refactor(serde): DELTA_SENTINEL uses msgpack ext type EXT_DELTA_SENTINEL=8

Aligns DELTA_SENTINEL serialization with _DeltaSnapshot: both now go
through the msgpack ext hook (EXT_DELTA_SENTINEL=8, EXT_DELTA_SNAPSHOT=7)
rather than a separate "delta" type tag in dumps_typed/loads_typed.

Removes the special-case "delta" branch from dumps_typed and loads_typed.
Updates prune() sentinel detection to deserialize and compare rather than
checking the raw type tag string.

Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>

.gitignore

@@ -100,3 +100,4 @@ dmypy.json
 .turbo
 .editorconfig
 .scratch
+.worktrees/

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

@@ -8,11 +8,13 @@ from typing import Any
 
 from langchain_core.runnables import RunnableConfig
 from langgraph.checkpoint.base import (
+    DELTA_SENTINEL,
     WRITES_IDX_MAP,
     ChannelVersions,
     Checkpoint,
     CheckpointMetadata,
     CheckpointTuple,
+    _ChannelWritesHistory,
     get_checkpoint_id,
     get_serializable_checkpoint_metadata,
 )
@@ -23,7 +25,12 @@ from psycopg.types.json import Jsonb
 from psycopg_pool import ConnectionPool
 
 from langgraph.checkpoint.postgres import _internal
-from langgraph.checkpoint.postgres.base import BasePostgresSaver
+from langgraph.checkpoint.postgres.base import (
+    SELECT_DELTA_BLOBS_SQL,
+    SELECT_DELTA_PARENTS_SQL,
+    SELECT_DELTA_WRITES_SQL,
+    BasePostgresSaver,
+)
 from langgraph.checkpoint.postgres.shallow import ShallowPostgresSaver
 
 Conn = _internal.Conn  # For backward compatibility
@@ -430,6 +437,42 @@ class PostgresSaver(BasePostgresSaver):
                 with conn.cursor(binary=True, row_factory=dict_row) as cur:
                     yield cur
 
+    def _get_channel_writes_history(
+        self, config: RunnableConfig, channel: str
+    ) -> _ChannelWritesHistory:
+        """Fast-path override of `BaseCheckpointSaver._get_channel_writes_history`.
+
+        Three indexed roundtrips (`checkpoints`, `checkpoint_writes`,
+        `checkpoint_blobs`) each filtered by `(thread_id, checkpoint_ns)` and
+        the per-table key. Plain SELECTs let the planner pick straight index
+        scans; rationale + benchmark in `notes/delta_channel_query_bench.md`.
+        """
+        thread_id = config["configurable"]["thread_id"]
+        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
+        checkpoint_id = get_checkpoint_id(config)
+        if checkpoint_id is None:
+            # Caller didn't specify a target — resolve to the latest
+            # checkpoint on the thread. `get_tuple` without `checkpoint_id`
+            # returns the newest; its config carries the resolved id.
+            target = self.get_tuple(config)
+            if target is None:
+                return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=[])
+            checkpoint_id = target.config["configurable"]["checkpoint_id"]
+        with self._cursor() as cur:
+            cur.execute(SELECT_DELTA_PARENTS_SQL, (channel, thread_id, checkpoint_ns))
+            parents_rows = cur.fetchall()
+            cur.execute(SELECT_DELTA_WRITES_SQL, (thread_id, checkpoint_ns, channel))
+            writes_rows = cur.fetchall()
+            cur.execute(SELECT_DELTA_BLOBS_SQL, (thread_id, checkpoint_ns, channel))
+            blobs_rows = cur.fetchall()
+        return self._build_delta_channel_writes_history(
+            channel=channel,
+            target_id=checkpoint_id,
+            parents_rows=parents_rows,
+            writes_rows=writes_rows,
+            blobs_rows=blobs_rows,
+        )
+
     def _load_checkpoint_tuple(self, value: DictRow) -> CheckpointTuple:
         """
         Convert a database row into a CheckpointTuple object.
@@ -442,6 +485,7 @@ class PostgresSaver(BasePostgresSaver):
             including its configuration, metadata, parent checkpoint (if any),
             and pending writes.
         """
+        channel_values = self._load_blobs(value["channel_values"])
         return CheckpointTuple(
             {
                 "configurable": {
@@ -454,7 +498,7 @@ class PostgresSaver(BasePostgresSaver):
                 **value["checkpoint"],
                 "channel_values": {
                     **(value["checkpoint"].get("channel_values") or {}),
-                    **self._load_blobs(value["channel_values"]),
+                    **channel_values,
                 },
             },
             value["metadata"],

