listen up

fix(cli): Escape variable substitution in compose codegen (#6594 )
feat(checkpoint): Validate checkpointer type at compile time (#6586 )
2026-08-28 18:59:42 +02:00 · 2025-12-17 23:06:05 +09:00 · 2025-12-15 14:51:03 -08:00 · 2025-12-15 07:22:47 +00:00 · 2025-12-12 17:56:30 -05:00 · 2025-12-12 17:16:48 -05:00
29 changed files with 1284 additions and 813 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
@@ -143,11 +143,13 @@ class PostgresSaver(BasePostgresSaver):
        """
        where, args = self._search_where(config, filter, before)
        query = self.SELECT_SQL + where + " ORDER BY checkpoint_id DESC"
-        if limit:
-            query += f" LIMIT {limit}"
+        params = list(args)
+        if limit is not None:
+            query += " LIMIT %s"
+            params.append(int(limit))
        # if we change this to use .stream() we need to make sure to close the cursor
        with self._cursor() as cur:
-            cur.execute(query, args)
+            cur.execute(query, params)
            values = cur.fetchall()
            if not values:
                return
@@ -132,11 +132,13 @@ class AsyncPostgresSaver(BasePostgresSaver):
        """
        where, args = self._search_where(config, filter, before)
        query = self.SELECT_SQL + where + " ORDER BY checkpoint_id DESC"
-        if limit:
-            query += f" LIMIT {limit}"
+        params = list(args)
+        if limit is not None:
+            query += " LIMIT %s"
+            params.append(int(limit))
        # if we change this to use .stream() we need to make sure to close the cursor
        async with self._cursor() as cur:
-            await cur.execute(query, args, binary=True)
+            await cur.execute(query, params, binary=True)
            values = await cur.fetchall()
            if not values:
                return
@@ -272,10 +272,12 @@ class ShallowPostgresSaver(BasePostgresSaver):
        """
        where, args = self._search_where(config, filter, before)
        query = self.SELECT_SQL + where
-        if limit:
-            query += f" LIMIT {limit}"
+        params = list(args)
+        if limit is not None:
+            query += " LIMIT %s"
+            params.append(int(limit))
        with self._cursor() as cur:
-            cur.execute(self.SELECT_SQL + where, args, binary=True)
+            cur.execute(query, params, binary=True)
            for value in cur:
                checkpoint: Checkpoint = {
                    **value["checkpoint"],
@@ -636,10 +638,12 @@ class AsyncShallowPostgresSaver(BasePostgresSaver):
        """
        where, args = self._search_where(config, filter, before)
        query = self.SELECT_SQL + where
-        if limit:
-            query += f" LIMIT {limit}"
+        params = list(args)
+        if limit is not None:
+            query += " LIMIT %s"
+            params.append(int(limit))
        async with self._cursor() as cur:
-            await cur.execute(self.SELECT_SQL + where, args, binary=True)
+            await cur.execute(query, params, binary=True)
            async for value in cur:
                checkpoint: Checkpoint = {
                    **value["checkpoint"],
@@ -266,6 +266,27 @@ class AsyncPostgresStore(AsyncBatchedBaseStore, BasePostgresStore[_ainternal.Con
                            k: v(self) if v is not None and callable(v) else v
                            for k, v in migration.params.items()
                        }
+                        if "dims" in params:
+                            try:
+                                params["dims"] = int(params["dims"])
+                            except Exception as e:
+                                raise ValueError(
+                                    f"Invalid dims for vector index: {params['dims']}"
+                                ) from e
+                        if "vector_type" in params:
+                            vt = str(params["vector_type"])
+                            if vt not in ("vector", "halfvec"):
+                                raise ValueError(
+                                    f"Invalid vector_type for pgvector: {vt}"
+                                )
+                            params["vector_type"] = vt
+                        if "index_type" in params:
+                            it = str(params["index_type"])
+                            if it not in ("hnsw", "ivfflat"):
+                                raise ValueError(
+                                    f"Invalid index_type for pgvector: {it}"
+                                )
+                            params["index_type"] = it
                        sql = sql % params
                    await cur.execute(sql)
                    await cur.execute(
@@ -327,31 +327,36 @@ class BasePostgresStore(Generic[C]):
        embedding_request: tuple[str, Sequence[tuple[str, str, str, str]]] | None = None
        if inserts:
            values = []
-            insertion_params = []
+            insertion_params: list[Any] = []
            vector_values = []
            embedding_request_params = []
            # Handle TTL expiration

