chore: import upstream snapshot with attribution

libs/checkpoint-postgres/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 LangChain, Inc.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

libs/checkpoint-postgres/Makefile

@@ -0,0 +1,66 @@
+.PHONY: test test_watch lint type format
+
+######################
+# TESTING AND COVERAGE
+######################
+
+start-postgres:
+	POSTGRES_VERSION=${POSTGRES_VERSION:-16} docker compose -f tests/compose-postgres.yml up -V --force-recreate --wait || ( \
+		echo "Failed to start PostgreSQL, printing logs..."; \
+		docker compose -f tests/compose-postgres.yml logs; \
+		exit 1 \
+	)
+
+stop-postgres:
+	docker compose -f tests/compose-postgres.yml down
+
+POSTGRES_VERSIONS ?= 15 16
+test_pg_version:
+	@echo "Testing PostgreSQL $(POSTGRES_VERSION)"
+	@POSTGRES_VERSION=$(POSTGRES_VERSION) make start-postgres
+	@uv run pytest $(TEST)
+	@EXIT_CODE=$$?; \
+	make stop-postgres; \
+	echo "Finished testing PostgreSQL $(POSTGRES_VERSION); Exit code: $$EXIT_CODE"; \
+	exit $$EXIT_CODE
+
+test:
+	@for version in $(POSTGRES_VERSIONS); do \
+		if ! make test_pg_version POSTGRES_VERSION=$$version; then \
+			echo "Test failed for PostgreSQL $$version"; \
+			exit 1; \
+		fi; \
+	done
+	@echo "All PostgreSQL versions tested successfully"
+
+TEST ?= .
+test_watch:
+	POSTGRES_VERSION=${POSTGRES_VERSION:-16} make start-postgres; \
+	uv run ptw $(TEST); \
+	EXIT_CODE=$$?; \
+	make stop-postgres; \
+	exit $$EXIT_CODE
+
+######################
+# LINTING AND FORMATTING
+######################
+
+# Define a variable for Python and notebook files.
+PYTHON_FILES=.
+lint format: PYTHON_FILES=.
+lint_diff format_diff: PYTHON_FILES=$(shell git diff --name-only --relative --diff-filter=d main . | grep -E '\.py$$|\.ipynb$$')
+lint_package: PYTHON_FILES=langgraph
+lint_tests: PYTHON_FILES=tests
+
+lint lint_diff lint_package lint_tests:
+	uv run ruff check .
+	[ "$(PYTHON_FILES)" = "" ] || uv run ruff format $(PYTHON_FILES) --diff
+	[ "$(PYTHON_FILES)" = "" ] || uv run ruff check --select I $(PYTHON_FILES)
+	[ "$(PYTHON_FILES)" = "" ] || uv run ty check $(PYTHON_FILES)
+
+type:
+	uv run ty check $(PYTHON_FILES)
+
+format format_diff:
+	uv run ruff format $(PYTHON_FILES)
+	uv run ruff check --select I --fix $(PYTHON_FILES)

libs/checkpoint-postgres/README.md

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+# LangGraph Checkpoint Postgres
+
+[![PyPI - Version](https://img.shields.io/pypi/v/langgraph-checkpoint-postgres?label=%20)](https://pypi.org/project/langgraph-checkpoint-postgres/#history)
+[![PyPI - License](https://img.shields.io/pypi/l/langgraph-checkpoint-postgres)](https://opensource.org/licenses/MIT)
+[![PyPI - Downloads](https://img.shields.io/pepy/dt/langgraph-checkpoint-postgres)](https://pypistats.org/packages/langgraph-checkpoint-postgres)
+[![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchain_oss.svg?style=social&label=Follow%20%40LangChain)](https://x.com/langchain_oss)
+
+To help you ship LangGraph apps to production faster, check out [LangSmith](https://www.langchain.com/langsmith).
+[LangSmith](https://www.langchain.com/langsmith) is a unified developer platform for building, testing, and monitoring LLM applications.
+
+## Quick Install
+
+```bash
+uv add langgraph-checkpoint-postgres
+```
+
+## 🤔 What is this?
+
+This library provides a Postgres implementation of LangGraph's checkpoint saver. Use it when you want LangGraph state persistence backed by Postgres for durable, long-running workflows and agents.
+
+By default, `langgraph-checkpoint-postgres` installs `psycopg` (Psycopg 3) without any extras. You can choose a specific installation that best suits your needs in the [Psycopg installation docs](https://www.psycopg.org/psycopg3/docs/basic/install.html), for example `psycopg[binary]`.
+
+## 📖 Documentation
+
+For full documentation, see the [API reference](https://reference.langchain.com/python/langgraph.checkpoint.postgres). For conceptual guides on persistence and memory, see the [LangGraph Docs](https://docs.langchain.com/oss/python/langgraph/overview).
+
+## Security
+
+> [!IMPORTANT]
+> Set `LANGGRAPH_STRICT_MSGPACK=true` or pass an explicit `allowed_msgpack_modules` list when creating your checkpointer. This restricts checkpoint deserialization to known-safe types, preventing code execution if the database is compromised. See the [langgraph-checkpoint README](https://github.com/langchain-ai/langgraph/tree/main/libs/checkpoint#serde) for details.
+
+## Usage
+
+> [!IMPORTANT]
+> When using Postgres checkpointers for the first time, make sure to call `.setup()` method on them to create required tables. See example below.
+
+> [!IMPORTANT]
+> When manually creating Postgres connections and passing them to `PostgresSaver` or `AsyncPostgresSaver`, make sure to include `autocommit=True` and `row_factory=dict_row` (`from psycopg.rows import dict_row`). See a full example in this [how-to guide](https://langchain-ai.github.io/langgraph/how-tos/persistence_postgres/).
+>
+> **Why these parameters are required:**
+>
+> - `autocommit=True`: Required for the `.setup()` method to properly commit the checkpoint tables to the database. Without this, table creation may not be persisted.
+> - `row_factory=dict_row`: Required because the PostgresSaver implementation accesses database rows using dictionary-style syntax (e.g., `row["column_name"]`). The default `tuple_row` factory returns tuples that only support index-based access (e.g., `row[0]`), which will cause `TypeError` exceptions when the checkpointer tries to access columns by name.
+>
+> **Example of incorrect usage:**
+>
+> ```python
+> # ❌ This will fail with TypeError during checkpointer operations
+> with psycopg.connect(DB_URI) as conn:  # Missing autocommit=True and row_factory=dict_row
+>     checkpointer = PostgresSaver(conn)
+>     checkpointer.setup()  # May not persist tables properly
+>     # Any operation that reads from database will fail with:
+>     # TypeError: tuple indices must be integers or slices, not str
+> ```
+
+```python
+from langgraph.checkpoint.postgres import PostgresSaver
+
+write_config = {"configurable": {"thread_id": "1", "checkpoint_ns": ""}}
+read_config = {"configurable": {"thread_id": "1"}}
+
+DB_URI = "postgres://postgres:postgres@localhost:5432/postgres?sslmode=disable"
+with PostgresSaver.from_conn_string(DB_URI) as checkpointer:
+    # call .setup() the first time you're using the checkpointer
+    checkpointer.setup()
+    checkpoint = {
+        "v": 4,
+        "ts": "2024-07-31T20:14:19.804150+00:00",
+        "id": "1ef4f797-8335-6428-8001-8a1503f9b875",
+        "channel_values": {
+            "my_key": "meow",
+            "node": "node"
+        },
+        "channel_versions": {
+            "__start__": 2,
+            "my_key": 3,
+            "start:node": 3,
+            "node": 3
+        },
+        "versions_seen": {
+            "__input__": {},
+            "__start__": {
+            "__start__": 1
+            },
+            "node": {
+            "start:node": 2
+            }
+        },
+    }
+
+    # store checkpoint
+    checkpointer.put(write_config, checkpoint, {}, {})
+
+    # load checkpoint
+    checkpointer.get(read_config)
+
+    # list checkpoints
+    list(checkpointer.list(read_config))
+```
+
+### Async
+
+```python
+from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
+
+async with AsyncPostgresSaver.from_conn_string(DB_URI) as checkpointer:
+    checkpoint = {
+        "v": 4,
+        "ts": "2024-07-31T20:14:19.804150+00:00",
+        "id": "1ef4f797-8335-6428-8001-8a1503f9b875",
+        "channel_values": {
+            "my_key": "meow",
+            "node": "node"
+        },
+        "channel_versions": {
+            "__start__": 2,
+            "my_key": 3,
+            "start:node": 3,
+            "node": 3
+        },
+        "versions_seen": {
+            "__input__": {},
+            "__start__": {
+            "__start__": 1
+            },
+            "node": {
+            "start:node": 2
+            }
+        },
+    }
+
+    # store checkpoint
+    await checkpointer.aput(write_config, checkpoint, {}, {})
+
+    # load checkpoint
+    await checkpointer.aget(read_config)
+
+    # list checkpoints
+    [c async for c in checkpointer.alist(read_config)]
+```
+
+## 📕 Releases & Versioning
+
+See our [Releases](https://docs.langchain.com/oss/python/release-policy) and [Versioning](https://docs.langchain.com/oss/python/versioning) policies.
+
+## 💁 Contributing
+
+As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
+
+For detailed information on how to contribute, see the [Contributing Guide](https://docs.langchain.com/oss/python/contributing/overview).

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

@@ -0,0 +1,595 @@
+from __future__ import annotations
+
+import threading
+from collections import defaultdict
+from collections.abc import Iterator, Mapping, Sequence
+from contextlib import contextmanager
+from typing import Any, cast
+
+from langchain_core.runnables import RunnableConfig
+from langgraph.checkpoint.base import (
+    WRITES_IDX_MAP,
+    ChannelVersions,
+    Checkpoint,
+    CheckpointMetadata,
+    CheckpointTuple,
+    DeltaChannelHistory,
+    get_checkpoint_id,
+    get_serializable_checkpoint_metadata,
+)
+from langgraph.checkpoint.serde.base import SerializerProtocol
+from langgraph.checkpoint.serde.types import _DeltaSnapshot
+from psycopg import Capabilities, Connection, Cursor, Pipeline
+from psycopg.rows import DictRow, dict_row
+from psycopg.types.json import Jsonb
+from psycopg_pool import ConnectionPool
+
+from langgraph.checkpoint.postgres import _internal
+from langgraph.checkpoint.postgres.base import (
+    _DELTA_PAGE_SIZE,
+    BasePostgresSaver,
+    _build_delta_stage1_sql,
+    _build_delta_stage2_sql,
+    _DeltaStage2Row,
+)
+from langgraph.checkpoint.postgres.shallow import ShallowPostgresSaver
+
+Conn = _internal.Conn  # For backward compatibility
+
+
+class PostgresSaver(BasePostgresSaver):
+    """Checkpointer that stores checkpoints in a Postgres database."""
+
+    lock: threading.Lock
+
+    def __init__(
+        self,
+        conn: _internal.Conn,
+        pipe: Pipeline | None = None,
+        serde: SerializerProtocol | None = None,
+    ) -> None:
+        super().__init__(serde=serde)
+        if isinstance(conn, ConnectionPool) and pipe is not None:
+            raise ValueError(
+                "Pipeline should be used only with a single Connection, not ConnectionPool."
+            )
+
+        self.conn = conn
+        self.pipe = pipe
+        self.lock = threading.Lock()
+        self.supports_pipeline = Capabilities().has_pipeline()
+
+    @classmethod
+    @contextmanager
+    def from_conn_string(
+        cls, conn_string: str, *, pipeline: bool = False
+    ) -> Iterator[PostgresSaver]:
+        """Create a new PostgresSaver instance from a connection string.
+
+        Args:
+            conn_string: The Postgres connection info string.
+            pipeline: whether to use Pipeline
+
+        Returns:
+            PostgresSaver: A new PostgresSaver instance.
+        """
+        with Connection.connect(
+            conn_string, autocommit=True, prepare_threshold=0, row_factory=dict_row
+        ) as conn:
+            if pipeline:
+                with conn.pipeline() as pipe:
+                    yield cls(conn, pipe)
+            else:
+                yield cls(conn)
+
+    def setup(self) -> None:
+        """Set up the checkpoint database asynchronously.
+
+        This method creates the necessary tables in the Postgres database if they don't
+        already exist and runs database migrations. It MUST be called directly by the user
+        the first time checkpointer is used.
+        """
+        with self._cursor() as cur:
+            cur.execute(self.MIGRATIONS[0])
+            results = cur.execute(
+                "SELECT v FROM checkpoint_migrations ORDER BY v DESC LIMIT 1"
+            )
+            row = results.fetchone()
+            if row is None:
+                version = -1
+            else:
+                version = row["v"]
+            for v, migration in zip(
+                range(version + 1, len(self.MIGRATIONS)),
+                self.MIGRATIONS[version + 1 :],
+                strict=False,
+            ):
+                cur.execute(migration)
+                cur.execute("INSERT INTO checkpoint_migrations (v) VALUES (%s)", (v,))
+        if self.pipe:
+            self.pipe.sync()
+
+    def list(
+        self,
+        config: RunnableConfig | None,
+        *,
+        filter: dict[str, Any] | None = None,
+        before: RunnableConfig | None = None,
+        limit: int | None = None,
+    ) -> Iterator[CheckpointTuple]:
+        """List checkpoints from the database.
+
+        This method retrieves a list of checkpoint tuples from the Postgres database based
+        on the provided config. The checkpoints are ordered by checkpoint ID in descending order (newest first).
+
+        Args:
+            config: The config to use for listing the checkpoints.
+            filter: Additional filtering criteria for metadata.
+            before: If provided, only checkpoints before the specified checkpoint ID are returned.
+            limit: The maximum number of checkpoints to return.
+
+        Yields:
+            An iterator of checkpoint tuples.
+
+        Examples:
+            >>> from langgraph.checkpoint.postgres import PostgresSaver
+            >>> DB_URI = "postgres://postgres:postgres@localhost:5432/postgres?sslmode=disable"
+            >>> with PostgresSaver.from_conn_string(DB_URI) as memory:
+            ... # Run a graph, then list the checkpoints
+            >>>     config = {"configurable": {"thread_id": "1"}}
+            >>>     checkpoints = list(memory.list(config, limit=2))
+            >>> print(checkpoints)
+            [CheckpointTuple(...), CheckpointTuple(...)]
+
+            >>> config = {"configurable": {"thread_id": "1"}}
+            >>> before = {"configurable": {"checkpoint_id": "1ef4f797-8335-6428-8001-8a1503f9b875"}}
+            >>> with PostgresSaver.from_conn_string(DB_URI) as memory:
+            ... # Run a graph, then list the checkpoints
+            >>>     checkpoints = list(memory.list(config, before=before))
+            >>> print(checkpoints)
+            [CheckpointTuple(...), ...]
+        """
+        where, args = self._search_where(config, filter, before)
+        query = self.SELECT_SQL + where + " ORDER BY checkpoint_id DESC"
+        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, params)
+            values = cur.fetchall()
+            if not values:
+                return
+            # migrate pending sends if necessary
+            if to_migrate := [
+                v
+                for v in values
+                if v["checkpoint"]["v"] < 4 and v["parent_checkpoint_id"]
+            ]:
+                cur.execute(
+                    self.SELECT_PENDING_SENDS_SQL,
+                    (
+                        values[0]["thread_id"],
+                        [v["parent_checkpoint_id"] for v in to_migrate],
+                    ),
+                )
+                grouped_by_parent = defaultdict(list)
+                for value in to_migrate:
+                    grouped_by_parent[value["parent_checkpoint_id"]].append(value)
+                for sends in cur:
+                    for value in grouped_by_parent[sends["checkpoint_id"]]:
+                        if value["channel_values"] is None:
+                            value["channel_values"] = []
+                        self._migrate_pending_sends(
+                            sends["sends"],
+                            value["checkpoint"],
+                            value["channel_values"],
+                        )
+            for value in values:
+                yield self._load_checkpoint_tuple(value)
+
+    def get_tuple(self, config: RunnableConfig) -> CheckpointTuple | None:
+        """Get a checkpoint tuple from the database.
+
+        This method retrieves a checkpoint tuple from the Postgres database based on the
+        provided config. If the config contains a `checkpoint_id` key, the checkpoint with
+        the matching thread ID and timestamp is retrieved. Otherwise, the latest checkpoint
+        for the given thread ID is retrieved.
+
+        Args:
+            config: The config to use for retrieving the checkpoint.
+
+        Returns:
+            The retrieved checkpoint tuple, or None if no matching checkpoint was found.
+
+        Examples:
+
+            Basic:
+            >>> config = {"configurable": {"thread_id": "1"}}
+            >>> checkpoint_tuple = memory.get_tuple(config)
+            >>> print(checkpoint_tuple)
+            CheckpointTuple(...)
+
+            With timestamp:
+
+            >>> config = {
+            ...    "configurable": {
+            ...        "thread_id": "1",
+            ...        "checkpoint_ns": "",
+            ...        "checkpoint_id": "1ef4f797-8335-6428-8001-8a1503f9b875",
+            ...    }
+            ... }
+            >>> checkpoint_tuple = memory.get_tuple(config)
+            >>> print(checkpoint_tuple)
+            CheckpointTuple(...)
+        """  # noqa
+        thread_id = config["configurable"]["thread_id"]
+        checkpoint_id = get_checkpoint_id(config)
+        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
+        if checkpoint_id:
+            args: tuple[Any, ...] = (thread_id, checkpoint_ns, checkpoint_id)
+            where = "WHERE thread_id = %s AND checkpoint_ns = %s AND checkpoint_id = %s"
+        else:
+            args = (thread_id, checkpoint_ns)
+            where = "WHERE thread_id = %s AND checkpoint_ns = %s ORDER BY checkpoint_id DESC LIMIT 1"
+
+        with self._cursor() as cur:
+            cur.execute(
+                self.SELECT_SQL + where,
+                args,
+            )
+            value = cur.fetchone()
+            if value is None:
+                return None
+
+            # migrate pending sends if necessary
+            if value["checkpoint"]["v"] < 4 and value["parent_checkpoint_id"]:
+                cur.execute(
+                    self.SELECT_PENDING_SENDS_SQL,
+                    (thread_id, [value["parent_checkpoint_id"]]),
+                )
+                if sends := cur.fetchone():
+                    if value["channel_values"] is None:
+                        value["channel_values"] = []
+                    self._migrate_pending_sends(
+                        sends["sends"],
+                        value["checkpoint"],
+                        value["channel_values"],
+                    )
+
+            return self._load_checkpoint_tuple(value)
+
+    def put(
+        self,
+        config: RunnableConfig,
+        checkpoint: Checkpoint,
+        metadata: CheckpointMetadata,
+        new_versions: ChannelVersions,
+    ) -> RunnableConfig:
+        """Save a checkpoint to the database.
+
+        This method saves a checkpoint to the Postgres database. The checkpoint is associated
+        with the provided config and its parent config (if any).
+
+        Args:
+            config: The config to associate with the checkpoint.
+            checkpoint: The checkpoint to save.
+            metadata: Additional metadata to save with the checkpoint.
+            new_versions: New channel versions as of this write.
+
+        Returns:
+            RunnableConfig: Updated configuration after storing the checkpoint.
+
+        Examples:
+
+            >>> from langgraph.checkpoint.postgres import PostgresSaver
+            >>> DB_URI = "postgres://postgres:postgres@localhost:5432/postgres?sslmode=disable"
+            >>> with PostgresSaver.from_conn_string(DB_URI) as memory:
+            >>>     config = {"configurable": {"thread_id": "1", "checkpoint_ns": ""}}
+            >>>     checkpoint = {"ts": "2024-05-04T06:32:42.235444+00:00", "id": "1ef4f797-8335-6428-8001-8a1503f9b875", "channel_values": {"key": "value"}}
+            >>>     saved_config = memory.put(config, checkpoint, {"source": "input", "step": 1, "writes": {"key": "value"}}, {})
+            >>> print(saved_config)
+            {'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef4f797-8335-6428-8001-8a1503f9b875'}}
+        """
+        configurable = config["configurable"].copy()
+        thread_id = configurable.pop("thread_id")
+        checkpoint_ns = configurable.pop("checkpoint_ns")
+        checkpoint_id = configurable.pop("checkpoint_id", None)
+        copy = checkpoint.copy()
+        copy["channel_values"] = copy["channel_values"].copy()
+        next_config = {
+            "configurable": {
+                "thread_id": thread_id,
+                "checkpoint_ns": checkpoint_ns,
+                "checkpoint_id": checkpoint["id"],
+            }
+        }
+
+        # inline primitive values in checkpoint table
+        # others are stored in blobs table
+        blob_values = {}
+        for k, v in checkpoint["channel_values"].items():
+            if isinstance(v, _DeltaSnapshot):
+                blob_values[k] = copy["channel_values"].pop(k)
+                copy["channel_values"][k] = True
+            elif v is None or isinstance(v, (str, int, float, bool)):
+                pass
+            else:
+                blob_values[k] = copy["channel_values"].pop(k)
+
+        with self._cursor(pipeline=True) as cur:
+            if blob_versions := {
+                k: v for k, v in new_versions.items() if k in blob_values
+            }:
+                cur.executemany(
+                    self.UPSERT_CHECKPOINT_BLOBS_SQL,
+                    self._dump_blobs(
+                        thread_id,
+                        checkpoint_ns,
+                        blob_values,
+                        blob_versions,
+                    ),
+                )
+            cur.execute(
+                self.UPSERT_CHECKPOINTS_SQL,
+                (
+                    thread_id,
+                    checkpoint_ns,
+                    checkpoint["id"],
+                    checkpoint_id,
+                    Jsonb(copy),
+                    Jsonb(get_serializable_checkpoint_metadata(config, metadata)),
+                ),
+            )
+        return next_config
+
+    def put_writes(
+        self,
+        config: RunnableConfig,
+        writes: Sequence[tuple[str, Any]],
+        task_id: str,
+        task_path: str = "",
+    ) -> None:
+        """Store intermediate writes linked to a checkpoint.
+
+        This method saves intermediate writes associated with a checkpoint to the Postgres database.
+
+        Args:
+            config: Configuration of the related checkpoint.
+            writes: List of writes to store.
+            task_id: Identifier for the task creating the writes.
+        """
+        query = (
+            self.UPSERT_CHECKPOINT_WRITES_SQL
+            if all(w[0] in WRITES_IDX_MAP for w in writes)
+            else self.INSERT_CHECKPOINT_WRITES_SQL
+        )
+        with self._cursor(pipeline=True) as cur:
+            cur.executemany(
+                query,
+                self._dump_writes(
+                    config["configurable"]["thread_id"],
+                    config["configurable"]["checkpoint_ns"],
+                    config["configurable"]["checkpoint_id"],
+                    task_id,
+                    task_path,
+                    writes,
+                ),
+            )
+
+    def delete_thread(self, thread_id: str) -> None:
+        """Delete all checkpoints and writes associated with a thread ID.
+
+        Args:
+            thread_id: The thread ID to delete.
+
+        Returns:
+            None
+        """
+        with self._cursor(pipeline=True) as cur:
+            cur.execute(
+                "DELETE FROM checkpoints WHERE thread_id = %s",
+                (str(thread_id),),
+            )
+            cur.execute(
+                "DELETE FROM checkpoint_blobs WHERE thread_id = %s",
+                (str(thread_id),),
+            )
+            cur.execute(
+                "DELETE FROM checkpoint_writes WHERE thread_id = %s",
+                (str(thread_id),),
+            )
+
+    @contextmanager
+    def _cursor(self, *, pipeline: bool = False) -> Iterator[Cursor[DictRow]]:
+        """Create a database cursor as a context manager.
+
+        Args:
+            pipeline: whether to use pipeline for the DB operations inside the context manager.
+                Will be applied regardless of whether the PostgresSaver instance was initialized with a pipeline.
+                If pipeline mode is not supported, will fall back to using transaction context manager.
+        """
+        with self.lock, _internal.get_connection(self.conn) as conn:
+            if self.pipe:
+                # a connection in pipeline mode can be used concurrently
+                # in multiple threads/coroutines, but only one cursor can be
+                # used at a time
+                try:
+                    with conn.cursor(binary=True, row_factory=dict_row) as cur:
+                        yield cur
+                finally:
+                    if pipeline:
+                        self.pipe.sync()
+            elif pipeline:
+                # a connection not in pipeline mode can only be used by one
+                # thread/coroutine at a time, so we acquire a lock
+                if self.supports_pipeline:
+                    with (
+                        conn.pipeline(),
+                        conn.cursor(binary=True, row_factory=dict_row) as cur,
+                    ):
+                        yield cur
+                else:
+                    # Use connection's transaction context manager when pipeline mode not supported
+                    with (
+                        conn.transaction(),
+                        conn.cursor(binary=True, row_factory=dict_row) as cur,
+                    ):
+                        yield cur
+            else:
+                with conn.cursor(binary=True, row_factory=dict_row) as cur:
+                    yield cur
+
+    def get_delta_channel_history(
+        self, *, config: RunnableConfig, channels: Sequence[str]
+    ) -> Mapping[str, DeltaChannelHistory]:
+        """Fast-path override of `BaseCheckpointSaver.get_delta_channel_history`.
+
+        Two-stage query, both stages cover ALL requested channels:
+
+        * Stage 1 (paged): dynamic SELECT over `checkpoints` with K parallel
+          JSONB key lookups (one column pair per channel) — no subquery, no
+          aggregation. Pages newest-first by `checkpoint_id` with a cursor;
+          page size is `_DELTA_PAGE_SIZE`. Stops paging when every channel
+          has found its seed or the chain is exhausted.
+
+        * Stage 2 (per-channel UNION ALL): one branch per channel reading
+          `checkpoint_writes` filtered to that channel's specific
+          `chain_cids`, plus one branch per channel that has a seed reading
+          `checkpoint_blobs` for that channel + version. Avoids the
+          over-fetch of a single `channel = ANY(channels)` filter when
+          channels have different chain depths.
+        """
+        if not channels:
+            return {}
+        channels = list(channels)
+        thread_id = config["configurable"]["thread_id"]
+        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
+        checkpoint_id = get_checkpoint_id(config)
+        if checkpoint_id is None:
+            target = self.get_tuple(config)
+            if target is None:
+                return {ch: {"writes": []} for ch in channels}
+            checkpoint_id = target.config["configurable"]["checkpoint_id"]
+
+        # Stage 1: paged K-JSONB-lookup scan, walking the parent chain in
+        # Python after each page. Stops as soon as every channel has its seed.
+        stage1_sql = _build_delta_stage1_sql(channels, paged=True)
+        parent_of: dict[str, str | None] = {}
+        ver_by_i_by_cid: list[dict[str, str | None]] = [{} for _ in channels]
+        hs_by_i_by_cid: list[dict[str, bool]] = [{} for _ in channels]
+        chain_by_ch: dict[str, list[str]] = {ch: [] for ch in channels}
+        seed_ver_by_ch: dict[str, str | None] = {ch: None for ch in channels}
+        walk_cursor_by_ch: dict[str, str | None] = {}
+        seeded: set[str] = set()
+        cursor: str | None = None
+
+        with self._cursor() as cur:
+            while True:
+                stage1_params: list[Any] = []
+                for ch in channels:
+                    stage1_params.extend([ch, ch])
+                stage1_params.extend(
+                    [thread_id, checkpoint_ns, cursor, cursor, _DELTA_PAGE_SIZE]
+                )
+                cur.execute(stage1_sql, stage1_params)
+                page = cur.fetchall()
+                if not page:
+                    break
+                oldest = self._ingest_stage1_page(
+                    cast("list[Mapping[str, Any]]", page),
+                    channels,
+                    parent_of,
+                    ver_by_i_by_cid,
+                    hs_by_i_by_cid,
+                )
+                self._try_advance_walks(
+                    checkpoint_id,
+                    channels,
+                    parent_of,
+                    ver_by_i_by_cid,
+                    hs_by_i_by_cid,
+                    chain_by_ch,
+                    seed_ver_by_ch,
+                    walk_cursor_by_ch,
+                    seeded,
+                )
+                # Stop if every channel is seeded, or the page was short
+                # (chain exhausted — no more rows to fetch).
+                if len(seeded) == len(channels) or len(page) < _DELTA_PAGE_SIZE:
+                    break
+                cursor = oldest
+
+        # Stage 2: per-channel UNION ALL — one writes branch per channel
+        # with non-empty chain, plus one blob branch per seeded channel.
+        channels_with_chain = [ch for ch in channels if chain_by_ch[ch]]
+        channels_with_seed = [ch for ch in channels if seed_ver_by_ch[ch] is not None]
+        stage2_sql = _build_delta_stage2_sql(
+            channels_with_chain=channels_with_chain,
+            channels_with_seed=channels_with_seed,
+        )
+
+        if stage2_sql:
+            stage2_params: list[Any] = []
+            for ch in channels_with_chain:
+                stage2_params.extend([thread_id, checkpoint_ns, ch, chain_by_ch[ch]])
+            for ch in channels_with_seed:
+                stage2_params.extend([thread_id, checkpoint_ns, ch, seed_ver_by_ch[ch]])
+            with self._cursor() as cur:
+                cur.execute(stage2_sql, stage2_params)
+                stage2_rows = cur.fetchall()
+        else:
+            stage2_rows = []
+
+        return self._build_delta_channels_writes_history(
+            channels=channels,
+            chain_by_ch=chain_by_ch,
+            seed_ver_by_ch=seed_ver_by_ch,
+            stage2_rows=cast("list[_DeltaStage2Row]", stage2_rows),
+        )
+
+    def _load_checkpoint_tuple(self, value: DictRow) -> CheckpointTuple:
+        """
+        Convert a database row into a CheckpointTuple object.
+
+        Args:
+            value: A row from the database containing checkpoint data.
+
+        Returns:
+            CheckpointTuple: A structured representation of the checkpoint,
+            including its configuration, metadata, parent checkpoint (if any),
+            and pending writes.
+        """
+        return CheckpointTuple(
+            {
+                "configurable": {
+                    "thread_id": value["thread_id"],
+                    "checkpoint_ns": value["checkpoint_ns"],
+                    "checkpoint_id": value["checkpoint_id"],
+                }
+            },
+            {
+                **value["checkpoint"],
+                "channel_values": {
+                    **(value["checkpoint"].get("channel_values") or {}),
+                    **self._load_blobs(value["channel_values"]),
+                },
+            },
+            value["metadata"],
+            (
+                {
+                    "configurable": {
+                        "thread_id": value["thread_id"],
+                        "checkpoint_ns": value["checkpoint_ns"],
+                        "checkpoint_id": value["parent_checkpoint_id"],
+                    }
+                }
+                if value["parent_checkpoint_id"]
+                else None
+            ),
+            self._load_writes(value["pending_writes"]),
+        )
+
+
+__all__ = ["PostgresSaver", "BasePostgresSaver", "ShallowPostgresSaver", "Conn"]