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

@@ -8,11 +8,13 @@ from typing import Any
 
 from langchain_core.runnables import RunnableConfig
 from langgraph.checkpoint.base import (
+    DELTA_SENTINEL,
     WRITES_IDX_MAP,
     ChannelVersions,
     Checkpoint,
     CheckpointMetadata,
     CheckpointTuple,
+    _ChannelWritesHistory,
     get_checkpoint_id,
     get_serializable_checkpoint_metadata,
 )
@@ -23,7 +25,12 @@ from psycopg.types.json import Jsonb
 from psycopg_pool import AsyncConnectionPool
 
 from langgraph.checkpoint.postgres import _ainternal
-from langgraph.checkpoint.postgres.base import BasePostgresSaver
+from langgraph.checkpoint.postgres.base import (
+    SELECT_DELTA_BLOBS_SQL,
+    SELECT_DELTA_PARENTS_SQL,
+    SELECT_DELTA_WRITES_SQL,
+    BasePostgresSaver,
+)
 from langgraph.checkpoint.postgres.shallow import AsyncShallowPostgresSaver
 
 Conn = _ainternal.Conn  # For backward compatibility
@@ -391,6 +398,45 @@ class AsyncPostgresSaver(BasePostgresSaver):
                 async with conn.cursor(binary=True, row_factory=dict_row) as cur:
                     yield cur
 
+    async def _aget_channel_writes_history(
+        self, config: RunnableConfig, channel: str
+    ) -> _ChannelWritesHistory:
+        """Fast-path override of `BaseCheckpointSaver._aget_channel_writes_history`.
+
+        Three indexed roundtrips (`checkpoints`, `checkpoint_writes`,
+        `checkpoint_blobs`); rows assembled by the shared pure helper on
+        `BasePostgresSaver`. Rationale + benchmark in
+        `notes/delta_channel_query_bench.md`.
+        """
+        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 _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=[])
+            checkpoint_id = target.config["configurable"]["checkpoint_id"]
+        async with self._cursor() as cur:
+            await cur.execute(
+                SELECT_DELTA_PARENTS_SQL, (channel, thread_id, checkpoint_ns)
+            )
+            parents_rows = await cur.fetchall()
+            await cur.execute(
+                SELECT_DELTA_WRITES_SQL, (thread_id, checkpoint_ns, channel)
+            )
+            writes_rows = await cur.fetchall()
+            await cur.execute(
+                SELECT_DELTA_BLOBS_SQL, (thread_id, checkpoint_ns, channel)
+            )
+            blobs_rows = await cur.fetchall()
+        return self._build_delta_channel_writes_history(
+            channel=channel,
+            target_id=checkpoint_id,
+            parents_rows=parents_rows,
+            writes_rows=writes_rows,
+            blobs_rows=blobs_rows,
+        )
+
     async def _load_checkpoint_tuple(self, value: DictRow) -> CheckpointTuple:
         """
         Convert a database row into a CheckpointTuple object.
@@ -403,11 +449,18 @@ class AsyncPostgresSaver(BasePostgresSaver):
             including its configuration, metadata, parent checkpoint (if any),
             and pending writes.
         """
+        thread_id = value["thread_id"]
+        checkpoint_ns = value["checkpoint_ns"]
+        blob_values = value["channel_values"]
+        channel_values: dict[str, Any] = {}
+        if blob_values:
+            channel_values = self._load_blobs(blob_values)
+
         return CheckpointTuple(
             {
                 "configurable": {
-                    "thread_id": value["thread_id"],
-                    "checkpoint_ns": value["checkpoint_ns"],
+                    "thread_id": thread_id,
+                    "checkpoint_ns": checkpoint_ns,
                     "checkpoint_id": value["checkpoint_id"],
                 }
             },
@@ -415,15 +468,15 @@ class AsyncPostgresSaver(BasePostgresSaver):
                 **value["checkpoint"],
                 "channel_values": {
                     **(value["checkpoint"].get("channel_values") or {}),
-                    **self._load_blobs(value["channel_values"]),
+                    **channel_values,
                 },
             },
             value["metadata"],
             (
                 {
                     "configurable": {
-                        "thread_id": value["thread_id"],
-                        "checkpoint_ns": value["checkpoint_ns"],
+                        "thread_id": thread_id,
+                        "checkpoint_ns": checkpoint_ns,
                         "checkpoint_id": value["parent_checkpoint_id"],
                     }
                 }

