update docstring

bumping sdk-py for custom encryption cleanup changes (#6595) - these are
breaking changes on this beta, unreleased and unused feature.

Signed-off-by: Connor Braa <cwlbraa@langchain.dev>

README.md

@@ -11,7 +11,7 @@
 [![Version](https://img.shields.io/pypi/v/langgraph.svg)](https://pypi.org/project/langgraph/)
 [![Downloads](https://static.pepy.tech/badge/langgraph/month)](https://pepy.tech/project/langgraph)
 [![Open Issues](https://img.shields.io/github/issues-raw/langchain-ai/langgraph)](https://github.com/langchain-ai/langgraph/issues)
-[![Docs](https://img.shields.io/badge/docs-latest-blue)](https://langchain-ai.github.io/langgraph/)
+[![Docs](https://img.shields.io/badge/docs-latest-blue)](https://docs.langchain.com/oss/python/langgraph/overview)
 
 Trusted by companies shaping the future of agents – including Klarna, Replit, Elastic, and more – LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents.
 
@@ -23,47 +23,55 @@ Install LangGraph:
 pip install -U langgraph
 ```
 
-Then, create an agent [using prebuilt components](https://langchain-ai.github.io/langgraph/agents/agents/):
+Create a simple workflow:
 
 ```python
-# pip install -qU "langchain[anthropic]" to call the model
+from langgraph.graph import START, StateGraph
+from typing_extensions import TypedDict
 
-from langgraph.prebuilt import create_react_agent
 
-def get_weather(city: str) -> str:
-    """Get weather for a given city."""
-    return f"It's always sunny in {city}!"
+class State(TypedDict):
+    text: str
 
-agent = create_react_agent(
-    model="anthropic:claude-3-7-sonnet-latest",
-    tools=[get_weather],
-    prompt="You are a helpful assistant"
-)
 