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

@@ -0,0 +1,23 @@
+"""Shared async utility functions for the Postgres checkpoint & storage classes."""
+
+from collections.abc import AsyncIterator
+from contextlib import asynccontextmanager
+
+from psycopg import AsyncConnection
+from psycopg.rows import DictRow
+from psycopg_pool import AsyncConnectionPool
+
+Conn = AsyncConnection[DictRow] | AsyncConnectionPool[AsyncConnection[DictRow]]
+
+
+@asynccontextmanager
+async def get_connection(
+    conn: Conn,
+) -> AsyncIterator[AsyncConnection[DictRow]]:
+    if isinstance(conn, AsyncConnection):
+        yield conn
+    elif isinstance(conn, AsyncConnectionPool):
+        async with conn.connection() as conn:
+            yield conn
+    else:
+        raise TypeError(f"Invalid connection type: {type(conn)}")

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

@@ -0,0 +1,21 @@
+"""Shared utility functions for the Postgres checkpoint & storage classes."""
+
+from collections.abc import Iterator
+from contextlib import contextmanager
+
+from psycopg import Connection
+from psycopg.rows import DictRow
+from psycopg_pool import ConnectionPool
+
+Conn = Connection[DictRow] | ConnectionPool[Connection[DictRow]]
+
+
+@contextmanager
+def get_connection(conn: Conn) -> Iterator[Connection[DictRow]]:
+    if isinstance(conn, Connection):
+        yield conn
+    elif isinstance(conn, ConnectionPool):
+        with conn.connection() as conn:
+            yield conn
+    else:
+        raise TypeError(f"Invalid connection type: {type(conn)}")

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

@@ -0,0 +1,684 @@
+from __future__ import annotations
+
+import asyncio
+from collections import defaultdict
+from collections.abc import AsyncIterator, Iterator, Mapping, Sequence
+from contextlib import asynccontextmanager
+from typing import Any, cast
+
+from langchain_core.runnables import RunnableConfig
+from langgraph.checkpoint.base import (
+    WRITES_IDX_MAP,
+    ChannelVersions,
+    Checkpoint,
+    CheckpointMetadata,
+    CheckpointTuple,
+    DeltaChannelHistory,
+    get_checkpoint_id,
+    get_serializable_checkpoint_metadata,
+)
+from langgraph.checkpoint.serde.base import SerializerProtocol
+from langgraph.checkpoint.serde.types import _DeltaSnapshot
+from psycopg import AsyncConnection, AsyncCursor, AsyncPipeline, Capabilities
+from psycopg.rows import DictRow, dict_row
+from psycopg.types.json import Jsonb
+from psycopg_pool import AsyncConnectionPool
+
+from langgraph.checkpoint.postgres import _ainternal
+from langgraph.checkpoint.postgres.base import (
+    _DELTA_PAGE_SIZE,
+    BasePostgresSaver,
+    _build_delta_stage1_sql,
+    _build_delta_stage2_sql,
+    _DeltaStage2Row,
+)
+from langgraph.checkpoint.postgres.shallow import AsyncShallowPostgresSaver
+
+Conn = _ainternal.Conn  # For backward compatibility
+
+
+class AsyncPostgresSaver(BasePostgresSaver):
+    """Asynchronous checkpointer that stores checkpoints in a Postgres database."""
+
+    lock: asyncio.Lock
+
+    def __init__(
+        self,
+        conn: _ainternal.Conn,
+        pipe: AsyncPipeline | None = None,
+        serde: SerializerProtocol | None = None,
+    ) -> None:
+        super().__init__(serde=serde)
+        if isinstance(conn, AsyncConnectionPool) and pipe is not None:
+            raise ValueError(
+                "Pipeline should be used only with a single AsyncConnection, not AsyncConnectionPool."
+            )
+
+        self.conn = conn
+        self.pipe = pipe
+        self.lock = asyncio.Lock()
+        self.loop = asyncio.get_running_loop()
+        self.supports_pipeline = Capabilities().has_pipeline()
+
+    @classmethod
+    @asynccontextmanager
+    async def from_conn_string(
+        cls,
+        conn_string: str,
+        *,
+        pipeline: bool = False,
+        serde: SerializerProtocol | None = None,
+    ) -> AsyncIterator[AsyncPostgresSaver]:
+        """Create a new AsyncPostgresSaver instance from a connection string.
+
+        Args:
+            conn_string: The Postgres connection info string.
+            pipeline: whether to use AsyncPipeline
+
+        Returns:
+            AsyncPostgresSaver: A new AsyncPostgresSaver instance.
+        """
+        async with await AsyncConnection.connect(
+            conn_string, autocommit=True, prepare_threshold=0, row_factory=dict_row
+        ) as conn:
+            if pipeline:
+                async with conn.pipeline() as pipe:
+                    yield cls(conn=conn, pipe=pipe, serde=serde)
+            else:
+                yield cls(conn=conn, serde=serde)
+
+    async def setup(self) -> None:
+        """Set up the checkpoint database asynchronously.
+
+        This method creates the necessary tables in the Postgres database if they don't
+        already exist and runs database migrations. It MUST be called directly by the user
+        the first time checkpointer is used.
+        """
+        async with self._cursor() as cur:
+            await cur.execute(self.MIGRATIONS[0])
+            results = await cur.execute(
+                "SELECT v FROM checkpoint_migrations ORDER BY v DESC LIMIT 1"
+            )
+            row = await results.fetchone()
+            if row is None:
+                version = -1
+            else:
+                version = row["v"]
+            for v, migration in zip(
+                range(version + 1, len(self.MIGRATIONS)),
+                self.MIGRATIONS[version + 1 :],
+                strict=False,
+            ):
+                await cur.execute(migration)
+                await cur.execute(
+                    "INSERT INTO checkpoint_migrations (v) VALUES (%s)", (v,)
+                )
+        if self.pipe:
+            await self.pipe.sync()
+
+    async def alist(
+        self,
+        config: RunnableConfig | None,
+        *,
+        filter: dict[str, Any] | None = None,
+        before: RunnableConfig | None = None,
+        limit: int | None = None,
+    ) -> AsyncIterator[CheckpointTuple]:
+        """List checkpoints from the database asynchronously.
+
+        This method retrieves a list of checkpoint tuples from the Postgres database based
+        on the provided config. The checkpoints are ordered by checkpoint ID in descending order (newest first).
+
+        Args:
+            config: Base configuration for filtering checkpoints.
+            filter: Additional filtering criteria for metadata.
+            before: If provided, only checkpoints before the specified checkpoint ID are returned.
+            limit: Maximum number of checkpoints to return.
+
+        Yields:
+            An asynchronous iterator of matching checkpoint tuples.
+        """
+        where, args = self._search_where(config, filter, before)
+        query = self.SELECT_SQL + where + " ORDER BY checkpoint_id DESC"
+        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, params, binary=True)
+            values = await cur.fetchall()
+            if not values:
+                return
+            # migrate pending sends if necessary
+            if to_migrate := [
+                v
+                for v in values
+                if v["checkpoint"]["v"] < 4 and v["parent_checkpoint_id"]
+            ]:
+                await cur.execute(
+                    self.SELECT_PENDING_SENDS_SQL,
+                    (
+                        values[0]["thread_id"],
+                        [v["parent_checkpoint_id"] for v in to_migrate],
+                    ),
+                )
+                grouped_by_parent = defaultdict(list)
+                for value in to_migrate:
+                    grouped_by_parent[value["parent_checkpoint_id"]].append(value)
+                async for sends in cur:
+                    for value in grouped_by_parent[sends["checkpoint_id"]]:
+                        if value["channel_values"] is None:
+                            value["channel_values"] = []
+                        self._migrate_pending_sends(
+                            sends["sends"],
+                            value["checkpoint"],
+                            value["channel_values"],
+                        )
+            for value in values:
+                yield await self._load_checkpoint_tuple(value)
+
+    async def aget_tuple(self, config: RunnableConfig) -> CheckpointTuple | None:
+        """Get a checkpoint tuple from the database asynchronously.
+
+        This method retrieves a checkpoint tuple from the Postgres database based on the
+        provided config. If the config contains a `checkpoint_id` key, the checkpoint with
+        the matching thread ID and "checkpoint_id" is retrieved. Otherwise, the latest checkpoint
+        for the given thread ID is retrieved.
+
+        Args:
+            config: The config to use for retrieving the checkpoint.
+
+        Returns:
+            The retrieved checkpoint tuple, or None if no matching checkpoint was found.
+        """
+        thread_id = config["configurable"]["thread_id"]
+        checkpoint_id = get_checkpoint_id(config)
+        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
+        if checkpoint_id:
+            args: tuple[Any, ...] = (thread_id, checkpoint_ns, checkpoint_id)
+            where = "WHERE thread_id = %s AND checkpoint_ns = %s AND checkpoint_id = %s"
+        else:
+            args = (thread_id, checkpoint_ns)
+            where = "WHERE thread_id = %s AND checkpoint_ns = %s ORDER BY checkpoint_id DESC LIMIT 1"
+
+        async with self._cursor() as cur:
+            await cur.execute(
+                self.SELECT_SQL + where,
+                args,
+                binary=True,
+            )
+            value = await cur.fetchone()
+            if value is None:
+                return None
+
+            # migrate pending sends if necessary
+            if value["checkpoint"]["v"] < 4 and value["parent_checkpoint_id"]:
+                await cur.execute(
+                    self.SELECT_PENDING_SENDS_SQL,
+                    (thread_id, [value["parent_checkpoint_id"]]),
+                )
+                if sends := await cur.fetchone():
+                    if value["channel_values"] is None:
+                        value["channel_values"] = []
+                    self._migrate_pending_sends(
+                        sends["sends"],
+                        value["checkpoint"],
+                        value["channel_values"],
+                    )
+
+            return await self._load_checkpoint_tuple(value)
+
+    async def aput(
+        self,
+        config: RunnableConfig,
+        checkpoint: Checkpoint,
+        metadata: CheckpointMetadata,
+        new_versions: ChannelVersions,
+    ) -> RunnableConfig:
+        """Save a checkpoint to the database asynchronously.
+
+        This method saves a checkpoint to the Postgres database. The checkpoint is associated
+        with the provided config and its parent config (if any).
+
+        Args:
+            config: The config to associate with the checkpoint.
+            checkpoint: The checkpoint to save.
+            metadata: Additional metadata to save with the checkpoint.
+            new_versions: New channel versions as of this write.
+
+        Returns:
+            RunnableConfig: Updated configuration after storing the checkpoint.
+        """
+        configurable = config["configurable"].copy()
+        thread_id = configurable.pop("thread_id")
+        checkpoint_ns = configurable.pop("checkpoint_ns")
+        checkpoint_id = configurable.pop("checkpoint_id", None)
+
+        copy = checkpoint.copy()
+        copy["channel_values"] = copy["channel_values"].copy()
+        next_config = {
+            "configurable": {
+                "thread_id": thread_id,
+                "checkpoint_ns": checkpoint_ns,
+                "checkpoint_id": checkpoint["id"],
+            }
+        }
+
+        # inline primitive values in checkpoint table
+        # others are stored in blobs table
+        blob_values = {}
+        for k, v in checkpoint["channel_values"].items():
+            if isinstance(v, _DeltaSnapshot):
+                blob_values[k] = copy["channel_values"].pop(k)
+                copy["channel_values"][k] = True
+            elif v is None or isinstance(v, (str, int, float, bool)):
+                pass
+            else:
+                blob_values[k] = copy["channel_values"].pop(k)
+
+        async with self._cursor(pipeline=True) as cur:
+            if blob_versions := {
+                k: v for k, v in new_versions.items() if k in blob_values
+            }:
+                await cur.executemany(
+                    self.UPSERT_CHECKPOINT_BLOBS_SQL,
+                    await asyncio.to_thread(
+                        self._dump_blobs,
+                        thread_id,
+                        checkpoint_ns,
+                        blob_values,
+                        blob_versions,
+                    ),
+                )
+            await cur.execute(
+                self.UPSERT_CHECKPOINTS_SQL,
+                (
+                    thread_id,
+                    checkpoint_ns,
+                    checkpoint["id"],
+                    checkpoint_id,
+                    Jsonb(copy),
+                    Jsonb(get_serializable_checkpoint_metadata(config, metadata)),
+                ),
+            )
+        return next_config
+
+    async def aput_writes(
+        self,
+        config: RunnableConfig,
+        writes: Sequence[tuple[str, Any]],
+        task_id: str,
+        task_path: str = "",
+    ) -> None:
+        """Store intermediate writes linked to a checkpoint asynchronously.
+
+        This method saves intermediate writes associated with a checkpoint to the database.
+
+        Args:
+            config: Configuration of the related checkpoint.
+            writes: List of writes to store, each as (channel, value) pair.
+            task_id: Identifier for the task creating the writes.
+        """
+        query = (
+            self.UPSERT_CHECKPOINT_WRITES_SQL
+            if all(w[0] in WRITES_IDX_MAP for w in writes)
+            else self.INSERT_CHECKPOINT_WRITES_SQL
+        )
+        params = await asyncio.to_thread(
+            self._dump_writes,
+            config["configurable"]["thread_id"],
+            config["configurable"]["checkpoint_ns"],
+            config["configurable"]["checkpoint_id"],
+            task_id,
+            task_path,
+            writes,
+        )
+        async with self._cursor(pipeline=True) as cur:
+            await cur.executemany(query, params)
+
+    async def adelete_thread(self, thread_id: str) -> None:
+        """Delete all checkpoints and writes associated with a thread ID.
+
+        Args:
+            thread_id: The thread ID to delete.
+
+        Returns:
+            None
+        """
+        async with self._cursor(pipeline=True) as cur:
+            await cur.execute(
+                "DELETE FROM checkpoints WHERE thread_id = %s",
+                (str(thread_id),),
+            )
+            await cur.execute(
+                "DELETE FROM checkpoint_blobs WHERE thread_id = %s",
+                (str(thread_id),),
+            )
+            await cur.execute(
+                "DELETE FROM checkpoint_writes WHERE thread_id = %s",
+                (str(thread_id),),
+            )
+
+    @asynccontextmanager
+    async def _cursor(
+        self, *, pipeline: bool = False
+    ) -> AsyncIterator[AsyncCursor[DictRow]]:
+        """Create a database cursor as a context manager.
+
+        Args:
+            pipeline: whether to use pipeline for the DB operations inside the context manager.
+                Will be applied regardless of whether the AsyncPostgresSaver instance was initialized with a pipeline.
+                If pipeline mode is not supported, will fall back to using transaction context manager.
+        """
+        async with self.lock, _ainternal.get_connection(self.conn) as conn:
+            if self.pipe:
+                # a connection in pipeline mode can be used concurrently
+                # in multiple threads/coroutines, but only one cursor can be
+                # used at a time
+                try:
+                    async with conn.cursor(binary=True, row_factory=dict_row) as cur:
+                        yield cur
+                finally:
+                    if pipeline:
+                        await self.pipe.sync()
+            elif pipeline:
+                # a connection not in pipeline mode can only be used by one
+                # thread/coroutine at a time, so we acquire a lock
+                if self.supports_pipeline:
+                    async with (
+                        conn.pipeline(),
+                        conn.cursor(binary=True, row_factory=dict_row) as cur,
+                    ):
+                        yield cur
+                else:
+                    # Use connection's transaction context manager when pipeline mode not supported
+                    async with (
+                        conn.transaction(),
+                        conn.cursor(binary=True, row_factory=dict_row) as cur,
+                    ):
+                        yield cur
+            else:
+                async with conn.cursor(binary=True, row_factory=dict_row) as cur:
+                    yield cur
+
+    async def aget_delta_channel_history(
+        self, *, config: RunnableConfig, channels: Sequence[str]
+    ) -> Mapping[str, DeltaChannelHistory]:
+        """Fast-path override of `BaseCheckpointSaver.aget_delta_channel_history`.
+
+        See `PostgresSaver.get_delta_channel_history` for design notes; this is
+        the async equivalent with internal stage-1 paging and per-channel
+        UNION ALL stage-2.
+        """
+        if not channels:
+            return {}
+        channels = list(channels)
+        thread_id = config["configurable"]["thread_id"]
+        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
+        checkpoint_id = get_checkpoint_id(config)
+        if checkpoint_id is None:
+            target = await self.aget_tuple(config)
+            if target is None:
+                return {ch: {"writes": []} for ch in channels}
+            checkpoint_id = target.config["configurable"]["checkpoint_id"]
+
+        stage1_sql = _build_delta_stage1_sql(channels, paged=True)
+        parent_of: dict[str, str | None] = {}
+        ver_by_i_by_cid: list[dict[str, str | None]] = [{} for _ in channels]
+        hs_by_i_by_cid: list[dict[str, bool]] = [{} for _ in channels]
+        chain_by_ch: dict[str, list[str]] = {ch: [] for ch in channels}
+        seed_ver_by_ch: dict[str, str | None] = {ch: None for ch in channels}
+        walk_cursor_by_ch: dict[str, str | None] = {}
+        seeded: set[str] = set()
+        cursor: str | None = None
+
+        async with self._cursor() as cur:
+            while True:
+                stage1_params: list[Any] = []
+                for ch in channels:
+                    stage1_params.extend([ch, ch])
+                stage1_params.extend(
+                    [thread_id, checkpoint_ns, cursor, cursor, _DELTA_PAGE_SIZE]
+                )
+                await cur.execute(stage1_sql, stage1_params)
+                page = await cur.fetchall()
+                if not page:
+                    break
+                oldest = self._ingest_stage1_page(
+                    cast("list[Mapping[str, Any]]", page),
+                    channels,
+                    parent_of,
+                    ver_by_i_by_cid,
+                    hs_by_i_by_cid,
+                )
+                self._try_advance_walks(
+                    checkpoint_id,
+                    channels,
+                    parent_of,
+                    ver_by_i_by_cid,
+                    hs_by_i_by_cid,
+                    chain_by_ch,
+                    seed_ver_by_ch,
+                    walk_cursor_by_ch,
+                    seeded,
+                )
+                if len(seeded) == len(channels) or len(page) < _DELTA_PAGE_SIZE:
+                    break
+                cursor = oldest
+
+        channels_with_chain = [ch for ch in channels if chain_by_ch[ch]]
+        channels_with_seed = [ch for ch in channels if seed_ver_by_ch[ch] is not None]
+        stage2_sql = _build_delta_stage2_sql(
+            channels_with_chain=channels_with_chain,
+            channels_with_seed=channels_with_seed,
+        )
+
+        if stage2_sql:
+            stage2_params: list[Any] = []
+            for ch in channels_with_chain:
+                stage2_params.extend([thread_id, checkpoint_ns, ch, chain_by_ch[ch]])
+            for ch in channels_with_seed:
+                stage2_params.extend([thread_id, checkpoint_ns, ch, seed_ver_by_ch[ch]])
+            async with self._cursor() as cur:
+                await cur.execute(stage2_sql, stage2_params)
+                stage2_rows = await cur.fetchall()
+        else:
+            stage2_rows = []
+
+        return self._build_delta_channels_writes_history(
+            channels=channels,
+            chain_by_ch=chain_by_ch,
+            seed_ver_by_ch=seed_ver_by_ch,
+            stage2_rows=cast("list[_DeltaStage2Row]", stage2_rows),
+        )
+
+    async def _load_checkpoint_tuple(self, value: DictRow) -> CheckpointTuple:
+        """
+        Convert a database row into a CheckpointTuple object.
+
+        Args:
+            value: A row from the database containing checkpoint data.
+
+        Returns:
+            CheckpointTuple: A structured representation of the checkpoint,
+            including its configuration, metadata, parent checkpoint (if any),
+            and pending writes.
+        """
+        return CheckpointTuple(
+            {
+                "configurable": {
+                    "thread_id": value["thread_id"],
+                    "checkpoint_ns": value["checkpoint_ns"],
+                    "checkpoint_id": value["checkpoint_id"],
+                }
+            },
+            {
+                **value["checkpoint"],
+                "channel_values": {
+                    **(value["checkpoint"].get("channel_values") or {}),
+                    **self._load_blobs(value["channel_values"]),
+                },
+            },
+            value["metadata"],
+            (
+                {
+                    "configurable": {
+                        "thread_id": value["thread_id"],
+                        "checkpoint_ns": value["checkpoint_ns"],
+                        "checkpoint_id": value["parent_checkpoint_id"],
+                    }
+                }
+                if value["parent_checkpoint_id"]
+                else None
+            ),
+            await asyncio.to_thread(self._load_writes, value["pending_writes"]),
+        )
+
+    def list(
+        self,
+        config: RunnableConfig | None,
+        *,
+        filter: dict[str, Any] | None = None,
+        before: RunnableConfig | None = None,
+        limit: int | None = None,
+    ) -> Iterator[CheckpointTuple]:
+        """List checkpoints from the database.
+
+        This method retrieves a list of checkpoint tuples from the Postgres database based
+        on the provided config. The checkpoints are ordered by checkpoint ID in descending order (newest first).
+
+        Args:
+            config: Base configuration for filtering checkpoints.
+            filter: Additional filtering criteria for metadata.
+            before: If provided, only checkpoints before the specified checkpoint ID are returned.
+            limit: Maximum number of checkpoints to return.
+
+        Yields:
+            An iterator of matching checkpoint tuples.
+        """
+        try:
+            # check if we are in the main thread, only bg threads can block
+            # we don't check in other methods to avoid the overhead
+            if asyncio.get_running_loop() is self.loop:
+                raise asyncio.InvalidStateError(
+                    "Synchronous calls to AsyncPostgresSaver are only allowed from a "
+                    "different thread. From the main thread, use the async interface. "
+                    "For example, use `checkpointer.alist(...)` or `await "
+                    "graph.ainvoke(...)`."
+                )
+        except RuntimeError:
+            pass
+        aiter_ = self.alist(config, filter=filter, before=before, limit=limit)
+        while True:
+            try:
+                yield asyncio.run_coroutine_threadsafe(
+                    anext(aiter_),  # type: ignore[arg-type]  # noqa: F821
+                    self.loop,
+                ).result()
+            except StopAsyncIteration:
+                break
+
+    def get_tuple(self, config: RunnableConfig) -> CheckpointTuple | None:
+        """Get a checkpoint tuple from the database.
+
+        This method retrieves a checkpoint tuple from the Postgres database based on the
+        provided config. If the config contains a `checkpoint_id` key, the checkpoint with
+        the matching thread ID and "checkpoint_id" is retrieved. Otherwise, the latest checkpoint
+        for the given thread ID is retrieved.
+
+        Args:
+            config: The config to use for retrieving the checkpoint.
+
+        Returns:
+            The retrieved checkpoint tuple, or None if no matching checkpoint was found.
+        """
+        try:
+            # check if we are in the main thread, only bg threads can block
+            # we don't check in other methods to avoid the overhead
+            if asyncio.get_running_loop() is self.loop:
+                raise asyncio.InvalidStateError(
+                    "Synchronous calls to AsyncPostgresSaver are only allowed from a "
+                    "different thread. From the main thread, use the async interface. "
+                    "For example, use `await checkpointer.aget_tuple(...)` or `await "
+                    "graph.ainvoke(...)`."
+                )
+        except RuntimeError:
+            pass
+        return asyncio.run_coroutine_threadsafe(
+            self.aget_tuple(config), self.loop
+        ).result()
+
+    def put(
+        self,
+        config: RunnableConfig,
+        checkpoint: Checkpoint,
+        metadata: CheckpointMetadata,
+        new_versions: ChannelVersions,
+    ) -> RunnableConfig:
+        """Save a checkpoint to the database.
+
+        This method saves a checkpoint to the Postgres database. The checkpoint is associated
+        with the provided config and its parent config (if any).
+
+        Args:
+            config: The config to associate with the checkpoint.
+            checkpoint: The checkpoint to save.
+            metadata: Additional metadata to save with the checkpoint.
+            new_versions: New channel versions as of this write.
+
+        Returns:
+            RunnableConfig: Updated configuration after storing the checkpoint.
+        """
+        return asyncio.run_coroutine_threadsafe(
+            self.aput(config, checkpoint, metadata, new_versions), self.loop
+        ).result()
+
+    def put_writes(
+        self,
+        config: RunnableConfig,
+        writes: Sequence[tuple[str, Any]],
+        task_id: str,
+        task_path: str = "",
+    ) -> None:
+        """Store intermediate writes linked to a checkpoint.
+
+        This method saves intermediate writes associated with a checkpoint to the database.
+
+        Args:
+            config: Configuration of the related checkpoint.
+            writes: List of writes to store, each as (channel, value) pair.
+            task_id: Identifier for the task creating the writes.
+            task_path: Path of the task creating the writes.
+        """
+        return asyncio.run_coroutine_threadsafe(
+            self.aput_writes(config, writes, task_id, task_path), self.loop
+        ).result()
+
+    def delete_thread(self, thread_id: str) -> None:
+        """Delete all checkpoints and writes associated with a thread ID.
+
+        Args:
+            thread_id: The thread ID to delete.
+
+        Returns:
+            None
+        """
+        try:
+            # check if we are in the main thread, only bg threads can block
+            # we don't check in other methods to avoid the overhead
+            if asyncio.get_running_loop() is self.loop:
+                raise asyncio.InvalidStateError(
+                    "Synchronous calls to AsyncPostgresSaver are only allowed from a "
+                    "different thread. From the main thread, use the async interface. "
+                    "For example, use `await checkpointer.aget_tuple(...)` or `await "
+                    "graph.ainvoke(...)`."
+                )
+        except RuntimeError:
+            pass
+        return asyncio.run_coroutine_threadsafe(
+            self.adelete_thread(thread_id), self.loop
+        ).result()
+
+
+__all__ = ["AsyncPostgresSaver", "AsyncShallowPostgresSaver", "Conn"]