            # First handle main store insertions
            for op in inserts:
-                if op.ttl is not None:
-                    expires_at_str = f"NOW() + INTERVAL '{op.ttl * 60} seconds'"
-                    ttl_minutes = op.ttl
-                else:
-                    expires_at_str = "NULL"
-                    ttl_minutes = None
-
-                values.append(
-                    f"(%s, %s, %s, CURRENT_TIMESTAMP, CURRENT_TIMESTAMP, {expires_at_str}, %s)"
-                )
                insertion_params.extend(
-                    [
+                    (
                        _namespace_to_text(op.namespace),
                        op.key,
                        Jsonb(cast(dict, op.value)),
-                        ttl_minutes,
-                    ]
+                    )
                )
+                if op.ttl is not None:
+                    values.append(
+                        "(%s, %s, %s, CURRENT_TIMESTAMP, CURRENT_TIMESTAMP, NOW() + %s::interval, %s)"
+                    )
+                    ttl_minutes = float(op.ttl)
+                    insertion_params.extend(
+                        (
+                            f"{ttl_minutes * 60} seconds",
+                            ttl_minutes,
+                        )
+                    )
+                else:
+                    values.append(
+                        "(%s, %s, %s, CURRENT_TIMESTAMP, CURRENT_TIMESTAMP, NULL, %s)"
+                    )
+                    insertion_params.append(None)

            # Then handle embeddings if configured
            if self.index_config:
@@ -465,6 +470,10 @@ class BasePostgresStore(Generic[C]):
                        cast(dict, self.index_config)["dims"],
                    )
                else:
+                    if vector_type not in ("vector", "halfvec"):
+                        raise ValueError(
+                            f"Invalid vector_type for pgvector: {vector_type}"
+                        )
                    score_operator = score_operator % ("%s", vector_type)

                vectors_per_doc_estimate = cast(dict, self.index_config)[
@@ -1122,6 +1131,27 @@ class PostgresStore(BaseStore, BasePostgresStore[_pg_internal.Conn]):
                            k: v(self) if v is not None and callable(v) else v
                            for k, v in migration.params.items()
                        }
+                        if "dims" in params:
+                            try:
+                                params["dims"] = int(params["dims"])
+                            except Exception as e:
+                                raise ValueError(
+                                    f"Invalid dims for vector index: {params['dims']}"
+                                ) from e
+                        if "vector_type" in params:
+                            vt = str(params["vector_type"])
+                            if vt not in ("vector", "halfvec"):
+                                raise ValueError(
+                                    f"Invalid vector_type for pgvector: {vt}"
+                                )
+                            params["vector_type"] = vt
+                        if "index_type" in params:
+                            it = str(params["index_type"])
+                            if it not in ("hnsw", "ivfflat"):
+                                raise ValueError(
+                                    f"Invalid index_type for pgvector: {it}"
+                                )
+                            params["index_type"] = it
                        sql = sql % params
                    cur.execute(sql)
                    cur.execute("INSERT INTO vector_migrations (v) VALUES (%s)", (v,))
@@ -1175,15 +1205,44 @@ def _get_vector_type_ops(store: BasePostgresStore) -> str:


 def _get_index_params(store: Any) -> tuple[str, dict[str, Any]]:
-    """Get the index type and configuration based on config."""
+    """Get a sanitized index type and configuration based on config.
+
+    Only allow known-safe kinds and integer parameters to avoid SQL injection
+    when constructing DDL strings for index creation.
+    """
    if not store.index_config:
        return "hnsw", {}