-# Run the agent
-agent.invoke(
-    {"messages": [{"role": "user", "content": "what is the weather in sf"}]}
-)
+def node_a(state: State) -> dict:
+    return {"text": state["text"] + "a"}
+
+
+def node_b(state: State) -> dict:
+    return {"text": state["text"] + "b"}
+
+
+graph = StateGraph(State)
+graph.add_node("node_a", node_a)
+graph.add_node("node_b", node_b)
+graph.add_edge(START, "node_a")
+graph.add_edge("node_a", "node_b")
+
+print(graph.compile().invoke({"text": ""}))
+# {'text': 'ab'}
 ```
 
-For more information, see the [Quickstart](https://langchain-ai.github.io/langgraph/agents/agents/). Or, to learn how to build an [agent workflow](https://langchain-ai.github.io/langgraph/concepts/low_level/) with a customizable architecture, long-term memory, and other complex task handling, see the [LangGraph basics tutorials](https://langchain-ai.github.io/langgraph/tutorials/get-started/1-build-basic-chatbot/).
+Get started with the [LangGraph Quickstart](https://docs.langchain.com/oss/python/langgraph/quickstart).
+
+To quickly build agents with LangChain's `create_agent` (built on LangGraph), see the [LangChain Agents documentation](https://docs.langchain.com/oss/python/langchain/agents).
 
 ## Core benefits
 
 LangGraph provides low-level supporting infrastructure for *any* long-running, stateful workflow or agent. LangGraph does not abstract prompts or architecture, and provides the following central benefits:
 
-- [Durable execution](https://langchain-ai.github.io/langgraph/concepts/durable_execution/): Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off.
-- [Human-in-the-loop](https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/): Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution.
-- [Comprehensive memory](https://langchain-ai.github.io/langgraph/concepts/memory/): Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions.
+- [Durable execution](https://docs.langchain.com/oss/python/langgraph/durable-execution): Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off.
+- [Human-in-the-loop](https://docs.langchain.com/oss/python/langgraph/interrupts): Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution.
+- [Comprehensive memory](https://docs.langchain.com/oss/python/langgraph/memory): Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions.
 - [Debugging with LangSmith](http://www.langchain.com/langsmith): Gain deep visibility into complex agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics.
-- [Production-ready deployment](https://langchain-ai.github.io/langgraph/concepts/deployment_options/): Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows.
+- [Production-ready deployment](https://docs.langchain.com/langsmith/app-development): Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows.
 
 ## LangGraph’s ecosystem
 
 While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents. To improve your LLM application development, pair LangGraph with:
 
 - [LangSmith](http://www.langchain.com/langsmith) — Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
-- [LangSmith Deployment](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/) — Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/).
+- [LangSmith Deployment](https://docs.langchain.com/langsmith/deployments) — Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangGraph Studio](https://docs.langchain.com/oss/python/langgraph/studio).
 - [LangChain](https://docs.langchain.com/oss/python/langchain/overview) – Provides integrations and composable components to streamline LLM application development.
 
 > [!NOTE]
@@ -71,12 +79,11 @@ While LangGraph can be used standalone, it also integrates seamlessly with any L
 
 ## Additional resources
 
-- [Guides](https://langchain-ai.github.io/langgraph/guides/): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
-- [Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
-- [Examples](https://langchain-ai.github.io/langgraph/examples/): Guided examples on getting started with LangGraph.
+- [Guides](https://docs.langchain.com/oss/python/langgraph/guides): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
+- [Reference](https://reference.langchain.com/python/langgraph/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
+- [Examples](https://docs.langchain.com/oss/python/langgraph/agentic-rag): Guided examples on getting started with LangGraph.
 - [LangChain Forum](https://forum.langchain.com/): Connect with the community and share all of your technical questions, ideas, and feedback.
 - [LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph): Learn the basics of LangGraph in our free, structured course.
-- [Templates](https://langchain-ai.github.io/langgraph/concepts/template_applications/): Pre-built reference apps for common agentic workflows (e.g. ReAct agent, memory, retrieval etc.) that can be cloned and adapted.
 - [Case studies](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship AI applications at scale.
 
 ## Acknowledgements

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

@@ -143,11 +143,13 @@ class PostgresSaver(BasePostgresSaver):
         """
         where, args = self._search_where(config, filter, before)
         query = self.SELECT_SQL + where + " ORDER BY checkpoint_id DESC"
-        if limit:
-            query += f" LIMIT {limit}"
+        params = list(args)
+        if limit is not None:
+            query += " LIMIT %s"
+            params.append(int(limit))
         # if we change this to use .stream() we need to make sure to close the cursor
         with self._cursor() as cur:
-            cur.execute(query, args)
+            cur.execute(query, params)
             values = cur.fetchall()
             if not values:
                 return

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

@@ -132,11 +132,13 @@ class AsyncPostgresSaver(BasePostgresSaver):
         """
         where, args = self._search_where(config, filter, before)
         query = self.SELECT_SQL + where + " ORDER BY checkpoint_id DESC"
-        if limit:
-            query += f" LIMIT {limit}"
+        params = list(args)
+        if limit is not None:
+            query += " LIMIT %s"
+            params.append(int(limit))
         # if we change this to use .stream() we need to make sure to close the cursor
         async with self._cursor() as cur:
-            await cur.execute(query, args, binary=True)
+            await cur.execute(query, params, binary=True)
             values = await cur.fetchall()
             if not values:
                 return

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

@@ -272,10 +272,12 @@ class ShallowPostgresSaver(BasePostgresSaver):
         """
         where, args = self._search_where(config, filter, before)
         query = self.SELECT_SQL + where
-        if limit:
-            query += f" LIMIT {limit}"
+        params = list(args)
+        if limit is not None:
+            query += " LIMIT %s"
+            params.append(int(limit))
         with self._cursor() as cur:
-            cur.execute(self.SELECT_SQL + where, args, binary=True)
+            cur.execute(query, params, binary=True)
             for value in cur:
                 checkpoint: Checkpoint = {
                     **value["checkpoint"],
@@ -636,10 +638,12 @@ class AsyncShallowPostgresSaver(BasePostgresSaver):
         """
         where, args = self._search_where(config, filter, before)
         query = self.SELECT_SQL + where
-        if limit:
-            query += f" LIMIT {limit}"
+        params = list(args)
+        if limit is not None:
+            query += " LIMIT %s"
+            params.append(int(limit))
         async with self._cursor() as cur:
-            await cur.execute(self.SELECT_SQL + where, args, binary=True)
+            await cur.execute(query, params, binary=True)
             async for value in cur:
                 checkpoint: Checkpoint = {
                     **value["checkpoint"],

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

@@ -266,6 +266,27 @@ class AsyncPostgresStore(AsyncBatchedBaseStore, BasePostgresStore[_ainternal.Con
                             k: v(self) if v is not None and callable(v) else v
                             for k, v in migration.params.items()
                         }
+                        if "dims" in params:
+                            try:
+                                params["dims"] = int(params["dims"])
+                            except Exception as e:
+                                raise ValueError(
+                                    f"Invalid dims for vector index: {params['dims']}"
+                                ) from e
+                        if "vector_type" in params:
+                            vt = str(params["vector_type"])
+                            if vt not in ("vector", "halfvec"):
+                                raise ValueError(
+                                    f"Invalid vector_type for pgvector: {vt}"
+                                )
+                            params["vector_type"] = vt
+                        if "index_type" in params:
+                            it = str(params["index_type"])
+                            if it not in ("hnsw", "ivfflat"):
+                                raise ValueError(
+                                    f"Invalid index_type for pgvector: {it}"
+                                )
+                            params["index_type"] = it
                         sql = sql % params
                     await cur.execute(sql)
                     await cur.execute(

libs/checkpoint-postgres/langgraph/store/postgres/base.py

@@ -327,31 +327,36 @@ class BasePostgresStore(Generic[C]):
         embedding_request: tuple[str, Sequence[tuple[str, str, str, str]]] | None = None
         if inserts:
             values = []
-            insertion_params = []
+            insertion_params: list[Any] = []
             vector_values = []
             embedding_request_params = []
             # Handle TTL expiration
 
             # First handle main store insertions
             for op in inserts:
-                if op.ttl is not None:
-                    expires_at_str = f"NOW() + INTERVAL '{op.ttl * 60} seconds'"
-                    ttl_minutes = op.ttl
-                else:
-                    expires_at_str = "NULL"
-                    ttl_minutes = None
-
-                values.append(
-                    f"(%s, %s, %s, CURRENT_TIMESTAMP, CURRENT_TIMESTAMP, {expires_at_str}, %s)"
-                )
                 insertion_params.extend(
-                    [
+                    (
                         _namespace_to_text(op.namespace),
                         op.key,
                         Jsonb(cast(dict, op.value)),
-                        ttl_minutes,
-                    ]
+                    )
                 )
+                if op.ttl is not None:
+                    values.append(
+                        "(%s, %s, %s, CURRENT_TIMESTAMP, CURRENT_TIMESTAMP, NOW() + %s::interval, %s)"
+                    )
+                    ttl_minutes = float(op.ttl)
+                    insertion_params.extend(
+                        (
+                            f"{ttl_minutes * 60} seconds",
+                            ttl_minutes,
+                        )
+                    )
+                else:
+                    values.append(
+                        "(%s, %s, %s, CURRENT_TIMESTAMP, CURRENT_TIMESTAMP, NULL, %s)"
+                    )
+                    insertion_params.append(None)
 
             # Then handle embeddings if configured
             if self.index_config:
@@ -465,6 +470,10 @@ class BasePostgresStore(Generic[C]):
                         cast(dict, self.index_config)["dims"],
                     )
                 else:
+                    if vector_type not in ("vector", "halfvec"):
+                        raise ValueError(
+                            f"Invalid vector_type for pgvector: {vector_type}"
+                        )
                     score_operator = score_operator % ("%s", vector_type)
 
                 vectors_per_doc_estimate = cast(dict, self.index_config)[
@@ -1122,6 +1131,27 @@ class PostgresStore(BaseStore, BasePostgresStore[_pg_internal.Conn]):
                             k: v(self) if v is not None and callable(v) else v
                             for k, v in migration.params.items()
                         }
+                        if "dims" in params:
+                            try:
+                                params["dims"] = int(params["dims"])
+                            except Exception as e:
+                                raise ValueError(
+                                    f"Invalid dims for vector index: {params['dims']}"
+                                ) from e
+                        if "vector_type" in params:
+                            vt = str(params["vector_type"])
+                            if vt not in ("vector", "halfvec"):
+                                raise ValueError(
+                                    f"Invalid vector_type for pgvector: {vt}"
+                                )
+                            params["vector_type"] = vt
+                        if "index_type" in params:
+                            it = str(params["index_type"])
+                            if it not in ("hnsw", "ivfflat"):
+                                raise ValueError(
+                                    f"Invalid index_type for pgvector: {it}"
+                                )
+                            params["index_type"] = it
                         sql = sql % params
                     cur.execute(sql)
                     cur.execute("INSERT INTO vector_migrations (v) VALUES (%s)", (v,))
@@ -1175,15 +1205,44 @@ def _get_vector_type_ops(store: BasePostgresStore) -> str:
 
 
 def _get_index_params(store: Any) -> tuple[str, dict[str, Any]]:
-    """Get the index type and configuration based on config."""
+    """Get a sanitized index type and configuration based on config.
+
+    Only allow known-safe kinds and integer parameters to avoid SQL injection
+    when constructing DDL strings for index creation.
+    """
     if not store.index_config:
         return "hnsw", {}
 
     config = cast(PostgresIndexConfig, store.index_config)
-    index_config = config.get("ann_index_config", _DEFAULT_ANN_CONFIG).copy()
-    kind = index_config.pop("kind", "hnsw")
-    index_config.pop("vector_type", None)
-    return kind, index_config
+    raw = config.get("ann_index_config", _DEFAULT_ANN_CONFIG).copy()
+
+    kind = str(raw.pop("kind", "hnsw"))
+    if kind not in ("hnsw", "ivfflat", "flat"):
+        raise ValueError(
+            f"Invalid index kind for pgvector: {kind}. Expected 'hnsw', 'ivfflat', or 'flat'."
+        )
+
+    raw.pop("vector_type", None)
+
+    if kind == "hnsw":
+        allowed_keys = {"m", "ef_construction"}
+    else:  # ivfflat/flat
+        allowed_keys = {"lists", "nlist"}
+
+    sanitized: dict[str, int] = {}
+    for k, v in list(raw.items()):
+        if k not in allowed_keys:
+            continue
+        key = "lists" if k == "nlist" else k
+        try:
+            ivalue = int(v)  # type: ignore[call-overload]
+        except Exception as e:
+            raise ValueError(f"Invalid index parameter value for {k}: {v}") from e
+        if ivalue <= 0:
+            continue
+        sanitized[key] = ivalue
+
+    return kind, sanitized
 
 
 def _namespace_to_text(

libs/checkpoint-postgres/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-checkpoint-postgres"
-version = "3.0.1"
+version = "3.0.2"
 description = "Library with a Postgres implementation of LangGraph checkpoint saver."
 authors = []
 requires-python = ">=3.10"

libs/cli/langgraph_cli/__init__.py

@@ -1 +1 @@
-__version__ = "0.4.9"
+__version__ = "0.4.11"

libs/cli/langgraph_cli/cli.py

@@ -336,13 +336,13 @@ def _build(
 
     # apply config
     stdin, additional_contexts = langgraph_cli.config.config_to_docker(
-        config,
-        config_json,
-        base_image,
-        api_version,
-        install_command,
-        build_command,
-        build_context,
+        config_path=config,
+        config=config_json,
+        base_image=base_image,
+        api_version=api_version,
+        install_command=install_command,
+        build_command=build_command,
+        build_context=build_context,
     )
     # add additional_contexts
     if additional_contexts:
@@ -522,8 +522,8 @@ def dockerfile(
 
     secho(f"📝 Generating Dockerfile at {save_path}", fg="yellow")
     dockerfile, additional_contexts = langgraph_cli.config.config_to_docker(
-        config,
-        config_json,
+        config_path=config,
+        config=config_json,
         base_image=base_image,
         api_version=api_version,
     )

libs/cli/langgraph_cli/config.py

@@ -824,6 +824,8 @@ def python_config_to_docker(
     config: Config,
     base_image: str,
     api_version: str | None = None,
+    *,
+    escape_variables: bool = False,
 ) -> tuple[str, dict[str, str]]:
     """Generate a Dockerfile from the configuration."""
     pip_installer = config.get("pip_installer", "auto")
@@ -1003,6 +1005,7 @@ ADD {relpath} /deps/{name}
         )
 
     # Add main dockerfile content
+    dep_vname = "$$dep" if escape_variables else "$dep"
     docker_file_contents.extend(
         [
             f"FROM {image_str}",
@@ -1013,10 +1016,10 @@ ADD {relpath} /deps/{name}
             "",
             "# -- Installing all local dependencies --",
             f"""RUN for dep in /deps/*; do \
-            echo "Installing $dep"; \
-            if [ -d "$dep" ]; then \
-                echo "Installing $dep"; \
-                (cd "$dep" && {global_reqs_pip_install} -e .); \
+            echo "Installing {dep_vname}"; \
+            if [ -d "{dep_vname}" ]; then \
+                echo "Installing {dep_vname}"; \
+                (cd "{dep_vname}" && {global_reqs_pip_install} -e .); \
             fi; \
         done""",
             "# -- End of local dependencies install --",
@@ -1202,26 +1205,34 @@ def _calculate_relative_workdir(config_path: pathlib.Path, build_context: str) -
 def config_to_docker(
     config_path: pathlib.Path,
     config: Config,
+    *,
     base_image: str | None = None,
     api_version: str | None = None,
     install_command: str | None = None,
     build_command: str | None = None,
     build_context: str | None = None,
+    escape_variables: bool = False,
 ) -> tuple[str, dict[str, str]]:
     base_image = base_image or default_base_image(config)
 
     if config.get("node_version") and not config.get("python_version"):