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

@@ -0,0 +1,596 @@
+from __future__ import annotations
+
+import random
+import warnings
+from collections.abc import Mapping, Sequence
+from importlib.metadata import version as get_version
+from typing import Any, TypedDict, cast
+
+from langchain_core.runnables import RunnableConfig
+from langgraph.checkpoint.base import (
+    WRITES_IDX_MAP,
+    BaseCheckpointSaver,
+    ChannelVersions,
+    DeltaChannelHistory,
+    PendingWrite,
+    get_checkpoint_id,
+)
+from langgraph.checkpoint.serde.types import TASKS
+from psycopg.types.json import Jsonb
+
+# Page size for stage-1 paged scan in `get_delta_channel_history`. Internal
+# constant — exposing this as a kwarg is left as a follow-up.
+_DELTA_PAGE_SIZE = 1024
+
+MetadataInput = dict[str, Any] | None
+
+try:
+    major, minor = get_version("langgraph").split(".")[:2]
+    if int(major) == 0 and int(minor) < 5:
+        warnings.warn(
+            "You're using incompatible versions of langgraph and checkpoint-postgres. Please upgrade langgraph to avoid unexpected behavior.",
+            DeprecationWarning,
+            stacklevel=2,
+        )
+except Exception:
+    # skip version check if running from source
+    pass
+
+"""
+To add a new migration, add a new string to the MIGRATIONS list.
+The position of the migration in the list is the version number.
+"""
+MIGRATIONS = [
+    """CREATE TABLE IF NOT EXISTS checkpoint_migrations (
+    v INTEGER PRIMARY KEY
+);""",
+    """CREATE TABLE IF NOT EXISTS checkpoints (
+    thread_id TEXT NOT NULL,
+    checkpoint_ns TEXT NOT NULL DEFAULT '',
+    checkpoint_id TEXT NOT NULL,
+    parent_checkpoint_id TEXT,
+    type TEXT,
+    checkpoint JSONB NOT NULL,
+    metadata JSONB NOT NULL DEFAULT '{}',
+    PRIMARY KEY (thread_id, checkpoint_ns, checkpoint_id)
+);""",
+    """CREATE TABLE IF NOT EXISTS checkpoint_blobs (
+    thread_id TEXT NOT NULL,
+    checkpoint_ns TEXT NOT NULL DEFAULT '',
+    channel TEXT NOT NULL,
+    version TEXT NOT NULL,
+    type TEXT NOT NULL,
+    blob BYTEA,
+    PRIMARY KEY (thread_id, checkpoint_ns, channel, version)
+);""",
+    """CREATE TABLE IF NOT EXISTS checkpoint_writes (
+    thread_id TEXT NOT NULL,
+    checkpoint_ns TEXT NOT NULL DEFAULT '',
+    checkpoint_id TEXT NOT NULL,
+    task_id TEXT NOT NULL,
+    idx INTEGER NOT NULL,
+    channel TEXT NOT NULL,
+    type TEXT,
+    blob BYTEA NOT NULL,
+    PRIMARY KEY (thread_id, checkpoint_ns, checkpoint_id, task_id, idx)
+);""",
+    "ALTER TABLE checkpoint_blobs ALTER COLUMN blob DROP not null;",
+    # NOTE: this is a no-op migration to ensure that the versions in the migrations table are correct.
+    # This is necessary due to an empty migration previously added to the list.
+    "SELECT 1;",
+    """
+    CREATE INDEX CONCURRENTLY IF NOT EXISTS checkpoints_thread_id_idx ON checkpoints(thread_id);
+    """,
+    """
+    CREATE INDEX CONCURRENTLY IF NOT EXISTS checkpoint_blobs_thread_id_idx ON checkpoint_blobs(thread_id);
+    """,
+    """
+    CREATE INDEX CONCURRENTLY IF NOT EXISTS checkpoint_writes_thread_id_idx ON checkpoint_writes(thread_id);
+    """,
+    """ALTER TABLE checkpoint_writes ADD COLUMN IF NOT EXISTS task_path TEXT NOT NULL DEFAULT '';""",
+]
+
+SELECT_SQL = """
+select
+    thread_id,
+    checkpoint,
+    checkpoint_ns,
+    checkpoint_id,
+    parent_checkpoint_id,
+    metadata,
+    (
+        select array_agg(array[bl.channel::bytea, bl.type::bytea, bl.blob])
+        from jsonb_each_text(checkpoint -> 'channel_versions')
+        inner join checkpoint_blobs bl
+            on bl.thread_id = checkpoints.thread_id
+            and bl.checkpoint_ns = checkpoints.checkpoint_ns
+            and bl.channel = jsonb_each_text.key
+            and bl.version = jsonb_each_text.value
+    ) as channel_values,
+    (
+        select
+        array_agg(array[cw.task_id::text::bytea, cw.channel::bytea, cw.type::bytea, cw.blob] order by cw.task_id, cw.idx)
+        from checkpoint_writes cw
+        where cw.thread_id = checkpoints.thread_id
+            and cw.checkpoint_ns = checkpoints.checkpoint_ns
+            and cw.checkpoint_id = checkpoints.checkpoint_id
+    ) as pending_writes
+from checkpoints """
+
+SELECT_PENDING_SENDS_SQL = f"""
+select
+    checkpoint_id,
+    array_agg(array[type::bytea, blob] order by task_path, task_id, idx) as sends
+from checkpoint_writes
+where thread_id = %s
+    and checkpoint_id = any(%s)
+    and channel = '{TASKS}'
+group by checkpoint_id
+"""
+
+UPSERT_CHECKPOINT_BLOBS_SQL = """
+    INSERT INTO checkpoint_blobs (thread_id, checkpoint_ns, channel, version, type, blob)
+    VALUES (%s, %s, %s, %s, %s, %s)
+    ON CONFLICT (thread_id, checkpoint_ns, channel, version) DO NOTHING
+"""
+
+UPSERT_CHECKPOINTS_SQL = """
+    INSERT INTO checkpoints (thread_id, checkpoint_ns, checkpoint_id, parent_checkpoint_id, checkpoint, metadata)
+    VALUES (%s, %s, %s, %s, %s, %s)
+    ON CONFLICT (thread_id, checkpoint_ns, checkpoint_id)
+    DO UPDATE SET
+        checkpoint = EXCLUDED.checkpoint,
+        metadata = EXCLUDED.metadata;
+"""
+
+UPSERT_CHECKPOINT_WRITES_SQL = """
+    INSERT INTO checkpoint_writes (thread_id, checkpoint_ns, checkpoint_id, task_id, task_path, idx, channel, type, blob)
+    VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s)
+    ON CONFLICT (thread_id, checkpoint_ns, checkpoint_id, task_id, idx) DO UPDATE SET
+        channel = EXCLUDED.channel,
+        type = EXCLUDED.type,
+        blob = EXCLUDED.blob;
+"""
+
+INSERT_CHECKPOINT_WRITES_SQL = """
+    INSERT INTO checkpoint_writes (thread_id, checkpoint_ns, checkpoint_id, task_id, task_path, idx, channel, type, blob)
+    VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s)
+    ON CONFLICT (thread_id, checkpoint_ns, checkpoint_id, task_id, idx) DO NOTHING
+"""
+
+
+class _DeltaStage2Row(TypedDict, total=False):
+    """One row from `_build_delta_stage2_sql` (a UNION ALL of writes and blobs)."""
+
+    _kind: str  # "w" or "b"
+    checkpoint_id: str | None  # "w" rows only
+    channel: str | None  # set on both "w" and "b" rows
+    type: str | None
+    blob: bytes | None
+    task_id: str | None  # "w" rows only
+    idx: int | None  # "w" rows only
+    version: str | None  # "b" rows only
+
+
+# Multi-channel two-stage DeltaChannel reconstruction.
+#
+# Stage 1 scans checkpoint metadata (no blob bytes) and emits one row per
+# checkpoint with K parallel JSONB key lookups (one column pair per
+# requested delta channel: ver_i / hs_i).  No subqueries, no aggregation.
+# Python walks the parent chain once across all channels.
+#
+# Stage 2 fetches all writes and the seed blobs for ALL channels in a
+# single roundtrip via `channel = ANY(%s)` and chain/seed-version
+# filtering.
+#
+# Empirical comparison vs an alternative "ship full channel_versions /
+# channel_values JSONB and let Python pick" form (1000 checkpoints,
+# 8 total channels in graph, 3 delta channels requested):
+#
+#   Postgres execution:    A=0.24ms vs B=0.38ms   (both negligible)
+#   End-to-end latency:    A=6.83ms vs B=2.28ms   (B is 3.0x faster)
+#   Wire payload:          A=836KB  vs B=330KB    (61% smaller)
+#   Buffer hits:           identical (167 blocks)
+#
+# B (this dynamic-columns design) wins because it avoids JSONB
+# serialization on the wire and JSONB-to-dict deserialization in
+# psycopg.  Even at K=8 (8 delta channels = 16 dynamic columns), B
+# still beats A end-to-end (4.2ms vs 6.8ms).
+
+
+def _build_delta_stage1_sql(channels: Sequence[str], *, paged: bool) -> str:
+    """Build stage 1 SQL with 2K parallel JSONB key lookups.
+
+    For channels=["messages", "files"] (with `paged=True`) the result is::
+
+        SELECT checkpoint_id, parent_checkpoint_id,
+               checkpoint -> 'channel_versions' ->> %s AS ver_0,
+               (checkpoint -> 'channel_values' -> %s) IS NOT NULL AS hs_0,
+               checkpoint -> 'channel_versions' ->> %s AS ver_1,
+               (checkpoint -> 'channel_values' -> %s) IS NOT NULL AS hs_1
+        FROM checkpoints
+        WHERE thread_id = %s AND checkpoint_ns = %s
+          AND (%s::text IS NULL OR checkpoint_id < %s)
+        ORDER BY checkpoint_id DESC
+        LIMIT %s
+
+    Channel names are passed as `%s` parameters (safe from SQL injection).
+    Only the column aliases `ver_i` / `hs_i` are interpolated into the
+    SQL string (i is bounded by len(channels) and uses safe identifiers).
+
+    Caller must extend params with `[ch_0, ch_0, ch_1, ch_1, ...,
+    thread_id, ns, cursor, cursor, page_size]` when `paged=True`.
+
+    When `paged=False`, the WHERE has no cursor predicate and there's no
+    LIMIT/ORDER BY — kept as a non-public helper for tests/diagnostics.
+    """
+    cols = []
+    for i in range(len(channels)):
+        cols.append(
+            f"checkpoint -> 'channel_versions' ->> %s AS ver_{i}, "
+            f"(checkpoint -> 'channel_values' -> %s) IS NOT NULL AS hs_{i}"
+        )
+    sql = (
+        "SELECT checkpoint_id, parent_checkpoint_id, "
+        + ", ".join(cols)
+        + " FROM checkpoints WHERE thread_id = %s AND checkpoint_ns = %s"
+    )
+    if paged:
+        sql += (
+            " AND (%s::text IS NULL OR checkpoint_id < %s)"
+            " ORDER BY checkpoint_id DESC LIMIT %s"
+        )
+    return sql
+
+
+def _build_delta_stage2_sql(
+    *,
+    channels_with_chain: Sequence[str],
+    channels_with_seed: Sequence[str],
+) -> str:
+    """Build stage 2 SQL as a per-channel UNION ALL.
+
+    For each channel with a non-empty chain, emit one branch reading
+    `checkpoint_writes` for that specific channel + chain_cids. For each
+    channel with a seed_version, emit one branch reading `checkpoint_blobs`
+    for that channel + version. This avoids the over-fetch of the prior
+    `channel = ANY(channels) AND checkpoint_id = ANY(union)` form when
+    channels have different chain depths.
+
+    The caller must pass parameters in matching order:
+
+        for ch in channels_with_chain:
+            params += [thread_id, checkpoint_ns, ch, chain_cids[ch]]
+        for ch in channels_with_seed:
+            params += [thread_id, checkpoint_ns, ch, seed_version[ch]]
+
+    Returns an empty SQL string if both channel lists are empty (caller
+    must skip executing in that case).
+    """
+    branches: list[str] = []
+    for _ in channels_with_chain:
+        branches.append(
+            "SELECT 'w'::text AS _kind, "
+            "checkpoint_id, channel, "
+            "type, blob, task_id, idx, NULL::text AS version "
+            "FROM checkpoint_writes "
+            "WHERE thread_id = %s AND checkpoint_ns = %s AND channel = %s "
+            "AND checkpoint_id = ANY(%s)"
+        )
+    for _ in channels_with_seed:
+        branches.append(
+            "SELECT 'b'::text AS _kind, NULL::text AS checkpoint_id, channel, "
+            "type, blob, NULL::text AS task_id, NULL::int AS idx, version "
+            "FROM checkpoint_blobs "
+            "WHERE thread_id = %s AND checkpoint_ns = %s AND channel = %s "
+            "AND version = %s"
+        )
+    return " UNION ALL ".join(branches)
+
+
+# Stage 1 rows are dynamic-shape dicts: {checkpoint_id, parent_checkpoint_id,
+# ver_0, hs_0, ver_1, hs_1, ...}.  Walking is parameterized by the channel
+# list to map indices back to channel names — no static TypedDict here.
+# `dict[str, Any]` is the practical signature.
+
+
+class BasePostgresSaver(BaseCheckpointSaver[str]):
+    SELECT_SQL = SELECT_SQL
+    SELECT_PENDING_SENDS_SQL = SELECT_PENDING_SENDS_SQL
+    MIGRATIONS = MIGRATIONS
+    UPSERT_CHECKPOINT_BLOBS_SQL = UPSERT_CHECKPOINT_BLOBS_SQL
+    UPSERT_CHECKPOINTS_SQL = UPSERT_CHECKPOINTS_SQL
+    UPSERT_CHECKPOINT_WRITES_SQL = UPSERT_CHECKPOINT_WRITES_SQL
+    INSERT_CHECKPOINT_WRITES_SQL = INSERT_CHECKPOINT_WRITES_SQL
+
+    supports_pipeline: bool
+
+    def _migrate_pending_sends(
+        self,
+        pending_sends: list[tuple[bytes, bytes]],
+        checkpoint: dict[str, Any],
+        channel_values: list[tuple[bytes, bytes, bytes]],
+    ) -> None:
+        if not pending_sends:
+            return
+        # add to values
+        enc, blob = self.serde.dumps_typed(
+            [self.serde.loads_typed((c.decode(), b)) for c, b in pending_sends],
+        )
+        channel_values.append((TASKS.encode(), enc.encode(), blob))
+        # add to versions
+        checkpoint["channel_versions"][TASKS] = (
+            max(checkpoint["channel_versions"].values())
+            if checkpoint["channel_versions"]
+            else self.get_next_version(None, None)
+        )
+
+    def _load_blobs(
+        self, blob_values: list[tuple[bytes, bytes, bytes]]
+    ) -> dict[str, Any]:
+        if not blob_values:
+            return {}
+        return {
+            k.decode(): self.serde.loads_typed((t.decode(), v))
+            for k, t, v in blob_values
+            if t.decode() != "empty"
+        }
+
+    @staticmethod
+    def _ingest_stage1_page(
+        stage1_rows: Sequence[Mapping[str, Any]],
+        channels: Sequence[str],
+        parent_of: dict[str, str | None],
+        ver_by_i_by_cid: list[dict[str, str | None]],
+        hs_by_i_by_cid: list[dict[str, bool]],
+    ) -> str | None:
+        """Fold one stage-1 page into the running walk-state mappings.
+
+        Returns the oldest checkpoint_id seen on this page (smallest, since
+        pages come back DESC). Caller uses it as the cursor for the next
+        page (`AND checkpoint_id < cursor`).
+        """
+        oldest: str | None = None
+        for r in stage1_rows:
+            cid = cast(str, r["checkpoint_id"])
+            parent_of[cid] = cast("str | None", r["parent_checkpoint_id"])
+            for i in range(len(channels)):
+                ver_by_i_by_cid[i][cid] = cast("str | None", r.get(f"ver_{i}"))
+                hs_by_i_by_cid[i][cid] = bool(r.get(f"hs_{i}"))
+            # Rows are DESC; the last one is the smallest cid in the page.
+            oldest = cid
+        return oldest
+
+    @staticmethod
+    def _try_advance_walks(
+        target_id: str,
+        channels: Sequence[str],
+        parent_of: Mapping[str, str | None],
+        ver_by_i_by_cid: Sequence[Mapping[str, str | None]],
+        hs_by_i_by_cid: Sequence[Mapping[str, bool]],
+        chain_by_ch: dict[str, list[str]],
+        seed_ver_by_ch: dict[str, str | None],
+        walk_cursor_by_ch: dict[str, str | None],
+        seeded: set[str],
+    ) -> None:
+        """Advance each not-yet-seeded channel's walk as far as possible.
+
+        Uses the partial `parent_of` map accumulated so far. A walk stops
+        either because:
+          (a) it found a snapshot for its channel (channel becomes seeded),
+          (b) it reached a real root (parent_of[cid] is None — fully
+              materialized at this point), or
+          (c) the next ancestor cid isn't in `parent_of` yet (waiting for
+              a later page; the cursor stays put).
+
+        Mutates `chain_by_ch`, `seed_ver_by_ch`, `walk_cursor_by_ch`, and
+        `seeded` in place.
+        """
+        for i, ch in enumerate(channels):
+            if ch in seeded:
+                continue
+            # First-time entry: cursor starts at the target's parent.
+            if ch not in walk_cursor_by_ch:
+                walk_cursor_by_ch[ch] = parent_of.get(target_id)
+            cur_cid = walk_cursor_by_ch[ch]
+            ch_chain = chain_by_ch[ch]
+            hs_i = hs_by_i_by_cid[i]
+            ver_i = ver_by_i_by_cid[i]
+            while cur_cid is not None:
+                if cur_cid not in parent_of:
+                    # Need more pages to continue this walk.
+                    break
+                ch_chain.append(cur_cid)
+                if hs_i.get(cur_cid, False):
+                    seed_ver_by_ch[ch] = ver_i.get(cur_cid)
+                    seeded.add(ch)
+                    cur_cid = None
+                    break
+                cur_cid = parent_of[cur_cid]
+            walk_cursor_by_ch[ch] = cur_cid
+
+    def _build_delta_channels_writes_history(
+        self,
+        *,
+        channels: Sequence[str],
+        chain_by_ch: Mapping[str, list[str]],
+        seed_ver_by_ch: Mapping[str, str | None],
+        stage2_rows: Sequence[_DeltaStage2Row],
+    ) -> dict[str, DeltaChannelHistory]:
+        """Demux stage 2 rows per channel; produce per-channel histories.
+
+        stage2_rows carry `channel` on every row. We build per-channel
+        `writes_by_cid` and per-channel `seed_blob` dicts, then assemble
+        a `DeltaChannelHistory` per requested channel. The `seed` key is omitted
+        when the walk reached root with no snapshot found, or when the
+        seed blob is sentinel "empty" — in both cases the consumer treats
+        absence as "start empty".
+        """
+        # writes_by_ch_by_cid[channel][cid] = list of (type, blob, task_id, idx)
+        writes_by_ch_by_cid: dict[str, dict[str, list[tuple[str, bytes, str, int]]]] = {
+            ch: {} for ch in channels
+        }
+        # seed_blob_by_ver[(channel, version)] = (type, blob)
+        seed_blob_by_ver: dict[tuple[str, str], tuple[str, bytes]] = {}
+
+        for r in stage2_rows:
+            ch = cast(str, r["channel"])
+            kind = r["_kind"]
+            if kind == "w":
+                cid = cast(str, r["checkpoint_id"])
+                writes_by_ch_by_cid.setdefault(ch, {}).setdefault(cid, []).append(
+                    cast(
+                        "tuple[str, bytes, str, int]",
+                        (r["type"], r["blob"], r["task_id"], r["idx"]),
+                    )
+                )
+            else:  # kind == "b"
+                ver = cast(str, r["version"])
+                seed_blob_by_ver[(ch, ver)] = cast(
+                    "tuple[str, bytes]", (r["type"], r["blob"])
+                )
+
+        # Sort writes per (channel, cid) newest-first by (task_id, idx)
+        for cid_map in writes_by_ch_by_cid.values():
+            for ws in cid_map.values():
+                ws.sort(key=lambda w: (w[2], w[3]), reverse=True)
+
+        result: dict[str, DeltaChannelHistory] = {}
+        for ch in channels:
+            chain_cids = chain_by_ch.get(ch, [])
+            seed_version = seed_ver_by_ch.get(ch)
+
+            collected: list[PendingWrite] = []
+            cid_writes = writes_by_ch_by_cid.get(ch, {})
+            for cid in chain_cids:
+                for type_tag, write_blob, task_id, _idx in cid_writes.get(cid, []):
+                    val = self.serde.loads_typed((type_tag, write_blob))
+                    collected.append((task_id, ch, val))
+            collected.reverse()
+
+            entry: DeltaChannelHistory = {"writes": collected}
+            if seed_version is not None:
+                blob = seed_blob_by_ver.get((ch, seed_version))
+                if blob is not None and blob[0] != "empty":
+                    entry["seed"] = self.serde.loads_typed(blob)
+            result[ch] = entry
+        return result
+
+    def _dump_blobs(
+        self,
+        thread_id: str,
+        checkpoint_ns: str,
+        values: dict[str, Any],
+        versions: ChannelVersions,
+    ) -> list[tuple[str, str, str, str, str, bytes | None]]:
+        if not versions:
+            return []
+
+        return [
+            (
+                thread_id,
+                checkpoint_ns,
+                k,
+                cast(str, ver),
+                *(
+                    self.serde.dumps_typed(values[k])
+                    if k in values
+                    else ("empty", None)
+                ),
+            )
+            for k, ver in versions.items()
+        ]
+
+    def _load_writes(
+        self, writes: list[tuple[bytes, bytes, bytes, bytes]]
+    ) -> list[tuple[str, str, Any]]:
+        return (
+            [
+                (
+                    tid.decode(),
+                    channel.decode(),
+                    self.serde.loads_typed((t.decode(), v)),
+                )
+                for tid, channel, t, v in writes
+            ]
+            if writes
+            else []
+        )
+
+    def _dump_writes(
+        self,
+        thread_id: str,
+        checkpoint_ns: str,
+        checkpoint_id: str,
+        task_id: str,
+        task_path: str,
+        writes: Sequence[tuple[str, Any]],
+    ) -> list[tuple[str, str, str, str, str, int, str, str, bytes]]:
+        return [
+            (
+                thread_id,
+                checkpoint_ns,
+                checkpoint_id,
+                task_id,
+                task_path,
+                WRITES_IDX_MAP.get(channel, idx),
+                channel,
+                *self.serde.dumps_typed(value),
+            )
+            for idx, (channel, value) in enumerate(writes)
+        ]
+
+    def get_next_version(self, current: str | None, channel: None) -> str:
+        if current is None:
+            current_v = 0
+        elif isinstance(current, int):
+            current_v = current
+        else:
+            current_v = int(current.split(".")[0])
+        next_v = current_v + 1
+        next_h = random.random()
+        return f"{next_v:032}.{next_h:016}"
+
+    def _search_where(
+        self,
+        config: RunnableConfig | None,
+        filter: MetadataInput,
+        before: RunnableConfig | None = None,
+    ) -> tuple[str, list[Any]]:
+        """Return WHERE clause predicates for alist() given config, filter, before.
+
+        This method returns a tuple of a string and a tuple of values. The string
+        is the parametered WHERE clause predicate (including the WHERE keyword):
+        "WHERE column1 = $1 AND column2 IS $2". The list of values contains the
+        values for each of the corresponding parameters.
+        """
+        wheres = []
+        param_values = []
+
+        # construct predicate for config filter
+        if config:
+            wheres.append("thread_id = %s ")
+            param_values.append(config["configurable"]["thread_id"])
+            checkpoint_ns = config["configurable"].get("checkpoint_ns")
+            if checkpoint_ns is not None:
+                wheres.append("checkpoint_ns = %s")
+                param_values.append(checkpoint_ns)
+
+            if checkpoint_id := get_checkpoint_id(config):
+                wheres.append("checkpoint_id = %s ")
+                param_values.append(checkpoint_id)
+
+        # construct predicate for metadata filter
+        if filter:
+            wheres.append("metadata @> %s ")
+            param_values.append(Jsonb(filter))
+
+        # construct predicate for `before`
+        if before is not None:
+            wheres.append("checkpoint_id < %s ")
+            param_values.append(get_checkpoint_id(before))
+
+        return (
+            "WHERE " + " AND ".join(wheres) if wheres else "",
+            param_values,
+        )