    config = cast(PostgresIndexConfig, store.index_config)
-    index_config = config.get("ann_index_config", _DEFAULT_ANN_CONFIG).copy()
-    kind = index_config.pop("kind", "hnsw")
-    index_config.pop("vector_type", None)
-    return kind, index_config
+    raw = config.get("ann_index_config", _DEFAULT_ANN_CONFIG).copy()
+
+    kind = str(raw.pop("kind", "hnsw"))
+    if kind not in ("hnsw", "ivfflat", "flat"):
+        raise ValueError(
+            f"Invalid index kind for pgvector: {kind}. Expected 'hnsw', 'ivfflat', or 'flat'."
+        )
+
+    raw.pop("vector_type", None)
+
+    if kind == "hnsw":
+        allowed_keys = {"m", "ef_construction"}
+    else:  # ivfflat/flat
+        allowed_keys = {"lists", "nlist"}
+
+    sanitized: dict[str, int] = {}
+    for k, v in list(raw.items()):
+        if k not in allowed_keys:
+            continue
+        key = "lists" if k == "nlist" else k
+        try:
+            ivalue = int(v)  # type: ignore[call-overload]
+        except Exception as e:
+            raise ValueError(f"Invalid index parameter value for {k}: {v}") from e
+        if ivalue <= 0:
+            continue
+        sanitized[key] = ivalue
+
+    return kind, sanitized


 def _namespace_to_text(
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"

 [project]
 name = "langgraph-checkpoint-postgres"
-version = "3.0.1"
+version = "3.0.2"
 description = "Library with a Postgres implementation of LangGraph checkpoint saver."
 authors = []
 requires-python = ">=3.10"
@@ -1 +1 @@
-__version__ = "0.4.9"
+__version__ = "0.4.10"
@@ -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(
@@ -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",
    )

@@ -853,7 +853,7 @@ wheels = [

 [[package]]
 name = "langchain-core"
-version = "1.1.2"
+version = "1.2.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "jsonpatch", marker = "python_full_version >= '3.11'" },
@@ -865,14 +865,14 @@ dependencies = [
    { name = "typing-extensions", marker = "python_full_version >= '3.11'" },
    { name = "uuid-utils", marker = "python_full_version >= '3.11'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/67/8d/99652acb7feaa4e16c9162429bc7a446f04749ef438aa02fce74b4319a00/langchain_core-1.1.2.tar.gz", hash = "sha256:75456c5cc10c3b53b80488bf5c6a4bcc3447b53e011533a8744bb0638b85dd78", size = 802560, upload-time = "2025-12-08T15:28:17.689Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/f5/a0/2177f4ef4dfbea8edeba377b7b4889d177b8356ce186640e4651b240fd4d/langchain_core-1.2.1.tar.gz", hash = "sha256:131e6ad105b47ec2adc4d4d973f569276688f48cd890ba44603d48e76d9993ce", size = 802986, upload-time = "2025-12-15T14:32:50.845Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/fb/68/2caf612e4b5e25d7938c96809b7ccbafb5906958bcad8c18d9211f092679/langchain_core-1.1.2-py3-none-any.whl", hash = "sha256:74dfd4dcc10a290e3701a64e35e0bea3f68420f5b7527820ced9414f5b2dc281", size = 475847, upload-time = "2025-12-08T15:28:16.467Z" },
+    { url = "https://files.pythonhosted.org/packages/cc/95/98c47dbb4b6098934ff70e0f52efef3a85505dbcccc9eb63587e21fde4c9/langchain_core-1.2.1-py3-none-any.whl", hash = "sha256:2f63859f85dc3d95f768e35fed605702e3ff5aa3e92c7b253103119613e79768", size = 475972, upload-time = "2025-12-15T14:32:49.698Z" },
 ]