         return node_config_to_docker(
-            config_path,
-            config,
-            base_image,
-            api_version,
-            install_command,
-            build_command,
-            build_context,
+            config_path=config_path,
+            config=config,
+            base_image=base_image,
+            api_version=api_version,
+            install_command=install_command,
+            build_command=build_command,
+            build_context=build_context,
         )
 
-    return python_config_to_docker(config_path, config, base_image, api_version)
+    return python_config_to_docker(
+        config_path=config_path,
+        config=config,
+        base_image=base_image,
+        api_version=api_version,
+        escape_variables=escape_variables,
+    )
 
 
 def config_to_compose(
@@ -1265,7 +1276,11 @@ def config_to_compose(
 
     else:
         dockerfile, additional_contexts = config_to_docker(
-            config_path, config, base_image, api_version
+            config_path=config_path,
+            config=config,
+            base_image=base_image,
+            api_version=api_version,
+            escape_variables=True,
         )
 
         additional_contexts_str = "\n".join(

libs/cli/pyproject.toml

@@ -19,7 +19,7 @@ dependencies = [
 path = "langgraph_cli/__init__.py"
 [project.optional-dependencies]
 inmem = [
-    "langgraph-api>=0.5.35,<0.6.0 ; python_version >= '3.11'",
+    "langgraph-api>=0.5.35,<0.7.0 ; python_version >= '3.11'",
     "langgraph-runtime-inmem>=0.7 ; python_version >= '3.11'",
     "python-dotenv>=0.8.0",
 ]

libs/cli/tests/unit_tests/cli/test_cli.py

@@ -150,7 +150,7 @@ services:
                 COPY --from=cli_1 . /deps/cli_1
                 # -- End of local package ../../.. --
                 # -- Installing all local dependencies --
-                RUN for dep in /deps/*; do             echo "Installing $dep";             if [ -d "$dep" ]; then                 echo "Installing $dep";                 (cd "$dep" && PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e .);             fi;         done
+                RUN for dep in /deps/*; do             echo "Installing $$dep";             if [ -d "$$dep" ]; then                 echo "Installing $$dep";                 (cd "$$dep" && PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e .);             fi;         done
                 # -- End of local dependencies install --
                 ENV LANGSERVE_GRAPHS='{{"agent": "agent.py:graph"}}'
 {textwrap.indent(textwrap.dedent(FORMATTED_CLEANUP_LINES), "                ")}

libs/cli/tests/unit_tests/test_config.py

@@ -420,7 +420,7 @@ def test_config_to_docker_simple():
                 "http": {"app": "../../examples/my_app.py:app"},
             }
         ),
-        "langchain/langgraph-api",
+        base_image="langchain/langgraph-api",
     )
     expected_docker_stdin = f"""\
 # syntax=docker/dockerfile:1.4
@@ -483,7 +483,7 @@ def test_config_to_docker_outside_path():
     actual_docker_stdin, additional_contexts = config_to_docker(
         PATH_TO_CONFIG,
         validate_config({"dependencies": [".", ".."], "graphs": graphs}),
-        "langchain/langgraph-api",
+        base_image="langchain/langgraph-api",
     )
     expected_docker_stdin = (
         """\
@@ -544,7 +544,7 @@ def test_config_to_docker_pipconfig():
                 "pip_config_file": "pipconfig.txt",
             }
         ),
-        "langchain/langgraph-api",
+        base_image="langchain/langgraph-api",
     )
     expected_docker_stdin = (
         """\
@@ -585,7 +585,7 @@ def test_config_to_docker_invalid_inputs():
         config_to_docker(
             PATH_TO_CONFIG,
             validate_config({"dependencies": ["./missing"], "graphs": graphs}),
-            "langchain/langgraph-api",
+            base_image="langchain/langgraph-api",
         )
 
     # test missing local module
@@ -594,7 +594,7 @@ def test_config_to_docker_invalid_inputs():
         config_to_docker(
             PATH_TO_CONFIG,
             validate_config({"dependencies": ["."], "graphs": graphs}),
-            "langchain/langgraph-api",
+            base_image="langchain/langgraph-api",
         )
 
 
@@ -608,7 +608,7 @@ def test_config_to_docker_local_deps():
                 "graphs": graphs,
             }
         ),
-        "langchain/langgraph-api-custom",
+        base_image="langchain/langgraph-api-custom",
     )
     expected_docker_stdin = f"""\
 FROM langchain/langgraph-api-custom:3.11
@@ -654,7 +654,7 @@ dependencies = ["langchain"]"""
                 "graphs": graphs,
             }
         ),
-        "langchain/langgraph-api",
+        base_image="langchain/langgraph-api",
     )
     os.remove(pyproject_path)
     expected_docker_stdin = (
@@ -689,7 +689,7 @@ def test_config_to_docker_end_to_end():
                 "dockerfile_lines": ["ARG meow", "ARG foo"],
             }
         ),
-        "langchain/langgraph-api",
+        base_image="langchain/langgraph-api",
     )
     expected_docker_stdin = f"""FROM langchain/langgraph-api:3.12
 ARG meow
@@ -734,7 +734,7 @@ def test_config_to_docker_nodejs():
                 "ui_config": {"shared": ["nuqs"]},
             }
         ),
-        "langchain/langgraphjs-api",
+        base_image="langchain/langgraphjs-api",
     )
     expected_docker_stdin = """FROM langchain/langgraphjs-api:20
 ARG meow
@@ -786,7 +786,7 @@ def test_config_to_docker_python_encryption_bad_path():
 def test_config_to_docker_python_encryption_formatted():
     # Test that encryption config is properly formatted in Docker output
     graphs = {"agent": "./graphs/agent.py:graph"}
-    actual_docker_stdin, additional_contexts = config_to_docker(
+    actual_docker_stdin, _ = config_to_docker(
         PATH_TO_CONFIG,
         validate_config(
             {
@@ -796,7 +796,7 @@ def test_config_to_docker_python_encryption_formatted():
                 "encryption": {"path": "./agent.py:my_encryption"},
             }
         ),
-        "langchain/langgraph-api",
+        base_image="langchain/langgraph-api",
     )
     # Verify that LANGGRAPH_ENCRYPTION is in the docker output with the correct path
     assert "LANGGRAPH_ENCRYPTION=" in actual_docker_stdin
@@ -821,7 +821,7 @@ def test_config_to_docker_nodejs_internal_docker_tag():
                 "_INTERNAL_docker_tag": "my-tag",
             }
         ),
-        "langchain/langgraphjs-api",
+        base_image="langchain/langgraphjs-api",
     )
     expected_docker_stdin = """FROM langchain/langgraphjs-api:my-tag
 ARG meow
@@ -875,7 +875,7 @@ def test_config_to_docker_webhooks_python():
                 "webhooks": webhooks,
             }
         ),
-        "langchain/langgraph-api",
+        base_image="langchain/langgraph-api",
     )
 
     # Ensure the ENV line is present and the payload round-trips via JSON
@@ -900,7 +900,7 @@ def test_config_to_docker_webhooks_node():
                 "webhooks": webhooks,
             }
         ),
-        "langchain/langgraphjs-api",
+        base_image="langchain/langgraphjs-api",
     )
 
     parsed = _extract_env_json(dockerfile, "LANGGRAPH_WEBHOOKS")
@@ -912,7 +912,7 @@ def test_config_to_docker_no_webhooks():
     dockerfile, _ = config_to_docker(
         PATH_TO_CONFIG,
         validate_config({"dependencies": ["."], "graphs": graphs}),
-        "langchain/langgraph-api",
+        base_image="langchain/langgraph-api",
     )
 
     assert "ENV LANGGRAPH_WEBHOOKS=" not in dockerfile
@@ -930,7 +930,7 @@ def test_config_to_docker_gen_ui_python():
                 "ui_config": {"shared": ["nuqs"]},
             }
         ),
-        "langchain/langgraph-api",
+        base_image="langchain/langgraph-api",
     )
 
     expected_docker_stdin = f"""FROM langchain/langgraph-api:3.11
@@ -976,7 +976,7 @@ def test_config_to_docker_multiplatform():
         validate_config(
             {"node_version": "22", "dependencies": ["."], "graphs": graphs}
         ),
-        "langchain/langgraph-api",
+        base_image="langchain/langgraph-api",
     )
 
     expected_docker_stdin = f"""FROM langchain/langgraph-api:3.11
@@ -1024,7 +1024,7 @@ def test_config_to_docker_pip_installer():
         {**copy.deepcopy(base_config), "pip_installer": "auto"}
     )
     docker_auto, _ = config_to_docker(
-        PATH_TO_CONFIG, config_auto, "langchain/langgraph-api:0.2.47"
+        PATH_TO_CONFIG, config_auto, base_image="langchain/langgraph-api:0.2.47"
     )
     assert "uv pip install --system " in docker_auto
     assert "rm /usr/bin/uv /usr/bin/uvx" in docker_auto
@@ -1032,7 +1032,7 @@ def test_config_to_docker_pip_installer():
     # Test explicit pip setting
     config_pip = validate_config({**copy.deepcopy(base_config), "pip_installer": "pip"})
     docker_pip, _ = config_to_docker(
-        PATH_TO_CONFIG, config_pip, "langchain/langgraph-api:0.2.47"
+        PATH_TO_CONFIG, config_pip, base_image="langchain/langgraph-api:0.2.47"
     )
     assert "uv pip install --system " not in docker_pip
     assert "pip install" in docker_pip
@@ -1041,7 +1041,7 @@ def test_config_to_docker_pip_installer():
     # Test explicit uv setting
     config_uv = validate_config({**copy.deepcopy(base_config), "pip_installer": "uv"})
     docker_uv, _ = config_to_docker(
-        PATH_TO_CONFIG, config_uv, "langchain/langgraph-api:0.2.47"
+        PATH_TO_CONFIG, config_uv, base_image="langchain/langgraph-api:0.2.47"
     )
     assert "uv pip install --system " in docker_uv
     assert "rm /usr/bin/uv /usr/bin/uvx" in docker_uv
@@ -1051,7 +1051,7 @@ def test_config_to_docker_pip_installer():
         {**copy.deepcopy(base_config), "pip_installer": "auto"}
     )
     docker_auto_old, _ = config_to_docker(
-        PATH_TO_CONFIG, config_auto_old, "langchain/langgraph-api:0.2.46"
+        PATH_TO_CONFIG, config_auto_old, base_image="langchain/langgraph-api:0.2.46"
     )
     assert "uv pip install --system " not in docker_auto_old
     assert "pip install" in docker_auto_old
@@ -1060,7 +1060,7 @@ def test_config_to_docker_pip_installer():
     # Test that missing pip_installer defaults to auto behavior
     config_default = validate_config(copy.deepcopy(base_config))
     docker_default, _ = config_to_docker(
-        PATH_TO_CONFIG, config_default, "langchain/langgraph-api:0.2.47"
+        PATH_TO_CONFIG, config_default, base_image="langchain/langgraph-api:0.2.47"
     )
     assert "uv pip install --system " in docker_default
 
@@ -1076,7 +1076,7 @@ def test_config_retain_build_tools():
         {**copy.deepcopy(base_config), "keep_pkg_tools": True}
     )
     docker_true, _ = config_to_docker(
-        PATH_TO_CONFIG, config_true, "langchain/langgraph-api:0.2.47"
+        PATH_TO_CONFIG, config_true, base_image="langchain/langgraph-api:0.2.47"
     )
     assert not any(
         "/usr/local/lib/python*/site-packages/" + pckg + "*" in docker_true
@@ -1087,7 +1087,7 @@ def test_config_retain_build_tools():
         {**copy.deepcopy(base_config), "keep_pkg_tools": False}
     )
     docker_false, _ = config_to_docker(
-        PATH_TO_CONFIG, config_false, "langchain/langgraph-api:0.2.47"
+        PATH_TO_CONFIG, config_false, base_image="langchain/langgraph-api:0.2.47"
     )
     assert all(
         "/usr/local/lib/python*/site-packages/" + pckg + "*" in docker_false
@@ -1098,7 +1098,7 @@ def test_config_retain_build_tools():
         {**copy.deepcopy(base_config), "keep_pkg_tools": ["pip", "setuptools"]}
     )
     docker_list, _ = config_to_docker(
-        PATH_TO_CONFIG, config_list, "langchain/langgraph-api:0.2.47"
+        PATH_TO_CONFIG, config_list, base_image="langchain/langgraph-api:0.2.47"
     )
     assert all(
         "/usr/local/lib/python*/site-packages/" + pckg + "*" in docker_list
@@ -1137,7 +1137,7 @@ def test_config_to_compose_simple_config():
                     done
                 # -- End of non-package dependency unit_tests --
                 # -- Installing all local dependencies --
-                RUN for dep in /deps/*; do             echo "Installing $dep";             if [ -d "$dep" ]; then                 echo "Installing $dep";                 (cd "$dep" && PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e .);             fi;         done
+                RUN for dep in /deps/*; do             echo "Installing $$dep";             if [ -d "$$dep" ]; then                 echo "Installing $$dep";                 (cd "$$dep" && PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e .);             fi;         done
                 # -- End of local dependencies install --
                 ENV LANGSERVE_GRAPHS='{{"agent": "/deps/outer-unit_tests/unit_tests/agent.py:graph"}}'
 {textwrap.indent(textwrap.dedent(FORMATTED_CLEANUP_LINES), "                ")}
@@ -1178,7 +1178,7 @@ def test_config_to_compose_env_vars():
                     done
                 # -- End of non-package dependency unit_tests --
                 # -- Installing all local dependencies --
-                RUN for dep in /deps/*; do             echo "Installing $dep";             if [ -d "$dep" ]; then                 echo "Installing $dep";                 (cd "$dep" && PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e .);             fi;         done
+                RUN for dep in /deps/*; do             echo "Installing $$dep";             if [ -d "$$dep" ]; then                 echo "Installing $$dep";                 (cd "$$dep" && PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e .);             fi;         done
                 # -- End of local dependencies install --
                 ENV LANGSERVE_GRAPHS='{{"agent": "/deps/outer-unit_tests/unit_tests/agent.py:graph"}}'
 {textwrap.indent(textwrap.dedent(FORMATTED_CLEANUP_LINES), "                ")}
@@ -1223,7 +1223,7 @@ def test_config_to_compose_env_file():
                     done
                 # -- End of non-package dependency unit_tests --
                 # -- Installing all local dependencies --
-                RUN for dep in /deps/*; do             echo "Installing $dep";             if [ -d "$dep" ]; then                 echo "Installing $dep";                 (cd "$dep" && PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e .);             fi;         done
+                RUN for dep in /deps/*; do             echo "Installing $$dep";             if [ -d "$$dep" ]; then                 echo "Installing $$dep";                 (cd "$$dep" && PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e .);             fi;         done
                 # -- End of local dependencies install --
                 ENV LANGSERVE_GRAPHS='{{"agent": "/deps/outer-unit_tests/unit_tests/agent.py:graph"}}'
 {textwrap.indent(textwrap.dedent(FORMATTED_CLEANUP_LINES), "                ")}
@@ -1261,7 +1261,7 @@ def test_config_to_compose_watch():
                     done
                 # -- End of non-package dependency unit_tests --
                 # -- Installing all local dependencies --
-                RUN for dep in /deps/*; do             echo "Installing $dep";             if [ -d "$dep" ]; then                 echo "Installing $dep";                 (cd "$dep" && PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e .);             fi;         done
+                RUN for dep in /deps/*; do             echo "Installing $$dep";             if [ -d "$$dep" ]; then                 echo "Installing $$dep";                 (cd "$$dep" && PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e .);             fi;         done
                 # -- End of local dependencies install --
                 ENV LANGSERVE_GRAPHS='{{"agent": "/deps/outer-unit_tests/unit_tests/agent.py:graph"}}'
 {textwrap.indent(textwrap.dedent(FORMATTED_CLEANUP_LINES), "                ")}
@@ -1308,7 +1308,7 @@ def test_config_to_compose_end_to_end():
                     done
                 # -- End of non-package dependency unit_tests --
                 # -- Installing all local dependencies --
-                RUN for dep in /deps/*; do             echo "Installing $dep";             if [ -d "$dep" ]; then                 echo "Installing $dep";                 (cd "$dep" && PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e .);             fi;         done
+                RUN for dep in /deps/*; do             echo "Installing $$dep";             if [ -d "$$dep" ]; then                 echo "Installing $$dep";                 (cd "$$dep" && PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e .);             fi;         done
                 # -- End of local dependencies install --
                 ENV LANGSERVE_GRAPHS='{{"agent": "/deps/outer-unit_tests/unit_tests/agent.py:graph"}}'
 {textwrap.indent(textwrap.dedent(FORMATTED_CLEANUP_LINES), "                ")}
@@ -1619,7 +1619,7 @@ def test_config_to_docker_with_api_version():
     actual_docker_stdin, additional_contexts = config_to_docker(
         PATH_TO_CONFIG,
         validate_config({"dependencies": ["."], "graphs": graphs}),
-        "langchain/langgraph-api",
+        base_image="langchain/langgraph-api",
         api_version="0.2.74",
     )
 
@@ -1633,7 +1633,7 @@ def test_config_to_docker_with_api_version():
     actual_docker_stdin, additional_contexts = config_to_docker(
         PATH_TO_CONFIG,
         validate_config({"node_version": "20", "graphs": graphs}),
-        "langchain/langgraphjs-api",
+        base_image="langchain/langgraphjs-api",
         api_version="0.2.74",
     )
 

libs/cli/uv.lock

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+version = "46.0.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "cffi", marker = "platform_python_implementation != 'PyPy'" },
+    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
 ]
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 [[package]]

libs/langgraph/README.md

@@ -11,7 +11,7 @@
 [![Version](https://img.shields.io/pypi/v/langgraph.svg)](https://pypi.org/project/langgraph/)
 [![Downloads](https://static.pepy.tech/badge/langgraph/month)](https://pepy.tech/project/langgraph)
 [![Open Issues](https://img.shields.io/github/issues-raw/langchain-ai/langgraph)](https://github.com/langchain-ai/langgraph/issues)
-[![Docs](https://img.shields.io/badge/docs-latest-blue)](https://langchain-ai.github.io/langgraph/)
+[![Docs](https://img.shields.io/badge/docs-latest-blue)](https://docs.langchain.com/oss/python/langgraph/overview)
 
 Trusted by companies shaping the future of agents – including Klarna, Replit, Elastic, and more – LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents.
 
@@ -23,47 +23,55 @@ Install LangGraph:
 pip install -U langgraph
 ```
 