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

@@ -8,12 +8,15 @@ from typing import Any, cast
 
 from langchain_core.runnables import RunnableConfig
 from langgraph.checkpoint.base import (
+    DELTA_SENTINEL,
     WRITES_IDX_MAP,
     BaseCheckpointSaver,
     ChannelVersions,
+    PendingWrite,
+    _ChannelWritesHistory,
     get_checkpoint_id,
 )
-from langgraph.checkpoint.serde.types import TASKS
+from langgraph.checkpoint.serde.types import TASKS, _DeltaSnapshot
 from psycopg.types.json import Jsonb
 
 MetadataInput = dict[str, Any] | None
@@ -152,6 +155,30 @@ INSERT_CHECKPOINT_WRITES_SQL = """
     ON CONFLICT (thread_id, checkpoint_ns, checkpoint_id, task_id, idx) DO NOTHING
 """
 
+# DeltaChannel reconstruction: three plain indexed SELECTs per channel.
+# Bench (notes/delta_channel_query_bench.md) showed the prior recursive CTE
+# carried a hidden O(ancestors x blobs_in_thread) join; plain SELECTs are
+# 3x-100x faster in the realistic depth range and the Python walk is O(n).
+SELECT_DELTA_PARENTS_SQL = """
+    SELECT checkpoint_id,
+           parent_checkpoint_id,
+           checkpoint -> 'channel_versions' ->> %s AS ver
+    FROM checkpoints
+    WHERE thread_id = %s AND checkpoint_ns = %s
+"""
+
+SELECT_DELTA_WRITES_SQL = """
+    SELECT checkpoint_id, type, blob, task_id, idx
+    FROM checkpoint_writes
+    WHERE thread_id = %s AND checkpoint_ns = %s AND channel = %s
+"""
+
+SELECT_DELTA_BLOBS_SQL = """
+    SELECT version, type, blob
+    FROM checkpoint_blobs
+    WHERE thread_id = %s AND checkpoint_ns = %s AND channel = %s
+"""
+
 
 class BasePostgresSaver(BaseCheckpointSaver[str]):
     SELECT_SQL = SELECT_SQL
@@ -185,16 +212,107 @@ class BasePostgresSaver(BaseCheckpointSaver[str]):
         )
 
     def _load_blobs(
-        self, blob_values: list[tuple[bytes, bytes, bytes]]
+        self,
+        blob_values: Any,
     ) -> 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"
+        result: dict[str, Any] = {}
+        for k, t, v in blob_values:
+            type_tag = t.decode()
+            if type_tag != "empty":
+                result[k.decode()] = self.serde.loads_typed((type_tag, v))
+        return result
+
+    def _build_delta_channel_writes_history(
+        self,
+        *,
+        channel: str,
+        target_id: str,
+        parents_rows: Sequence[Any],
+        writes_rows: Sequence[Any],
+        blobs_rows: Sequence[Any],
+    ) -> _ChannelWritesHistory:
+        """Reconstruct one delta channel's history from rows of the three SELECTs.
+
+        Pure data transform shared by sync (`PostgresSaver`) and async
+        (`AsyncPostgresSaver`); both paths run the queries themselves and
+        feed the rows here.
+
+        Walk is newest → oldest from the target's parent. A non-sentinel
+        blob in `checkpoint_blobs` (a pre-delta snapshot) terminates the
+        walk and is returned as the seed so replay starts from it.
+
+        Writes stored at `target_id` itself are pending writes for the next
+        step and are excluded — the walk begins at the target's parent.
+        """
+        parent_of: dict[str, str | None] = {}
+        ver_of: dict[str, str | None] = {}
+        for r in parents_rows:
+            cid = r["checkpoint_id"]
+            parent_of[cid] = r["parent_checkpoint_id"]
+            ver_of[cid] = r["ver"]
+
+        ancestors: list[str] = []
+        cid = parent_of.get(target_id)
+        while cid is not None:
+            ancestors.append(cid)
+            cid = parent_of.get(cid)
+        if not ancestors:
+            return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=[])
+        ancestor_set = set(ancestors)
+
+        # Group writes by ancestor cid; sort within (task_id DESC, idx DESC)
+        # to match the prior CTE ordering — newest write first per ancestor.
+        writes_by_cid: dict[str, list[tuple[str, bytes, str, int]]] = {}
+        for r in writes_rows:
+            cid = r["checkpoint_id"]
+            if cid not in ancestor_set:
+                continue
+            writes_by_cid.setdefault(cid, []).append(
+                (r["type"], r["blob"], r["task_id"], r["idx"])
+            )
+        for ws in writes_by_cid.values():
+            ws.sort(key=lambda w: (w[2], w[3]), reverse=True)
+
+        blob_by_ver: dict[str, tuple[str, bytes]] = {
+            r["version"]: (r["type"], r["blob"]) for r in blobs_rows
         }
 