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

@@ -0,0 +1,967 @@
+import asyncio
+import threading
+import warnings
+from collections.abc import AsyncIterator, Iterator, Sequence
+from contextlib import asynccontextmanager, contextmanager
+from typing import Any
+
+from langchain_core.runnables import RunnableConfig
+from langgraph.checkpoint.base import (
+    WRITES_IDX_MAP,
+    ChannelVersions,
+    Checkpoint,
+    CheckpointMetadata,
+    CheckpointTuple,
+    get_serializable_checkpoint_metadata,
+)
+from langgraph.checkpoint.serde.base import SerializerProtocol
+from langgraph.checkpoint.serde.types import TASKS
+from psycopg import (
+    AsyncConnection,
+    AsyncCursor,
+    AsyncPipeline,
+    Capabilities,
+    Connection,
+    Cursor,
+    Pipeline,
+)
+from psycopg.rows import DictRow, dict_row
+from psycopg.types.json import Jsonb
+from psycopg_pool import AsyncConnectionPool, ConnectionPool
+
+from langgraph.checkpoint.postgres import _ainternal, _internal
+from langgraph.checkpoint.postgres.base import BasePostgresSaver
+
+"""
+To add a new migration, add a new string to the MIGRATIONS list.
+The position of the migration in the list is the version number.
+"""
+MIGRATIONS = [
+    """CREATE TABLE IF NOT EXISTS checkpoint_migrations (
+    v INTEGER PRIMARY KEY
+);""",
+    """CREATE TABLE IF NOT EXISTS checkpoints (
+    thread_id TEXT NOT NULL,
+    checkpoint_ns TEXT NOT NULL DEFAULT '',
+    type TEXT,
+    checkpoint JSONB NOT NULL,
+    metadata JSONB NOT NULL DEFAULT '{}',
+    PRIMARY KEY (thread_id, checkpoint_ns)
+);""",
+    """CREATE TABLE IF NOT EXISTS checkpoint_blobs (
+    thread_id TEXT NOT NULL,
+    checkpoint_ns TEXT NOT NULL DEFAULT '',
+    channel TEXT NOT NULL,
+    type TEXT NOT NULL,
+    blob BYTEA,
+    PRIMARY KEY (thread_id, checkpoint_ns, channel)
+);""",
+    """CREATE TABLE IF NOT EXISTS checkpoint_writes (
+    thread_id TEXT NOT NULL,
+    checkpoint_ns TEXT NOT NULL DEFAULT '',
+    checkpoint_id TEXT NOT NULL,
+    task_id TEXT NOT NULL,
+    idx INTEGER NOT NULL,
+    channel TEXT NOT NULL,
+    type TEXT,
+    blob BYTEA NOT NULL,
+    PRIMARY KEY (thread_id, checkpoint_ns, checkpoint_id, task_id, idx)
+);""",
+    """
+    CREATE INDEX CONCURRENTLY IF NOT EXISTS checkpoints_thread_id_idx ON checkpoints(thread_id);
+    """,
+    """
+    CREATE INDEX CONCURRENTLY IF NOT EXISTS checkpoint_blobs_thread_id_idx ON checkpoint_blobs(thread_id);
+    """,
+    """
+    CREATE INDEX CONCURRENTLY IF NOT EXISTS checkpoint_writes_thread_id_idx ON checkpoint_writes(thread_id);
+    """,
+    """
+    ALTER TABLE checkpoint_writes ADD COLUMN IF NOT EXISTS task_path TEXT NOT NULL DEFAULT '';
+    """,
+]
+
+SELECT_SQL = f"""
+select
+    thread_id,
+    checkpoint,
+    checkpoint_ns,
+    metadata,
+    (
+        select array_agg(array[bl.channel::bytea, bl.type::bytea, bl.blob])
+        from jsonb_each_text(checkpoint -> 'channel_versions')
+        inner join checkpoint_blobs bl
+            on bl.thread_id = checkpoints.thread_id
+            and bl.checkpoint_ns = checkpoints.checkpoint_ns
+            and bl.channel = jsonb_each_text.key
+    ) as channel_values,
+    (
+        select
+        array_agg(array[cw.task_id::text::bytea, cw.channel::bytea, cw.type::bytea, cw.blob] order by cw.task_id, cw.idx)
+        from checkpoint_writes cw
+        where cw.thread_id = checkpoints.thread_id
+            and cw.checkpoint_ns = checkpoints.checkpoint_ns
+            and cw.checkpoint_id = (checkpoint->>'id')
+    ) as pending_writes,
+    (
+        select array_agg(array[cw.type::bytea, cw.blob] order by cw.task_path, cw.task_id, cw.idx)
+        from checkpoint_writes cw
+        where cw.thread_id = checkpoints.thread_id
+            and cw.checkpoint_ns = checkpoints.checkpoint_ns
+            and cw.channel = '{TASKS}'
+    ) as pending_sends
+from checkpoints """
+
+UPSERT_CHECKPOINT_BLOBS_SQL = """
+    INSERT INTO checkpoint_blobs (thread_id, checkpoint_ns, channel, type, blob)
+    VALUES (%s, %s, %s, %s, %s)
+    ON CONFLICT (thread_id, checkpoint_ns, channel) DO UPDATE SET
+        type = EXCLUDED.type,
+        blob = EXCLUDED.blob;
+"""
+
+UPSERT_CHECKPOINTS_SQL = """
+    INSERT INTO checkpoints (thread_id, checkpoint_ns, checkpoint, metadata)
+    VALUES (%s, %s, %s, %s)
+    ON CONFLICT (thread_id, checkpoint_ns)
+    DO UPDATE SET
+        checkpoint = EXCLUDED.checkpoint,
+        metadata = EXCLUDED.metadata;
+"""
+
+UPSERT_CHECKPOINT_WRITES_SQL = """
+    INSERT INTO checkpoint_writes (thread_id, checkpoint_ns, checkpoint_id, task_id, task_path, idx, channel, type, blob)
+    VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s)
+    ON CONFLICT (thread_id, checkpoint_ns, checkpoint_id, task_id, idx) DO UPDATE SET
+        channel = EXCLUDED.channel,
+        type = EXCLUDED.type,
+        blob = EXCLUDED.blob;
+"""
+
+INSERT_CHECKPOINT_WRITES_SQL = """
+    INSERT INTO checkpoint_writes (thread_id, checkpoint_ns, checkpoint_id, task_id, task_path, idx, channel, type, blob)
+    VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s)
+    ON CONFLICT (thread_id, checkpoint_ns, checkpoint_id, task_id, idx) DO NOTHING
+"""
+
+
+def _dump_blobs(
+    serde: SerializerProtocol,
+    thread_id: str,
+    checkpoint_ns: str,
+    values: dict[str, Any],
+    versions: ChannelVersions,
+) -> list[tuple[str, str, str, str, bytes | None]]:
+    if not versions:
+        return []
+
+    return [
+        (
+            thread_id,
+            checkpoint_ns,
+            k,
+            *(serde.dumps_typed(values[k]) if k in values else ("empty", None)),
+        )
+        for k in versions
+    ]
+
+
+class ShallowPostgresSaver(BasePostgresSaver):
+    """A checkpoint saver that uses Postgres to store checkpoints.
+
+    This checkpointer ONLY stores the most recent checkpoint and does NOT retain any history.
+    It is meant to be a light-weight drop-in replacement for the PostgresSaver that
+    supports most of the LangGraph persistence functionality with the exception of time travel.
+    """
+
+    SELECT_SQL = SELECT_SQL
+    MIGRATIONS = MIGRATIONS
+    UPSERT_CHECKPOINT_BLOBS_SQL = UPSERT_CHECKPOINT_BLOBS_SQL
+    UPSERT_CHECKPOINTS_SQL = UPSERT_CHECKPOINTS_SQL
+    UPSERT_CHECKPOINT_WRITES_SQL = UPSERT_CHECKPOINT_WRITES_SQL
+    INSERT_CHECKPOINT_WRITES_SQL = INSERT_CHECKPOINT_WRITES_SQL
+
+    lock: threading.Lock
+
+    def __init__(
+        self,
+        conn: _internal.Conn,
+        pipe: Pipeline | None = None,
+        serde: SerializerProtocol | None = None,
+    ) -> None:
+        warnings.warn(
+            "ShallowPostgresSaver is deprecated as of version 2.0.20 and will be removed in 3.0.0. "
+            "Use PostgresSaver instead, and invoke the graph with `graph.invoke(..., durability='exit')`.",
+            DeprecationWarning,
+            stacklevel=2,
+        )
+        super().__init__(serde=serde)
+        if isinstance(conn, ConnectionPool) and pipe is not None:
+            raise ValueError(
+                "Pipeline should be used only with a single Connection, not ConnectionPool."
+            )
+
+        self.conn = conn
+        self.pipe = pipe
+        self.lock = threading.Lock()
+        self.supports_pipeline = Capabilities().has_pipeline()
+
+    @classmethod
+    @contextmanager
+    def from_conn_string(
+        cls, conn_string: str, *, pipeline: bool = False
+    ) -> Iterator["ShallowPostgresSaver"]:
+        """Create a new ShallowPostgresSaver instance from a connection string.
+
+        Args:
+            conn_string: The Postgres connection info string.
+            pipeline: whether to use Pipeline
+
+        Returns:
+            ShallowPostgresSaver: A new ShallowPostgresSaver instance.
+        """
+        with Connection.connect(
+            conn_string, autocommit=True, prepare_threshold=0, row_factory=dict_row
+        ) as conn:
+            if pipeline:
+                with conn.pipeline() as pipe:
+                    yield cls(conn, pipe)
+            else:
+                yield cls(conn)
+
+    def setup(self) -> None:
+        """Set up the checkpoint database asynchronously.
+
+        This method creates the necessary tables in the Postgres database if they don't
+        already exist and runs database migrations. It MUST be called directly by the user
+        the first time checkpointer is used.
+        """
+        with self._cursor() as cur:
+            cur.execute(self.MIGRATIONS[0])
+            results = cur.execute(
+                "SELECT v FROM checkpoint_migrations ORDER BY v DESC LIMIT 1"
+            )
+            row = results.fetchone()
+            if row is None:
+                version = -1
+            else:
+                version = row["v"]
+            for v, migration in zip(
+                range(version + 1, len(self.MIGRATIONS)),
+                self.MIGRATIONS[version + 1 :],
+                strict=False,
+            ):
+                cur.execute(migration)
+                cur.execute("INSERT INTO checkpoint_migrations (v) VALUES (%s)", (v,))
+        if self.pipe:
+            self.pipe.sync()
+
+    def list(
+        self,
+        config: RunnableConfig | None,
+        *,
+        filter: dict[str, Any] | None = None,
+        before: RunnableConfig | None = None,
+        limit: int | None = None,
+    ) -> Iterator[CheckpointTuple]:
+        """List checkpoints from the database.
+
+        This method retrieves a list of checkpoint tuples from the Postgres database based
+        on the provided config. For ShallowPostgresSaver, this method returns a list with
+        ONLY the most recent checkpoint.
+        """
+        where, args = self._search_where(config, filter, before)
+        query = self.SELECT_SQL + where
+        params = list(args)
+        if limit is not None:
+            query += " LIMIT %s"
+            params.append(int(limit))
+        with self._cursor() as cur:
+            cur.execute(query, params, binary=True)
+            for value in cur:
+                checkpoint: Checkpoint = {
+                    **value["checkpoint"],
+                    "channel_values": self._load_blobs(value["channel_values"]),
+                    "pending_sends": [
+                        self.serde.loads_typed((t.decode(), v))
+                        for t, v in value["pending_sends"]
+                    ]
+                    if value["pending_sends"]
+                    else [],
+                }
+                yield CheckpointTuple(
+                    config={
+                        "configurable": {
+                            "thread_id": value["thread_id"],
+                            "checkpoint_ns": value["checkpoint_ns"],
+                            "checkpoint_id": checkpoint["id"],
+                        }
+                    },
+                    checkpoint=checkpoint,
+                    metadata=value["metadata"],
+                    pending_writes=self._load_writes(value["pending_writes"]),
+                )
+
+    def get_tuple(self, config: RunnableConfig) -> CheckpointTuple | None:
+        """Get a checkpoint tuple from the database.
+
+        This method retrieves a checkpoint tuple from the Postgres database based on the
+        provided config (matching the thread ID in the config).
+
+        Args:
+            config: The config to use for retrieving the checkpoint.
+
+        Returns:
+            The retrieved checkpoint tuple, or None if no matching checkpoint was found.
+
+        Examples:
+
+            Basic:
+            >>> config = {"configurable": {"thread_id": "1"}}
+            >>> checkpoint_tuple = memory.get_tuple(config)
+            >>> print(checkpoint_tuple)
+            CheckpointTuple(...)
+
+            With timestamp:
+
+            >>> config = {
+            ...    "configurable": {
+            ...        "thread_id": "1",
+            ...        "checkpoint_ns": "",
+            ...        "checkpoint_id": "1ef4f797-8335-6428-8001-8a1503f9b875",
+            ...    }
+            ... }
+            >>> checkpoint_tuple = memory.get_tuple(config)
+            >>> print(checkpoint_tuple)
+            CheckpointTuple(...)
+        """  # noqa
+        thread_id = config["configurable"]["thread_id"]
+        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
+        args = (thread_id, checkpoint_ns)
+        where = "WHERE thread_id = %s AND checkpoint_ns = %s"
+
+        with self._cursor() as cur:
+            cur.execute(
+                self.SELECT_SQL + where,
+                args,
+                binary=True,
+            )
+
+            for value in cur:
+                checkpoint: Checkpoint = {
+                    **value["checkpoint"],
+                    "channel_values": self._load_blobs(value["channel_values"]),
+                    "pending_sends": [
+                        self.serde.loads_typed((t.decode(), v))
+                        for t, v in value["pending_sends"]
+                    ]
+                    if value["pending_sends"]
+                    else [],
+                }
+                return CheckpointTuple(
+                    config={
+                        "configurable": {
+                            "thread_id": thread_id,
+                            "checkpoint_ns": checkpoint_ns,
+                            "checkpoint_id": checkpoint["id"],
+                        }
+                    },
+                    checkpoint=checkpoint,
+                    metadata=value["metadata"],
+                    pending_writes=self._load_writes(value["pending_writes"]),
+                )
+
+    def put(
+        self,
+        config: RunnableConfig,
+        checkpoint: Checkpoint,
+        metadata: CheckpointMetadata,
+        new_versions: ChannelVersions,
+    ) -> RunnableConfig:
+        """Save a checkpoint to the database.
+
+        This method saves a checkpoint to the Postgres database. The checkpoint is associated
+        with the provided config. For ShallowPostgresSaver, this method saves ONLY the most recent
+        checkpoint and overwrites a previous checkpoint, if it exists.
+
+        Args:
+            config: The config to associate with the checkpoint.
+            checkpoint: The checkpoint to save.
+            metadata: Additional metadata to save with the checkpoint.
+            new_versions: New channel versions as of this write.
+
+        Returns:
+            RunnableConfig: Updated configuration after storing the checkpoint.
+
+        Examples:
+
+            >>> from langgraph.checkpoint.postgres import ShallowPostgresSaver
+            >>> DB_URI = "postgres://postgres:postgres@localhost:5432/postgres?sslmode=disable"
+            >>> with ShallowPostgresSaver.from_conn_string(DB_URI) as memory:
+            >>>     config = {"configurable": {"thread_id": "1", "checkpoint_ns": ""}}
+            >>>     checkpoint = {"ts": "2024-05-04T06:32:42.235444+00:00", "id": "1ef4f797-8335-6428-8001-8a1503f9b875", "channel_values": {"key": "value"}}
+            >>>     saved_config = memory.put(config, checkpoint, {"source": "input", "step": 1, "writes": {"key": "value"}}, {})
+            >>> print(saved_config)
+            {'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef4f797-8335-6428-8001-8a1503f9b875'}}
+        """
+        configurable = config["configurable"].copy()
+        thread_id = configurable.pop("thread_id")
+        checkpoint_ns = configurable.pop("checkpoint_ns")
+
+        copy = checkpoint.copy()
+        next_config = {
+            "configurable": {
+                "thread_id": thread_id,
+                "checkpoint_ns": checkpoint_ns,
+                "checkpoint_id": checkpoint["id"],
+            }
+        }
+
+        with self._cursor(pipeline=True) as cur:
+            cur.execute(
+                """DELETE FROM checkpoint_writes
+                WHERE thread_id = %s AND checkpoint_ns = %s AND checkpoint_id NOT IN (%s, %s)""",
+                (
+                    thread_id,
+                    checkpoint_ns,
+                    checkpoint["id"],
+                    configurable.get("checkpoint_id", ""),
+                ),
+            )
+            cur.executemany(
+                self.UPSERT_CHECKPOINT_BLOBS_SQL,
+                _dump_blobs(
+                    self.serde,
+                    thread_id,
+                    checkpoint_ns,
+                    copy.pop("channel_values"),  # type: ignore[misc]
+                    new_versions,
+                ),
+            )
+            cur.execute(
+                self.UPSERT_CHECKPOINTS_SQL,
+                (
+                    thread_id,
+                    checkpoint_ns,
+                    Jsonb(copy),
+                    Jsonb(get_serializable_checkpoint_metadata(config, metadata)),
+                ),
+            )
+        return next_config
+
+    def put_writes(
+        self,
+        config: RunnableConfig,
+        writes: Sequence[tuple[str, Any]],
+        task_id: str,
+        task_path: str = "",
+    ) -> None:
+        """Store intermediate writes linked to a checkpoint.
+
+        This method saves intermediate writes associated with a checkpoint to the Postgres database.
+
+        Args:
+            config: Configuration of the related checkpoint.
+            writes: List of writes to store.
+            task_id: Identifier for the task creating the writes.
+        """
+        query = (
+            self.UPSERT_CHECKPOINT_WRITES_SQL
+            if all(w[0] in WRITES_IDX_MAP for w in writes)
+            else self.INSERT_CHECKPOINT_WRITES_SQL
+        )
+        with self._cursor(pipeline=True) as cur:
+            cur.executemany(
+                query,
+                self._dump_writes(
+                    config["configurable"]["thread_id"],
+                    config["configurable"]["checkpoint_ns"],
+                    config["configurable"]["checkpoint_id"],
+                    task_id,
+                    task_path,
+                    writes,
+                ),
+            )
+
+    @contextmanager
+    def _cursor(self, *, pipeline: bool = False) -> Iterator[Cursor[DictRow]]:
+        """Create a database cursor as a context manager.
+
+        Args:
+            pipeline: whether to use pipeline for the DB operations inside the context manager.
+                Will be applied regardless of whether the ShallowPostgresSaver instance was initialized with a pipeline.
+                If pipeline mode is not supported, will fall back to using transaction context manager.
+        """
+        with _internal.get_connection(self.conn) as conn:
+            if self.pipe:
+                # a connection in pipeline mode can be used concurrently
+                # in multiple threads/coroutines, but only one cursor can be
+                # used at a time
+                try:
+                    with conn.cursor(binary=True, row_factory=dict_row) as cur:
+                        yield cur
+                finally:
+                    if pipeline:
+                        self.pipe.sync()
+            elif pipeline:
+                # a connection not in pipeline mode can only be used by one
+                # thread/coroutine at a time, so we acquire a lock
+                if self.supports_pipeline:
+                    with (
+                        self.lock,
+                        conn.pipeline(),
+                        conn.cursor(binary=True, row_factory=dict_row) as cur,
+                    ):
+                        yield cur
+                else:
+                    # Use connection's transaction context manager when pipeline mode not supported
+                    with (
+                        self.lock,
+                        conn.transaction(),
+                        conn.cursor(binary=True, row_factory=dict_row) as cur,
+                    ):
+                        yield cur
+            else:
+                with self.lock, conn.cursor(binary=True, row_factory=dict_row) as cur:
+                    yield cur
+
+
+class AsyncShallowPostgresSaver(BasePostgresSaver):
+    """A checkpoint saver that uses Postgres to store checkpoints asynchronously.
+
+    This checkpointer ONLY stores the most recent checkpoint and does NOT retain any history.
+    It is meant to be a light-weight drop-in replacement for the AsyncPostgresSaver that
+    supports most of the LangGraph persistence functionality with the exception of time travel.
+    """
+
+    SELECT_SQL = SELECT_SQL
+    MIGRATIONS = MIGRATIONS
+    UPSERT_CHECKPOINT_BLOBS_SQL = UPSERT_CHECKPOINT_BLOBS_SQL
+    UPSERT_CHECKPOINTS_SQL = UPSERT_CHECKPOINTS_SQL
+    UPSERT_CHECKPOINT_WRITES_SQL = UPSERT_CHECKPOINT_WRITES_SQL
+    INSERT_CHECKPOINT_WRITES_SQL = INSERT_CHECKPOINT_WRITES_SQL
+    lock: asyncio.Lock
+
+    def __init__(
+        self,
+        conn: _ainternal.Conn,
+        pipe: AsyncPipeline | None = None,
+        serde: SerializerProtocol | None = None,
+    ) -> None:
+        warnings.warn(
+            "AsyncShallowPostgresSaver is deprecated as of version 2.0.20 and will be removed in 3.0.0. "
+            "Use AsyncPostgresSaver instead, and invoke the graph with `await graph.ainvoke(..., durability='exit')`.",
+            DeprecationWarning,
+            stacklevel=2,
+        )
+        super().__init__(serde=serde)
+        if isinstance(conn, AsyncConnectionPool) and pipe is not None:
+            raise ValueError(
+                "Pipeline should be used only with a single AsyncConnection, not AsyncConnectionPool."
+            )
+
+        self.conn = conn
+        self.pipe = pipe
+        self.lock = asyncio.Lock()
+        self.loop = asyncio.get_running_loop()
+        self.supports_pipeline = Capabilities().has_pipeline()
+
+    @classmethod
+    @asynccontextmanager
+    async def from_conn_string(
+        cls,
+        conn_string: str,
+        *,
+        pipeline: bool = False,
+        serde: SerializerProtocol | None = None,
+    ) -> AsyncIterator["AsyncShallowPostgresSaver"]:
+        """Create a new AsyncShallowPostgresSaver instance from a connection string.
+
+        Args:
+            conn_string: The Postgres connection info string.
+            pipeline: whether to use AsyncPipeline
+
+        Returns:
+            AsyncShallowPostgresSaver: A new AsyncShallowPostgresSaver instance.
+        """
+        async with await AsyncConnection.connect(
+            conn_string, autocommit=True, prepare_threshold=0, row_factory=dict_row
+        ) as conn:
+            if pipeline:
+                async with conn.pipeline() as pipe:
+                    yield cls(conn=conn, pipe=pipe, serde=serde)
+            else:
+                yield cls(conn=conn, serde=serde)
+
+    async def setup(self) -> None:
+        """Set up the checkpoint database asynchronously.
+
+        This method creates the necessary tables in the Postgres database if they don't
+        already exist and runs database migrations. It MUST be called directly by the user
+        the first time checkpointer is used.
+        """
+        async with self._cursor() as cur:
+            await cur.execute(self.MIGRATIONS[0])
+            results = await cur.execute(
+                "SELECT v FROM checkpoint_migrations ORDER BY v DESC LIMIT 1"
+            )
+            row = await results.fetchone()
+            if row is None:
+                version = -1
+            else:
+                version = row["v"]
+            for v, migration in zip(
+                range(version + 1, len(self.MIGRATIONS)),
+                self.MIGRATIONS[version + 1 :],
+                strict=False,
+            ):
+                await cur.execute(migration)
+                await cur.execute(
+                    "INSERT INTO checkpoint_migrations (v) VALUES (%s)", (v,)
+                )
+        if self.pipe:
+            await self.pipe.sync()
+
+    async def alist(
+        self,
+        config: RunnableConfig | None,
+        *,
+        filter: dict[str, Any] | None = None,
+        before: RunnableConfig | None = None,
+        limit: int | None = None,
+    ) -> AsyncIterator[CheckpointTuple]:
+        """List checkpoints from the database asynchronously.
+
+        This method retrieves a list of checkpoint tuples from the Postgres database based
+        on the provided config. For ShallowPostgresSaver, this method returns a list with
+        ONLY the most recent checkpoint.
+        """
+        where, args = self._search_where(config, filter, before)
+        query = self.SELECT_SQL + where
+        params = list(args)
+        if limit is not None:
+            query += " LIMIT %s"
+            params.append(int(limit))
+        async with self._cursor() as cur:
+            await cur.execute(query, params, binary=True)
+            async for value in cur:
+                checkpoint: Checkpoint = {
+                    **value["checkpoint"],
+                    "channel_values": self._load_blobs(value["channel_values"]),
+                    "pending_sends": [
+                        self.serde.loads_typed((t.decode(), v))
+                        for t, v in value["pending_sends"]
+                    ]
+                    if value["pending_sends"]
+                    else [],
+                }
+                yield CheckpointTuple(
+                    config={
+                        "configurable": {
+                            "thread_id": value["thread_id"],
+                            "checkpoint_ns": value["checkpoint_ns"],
+                            "checkpoint_id": checkpoint["id"],
+                        }
+                    },
+                    checkpoint=checkpoint,
+                    metadata=value["metadata"],
+                    pending_writes=await asyncio.to_thread(
+                        self._load_writes, value["pending_writes"]
+                    ),
+                )
+
+    async def aget_tuple(self, config: RunnableConfig) -> CheckpointTuple | None:
+        """Get a checkpoint tuple from the database asynchronously.
+
+        This method retrieves a checkpoint tuple from the Postgres database based on the
+        provided config (matching the thread ID in the config).
+
+        Args:
+            config: The config to use for retrieving the checkpoint.
+
+        Returns:
+            The retrieved checkpoint tuple, or None if no matching checkpoint was found.
+        """
+        thread_id = config["configurable"]["thread_id"]
+        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
+        args = (thread_id, checkpoint_ns)
+        where = "WHERE thread_id = %s AND checkpoint_ns = %s"
+
+        async with self._cursor() as cur:
+            await cur.execute(
+                self.SELECT_SQL + where,
+                args,
+                binary=True,
+            )
+
+            async for value in cur:
+                checkpoint: Checkpoint = {
+                    **value["checkpoint"],
+                    "channel_values": self._load_blobs(value["channel_values"]),
+                    "pending_sends": [
+                        self.serde.loads_typed((t.decode(), v))
+                        for t, v in value["pending_sends"]
+                    ]
+                    if value["pending_sends"]
+                    else [],
+                }
+                return CheckpointTuple(
+                    config={
+                        "configurable": {
+                            "thread_id": thread_id,
+                            "checkpoint_ns": checkpoint_ns,
+                            "checkpoint_id": checkpoint["id"],
+                        }
+                    },
+                    checkpoint=checkpoint,
+                    metadata=value["metadata"],
+                    pending_writes=await asyncio.to_thread(
+                        self._load_writes, value["pending_writes"]
+                    ),
+                )
+
+    async def aput(
+        self,
+        config: RunnableConfig,
+        checkpoint: Checkpoint,
+        metadata: CheckpointMetadata,
+        new_versions: ChannelVersions,
+    ) -> RunnableConfig:
+        """Save a checkpoint to the database asynchronously.
+
+        This method saves a checkpoint to the Postgres database. The checkpoint is associated
+        with the provided config. For AsyncShallowPostgresSaver, this method saves ONLY the most recent
+        checkpoint and overwrites a previous checkpoint, if it exists.
+
+        Args:
+            config: The config to associate with the checkpoint.
+            checkpoint: The checkpoint to save.
+            metadata: Additional metadata to save with the checkpoint.
+            new_versions: New channel versions as of this write.
+
+        Returns:
+            RunnableConfig: Updated configuration after storing the checkpoint.
+        """
+        configurable = config["configurable"].copy()
+        thread_id = configurable.pop("thread_id")
+        checkpoint_ns = configurable.pop("checkpoint_ns")
+
+        copy = checkpoint.copy()
+        next_config = {
+            "configurable": {
+                "thread_id": thread_id,
+                "checkpoint_ns": checkpoint_ns,
+                "checkpoint_id": checkpoint["id"],
+            }
+        }
+
+        async with self._cursor(pipeline=True) as cur:
+            await cur.execute(
+                """DELETE FROM checkpoint_writes
+                WHERE thread_id = %s AND checkpoint_ns = %s AND checkpoint_id NOT IN (%s, %s)""",
+                (
+                    thread_id,
+                    checkpoint_ns,
+                    checkpoint["id"],
+                    configurable.get("checkpoint_id", ""),
+                ),
+            )
+            await cur.executemany(
+                self.UPSERT_CHECKPOINT_BLOBS_SQL,
+                _dump_blobs(
+                    self.serde,
+                    thread_id,
+                    checkpoint_ns,
+                    copy.pop("channel_values"),  # type: ignore[misc]
+                    new_versions,
+                ),
+            )
+            await cur.execute(
+                self.UPSERT_CHECKPOINTS_SQL,
+                (
+                    thread_id,
+                    checkpoint_ns,
+                    Jsonb(copy),
+                    Jsonb(get_serializable_checkpoint_metadata(config, metadata)),
+                ),
+            )
+        return next_config
+
+    async def aput_writes(
+        self,
+        config: RunnableConfig,
+        writes: Sequence[tuple[str, Any]],
+        task_id: str,
+        task_path: str = "",
+    ) -> None:
+        """Store intermediate writes linked to a checkpoint asynchronously.
+
+        This method saves intermediate writes associated with a checkpoint to the database.
+
+        Args:
+            config: Configuration of the related checkpoint.
+            writes: List of writes to store, each as (channel, value) pair.
+            task_id: Identifier for the task creating the writes.
+        """
+        query = (
+            self.UPSERT_CHECKPOINT_WRITES_SQL
+            if all(w[0] in WRITES_IDX_MAP for w in writes)
+            else self.INSERT_CHECKPOINT_WRITES_SQL
+        )
+        params = await asyncio.to_thread(
+            self._dump_writes,
+            config["configurable"]["thread_id"],
+            config["configurable"]["checkpoint_ns"],
+            config["configurable"]["checkpoint_id"],
+            task_id,
+            task_path,
+            writes,
+        )
+        async with self._cursor(pipeline=True) as cur:
+            await cur.executemany(query, params)
+
+    @asynccontextmanager
+    async def _cursor(
+        self, *, pipeline: bool = False
+    ) -> AsyncIterator[AsyncCursor[DictRow]]:
+        """Create a database cursor as a context manager.
+
+        Args:
+            pipeline: whether to use pipeline for the DB operations inside the context manager.
+                Will be applied regardless of whether the AsyncShallowPostgresSaver instance was initialized with a pipeline.
+                If pipeline mode is not supported, will fall back to using transaction context manager.
+        """
+        async with _ainternal.get_connection(self.conn) as conn:
+            if self.pipe:
+                # a connection in pipeline mode can be used concurrently
+                # in multiple threads/coroutines, but only one cursor can be
+                # used at a time
+                try:
+                    async with conn.cursor(binary=True, row_factory=dict_row) as cur:
+                        yield cur
+                finally:
+                    if pipeline:
+                        await self.pipe.sync()
+            elif pipeline:
+                # a connection not in pipeline mode can only be used by one
+                # thread/coroutine at a time, so we acquire a lock
+                if self.supports_pipeline:
+                    async with (
+                        self.lock,
+                        conn.pipeline(),
+                        conn.cursor(binary=True, row_factory=dict_row) as cur,
+                    ):
+                        yield cur
+                else:
+                    # Use connection's transaction context manager when pipeline mode not supported
+                    async with (
+                        self.lock,
+                        conn.transaction(),
+                        conn.cursor(binary=True, row_factory=dict_row) as cur,
+                    ):
+                        yield cur
+            else:
+                async with (
+                    self.lock,
+                    conn.cursor(binary=True, row_factory=dict_row) as cur,
+                ):
+                    yield cur
+
+    def list(
+        self,
+        config: RunnableConfig | None,
+        *,
+        filter: dict[str, Any] | None = None,
+        before: RunnableConfig | None = None,
+        limit: int | None = None,
+    ) -> Iterator[CheckpointTuple]:
+        """List checkpoints from the database.
+
+        This method retrieves a list of checkpoint tuples from the Postgres database based
+        on the provided config. For ShallowPostgresSaver, this method returns a list with
+        ONLY the most recent checkpoint.
+        """
+        aiter_ = self.alist(config, filter=filter, before=before, limit=limit)
+        while True:
+            try:
+                yield asyncio.run_coroutine_threadsafe(
+                    anext(aiter_),  # type: ignore[arg-type]  # noqa: F821
+                    self.loop,
+                ).result()
+            except StopAsyncIteration:
+                break
+
+    def get_tuple(self, config: RunnableConfig) -> CheckpointTuple | None:
+        """Get a checkpoint tuple from the database.
+
+        This method retrieves a checkpoint tuple from the Postgres database based on the
+        provided config (matching the thread ID in the config).
+
+        Args:
+            config: The config to use for retrieving the checkpoint.
+
+        Returns:
+            The retrieved checkpoint tuple, or None if no matching checkpoint was found.
+        """
+        try:
+            # check if we are in the main thread, only bg threads can block
+            # we don't check in other methods to avoid the overhead
+            if asyncio.get_running_loop() is self.loop:
+                raise asyncio.InvalidStateError(
+                    "Synchronous calls to AsyncShallowPostgresSaver are only allowed from a "
+                    "different thread. From the main thread, use the async interface."
+                    "For example, use `await checkpointer.aget_tuple(...)` or `await "
+                    "graph.ainvoke(...)`."
+                )
+        except RuntimeError:
+            pass
+        return asyncio.run_coroutine_threadsafe(
+            self.aget_tuple(config), self.loop
+        ).result()
+
+    def put(
+        self,
+        config: RunnableConfig,
+        checkpoint: Checkpoint,
+        metadata: CheckpointMetadata,
+        new_versions: ChannelVersions,
+    ) -> RunnableConfig:
+        """Save a checkpoint to the database.
+
+        This method saves a checkpoint to the Postgres database. The checkpoint is associated
+        with the provided config. For AsyncShallowPostgresSaver, this method saves ONLY the most recent
+        checkpoint and overwrites a previous checkpoint, if it exists.
+
+        Args:
+            config: The config to associate with the checkpoint.
+            checkpoint: The checkpoint to save.
+            metadata: Additional metadata to save with the checkpoint.
+            new_versions: New channel versions as of this write.
+
+        Returns:
+            RunnableConfig: Updated configuration after storing the checkpoint.
+        """
+        return asyncio.run_coroutine_threadsafe(
+            self.aput(config, checkpoint, metadata, new_versions), self.loop
+        ).result()
+
+    def put_writes(
+        self,
+        config: RunnableConfig,
+        writes: Sequence[tuple[str, Any]],
+        task_id: str,
+        task_path: str = "",
+    ) -> None:
+        """Store intermediate writes linked to a checkpoint.
+
+        This method saves intermediate writes associated with a checkpoint to the database.
+
+        Args:
+            config: Configuration of the related checkpoint.
+            writes: List of writes to store, each as (channel, value) pair.
+            task_id: Identifier for the task creating the writes.
+            task_path: Path of the task creating the writes.
+        """
+        return asyncio.run_coroutine_threadsafe(
+            self.aput_writes(config, writes, task_id, task_path), self.loop
+        ).result()