-Then, create an agent [using prebuilt components](https://langchain-ai.github.io/langgraph/agents/agents/):
+Create a simple workflow:
 
 ```python
-# pip install -qU "langchain[anthropic]" to call the model
+from langgraph.graph import START, StateGraph
+from typing_extensions import TypedDict
 
-from langgraph.prebuilt import create_react_agent
 
-def get_weather(city: str) -> str:
-    """Get weather for a given city."""
-    return f"It's always sunny in {city}!"
+class State(TypedDict):
+    text: str
 
-agent = create_react_agent(
-    model="anthropic:claude-3-7-sonnet-latest",
-    tools=[get_weather],
-    prompt="You are a helpful assistant"
-)
 
-# Run the agent
-agent.invoke(
-    {"messages": [{"role": "user", "content": "what is the weather in sf"}]}
-)
+def node_a(state: State) -> dict:
+    return {"text": state["text"] + "a"}
+
+
+def node_b(state: State) -> dict:
+    return {"text": state["text"] + "b"}
+
+
+graph = StateGraph(State)
+graph.add_node("node_a", node_a)
+graph.add_node("node_b", node_b)
+graph.add_edge(START, "node_a")
+graph.add_edge("node_a", "node_b")
+
+print(graph.compile().invoke({"text": ""}))
+# {'text': 'ab'}
 ```
 
