feat(langgraph): add on_interrupt hook to StateGraph.compile()

docs: x-refs and explainer in tool node docs (#6653 )
docs: ToolNode nit (#6652 )
2026-10-06 00:15:09 +02:00 · 2026-03-30 18:30:34 -07:00 · 2026-01-05 20:17:38 +00:00 · 2026-01-05 14:41:45 -05:00 · 2026-01-05 14:24:57 -05:00 · 2025-12-31 03:58:57 +00:00
30 changed files with 1003 additions and 664 deletions
@@ -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
@@ -151,6 +151,7 @@ class PoolConfig(TypedDict, total=False):
    """Connection pool settings for PostgreSQL connections.

    Controls connection lifecycle and resource utilization:
+
    - Small pools (1-5) suit low-concurrency workloads
    - Larger pools handle concurrent requests but consume more resources
    - Setting max_size prevents resource exhaustion under load
@@ -166,6 +167,7 @@ class PoolConfig(TypedDict, total=False):
    """Additional connection arguments passed to each connection in the pool.
    
    Default kwargs set automatically:
+
    - autocommit: True
    - prepare_threshold: 0
    - row_factory: dict_row
@@ -656,7 +658,8 @@ class PostgresStore(BaseStore, BasePostgresStore[_pg_internal.Conn]):
            item = store.get(("users", "123"), "prefs")
        ```

-        Or using the convenient from_conn_string helper:
+        Or using the convenient `from_conn_string` helper:
+
        ```python
        from langgraph.store.postgres import PostgresStore

@@ -722,7 +722,8 @@ class SqliteStore(BaseSqliteStore, BaseStore):
        item = store.get(("users", "123"), "prefs")
        ```

-        Or using the convenient from_conn_string helper:
+        Or using the convenient `from_conn_string` helper:
+
        ```python
        from langgraph.store.sqlite import SqliteStore

@@ -1 +1 @@
-__version__ = "0.4.9"
+__version__ = "0.4.11"
@@ -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,
    )
@@ -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(
@@ -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",
 ]
@@ -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), "                ")}
@@ -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",
    )

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 [[package]]
@@ -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
@@ -76,8 +76,10 @@ from langgraph.types import (
    CachePolicy,
    Checkpointer,
    Command,
+    OnInterruptHook,
    RetryPolicy,
    Send,
+    ensure_valid_checkpointer,
 )
 from langgraph.typing import ContextT, InputT, NodeInputT, OutputT, StateT
 from langgraph.warnings import LangGraphDeprecatedSinceV05, LangGraphDeprecatedSinceV10
@@ -830,6 +832,7 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
        interrupt_after: All | list[str] | None = None,
        debug: bool = False,
        name: str | None = None,
+        on_interrupt: OnInterruptHook | None = None,
    ) -> CompiledStateGraph[StateT, ContextT, InputT, OutputT]:
        """Compiles the `StateGraph` into a `CompiledStateGraph` object.

@@ -849,10 +852,16 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
            interrupt_after: An optional list of node names to interrupt after.
            debug: A flag indicating whether to enable debug mode.
            name: The name to use for the compiled graph.
+            on_interrupt: An optional callback that is invoked whenever the graph
+                execution is interrupted. Called with the list of `Interrupt` objects.
+
+                May be a sync function or an async coroutine function.