+        collected: list[PendingWrite] = []  # newest first; reversed at the end
+        for cid in ancestors:
+            ver = ver_of.get(cid)
+            if ver is not None:
+                seed_blob = blob_by_ver.get(ver)
+                if seed_blob is not None and seed_blob[0] != "empty":
+                    blob_value = self.serde.loads_typed(seed_blob)
+                    if blob_value is not DELTA_SENTINEL:
+                        if isinstance(blob_value, _DeltaSnapshot):
+                            # Step-based snapshot: collect this ancestor's
+                            # pending_writes first (they encode the NEXT step's
+                            # transition, not subsumed by the snapshot blob).
+                            for (
+                                type_tag,
+                                write_blob,
+                                task_id,
+                                _idx,
+                            ) in writes_by_cid.get(cid, []):
+                                val = self.serde.loads_typed((type_tag, write_blob))
+                                collected.append((task_id, channel, val))
+                            collected.reverse()
+                            return _ChannelWritesHistory(
+                                seed=blob_value, writes=collected
+                            )
+                        # Pre-delta blob: subsumes this ancestor's writes.
+                        collected.reverse()
+                        return _ChannelWritesHistory(seed=blob_value, writes=collected)
+            for type_tag, write_blob, task_id, _idx in writes_by_cid.get(cid, []):
+                val = self.serde.loads_typed((type_tag, write_blob))
+                collected.append((task_id, channel, val))
+
+        collected.reverse()  # oldest → newest
+        return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=collected)
+
     def _dump_blobs(
         self,
         thread_id: str,

libs/checkpoint-postgres/tests/test_async.py

@@ -361,9 +361,9 @@ async def test_get_checkpoint_no_channel_values(
 
         load_checkpoint_tuple = saver._load_checkpoint_tuple
 
-        def patched_load_checkpoint_tuple(value):
+        async def patched_load_checkpoint_tuple(value):
             value["checkpoint"].pop("channel_values", None)
-            return load_checkpoint_tuple(value)
+            return await load_checkpoint_tuple(value)
 
         monkeypatch.setattr(
             saver, "_load_checkpoint_tuple", patched_load_checkpoint_tuple
@@ -371,3 +371,47 @@ async def test_get_checkpoint_no_channel_values(
 
         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 add_messages
+    from typing_extensions import TypedDict
+
+    class State(TypedDict):
+        messages: Annotated[list, DeltaChannel(add_messages)]
+
+    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/langgraph/checkpoint/base/__init__.py

@@ -1,9 +1,10 @@
 from __future__ import annotations
 
+import contextvars
 import copy
 import logging
 from collections.abc import AsyncIterator, Collection, Iterator, Mapping, Sequence
-from typing import (  # noqa: UP035
+from typing import (
     Any,
     Generic,
     Literal,
@@ -18,16 +19,30 @@ from langgraph.checkpoint.base.id import uuid6
 from langgraph.checkpoint.serde.base import SerializerProtocol, maybe_add_typed_methods
 from langgraph.checkpoint.serde.encrypted import EncryptedSerializer
 from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
+from langgraph.checkpoint.serde.types import (
+    DELTA_SENTINEL as DELTA_SENTINEL,
+)
 from langgraph.checkpoint.serde.types import (
     ERROR,
     INTERRUPT,
     RESUME,
     SCHEDULED,
     ChannelProtocol,
+    _DeltaSnapshot,
 )
 
 V = TypeVar("V", int, float, str)
 PendingWrite = tuple[str, str, Any]
+
+# Task-local guard: ContextVar is copied per asyncio Task, so concurrent
+# requests on the same event-loop thread do not share this flag. A plain
+# `threading.local()` would leak across tasks and let one in-flight
+# reconstruction silently short-circuit another.
+_DELTA_RECONSTRUCTION: contextvars.ContextVar[bool] = contextvars.ContextVar(
+    "_DELTA_RECONSTRUCTION", default=False
+)
+
+
 logger = logging.getLogger(__name__)
 
 
@@ -119,6 +134,30 @@ class CheckpointTuple(NamedTuple):
     pending_writes: list[PendingWrite] | None = None
 
