the-loop:
+            - Human-in-the-loop: how-tos#human-in-the-loop
+            - how-tos/human_in_the_loop/breakpoints.ipynb
+            - how-tos/human_in_the_loop/dynamic_breakpoints.ipynb
+            - how-tos/human_in_the_loop/edit-graph-state.ipynb
+            - how-tos/human_in_the_loop/wait-user-input.ipynb
+            - how-tos/human_in_the_loop/time-travel.ipynb
+            - how-tos/human_in_the_loop/review-tool-calls.ipynb
+        - Streaming:
+            - Streaming: how-tos#streaming
+            - how-tos/stream-values.ipynb
+            - how-tos/stream-updates.ipynb
+            - how-tos/streaming-tokens.ipynb
+            - how-tos/streaming-tokens-without-langchain.ipynb
+            - how-tos/streaming-content.ipynb
+            - how-tos/stream-multiple.ipynb
+            - how-tos/streaming-events-from-within-tools.ipynb
+            - how-tos/streaming-events-from-within-tools-without-langchain.ipynb
+            - how-tos/streaming-from-final-node.ipynb
+            - how-tos/streaming-subgraphs.ipynb
+            - how-tos/disable-streaming.ipynb
+        - Tool calling:
+            - Tool calling: how-tos#tool-calling
+            - how-tos/tool-calling.ipynb
+            - how-tos/tool-calling-errors.ipynb
+            - how-tos/pass-run-time-values-to-tools.ipynb
+            - how-tos/pass-config-to-tools.ipynb
+            - how-tos/many-tools.ipynb
+        - Subgraphs:
+            - Subgraphs: how-tos#subgraphs
+            - how-tos/subgraph.ipynb
+            - how-tos/subgraphs-manage-state.ipynb
+            - how-tos/subgraph-transform-state.ipynb
+        - State Management:
+            - State Management: how-tos#state-management
+            - how-tos/state-model.ipynb
+            - how-tos/input_output_schema.ipynb
+            - how-tos/pass_private_state.ipynb
+        - Other:
+            - Other: how-tos#other
+            - how-tos/async.ipynb
+            - how-tos/visualization.ipynb
+            - how-tos/configuration.ipynb
+            - how-tos/node-retries.ipynb
+            - how-tos/react-agent-structured-output.ipynb
+            - how-tos/run-id-langsmith.ipynb
+            - how-tos/return-when-recursion-limit-hits.ipynb
+        - Prebuilt ReAct Agent:
+            - Prebuilt ReAct Agent: how-tos#prebuilt-react-agent
+            - how-tos/create-react-agent.ipynb
+            - how-tos/create-react-agent-memory.ipynb
+            - how-tos/create-react-agent-system-prompt.ipynb
+            - how-tos/create-react-agent-hitl.ipynb
+            - how-tos/react-agent-from-scratch.ipynb
+      - LangGraph Platform:
+          - LangGraph Platform: how-tos#langgraph-platform
+          - Application Structure:
+              - Application Structure: how-tos#application-structure
+              - cloud/deployment/setup.md
+              - cloud/deployment/setup_pyproject.md
+              - cloud/deployment/setup_javascript.md
+              - cloud/deployment/custom_docker.md
+              - cloud/deployment/test_locally.md
+              - cloud/deployment/graph_rebuild.md
           - Deployment:
-              - Deploy to Cloud: "cloud/deployment/cloud.md"
+              - Deployment: how-tos#deployment
+              - cloud/deployment/cloud.md
+              - how-tos/deploy-self-hosted.md
+              - how-tos/use-remote-graph.md
+          - Assistants:
+              - Assistants: how-tos#assistants
+              - cloud/how-tos/configuration_cloud.md
+              - cloud/how-tos/assistant_versioning.md
+          - Threads:
+              - Threads: how-tos#threads
+              - cloud/how-tos/copy_threads.md
+              - cloud/how-tos/check_thread_status.md
+          - Runs:
+              - Runs: how-tos#runs
+              - cloud/how-tos/background_run.md
+              - cloud/how-tos/same-thread.md
+              - cloud/how-tos/cron_jobs.md