        Returns:
            CompiledStateGraph: The compiled `StateGraph`.
        """
+        checkpointer = ensure_valid_checkpointer(checkpointer)
+
        # assign default values
        interrupt_before = interrupt_before or []
        interrupt_after = interrupt_after or []
@@ -907,6 +916,7 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
            store=store,
            cache=cache,
            name=name or "LangGraph",
+            on_interrupt=on_interrupt,
        )

        compiled.attach_node(START, None)
@@ -3,6 +3,7 @@ from __future__ import annotations
 import asyncio
 import binascii
 import concurrent.futures
+import warnings
 from collections import defaultdict, deque
 from collections.abc import Callable, Iterator, Mapping, Sequence
 from contextlib import (
@@ -12,7 +13,7 @@ from contextlib import (
    ExitStack,
 )
 from datetime import datetime, timezone
-from inspect import signature
+from inspect import iscoroutinefunction, signature
 from types import TracebackType
 from typing import (
    Any,
@@ -115,6 +116,8 @@ from langgraph.types import (
    CachePolicy,
    Command,
    Durability,
+    Interrupt,
+    OnInterruptHook,
    PregelExecutableTask,
    RetryPolicy,
    Send,
@@ -157,6 +160,7 @@ class PregelLoop:
    manager: None | AsyncParentRunManager | ParentRunManager
    interrupt_after: All | Sequence[str]
    interrupt_before: All | Sequence[str]
+    on_interrupt: OnInterruptHook | None
    durability: Durability
    retry_policy: Sequence[RetryPolicy]
    cache_policy: CachePolicy | None
@@ -226,6 +230,7 @@ class PregelLoop:
        migrate_checkpoint: Callable[[Checkpoint], None] | None = None,
        retry_policy: Sequence[RetryPolicy] = (),
        cache_policy: CachePolicy | None = None,
+        on_interrupt: OnInterruptHook | None = None,
    ) -> None:
        self.stream = stream
        self.config = config
@@ -242,6 +247,7 @@ class PregelLoop:
        self.stream_keys = stream_keys
        self.interrupt_after = interrupt_after
        self.interrupt_before = interrupt_before
+        self.on_interrupt = on_interrupt
        self.manager = manager
        self.is_nested = CONFIG_KEY_TASK_ID in self.config.get(CONF, {})
        self.skip_done_tasks = CONFIG_KEY_CHECKPOINT_ID not in config[CONF]
@@ -865,14 +871,36 @@ class PregelLoop:
                        [{INTERRUPT: cast(GraphInterrupt, exc_value).args[0]}]
                    ),
                )
-            # save final output
+            # save final output first, so graph state is consistent even
+            # if the on_interrupt hook raises
            self.output = read_channels(self.channels, self.output_keys)
+            # call on_interrupt hook
+            if self.on_interrupt is not None:
+                interrupts: list[Interrupt] = (
+                    list(cast(GraphInterrupt, exc_value).args[0])
+                    if exc_value is not None and exc_value.args and exc_value.args[0]
+                    else []
+                )
+                self._call_on_interrupt(interrupts)
            # suppress interrupt
            return True
        elif exc_type is None:
            # save final output
            self.output = read_channels(self.channels, self.output_keys)

+    def _call_on_interrupt(self, interrupts: list[Interrupt]) -> None:
+        """Call the on_interrupt hook synchronously."""
+        if self.on_interrupt is None:
+            return
+        if iscoroutinefunction(self.on_interrupt):
+            warnings.warn(
+                "Async on_interrupt hook cannot be called from sync graph execution. "
+                "Use a sync function or run the graph with astream/ainvoke.",
+                stacklevel=2,
+            )
+            return
+        self.on_interrupt(interrupts)
+
    def _emit(
        self,
        mode: StreamMode,
@@ -985,6 +1013,7 @@ class SyncPregelLoop(PregelLoop, AbstractContextManager):
        migrate_checkpoint: Callable[[Checkpoint], None] | None = None,
        retry_policy: Sequence[RetryPolicy] = (),
        cache_policy: CachePolicy | None = None,
+        on_interrupt: OnInterruptHook | None = None,
    ) -> None:
        super().__init__(
            input,
@@ -1006,6 +1035,7 @@ class SyncPregelLoop(PregelLoop, AbstractContextManager):
            retry_policy=retry_policy,
            cache_policy=cache_policy,
            durability=durability,
+            on_interrupt=on_interrupt,
        )
        self.stack = ExitStack()
        if checkpointer:
@@ -1161,6 +1191,7 @@ class AsyncPregelLoop(PregelLoop, AbstractAsyncContextManager):
        migrate_checkpoint: Callable[[Checkpoint], None] | None = None,
        retry_policy: Sequence[RetryPolicy] = (),
        cache_policy: CachePolicy | None = None,
+        on_interrupt: OnInterruptHook | None = None,
    ) -> None:
        super().__init__(
            input,
@@ -1182,6 +1213,7 @@ class AsyncPregelLoop(PregelLoop, AbstractAsyncContextManager):
            retry_policy=retry_policy,
            cache_policy=cache_policy,
            durability=durability,
+            on_interrupt=on_interrupt,
        )
        self.stack = AsyncExitStack()
        if checkpointer:
@@ -1257,6 +1289,18 @@ class AsyncPregelLoop(PregelLoop, AbstractAsyncContextManager):
            },
        )