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

@@ -0,0 +1,4 @@
+from langgraph.store.postgres.aio import AsyncPostgresStore
+from langgraph.store.postgres.base import PoolConfig, PostgresStore
+
+__all__ = ["AsyncPostgresStore", "PoolConfig", "PostgresStore"]

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

@@ -0,0 +1,592 @@
+from __future__ import annotations
+
+import asyncio
+import logging
+from collections.abc import AsyncIterator, Callable, Iterable, Sequence
+from contextlib import asynccontextmanager
+from types import TracebackType
+from typing import Any, cast
+
+import orjson
+from langgraph.store.base import (
+    GetOp,
+    ListNamespacesOp,
+    Op,
+    PutOp,
+    Result,
+    SearchOp,
+)
+from langgraph.store.base.batch import AsyncBatchedBaseStore
+from psycopg import AsyncConnection, AsyncCursor, AsyncPipeline, Capabilities
+from psycopg.rows import DictRow, dict_row
+from psycopg_pool import AsyncConnectionPool
+
+from langgraph.checkpoint.postgres import _ainternal
+from langgraph.store.postgres.base import (
+    PLACEHOLDER,
+    BasePostgresStore,
+    PoolConfig,
+    PostgresIndexConfig,
+    Row,
+    TTLConfig,
+    _decode_ns_bytes,
+    _ensure_index_config,
+    _group_ops,
+    _row_to_item,
+    _row_to_search_item,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class AsyncPostgresStore(AsyncBatchedBaseStore, BasePostgresStore[_ainternal.Conn]):
+    """Asynchronous Postgres-backed store with optional vector search using pgvector.
+
+    !!! example "Examples"
+        Basic setup and usage:
+        ```python
+        from langgraph.store.postgres import AsyncPostgresStore
+
+        conn_string = "postgresql://user:pass@localhost:5432/dbname"
+
+        async with AsyncPostgresStore.from_conn_string(conn_string) as store:
+            await store.setup()  # Run migrations. Done once
+
+            # Store and retrieve data
+            await store.aput(("users", "123"), "prefs", {"theme": "dark"})
+            item = await store.aget(("users", "123"), "prefs")
+        ```
+
+        Vector search using LangChain embeddings:
+        ```python
+        from langchain.embeddings import init_embeddings
+        from langgraph.store.postgres import AsyncPostgresStore
+
+        conn_string = "postgresql://user:pass@localhost:5432/dbname"
+
+        async with AsyncPostgresStore.from_conn_string(
+            conn_string,
+            index={
+                "dims": 1536,
+                "embed": init_embeddings("openai:text-embedding-3-small"),
+                "fields": ["text"]  # specify which fields to embed. Default is the whole serialized value
+            }
+        ) as store:
+            await store.setup()  # Run migrations. Done once
+
+            # Store documents
+            await store.aput(("docs",), "doc1", {"text": "Python tutorial"})
+            await store.aput(("docs",), "doc2", {"text": "TypeScript guide"})
+            await store.aput(("docs",), "doc3", {"text": "Other guide"}, index=False)  # don't index
+
+            # Search by similarity
+            results = await store.asearch(("docs",), query="programming guides", limit=2)
+        ```
+
+        Using connection pooling for better performance:
+        ```python
+        from langgraph.store.postgres import AsyncPostgresStore, PoolConfig
+
+        conn_string = "postgresql://user:pass@localhost:5432/dbname"
+
+        async with AsyncPostgresStore.from_conn_string(
+            conn_string,
+            pool_config=PoolConfig(
+                min_size=5,
+                max_size=20
+            )
+        ) as store:
+            await store.setup()  # Run migrations. Done once
+            # Use store with connection pooling...
+        ```
+
+    Warning:
+        Make sure to:
+        1. Call `setup()` before first use to create necessary tables and indexes
+        2. Have the pgvector extension available to use vector search
+        3. Use Python 3.10+ for async functionality
+
+    Note:
+        Semantic search is disabled by default. You can enable it by providing an `index` configuration
+        when creating the store. Without this configuration, all `index` arguments passed to
+        `put` or `aput` will have no effect.
+
+    Note:
+        If you provide a TTL configuration, you must explicitly call `start_ttl_sweeper()` to begin
+        the background task that removes expired items. Call `stop_ttl_sweeper()` to properly
+        clean up resources when you're done with the store.
+    """
+
+    __slots__ = (
+        "_deserializer",
+        "pipe",
+        "lock",
+        "supports_pipeline",
+        "index_config",
+        "embeddings",
+        "ttl_config",
+        "_ttl_sweeper_task",
+        "_ttl_stop_event",
+    )
+    supports_ttl: bool = True
+
+    def __init__(
+        self,
+        conn: _ainternal.Conn,
+        *,
+        pipe: AsyncPipeline | None = None,
+        deserializer: Callable[[bytes | orjson.Fragment], dict[str, Any]] | None = None,
+        index: PostgresIndexConfig | None = None,
+        ttl: TTLConfig | None = None,
+    ) -> None:
+        if isinstance(conn, AsyncConnectionPool) and pipe is not None:
+            raise ValueError(
+                "Pipeline should be used only with a single AsyncConnection, not AsyncConnectionPool."
+            )
+        super().__init__()
+        self._deserializer = deserializer
+        self.conn = conn
+        self.pipe = pipe
+        self.lock = asyncio.Lock()
+        self.loop = asyncio.get_running_loop()
+        self.supports_pipeline = Capabilities().has_pipeline()
+        self.index_config = index
+        if self.index_config:
+            self.embeddings, self.index_config = _ensure_index_config(self.index_config)
+        else:
+            self.embeddings = None
+
+        self.ttl_config = ttl
+        self._ttl_sweeper_task: asyncio.Task[None] | None = None
+        self._ttl_stop_event = asyncio.Event()
+
+    async def abatch(self, ops: Iterable[Op]) -> list[Result]:
+        grouped_ops, num_ops = _group_ops(ops)
+        results: list[Result] = [None] * num_ops
+
+        if self.pipe:
+            async with self.pipe:
+                await self._execute_batch(grouped_ops, results)
+        else:
+            await self._execute_batch(grouped_ops, results)
+
+        return results
+
+    @classmethod
+    @asynccontextmanager
+    async def from_conn_string(
+        cls,
+        conn_string: str,
+        *,
+        pipeline: bool = False,
+        pool_config: PoolConfig | None = None,
+        index: PostgresIndexConfig | None = None,
+        ttl: TTLConfig | None = None,
+    ) -> AsyncIterator[AsyncPostgresStore]:
+        """Create a new AsyncPostgresStore instance from a connection string.
+
+        Args:
+            conn_string: The Postgres connection info string.
+            pipeline: Whether to use AsyncPipeline (only for single connections)
+            pool_config: Configuration for the connection pool.
+                If provided, will create a connection pool and use it instead of a single connection.
+                This overrides the `pipeline` argument.
+            index: The embedding config.
+
+        Returns:
+            AsyncPostgresStore: A new AsyncPostgresStore instance.
+        """
+        if pool_config is not None:
+            pc = pool_config.copy()
+            async with cast(
+                AsyncConnectionPool[AsyncConnection[DictRow]],
+                AsyncConnectionPool(
+                    conn_string,
+                    min_size=pc.pop("min_size", 1),
+                    max_size=pc.pop("max_size", None),
+                    kwargs={
+                        "autocommit": True,
+                        "prepare_threshold": 0,
+                        "row_factory": dict_row,
+                        **(pc.pop("kwargs", None) or {}),
+                    },
+                    **cast(dict, pc),
+                ),
+            ) as pool:
+                yield cls(conn=pool, index=index, ttl=ttl)
+        else:
+            async with await AsyncConnection.connect(
+                conn_string, autocommit=True, prepare_threshold=0, row_factory=dict_row
+            ) as conn:
+                if pipeline:
+                    async with conn.pipeline() as pipe:
+                        yield cls(conn=conn, pipe=pipe, index=index, ttl=ttl)
+                else:
+                    yield cls(conn=conn, index=index, ttl=ttl)
+
+    async def setup(self) -> None:
+        """Set up the store database asynchronously.
+
+        This method creates the necessary tables in the Postgres database if they don't
+        already exist and runs database migrations. It MUST be called directly by the user
+        the first time the store is used.
+        """
+
+        async def _get_version(cur: AsyncCursor[DictRow], table: str) -> int:
+            await cur.execute(
+                f"""
+                CREATE TABLE IF NOT EXISTS {table} (
+                    v INTEGER PRIMARY KEY
+                )
+            """
+            )
+            await cur.execute(f"SELECT v FROM {table} ORDER BY v DESC LIMIT 1")
+            row = cast(dict, await cur.fetchone())
+            if row is None:
+                version = -1
+            else:
+                version = row["v"]
+            return version
+
+        async with self._cursor() as cur:
+            version = await _get_version(cur, table="store_migrations")
+            for v, sql in enumerate(self.MIGRATIONS[version + 1 :], start=version + 1):
+                await cur.execute(sql)
+                await cur.execute("INSERT INTO store_migrations (v) VALUES (%s)", (v,))
+
+            if self.index_config:
+                version = await _get_version(cur, table="vector_migrations")
+                for v, migration in enumerate(
+                    self.VECTOR_MIGRATIONS[version + 1 :], start=version + 1
+                ):
+                    sql = migration.sql
+                    if migration.params:
+                        params = {
+                            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(
+                        "INSERT INTO vector_migrations (v) VALUES (%s)", (v,)
+                    )
+
+    async def sweep_ttl(self) -> int:
+        """Delete expired store items based on TTL.
+
+        Returns:
+            int: The number of deleted items.
+        """
+        async with self._cursor() as cur:
+            await cur.execute(
+                """
+                DELETE FROM store
+                WHERE expires_at IS NOT NULL AND expires_at < NOW()
+                """
+            )
+            deleted_count = cur.rowcount
+            return deleted_count
+
+    async def start_ttl_sweeper(
+        self, sweep_interval_minutes: int | None = None
+    ) -> asyncio.Task[None]:
+        """Periodically delete expired store items based on TTL.
+
+        Returns:
+            Task that can be awaited or cancelled.
+        """
+        if not self.ttl_config:
+            return asyncio.create_task(asyncio.sleep(0))
+
+        if self._ttl_sweeper_task is not None and not self._ttl_sweeper_task.done():
+            return self._ttl_sweeper_task
+
+        self._ttl_stop_event.clear()
+
+        interval = float(
+            sweep_interval_minutes or self.ttl_config.get("sweep_interval_minutes") or 5
+        )
+        logger.info(f"Starting store TTL sweeper with interval {interval} minutes")
+
+        async def _sweep_loop() -> None:
+            while not self._ttl_stop_event.is_set():
+                try:
+                    try:
+                        await asyncio.wait_for(
+                            self._ttl_stop_event.wait(),
+                            timeout=interval * 60,
+                        )
+                        break
+                    except asyncio.TimeoutError:
+                        pass
+
+                    expired_items = await self.sweep_ttl()
+                    if expired_items > 0:
+                        logger.info(f"Store swept {expired_items} expired items")
+                except asyncio.CancelledError:
+                    break
+                except Exception as exc:
+                    logger.exception("Store TTL sweep iteration failed", exc_info=exc)
+
+        task = asyncio.create_task(_sweep_loop())
+        task.set_name("ttl_sweeper")
+        self._ttl_sweeper_task = task
+        return task
+
+    async def stop_ttl_sweeper(self, timeout: float | None = None) -> bool:
+        """Stop the TTL sweeper task if it's running.
+
+        Args:
+            timeout: Maximum time to wait for the task to stop, in seconds.
+                If `None`, wait indefinitely.
+
+        Returns:
+            bool: True if the task was successfully stopped or wasn't running,
+                False if the timeout was reached before the task stopped.
+        """
+        if self._ttl_sweeper_task is None or self._ttl_sweeper_task.done():
+            return True
+
+        logger.info("Stopping TTL sweeper task")
+        self._ttl_stop_event.set()
+
+        if timeout is not None:
+            try:
+                await asyncio.wait_for(self._ttl_sweeper_task, timeout=timeout)
+                success = True
+            except asyncio.TimeoutError:
+                success = False
+        else:
+            await self._ttl_sweeper_task
+            success = True
+
+        if success:
+            self._ttl_sweeper_task = None
+            logger.info("TTL sweeper task stopped")
+        else:
+            logger.warning("Timed out waiting for TTL sweeper task to stop")
+
+        return success
+
+    async def __aenter__(self) -> AsyncPostgresStore:
+        return self
+
+    async def __aexit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_val: BaseException | None,
+        exc_tb: TracebackType | None,
+    ) -> None:
+        # Ensure the TTL sweeper task is stopped when exiting the context
+        if hasattr(self, "_ttl_sweeper_task") and self._ttl_sweeper_task is not None:
+            # Set the event to signal the task to stop
+            self._ttl_stop_event.set()
+            # We don't wait for the task to complete here to avoid blocking
+            # The task will clean up itself gracefully
+
+    async def _execute_batch(
+        self,
+        grouped_ops: dict,
+        results: list[Result],
+        conn: AsyncConnection[DictRow] | None = None,
+    ) -> None:
+        # Keep `conn` for compatibility with subclasses overriding this private hook.
+        # All database I/O goes through `_cursor()`, which owns connection acquisition.
+        async with self._cursor(pipeline=True) as cur:
+            if GetOp in grouped_ops:
+                await self._batch_get_ops(
+                    cast(Sequence[tuple[int, GetOp]], grouped_ops[GetOp]),
+                    results,
+                    cur,
+                )
+
+            if SearchOp in grouped_ops:
+                await self._batch_search_ops(
+                    cast(Sequence[tuple[int, SearchOp]], grouped_ops[SearchOp]),
+                    results,
+                    cur,
+                )
+
+            if ListNamespacesOp in grouped_ops:
+                await self._batch_list_namespaces_ops(
+                    cast(
+                        Sequence[tuple[int, ListNamespacesOp]],
+                        grouped_ops[ListNamespacesOp],
+                    ),
+                    results,
+                    cur,
+                )
+
+            if PutOp in grouped_ops:
+                await self._batch_put_ops(
+                    cast(Sequence[tuple[int, PutOp]], grouped_ops[PutOp]),
+                    cur,
+                )
+
+    async def _batch_get_ops(
+        self,
+        get_ops: Sequence[tuple[int, GetOp]],
+        results: list[Result],
+        cur: AsyncCursor[DictRow],
+    ) -> None:
+        for query, params, namespace, items in self._get_batch_GET_ops_queries(get_ops):
+            await cur.execute(query, params)
+            rows = cast(list[Row], await cur.fetchall())
+            key_to_row = {row["key"]: row for row in rows}
+            for idx, key in items:
+                row = key_to_row.get(key)
+                if row:
+                    results[idx] = _row_to_item(
+                        namespace, row, loader=self._deserializer
+                    )
+                else:
+                    results[idx] = None
+
+    async def _batch_put_ops(
+        self,
+        put_ops: Sequence[tuple[int, PutOp]],
+        cur: AsyncCursor[DictRow],
+    ) -> None:
+        queries, embedding_request = self._prepare_batch_PUT_queries(put_ops)
+        if embedding_request:
+            if self.embeddings is None:
+                # Should not get here since the embedding config is required
+                # to return an embedding_request above
+                raise ValueError(
+                    "Embedding configuration is required for vector operations "
+                    f"(for semantic search). "
+                    f"Please provide an EmbeddingConfig when initializing the {self.__class__.__name__}."
+                )
+            query, txt_params = embedding_request
+            vectors = await self.embeddings.aembed_documents(
+                [param[-1] for param in txt_params]
+            )
+            queries.append(
+                (
+                    query,
+                    [
+                        p
+                        for (ns, k, pathname, _), vector in zip(
+                            txt_params, vectors, strict=False
+                        )
+                        for p in (ns, k, pathname, vector)
+                    ],
+                )
+            )
+
+        for query, params in queries:
+            await cur.execute(query, params)
+
+    async def _batch_search_ops(
+        self,
+        search_ops: Sequence[tuple[int, SearchOp]],
+        results: list[Result],
+        cur: AsyncCursor[DictRow],
+    ) -> None:
+        queries, embedding_requests = self._prepare_batch_search_queries(search_ops)
+
+        if embedding_requests and self.embeddings:
+            vectors = await self.embeddings.aembed_documents(
+                [query for _, query in embedding_requests]
+            )
+            for (idx, _), vector in zip(embedding_requests, vectors, strict=False):
+                _paramslist = queries[idx][1]
+                for i in range(len(_paramslist)):
+                    if _paramslist[i] is PLACEHOLDER:
+                        _paramslist[i] = vector
+
+        for (idx, _), (query, params) in zip(search_ops, queries, strict=False):
+            await cur.execute(query, params)
+            rows = cast(list[Row], await cur.fetchall())
+            items = [
+                _row_to_search_item(
+                    _decode_ns_bytes(row["prefix"]), row, loader=self._deserializer
+                )
+                for row in rows
+            ]
+            results[idx] = items
+
+    async def _batch_list_namespaces_ops(
+        self,
+        list_ops: Sequence[tuple[int, ListNamespacesOp]],
+        results: list[Result],
+        cur: AsyncCursor[DictRow],
+    ) -> None:
+        queries = self._get_batch_list_namespaces_queries(list_ops)
+        for (query, params), (idx, _) in zip(queries, list_ops, strict=False):
+            await cur.execute(query, params)
+            rows = cast(list[dict], await cur.fetchall())
+            namespaces = [_decode_ns_bytes(row["truncated_prefix"]) for row in rows]
+            results[idx] = namespaces
+
+    @asynccontextmanager
+    async def _cursor(
+        self, *, pipeline: bool = False
+    ) -> AsyncIterator[AsyncCursor[DictRow]]:
+        """Create a database cursor as a context manager.
+
+        Args:
+            pipeline: whether to use pipeline for the DB operations inside the context manager.
+                Will be applied regardless of whether the PostgresStore instance was initialized with a pipeline.
+                If pipeline mode is not supported, will fall back to using transaction context manager.
+        """
+        is_pooled_conn = isinstance(self.conn, AsyncConnectionPool)
+        # With AsyncConnectionPool, each _cursor() call checks out its own connection.
+        # The pool does not hand out the same connection concurrently, so a shared lock
+        # across calls is unnecessary here.
+        lock = asyncio.Lock() if is_pooled_conn else self.lock
+        async with _ainternal.get_connection(self.conn) as conn:
+            if self.pipe:
+                # a connection in pipeline mode can be used concurrently
+                # in multiple threads/coroutines, but only one cursor can be
+                # used at a time
+                try:
+                    async with conn.cursor(binary=True, row_factory=dict_row) as cur:
+                        yield cur
+                finally:
+                    if pipeline:
+                        await self.pipe.sync()
+            elif pipeline:
+                # a connection not in pipeline mode can only be used by one
+                # thread/coroutine at a time, so we acquire a lock
+                if self.supports_pipeline:
+                    async with (
+                        lock,
+                        conn.pipeline(),
+                        conn.cursor(binary=True, row_factory=dict_row) as cur,
+                    ):
+                        yield cur
+                else:
+                    async with (
+                        lock,
+                        conn.transaction(),
+                        conn.cursor(binary=True, row_factory=dict_row) as cur,
+                    ):
+                        yield cur
+            else:
+                async with (
+                    lock,
+                    conn.cursor(binary=True, row_factory=dict_row) as cur,
+                ):
+                    yield cur

libs/checkpoint-postgres/pyproject.toml

@@ -0,0 +1,86 @@
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[project]
+name = "langgraph-checkpoint-postgres"
+version = "3.1.0"
+description = "Library with a Postgres implementation of LangGraph checkpoint saver."
+authors = []
+requires-python = ">=3.10"
+readme = "README.md"
+license = "MIT"
+license-files = ['LICENSE']
+dependencies = [
+  "langgraph-checkpoint>=4.1.0,<5.0.0",
+  "orjson>=3.11.5",
+  "psycopg>=3.2.0",
+  "psycopg-pool>=3.2.0",
+]
+
+[project.urls]
+Source = "https://github.com/langchain-ai/langgraph/tree/main/libs/checkpoint-postgres"
+Twitter = "https://x.com/langchain_oss"
+Slack = "https://www.langchain.com/join-community"
+Reddit = "https://www.reddit.com/r/LangChain/"
+
+[dependency-groups]
+test = [
+  "pytest",
+  "anyio",
+  "pytest-asyncio",
+  "pytest-mock",
+  "psycopg[binary]",
+  "langgraph-checkpoint",
+  "pytest-watcher",
+]
+lint = [
+  "ruff",
+  "codespell",
+  "ty",
+]
+dev = [
+  {include-group = "test"},
+  {include-group = "lint"},
+]
+
+[tool.uv]
+default-groups = ['dev']
+
+[tool.uv.sources]
+langgraph-checkpoint = { path = "../checkpoint", editable = true }
+
+[tool.hatch.build.targets.wheel]
+include = ["langgraph"]
+
+[tool.pytest.ini_options]
+addopts = "--strict-markers --strict-config --durations=5 -vv"
+asyncio_mode = "auto"
+
+[tool.ruff]
+lint.select = [
+  "E",  # pycodestyle
+  "F",  # Pyflakes
+  "UP", # pyupgrade
+  "B",  # flake8-bugbear
+  "I",  # isort
+  "UP", # pyupgrade
+]
+lint.ignore = ["E501", "B008"]
+target-version = "py310"
+
+[tool.ty.rules]
+invalid-argument-type = "ignore"
+invalid-assignment = "ignore"
+invalid-key = "ignore"
+invalid-return-type = "ignore"
+invalid-typed-dict-field = "ignore"
+invalid-yield = "ignore"
+no-matching-overload = "ignore"
+unused-type-ignore-comment = "ignore"
+
+[tool.pytest-watcher]
+now = true
+delay = 0.1
+runner_args = ["--ff", "-x", "-v", "--tb", "short"]
+patterns = ["*.py"]

libs/checkpoint-postgres/tests/compose-postgres.yml

@@ -0,0 +1,17 @@
+services:
+  postgres-test:
+    image: pgvector/pgvector:pg${POSTGRES_VERSION:-16}
+    ports:
+      - "5441:5432"
+    environment:
+      POSTGRES_DB: postgres
+      POSTGRES_USER: postgres
+      POSTGRES_PASSWORD: postgres
+    command: ["postgres", "-c", "shared_preload_libraries=vector"]
+    healthcheck:
+      test: pg_isready -U postgres
+      start_period: 10s
+      timeout: 1s
+      retries: 5
+      interval: 60s
+      start_interval: 1s

libs/checkpoint-postgres/tests/conftest.py

@@ -0,0 +1,44 @@
+from collections.abc import AsyncIterator
+
+import pytest
+from psycopg import AsyncConnection
+from psycopg.errors import UndefinedTable
+from psycopg.rows import DictRow, dict_row
+
+from tests.embed_test_utils import CharacterEmbeddings
+
+DEFAULT_POSTGRES_URI = "postgres://postgres:postgres@localhost:5441/"
+DEFAULT_URI = "postgres://postgres:postgres@localhost:5441/postgres?sslmode=disable"
+
+
+@pytest.fixture(scope="function")
+async def conn() -> AsyncIterator[AsyncConnection[DictRow]]:
+    async with await AsyncConnection.connect(
+        DEFAULT_URI, autocommit=True, prepare_threshold=0, row_factory=dict_row
+    ) as conn:
+        yield conn
+
+
+@pytest.fixture(scope="function", autouse=True)
+async def clear_test_db(conn: AsyncConnection[DictRow]) -> None:
+    """Delete all tables before each test."""
+    try:
+        await conn.execute("DELETE FROM checkpoints")
+        await conn.execute("DELETE FROM checkpoint_blobs")
+        await conn.execute("DELETE FROM checkpoint_writes")
+        await conn.execute("DELETE FROM checkpoint_migrations")
+    except UndefinedTable:
+        pass
+    try:
+        await conn.execute("DELETE FROM store_migrations")
+        await conn.execute("DELETE FROM store")
+    except UndefinedTable:
+        pass
+
+
+@pytest.fixture
+def fake_embeddings() -> CharacterEmbeddings:
+    return CharacterEmbeddings(dims=500)
+
+
+VECTOR_TYPES = ["vector", "halfvec"]

libs/checkpoint-postgres/tests/embed_test_utils.py

@@ -0,0 +1,55 @@
+"""Embedding utilities for testing."""
+
+import math
+import random
+from collections import Counter, defaultdict
+from typing import Any
+
+from langchain_core.embeddings import Embeddings
+
+
+class CharacterEmbeddings(Embeddings):
+    """Simple character-frequency based embeddings using random projections."""
+
+    def __init__(self, dims: int = 50, seed: int = 42):
+        """Initialize with embedding dimensions and random seed."""
+        self._rng = random.Random(seed)
+        self.dims = dims
+        # Create projection vector for each character lazily
+        self._char_projections: defaultdict[str, list[float]] = defaultdict(
+            lambda: [
+                self._rng.gauss(0, 1 / math.sqrt(self.dims)) for _ in range(self.dims)
+            ]
+        )
+
+    def _embed_one(self, text: str) -> list[float]:
+        """Embed a single text."""
+        counts = Counter(text)
+        total = sum(counts.values())
+
+        if total == 0:
+            return [0.0] * self.dims
+
+        embedding = [0.0] * self.dims
+        for char, count in counts.items():
+            weight = count / total
+            char_proj = self._char_projections[char]
+            for i, proj in enumerate(char_proj):
+                embedding[i] += weight * proj
+
+        norm = math.sqrt(sum(x * x for x in embedding))
+        if norm > 0:
+            embedding = [x / norm for x in embedding]
+
+        return embedding
+
+    def embed_documents(self, texts: list[str]) -> list[list[float]]:
+        """Embed a list of documents."""
+        return [self._embed_one(text) for text in texts]
+
+    def embed_query(self, text: str) -> list[float]:
+        """Embed a query string."""
+        return self._embed_one(text)
+
+    def __eq__(self, other: Any) -> bool:
+        return isinstance(other, CharacterEmbeddings) and self.dims == other.dims