 [[package]]
 name = "langgraph"
-version = "1.0.4"
+version = "1.0.5"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "langchain-core", marker = "python_full_version >= '3.11'" },
@@ -882,14 +882,14 @@ dependencies = [
    { name = "pydantic", marker = "python_full_version >= '3.11'" },
    { name = "xxhash", marker = "python_full_version >= '3.11'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/d6/3c/af87902d300c1f467165558c8966d8b1e1f896dace271d3f35a410a5c26a/langgraph-1.0.4.tar.gz", hash = "sha256:86d08e25d7244340f59c5200fa69fdd11066aa999b3164b531e2a20036fac156", size = 484397, upload-time = "2025-11-25T20:31:48.608Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/7d/47/28f4d4d33d88f69de26f7a54065961ac0c662cec2479b36a2db081ef5cb6/langgraph-1.0.5.tar.gz", hash = "sha256:7f6ae59622386b60fe9fa0ad4c53f42016b668455ed604329e7dc7904adbf3f8", size = 493969, upload-time = "2025-12-12T23:05:48.224Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/14/52/4eb25a3f60399da34ba34adff1b3e324cf0d87eb7a08cebf1882a9b5e0d5/langgraph-1.0.4-py3-none-any.whl", hash = "sha256:b1a835ceb0a8d69b9db48075e1939e28b1ad70ee23fa3fa8f90149904778bacf", size = 157271, upload-time = "2025-11-25T20:31:47.518Z" },
+    { url = "https://files.pythonhosted.org/packages/23/1b/e318ee76e42d28f515d87356ac5bd7a7acc8bad3b8f54ee377bef62e1cbf/langgraph-1.0.5-py3-none-any.whl", hash = "sha256:b4cfd173dca3c389735b47228ad8b295e6f7b3df779aba3a1e0c23871f81281e", size = 157056, upload-time = "2025-12-12T23:05:46.499Z" },
 ]

 [[package]]
 name = "langgraph-api"
-version = "0.5.35"
+version = "0.5.42"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "cloudpickle", marker = "python_full_version >= '3.11'" },
@@ -919,9 +919,9 @@ dependencies = [
    { name = "uvicorn", marker = "python_full_version >= '3.11'" },
    { name = "watchfiles", marker = "python_full_version >= '3.11'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/5a/27/4dd4287ec65690e3a212d7154e20504b4e88e861fd62625053be8903bc57/langgraph_api-0.5.35.tar.gz", hash = "sha256:b5687a5201ff365e1bc016042a7103ed8a2c2440f57b71f8480c223585bbfca1", size = 378029, upload-time = "2025-12-09T00:37:35.091Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/80/22/454e81273f77fe33c20b75e046f5a3ca2f3f7fc18f13df7d95bddad372d2/langgraph_api-0.5.42.tar.gz", hash = "sha256:b2e71dbb7392b459ee6218f9fe8deca1be97c0d89891e867990a311912257fe6", size = 392953, upload-time = "2025-12-12T19:40:43.199Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/5a/80/296db2db262a90b0fe3cb2562790025e018e33da9d171cc64f12076e5911/langgraph_api-0.5.35-py3-none-any.whl", hash = "sha256:6aaf967c52ff719861b80e4dc8066968baa185d9daae8433f0d84b5a27708a65", size = 305523, upload-time = "2025-12-09T00:37:34.001Z" },
+    { url = "https://files.pythonhosted.org/packages/a0/05/6fc28638791fd021989279c90a2053783170ad273b79fbb487c241022ade/langgraph_api-0.5.42-py3-none-any.whl", hash = "sha256:42560a57870d7f2df3d0726392b1ad52670e5b4170e23790f77fea1610891446", size = 312558, upload-time = "2025-12-12T19:40:42.107Z" },
 ]