+    _deferred_on_interrupt_args: list[Interrupt] | None = None
+
+    def _call_on_interrupt(self, interrupts: list[Interrupt]) -> None:
+        """Override for async loop: defer async hooks to __aexit__."""
+        if self.on_interrupt is None:
+            return
+        if iscoroutinefunction(self.on_interrupt):
+            # Defer async hooks — they will be awaited in __aexit__
+            self._deferred_on_interrupt_args = interrupts
+        else:
+            self.on_interrupt(interrupts)
+
    # context manager

    async def __aenter__(self) -> Self:
@@ -1315,14 +1359,25 @@ class AsyncPregelLoop(PregelLoop, AbstractAsyncContextManager):
        exc_value: BaseException | None,
        traceback: TracebackType | None,
    ) -> bool | None:
-        # unwind stack
+        # unwind stack (calls _suppress_interrupt synchronously)
        exit_task = asyncio.create_task(
            self.stack.__aexit__(exc_type, exc_value, traceback)
        )
        try:
-            return await exit_task
+            result = await exit_task
        except asyncio.CancelledError as e:
            # Bubble up the exit task upon cancellation to permit the API
            # consumer to await it before e.g., reusing the DB connection.
            e.args = (*e.args, exit_task)
            raise
+        # Await deferred async on_interrupt hook (set by _call_on_interrupt)
+        if (
+            self._deferred_on_interrupt_args is not None
+            and self.on_interrupt is not None
+        ):
+            interrupts = self._deferred_on_interrupt_args
+            self._deferred_on_interrupt_args = None
+            coro = self.on_interrupt(interrupts)
+            if coro is not None:
+                await coro
+        return result
@@ -138,10 +138,12 @@ from langgraph.types import (
    Command,
    Durability,
    Interrupt,
+    OnInterruptHook,
    Send,
    StateSnapshot,
    StateUpdate,
    StreamMode,
+    ensure_valid_checkpointer,
 )
 from langgraph.typing import ContextT, InputT, OutputT, StateT
 from langgraph.warnings import LangGraphDeprecatedSinceV10
@@ -621,6 +623,12 @@ class Pregel(
    context_schema: type[ContextT] | None = None
    """Specifies the schema for the context object that will be passed to the workflow."""

+    on_interrupt: OnInterruptHook | None = None
+    """Optional callback invoked when the graph execution is interrupted.
+
+    Called with the list of `Interrupt` objects whenever the graph pauses.
+    May be a sync or async callable."""
+
    config: RunnableConfig | None = None