libs/checkpoint-postgres/tests/test_async.py

@@ -0,0 +1,417 @@
+# type: ignore
+
+from contextlib import asynccontextmanager
+from typing import Any
+from uuid import uuid4
+
+import pytest
+from langchain_core.runnables import RunnableConfig
+from langgraph.checkpoint.base import (
+    EXCLUDED_METADATA_KEYS,
+    Checkpoint,
+    CheckpointMetadata,
+    create_checkpoint,
+    empty_checkpoint,
+)
+from langgraph.checkpoint.serde.types import TASKS
+from psycopg import AsyncConnection
+from psycopg.rows import dict_row
+from psycopg_pool import AsyncConnectionPool
+
+from langgraph.checkpoint.postgres.aio import (
+    AsyncPostgresSaver,
+    AsyncShallowPostgresSaver,
+)
+from tests.conftest import DEFAULT_POSTGRES_URI
+
+
+def _exclude_keys(config: dict[str, Any]) -> dict[str, Any]:
+    return {k: v for k, v in config.items() if k not in EXCLUDED_METADATA_KEYS}
+
+
+@asynccontextmanager
+async def _pool_saver():
+    """Fixture for pool mode testing."""
+    database = f"test_{uuid4().hex[:16]}"
+    # create unique db
+    async with await AsyncConnection.connect(
+        DEFAULT_POSTGRES_URI, autocommit=True
+    ) as conn:
+        await conn.execute(f"CREATE DATABASE {database}")
+    try:
+        # yield checkpointer
+        async with AsyncConnectionPool(
+            DEFAULT_POSTGRES_URI + database,
+            max_size=10,
+            kwargs={"autocommit": True, "row_factory": dict_row},
+        ) as pool:
+            checkpointer = AsyncPostgresSaver(pool)
+            await checkpointer.setup()
+            yield checkpointer
+    finally:
+        # drop unique db
+        async with await AsyncConnection.connect(
+            DEFAULT_POSTGRES_URI, autocommit=True
+        ) as conn:
+            await conn.execute(f"DROP DATABASE {database}")
+
+
+@asynccontextmanager
+async def _pipe_saver():
+    """Fixture for pipeline mode testing."""
+    database = f"test_{uuid4().hex[:16]}"
+    # create unique db
+    async with await AsyncConnection.connect(
+        DEFAULT_POSTGRES_URI, autocommit=True
+    ) as conn:
+        await conn.execute(f"CREATE DATABASE {database}")
+    try:
+        async with await AsyncConnection.connect(
+            DEFAULT_POSTGRES_URI + database,
+            autocommit=True,
+            prepare_threshold=0,
+            row_factory=dict_row,
+        ) as conn:
+            checkpointer = AsyncPostgresSaver(conn)
+            await checkpointer.setup()
+            async with conn.pipeline() as pipe:
+                checkpointer = AsyncPostgresSaver(conn, pipe=pipe)
+                yield checkpointer
+    finally:
+        # drop unique db
+        async with await AsyncConnection.connect(
+            DEFAULT_POSTGRES_URI, autocommit=True
+        ) as conn:
+            await conn.execute(f"DROP DATABASE {database}")
+
+
+@asynccontextmanager
+async def _base_saver():
+    """Fixture for regular connection mode testing."""
+    database = f"test_{uuid4().hex[:16]}"
+    # create unique db
+    async with await AsyncConnection.connect(
+        DEFAULT_POSTGRES_URI, autocommit=True
+    ) as conn:
+        await conn.execute(f"CREATE DATABASE {database}")
+    try:
+        async with await AsyncConnection.connect(
+            DEFAULT_POSTGRES_URI + database,
+            autocommit=True,
+            prepare_threshold=0,
+            row_factory=dict_row,
+        ) as conn:
+            checkpointer = AsyncPostgresSaver(conn)
+            await checkpointer.setup()
+            yield checkpointer
+    finally:
+        # drop unique db
+        async with await AsyncConnection.connect(
+            DEFAULT_POSTGRES_URI, autocommit=True
+        ) as conn:
+            await conn.execute(f"DROP DATABASE {database}")
+
+
+@asynccontextmanager
+async def _shallow_saver():
+    """Fixture for shallow connection mode testing."""
+    database = f"test_{uuid4().hex[:16]}"
+    # create unique db
+    async with await AsyncConnection.connect(
+        DEFAULT_POSTGRES_URI, autocommit=True
+    ) as conn:
+        await conn.execute(f"CREATE DATABASE {database}")
+    try:
+        async with await AsyncConnection.connect(
+            DEFAULT_POSTGRES_URI + database,
+            autocommit=True,
+            prepare_threshold=0,
+            row_factory=dict_row,
+        ) as conn:
+            checkpointer = AsyncShallowPostgresSaver(conn)
+            await checkpointer.setup()
+            yield checkpointer
+    finally:
+        # drop unique db
+        async with await AsyncConnection.connect(
+            DEFAULT_POSTGRES_URI, autocommit=True
+        ) as conn:
+            await conn.execute(f"DROP DATABASE {database}")
+
+
+@asynccontextmanager
+async def _saver(name: str):
+    if name == "base":
+        async with _base_saver() as saver:
+            yield saver
+    elif name == "shallow":
+        async with _shallow_saver() as saver:
+            yield saver
+    elif name == "pool":
+        async with _pool_saver() as saver:
+            yield saver
+    elif name == "pipe":
+        async with _pipe_saver() as saver:
+            yield saver
+
+
+@pytest.fixture
+def test_data():
+    """Fixture providing test data for checkpoint tests."""
+    config_1: RunnableConfig = {
+        "configurable": {
+            "thread_id": "thread-1",
+            "checkpoint_id": "1",
+            "checkpoint_ns": "",
+        }
+    }
+    config_2: RunnableConfig = {
+        "configurable": {
+            "thread_id": "thread-2",
+            "checkpoint_id": "2",
+            "checkpoint_ns": "",
+        }
+    }
+    config_3: RunnableConfig = {
+        "configurable": {
+            "thread_id": "thread-2",
+            "checkpoint_id": "2-inner",
+            "checkpoint_ns": "inner",
+        }
+    }
+
+    chkpnt_1: Checkpoint = empty_checkpoint()
+    chkpnt_2: Checkpoint = create_checkpoint(chkpnt_1, {}, 1)
+    chkpnt_3: Checkpoint = empty_checkpoint()
+
+    metadata_1: CheckpointMetadata = {
+        "source": "input",
+        "step": 2,
+        "score": 1,
+    }
+    metadata_2: CheckpointMetadata = {
+        "source": "loop",
+        "step": 1,
+        "score": None,
+    }
+    metadata_3: CheckpointMetadata = {}
+
+    return {
+        "configs": [config_1, config_2, config_3],
+        "checkpoints": [chkpnt_1, chkpnt_2, chkpnt_3],
+        "metadata": [metadata_1, metadata_2, metadata_3],
+    }
+
+
+@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe", "shallow"])
+async def test_combined_metadata(saver_name: str, test_data) -> None:
+    async with _saver(saver_name) as saver:
+        config = {
+            "configurable": {
+                "thread_id": "thread-2",
+                "checkpoint_ns": "",
+                "__super_private_key": "super_private_value",
+            },
+            "metadata": {"run_id": "my_run_id"},
+        }
+        chkpnt: Checkpoint = create_checkpoint(empty_checkpoint(), {}, 1)
+        metadata: CheckpointMetadata = {
+            "source": "loop",
+            "step": 1,
+            "score": None,
+        }
+        await saver.aput(config, chkpnt, metadata, {})
+        checkpoint = await saver.aget_tuple(config)
+        assert checkpoint.metadata == {
+            **metadata,
+            "run_id": "my_run_id",
+        }
+
+
+@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe", "shallow"])
+async def test_asearch(saver_name: str, test_data) -> None:
+    async with _saver(saver_name) as saver:
+        configs = test_data["configs"]
+        checkpoints = test_data["checkpoints"]
+        metadata = test_data["metadata"]
+
+        await saver.aput(configs[0], checkpoints[0], metadata[0], {})
+        await saver.aput(configs[1], checkpoints[1], metadata[1], {})
+        await saver.aput(configs[2], checkpoints[2], metadata[2], {})
+
+        # call method / assertions
+        query_1 = {"source": "input"}  # search by 1 key
+        query_2 = {
+            "step": 1,
+        }  # search by multiple keys
+        query_3: dict[str, Any] = {}  # search by no keys, return all checkpoints
+        query_4 = {"source": "update", "step": 1}  # no match
+
+        search_results_1 = [c async for c in saver.alist(None, filter=query_1)]
+        assert len(search_results_1) == 1
+        assert search_results_1[0].metadata == {
+            **_exclude_keys(configs[0]["configurable"]),
+            **metadata[0],
+        }
+
+        search_results_2 = [c async for c in saver.alist(None, filter=query_2)]
+        assert len(search_results_2) == 1
+        assert search_results_2[0].metadata == {
+            **_exclude_keys(configs[1]["configurable"]),
+            **metadata[1],
+        }
+
+        search_results_3 = [c async for c in saver.alist(None, filter=query_3)]
+        assert len(search_results_3) == 3
+
+        search_results_4 = [c async for c in saver.alist(None, filter=query_4)]
+        assert len(search_results_4) == 0
+
+        # search by config (defaults to checkpoints across all namespaces)
+        search_results_5 = [
+            c async for c in saver.alist({"configurable": {"thread_id": "thread-2"}})
+        ]
+        assert len(search_results_5) == 2
+        assert {
+            search_results_5[0].config["configurable"]["checkpoint_ns"],
+            search_results_5[1].config["configurable"]["checkpoint_ns"],
+        } == {"", "inner"}
+
+
+@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe", "shallow"])
+async def test_null_chars(saver_name: str, test_data) -> None:
+    async with _saver(saver_name) as saver:
+        config = await saver.aput(
+            test_data["configs"][0],
+            test_data["checkpoints"][0],
+            {"my_key": "\x00abc"},
+            {},
+        )
+        assert (await saver.aget_tuple(config)).metadata["my_key"] == "abc"  # type: ignore
+        assert [c async for c in saver.alist(None, filter={"my_key": "abc"})][
+            0
+        ].metadata["my_key"] == "abc"
+
+
+@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe"])
+async def test_pending_sends_migration(saver_name: str) -> None:
+    async with _saver(saver_name) as saver:
+        config = {
+            "configurable": {
+                "thread_id": "thread-1",
+                "checkpoint_ns": "",
+            }
+        }
+
+        # create the first checkpoint
+        # and put some pending sends
+        checkpoint_0 = empty_checkpoint()
+        config = await saver.aput(config, checkpoint_0, {}, {})
+        await saver.aput_writes(
+            config, [(TASKS, "send-1"), (TASKS, "send-2")], task_id="task-1"
+        )
+        await saver.aput_writes(config, [(TASKS, "send-3")], task_id="task-2")
+
+        # check that fetching checkpoint_0 doesn't attach pending sends
+        # (they should be attached to the next checkpoint)
+        tuple_0 = await saver.aget_tuple(config)
+        assert tuple_0.checkpoint["channel_values"] == {}
+        assert tuple_0.checkpoint["channel_versions"] == {}
+
+        # create the second checkpoint
+        checkpoint_1 = create_checkpoint(checkpoint_0, {}, 1)
+        config = await saver.aput(config, checkpoint_1, {}, {})
+
+        # check that pending sends are attached to checkpoint_1
+        tuple_1 = await saver.aget_tuple(config)
+        assert tuple_1.checkpoint["channel_values"] == {
+            TASKS: ["send-1", "send-2", "send-3"]
+        }
+        assert TASKS in tuple_1.checkpoint["channel_versions"]
+
+        # check that list also applies the migration
+        search_results = [
+            c async for c in saver.alist({"configurable": {"thread_id": "thread-1"}})
+        ]
+        assert len(search_results) == 2
+        assert search_results[-1].checkpoint["channel_values"] == {}
+        assert search_results[-1].checkpoint["channel_versions"] == {}
+        assert search_results[0].checkpoint["channel_values"] == {
+            TASKS: ["send-1", "send-2", "send-3"]
+        }
+        assert TASKS in search_results[0].checkpoint["channel_versions"]
+
+
+@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe"])
+async def test_get_checkpoint_no_channel_values(
+    monkeypatch, saver_name: str, test_data
+) -> None:
+    """Backwards compatibility test that verifies a checkpoint with no channel_values key can be retrieved without throwing an error."""
+    async with _saver(saver_name) as saver:
+        config = {
+            "configurable": {
+                "thread_id": "thread-2",
+                "checkpoint_ns": "",
+                "__super_private_key": "super_private_value",
+            },
+            "metadata": {"run_id": "my_run_id"},
+        }
+        chkpnt: Checkpoint = create_checkpoint(empty_checkpoint(), {}, 1)
+        await saver.aput(config, chkpnt, {}, {})
+
+        load_checkpoint_tuple = saver._load_checkpoint_tuple
+
+        async def patched_load_checkpoint_tuple(value):
+            value["checkpoint"].pop("channel_values", None)
+            return await load_checkpoint_tuple(value)
+
+        monkeypatch.setattr(
+            saver, "_load_checkpoint_tuple", patched_load_checkpoint_tuple
+        )
+
+        checkpoint = await saver.aget_tuple(config)
+        assert checkpoint.checkpoint["channel_values"] == {}
+
+
+@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe"])
+async def test_delta_channel_chain_reconstruction(saver_name: str) -> None:
+    """AsyncPostgresSaver reconstructs DeltaChannel chain via point-lookup traversal."""
+    pytest.importorskip(
+        "langgraph.channels.delta", reason="langgraph core not installed"
+    )
+
+    from typing import Annotated
+
+    from langchain_core.messages import AIMessage, HumanMessage
+    from langgraph.channels.delta import DeltaChannel
+    from langgraph.graph import START, StateGraph
+    from langgraph.graph.message import _messages_delta_reducer
+    from typing_extensions import TypedDict
+
+    class State(TypedDict):
+        messages: Annotated[list, DeltaChannel(_messages_delta_reducer)]
+
+    def respond(state: State) -> dict:
+        n = len(state["messages"])
+        return {"messages": [AIMessage(content=f"reply-{n}", id=f"ai-{n}")]}
+
+    builder = StateGraph(State)
+    builder.add_node("respond", respond)
+    builder.add_edge(START, "respond")
+
+    async with _saver(saver_name) as saver:
+        graph = builder.compile(checkpointer=saver)
+        config = {"configurable": {"thread_id": "diff-channel-test-1"}}
+
+        await graph.ainvoke({"messages": [HumanMessage(content="hi", id="h1")]}, config)
+        await graph.ainvoke(
+            {"messages": [HumanMessage(content="there", id="h2")]}, config
+        )
+
+        state = await graph.aget_state(config)
+        msgs = state.values["messages"]
+        assert len(msgs) == 4, f"expected 4, got {len(msgs)}: {msgs}"
+        assert msgs[0].content == "hi"
+        assert msgs[1].content == "reply-1"
+        assert msgs[2].content == "there"
+        assert msgs[3].content == "reply-3"