 [[package]]
@@ -1027,7 +1027,7 @@ wheels = [

 [[package]]
 name = "langgraph-runtime-inmem"
-version = "0.19.1"
+version = "0.20.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "blockbuster", marker = "python_full_version >= '3.11'" },
@@ -1037,27 +1037,27 @@ dependencies = [
    { name = "starlette", marker = "python_full_version >= '3.11'" },
    { name = "structlog", marker = "python_full_version >= '3.11'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/f4/9e/6e7b321ef02834059983d6d5a635cc20f9987b19fe6a4666332c8b9b0ede/langgraph_runtime_inmem-0.19.1.tar.gz", hash = "sha256:573d576cf38392fcace76d772be9adc4d54b2af129ae54cb9780bab4fb55ee69", size = 98975, upload-time = "2025-12-04T07:01:40.105Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/31/12/219cc68f58e69fe9a92518ae2563cbf20b118e1a6abb8827daafb26a83a2/langgraph_runtime_inmem-0.20.1.tar.gz", hash = "sha256:6ee918f0a839f3044cbed69580b44eb9cadf31a4771fc2e6d66842c7b7722fb0", size = 101516, upload-time = "2025-12-09T22:01:54.588Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/8d/32/2230e8f4bd1a61518b594aefda60a14ea0fdf50b4bb14da7962f8590fa45/langgraph_runtime_inmem-0.19.1-py3-none-any.whl", hash = "sha256:5aba07ba358a493a8f40590fad8e5342d1141a479410289ca5ae6f6b4fa6ab84", size = 35100, upload-time = "2025-12-04T07:01:38.758Z" },
+    { url = "https://files.pythonhosted.org/packages/4c/9c/b3883f862c46379bdd5fe4bd1eb8dce6c3897d7ec2d226b019e0866fad47/langgraph_runtime_inmem-0.20.1-py3-none-any.whl", hash = "sha256:27f4401372679c1132a31937524ca9fded071fc8e9a6cd5ac25e3860ebc8238b", size = 35183, upload-time = "2025-12-09T22:01:53.462Z" },
 ]

 [[package]]
 name = "langgraph-sdk"
-version = "0.2.14"
+version = "0.3.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "httpx", marker = "python_full_version >= '3.11'" },
    { name = "orjson", marker = "python_full_version >= '3.11'" },
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+sdist = { url = "https://files.pythonhosted.org/packages/2b/1b/f328afb4f24f6e18333ff357d9580a3bb5b133ff2c7aae34fef7f5b87f31/langgraph_sdk-0.3.0.tar.gz", hash = "sha256:4145bc3c34feae227ae918341f66d3ba7d1499722c1ef4a8aae5ea828897d1d4", size = 130366, upload-time = "2025-12-12T22:19:30.323Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/69/48/ee4d7afb3c3d38bd2ebe51a4d37f1ed7f1058dd242f35994b562203067aa/langgraph_sdk-0.3.0-py3-none-any.whl", hash = "sha256:c1ade483fba17ae354ee920e4779042b18d5aba875f2a858ba569f62f628f26f", size = 66489, upload-time = "2025-12-12T22:19:29.228Z" },
 ]

 [[package]]
 name = "langsmith"
-version = "0.4.56"
+version = "0.4.59"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "httpx", marker = "python_full_version >= '3.11'" },
@@ -1069,82 +1069,82 @@ dependencies = [
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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
@@ -78,6 +78,7 @@ from langgraph.types import (
    Command,
    RetryPolicy,
    Send,
+    ensure_valid_checkpointer,
 )
 from langgraph.typing import ContextT, InputT, NodeInputT, OutputT, StateT
 from langgraph.warnings import LangGraphDeprecatedSinceV05, LangGraphDeprecatedSinceV10
@@ -853,6 +854,8 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
        Returns:
            CompiledStateGraph: The compiled `StateGraph`.
        """
+        checkpointer = ensure_valid_checkpointer(checkpointer)
+
        # assign default values
        interrupt_before = interrupt_before or []
        interrupt_after = interrupt_after or []
@@ -142,6 +142,7 @@ from langgraph.types import (
    StateSnapshot,
    StateUpdate,
    StreamMode,
+    ensure_valid_checkpointer,
 )
 from langgraph.typing import ContextT, InputT, OutputT, StateT
 from langgraph.warnings import LangGraphDeprecatedSinceV10
@@ -642,7 +643,7 @@ 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] = (),
@@ -653,6 +654,8 @@ class Pregel(
        name: str = "LangGraph",
        **deprecated_kwargs: Unpack[DeprecatedKwargs],
    ) -> None:
+        root_listeners = deprecated_kwargs.pop("_root_listeners", None)
+        aroot_listeners = deprecated_kwargs.pop("_aroot_listeners", None)
        if (
            config_type := deprecated_kwargs.get("config_type", MISSING)
        ) is not MISSING:
@@ -665,6 +668,8 @@ class Pregel(
            if context_schema is None:
                context_schema = cast(type[ContextT], config_type)