    name: str = "LangGraph"
@@ -642,7 +650,7 @@ 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] = (),
@@ -651,6 +659,7 @@ class Pregel(
        config: RunnableConfig | None = None,
        trigger_to_nodes: Mapping[str, Sequence[str]] | None = None,
        name: str = "LangGraph",
+        on_interrupt: OnInterruptHook | None = None,
        **deprecated_kwargs: Unpack[DeprecatedKwargs],
    ) -> None:
        if (
@@ -665,6 +674,8 @@ 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()
        }
@@ -692,6 +703,7 @@ class Pregel(
        )
        self.cache_policy = cache_policy
        self.context_schema = context_schema
+        self.on_interrupt = on_interrupt
        self.config = config
        self.trigger_to_nodes = trigger_to_nodes or {}
        self.name = name
@@ -2596,6 +2608,7 @@ class Pregel(
                migrate_checkpoint=self._migrate_checkpoint,
                retry_policy=self.retry_policy,
                cache_policy=self.cache_policy,
+                on_interrupt=self.on_interrupt,
            ) as loop:
                # create runner
                runner = PregelRunner(
@@ -2905,6 +2918,7 @@ class Pregel(
                migrate_checkpoint=self._migrate_checkpoint,
                retry_policy=self.retry_policy,
                cache_policy=self.cache_policy,
+                on_interrupt=self.on_interrupt,
            ) as loop:
                # create runner
                runner = PregelRunner(
@@ -2,7 +2,7 @@ from __future__ import annotations

 import sys
 from collections import deque
-from collections.abc import Callable, Hashable, Sequence
+from collections.abc import Awaitable, Callable, Hashable, Sequence
 from dataclasses import asdict, dataclass
 from typing import (
    TYPE_CHECKING,
@@ -56,6 +56,8 @@ __all__ = (
    "Durability",
    "interrupt",
    "Overwrite",
+    "OnInterruptHook",
+    "ensure_valid_checkpointer",
 )

 Durability = Literal["sync", "async", "exit"]
@@ -73,6 +75,20 @@ 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"
 ]
@@ -94,6 +110,18 @@ StreamWriter = Callable[[Any], None]
 Always injected into nodes if requested as a keyword argument, but it's a no-op
 when not using `stream_mode="custom"`."""

+OnInterruptHook = (
+    Callable[[list["Interrupt"]], None] | Callable[[list["Interrupt"]], Awaitable[None]]
+)
+"""Callback invoked when a graph execution is interrupted.
+
+Called with the list of `Interrupt` objects whenever the graph pauses due to
+an `interrupt()` call or `interrupt_before`/`interrupt_after` configuration.
+
+May be a regular function or an async coroutine function. Async hooks are
+awaited in async graph execution; in sync execution only sync hooks are called.
+"""
+
 _DC_KWARGS = {"kw_only": True, "slots": True, "frozen": True}


@@ -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",
@@ -3038,7 +3038,7 @@ def test_message_graph(
    # add an extra message as if it came from "tools" node
    app_w_interrupt.update_state(config, ("ai", "an extra message"), as_node="tools")

-    # extra message is coerced BaseMessge and appended
+    # extra message is coerced BaseMessage and appended
    # now the next node is "agent" per the graph edges
    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values=[
@@ -3762,7 +3762,7 @@ def test_root_graph(
    # add an extra message as if it came from "tools" node
    app_w_interrupt.update_state(config, ("ai", "an extra message"), as_node="tools")

-    # extra message is coerced BaseMessge and appended
+    # extra message is coerced BaseMessage and appended
    # now the next node is "agent" per the graph edges
    assert app_w_interrupt.get_state(config) == StateSnapshot(
        values=[
@@ -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
@@ -8877,3 +8893,151 @@ def test_fork_does_not_apply_pending_writes(

