Update error message for missing dev command

Example screenshot:

![image](https://github.com/user-attachments/assets/8304b08d-bc9b-4cd3-8ff4-ed2471d3d6c7)

docs/docs/concepts/deployment_options.md

@@ -28,6 +28,10 @@ The guide below will explain the differences between the deployment options.
 
     The Self-Hosted Enterprise version is only available for the **Enterprise** plan.
 
+!!! warning "Note"
+
+    The LangGraph Platform Deployments view (within LangSmith SaaS and self-hosted LangSmith) is not available for Self-Hosted Enterprise LangGraph deployments. Self-hosted LangGraph deployments are managed externally from LangSmith (e.g. there is no UI to manage these deployments).
+
 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.
@@ -43,6 +47,10 @@ For more information, please see:
 
     The Self-Hosted Lite version is available for all plans.
 
+!!! warning "Note"
+
+    The LangGraph Platform Deployments view (within LangSmith SaaS and self-hosted LangSmith) is not available for Self-Hosted Lite LangGraph deployments. Self-hosted LangGraph deployments are managed externally from LangSmith (e.g. there is no UI to manage these deployments).
+
 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.
@@ -61,12 +69,11 @@ For more information, please see:
 
     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.
+This deployment option provides access to the LangGraph Platform UI (within LangSmith) and an integration with GitHub, allowing you to deploy code from any of your repositories on GitHub.
 
 For more information, please see:
 
@@ -81,7 +88,7 @@ For more information, please see:
     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.
+This combines the best of both worlds for Cloud and Self-Hosted. Create your deployments through the LangGraph Platform UI (within LangSmith) and we manage the infrastructure so you don't have to. The infrastructure all runs within your cloud. This is currently only available on AWS.
 
 For more information please see:
 

docs/docs/concepts/multi_agent.md

@@ -112,7 +112,7 @@ In this architecture, agents are defined as graph nodes. Each agent can communic
 ```python
 from typing import Literal
 from langchain_openai import ChatOpenAI
-from langgraph.graph import StateGraph, MessagesState, START
+from langgraph.graph import StateGraph, MessagesState, START, END
 
 model = ChatOpenAI()
 

docs/docs/concepts/persistence.md

@@ -147,24 +147,21 @@ In our example, the output of `get_state_history` will look like this:
 
 ### Replay
 
-It's also possible to play-back a prior graph execution. If we `invoking` a graph with a `thread_id` and a `checkpoint_id`, then we will *re-play* the graph from a checkpoint that corresponds to the `checkpoint_id`.
+It's also possible to play-back a prior graph execution. If we `invoke` a graph with a `thread_id` and a `checkpoint_id`, then we will *re-play* the previously executed steps _before_ a checkpoint that corresponds to the `checkpoint_id`, and only execute the steps _after_ the checkpoint.
 
-* `thread_id` is simply the ID of a thread. This is always required.
-* `checkpoint_id` This identifier refers to a specific checkpoint within a thread. 
+* `thread_id` is the ID of a thread.
+* `checkpoint_id` is an identifier that refers to a specific checkpoint within a thread.
 
 You must pass these when invoking the graph as part of the `configurable` portion of the config:
 
 ```python
-# {"configurable": {"thread_id": "1"}}  # valid config
-# {"configurable": {"thread_id": "1", "checkpoint_id": "0c62ca34-ac19-445d-bbb0-5b4984975b2a"}}  # also valid config
-
-config = {"configurable": {"thread_id": "1"}}
+config = {"configurable": {"thread_id": "1", "checkpoint_id": "0c62ca34-ac19-445d-bbb0-5b4984975b2a"}}
 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).
+Importantly, LangGraph knows whether a particular step has been executed previously. If it has, LangGraph simply *re-plays* that particular step in the graph and does not re-execute the step, but only for the steps _before_ the provided `checkpoint_id`. All of the steps _after_ `checkpoint_id` will be executed (i.e., a new fork), even if they have been executed previously. See this [how to guide on time-travel to learn more about replaying](../how-tos/human_in_the_loop/time-travel.ipynb).
 
-![Replay](img/persistence/re_play.jpg)
+![Replay](img/persistence/re_play.png)
 
 ### Update state
 

docs/docs/concepts/self_hosted.md

@@ -32,6 +32,10 @@ To use the Self-Hosted Enterprise version, you must acquire a license key that y
 - 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.
 