+              - cloud/how-tos/stateless_runs.md
           - Streaming:
-              - Stream Values: "cloud/how-tos/stream_values.md"
-              - Stream Updates: "cloud/how-tos/stream_updates.md"
-              - Stream Messages: "cloud/how-tos/stream_messages.md"
-              - Stream Events: "cloud/how-tos/stream_events.md"
-              - Stream Debug: "cloud/how-tos/stream_debug.md"
-              - Multiple Modes: "cloud/how-tos/stream_multiple.md"
-          - Double Texting:
-              - Interrupt: "cloud/how-tos/interrupt_concurrent.md"
-              - Rollback: "cloud/how-tos/rollback_concurrent.md"
-              - Reject: "cloud/how-tos/reject_concurrent.md"
-              - Enqueue: "cloud/how-tos/enqueue_concurrent.md"
-          - Human-in-the-Loop:
-              - Add Breakpoint: "cloud/how-tos/human_in_the_loop_breakpoint.md"
-              - Wait for User Input: "cloud/how-tos/human_in_the_loop_user_input.md"
-              - Edit Graph State: "cloud/how-tos/human_in_the_loop_edit_state.md"
-              - Replay and Branch from Prior States: "cloud/how-tos/human_in_the_loop_time_travel.md"
-              - Review Tool Calls: "cloud/how-tos/human_in_the_loop_review_tool_calls.md"
+              - Streaming: how-tos#streaming_1
+              - cloud/how-tos/stream_values.md
+              - cloud/how-tos/stream_updates.md
+              - cloud/how-tos/stream_messages.md
+              - cloud/how-tos/stream_events.md
+              - cloud/how-tos/stream_debug.md
+              - cloud/how-tos/stream_multiple.md
+          - Human-in-the-loop:
+              - Human-in-the-loop: how-tos#human-in-the-loop_1
+              - cloud/how-tos/human_in_the_loop_breakpoint.md
+              - cloud/how-tos/human_in_the_loop_user_input.md
+              - cloud/how-tos/human_in_the_loop_edit_state.md
+              - cloud/how-tos/human_in_the_loop_time_travel.md
+              - cloud/how-tos/human_in_the_loop_review_tool_calls.md
+          - Double-texting:
+              - Double-texting: how-tos#double-texting
+              - cloud/how-tos/interrupt_concurrent.md
+              - cloud/how-tos/rollback_concurrent.md
+              - cloud/how-tos/reject_concurrent.md
+              - cloud/how-tos/enqueue_concurrent.md
+          - Webhooks:
+              - cloud/how-tos/webhooks.md
+          - Cron Jobs:
+              - cloud/how-tos/cron_jobs.md
           - LangGraph Studio:
-              - Test Cloud Deployment: "cloud/how-tos/test_deployment.md"
-              - Test Local Deployment: "cloud/how-tos/test_local_deployment.md"
-              - Invoke graph in LangGraph Studio: "cloud/how-tos/invoke_studio.md"
-              - Interact with threads in LangGraph Studio: "cloud/how-tos/threads_studio.md"
-          - Different Types of Runs:
-              - Run an Agent in the Background: "cloud/how-tos/background_run.md"
-              - Run Multiple Agents in Same Thread: "cloud/how-tos/same-thread.md"
-              - Create Cron Jobs: "cloud/how-tos/cron_jobs.md"
-              - Create Stateless Runs: "cloud/how-tos/stateless_runs.md"
-          - Other:
-              - Configure Agents: "cloud/how-tos/configuration_cloud.md"
-              - Versioning Assistants: "cloud/how-tos/assistant_versioning.md"
-              - Convert LangGraph calls to LangGraph Cloud calls: "cloud/how-tos/langgraph_to_langgraph_cloud.ipynb"
-              - Integrate Webhooks: "cloud/how-tos/webhooks.md"
-              - Copy Threads: "cloud/how-tos/copy_threads.md"
-              - Check Status of Threads: "cloud/how-tos/check_thread_status.md"
-      - Conceptual Guides:
-          - API Concepts: "cloud/concepts/api.md"
-          - Cloud Concepts: "cloud/concepts/cloud.md"
-      - Reference:
-          - API: "cloud/reference/api/api_ref.md"
-          - SDK:
-              - Python: "cloud/reference/sdk/python_sdk_ref.md"