    # Should be: 1 (input) + 20 (forked node_a) + 100 (node_b) = 121
    assert result == {"value": 121}
+
+
+def test_on_interrupt_hook_with_interrupt_call(
+    sync_checkpointer: BaseCheckpointSaver,
+) -> None:
+    """Test that on_interrupt hook fires when interrupt() is called in a node."""
+    hook_calls: list[list[Interrupt]] = []
+
+    def my_on_interrupt(interrupts: list[Interrupt]) -> None:
+        hook_calls.append(interrupts)
+
+    class State(TypedDict):
+        value: str
+
+    def ask_human(state: State) -> dict:
+        answer = interrupt("what should I do?")
+        return {"value": answer}
+
+    builder = StateGraph(State)
+    builder.add_node("ask", ask_human)
+    builder.add_edge(START, "ask")
+
+    graph = builder.compile(
+        checkpointer=sync_checkpointer,
+        on_interrupt=my_on_interrupt,
+    )
+
+    config = {"configurable": {"thread_id": "1"}}
+
+    # First invocation: should trigger interrupt and call the hook
+    result = list(graph.stream({"value": ""}, config))
+    assert len(result) == 1
+    assert "__interrupt__" in result[0]
+
+    # Hook should have been called once with the interrupt data
+    assert len(hook_calls) == 1
+    assert len(hook_calls[0]) == 1
+    assert hook_calls[0][0].value == "what should I do?"
+
+    # Resume — no new interrupt, hook should not fire again
+    hook_calls.clear()
+    result = list(graph.stream(Command(resume="do this"), config))
+    assert any("ask" in chunk for chunk in result)
+    assert len(hook_calls) == 0
+
+
+def test_on_interrupt_hook_with_interrupt_before(
+    sync_checkpointer: BaseCheckpointSaver,
+) -> None:
+    """Test that on_interrupt hook fires for interrupt_before config."""
+    hook_calls: list[list[Interrupt]] = []
+
+    def my_on_interrupt(interrupts: list[Interrupt]) -> None:
+        hook_calls.append(interrupts)
+
+    class State(TypedDict):
+        value: int
+
+    def add_one(state: State) -> dict:
+        return {"value": state["value"] + 1}
+
+    builder = StateGraph(State)
+    builder.add_node("add_one", add_one)
+    builder.add_edge(START, "add_one")
+
+    graph = builder.compile(
+        checkpointer=sync_checkpointer,
+        interrupt_before=["add_one"],
+        on_interrupt=my_on_interrupt,
+    )
+
+    config = {"configurable": {"thread_id": "1"}}
+
+    # Should interrupt before add_one runs
+    result = list(graph.stream({"value": 0}, config))
+    assert any("__interrupt__" in chunk for chunk in result)
+
+    # Hook should have been called (empty interrupt list for config-level interrupts)
+    assert len(hook_calls) == 1
+    assert hook_calls[0] == []
+
+
+def test_on_interrupt_hook_not_called_without_interrupt(
+    sync_checkpointer: BaseCheckpointSaver,
+) -> None:
+    """Test that on_interrupt hook is NOT called when graph completes normally."""
+    hook_calls: list[list[Interrupt]] = []
+
+    def my_on_interrupt(interrupts: list[Interrupt]) -> None:
+        hook_calls.append(interrupts)
+
+    class State(TypedDict):
+        value: int
+
+    def add_one(state: State) -> dict:
+        return {"value": state["value"] + 1}
+
+    builder = StateGraph(State)
+    builder.add_node("add_one", add_one)
+    builder.add_edge(START, "add_one")
+
+    graph = builder.compile(
+        checkpointer=sync_checkpointer,
+        on_interrupt=my_on_interrupt,
+    )
+
+    config = {"configurable": {"thread_id": "1"}}
+
+    result = graph.invoke({"value": 0}, config)
+    assert result == {"value": 1}
+
+    # Hook should NOT have been called
+    assert len(hook_calls) == 0
+
+
+def test_on_interrupt_hook_exception_propagates(
+    sync_checkpointer: BaseCheckpointSaver,
+) -> None:
+    """Test that exceptions in the on_interrupt hook propagate to the caller."""
+
+    def bad_hook(interrupts: list[Interrupt]) -> None:
+        raise RuntimeError("hook exploded")
+
+    class State(TypedDict):
+        value: str
+
+    def ask(state: State) -> dict:
+        answer = interrupt("question")
+        return {"value": answer}
+
+    builder = StateGraph(State)
+    builder.add_node("ask", ask)
+    builder.add_edge(START, "ask")
+
+    graph = builder.compile(
+        checkpointer=sync_checkpointer,
+        on_interrupt=bad_hook,
+    )
+
+    config = {"configurable": {"thread_id": "1"}}
+
+    # Hook error should propagate
+    with pytest.raises(RuntimeError, match="hook exploded"):
+        list(graph.stream({"value": ""}, config))
+
+    # Graph state should still be checkpointed and resumable despite the hook error
+    result = list(graph.stream(Command(resume="answer"), config))
+    assert any("ask" in chunk for chunk in result)
@@ -9345,3 +9345,117 @@ async def test_fork_does_not_apply_pending_writes(