-For more information, see the [Quickstart](https://langchain-ai.github.io/langgraph/agents/agents/). Or, to learn how to build an [agent workflow](https://langchain-ai.github.io/langgraph/concepts/low_level/) with a customizable architecture, long-term memory, and other complex task handling, see the [LangGraph basics tutorials](https://langchain-ai.github.io/langgraph/tutorials/get-started/1-build-basic-chatbot/).
+Get started with the [LangGraph Quickstart](https://docs.langchain.com/oss/python/langgraph/quickstart).
+
+To quickly build agents with LangChain's `create_agent` (built on LangGraph), see the [LangChain Agents documentation](https://docs.langchain.com/oss/python/langchain/agents).
 
 ## Core benefits
 
 LangGraph provides low-level supporting infrastructure for *any* long-running, stateful workflow or agent. LangGraph does not abstract prompts or architecture, and provides the following central benefits:
 
-- [Durable execution](https://langchain-ai.github.io/langgraph/concepts/durable_execution/): Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off.
-- [Human-in-the-loop](https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/): Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution.
-- [Comprehensive memory](https://langchain-ai.github.io/langgraph/concepts/memory/): Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions.
+- [Durable execution](https://docs.langchain.com/oss/python/langgraph/durable-execution): Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off.
+- [Human-in-the-loop](https://docs.langchain.com/oss/python/langgraph/interrupts): Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution.
+- [Comprehensive memory](https://docs.langchain.com/oss/python/langgraph/memory): Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions.
 - [Debugging with LangSmith](http://www.langchain.com/langsmith): Gain deep visibility into complex agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics.
-- [Production-ready deployment](https://langchain-ai.github.io/langgraph/concepts/deployment_options/): Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows.
+- [Production-ready deployment](https://docs.langchain.com/langsmith/app-development): Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows.
 
 ## LangGraph’s ecosystem
 
 While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents. To improve your LLM application development, pair LangGraph with:
 
 - [LangSmith](http://www.langchain.com/langsmith) — Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
-- [LangSmith Deployment](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/) — Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/).
+- [LangSmith Deployment](https://docs.langchain.com/langsmith/deployments) — Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangGraph Studio](https://docs.langchain.com/oss/python/langgraph/studio).
 - [LangChain](https://docs.langchain.com/oss/python/langchain/overview) – Provides integrations and composable components to streamline LLM application development.
 
 > [!NOTE]
@@ -71,12 +79,11 @@ While LangGraph can be used standalone, it also integrates seamlessly with any L
 
 ## Additional resources
 
-- [Guides](https://langchain-ai.github.io/langgraph/guides/): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
-- [Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
-- [Examples](https://langchain-ai.github.io/langgraph/examples/): Guided examples on getting started with LangGraph.
+- [Guides](https://docs.langchain.com/oss/python/langgraph/guides): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
+- [Reference](https://reference.langchain.com/python/langgraph/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
+- [Examples](https://docs.langchain.com/oss/python/langgraph/agentic-rag): Guided examples on getting started with LangGraph.
 - [LangChain Forum](https://forum.langchain.com/): Connect with the community and share all of your technical questions, ideas, and feedback.
 - [LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph): Learn the basics of LangGraph in our free, structured course.
-- [Templates](https://langchain-ai.github.io/langgraph/concepts/template_applications/): Pre-built reference apps for common agentic workflows (e.g. ReAct agent, memory, retrieval etc.) that can be cloned and adapted.
 - [Case studies](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship AI applications at scale.
 
 ## Acknowledgements

libs/langgraph/langgraph/graph/state.py

@@ -78,6 +78,7 @@ from langgraph.types import (
     Command,
     RetryPolicy,
     Send,
+    ensure_valid_checkpointer,
 )
 from langgraph.typing import ContextT, InputT, NodeInputT, OutputT, StateT
 from langgraph.warnings import LangGraphDeprecatedSinceV05, LangGraphDeprecatedSinceV10
@@ -853,6 +854,8 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
         Returns:
             CompiledStateGraph: The compiled `StateGraph`.
         """
+        checkpointer = ensure_valid_checkpointer(checkpointer)
+
         # assign default values
         interrupt_before = interrupt_before or []
         interrupt_after = interrupt_after or []

libs/langgraph/langgraph/pregel/main.py

@@ -22,6 +22,7 @@ from inspect import isclass
 from typing import (
     Any,
     Generic,
+    Literal,
     cast,
     get_type_hints,
 )
@@ -38,6 +39,7 @@ from langchain_core.runnables.config import (
     get_callback_manager_for_config,
 )
 from langchain_core.runnables.graph import Graph
+from langchain_core.runnables.schema import CustomStreamEvent, StandardStreamEvent
 from langgraph.cache.base import BaseCache
 from langgraph.checkpoint.base import (
     BaseCheckpointSaver,
@@ -142,6 +144,7 @@ from langgraph.types import (
     StateSnapshot,
     StateUpdate,
     StreamMode,
+    ensure_valid_checkpointer,
 )
 from langgraph.typing import ContextT, InputT, OutputT, StateT
 from langgraph.warnings import LangGraphDeprecatedSinceV10
@@ -642,7 +645,7 @@ class Pregel(
         input_channels: str | Sequence[str],
         step_timeout: float | None = None,
         debug: bool | None = None,
-        checkpointer: BaseCheckpointSaver | None = None,
+        checkpointer: Checkpointer = None,
         store: BaseStore | None = None,
         cache: BaseCache | None = None,
         retry_policy: RetryPolicy | Sequence[RetryPolicy] = (),
@@ -665,6 +668,8 @@ class Pregel(
             if context_schema is None:
                 context_schema = cast(type[ContextT], config_type)
 
+        checkpointer = ensure_valid_checkpointer(checkpointer)
+
         self.nodes = {
             k: v.build() if isinstance(v, NodeBuilder) else v for k, v in nodes.items()
         }
@@ -3018,6 +3023,61 @@ class Pregel(
             await asyncio.shield(run_manager.on_chain_error(e))
             raise
 
+    async def astream_events(
+        self,
+        input: InputT | Command | None,
+        config: RunnableConfig | None = None,
+        *,
+        context: ContextT | None = None,
+        version: Literal["v2"],
+        include_names: Sequence[str] | None = None,
+        include_types: Sequence[str] | None = None,
+        include_tags: Sequence[str] | None = None,
+        exclude_names: Sequence[str] | None = None,
+        exclude_types: Sequence[str] | None = None,
+        exclude_tags: Sequence[str] | None = None,
+        **kwargs: Any,
+    ) -> AsyncIterator[StandardStreamEvent | CustomStreamEvent]:
+        """Stream events from the graph execution.
+
+        This method extends the base Runnable.astream_events with support for
+        the LangGraph `context` parameter.
+
+        Args:
+            input: The input to the graph.
+            config: The configuration to use for the run.
+            context: The static context to use for the run.
+                !!! version-added "Added in version 1.0.6"
+            version: The version of the event stream schema to use ("v2").
+            include_names: Only include events from runnables with matching names.
+            include_types: Only include events from runnables with matching types.
+            include_tags: Only include events from runnables with matching tags.
+            exclude_names: Exclude events from runnables with matching names.
+            exclude_types: Exclude events from runnables with matching types.
+            exclude_tags: Exclude events from runnables with matching tags.
+            **kwargs: Additional arguments passed to the underlying stream.
+
+        Yields:
+            Events from the graph execution.
+        """
+        async with contextlib.aclosing(
+            super().astream_events(
+                input,
+                config,
+                version=version,
+                include_names=include_names,
+                include_types=include_types,
+                include_tags=include_tags,
+                exclude_names=exclude_names,
+                exclude_types=exclude_types,
+                exclude_tags=exclude_tags,
+                context=context,
+                **kwargs,
+            )
+        ) as stream:  # type: ignore[type-var]
+            async for event in stream:
+                yield event
+
     def invoke(
         self,
         input: InputT | Command | None,

libs/langgraph/langgraph/pregel/remote.py

@@ -907,6 +907,7 @@ class RemoteGraph(PregelProtocol):
         input: Any,
         config: RunnableConfig | None = None,
         *,
+        context: Any = None,
         version: Literal["v1", "v2"],
         include_names: Sequence[All] | None = None,
         include_types: Sequence[All] | None = None,

libs/langgraph/langgraph/types.py

@@ -56,6 +56,7 @@ __all__ = (
     "Durability",
     "interrupt",
     "Overwrite",
+    "ensure_valid_checkpointer",
 )
 
 Durability = Literal["sync", "async", "exit"]
@@ -73,6 +74,20 @@ Checkpointer = None | bool | BaseCheckpointSaver
 - False disables checkpointing, even if the parent graph has a checkpointer.
 - None inherits checkpointer from the parent graph."""
 