+!!! warning "Note"
+
+    The LangGraph Platform Deployments view (within LangSmith SaaS and self-hosted LangSmith) is not available for Self-Hosted Lite or Self-Hosted Enterprise LangGraph deployments. Self-hosted LangGraph deployments are managed externally from LangSmith (e.g. there is no UI to manage these deployments).
+
 For step-by-step instructions, see [How to set up a self-hosted deployment of LangGraph](../how-tos/deploy-self-hosted.md).
 
 ## Helm Chart

docs/docs/concepts/time-travel.md

@@ -17,17 +17,9 @@ We call these debugging techniques **Time Travel**, composed of two key actions:
 
 ![](./img/human_in_the_loop/replay.png)
 
-Replaying allows us to revisit and reproduce an agent's past actions. This can be done either from the current state (or checkpoint) of the graph or from a specific checkpoint.
+Replaying allows us to revisit and reproduce an agent's past actions, up to and including a specific step (checkpoint).
 
-To replay from the current state, simply pass `None` as the input along with a `thread`:
-
-```python
-thread = {"configurable": {"thread_id": "1"}}
-for event in graph.stream(None, thread, stream_mode="values"):
-    print(event)
-```
-
-To replay actions from a specific checkpoint, start by retrieving all checkpoints for the thread:
+To replay actions before a specific checkpoint, start by retrieving all checkpoints for the thread:
 
 ```python
 all_checkpoints = []
@@ -43,7 +35,7 @@ for event in graph.stream(None, config, stream_mode="values"):
     print(event)
 ```
 
-The graph efficiently replays previously executed nodes instead of re-executing them, leveraging its awareness of prior checkpoint executions.
+The graph replays previously executed steps _before_ the provided `checkpoint_id` and executes the steps _after_ `checkpoint_id` (i.e., a new fork), even if they have been executed previously.
 
 ## Forking
 

docs/docs/troubleshooting/errors/MULTIPLE_SUBGRAPHS.md

@@ -1,6 +1,6 @@
 # MULTIPLE_SUBGRAPHS
 
-You are calling the same subgraph multiple times within a single LangGraph node with checkpointing enabled for each subgraph.
+You are calling subgraphs multiple times within a single LangGraph node with checkpointing enabled for each subgraph.
 
 This is currently not allowed due to internal restrictions on how checkpoint namespacing for subgraphs works.
 
@@ -9,4 +9,4 @@ This is currently not allowed due to internal restrictions on how checkpoint nam
 The following may help resolve this error:
 
 - If you don't need to interrupt/resume from a subgraph, pass `checkpointer=False` when compiling it like this: `.compile(checkpointer=False)`
-- Don't imperatively call graphs multiple times in the same node, and instead use the [`Send`](https://langchain-ai.github.io/langgraph/concepts/low_level/#send) API.
+- Don't imperatively call graphs multiple times in the same node, and instead use the [`Send`](https://langchain-ai.github.io/langgraph/concepts/low_level/#send) API.

libs/checkpoint-postgres/langgraph/checkpoint/postgres/aio.py

@@ -380,6 +380,18 @@ class AsyncPostgresSaver(BasePostgresSaver):
         Yields:
             Iterator[CheckpointTuple]: An iterator of matching checkpoint tuples.
         """
+        try:
+            # check if we are in the main thread, only bg threads can block
+            # we don't check in other methods to avoid the overhead
+            if asyncio.get_running_loop() is self.loop:
+                raise asyncio.InvalidStateError(
+                    "Synchronous calls to AsyncSqliteSaver are only allowed from a "
+                    "different thread. From the main thread, use the async interface. "
+                    "For example, use `checkpointer.alist(...)` or `await "
+                    "graph.ainvoke(...)`."
+                )
+        except RuntimeError:
+            pass
         aiter_ = self.alist(config, filter=filter, before=before, limit=limit)
         while True:
             try:
@@ -410,7 +422,7 @@ class AsyncPostgresSaver(BasePostgresSaver):
             if asyncio.get_running_loop() is self.loop:
                 raise asyncio.InvalidStateError(
                     "Synchronous calls to AsyncPostgresSaver are only allowed from a "
-                    "different thread. From the main thread, use the async interface."
+                    "different thread. From the main thread, use the async interface. "
                     "For example, use `await checkpointer.aget_tuple(...)` or `await "
                     "graph.ainvoke(...)`."
                 )