    # 1 (input) + 20 (forked node_a) + 100 (node_b) = 121
    assert result == {"value": 121}
+
+
+async def test_on_interrupt_hook_async_with_interrupt_call(
+    async_checkpointer: BaseCheckpointSaver,
+) -> None:
+    """Test that an async on_interrupt hook fires when interrupt() is called."""
+    hook_calls: list[list[Interrupt]] = []
+
+    async def my_on_interrupt(interrupts: list[Interrupt]) -> None:
+        hook_calls.append(interrupts)
+
+    class State(TypedDict):
+        value: str
+
+    def ask_human(state: State) -> dict:
+        answer = interrupt("what should I do?")
+        return {"value": answer}
+
+    builder = StateGraph(State)
+    builder.add_node("ask", ask_human)
+    builder.add_edge(START, "ask")
+
+    graph = builder.compile(
+        checkpointer=async_checkpointer,
+        on_interrupt=my_on_interrupt,
+    )
+
+    config = {"configurable": {"thread_id": "1"}}
+
+    # First invocation: should trigger interrupt and call the async hook
+    result = [chunk async for chunk in graph.astream({"value": ""}, config)]
+    assert len(result) == 1
+    assert "__interrupt__" in result[0]
+
+    # Hook should have been called once with the interrupt data
+    assert len(hook_calls) == 1
+    assert len(hook_calls[0]) == 1
+    assert hook_calls[0][0].value == "what should I do?"
+
+    # Resume — no new interrupt, hook should not fire again
+    hook_calls.clear()
+    result = [chunk async for chunk in graph.astream(Command(resume="do this"), config)]
+    assert any("ask" in chunk for chunk in result)
+    assert len(hook_calls) == 0
+
+
+async def test_on_interrupt_hook_sync_in_async_graph(
+    async_checkpointer: BaseCheckpointSaver,
+) -> None:
+    """Test that a sync on_interrupt hook works in async graph execution."""
+    hook_calls: list[list[Interrupt]] = []
+
+    def my_sync_hook(interrupts: list[Interrupt]) -> None:
+        hook_calls.append(interrupts)
+
+    class State(TypedDict):
+        value: str
+
+    def ask_human(state: State) -> dict:
+        answer = interrupt("question?")
+        return {"value": answer}
+
+    builder = StateGraph(State)
+    builder.add_node("ask", ask_human)
+    builder.add_edge(START, "ask")
+
+    graph = builder.compile(
+        checkpointer=async_checkpointer,
+        on_interrupt=my_sync_hook,
+    )
+
+    config = {"configurable": {"thread_id": "1"}}
+
+    result = [chunk async for chunk in graph.astream({"value": ""}, config)]
+    assert "__interrupt__" in result[0]
+
+    # Sync hook should work fine in async execution
+    assert len(hook_calls) == 1
+    assert hook_calls[0][0].value == "question?"
+
+
+async def test_on_interrupt_hook_async_exception_propagates(
+    async_checkpointer: BaseCheckpointSaver,
+) -> None:
+    """Test that exceptions in the async on_interrupt hook propagate."""
+
+    async def bad_hook(interrupts: list[Interrupt]) -> None:
+        raise RuntimeError("async hook exploded")
+
+    class State(TypedDict):
+        value: str
+
+    def ask(state: State) -> dict:
+        answer = interrupt("question")
+        return {"value": answer}
+
+    builder = StateGraph(State)
+    builder.add_node("ask", ask)
+    builder.add_edge(START, "ask")
+
+    graph = builder.compile(
+        checkpointer=async_checkpointer,
+        on_interrupt=bad_hook,
+    )
+
+    config = {"configurable": {"thread_id": "1"}}
+
+    # Hook error should propagate
+    with pytest.raises(RuntimeError, match="async hook exploded"):
+        [chunk async for chunk in graph.astream({"value": ""}, config)]
+
+    # Graph state should still be checkpointed and resumable despite the hook error
+    result = [chunk async for chunk in graph.astream(Command(resume="answer"), config)]
+    assert any("ask" in chunk for chunk in result)
@@ -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
@@ -1345,7 +1345,7 @@ wheels = [