+
+def ensure_valid_checkpointer(checkpointer: Checkpointer) -> Checkpointer:
+    if checkpointer not in (None, True, False) and not isinstance(
+        checkpointer, BaseCheckpointSaver
+    ):
+        raise TypeError(
+            "Invalid checkpointer provided. Expected an instance of "
+            "`BaseCheckpointSaver`, `True`, `False`, or `None`. "
+            f"Received {type(checkpointer).__name__!s}. "
+            "Pass a proper saver (e.g., InMemorySaver, AsyncPostgresSaver)."
+        )
+    return checkpointer
+
+
 StreamMode = Literal[
     "values", "updates", "checkpoints", "tasks", "debug", "messages", "custom"
 ]

libs/langgraph/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph"
-version = "1.0.4"
+version = "1.0.5"
 description = "Building stateful, multi-actor applications with LLMs"
 authors = []
 requires-python = ">=3.10"
@@ -26,7 +26,7 @@ classifiers = [
 dependencies = [
     "langchain-core>=0.1",
     "langgraph-checkpoint>=2.1.0,<4.0.0",
-    "langgraph-sdk>=0.2.2,<0.3.0",
+    "langgraph-sdk>=0.3.0,<0.4.0",
     "langgraph-prebuilt>=1.0.2,<1.1.0",
     "xxhash>=3.5.0",
     "pydantic>=2.7.4",

libs/langgraph/tests/test_pregel.py

@@ -120,6 +120,22 @@ def test_graph_validation() -> None:
         graph.invoke({"hello": "there"})
 
 
+def test_invalid_checkpointer_type() -> None:
+    class State(TypedDict):
+        foo: str
+
+    builder = StateGraph(State)
+    builder.add_node("start", lambda state: state)
+    builder.set_entry_point("start")
+    builder.set_finish_point("start")
+
+    class NotACheckpointer:
+        pass
+
+    with pytest.raises(TypeError, match="Invalid checkpointer provided"):
+        builder.compile(checkpointer=NotACheckpointer())
+
+
 def test_graph_validation_with_command() -> None:
     class State(TypedDict):
         foo: str

libs/langgraph/tests/test_remote_graph.py

@@ -567,9 +567,9 @@ def test_stream():
         stream_parts.append(stream_part)
 
     assert stream_parts == [
-        ("updates", {"chunk": "data3"}),
-        ("updates", {"chunk": "data4"}),
-        ("updates", {"__interrupt__": ()}),
+        ("updates", {"chunk": "data3"}, None),
+        ("updates", {"chunk": "data4"}, None),
+        ("updates", {"__interrupt__": ()}, None),
     ]
 
     # subgraphs + list modes
@@ -739,9 +739,9 @@ async def test_astream():
         stream_parts.append(stream_part)
 
     assert stream_parts == [
-        ("updates", {"chunk": "data3"}),
-        ("updates", {"chunk": "data4"}),
-        ("updates", {"__interrupt__": ()}),
+        ("updates", {"chunk": "data3"}, None),
+        ("updates", {"chunk": "data4"}, None),
+        ("updates", {"__interrupt__": ()}, None),
     ]
 
     # subgraphs + list modes

libs/langgraph/tests/test_runtime.py

@@ -389,3 +389,45 @@ def test_context_coercion_pydantic_validation_errors() -> None:
         compiled.invoke(
             {"message": "test"}, context={"api_key": "sk_test", "timeout": "not_an_int"}
         )
+
+
+@pytest.mark.anyio
+async def test_context_with_astream_events() -> None:
+    """Test that context is properly passed through astream_events."""
+
+    @dataclass
+    class Context:
+        api_key: str
+
+    class State(TypedDict):
+        message: str
+
+    def node_with_context(state: State, runtime: Runtime[Context]) -> dict[str, Any]:
+        return {"message": f"api_key: {runtime.context.api_key}"}
+
+    graph = StateGraph(state_schema=State, context_schema=Context)
+    graph.add_node("node", node_with_context)
+    graph.add_edge(START, "node")
+    graph.add_edge("node", END)
+    compiled = graph.compile()
+
+    events = []
+    async for event in compiled.astream_events(
+        {"message": "test"},
+        version="v2",
+        context=Context(api_key="sk_events_123"),
+    ):
+        events.append(event)
+
+    # Verify we got events
+    assert len(events) > 0
+
+    # Find the final on_chain_end event (no parent_ids means it's the root)
+    end_events = [
+        e for e in events if e["event"] == "on_chain_end" and not e.get("parent_ids")
+    ]
+    assert len(end_events) == 1
+
+    # Verify the output contains our context value
+    output = end_events[0]["data"]["output"]
+    assert output["message"] == "api_key: sk_events_123"

libs/langgraph/uv.lock

@@ -1,5 +1,5 @@
 version = 1
-revision = 2
+revision = 3
 requires-python = ">=3.10"
 resolution-markers = [
     "python_full_version >= '3.14'",
@@ -1345,7 +1345,7 @@ wheels = [
 
 [[package]]
 name = "langgraph"
-version = "1.0.4"
+version = "1.0.5"
 source = { editable = "." }
 dependencies = [
     { name = "langchain-core" },
@@ -1572,7 +1572,7 @@ test = [
 
 [[package]]
 name = "langgraph-checkpoint-postgres"
-version = "3.0.1"
+version = "3.0.2"
 source = { editable = "../checkpoint-postgres" }
 dependencies = [
     { name = "langgraph-checkpoint" },
@@ -1678,7 +1678,7 @@ inmem = [
 [package.metadata]
 requires-dist = [
     { name = "click", specifier = ">=8.1.7" },
-    { name = "langgraph-api", marker = "python_full_version >= '3.11' and extra == 'inmem'", specifier = ">=0.5.35,<0.6.0" },
+    { name = "langgraph-api", marker = "python_full_version >= '3.11' and extra == 'inmem'", specifier = ">=0.5.35,<0.7.0" },
     { name = "langgraph-runtime-inmem", marker = "python_full_version >= '3.11' and extra == 'inmem'", specifier = ">=0.7" },
     { name = "langgraph-sdk", marker = "python_full_version >= '3.11'", specifier = ">=0.1.0" },
     { name = "python-dotenv", marker = "extra == 'inmem'", specifier = ">=0.8.0" },

libs/prebuilt/tests/test_react_agent.py

@@ -638,7 +638,7 @@ def test_react_agent_parallel_tool_calls(
     for event in agent.stream(
         {"messages": [("user", query)]}, config, stream_mode="values"
     ):
-        if "__interrupt__" not in event: 
+        if "__interrupt__" not in event:
             if messages := event.get("messages"):
                 message_types.append([m.type for m in messages])
 

libs/prebuilt/uv.lock

@@ -1,5 +1,5 @@
 version = 1
-revision = 2
+revision = 3
 requires-python = ">=3.10"
 
 [[package]]
@@ -271,7 +271,7 @@ wheels = [
 
 [[package]]
 name = "langgraph"
-version = "1.0.4"
+version = "1.0.5"
 source = { editable = "../langgraph" }
 dependencies = [
     { name = "langchain-core" },
@@ -402,7 +402,7 @@ test = [
 
 [[package]]
 name = "langgraph-checkpoint-postgres"
-version = "3.0.1"
+version = "3.0.2"
 source = { editable = "../checkpoint-postgres" }
 dependencies = [
     { name = "langgraph-checkpoint" },

libs/sdk-py/langgraph_sdk/__init__.py

@@ -3,6 +3,6 @@ from langgraph_sdk.client import get_client, get_sync_client
 from langgraph_sdk.encryption import Encryption
 from langgraph_sdk.encryption.types import EncryptionContext
 
-__version__ = "0.2.15"
+__version__ = "0.3.1"
 
 __all__ = ["Auth", "Encryption", "EncryptionContext", "get_client", "get_sync_client"]

libs/sdk-py/langgraph_sdk/encryption/__init__.py

@@ -64,114 +64,6 @@ def _validate_handler(fn: typing.Callable, handler_type: str) -> None:
         )
 
 
-class _JsonEncryptDecorators:
-    """Dynamic decorator factory for JSON encryption handlers.
-
-    Supports both default and model-specific handlers:
-    - @encrypt.json - default handler for all models
-    - @encrypt.json.thread - handler for thread model
-    """
-
-    def __init__(self, parent: Encryption):
-        self._parent = parent
-
-    def __call__(self, fn: types.JsonEncryptor) -> types.JsonEncryptor:
-        """Register the default JSON encryption handler.
-
-        Args:
-            fn: The handler function
-
-        Returns:
-            The registered handler function
-
-        Raises:
-            DuplicateHandlerError: If handler already registered
-            TypeError: If handler has invalid signature
-        """
-        if self._parent._json_encryptor is not None:
-            raise DuplicateHandlerError("Default JSON encryptor already registered")
-        _validate_handler(fn, "Default JSON encryptor")
-        self._parent._json_encryptor = fn
-        return fn
-
-    def __getattr__(
-        self, model: str
-    ) -> typing.Callable[[types.JsonEncryptor], types.JsonEncryptor]:
-        """Dynamic attribute access for model-specific handlers.
-
-        Allows @encryption.encrypt.json.thread, @encryption.encrypt.json.assistant, etc.
-
-        Raises:
-            DuplicateHandlerError: If handler already registered for this model
-            TypeError: If handler has invalid signature
-        """
-
-        def decorator(fn: types.JsonEncryptor) -> types.JsonEncryptor:
-            if model in self._parent._json_encryptors:
-                raise DuplicateHandlerError(
-                    f"JSON encryptor for model '{model}' already registered"
-                )
-            _validate_handler(fn, f"JSON encryptor for model '{model}'")
-            self._parent._json_encryptors[model] = fn
-            return fn
-
-        return decorator
-
-
-class _JsonDecryptDecorators:
-    """Dynamic decorator factory for JSON decryption handlers.
-
-    Supports both default and model-specific handlers:
-    - @encryption.decrypt.json - default handler for all models
-    - @encryption.decrypt.json.thread - handler for thread model
-    """
-
-    def __init__(self, parent: Encryption):
-        self._parent = parent
-
-    def __call__(self, fn: types.JsonDecryptor) -> types.JsonDecryptor:
-        """Register the default JSON decryption handler.
-
-        Args:
-            fn: The handler function
-
-        Returns:
-            The registered handler function
-
-        Raises:
-            DuplicateHandlerError: If handler already registered
-            TypeError: If handler has invalid signature
-        """
-        if self._parent._json_decryptor is not None:
-            raise DuplicateHandlerError("Default JSON decryptor already registered")
-        _validate_handler(fn, "Default JSON decryptor")
-        self._parent._json_decryptor = fn
-        return fn
-
-    def __getattr__(
-        self, model: str
-    ) -> typing.Callable[[types.JsonDecryptor], types.JsonDecryptor]:
-        """Dynamic attribute access for model-specific handlers.
-
-        Allows @encryption.decrypt.json.thread, @encryption.decrypt.json.assistant, etc.
-
-        Raises:
-            DuplicateHandlerError: If handler already registered for this model
-            TypeError: If handler has invalid signature
-        """
-
-        def decorator(fn: types.JsonDecryptor) -> types.JsonDecryptor:
-            if model in self._parent._json_decryptors:
-                raise DuplicateHandlerError(
-                    f"JSON decryptor for model '{model}' already registered"
-                )
-            _validate_handler(fn, f"JSON decryptor for model '{model}'")
-            self._parent._json_decryptors[model] = fn
-            return fn
-
-        return decorator
-
-
 class _EncryptDecorators:
     """Decorators for encryption handlers.
 