libs/checkpoint-sqlite/langgraph/checkpoint/sqlite/aio.py

@@ -159,7 +159,7 @@ class AsyncSqliteSaver(BaseCheckpointSaver[str]):
             if asyncio.get_running_loop() is self.loop:
                 raise asyncio.InvalidStateError(
                     "Synchronous calls to AsyncSqliteSaver are only allowed from a "
-                    "different thread. From the main thread, use the async interface."
+                    "different thread. From the main thread, use the async interface. "
                     "For example, use `await checkpointer.aget_tuple(...)` or `await "
                     "graph.ainvoke(...)`."
                 )
@@ -191,6 +191,18 @@ class AsyncSqliteSaver(BaseCheckpointSaver[str]):
         Yields:
             Iterator[CheckpointTuple]: An iterator of matching checkpoint tuples.
         """
+        try:
+            # check if we are in the main thread, only bg threads can block
+            # we don't check in other methods to avoid the overhead
+            if asyncio.get_running_loop() is self.loop:
+                raise asyncio.InvalidStateError(
+                    "Synchronous calls to AsyncSqliteSaver are only allowed from a "
+                    "different thread. From the main thread, use the async interface. "
+                    "For example, use `checkpointer.alist(...)` or `await "
+                    "graph.ainvoke(...)`."
+                )
+        except RuntimeError:
+            pass
         aiter_ = self.alist(config, filter=filter, before=before, limit=limit)
         while True:
             try:

libs/cli/langgraph_cli/cli.py

@@ -575,6 +575,13 @@ def dev(
     try:
         from langgraph_api.cli import run_server
     except ImportError:
+        py_version_msg = ""
+        if sys.version_info < (3, 11):
+            py_version_msg = (
+                "\n\nNote: The in-mem server requires Python 3.11 or higher to be installed."
+                f" You are currently using Python {sys.version_info.major}.{sys.version_info.minor}."
+                ' Please upgrade your Python version before installing "langgraph-cli[inmem]".'
+            )
         try:
             from importlib import util
 
@@ -582,16 +589,19 @@ def dev(
                 raise click.UsageError(
                     "Required package 'langgraph-api' is not installed.\n"
                     "Please install it with:\n\n"
-                    '    pip install -U "langgraph-cli[inmem]"\n\n'
+                    '    pip install -U "langgraph-cli[inmem]"'
+                    f"{py_version_msg}"
                 ) from None
         except ImportError:
             raise click.UsageError(
                 "Could not verify package installation. Please ensure Python is up to date and\n"
                 "langgraph-cli is installed with the 'inmem' extra: pip install -U \"langgraph-cli[inmem]\""
+                f"{py_version_msg}"
             ) from None
         raise click.UsageError(
             "Could not import run_server. This likely means your installation is incomplete.\n"
             "Please ensure langgraph-cli is installed with the 'inmem' extra: pip install -U \"langgraph-cli[inmem]\""
+            f"{py_version_msg}"
         ) from None
 
     config_json = langgraph_cli.config.validate_config_file(pathlib.Path(config))

libs/cli/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph-cli"
-version = "0.1.66"
+version = "0.1.67"
 description = "CLI for interacting with LangGraph API"
 authors = []
 license = "MIT"

libs/cli/tests/unit_tests/test_config.py

@@ -91,10 +91,22 @@ def test_validate_config():
         validate_config({"python_version": "3.10"})
     assert "Minimum required version" in str(exc_info.value)
 
-    config = validate_config({"python_version": "3.11-bullseye", "dependencies": ["."], "graphs": {"agent": "./agent.py:graph"}})
+    config = validate_config(
+        {
+            "python_version": "3.11-bullseye",
+            "dependencies": ["."],
+            "graphs": {"agent": "./agent.py:graph"},
+        }
+    )
     assert config["python_version"] == "3.11-bullseye"
 
-    config = validate_config({"python_version": "3.12-slim", "dependencies": ["."], "graphs": {"agent": "./agent.py:graph"}})
+    config = validate_config(
+        {
+            "python_version": "3.12-slim",
+            "dependencies": ["."],
+            "graphs": {"agent": "./agent.py:graph"},
+        }
+    )
     assert config["python_version"] == "3.12-slim"