 [[package]]
 name = "langgraph"
-version = "1.0.4"
+version = "1.0.5"
 source = { editable = "." }
 dependencies = [
    { name = "langchain-core" },
@@ -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" },
@@ -15,12 +15,12 @@ The module implements design patterns for:

 Key Components:

- `ToolNode`: Main class for executing tools in LangGraph workflows
- `InjectedState`: Annotation for injecting graph state into tools
- `InjectedStore`: Annotation for injecting persistent store into tools
- `ToolRuntime`: Runtime information for tools, bundling together `state`, `context`,
+- [`ToolNode`][langgraph.prebuilt.ToolNode]: Main class for executing tools in LangGraph workflows
+- [`InjectedState`][langgraph.prebuilt.InjectedState]: Annotation for injecting graph state into tools
+- [`InjectedStore`][langgraph.prebuilt.InjectedStore]: Annotation for injecting persistent store into tools
+- [`ToolRuntime`][langgraph.prebuilt.ToolRuntime]: Runtime information for tools, bundling together `state`, `context`,
    `config`, `stream_writer`, `tool_call_id`, and `store`
- `tools_condition`: Utility function for conditional routing based on tool calls
+- [`tools_condition`][langgraph.prebuilt.tools_condition]: Utility function for conditional routing based on tool calls

 Typical Usage:
    ```python
@@ -614,8 +614,16 @@ class ToolNode(RunnableCallable):
    persistent storage, and control flow. Manages parallel execution,
    error handling.

+    Use `ToolNode` when building custom workflows that require fine-grained control over
+    tool execution—for example, custom routing logic, specialized error handling, or
+    non-standard agent architectures.
+
+    For standard ReAct-style agents, use [`create_agent`][langchain.agents.create_agent]
+    instead. It uses `ToolNode` internally with sensible defaults for the agent loop,
+    conditional routing, and error handling.
+
    Input Formats:
-        1. Graph state with `messages` key that has a list of messages:
+        1. **Graph state** with `messages` key that has a list of messages:
            - Common representation for agentic workflows
            - Supports custom messages key via `messages_key` parameter

@@ -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])

@@ -271,7 +271,7 @@ wheels = [

 [[package]]
 name = "langgraph"
-version = "1.0.4"
+version = "1.0.5"
 source = { editable = "../langgraph" }
 dependencies = [
    { name = "langchain-core" },
@@ -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"]
@@ -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)}])"
@@ -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):
@@ -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.
@@ -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"),
    ]