 
+class _ChannelWritesHistory(NamedTuple):
+    """Result of `BaseCheckpointSaver._get_channel_writes_history`.
+
+    Storage-level view of what one channel wrote across the ancestor chain
+    of a target checkpoint:
+
+      * `seed` — the nearest ancestor's stored blob value for this channel,
+        or `DELTA_SENTINEL` if the walk reached the root without finding a
+        stored value. A non-sentinel seed typically indicates a pre-delta
+        snapshot preserved across a channel-type migration (e.g.
+        `BinaryOperatorAggregate` storage extended under `DeltaChannel`).
+      * `writes` — on-path deltas oldest→newest, one `PendingWrite` per
+        step that wrote to this channel. Writes stored at the target
+        checkpoint itself are pending for the next super-step and are
+        excluded.
+
+    Experimental: method surface may change; the NamedTuple shape is the
+    contract.
+    """
+
+    seed: Any
+    writes: list[PendingWrite]
+
+
 class BaseCheckpointSaver(Generic[V]):
     """Base class for creating a graph checkpointer.
 
@@ -457,6 +496,121 @@ class BaseCheckpointSaver(Generic[V]):
         """
         raise NotImplementedError
 
+    def _get_channel_writes_history(
+        self, config: RunnableConfig, channel: str
+    ) -> _ChannelWritesHistory:
+        """**Experimental.** Query one channel's writes along the parent chain.
+
+        Storage-level query, not channel semantics: returns `(seed, writes)`
+        reflecting what storage knows about a single channel across the
+        ancestor chain of the target checkpoint identified by `config`.
+
+        * `writes` — on-path deltas oldest→newest as `PendingWrite` tuples.
+          Writes stored at the target `checkpoint_id` itself are pending
+          for the next super-step and are excluded.
+        * `seed` — the nearest ancestor's stored blob value for this
+          channel; `DELTA_SENTINEL` if the walk reached the root without
+          finding a stored value. A non-sentinel seed typically indicates
+          a pre-delta snapshot preserved across a channel-type migration.
+
+        Walks the **parent chain** (not `list(before=...)`): for forked
+        threads, only on-path ancestors contribute.
+
+        Reference implementation walks `get_tuple` + `parent_config`,
+        inspecting each ancestor's `channel_values[channel]` for the seed
+        terminator. Savers with direct storage access (`InMemorySaver`,
+        `PostgresSaver`) override for performance; the return contract is
+        fixed here.
+
+        Underscore-prefixed because the method surface is experimental.
+        """
+        # Guard against re-entrant calls: when get_tuple() triggers
+        # reconstruction which calls get_tuple() again, the inner call
+        # short-circuits here.
+        if _DELTA_RECONSTRUCTION.get():
+            return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=[])
+
+        token = _DELTA_RECONSTRUCTION.set(True)
+        try:
+            collected: list[PendingWrite] = []  # newest first; reversed at the end
+            target_tuple = self.get_tuple(config)
+            cursor_config: RunnableConfig | None = (
+                target_tuple.parent_config if target_tuple else None
+            )
+            while cursor_config is not None:
+                tup = self.get_tuple(cursor_config)
+                if tup is None:
+                    break
+                # Pre-delta seed terminator: if the ancestor has a stored
+                # (non-sentinel) value for this channel, that snapshot
+                # subsumes any earlier writes on the chain. Stop here.
+                ancestor_value = tup.checkpoint["channel_values"].get(channel)
+                if ancestor_value is not None and ancestor_value is not DELTA_SENTINEL:
+                    if isinstance(ancestor_value, _DeltaSnapshot):
+                        # Step-based snapshot: the blob is state AT this ancestor,
+                        # but pending_writes encode the NEXT step's transition and
+                        # are NOT subsumed — collect them before terminating.
+                        if tup.pending_writes:
+                            for write in reversed(tup.pending_writes):
+                                if write[1] != channel:
+                                    continue
+                                collected.append(write)
+                    # Pre-delta blob: subsumes its own writes — stop immediately.
+                    collected.reverse()
+                    return _ChannelWritesHistory(seed=ancestor_value, writes=collected)
+                if tup.pending_writes:
+                    # Within a superstep, pending_writes are oldest→newest;
+                    # reverse to scan newest-first.
+                    for write in reversed(tup.pending_writes):
+                        if write[1] != channel:
+                            continue
+                        collected.append(write)
+                cursor_config = tup.parent_config
+            collected.reverse()
+            return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=collected)
+        finally:
+            _DELTA_RECONSTRUCTION.reset(token)
+
+    async def _aget_channel_writes_history(
+        self, config: RunnableConfig, channel: str
+    ) -> _ChannelWritesHistory:
+        """Async version of `_get_channel_writes_history`. See docstring there."""
+        if _DELTA_RECONSTRUCTION.get():
+            return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=[])
+
+        token = _DELTA_RECONSTRUCTION.set(True)
+        try:
+            collected: list[PendingWrite] = []
+            target_tuple = await self.aget_tuple(config)
+            cursor_config: RunnableConfig | None = (
+                target_tuple.parent_config if target_tuple else None
+            )
+            while cursor_config is not None:
+                tup = await self.aget_tuple(cursor_config)
+                if tup is None:
+                    break
+                # See sync variant for rationale.
+                ancestor_value = tup.checkpoint["channel_values"].get(channel)
+                if ancestor_value is not None and ancestor_value is not DELTA_SENTINEL:
+                    if isinstance(ancestor_value, _DeltaSnapshot):
+                        if tup.pending_writes:
+                            for write in reversed(tup.pending_writes):
+                                if write[1] != channel:
+                                    continue
+                                collected.append(write)
+                    collected.reverse()
+                    return _ChannelWritesHistory(seed=ancestor_value, writes=collected)
+                if tup.pending_writes:
+                    for write in reversed(tup.pending_writes):
+                        if write[1] != channel:
+                            continue
+                        collected.append(write)
+                cursor_config = tup.parent_config
+            collected.reverse()
+            return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=collected)
+        finally:
+            _DELTA_RECONSTRUCTION.reset(token)
+
     def get_next_version(self, current: V | None, channel: None) -> V:
         """Generate the next version ID for a channel.
 