-              - JS/TS: "cloud/reference/sdk/js_ts_sdk_ref.md"
-          - CLI: "cloud/reference/cli.md"
-          - Environment Variables: "cloud/reference/env_var.md"
-      - FAQ:
-          - Studio: "cloud/faq/studio.md"
+              - LangGraph Studio: how-tos#langgraph-studio
+              - cloud/how-tos/test_deployment.md
+              - cloud/how-tos/test_local_deployment.md
+              - cloud/how-tos/invoke_studio.md
+              - cloud/how-tos/threads_studio.md
+      - Troubleshooting:
+          - Troubleshooting: how-tos#troubleshooting
+          - troubleshooting/errors/index.md
+          - troubleshooting/errors/GRAPH_RECURSION_LIMIT.md
+          - troubleshooting/errors/INVALID_CONCURRENT_GRAPH_UPDATE.md
+          - troubleshooting/errors/INVALID_GRAPH_NODE_RETURN_VALUE.md
+          - troubleshooting/errors/MULTIPLE_SUBGRAPHS.md
+
+  - Conceptual Guides:
+      - concepts/index.md
+      - LangGraph:
+        - LangGraph: concepts#langgraph
+        - concepts/high_level.md
+        - concepts/low_level.md
+        - concepts/agentic_concepts.md
+        - concepts/multi_agent.md
+        - concepts/human_in_the_loop.md
+        - concepts/persistence.md
+        - concepts/memory.md
+        - concepts/streaming.md
+        - concepts/faq.md
+      - LangGraph Platform:
+        - LangGraph Platform: concepts#langgraph-platform
+        - High Level:
+          - High Level: concepts#high-level
+          - concepts/langgraph_platform.md
+          - concepts/deployment_options.md
+          - concepts/plans.md
+          - concepts/template_applications.md
+        - Components:
+          - Components: concepts#components
+          - concepts/langgraph_server.md
+          - concepts/langgraph_studio.md
+          - concepts/langgraph_cli.md
+          - concepts/sdk.md
+          - how-tos/use-remote-graph.md
+        - LangGraph Server:
+          - LangGraph Server: concepts#langgraph-server
+          - concepts/application_structure.md
+          - concepts/assistants.md
+          - concepts/double_texting.md
+        - Deployment Options:
+          - Deployment Options: concepts#deployment-options
+          - concepts/self_hosted.md
+          - concepts/langgraph_cloud.md
+          - concepts/bring_your_own_cloud.md
+
+  - Reference:
+      - "reference/index.md"
+      - Library:
+        - Graphs: reference/graphs.md
+        - Checkpointing: reference/checkpoints.md
+        - Storage: reference/store.md
+        - Prebuilt Components: reference/prebuilt.md
+        - Channels: reference/channels.md
+        - Errors: reference/errors.md
+        - Types: reference/types.md
+        - Constants: reference/constants.md
+      - LangGraph Platform:
+        - Server API: "cloud/reference/api/api_ref.md"
+        - CLI: "cloud/reference/cli.md"
+        - SDK (Python): "cloud/reference/sdk/python_sdk_ref.md"
+        - SDK (JS/TS): "cloud/reference/sdk/js_ts_sdk_ref.md"
+        - RemoteGraph: reference/remote_graph.md
+        - Environment Variables: "cloud/reference/env_var.md"
 
 markdown_extensions:
   - abbr
@@ -351,7 +422,14 @@ extra:
           note: >-
             Thanks for your feedback! Please help us improve this page by adding to the discussion below.
 validation:
-  omitted_files: warn
+  # https://www.mkdocs.org/user-guide/configuration/
+  # We're `ignoring` nav.omitted_files because we are going to rely
+  # on files being properly links to from the index pages of:
+  # - tutorials
+  # - concepts
+  # - how-tos
+  # - reference
+  omitted_files: ignore
   absolute_links: warn
   unrecognized_links: warn
   # TODO: figure out how to enable 'warn' for this
@@ -359,3 +437,5 @@ validation:
   # because it creates anchors in the generated report
   # and those anchors are not available in the actual doc
   anchors: info
+  # this is needed to handle headers with anchors for nav
+  not_found: info