+        checkpointer = ensure_valid_checkpointer(checkpointer)
+
        self.nodes = {
            k: v.build() if isinstance(v, NodeBuilder) else v for k, v in nodes.items()
        }
@@ -695,6 +700,14 @@ class Pregel(
        self.config = config
        self.trigger_to_nodes = trigger_to_nodes or {}
        self.name = name
+        self._root_listeners = cast(
+            "tuple[Callable[[Any], Any] | None, Callable[[Any], Any] | None, Callable[[Any], Any] | None] | None",
+            root_listeners,
+        )
+        self._aroot_listeners = cast(
+            "tuple[Callable[[Any], Awaitable[Any]] | None, Callable[[Any], Awaitable[Any]] | None, Callable[[Any], Awaitable[Any]] | None] | None",
+            aroot_listeners,
+        )
        if auto_validate:
            self.validate()

@@ -786,6 +799,26 @@ class Pregel(
            {"config": merge_configs(self.config, config, cast(RunnableConfig, kwargs))}
        )

+    def with_listeners(
+        self,
+        *,
+        on_start: Callable[[Any], Any] | None = None,
+        on_end: Callable[[Any], Any] | None = None,
+        on_error: Callable[[Any], Any] | None = None,
+    ) -> Self:
+        """Create a copy of the Pregel object with updated sync listeners."""
+        return self.copy({"_root_listeners": (on_start, on_end, on_error)})
+
+    def with_alisteners(
+        self,
+        *,
+        on_start: Callable[[Any], Awaitable[Any]] | None = None,
+        on_end: Callable[[Any], Awaitable[Any]] | None = None,
+        on_error: Callable[[Any], Awaitable[Any]] | None = None,
+    ) -> Self:
+        """Create a copy of the Pregel object with updated async listeners."""
+        return self.copy({"_aroot_listeners": (on_start, on_end, on_error)})
+
    def validate(self) -> Self:
        validate_graph(
            self.nodes,
@@ -2494,6 +2527,26 @@ class Pregel(
        stream = SyncQueue()

        config = ensure_config(self.config, config)
+        if self._root_listeners is not None:
+            on_start, on_end, on_error = self._root_listeners
+            from langchain_core.tracers.root_listeners import RootListenersTracer
+
+            tracer = RootListenersTracer(
+                config=config,
+                on_start=on_start,
+                on_end=on_end,
+                on_error=on_error,
+            )
+            existing_callbacks = config.get("callbacks")
+            config = config.copy()
+            if existing_callbacks is None:
+                config["callbacks"] = [tracer]
+            elif isinstance(existing_callbacks, list):
+                config["callbacks"] = [*existing_callbacks, tracer]
+            else:
+                callbacks_manager = existing_callbacks.copy()
+                callbacks_manager.add_handler(tracer, inherit=True)
+                config["callbacks"] = callbacks_manager
        callback_manager = get_callback_manager_for_config(config)
        run_manager = callback_manager.on_chain_start(
            None,
@@ -2773,6 +2826,46 @@ class Pregel(
        )