@@ -181,7 +73,6 @@ class _EncryptDecorators:
 
     def __init__(self, parent: Encryption):
         self._parent = parent
-        self._json = _JsonEncryptDecorators(parent)
 
     def blob(self, fn: types.BlobEncryptor) -> types.BlobEncryptor:
         """Register a blob encryption handler.
@@ -212,29 +103,32 @@ class _EncryptDecorators:
         self._parent._blob_encryptor = fn
         return fn
 
-    @property
-    def json(self) -> _JsonEncryptDecorators:
-        """Access JSON encryption decorators.
-
-        Supports model-specific handlers:
-        - @encryption.encrypt.json - default handler for all models
-        - @encryption.encrypt.json.thread - handler for thread model only
-        - @encryption.encrypt.json.assistant - handler for assistant model only
+    def json(self, fn: types.JsonEncryptor) -> types.JsonEncryptor:
+        """Register the JSON encryption handler.
 
         Example:
             ```python
             @encryption.encrypt.json
-            async def default_encrypt(ctx: EncryptionContext, data: dict) -> dict:
-                # Default encryption for all models
+            async def encrypt_json(ctx: EncryptionContext, data: dict) -> dict:
+                # Encrypt the data
                 return encrypt_data(data)
-
-            @encryption.encrypt.json.thread
-            async def encrypt_thread(ctx: EncryptionContext, data: dict) -> dict:
-                # Special encryption for thread model only
-                return encrypt_thread_data(data)
             ```
+
+        Args:
+            fn: The encryption handler function
+
+        Returns:
+            The registered handler function
+
+        Raises:
+            DuplicateHandlerError: If JSON encryptor already registered
+            TypeError: If handler has invalid signature
         """
-        return self._json
+        if self._parent._json_encryptor is not None:
+            raise DuplicateHandlerError("JSON encryptor already registered")
+        _validate_handler(fn, "JSON encryptor")
+        self._parent._json_encryptor = fn
+        return fn
 
 
 class _DecryptDecorators:
@@ -246,7 +140,6 @@ class _DecryptDecorators:
 
     def __init__(self, parent: Encryption):
         self._parent = parent
-        self._json = _JsonDecryptDecorators(parent)
 
     def blob(self, fn: types.BlobDecryptor) -> types.BlobDecryptor:
         """Register a blob decryption handler.
@@ -277,29 +170,32 @@ class _DecryptDecorators:
         self._parent._blob_decryptor = fn
         return fn
 
-    @property
-    def json(self) -> _JsonDecryptDecorators:
-        """Access JSON decryption decorators.
-
-        Supports model-specific handlers:
-        - @encryption.decrypt.json - default handler for all models
-        - @encryption.decrypt.json.thread - handler for thread model only
-        - @encryption.decrypt.json.assistant - handler for assistant model only
+    def json(self, fn: types.JsonDecryptor) -> types.JsonDecryptor:
+        """Register the JSON decryption handler.
 
         Example:
             ```python
             @encryption.decrypt.json
-            async def default_decrypt(ctx: EncryptionContext, data: dict) -> dict:
-                # Default decryption for all models
+            async def decrypt_json(ctx: EncryptionContext, data: dict) -> dict:
+                # Decrypt the data
                 return decrypt_data(data)
-
-            @encryption.decrypt.json.thread
-            async def decrypt_thread(ctx: EncryptionContext, data: dict) -> dict:
-                # Special decryption for thread model only
-                return decrypt_thread_data(data)
             ```
+
+        Args:
+            fn: The decryption handler function
+
+        Returns:
+            The registered handler function
+
+        Raises:
+            DuplicateHandlerError: If JSON decryptor already registered
+            TypeError: If handler has invalid signature
         """
-        return self._json
+        if self._parent._json_decryptor is not None:
+            raise DuplicateHandlerError("JSON decryptor already registered")
+        _validate_handler(fn, "JSON decryptor")
+        self._parent._json_decryptor = fn
+        return fn
 
 
 class Encryption:
@@ -336,6 +232,28 @@ class Encryption:
     Then the LangGraph server will load your encryption file and use it to
     encrypt/decrypt data at rest.
 
+    !!! warning "JSON Encryptors Must Preserve Keys"
+
+        JSON encryptors **must not add or remove keys** from the input dict.
+        Only values may be transformed. This constraint is **enforced at runtime
+        by the server** and exists because SQL JSONB merge operations (used for
+        partial updates) work at the key level.
+
+        **Correct (per-key encryption):**
+        ```python
+        # Input:  {"secret": "value", "plain": "x"}
+        # Output: {"secret": "<encrypted>", "plain": "x"}  ✓ Keys preserved
+        ```
+
+        **Incorrect (key consolidation):**
+        ```python
+        # Input:  {"secret": "value", "plain": "x"}
+        # Output: {"__encrypted__": "<blob>", "plain": "x"}  ✗ Key changed
+        ```
+
+        If your encryptor needs to store auxiliary data (DEK, IV, etc.), embed it
+        within the encrypted value itself, not as separate keys.
+
     ???+ example "Basic Usage"
 
         ```python
@@ -343,89 +261,48 @@ class Encryption:
 
         my_encryption = Encryption()
 
+        SKIP_FIELDS = {"tenant_id", "owner", "thread_id", "assistant_id"}
+        ENCRYPTED_PREFIX = "encrypted:"
+
         @my_encryption.encrypt.blob
         async def encrypt_blob(ctx: EncryptionContext, blob: bytes) -> bytes:
-            # Call your encryption service
-            return encrypted_blob
+            return your_encrypt_bytes(blob)
 
         @my_encryption.decrypt.blob
         async def decrypt_blob(ctx: EncryptionContext, blob: bytes) -> bytes:
-            # Call your decryption service
-            return decrypted_blob
+            return your_decrypt_bytes(blob)
 
         @my_encryption.encrypt.json
         async def encrypt_json(ctx: EncryptionContext, data: dict) -> dict:
-            # Practical encryption strategy:
-            # - "owner" field: unencrypted (for search/filtering)
-            # - "my.customer.org/" prefixed fields: encrypt VALUES only
-            # - All other fields: pass through unencrypted
-            encrypted = {}
-            for key, value in data.items():
-                if key.startswith("my.customer.org/"):
-                    # Encrypt VALUE for sensitive customer data
-                    encrypted[key] = encrypt_value(value)
+            result = {}
+            for k, v in data.items():
+                if k in SKIP_FIELDS or v is None:
+                    result[k] = v
                 else:
-                    # Pass through (including "owner" for search)
-                    encrypted[key] = value
-            return encrypted
+                    result[k] = ENCRYPTED_PREFIX + your_encrypt_string(v)
+            return result
 
         @my_encryption.decrypt.json
         async def decrypt_json(ctx: EncryptionContext, data: dict) -> dict:
-            # Decrypt VALUES for "my.customer.org/" prefixed fields
-            decrypted = {}
-            for key, value in data.items():
-                if key.startswith("my.customer.org/"):
-                    decrypted[key] = decrypt_value(value)
+            result = {}
+            for k, v in data.items():
+                if isinstance(v, str) and v.startswith(ENCRYPTED_PREFIX):
+                    result[k] = your_decrypt_string(v[len(ENCRYPTED_PREFIX):])
                 else:
-                    decrypted[key] = value
-            return decrypted
-        ```
-
-    ???+ example "Model-Specific Handlers"
-
-        You can register different encryption handlers for different model types
-        (thread, assistant, run, cron, checkpoint, etc.):
-
-        ```python
-        from langgraph_sdk import Encryption, EncryptionContext
-
-        my_encryption = Encryption()
-
-        # Default handler for models without specific handlers
-        @my_encryption.encrypt.json
-        async def default_encrypt(ctx: EncryptionContext, data: dict) -> dict:
-            return standard_encrypt(data)
-
-        # Thread-specific handler (uses different KMS key)
-        @my_encryption.encrypt.json.thread
-        async def encrypt_thread(ctx: EncryptionContext, data: dict) -> dict:
-            return encrypt_with_thread_key(data)
-
-        # Assistant-specific handler
-        @my_encryption.encrypt.json.assistant
-        async def encrypt_assistant(ctx: EncryptionContext, data: dict) -> dict:
-            return encrypt_with_assistant_key(data)
-
-        # Same pattern for decryption
-        @my_encryption.decrypt.json
-        async def default_decrypt(ctx: EncryptionContext, data: dict) -> dict:
-            return standard_decrypt(data)
-
-        @my_encryption.decrypt.json.thread
-        async def decrypt_thread(ctx: EncryptionContext, data: dict) -> dict:
-            return decrypt_with_thread_key(data)
+                    result[k] = v
+            return result
         ```
 
     ???+ example "Field-Specific Logic"
 
-        The `ctx.field` attribute tells you which specific field is being encrypted,
-        allowing different logic within the same model:
+        The `ctx.model` and `ctx.field` attributes tell you which model type and
+        specific field is being encrypted, allowing different logic:
 