libs/checkpoint/langgraph/checkpoint/memory/__init__.py

@@ -9,21 +9,25 @@ from collections import defaultdict
 from collections.abc import AsyncIterator, Iterator, Sequence
 from contextlib import AbstractAsyncContextManager, AbstractContextManager, ExitStack
 from types import TracebackType
-from typing import Any
+from typing import Any, cast
 
 from langchain_core.runnables import RunnableConfig
 
 from langgraph.checkpoint.base import (
+    DELTA_SENTINEL,
     WRITES_IDX_MAP,
     BaseCheckpointSaver,
     ChannelVersions,
     Checkpoint,
     CheckpointMetadata,
     CheckpointTuple,
+    PendingWrite,
     SerializerProtocol,
+    _ChannelWritesHistory,
     get_checkpoint_id,
     get_checkpoint_metadata,
 )
+from langgraph.checkpoint.serde.types import _DeltaSnapshot
 
 logger = logging.getLogger(__name__)
 
@@ -121,16 +125,114 @@ class InMemorySaver(
         return self.stack.__exit__(__exc_type, __exc_value, __traceback)
 
     def _load_blobs(
-        self, thread_id: str, checkpoint_ns: str, versions: ChannelVersions
+        self,
+        thread_id: str,
+        checkpoint_ns: str,
+        versions: ChannelVersions,
     ) -> dict[str, Any]:
-        channel_values: dict[str, Any] = {}
-        for k, v in versions.items():
-            kk = (thread_id, checkpoint_ns, k, v)
-            if kk in self.blobs:
-                vv = self.blobs[kk]
-                if vv[0] != "empty":
-                    channel_values[k] = self.serde.loads_typed(vv)
-        return channel_values
+        result: dict[str, Any] = {}
+        for k, ver in versions.items():
+            kk = (thread_id, checkpoint_ns, k, ver)
+            if kk not in self.blobs:
+                continue
+            vv = self.blobs[kk]
+            if vv[0] == "empty":
+                continue
+            result[k] = self.serde.loads_typed(vv)
+        return result
+
+    def _get_channel_writes_history(
+        self, config: RunnableConfig, channel: str
+    ) -> _ChannelWritesHistory:
+        thread_id = config["configurable"]["thread_id"]
+        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
+        checkpoint_id = config["configurable"].get("checkpoint_id", "")
+        ns_storage = self.storage.get(thread_id, {}).get(checkpoint_ns, {})
+        # Walk the parent chain newest→oldest. Skip the target itself —
+        # writes stored AT `checkpoint_id` are pending for the next step
+        # (pregel applies them via `apply_writes`; they aren't part of the
+        # snapshot value AT `checkpoint_id`).
+        chain: list[str] = []
+        target_entry = ns_storage.get(checkpoint_id)
+        current: str | None = target_entry[2] if target_entry is not None else None
+        while current is not None:
+            entry = ns_storage.get(current)
+            if entry is None:
+                break
+            chain.append(current)
+            _, _, parent = entry
+            current = parent
+        # Scan newest→oldest. A pre-delta blob on an ancestor terminates the
+        # walk and is bound as `seed`; without this, a thread migrated from
+        # pre-delta storage would replay ancestor writes all the way to the
+        # root AND miss any value that lived only in the old blob (e.g. from
+        # `update_state`).
+        #
+        # At each ancestor, check the blob BEFORE processing its pending
+        # writes: a pre-delta blob represents the state AT that ancestor,
+        # which already subsumes any writes stored under it. Processing
+        # those writes first would fold them into the reconstructed value
+        # twice (once via the blob, once via replay).
+        collected: list[PendingWrite] = []  # newest first
+        for cp_id in chain:  # newest → oldest
+            entry = ns_storage.get(cp_id)
+            if entry is not None:
+                ckpt = self.serde.loads_typed(entry[0])
+                ver = ckpt.get("channel_versions", {}).get(channel)
+                if ver is not None:
+                    blob_entry = self.blobs.get(
+                        (thread_id, checkpoint_ns, channel, ver)
+                    )
+                    if blob_entry is not None and blob_entry[0] != "empty":
+                        blob_value = self.serde.loads_typed(blob_entry)
+                        if blob_value is not DELTA_SENTINEL:
+                            if isinstance(blob_value, _DeltaSnapshot):
+                                # Step-based snapshot: the blob is state AT this
+                                # ancestor, but the ancestor's pending_writes
+                                # encode the NEXT step's transition and are NOT
+                                # subsumed by the snapshot — collect them first.
+                                step_writes = self.writes.get(