        config = ensure_config(self.config, config)
+        if self._root_listeners is not None:
+            on_start, on_end, on_error = self._root_listeners
+            from langchain_core.tracers.root_listeners import RootListenersTracer
+
+            sync_tracer = RootListenersTracer(
+                config=config,
+                on_start=on_start,
+                on_end=on_end,
+                on_error=on_error,
+            )
+            existing_callbacks = config.get("callbacks")
+            config = config.copy()
+            if existing_callbacks is None:
+                config["callbacks"] = [sync_tracer]
+            elif isinstance(existing_callbacks, list):
+                config["callbacks"] = [*existing_callbacks, sync_tracer]
+            else:
+                callbacks_manager = existing_callbacks.copy()
+                callbacks_manager.add_handler(sync_tracer, inherit=True)
+                config["callbacks"] = callbacks_manager
+        if self._aroot_listeners is not None:
+            on_start, on_end, on_error = self._aroot_listeners
+            from langchain_core.tracers.root_listeners import AsyncRootListenersTracer
+
+            async_tracer = AsyncRootListenersTracer(
+                config=config,
+                on_start=on_start,
+                on_end=on_end,
+                on_error=on_error,
+            )
+            existing_callbacks = config.get("callbacks")
+            config = config.copy()
+            if existing_callbacks is None:
+                config["callbacks"] = [async_tracer]
+            elif isinstance(existing_callbacks, list):
+                config["callbacks"] = [*existing_callbacks, async_tracer]
+            else:
+                callbacks_manager = existing_callbacks.copy()
+                callbacks_manager.add_handler(async_tracer, inherit=True)
+                config["callbacks"] = callbacks_manager
        callback_manager = get_async_callback_manager_for_config(config)
        run_manager = await callback_manager.on_chain_start(
            None,
@@ -1,7 +1,7 @@
 from __future__ import annotations

 from abc import abstractmethod
-from collections.abc import AsyncIterator, Callable, Iterator, Sequence
+from collections.abc import AsyncIterator, Awaitable, Callable, Iterator, Sequence
 from typing import Any, Generic, cast

 from langchain_core.runnables import Runnable, RunnableConfig
@@ -20,6 +20,24 @@ class PregelProtocol(Runnable[InputT, Any], Generic[StateT, ContextT, InputT, Ou
        self, config: RunnableConfig | None = None, **kwargs: Any
    ) -> Self: ...

+    @abstractmethod
+    def with_listeners(
+        self,
+        *,
+        on_start: Callable[[Any], Any] | None = None,
+        on_end: Callable[[Any], Any] | None = None,
+        on_error: Callable[[Any], Any] | None = None,
+    ) -> Self: ...
+
+    @abstractmethod
+    def with_alisteners(
+        self,
+        *,
+        on_start: Callable[[Any], Awaitable[Any]] | None = None,
+        on_end: Callable[[Any], Awaitable[Any]] | None = None,
+        on_error: Callable[[Any], Awaitable[Any]] | None = None,
+    ) -> Self: ...
+
    @abstractmethod
    def get_graph(
        self,
@@ -56,6 +56,7 @@ __all__ = (
    "Durability",
    "interrupt",
    "Overwrite",
+    "ensure_valid_checkpointer",
 )

 Durability = Literal["sync", "async", "exit"]
@@ -73,6 +74,20 @@ Checkpointer = None | bool | BaseCheckpointSaver
 - False disables checkpointing, even if the parent graph has a checkpointer.
 - None inherits checkpointer from the parent graph."""

+
+def ensure_valid_checkpointer(checkpointer: Checkpointer) -> Checkpointer:
+    if checkpointer not in (None, True, False) and not isinstance(
+        checkpointer, BaseCheckpointSaver
+    ):
+        raise TypeError(
+            "Invalid checkpointer provided. Expected an instance of "
+            "`BaseCheckpointSaver`, `True`, `False`, or `None`. "
+            f"Received {type(checkpointer).__name__!s}. "
+            "Pass a proper saver (e.g., InMemorySaver, AsyncPostgresSaver)."
+        )
+    return checkpointer
+
+
 StreamMode = Literal[
    "values", "updates", "checkpoints", "tasks", "debug", "messages", "custom"
 ]
@@ -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",
@@ -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
@@ -236,6 +252,64 @@ def test_context_json_schema() -> None:
    }