         ```python
-        @my_encryption.encrypt.json.thread
-        async def encrypt_thread(ctx: EncryptionContext, data: dict) -> dict:
+        @my_encryption.encrypt.json
+        async def encrypt_json(ctx: EncryptionContext, data: dict) -> dict:
             if ctx.field == "metadata":
-                # Thread metadata - standard encryption
+                # Metadata - standard encryption
                 return encrypt_standard(data)
             elif ctx.field == "values":
                 # Thread values - more sensitive, use stronger encryption
@@ -433,6 +310,49 @@ class Encryption:
             else:
                 return encrypt_standard(data)
         ```
+
+        !!! warning "Model/Field May Differ Between Encrypt and Decrypt"
+
+            Data encrypted with one `(model, field)` pair is **not guaranteed**
+            to be decrypted with the same pair. The server performs SQL JSONB
+            merges that can move encrypted values between models (e.g., cron
+            metadata → run metadata). Your decryption logic must handle data
+            regardless of the `ctx.model` or `ctx.field` values at decrypt time.
+
+            **Safe:** Use `ctx.model`/`ctx.field` for logging or metrics only.
+
+            **Safe:** Encrypt different keys based on `ctx.field`, but use a
+            single decrypt handler that decrypts any value with the encrypted
+            prefix (and passes through plaintext unchanged):
+
+            ```python
+            ENCRYPTED_PREFIX = "enc:"
+
+            @my_encryption.encrypt.json
+            async def encrypt_json(ctx: EncryptionContext, data: dict) -> dict:
+                # Encrypt different keys depending on the field
+                if ctx.field == "context":
+                    keys_to_encrypt = {"api_key", "secret_token"}
+                else:
+                    keys_to_encrypt = {"email", "ssn"}
+                return {
+                    k: ENCRYPTED_PREFIX + encrypt(v) if k in keys_to_encrypt else v
+                    for k, v in data.items()
+                }
+
+            @my_encryption.decrypt.json
+            async def decrypt_json(ctx: EncryptionContext, data: dict) -> dict:
+                # Decrypt ANY value with the prefix, regardless of model/field
+                return {
+                    k: decrypt(v[len(ENCRYPTED_PREFIX):])
+                       if isinstance(v, str) and v.startswith(ENCRYPTED_PREFIX)
+                       else v
+                    for k, v in data.items()
+                }
+            ```
+
+            **Unsafe:** Using different encryption keys or algorithms based on
+            `ctx.model`/`ctx.field` will cause decryption failures.
     """
 
     __slots__ = (
@@ -440,9 +360,7 @@ class Encryption:
         "_blob_encryptor",
         "_context_handler",
         "_json_decryptor",
-        "_json_decryptors",
         "_json_encryptor",
-        "_json_encryptors",
         "decrypt",
         "encrypt",
     )
@@ -464,8 +382,6 @@ class Encryption:
         self._blob_decryptor: types.BlobDecryptor | None = None
         self._json_encryptor: types.JsonEncryptor | None = None
         self._json_decryptor: types.JsonDecryptor | None = None
-        self._json_encryptors: dict[str, types.JsonEncryptor] = {}
-        self._json_decryptors: dict[str, types.JsonDecryptor] = {}
         self._context_handler: types.ContextHandler | None = None
 
     def context(self, fn: types.ContextHandler) -> types.ContextHandler:
@@ -506,33 +422,33 @@ class Encryption:
         return fn
 
     def get_json_encryptor(
-        self, model: str | None = None
+        self,
+        _model: str | None = None,  # kept for langgraph-api compat
     ) -> types.JsonEncryptor | None:
-        """Get the JSON encryptor for a specific model.
+        """Get the JSON encryptor.
 
         Args:
-            model: The model type (e.g., "thread", "assistant"). If None, returns default.
+            _model: Ignored. Kept for backwards compatibility with langgraph-api
+                which passes model_type to this method.
 
         Returns:
-            Model-specific encryptor if registered, otherwise default encryptor, or None.
+            The JSON encryptor, or None if not registered.
         """
-        if model and model in self._json_encryptors:
-            return self._json_encryptors[model]
         return self._json_encryptor
 
     def get_json_decryptor(
-        self, model: str | None = None
+        self,
+        _model: str | None = None,  # kept for langgraph-api compat
     ) -> types.JsonDecryptor | None:
-        """Get the JSON decryptor for a specific model.
+        """Get the JSON decryptor.
 
         Args:
-            model: The model type (e.g., "thread", "assistant"). If None, returns default.
+            _model: Ignored. Kept for backwards compatibility with langgraph-api
+                which passes model_type to this method.
 
         Returns:
-            Model-specific decryptor if registered, otherwise default decryptor, or None.
+            The JSON decryptor, or None if not registered.
         """
-        if model and model in self._json_decryptors:
-            return self._json_decryptors[model]
         return self._json_decryptor
 
     def __repr__(self) -> str:
@@ -545,10 +461,6 @@ class Encryption:
             handlers.append("json_encryptor")
         if self._json_decryptor:
             handlers.append("json_decryptor")
-        if self._json_encryptors:
-            handlers.append(f"json_encryptors({list(self._json_encryptors.keys())})")
-        if self._json_decryptors:
-            handlers.append(f"json_decryptors({list(self._json_decryptors.keys())})")
         if self._context_handler:
             handlers.append("context_handler")
         return f"Encryption(handlers=[{', '.join(handlers)}])"

libs/sdk-py/langgraph_sdk/schema.py

@@ -167,7 +167,7 @@ class Config(TypedDict, total=False):
     """
     Runtime values for attributes previously made configurable on this Runnable,
     or sub-Runnables, through .configurable_fields() or .configurable_alternatives().
-    Check .output_schema() for a description of the attributes that have been made 
+    Check .output_schema() for a description of the attributes that have been made
     configurable.
     """
 
@@ -301,7 +301,7 @@ class ThreadState(TypedDict):
     values: list[dict] | dict[str, Any]
     """The state values."""
     next: Sequence[str]
-    """The next nodes to execute. If empty, the thread is done until new input is 
+    """The next nodes to execute. If empty, the thread is done until new input is
     received."""
     checkpoint: Checkpoint
     """The ID of the checkpoint."""
@@ -476,7 +476,7 @@ class Item(TypedDict):
     """The namespace of the item. A namespace is analogous to a document's directory."""
     key: str
     """The unique identifier of the item within its namespace.
-    
+
     In general, keys needn't be globally unique.
     """
     value: dict[str, Any]
@@ -519,6 +519,8 @@ class StreamPart(NamedTuple):
     """The type of event for this stream part."""
     data: dict
     """The data payload associated with the event."""
+    id: str | None = None
+    """The ID of the event."""
 
 
 class Send(TypedDict):

libs/sdk-py/langgraph_sdk/sse.py

@@ -103,6 +103,7 @@ class SSEDecoder:
             sse = StreamPart(
                 event=self._event,
                 data=orjson.loads(self._data) if self._data else None,  # type: ignore[invalid-argument-type]
+                id=self.last_event_id,
             )
 
             # NOTE: as per the SSE spec, do not reset last_event_id.

libs/sdk-py/tests/test_client_stream.py

@@ -50,7 +50,7 @@ def iter_lines_raw(payload: list[bytes]) -> Iterator[BytesLike]:
     yield from decoder.flush()
 
 
-def test_stream_see():
+def test_stream_sse():
     for groups in (
         [RESPONSE_PAYLOAD],
         RESPONSE_PAYLOAD.splitlines(keepends=True),
@@ -150,9 +150,9 @@ def test_sync_http_client_stream_recovers_after_disconnect():
 
     assert call_count == 2
     assert parts == [
-        StreamPart(event="values", data={"step": 1}),
-        StreamPart(event="values", data={"step": 2}),
-        StreamPart(event="end", data=None),  # ty: ignore
+        StreamPart(event="values", data={"step": 1}, id="1"),
+        StreamPart(event="values", data={"step": 2}, id="2"),
+        StreamPart(event="end", data=None, id="2"),
     ]
 
 
@@ -222,9 +222,9 @@ async def test_http_client_stream_recovers_after_disconnect():
 
     assert call_count == 2
     assert parts == [
-        StreamPart(event="values", data={"step": 1}),
-        StreamPart(event="values", data={"step": 2}),
-        StreamPart(event="end", data=None),
+        StreamPart(event="values", data={"step": 1}, id="1"),
+        StreamPart(event="values", data={"step": 2}, id="2"),
+        StreamPart(event="end", data=None, id="2"),
     ]
 
 

libs/sdk-py/tests/test_encryption.py

@@ -26,14 +26,6 @@ class TestHandlerValidation:
         async def json_dec(_ctx, data):
             return data
 
-        @encryption.encrypt.json.thread
-        async def thread_enc(_ctx, data):
-            return data
-
-        @encryption.decrypt.json.custom
-        async def custom_dec(_ctx, data):
-            return data
-
         # All duplicates should raise
         with pytest.raises(DuplicateHandlerError):
 
@@ -59,18 +51,6 @@ class TestHandlerValidation:
             async def dup(_ctx, data):
                 return data
 
-        with pytest.raises(DuplicateHandlerError):
-
-            @encryption.encrypt.json.thread
-            async def dup(_ctx, data):
-                return data
-
-        with pytest.raises(DuplicateHandlerError):
-
-            @encryption.decrypt.json.custom
-            async def dup(_ctx, data):
-                return data
-
     def test_handlers_must_be_async(self):
         """Sync functions raise TypeError."""
         encryption = Encryption()