+                                    (thread_id, checkpoint_ns, cp_id), {}
+                                )
+                                for (_task_id, _idx), (
+                                    tid,
+                                    ch,
+                                    serialized,
+                                    _,
+                                ) in sorted(step_writes.items(), reverse=True):
+                                    if ch != channel:
+                                        continue
+                                    collected.append(
+                                        (tid, ch, self.serde.loads_typed(serialized))
+                                    )
+                                collected.reverse()
+                                return _ChannelWritesHistory(
+                                    seed=blob_value, writes=collected
+                                )
+                            # Pre-delta blob: state AT this ancestor already
+                            # subsumes its pending_writes — skip them.
+                            collected.reverse()
+                            return _ChannelWritesHistory(
+                                seed=blob_value, writes=collected
+                            )
+
+            step_writes = self.writes.get((thread_id, checkpoint_ns, cp_id), {})
+            # Within a superstep, sorted by (task_id, idx) = oldest → newest;
+            # reverse for newest-first scan.
+            for (_task_id, _idx), (tid, ch, serialized, _) in sorted(
+                step_writes.items(), reverse=True
+            ):
+                if ch != channel:
+                    continue
+                val = self.serde.loads_typed(serialized)
+                collected.append((tid, ch, val))
+        collected.reverse()
+        return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=collected)
+
+    async def _aget_channel_writes_history(
+        self, config: RunnableConfig, channel: str
+    ) -> _ChannelWritesHistory:
+        return self._get_channel_writes_history(config, channel)
 
     def get_tuple(self, config: RunnableConfig) -> CheckpointTuple | None:
         """Get a checkpoint tuple from the in-memory storage.
@@ -153,13 +255,16 @@ class InMemorySaver(
                 checkpoint, metadata, parent_checkpoint_id = saved
                 writes = self.writes[(thread_id, checkpoint_ns, checkpoint_id)].values()
                 checkpoint_: Checkpoint = self.serde.loads_typed(checkpoint)
+                channel_values = self._load_blobs(
+                    thread_id,
+                    checkpoint_ns,
+                    checkpoint_["channel_versions"],
+                )
                 return CheckpointTuple(
                     config=config,
                     checkpoint={
                         **checkpoint_,
-                        "channel_values": self._load_blobs(
-                            thread_id, checkpoint_ns, checkpoint_["channel_versions"]
-                        ),
+                        "channel_values": channel_values,
                     },
                     metadata=self.serde.loads_typed(metadata),
                     pending_writes=[
@@ -183,19 +288,26 @@ class InMemorySaver(
                 checkpoint, metadata, parent_checkpoint_id = checkpoints[checkpoint_id]
                 writes = self.writes[(thread_id, checkpoint_ns, checkpoint_id)].values()
                 checkpoint_ = self.serde.loads_typed(checkpoint)
-                return CheckpointTuple(
-                    config={
+                resolved_config = cast(
+                    RunnableConfig,
+                    {
                         "configurable": {
                             "thread_id": thread_id,
                             "checkpoint_ns": checkpoint_ns,
                             "checkpoint_id": checkpoint_id,
                         }
                     },
+                )
+                channel_values = self._load_blobs(
+                    thread_id,
+                    checkpoint_ns,
+                    checkpoint_["channel_versions"],
+                )
+                return CheckpointTuple(
+                    config=resolved_config,
                     checkpoint={
                         **checkpoint_,
-                        "channel_values": self._load_blobs(
-                            thread_id, checkpoint_ns, checkpoint_["channel_versions"]
-                        ),
+                        "channel_values": channel_values,
                     },
                     metadata=self.serde.loads_typed(metadata),
                     pending_writes=[
@@ -290,21 +402,27 @@ class InMemorySaver(
 
                     checkpoint_: Checkpoint = self.serde.loads_typed(checkpoint)
 