libs/checkpoint-postgres/tests/test_async_store.py

@@ -0,0 +1,741 @@
+# type: ignore
+from __future__ import annotations
+
+import asyncio
+import itertools
+import uuid
+from collections.abc import AsyncIterator
+from concurrent.futures import ThreadPoolExecutor
+from contextlib import asynccontextmanager
+from typing import Any
+
+import pytest
+from langchain_core.embeddings import Embeddings
+from langgraph.store.base import (
+    GetOp,
+    Item,
+    ListNamespacesOp,
+    PutOp,
+    SearchOp,
+)
+from psycopg import AsyncConnection
+
+from langgraph.checkpoint.postgres import _ainternal
+from langgraph.store.postgres import AsyncPostgresStore
+from tests.conftest import (
+    DEFAULT_URI,
+    VECTOR_TYPES,
+    CharacterEmbeddings,
+)
+
+TTL_SECONDS = 6
+TTL_MINUTES = TTL_SECONDS / 60
+
+
+@pytest.fixture(scope="function", params=["default", "pipe", "pool"])
+async def store(request) -> AsyncIterator[AsyncPostgresStore]:
+    database = f"test_{uuid.uuid4().hex[:16]}"
+    uri_parts = DEFAULT_URI.split("/")
+    uri_base = "/".join(uri_parts[:-1])
+    query_params = ""
+    if "?" in uri_parts[-1]:
+        db_name, query_params = uri_parts[-1].split("?", 1)
+        query_params = "?" + query_params
+
+    conn_string = f"{uri_base}/{database}{query_params}"
+    admin_conn_string = DEFAULT_URI
+    ttl_config = {
+        "default_ttl": TTL_MINUTES,
+        "refresh_on_read": True,
+        "sweep_interval_minutes": TTL_MINUTES / 2,
+    }
+    async with await AsyncConnection.connect(
+        admin_conn_string, autocommit=True
+    ) as conn:
+        await conn.execute(f"CREATE DATABASE {database}")
+    try:
+        async with AsyncPostgresStore.from_conn_string(
+            conn_string, ttl=ttl_config
+        ) as store:
+            store.MIGRATIONS = [
+                (
+                    mig.replace("ttl_minutes INT;", "ttl_minutes FLOAT;")
+                    if isinstance(mig, str)
+                    else mig
+                )
+                for mig in store.MIGRATIONS
+            ]
+            await store.setup()
+            async with store._cursor() as cur:
+                # drop the migration index
+                await cur.execute("DROP TABLE IF EXISTS store_migrations")
+            await store.setup()  # Will fail if migrations aren't idempotent
+
+        if request.param == "pipe":
+            async with AsyncPostgresStore.from_conn_string(
+                conn_string, pipeline=True, ttl=ttl_config
+            ) as store:
+                await store.start_ttl_sweeper()
+                yield store
+                await store.stop_ttl_sweeper()
+        elif request.param == "pool":
+            async with AsyncPostgresStore.from_conn_string(
+                conn_string, pool_config={"min_size": 1, "max_size": 10}, ttl=ttl_config
+            ) as store:
+                await store.start_ttl_sweeper()
+                yield store
+                await store.stop_ttl_sweeper()
+        else:  # default
+            async with AsyncPostgresStore.from_conn_string(
+                conn_string, ttl=ttl_config
+            ) as store:
+                await store.start_ttl_sweeper()
+                yield store
+                await store.stop_ttl_sweeper()
+    finally:
+        async with await AsyncConnection.connect(
+            admin_conn_string, autocommit=True
+        ) as conn:
+            await conn.execute(f"DROP DATABASE {database}")
+
+
+async def test_no_running_loop(store: AsyncPostgresStore) -> None:
+    with pytest.raises(asyncio.InvalidStateError):
+        store.put(("foo", "bar"), "baz", {"val": "baz"})
+    with pytest.raises(asyncio.InvalidStateError):
+        store.get(("foo", "bar"), "baz")
+    with pytest.raises(asyncio.InvalidStateError):
+        store.delete(("foo", "bar"), "baz")
+    with pytest.raises(asyncio.InvalidStateError):
+        store.search(("foo", "bar"))
+    with pytest.raises(asyncio.InvalidStateError):
+        store.list_namespaces(prefix=("foo",))
+    with pytest.raises(asyncio.InvalidStateError):
+        store.batch([PutOp(namespace=("foo", "bar"), key="baz", value={"val": "baz"})])
+    with ThreadPoolExecutor(max_workers=1) as executor:
+        future = executor.submit(store.put, ("foo", "bar"), "baz", {"val": "baz"})
+        result = await asyncio.wrap_future(future)
+        assert result is None
+        future = executor.submit(store.get, ("foo", "bar"), "baz")
+        result = await asyncio.wrap_future(future)
+        assert result.value == {"val": "baz"}
+        result = await asyncio.wrap_future(
+            executor.submit(store.list_namespaces, prefix=("foo",))
+        )
+
+
+async def test_large_batches(request: Any, store: AsyncPostgresStore) -> None:
+    N = 100  # less important that we are performant here
+    M = 10
+
+    with ThreadPoolExecutor(max_workers=10) as executor:
+        futures = []
+        for m in range(M):
+            for i in range(N):
+                futures += [
+                    executor.submit(
+                        store.put,
+                        ("test", "foo", "bar", "baz", str(m % 2)),
+                        f"key{i}",
+                        value={"foo": "bar" + str(i)},
+                    ),
+                    executor.submit(
+                        store.get,
+                        ("test", "foo", "bar", "baz", str(m % 2)),
+                        f"key{i}",
+                    ),
+                    executor.submit(
+                        store.list_namespaces,
+                        prefix=None,
+                        max_depth=m + 1,
+                    ),
+                    executor.submit(
+                        store.search,
+                        ("test",),
+                    ),
+                    executor.submit(
+                        store.put,
+                        ("test", "foo", "bar", "baz", str(m % 2)),
+                        f"key{i}",
+                        value={"foo": "bar" + str(i)},
+                    ),
+                    executor.submit(
+                        store.put,
+                        ("test", "foo", "bar", "baz", str(m % 2)),
+                        f"key{i}",
+                        None,
+                    ),
+                ]
+
+        results = await asyncio.gather(
+            *(asyncio.wrap_future(future) for future in futures)
+        )
+    assert len(results) == M * N * 6
+
+
+async def test_large_batches_async(store: AsyncPostgresStore) -> None:
+    N = 1000
+    M = 10
+    coros = []
+    for m in range(M):
+        for i in range(N):
+            coros.append(
+                store.aput(
+                    ("test", "foo", "bar", "baz", str(m % 2)),
+                    f"key{i}",
+                    value={"foo": "bar" + str(i)},
+                )
+            )
+            coros.append(
+                store.aget(
+                    ("test", "foo", "bar", "baz", str(m % 2)),
+                    f"key{i}",
+                )
+            )
+            coros.append(
+                store.alist_namespaces(
+                    prefix=None,
+                    max_depth=m + 1,
+                )
+            )
+            coros.append(
+                store.asearch(
+                    ("test",),
+                )
+            )
+            coros.append(
+                store.aput(
+                    ("test", "foo", "bar", "baz", str(m % 2)),
+                    f"key{i}",
+                    value={"foo": "bar" + str(i)},
+                )
+            )
+            coros.append(
+                store.adelete(
+                    ("test", "foo", "bar", "baz", str(m % 2)),
+                    f"key{i}",
+                )
+            )
+
+    results = await asyncio.gather(*coros)
+    assert len(results) == M * N * 6
+
+
+async def test_abatch_order(store: AsyncPostgresStore) -> None:
+    # Setup test data
+    await store.aput(("test", "foo"), "key1", {"data": "value1"})
+    await store.aput(("test", "bar"), "key2", {"data": "value2"})
+
+    ops = [
+        GetOp(namespace=("test", "foo"), key="key1"),
+        PutOp(namespace=("test", "bar"), key="key2", value={"data": "value2"}),
+        SearchOp(
+            namespace_prefix=("test",), filter={"data": "value1"}, limit=10, offset=0
+        ),
+        ListNamespacesOp(match_conditions=None, max_depth=None, limit=10, offset=0),
+        GetOp(namespace=("test",), key="key3"),
+    ]
+
+    results = await store.abatch(ops)
+    assert len(results) == 5
+    assert isinstance(results[0], Item)
+    assert isinstance(results[0].value, dict)
+    assert results[0].value == {"data": "value1"}
+    assert results[0].key == "key1"
+    assert results[1] is None
+    assert isinstance(results[2], list)
+    assert len(results[2]) == 1
+    assert isinstance(results[3], list)
+    assert ("test", "foo") in results[3] and ("test", "bar") in results[3]
+    assert results[4] is None
+
+    ops_reordered = [
+        SearchOp(namespace_prefix=("test",), filter=None, limit=5, offset=0),
+        GetOp(namespace=("test", "bar"), key="key2"),
+        ListNamespacesOp(match_conditions=None, max_depth=None, limit=5, offset=0),
+        PutOp(namespace=("test",), key="key3", value={"data": "value3"}),
+        GetOp(namespace=("test", "foo"), key="key1"),
+    ]
+
+    results_reordered = await store.abatch(ops_reordered)
+    assert len(results_reordered) == 5
+    assert isinstance(results_reordered[0], list)
+    assert len(results_reordered[0]) == 2
+    assert isinstance(results_reordered[1], Item)
+    assert results_reordered[1].value == {"data": "value2"}
+    assert results_reordered[1].key == "key2"
+    assert isinstance(results_reordered[2], list)
+    assert ("test", "foo") in results_reordered[2] and (
+        "test",
+        "bar",
+    ) in results_reordered[2]
+    assert results_reordered[3] is None
+    assert isinstance(results_reordered[4], Item)
+    assert results_reordered[4].value == {"data": "value1"}
+    assert results_reordered[4].key == "key1"
+
+
+async def test_batch_get_ops(store: AsyncPostgresStore) -> None:
+    # Setup test data
+    await store.aput(("test",), "key1", {"data": "value1"})
+    await store.aput(("test",), "key2", {"data": "value2"})
+
+    ops = [
+        GetOp(namespace=("test",), key="key1"),
+        GetOp(namespace=("test",), key="key2"),
+        GetOp(namespace=("test",), key="key3"),
+    ]
+
+    results = await store.abatch(ops)
+
+    assert len(results) == 3
+    assert results[0] is not None
+    assert results[1] is not None
+    assert results[2] is None
+    assert results[0].key == "key1"
+    assert results[1].key == "key2"
+
+
+async def test_batch_put_ops(store: AsyncPostgresStore) -> None:
+    ops = [
+        PutOp(namespace=("test",), key="key1", value={"data": "value1"}),
+        PutOp(namespace=("test",), key="key2", value={"data": "value2"}),
+        PutOp(namespace=("test",), key="key3", value=None),
+    ]
+
+    results = await store.abatch(ops)
+
+    assert len(results) == 3
+    assert all(result is None for result in results)
+
+    # Verify the puts worked
+    items = await store.asearch(["test"], limit=10)
+    assert len(items) == 2  # key3 had None value so wasn't stored
+
+
+async def test_batch_search_ops(store: AsyncPostgresStore) -> None:
+    # Setup test data
+    await store.aput(("test", "foo"), "key1", {"data": "value1"})
+    await store.aput(("test", "bar"), "key2", {"data": "value2"})
+
+    ops = [
+        SearchOp(
+            namespace_prefix=("test",), filter={"data": "value1"}, limit=10, offset=0
+        ),
+        SearchOp(namespace_prefix=("test",), filter=None, limit=5, offset=0),
+    ]
+
+    results = await store.abatch(ops)
+
+    assert len(results) == 2
+    assert len(results[0]) == 1  # Filtered results
+    assert len(results[1]) == 2  # All results
+
+
+async def test_batch_list_namespaces_ops(store: AsyncPostgresStore) -> None:
+    # Setup test data
+    await store.aput(("test", "namespace1"), "key1", {"data": "value1"})
+    await store.aput(("test", "namespace2"), "key2", {"data": "value2"})
+
+    ops = [ListNamespacesOp(match_conditions=None, max_depth=None, limit=10, offset=0)]
+
+    results = await store.abatch(ops)
+
+    assert len(results) == 1
+    assert len(results[0]) == 2
+    assert ("test", "namespace1") in results[0]
+    assert ("test", "namespace2") in results[0]
+
+
+@asynccontextmanager
+async def _create_pool_store() -> AsyncIterator[AsyncPostgresStore]:
+    database = f"test_{uuid.uuid4().hex[:16]}"
+    uri_parts = DEFAULT_URI.split("/")
+    uri_base = "/".join(uri_parts[:-1])
+    query_params = ""
+    if "?" in uri_parts[-1]:
+        _, query_params = uri_parts[-1].split("?", 1)
+        query_params = "?" + query_params
+
+    conn_string = f"{uri_base}/{database}{query_params}"
+    admin_conn_string = DEFAULT_URI
+    async with await AsyncConnection.connect(
+        admin_conn_string, autocommit=True
+    ) as conn:
+        await conn.execute(f"CREATE DATABASE {database}")
+    try:
+        async with AsyncPostgresStore.from_conn_string(
+            conn_string, pool_config={"min_size": 1, "max_size": 1}
+        ) as store:
+            await store.setup()
+            yield store
+    finally:
+        async with await AsyncConnection.connect(
+            admin_conn_string, autocommit=True
+        ) as conn:
+            await conn.execute(f"DROP DATABASE {database}")
+
+
+async def test_abatch_uses_single_pool_checkout(monkeypatch) -> None:
+    async with _create_pool_store() as store:
+        await store.aput(("test",), "key1", {"data": "value1"})
+
+        original_get_connection = _ainternal.get_connection
+        checkout_count = 0
+
+        @asynccontextmanager
+        async def counting_get_connection(conn):
+            nonlocal checkout_count
+            checkout_count += 1
+            async with original_get_connection(conn) as checked_out_conn:
+                yield checked_out_conn
+
+        monkeypatch.setattr(_ainternal, "get_connection", counting_get_connection)
+
+        results = await store.abatch([GetOp(namespace=("test",), key="key1")])
+
+        assert len(results) == 1
+        assert results[0] is not None
+        assert results[0].value == {"data": "value1"}
+        assert checkout_count == 1
+
+
+@asynccontextmanager
+async def _create_vector_store(
+    vector_type: str,
+    distance_type: str,
+    fake_embeddings: CharacterEmbeddings,
+    text_fields: list[str] | None = None,
+) -> AsyncIterator[AsyncPostgresStore]:
+    """Create a store with vector search enabled."""
+
+    database = f"test_{uuid.uuid4().hex[:16]}"
+    uri_parts = DEFAULT_URI.split("/")
+    uri_base = "/".join(uri_parts[:-1])
+    query_params = ""
+    if "?" in uri_parts[-1]:
+        db_name, query_params = uri_parts[-1].split("?", 1)
+        query_params = "?" + query_params
+
+    conn_string = f"{uri_base}/{database}{query_params}"
+    admin_conn_string = DEFAULT_URI
+
+    index_config = {
+        "dims": fake_embeddings.dims,
+        "embed": fake_embeddings,
+        "ann_index_config": {
+            "vector_type": vector_type,
+        },
+        "distance_type": distance_type,
+        "fields": text_fields,
+    }
+
+    async with await AsyncConnection.connect(
+        admin_conn_string, autocommit=True
+    ) as conn:
+        await conn.execute(f"CREATE DATABASE {database}")
+    try:
+        async with AsyncPostgresStore.from_conn_string(
+            conn_string,
+            index=index_config,
+        ) as store:
+            await store.setup()
+            yield store
+    finally:
+        async with await AsyncConnection.connect(
+            admin_conn_string, autocommit=True
+        ) as conn:
+            await conn.execute(f"DROP DATABASE {database}")
+
+
+@pytest.fixture(
+    scope="function",
+    params=[
+        (vector_type, distance_type)
+        for vector_type in VECTOR_TYPES
+        for distance_type in (
+            ["hamming"] if vector_type == "bit" else ["l2", "inner_product", "cosine"]
+        )
+    ],
+    ids=lambda p: f"{p[0]}_{p[1]}",
+)
+async def vector_store(
+    request,
+    fake_embeddings: CharacterEmbeddings,
+) -> AsyncIterator[AsyncPostgresStore]:
+    """Create a store with vector search enabled."""
+    vector_type, distance_type = request.param
+    async with _create_vector_store(
+        vector_type, distance_type, fake_embeddings
+    ) as store:
+        yield store
+
+
+async def test_vector_store_initialization(
+    vector_store: AsyncPostgresStore, fake_embeddings: CharacterEmbeddings
+) -> None:
+    """Test store initialization with embedding config."""
+    assert vector_store.index_config is not None
+    assert vector_store.index_config["dims"] == fake_embeddings.dims
+    if isinstance(vector_store.index_config["embed"], Embeddings):
+        assert vector_store.index_config["embed"] == fake_embeddings
+
+
+async def test_vector_insert_with_auto_embedding(
+    vector_store: AsyncPostgresStore,
+) -> None:
+    """Test inserting items that get auto-embedded."""
+    docs = [
+        ("doc1", {"text": "short text"}),
+        ("doc2", {"text": "longer text document"}),
+        ("doc3", {"text": "longest text document here"}),
+        ("doc4", {"description": "text in description field"}),
+        ("doc5", {"content": "text in content field"}),
+        ("doc6", {"body": "text in body field"}),
+    ]
+
+    for key, value in docs:
+        await vector_store.aput(("test",), key, value)
+
+    results = await vector_store.asearch(("test",), query="long text")
+    assert len(results) > 0
+
+    doc_order = [r.key for r in results]
+    assert "doc2" in doc_order
+    assert "doc3" in doc_order
+
+
+async def test_vector_update_with_embedding(vector_store: AsyncPostgresStore) -> None:
+    """Test that updating items properly updates their embeddings."""
+    await vector_store.aput(("test",), "doc1", {"text": "zany zebra Xerxes"})
+    await vector_store.aput(("test",), "doc2", {"text": "something about dogs"})
+    await vector_store.aput(("test",), "doc3", {"text": "text about birds"})
+
+    results_initial = await vector_store.asearch(("test",), query="Zany Xerxes")
+    assert len(results_initial) > 0
+    assert results_initial[0].key == "doc1"
+    initial_score = results_initial[0].score
+
+    await vector_store.aput(("test",), "doc1", {"text": "new text about dogs"})
+
+    results_after = await vector_store.asearch(("test",), query="Zany Xerxes")
+    after_score = next((r.score for r in results_after if r.key == "doc1"), 0.0)
+    assert after_score < initial_score
+
+    results_new = await vector_store.asearch(("test",), query="new text about dogs")
+    for r in results_new:
+        if r.key == "doc1":
+            assert r.score > after_score
+
+    # Don't index this one
+    await vector_store.aput(
+        ("test",), "doc4", {"text": "new text about dogs"}, index=False
+    )
+    results_new = await vector_store.asearch(
+        ("test",), query="new text about dogs", limit=3
+    )
+    assert not any(r.key == "doc4" for r in results_new)
+
+
+async def test_vector_search_with_filters(vector_store: AsyncPostgresStore) -> None:
+    """Test combining vector search with filters."""
+    docs = [
+        ("doc1", {"text": "red apple", "color": "red", "score": 4.5}),
+        ("doc2", {"text": "red car", "color": "red", "score": 3.0}),
+        ("doc3", {"text": "green apple", "color": "green", "score": 4.0}),
+        ("doc4", {"text": "blue car", "color": "blue", "score": 3.5}),
+    ]
+
+    for key, value in docs:
+        await vector_store.aput(("test",), key, value)
+
+    results = await vector_store.asearch(
+        ("test",), query="apple", filter={"color": "red"}
+    )
+    assert len(results) == 2
+    assert results[0].key == "doc1"
+
+    results = await vector_store.asearch(
+        ("test",), query="car", filter={"color": "red"}
+    )
+    assert len(results) == 2
+    assert results[0].key == "doc2"
+
+    results = await vector_store.asearch(
+        ("test",), query="bbbbluuu", filter={"score": {"$gt": 3.2}}
+    )
+    assert len(results) == 3
+    assert results[0].key == "doc4"
+
+    results = await vector_store.asearch(
+        ("test",), query="apple", filter={"score": {"$gte": 4.0}, "color": "green"}
+    )
+    assert len(results) == 1
+    assert results[0].key == "doc3"
+
+
+async def test_vector_search_pagination(vector_store: AsyncPostgresStore) -> None:
+    """Test pagination with vector search."""
+    for i in range(5):
+        await vector_store.aput(
+            ("test",), f"doc{i}", {"text": f"test document number {i}"}
+        )
+
+    results_page1 = await vector_store.asearch(("test",), query="test", limit=2)
+    results_page2 = await vector_store.asearch(
+        ("test",), query="test", limit=2, offset=2
+    )
+
+    assert len(results_page1) == 2
+    assert len(results_page2) == 2
+    assert results_page1[0].key != results_page2[0].key
+
+    all_results = await vector_store.asearch(("test",), query="test", limit=10)
+    assert len(all_results) == 5
+
+
+async def test_vector_search_edge_cases(vector_store: AsyncPostgresStore) -> None:
+    """Test edge cases in vector search."""
+    await vector_store.aput(("test",), "doc1", {"text": "test document"})
+
+    perfect_match = await vector_store.asearch(("test",), query="text test document")
+    perfect_score = perfect_match[0].score
+
+    results = await vector_store.asearch(("test",), query="")
+    assert len(results) == 1
+    assert results[0].score is None
+
+    results = await vector_store.asearch(("test",), query=None)
+    assert len(results) == 1
+    assert results[0].score is None
+
+    long_query = "foo " * 100
+    results = await vector_store.asearch(("test",), query=long_query)
+    assert len(results) == 1
+    assert results[0].score < perfect_score
+
+    special_query = "test!@#$%^&*()"
+    results = await vector_store.asearch(("test",), query=special_query)
+    assert len(results) == 1
+    assert results[0].score < perfect_score
+
+
+@pytest.mark.parametrize(
+    "vector_type,distance_type",
+    [
+        *itertools.product(["vector", "halfvec"], ["cosine", "inner_product", "l2"]),
+    ],
+)
+async def test_embed_with_path(
+    request: Any,
+    fake_embeddings: CharacterEmbeddings,
+    vector_type: str,
+    distance_type: str,
+) -> None:
+    """Test vector search with specific text fields in Postgres store."""
+    async with _create_vector_store(
+        vector_type,
+        distance_type,
+        fake_embeddings,
+        text_fields=["key0", "key1", "key3"],
+    ) as store:
+        # This will have 2 vectors representing it
+        doc1 = {
+            # Omit key0 - check it doesn't raise an error
+            "key1": "xxx",
+            "key2": "yyy",
+            "key3": "zzz",
+        }
+        # This will have 3 vectors representing it
+        doc2 = {
+            "key0": "uuu",
+            "key1": "vvv",
+            "key2": "www",
+            "key3": "xxx",
+        }
+        await store.aput(("test",), "doc1", doc1)
+        await store.aput(("test",), "doc2", doc2)
+
+        # doc2.key3 and doc1.key1 both would have the highest score
+        results = await store.asearch(("test",), query="xxx")
+        assert len(results) == 2
+        assert results[0].key != results[1].key
+        ascore = results[0].score
+        bscore = results[1].score
+        assert ascore == pytest.approx(bscore, abs=1e-3)
+
+        results = await store.asearch(("test",), query="uuu")
+        assert len(results) == 2
+        assert results[0].key != results[1].key
+        assert results[0].key == "doc2"
+        assert results[0].score > results[1].score
+        assert ascore == pytest.approx(results[0].score, abs=1e-3)
+
+        # Un-indexed - will have low results for both. Not zero (because we're projecting)
+        # but less than the above.
+        results = await store.asearch(("test",), query="www")
+        assert len(results) == 2
+        assert results[0].score < ascore
+        assert results[1].score < ascore
+
+
+@pytest.mark.parametrize(
+    "vector_type,distance_type",
+    [
+        *itertools.product(["vector", "halfvec"], ["cosine", "inner_product", "l2"]),
+    ],
+)
+async def test_search_sorting(
+    request: Any,
+    fake_embeddings: CharacterEmbeddings,
+    vector_type: str,
+    distance_type: str,
+) -> None:
+    """Test operation-level field configuration for vector search."""
+    async with _create_vector_store(
+        vector_type,
+        distance_type,
+        fake_embeddings,
+        text_fields=["key1"],  # Default fields that won't match our test data
+    ) as store:
+        amatch = {
+            "key1": "mmm",
+        }
+
+        await store.aput(("test", "M"), "M", amatch)
+        N = 100
+        for i in range(N):
+            await store.aput(("test", "A"), f"A{i}", {"key1": "no"})
+        for i in range(N):
+            await store.aput(("test", "Z"), f"Z{i}", {"key1": "no"})
+
+        results = await store.asearch(("test",), query="mmm", limit=10)
+        assert len(results) == 10
+        assert len(set(r.key for r in results)) == 10
+        assert results[0].key == "M"
+        assert results[0].score > results[1].score
+
+
+async def test_store_ttl(store):
+    # Assumes a TTL of 1 minute = 60 seconds
+    ns = ("foo",)
+    await store.start_ttl_sweeper()
+    await store.aput(
+        ns,
+        key="item1",
+        value={"foo": "bar"},
+        ttl=TTL_MINUTES,  # type: ignore
+    )
+    await asyncio.sleep(TTL_SECONDS - 2)
+    res = await store.aget(ns, key="item1", refresh_ttl=True)
+    assert res is not None
+    await asyncio.sleep(TTL_SECONDS - 2)
+    results = await store.asearch(ns, query="foo", refresh_ttl=True)
+    assert len(results) == 1
+    await asyncio.sleep(TTL_SECONDS - 2)
+    res = await store.aget(ns, key="item1", refresh_ttl=False)
+    assert res is not None
+    await asyncio.sleep(TTL_SECONDS - 1)
+    # Now has been (TTL_SECONDS-2)*2 > TTL_SECONDS + TTL_SECONDS/2
+    results = await store.asearch(ns, query="bar", refresh_ttl=False)
+    assert len(results) == 0