+def test_pregel_with_listeners() -> None:
+    chain = NodeBuilder().subscribe_only("input").do(lambda x: x + 1).write_to("output")
+
+    app = Pregel(
+        nodes={"one": chain},
+        channels={
+            "input": LastValue(int),
+            "output": LastValue(int),
+        },
+        input_channels="input",
+        output_channels="output",
+    )
+
+    events: list[tuple[str, list[str] | None]] = []
+
+    def on_start(_: Any, config: RunnableConfig) -> None:
+        events.append(("start", list(config.get("tags") or [])))
+
+    def on_end(_: Any, config: RunnableConfig) -> None:
+        events.append(("end", list(config.get("tags") or [])))
+
+    out = app.with_listeners(on_start=on_start, on_end=on_end).invoke(
+        1, {"tags": ["tag-a"]}
+    )
+
+    assert out == 2
+    assert events == [("start", ["tag-a"]), ("end", ["tag-a"])]
+
+
+async def test_pregel_with_alisteners() -> None:
+    chain = NodeBuilder().subscribe_only("input").do(lambda x: x + 1).write_to("output")
+
+    app = Pregel(
+        nodes={"one": chain},
+        channels={
+            "input": LastValue(int),
+            "output": LastValue(int),
+        },
+        input_channels="input",
+        output_channels="output",
+    )
+
+    events: list[tuple[str, list[str] | None]] = []
+
+    async def on_start(_: Any, config: RunnableConfig) -> None:
+        events.append(("start", list(config.get("tags") or [])))
+
+    async def on_end(_: Any, config: RunnableConfig) -> None:
+        events.append(("end", list(config.get("tags") or [])))
+
+    out = await app.with_alisteners(on_start=on_start, on_end=on_end).ainvoke(
+        1, {"tags": ["tag-a"]}
+    )
+
+    assert out == 2
+    assert events == [("start", ["tag-a"]), ("end", ["tag-a"])]
+
+
 def test_node_schemas_custom_output() -> None:
    class State(TypedDict):
        hello: str
@@ -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
@@ -1,5 +1,5 @@
 version = 1
-revision = 2
+revision = 3
 requires-python = ">=3.10"
 resolution-markers = [
    "python_full_version >= '3.14'",
@@ -1345,7 +1345,7 @@ wheels = [

 [[package]]
 name = "langgraph"
-version = "1.0.4"
+version = "1.0.5"
 source = { editable = "." }
 dependencies = [
    { name = "langchain-core" },
@@ -1572,7 +1572,7 @@ test = [

 [[package]]
 name = "langgraph-checkpoint-postgres"
-version = "3.0.1"
+version = "3.0.2"
 source = { editable = "../checkpoint-postgres" }
 dependencies = [
    { name = "langgraph-checkpoint" },
@@ -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])

@@ -1,5 +1,5 @@
 version = 1
-revision = 2
+revision = 3
 requires-python = ">=3.10"

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

 [[package]]
 name = "langgraph"
-version = "1.0.4"
+version = "1.0.5"
 source = { editable = "../langgraph" }
 dependencies = [
    { name = "langchain-core" },
@@ -402,7 +402,7 @@ test = [

 [[package]]
 name = "langgraph-checkpoint-postgres"
-version = "3.0.1"
+version = "3.0.2"
 source = { editable = "../checkpoint-postgres" }
 dependencies = [
    { name = "langgraph-checkpoint" },
@@ -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__ = "0.3.0"

 __all__ = ["Auth", "Encryption", "EncryptionContext", "get_client", "get_sync_client"]
@@ -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"),
    ]
Author	SHA1	Message	Date
William Fu-Hinthorn	2c2575370e	listen up	2025-12-17 23:06:05 +09:00
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
William FHandGitHub	4d01e69b82	release(checkpoint-postgres): 3.0.1 (#6568 )	2025-12-09 23:05:49 +00:00
William FHandGitHub	e86b5f4da2	chore: pgqs (#6567 ) Add more argument sanitization	2025-12-09 14:51:29 -08:00