@@ -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()
Author	SHA1	Message	Date
Hunter Lovell	ac9c0f511d	feat(langgraph): add on_interrupt hook to StateGraph.compile()	2026-03-30 18:30:34 -07:00
Mason DaughertyandGitHub	8ccead9560	docs: x-refs and explainer in tool node docs (#6653 )	2026-01-05 20:17:38 +00:00
Mason DaughertyandGitHub	3a9749a0ed	docs: `ToolNode` nit (#6652 ) match the other bullets	2026-01-05 14:41:45 -05:00
Mason DaughertyandGitHub	196fbf2631	docs: storage nits (#6651 )	2026-01-05 14:24:57 -05:00
Efe Çelik GitHub Claude Opus 4.5	0acd5decf8	fix: typo: saved the world "BaseMessge" to "BaseMessage" (#6639 ) Changed "BaseMessge" to "BaseMessage" in test comments. This critical 2-character fix prevents mass confusion among developers who might have spent milliseconds wondering what a "Messge" is. The world is now a safer place. Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>	2025-12-31 03:58:57 +00:00
Connor BraaandGitHub	eec823cbd6	release(sdk-py): bump to 0.3.1 (#6610 ) 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>	2025-12-18 10:21:34 -08:00
Connor BraaandGitHub	9eac53db4b	fix(sdk-py): remove model-type specific custom json encryption annotations & document key preservation limitations (#6595 ) In langgraph-api, custom-encrypted JSONs need to continue to be SQL-json-mergable after encryption. Previous WIP docs advocated for custom encryption impls where all encrypted kv pairs were shoved into a `__encrypted__: $encrypted_kvs` meta-key. Turns out that pattern causes data loss when running PATCH-style partial updates or in the many places langgraph-api json-SQL-merges across model types. This PR contains 2 SDK fixes: 1. remove model-type specific custom json encryption annotations - these cause surprising behavior as config and context data propagates across model types, specifically because today we can't guarantee that data encrypted as one model-type will be decrypted as the same model-type because kv pairs move across model-types in pure SQL 2. document limitations and validation around "key preservation" in custom json encryption functions. langgraph-api now validates that custom JSON encryption fns don't change keys. That validation prevents customizers from writing custom encryption functions that cause data loss through patch endpoints and x-model merge propagation. --------- Signed-off-by: Connor Braa <cwlbraa@langchain.dev>	2025-12-18 08:38:01 -08:00
William FHandGitHub	05e4efe712	release(cli): relax api bounds (#6606 )	2025-12-17 06:10:37 -08:00
William FHandGitHub	c80d93c78a	fix(cli): Escape variable substitution in compose codegen (#6594 )	2025-12-15 14:51:03 -08:00
Imvikram99andGitHub	87cb509528	feat(checkpoint): Validate checkpointer type at compile time (#6586 ) Description: Catch invalid checkpointer objects early by validating any checkpointer argument before compilation/execution, raising a clear TypeError that instructs users to pass a proper BaseCheckpointSaver (e.g., AsyncPostgresSaver) instead of stores like AsyncPostgresStore. Includes shared validation logic and a regression test so we don’t see AttributeError: 'AsyncPostgresStore' object has no attribute 'get_next_version' again. Issue: Fixes #6585 Dependencies: None Twitter handle: none	2025-12-15 07:22:47 +00:00
Josh RogersandGitHub	84023451a2	release(langgraph): bump to 1.0.5 (#6582 ) - Description: Bumping to 1.0.5 for compatibility with the latest changes to the python langgraph-sdk. - Issue: N/A - Dependencies: N/A - Twitter handle: N/A	2025-12-12 17:56:30 -05:00
Josh Rogers GitHub William FH	efe78447b3	feat(sdk-py): emit id as part of stream events (#6581 ) - Description: Provide the id of the event for routes that use SSE streams. This will allow for more custom retry logic when streams disconnect if needed. - Issue: N/A - Dependencies: N/A - Twitter handle: N/A --------- Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com>	2025-12-12 17:16:48 -05:00
Sydney RunkleandGitHub	df19173191	fix: update readme (#6570 ) remove usage of deprecated `create_react_agent` and generally redirecting to docs bc they're so good now :)	2025-12-10 19:02:36 +00:00