libs/checkpoint-postgres/tests/test_store.py

@@ -0,0 +1,901 @@
+# type: ignore
+from __future__ import annotations
+
+import re
+import time
+from contextlib import contextmanager
+from typing import Any
+from uuid import uuid4
+
+import pytest
+from langchain_core.embeddings import Embeddings
+from langgraph.store.base import (
+    GetOp,
+    Item,
+    ListNamespacesOp,
+    MatchCondition,
+    PutOp,
+    SearchOp,
+)
+from psycopg import Connection
+
+from langgraph.store.postgres import PostgresStore
+from tests.conftest import (
+    DEFAULT_URI,
+    VECTOR_TYPES,
+    CharacterEmbeddings,
+)
+
+TTL_SECONDS = 6
+TTL_MINUTES = TTL_SECONDS / 60
+
+
+@pytest.fixture(scope="function", params=["default", "pipe", "pool"])
+def store(request) -> PostgresStore:
+    database = f"test_{uuid4().hex[:16]}"
+    uri_parts = DEFAULT_URI.split("/")
+    uri_base = "/".join(uri_parts[:-1])
+    query_params = ""
+    if "?" in uri_parts[-1]:
+        _, query_params = uri_parts[-1].split("?", 1)
+        query_params = "?" + query_params
+
+    conn_string = f"{uri_base}/{database}{query_params}"
+    admin_conn_string = DEFAULT_URI
+    ttl_config = {
+        "default_ttl": TTL_MINUTES,
+        "refresh_on_read": True,
+        "sweep_interval_minutes": TTL_MINUTES / 2,
+    }
+    with Connection.connect(admin_conn_string, autocommit=True) as conn:
+        conn.execute(f"CREATE DATABASE {database}")
+    try:
+        with PostgresStore.from_conn_string(conn_string, ttl=ttl_config) as store:
+            store.MIGRATIONS = [
+                (
+                    mig.replace("ttl_minutes INT;", "ttl_minutes FLOAT;")
+                    if isinstance(mig, str)
+                    else mig
+                )
+                for mig in store.MIGRATIONS
+            ]
+            store.setup()
+
+        if request.param == "pipe":
+            with PostgresStore.from_conn_string(
+                conn_string,
+                pipeline=True,
+                ttl=ttl_config,
+            ) as store:
+                store.start_ttl_sweeper()
+                yield store
+
+                store.stop_ttl_sweeper()
+        elif request.param == "pool":
+            with PostgresStore.from_conn_string(
+                conn_string,
+                pool_config={"min_size": 1, "max_size": 10},
+                ttl=ttl_config,
+            ) as store:
+                store.start_ttl_sweeper()
+                yield store
+
+                store.stop_ttl_sweeper()
+        else:  # default
+            with PostgresStore.from_conn_string(conn_string, ttl=ttl_config) as store:
+                store.start_ttl_sweeper()
+                yield store
+
+                store.stop_ttl_sweeper()
+    finally:
+        with Connection.connect(admin_conn_string, autocommit=True) as conn:
+            conn.execute(f"DROP DATABASE {database}")
+
+
+def test_batch_order(store: PostgresStore) -> None:
+    # Setup test data
+    store.put(("test", "foo"), "key1", {"data": "value1"})
+    store.put(("test", "bar"), "key2", {"data": "value2"})
+
+    ops = [
+        GetOp(namespace=("test", "foo"), key="key1"),
+        PutOp(namespace=("test", "bar"), key="key2", value={"data": "value2"}),
+        SearchOp(
+            namespace_prefix=("test",), filter={"data": "value1"}, limit=10, offset=0
+        ),
+        ListNamespacesOp(match_conditions=None, max_depth=None, limit=10, offset=0),
+        GetOp(namespace=("test",), key="key3"),
+    ]
+
+    results = store.batch(ops)
+    assert len(results) == 5
+    assert isinstance(results[0], Item)
+    assert isinstance(results[0].value, dict)
+    assert results[0].value == {"data": "value1"}
+    assert results[0].key == "key1"
+    assert results[1] is None  # Put operation returns None
+    assert isinstance(results[2], list)
+    assert len(results[2]) == 1
+    assert isinstance(results[3], list)
+    assert len(results[3]) > 0  # Should contain at least our test namespaces
+    assert results[4] is None  # Non-existent key returns None
+
+    # Test reordered operations
+    ops_reordered = [
+        SearchOp(namespace_prefix=("test",), filter=None, limit=5, offset=0),
+        GetOp(namespace=("test", "bar"), key="key2"),
+        ListNamespacesOp(match_conditions=None, max_depth=None, limit=5, offset=0),
+        PutOp(namespace=("test",), key="key3", value={"data": "value3"}),
+        GetOp(namespace=("test", "foo"), key="key1"),
+    ]
+
+    results_reordered = store.batch(ops_reordered)
+    assert len(results_reordered) == 5
+    assert isinstance(results_reordered[0], list)
+    assert len(results_reordered[0]) >= 2  # Should find at least our two test items
+    assert isinstance(results_reordered[1], Item)
+    assert results_reordered[1].value == {"data": "value2"}
+    assert results_reordered[1].key == "key2"
+    assert isinstance(results_reordered[2], list)
+    assert len(results_reordered[2]) > 0
+    assert results_reordered[3] is None  # Put operation returns None
+    assert isinstance(results_reordered[4], Item)
+    assert results_reordered[4].value == {"data": "value1"}
+    assert results_reordered[4].key == "key1"
+
+
+def test_batch_get_ops(store: PostgresStore) -> None:
+    # Setup test data
+    store.put(("test",), "key1", {"data": "value1"})
+    store.put(("test",), "key2", {"data": "value2"})
+
+    ops = [
+        GetOp(namespace=("test",), key="key1"),
+        GetOp(namespace=("test",), key="key2"),
+        GetOp(namespace=("test",), key="key3"),  # Non-existent key
+    ]
+
+    results = store.batch(ops)
+
+    assert len(results) == 3
+    assert results[0] is not None
+    assert results[1] is not None
+    assert results[2] is None
+    assert results[0].key == "key1"
+    assert results[1].key == "key2"
+
+
+def test_batch_put_ops(store: PostgresStore) -> None:
+    ops = [
+        PutOp(namespace=("test",), key="key1", value={"data": "value1"}),
+        PutOp(namespace=("test",), key="key2", value={"data": "value2"}),
+        PutOp(namespace=("test",), key="key3", value=None),  # Delete operation
+    ]
+
+    results = store.batch(ops)
+    assert len(results) == 3
+    assert all(result is None for result in results)
+
+    # Verify the puts worked
+    item1 = store.get(("test",), "key1")
+    item2 = store.get(("test",), "key2")
+    item3 = store.get(("test",), "key3")
+
+    assert item1 and item1.value == {"data": "value1"}
+    assert item2 and item2.value == {"data": "value2"}
+    assert item3 is None
+
+
+def test_batch_search_ops(store: PostgresStore) -> None:
+    # Setup test data
+    test_data = [
+        (("test", "foo"), "key1", {"data": "value1", "tag": "a"}),
+        (("test", "bar"), "key2", {"data": "value2", "tag": "a"}),
+        (("test", "baz"), "key3", {"data": "value3", "tag": "b"}),
+    ]
+    for namespace, key, value in test_data:
+        store.put(namespace, key, value)
+
+    ops = [
+        SearchOp(namespace_prefix=("test",), filter={"tag": "a"}, limit=10, offset=0),
+        SearchOp(namespace_prefix=("test",), filter=None, limit=2, offset=0),
+        SearchOp(namespace_prefix=("test", "foo"), filter=None, limit=10, offset=0),
+    ]
+
+    results = store.batch(ops)
+    assert len(results) == 3
+
+    # First search should find items with tag "a"
+    assert len(results[0]) == 2
+    assert all(item.value["tag"] == "a" for item in results[0])
+
+    # Second search should return first 2 items
+    assert len(results[1]) == 2
+
+    # Third search should only find items in test/foo namespace
+    assert len(results[2]) == 1
+    assert results[2][0].namespace == ("test", "foo")
+
+
+def test_batch_list_namespaces_ops(store: PostgresStore) -> None:
+    # Setup test data with various namespaces
+    test_data = [
+        (("test", "documents", "public"), "doc1", {"content": "public doc"}),
+        (("test", "documents", "private"), "doc2", {"content": "private doc"}),
+        (("test", "images", "public"), "img1", {"content": "public image"}),
+        (("prod", "documents", "public"), "doc3", {"content": "prod doc"}),
+    ]
+    for namespace, key, value in test_data:
+        store.put(namespace, key, value)
+
+    ops = [
+        ListNamespacesOp(match_conditions=None, max_depth=None, limit=10, offset=0),
+        ListNamespacesOp(match_conditions=None, max_depth=2, limit=10, offset=0),
+        ListNamespacesOp(
+            match_conditions=[MatchCondition("suffix", "public")],
+            max_depth=None,
+            limit=10,
+            offset=0,
+        ),
+    ]
+
+    results = store.batch(ops)
+    assert len(results) == 3
+
+    # First operation should list all namespaces
+    assert len(results[0]) == len(test_data)
+
+    # Second operation should only return namespaces up to depth 2
+    assert all(len(ns) <= 2 for ns in results[1])
+
+    # Third operation should only return namespaces ending with "public"
+    assert all(ns[-1] == "public" for ns in results[2])
+
+
+def test_basic_store_ops(store) -> None:
+    namespace = ("test", "documents")
+    item_id = "doc1"
+    item_value = {"title": "Test Document", "content": "Hello, World!"}
+
+    store.put(namespace, item_id, item_value)
+    item = store.get(namespace, item_id)
+
+    assert item
+    assert item.namespace == namespace
+    assert item.key == item_id
+    assert item.value == item_value
+
+    # Test update
+    updated_value = {"title": "Updated Document", "content": "Hello, Updated!"}
+    store.put(namespace, item_id, updated_value)
+    updated_item = store.get(namespace, item_id)
+
+    assert updated_item.value == updated_value
+    assert updated_item.updated_at > item.updated_at
+
+    # Test get from non-existent namespace
+    different_namespace = ("test", "other_documents")
+    item_in_different_namespace = store.get(different_namespace, item_id)
+    assert item_in_different_namespace is None
+
+    # Test delete
+    store.delete(namespace, item_id)
+    deleted_item = store.get(namespace, item_id)
+    assert deleted_item is None
+
+
+def test_list_namespaces(store) -> None:
+    # Create test data with various namespaces
+    test_namespaces = [
+        ("test", "documents", "public"),
+        ("test", "documents", "private"),
+        ("test", "images", "public"),
+        ("test", "images", "private"),
+        ("prod", "documents", "public"),
+        ("prod", "documents", "private"),
+    ]
+
+    # Insert test data
+    for namespace in test_namespaces:
+        store.put(namespace, "dummy", {"content": "dummy"})
+
+    # Test listing with various filters
+    all_namespaces = store.list_namespaces()
+    assert len(all_namespaces) == len(test_namespaces)
+
+    # Test prefix filtering
+    test_prefix_namespaces = store.list_namespaces(prefix=["test"])
+    assert len(test_prefix_namespaces) == 4
+    assert all(ns[0] == "test" for ns in test_prefix_namespaces)
+
+    # Test suffix filtering
+    public_namespaces = store.list_namespaces(suffix=["public"])
+    assert len(public_namespaces) == 3
+    assert all(ns[-1] == "public" for ns in public_namespaces)
+
+    # Test max depth
+    depth_2_namespaces = store.list_namespaces(max_depth=2)
+    assert all(len(ns) <= 2 for ns in depth_2_namespaces)
+
+    # Test pagination
+    paginated_namespaces = store.list_namespaces(limit=3)
+    assert len(paginated_namespaces) == 3
+
+    # Cleanup
+    for namespace in test_namespaces:
+        store.delete(namespace, "dummy")
+
+
+def test_search(store) -> None:
+    # Create test data
+    test_data = [
+        (
+            ("test", "docs"),
+            "doc1",
+            {"title": "First Doc", "author": "Alice", "tags": ["important"]},
+        ),
+        (
+            ("test", "docs"),
+            "doc2",
+            {"title": "Second Doc", "author": "Bob", "tags": ["draft"]},
+        ),
+        (
+            ("test", "images"),
+            "img1",
+            {"title": "Image 1", "author": "Alice", "tags": ["final"]},
+        ),
+    ]
+
+    for namespace, key, value in test_data:
+        store.put(namespace, key, value)
+
+    # Test basic search
+    all_items = store.search(["test"])
+    assert len(all_items) == 3
+
+    # Test namespace filtering
+    docs_items = store.search(["test", "docs"])
+    assert len(docs_items) == 2
+    assert all(item.namespace == ("test", "docs") for item in docs_items)
+
+    # Test value filtering
+    alice_items = store.search(["test"], filter={"author": "Alice"})
+    assert len(alice_items) == 2
+    assert all(item.value["author"] == "Alice" for item in alice_items)
+
+    # Test pagination
+    paginated_items = store.search(["test"], limit=2)
+    assert len(paginated_items) == 2
+
+    offset_items = store.search(["test"], offset=2)
+    assert len(offset_items) == 1
+
+    # Cleanup
+    for namespace, key, _ in test_data:
+        store.delete(namespace, key)
+
+
+@contextmanager
+def _create_vector_store(
+    vector_type: str,
+    distance_type: str,
+    fake_embeddings: Embeddings,
+    text_fields: list[str] | None = None,
+    enable_ttl: bool = True,
+) -> PostgresStore:
+    """Create a store with vector search enabled."""
+    database = f"test_{uuid4().hex[:16]}"
+    uri_parts = DEFAULT_URI.split("/")
+    uri_base = "/".join(uri_parts[:-1])
+    query_params = ""
+    if "?" in uri_parts[-1]:
+        db_name, query_params = uri_parts[-1].split("?", 1)
+        query_params = "?" + query_params
+
+    conn_string = f"{uri_base}/{database}{query_params}"
+    admin_conn_string = DEFAULT_URI
+
+    index_config = {
+        "dims": fake_embeddings.dims,
+        "embed": fake_embeddings,
+        "ann_index_config": {
+            "vector_type": vector_type,
+        },
+        "distance_type": distance_type,
+        "fields": text_fields,
+    }
+
+    with Connection.connect(admin_conn_string, autocommit=True) as conn:
+        conn.execute(f"CREATE DATABASE {database}")
+    try:
+        with PostgresStore.from_conn_string(
+            conn_string,
+            index=index_config,
+            ttl={"default_ttl": 2, "refresh_on_read": True} if enable_ttl else None,
+        ) as store:
+            store.setup()
+            with store._cursor() as cur:
+                # drop the migration index
+                cur.execute("DROP TABLE IF EXISTS store_migrations")
+            store.setup()  # Will fail if migrations aren't idempotent
+            yield store
+    finally:
+        with Connection.connect(admin_conn_string, autocommit=True) as conn:
+            conn.execute(f"DROP DATABASE {database}")
+
+
+_vector_params = [
+    (vector_type, distance_type, True)
+    for vector_type in VECTOR_TYPES
+    for distance_type in (
+        ["hamming"] if vector_type == "bit" else ["l2", "inner_product", "cosine"]
+    )
+]
+_vector_params += [(*_vector_params[-1][:2], False)]
+
+
+@pytest.fixture(
+    scope="function",
+    params=_vector_params,
+    ids=lambda p: f"{p[0]}_{p[1]}",
+)
+def vector_store(
+    request,
+    fake_embeddings: Embeddings,
+) -> PostgresStore:
+    """Create a store with vector search enabled."""
+    vector_type, distance_type, enable_ttl = request.param
+    with _create_vector_store(
+        vector_type, distance_type, fake_embeddings, enable_ttl=enable_ttl
+    ) as store:
+        yield store
+
+
+def test_vector_store_initialization(
+    vector_store: PostgresStore, fake_embeddings: CharacterEmbeddings
+) -> None:
+    """Test store initialization with embedding config."""
+    # Store should be initialized with embedding config
+    assert vector_store.index_config is not None
+    assert vector_store.index_config["dims"] == fake_embeddings.dims
+    assert vector_store.index_config["embed"] == fake_embeddings
+
+
+def test_vector_insert_with_auto_embedding(vector_store: PostgresStore) -> None:
+    """Test inserting items that get auto-embedded."""
+    docs = [
+        ("doc1", {"text": "short text"}),
+        ("doc2", {"text": "longer text document"}),
+        ("doc3", {"text": "longest text document here"}),
+        ("doc4", {"description": "text in description field"}),
+        ("doc5", {"content": "text in content field"}),
+        ("doc6", {"body": "text in body field"}),
+    ]
+
+    for key, value in docs:
+        vector_store.put(("test",), key, value)
+
+    results = vector_store.search(("test",), query="long text")
+    assert len(results) > 0
+
+    doc_order = [r.key for r in results]
+    assert "doc2" in doc_order
+    assert "doc3" in doc_order
+
+
+def test_vector_update_with_embedding(vector_store: PostgresStore) -> None:
+    """Test that updating items properly updates their embeddings."""
+    vector_store.put(("test",), "doc1", {"text": "zany zebra Xerxes"})
+    vector_store.put(("test",), "doc2", {"text": "something about dogs"})
+    vector_store.put(("test",), "doc3", {"text": "text about birds"})
+
+    results_initial = vector_store.search(("test",), query="Zany Xerxes")
+    assert len(results_initial) > 0
+    assert results_initial[0].key == "doc1"
+    initial_score = results_initial[0].score
+
+    vector_store.put(("test",), "doc1", {"text": "new text about dogs"})
+
+    results_after = vector_store.search(("test",), query="Zany Xerxes")
+    after_score = next((r.score for r in results_after if r.key == "doc1"), 0.0)
+    assert after_score < initial_score
+
+    results_new = vector_store.search(("test",), query="new text about dogs")
+    for r in results_new:
+        if r.key == "doc1":
+            assert r.score > after_score
+
+    # Don't index this one
+    vector_store.put(("test",), "doc4", {"text": "new text about dogs"}, index=False)
+    results_new = vector_store.search(("test",), query="new text about dogs", limit=3)
+    assert not any(r.key == "doc4" for r in results_new)
+
+
+@pytest.mark.parametrize("refresh_ttl", [True, False])
+def test_vector_search_with_filters(
+    vector_store: PostgresStore, refresh_ttl: bool
+) -> None:
+    """Test combining vector search with filters."""
+    # Insert test documents
+    docs = [
+        ("doc1", {"text": "red apple", "color": "red", "score": 4.5}),
+        ("doc2", {"text": "red car", "color": "red", "score": 3.0}),
+        ("doc3", {"text": "green apple", "color": "green", "score": 4.0}),
+        ("doc4", {"text": "blue car", "color": "blue", "score": 3.5}),
+    ]
+
+    for key, value in docs:
+        vector_store.put(("test",), key, value)
+
+    results = vector_store.search(
+        ("test",), query="apple", filter={"color": "red"}, refresh_ttl=refresh_ttl
+    )
+    assert len(results) == 2
+    assert results[0].key == "doc1"
+
+    results = vector_store.search(
+        ("test",), query="car", filter={"color": "red"}, refresh_ttl=refresh_ttl
+    )
+    assert len(results) == 2
+    assert results[0].key == "doc2"
+
+    results = vector_store.search(
+        ("test",),
+        query="bbbbluuu",
+        filter={"score": {"$gt": 3.2}},
+        refresh_ttl=refresh_ttl,
+    )
+    assert len(results) == 3
+    assert results[0].key == "doc4"
+
+    # Multiple filters
+    results = vector_store.search(
+        ("test",), query="apple", filter={"score": {"$gte": 4.0}, "color": "green"}
+    )
+    assert len(results) == 1
+    assert results[0].key == "doc3"
+
+
+def test_vector_search_pagination(vector_store: PostgresStore) -> None:
+    """Test pagination with vector search."""
+    # Insert multiple similar documents
+    for i in range(5):
+        vector_store.put(("test",), f"doc{i}", {"text": f"test document number {i}"})
+
+    # Test with different page sizes
+    results_page1 = vector_store.search(("test",), query="test", limit=2)
+    results_page2 = vector_store.search(("test",), query="test", limit=2, offset=2)
+
+    assert len(results_page1) == 2
+    assert len(results_page2) == 2
+    assert results_page1[0].key != results_page2[0].key
+
+    # Get all results
+    all_results = vector_store.search(("test",), query="test", limit=10)
+    assert len(all_results) == 5
+
+
+def test_vector_search_edge_cases(vector_store: PostgresStore) -> None:
+    """Test edge cases in vector search."""
+    vector_store.put(("test",), "doc1", {"text": "test document"})
+
+    results = vector_store.search(("test",), query="")
+    assert len(results) == 1
+
+    results = vector_store.search(("test",), query=None)
+    assert len(results) == 1
+
+    long_query = "test " * 100
+    results = vector_store.search(("test",), query=long_query)
+    assert len(results) == 1
+
+    special_query = "test!@#$%^&*()"
+    results = vector_store.search(("test",), query=special_query)
+    assert len(results) == 1
+
+
+@pytest.mark.parametrize(
+    "vector_type,distance_type",
+    [
+        ("vector", "cosine"),
+        ("vector", "inner_product"),
+        ("halfvec", "cosine"),
+        ("halfvec", "inner_product"),
+    ],
+)
+def test_embed_with_path_sync(
+    request: Any,
+    fake_embeddings: CharacterEmbeddings,
+    vector_type: str,
+    distance_type: str,
+) -> None:
+    """Test vector search with specific text fields in Postgres store."""
+    with _create_vector_store(
+        vector_type,
+        distance_type,
+        fake_embeddings,
+        text_fields=["key0", "key1", "key3"],
+    ) as store:
+        # This will have 2 vectors representing it
+        doc1 = {
+            # Omit key0 - check it doesn't raise an error
+            "key1": "xxx",
+            "key2": "yyy",
+            "key3": "zzz",
+        }
+        # This will have 3 vectors representing it
+        doc2 = {
+            "key0": "uuu",
+            "key1": "vvv",
+            "key2": "www",
+            "key3": "xxx",
+        }
+        store.put(("test",), "doc1", doc1)
+        store.put(("test",), "doc2", doc2)
+
+        # doc2.key3 and doc1.key1 both would have the highest score
+        results = store.search(("test",), query="xxx")
+        assert len(results) == 2
+        assert results[0].key != results[1].key
+        ascore = results[0].score
+        bscore = results[1].score
+        assert ascore == pytest.approx(bscore, abs=1e-3)
+
+        # ~Only match doc2
+        results = store.search(("test",), query="uuu")
+        assert len(results) == 2
+        assert results[0].key != results[1].key
+        assert results[0].key == "doc2"
+        assert results[0].score > results[1].score
+        assert ascore == pytest.approx(results[0].score, abs=1e-3)
+
+        # ~Only match doc1
+        results = store.search(("test",), query="zzz")
+        assert len(results) == 2
+        assert results[0].key != results[1].key
+        assert results[0].key == "doc1"
+        assert results[0].score > results[1].score
+        assert ascore == pytest.approx(results[0].score, abs=1e-3)
+
+        # Un-indexed - will have low results for both. Not zero (because we're projecting)
+        # but less than the above.
+        results = store.search(("test",), query="www")
+        assert len(results) == 2
+        assert results[0].key != results[1].key
+        assert results[0].score < ascore
+        assert results[1].score < ascore
+
+
+@pytest.mark.parametrize(
+    "vector_type,distance_type",
+    [
+        ("vector", "cosine"),
+        ("vector", "inner_product"),
+        ("halfvec", "cosine"),
+        ("halfvec", "inner_product"),
+    ],
+)
+def test_embed_with_path_operation_config(
+    request: Any,
+    fake_embeddings: CharacterEmbeddings,
+    vector_type: str,
+    distance_type: str,
+) -> None:
+    """Test operation-level field configuration for vector search."""
+
+    with _create_vector_store(
+        vector_type,
+        distance_type,
+        fake_embeddings,
+        text_fields=["key17"],  # Default fields that won't match our test data
+    ) as store:
+        doc3 = {
+            "key0": "aaa",
+            "key1": "bbb",
+            "key2": "ccc",
+            "key3": "ddd",
+        }
+        doc4 = {
+            "key0": "eee",
+            "key1": "bbb",  # Same as doc3.key1
+            "key2": "fff",
+            "key3": "ggg",
+        }
+
+        store.put(("test",), "doc3", doc3, index=["key0", "key1"])
+        store.put(("test",), "doc4", doc4, index=["key1", "key3"])
+
+        results = store.search(("test",), query="aaa")
+        assert len(results) == 2
+        assert results[0].key == "doc3"
+        assert len(set(r.key for r in results)) == 2
+        assert results[0].score > results[1].score
+
+        results = store.search(("test",), query="ggg")
+        assert len(results) == 2
+        assert results[0].key == "doc4"
+        assert results[0].score > results[1].score
+
+        results = store.search(("test",), query="bbb")
+        assert len(results) == 2
+        assert results[0].key != results[1].key
+        assert results[0].score == pytest.approx(results[1].score, abs=1e-3)
+
+        results = store.search(("test",), query="ccc")
+        assert len(results) == 2
+        assert all(
+            r.score < 0.9 for r in results
+        )  # Unindexed field should have low scores
+
+        # Test index=False behavior
+        doc5 = {
+            "key0": "hhh",
+            "key1": "iii",
+        }
+        store.put(("test",), "doc5", doc5, index=False)
+        results = store.search(("test",))
+        assert len(results) == 3
+        assert all(r.score is None for r in results), f"{results}"
+        assert any(r.key == "doc5" for r in results)
+
+        results = store.search(("test",), query="hhh")
+        # TODO: We don't currently fill in additional results if there are not enough
+        # returned during vector search.
+        # assert len(results) == 3
+        # doc5_result = next(r for r in results if r.key == "doc5")
+        # assert doc5_result.score is None
+
+
+def _cosine_similarity(X: list[float], Y: list[list[float]]) -> list[float]:
+    """
+    Compute cosine similarity between a vector X and a matrix Y.
+    Lazy import numpy for efficiency.
+    """
+
+    similarities = []
+    for y in Y:
+        dot_product = sum(a * b for a, b in zip(X, y, strict=False))
+        norm1 = sum(a * a for a in X) ** 0.5
+        norm2 = sum(a * a for a in y) ** 0.5
+        similarity = dot_product / (norm1 * norm2) if norm1 > 0 and norm2 > 0 else 0.0
+        similarities.append(similarity)
+
+    return similarities
+
+
+def _inner_product(X: list[float], Y: list[list[float]]) -> list[float]:
+    """
+    Compute inner product between a vector X and a matrix Y.
+    Lazy import numpy for efficiency.
+    """
+
+    similarities = []
+    for y in Y:
+        similarity = sum(a * b for a, b in zip(X, y, strict=False))
+        similarities.append(similarity)
+
+    return similarities
+
+
+def _neg_l2_distance(X: list[float], Y: list[list[float]]) -> list[float]:
+    """
+    Compute l2 distance between a vector X and a matrix Y.
+    Lazy import numpy for efficiency.
+    """
+
+    similarities = []
+    for y in Y:
+        similarity = sum((a - b) ** 2 for a, b in zip(X, y, strict=False)) ** 0.5
+        similarities.append(-similarity)
+
+    return similarities
+
+
+@pytest.mark.parametrize(
+    "vector_type,distance_type",
+    [
+        ("vector", "cosine"),
+        ("vector", "inner_product"),
+        ("halfvec", "l2"),
+    ],
+)
+@pytest.mark.parametrize("query", ["aaa", "bbb", "ccc", "abcd", "poisson"])
+def test_scores(
+    fake_embeddings: CharacterEmbeddings,
+    vector_type: str,
+    distance_type: str,
+    query: str,
+) -> None:
+    """Test operation-level field configuration for vector search."""
+    with _create_vector_store(
+        vector_type,
+        distance_type,
+        fake_embeddings,
+        text_fields=["key0"],
+    ) as store:
+        doc = {
+            "key0": "aaa",
+        }
+        store.put(("test",), "doc", doc, index=["key0", "key1"])
+
+        results = store.search((), query=query)
+        vec0 = fake_embeddings.embed_query(doc["key0"])
+        vec1 = fake_embeddings.embed_query(query)
+        if distance_type == "cosine":
+            similarities = _cosine_similarity(vec1, [vec0])
+        elif distance_type == "inner_product":
+            similarities = _inner_product(vec1, [vec0])
+        elif distance_type == "l2":
+            similarities = _neg_l2_distance(vec1, [vec0])
+
+        assert len(results) == 1
+        assert results[0].score == pytest.approx(similarities[0], abs=1e-3)
+
+
+def test_nonnull_migrations() -> None:
+    _leading_comment_remover = re.compile(r"^/\*.*?\*/")
+    for migration in PostgresStore.MIGRATIONS:
+        statement = _leading_comment_remover.sub("", migration).split()[0]
+        assert statement.strip()
+
+
+def test_store_ttl(store):
+    # Assumes a TTL of 1 minute = 60 seconds
+    ns = ("foo",)
+    store.put(
+        ns,
+        key="item1",
+        value={"foo": "bar"},
+        ttl=TTL_MINUTES,  # type: ignore
+    )
+    time.sleep(TTL_SECONDS - 2)
+    res = store.get(ns, key="item1", refresh_ttl=True)
+    assert res is not None
+    time.sleep(TTL_SECONDS - 2)
+    results = store.search(ns, query="foo", refresh_ttl=True)
+    assert len(results) == 1
+    time.sleep(TTL_SECONDS - 2)
+    res = store.get(ns, key="item1", refresh_ttl=False)
+    assert res is not None
+    time.sleep(TTL_SECONDS - 1)
+    # Now has been (TTL_SECONDS-2)*2 > TTL_SECONDS + TTL_SECONDS/2
+    res = store.search(ns, query="bar", refresh_ttl=False)
+    assert len(res) == 0
+
+
+@pytest.mark.parametrize(
+    "vector_type,distance_type",
+    [
+        ("vector", "cosine"),
+        ("vector", "inner_product"),
+        ("halfvec", "cosine"),
+        ("halfvec", "inner_product"),
+    ],
+)
+def test_non_ascii(
+    request: Any,
+    fake_embeddings: CharacterEmbeddings,
+    vector_type: str,
+    distance_type: str,
+) -> None:
+    """Test support for non-ascii characters"""
+    with _create_vector_store(vector_type, distance_type, fake_embeddings) as store:
+        store.put(("user_123", "memories"), "1", {"text": "这是中文"})  # Chinese
+        store.put(
+            ("user_123", "memories"), "2", {"text": "これは日本語です"}
+        )  # Japanese
+        store.put(("user_123", "memories"), "3", {"text": "이건 한국어야"})  # Korean
+        store.put(("user_123", "memories"), "4", {"text": "Это русский"})  # Russian
+        store.put(("user_123", "memories"), "5", {"text": "यह रूसी है"})  # Hindi
+
+        result1 = store.search(("user_123", "memories"), query="这是中文")
+        result2 = store.search(("user_123", "memories"), query="これは日本語です")
+        result3 = store.search(("user_123", "memories"), query="이건 한국어야")
+        result4 = store.search(("user_123", "memories"), query="Это русский")
+        result5 = store.search(("user_123", "memories"), query="यह रूसी है")
+
+        assert result1[0].key == "1"
+        assert result2[0].key == "2"
+        assert result3[0].key == "3"
+        assert result4[0].key == "4"
+        assert result5[0].key == "5"

libs/checkpoint-postgres/tests/test_sync.py

@@ -0,0 +1,360 @@
+# type: ignore
+
+import re
+from contextlib import contextmanager
+from typing import Any
+from uuid import uuid4
+
+import pytest
+from langchain_core.runnables import RunnableConfig
+from langgraph.checkpoint.base import (
+    EXCLUDED_METADATA_KEYS,
+    Checkpoint,
+    CheckpointMetadata,
+    create_checkpoint,
+    empty_checkpoint,
+)
+from langgraph.checkpoint.serde.types import TASKS
+from psycopg import Connection
+from psycopg.rows import dict_row
+from psycopg_pool import ConnectionPool
+
+from langgraph.checkpoint.postgres import PostgresSaver, ShallowPostgresSaver
+from tests.conftest import DEFAULT_POSTGRES_URI
+
+
+def _exclude_keys(config: dict[str, Any]) -> dict[str, Any]:
+    return {k: v for k, v in config.items() if k not in EXCLUDED_METADATA_KEYS}
+
+
+@contextmanager
+def _pool_saver():
+    """Fixture for pool mode testing."""
+    database = f"test_{uuid4().hex[:16]}"
+    # create unique db
+    with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+        conn.execute(f"CREATE DATABASE {database}")
+    try:
+        # yield checkpointer
+        with ConnectionPool(
+            DEFAULT_POSTGRES_URI + database,
+            max_size=10,
+            kwargs={"autocommit": True, "row_factory": dict_row},
+        ) as pool:
+            checkpointer = PostgresSaver(pool)
+            checkpointer.setup()
+            yield checkpointer
+    finally:
+        # drop unique db
+        with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+            conn.execute(f"DROP DATABASE {database}")
+
+
+@contextmanager
+def _pipe_saver():
+    """Fixture for pipeline mode testing."""
+    database = f"test_{uuid4().hex[:16]}"
+    # create unique db
+    with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+        conn.execute(f"CREATE DATABASE {database}")
+    try:
+        with Connection.connect(
+            DEFAULT_POSTGRES_URI + database,
+            autocommit=True,
+            prepare_threshold=0,
+            row_factory=dict_row,
+        ) as conn:
+            checkpointer = PostgresSaver(conn)
+            checkpointer.setup()
+            with conn.pipeline() as pipe:
+                checkpointer = PostgresSaver(conn, pipe=pipe)
+                yield checkpointer
+    finally:
+        # drop unique db
+        with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+            conn.execute(f"DROP DATABASE {database}")
+
+
+@contextmanager
+def _base_saver():
+    """Fixture for regular connection mode testing."""
+    database = f"test_{uuid4().hex[:16]}"
+    # create unique db
+    with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+        conn.execute(f"CREATE DATABASE {database}")
+    try:
+        with Connection.connect(
+            DEFAULT_POSTGRES_URI + database,
+            autocommit=True,
+            prepare_threshold=0,
+            row_factory=dict_row,
+        ) as conn:
+            checkpointer = PostgresSaver(conn)
+            checkpointer.setup()
+            yield checkpointer
+    finally:
+        # drop unique db
+        with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+            conn.execute(f"DROP DATABASE {database}")
+
+
+@contextmanager
+def _shallow_saver():
+    """Fixture for regular connection mode testing with a shallow checkpointer."""
+    database = f"test_{uuid4().hex[:16]}"
+    # create unique db
+    with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+        conn.execute(f"CREATE DATABASE {database}")
+    try:
+        with Connection.connect(
+            DEFAULT_POSTGRES_URI + database,
+            autocommit=True,
+            prepare_threshold=0,
+            row_factory=dict_row,
+        ) as conn:
+            checkpointer = ShallowPostgresSaver(conn)
+            checkpointer.setup()
+            yield checkpointer
+    finally:
+        # drop unique db
+        with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+            conn.execute(f"DROP DATABASE {database}")
+
+
+@contextmanager
+def _saver(name: str):
+    if name == "base":
+        with _base_saver() as saver:
+            yield saver
+    elif name == "shallow":
+        with _shallow_saver() as saver:
+            yield saver
+    elif name == "pool":
+        with _pool_saver() as saver:
+            yield saver
+    elif name == "pipe":
+        with _pipe_saver() as saver:
+            yield saver
+
+
+@pytest.fixture
+def test_data():
+    """Fixture providing test data for checkpoint tests."""
+    config_1: RunnableConfig = {
+        "configurable": {
+            "thread_id": "thread-1",
+            "checkpoint_id": "1",
+            "checkpoint_ns": "",
+        }
+    }
+    config_2: RunnableConfig = {
+        "configurable": {
+            "thread_id": "thread-2",
+            "checkpoint_id": "2",
+            "checkpoint_ns": "",
+        }
+    }
+    config_3: RunnableConfig = {
+        "configurable": {
+            "thread_id": "thread-2",
+            "checkpoint_id": "2-inner",
+            "checkpoint_ns": "inner",
+        }
+    }
+
+    chkpnt_1: Checkpoint = empty_checkpoint()
+    chkpnt_2: Checkpoint = create_checkpoint(chkpnt_1, {}, 1)
+    chkpnt_3: Checkpoint = empty_checkpoint()
+
+    metadata_1: CheckpointMetadata = {
+        "source": "input",
+        "step": 2,
+        "score": 1,
+    }
+    metadata_2: CheckpointMetadata = {
+        "source": "loop",
+        "step": 1,
+        "score": None,
+    }
+    metadata_3: CheckpointMetadata = {}
+
+    return {
+        "configs": [config_1, config_2, config_3],
+        "checkpoints": [chkpnt_1, chkpnt_2, chkpnt_3],
+        "metadata": [metadata_1, metadata_2, metadata_3],
+    }
+
+
+@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe", "shallow"])
+def test_combined_metadata(saver_name: str, test_data) -> None:
+    with _saver(saver_name) as saver:
+        config = {
+            "configurable": {
+                "thread_id": "thread-2",
+                "checkpoint_ns": "",
+                "__super_private_key": "super_private_value",
+            },
+            "metadata": {"run_id": "my_run_id"},
+        }
+        chkpnt: Checkpoint = create_checkpoint(empty_checkpoint(), {}, 1)
+        metadata: CheckpointMetadata = {
+            "source": "loop",
+            "step": 1,
+            "score": None,
+        }
+        saver.put(config, chkpnt, metadata, {})
+        checkpoint = saver.get_tuple(config)
+        assert checkpoint.metadata == {
+            **metadata,
+            "run_id": "my_run_id",
+        }
+
+
+@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe", "shallow"])
+def test_search(saver_name: str, test_data) -> None:
+    with _saver(saver_name) as saver:
+        configs = test_data["configs"]
+        checkpoints = test_data["checkpoints"]
+        metadata = test_data["metadata"]
+
+        saver.put(configs[0], checkpoints[0], metadata[0], {})
+        saver.put(configs[1], checkpoints[1], metadata[1], {})
+        saver.put(configs[2], checkpoints[2], metadata[2], {})
+
+        # call method / assertions
+        query_1 = {"source": "input"}  # search by 1 key
+        query_2 = {
+            "step": 1,
+        }  # search by multiple keys
+        query_3: dict[str, Any] = {}  # search by no keys, return all checkpoints
+        query_4 = {"source": "update", "step": 1}  # no match
+
+        search_results_1 = list(saver.list(None, filter=query_1))
+        assert len(search_results_1) == 1
+        assert search_results_1[0].metadata == {
+            **_exclude_keys(configs[0]["configurable"]),
+            **metadata[0],
+        }
+
+        search_results_2 = list(saver.list(None, filter=query_2))
+        assert len(search_results_2) == 1
+        assert search_results_2[0].metadata == {
+            **_exclude_keys(configs[1]["configurable"]),
+            **metadata[1],
+        }
+
+        search_results_3 = list(saver.list(None, filter=query_3))
+        assert len(search_results_3) == 3
+
+        search_results_4 = list(saver.list(None, filter=query_4))
+        assert len(search_results_4) == 0
+
+        # search by config (defaults to checkpoints across all namespaces)
+        search_results_5 = list(saver.list({"configurable": {"thread_id": "thread-2"}}))
+        assert len(search_results_5) == 2
+        assert {
+            search_results_5[0].config["configurable"]["checkpoint_ns"],
+            search_results_5[1].config["configurable"]["checkpoint_ns"],
+        } == {"", "inner"}
+
+
+@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe", "shallow"])
+def test_null_chars(saver_name: str, test_data) -> None:
+    with _saver(saver_name) as saver:
+        config = saver.put(
+            test_data["configs"][0],
+            test_data["checkpoints"][0],
+            {"my_key": "\x00abc"},
+            {},
+        )
+        assert saver.get_tuple(config).metadata["my_key"] == "abc"  # type: ignore
+        assert (
+            list(saver.list(None, filter={"my_key": "abc"}))[0].metadata["my_key"]
+            == "abc"
+        )
+
+
+def test_nonnull_migrations() -> None:
+    _leading_comment_remover = re.compile(r"^/\*.*?\*/")
+    for migration in PostgresSaver.MIGRATIONS:
+        statement = _leading_comment_remover.sub("", migration).split()[0]
+        assert statement.strip()
+
+
+@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe"])
+def test_pending_sends_migration(saver_name: str) -> None:
+    with _saver(saver_name) as saver:
+        config = {
+            "configurable": {
+                "thread_id": "thread-1",
+                "checkpoint_ns": "",
+            }
+        }
+
+        # create the first checkpoint
+        # and put some pending sends
+        checkpoint_0 = empty_checkpoint()
+        config = saver.put(config, checkpoint_0, {}, {})
+        saver.put_writes(
+            config, [(TASKS, "send-1"), (TASKS, "send-2")], task_id="task-1"
+        )
+        saver.put_writes(config, [(TASKS, "send-3")], task_id="task-2")
+
+        # check that fetching checkpoint_0 doesn't attach pending sends
+        # (they should be attached to the next checkpoint)
+        tuple_0 = saver.get_tuple(config)
+        assert tuple_0.checkpoint["channel_values"] == {}
+        assert tuple_0.checkpoint["channel_versions"] == {}
+
+        # create the second checkpoint
+        checkpoint_1 = create_checkpoint(checkpoint_0, {}, 1)
+        config = saver.put(config, checkpoint_1, {}, {})
+
+        # check that pending sends are attached to checkpoint_1
+        checkpoint_1 = saver.get_tuple(config)
+        assert checkpoint_1.checkpoint["channel_values"] == {
+            TASKS: ["send-1", "send-2", "send-3"]
+        }
+        assert TASKS in checkpoint_1.checkpoint["channel_versions"]
+
+        # check that list also applies the migration
+        search_results = [
+            c for c in saver.list({"configurable": {"thread_id": "thread-1"}})
+        ]
+        assert len(search_results) == 2
+        assert search_results[-1].checkpoint["channel_values"] == {}
+        assert search_results[-1].checkpoint["channel_versions"] == {}
+        assert search_results[0].checkpoint["channel_values"] == {
+            TASKS: ["send-1", "send-2", "send-3"]
+        }
+        assert TASKS in search_results[0].checkpoint["channel_versions"]
+
+
+@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe"])
+def test_get_checkpoint_no_channel_values(
+    monkeypatch, saver_name: str, test_data
+) -> None:
+    """Backwards compatibility test that verifies a checkpoint with no channel_values key can be retrieved without throwing an error."""
+    with _saver(saver_name) as saver:
+        config = {
+            "configurable": {
+                "thread_id": "thread-2",
+                "checkpoint_ns": "",
+                "__super_private_key": "super_private_value",
+            },
+        }
+        chkpnt: Checkpoint = create_checkpoint(empty_checkpoint(), {}, 1)
+        saver.put(config, chkpnt, {}, {})
+
+        load_checkpoint_tuple = saver._load_checkpoint_tuple
+
+        def patched_load_checkpoint_tuple(value):
+            value["checkpoint"].pop("channel_values", None)
+            return load_checkpoint_tuple(value)
+
+        monkeypatch.setattr(
+            saver, "_load_checkpoint_tuple", patched_load_checkpoint_tuple
+        )
+
+        checkpoint = saver.get_tuple(config)
+        assert checkpoint.checkpoint["channel_values"] == {}