-                    yield CheckpointTuple(
-                        config={
+                    list_config = cast(
+                        RunnableConfig,
+                        {
                             "configurable": {
                                 "thread_id": thread_id,
                                 "checkpoint_ns": checkpoint_ns,
                                 "checkpoint_id": checkpoint_id,
                             }
                         },
+                    )
+                    channel_values = self._load_blobs(
+                        thread_id,
+                        checkpoint_ns,
+                        checkpoint_["channel_versions"],
+                    )
+
+                    yield CheckpointTuple(
+                        config=list_config,
                         checkpoint={
                             **checkpoint_,
-                            "channel_values": self._load_blobs(
-                                thread_id,
-                                checkpoint_ns,
-                                checkpoint_["channel_versions"],
-                            ),
+                            "channel_values": channel_values,
                         },
                         metadata=metadata,
                         parent_config=(
@@ -407,6 +525,101 @@ class InMemorySaver(
                 task_path,
             )
 
+    def prune(
+        self,
+        thread_ids: Sequence[str],
+        *,
+        strategy: str = "keep_latest",
+    ) -> None:
+        """Prune checkpoints for the given threads.
+
+        For DeltaChannel channels, a checkpoint is only deleted if the walk
+        from the latest checkpoint would not need to traverse it — i.e., a
+        `_DeltaSnapshot` blob exists in the kept ancestry that covers all
+        sentinel channels.  Checkpoints that are still in the active walk
+        chain (because no snapshot has been taken yet, e.g. with
+        `snapshot_frequency=None`) are retained.
+
+        Args:
+            thread_ids: Thread IDs to prune.
+            strategy: ``"keep_latest"`` keeps only the most recent checkpoint
+                per namespace; ``"delete"`` removes all checkpoints.
+        """
+        for thread_id in thread_ids:
+            if strategy == "delete":
+                self.delete_thread(thread_id)
+                continue
+
+            if strategy != "keep_latest":
+                raise ValueError(
+                    f"Unknown pruning strategy {strategy!r}. "
+                    "Expected 'keep_latest' or 'delete'."
+                )
+
+            for checkpoint_ns, ns_storage in list(
+                self.storage.get(thread_id, {}).items()
+            ):
+                if not ns_storage:
+                    continue
+
+                # Latest checkpoint (uuid6 IDs are lexicographically monotonic)
+                latest_id = max(ns_storage.keys())
+                latest_data, _, _ = ns_storage[latest_id]
+                latest_cp = self.serde.loads_typed(latest_data)
+
+                # Which channels in the latest checkpoint still have sentinels?
+                sentinel_channels: set[str] = set()
+                for ch, ver in latest_cp.get("channel_versions", {}).items():
+                    blob = self.blobs.get((thread_id, checkpoint_ns, ch, ver))
+                    if blob is not None and blob[0] != "empty":
+                        if self.serde.loads_typed(blob) is DELTA_SENTINEL:
+                            sentinel_channels.add(ch)
+
+                # Walk the parent chain to find the oldest ancestor still needed.
+                # We stop (and mark "safe to prune before here") when all
+                # sentinel channels are covered by a non-sentinel blob.
+                required_ids: set[str] = {latest_id}
+                if sentinel_channels:
+                    _, _, parent_id = ns_storage[latest_id]
+                    remaining = set(sentinel_channels)
+                    while parent_id is not None and remaining:
+                        entry = ns_storage.get(parent_id)
+                        if entry is None:
+                            break
+                        required_ids.add(parent_id)
+                        cp_data, _, grandparent_id = entry
+                        cp = self.serde.loads_typed(cp_data)
+                        resolved: set[str] = set()
+                        for ch in remaining:
+                            ver = cp.get("channel_versions", {}).get(ch)
+                            if ver is None:
+                                continue
+                            blob = self.blobs.get((thread_id, checkpoint_ns, ch, ver))
+                            if blob is not None and blob[0] != "empty":
+                                if self.serde.loads_typed(blob) is not DELTA_SENTINEL:
+                                    resolved.add(ch)
+                        remaining -= resolved
+                        parent_id = grandparent_id
+
+                # Delete everything outside the required set
+                for cp_id in list(ns_storage.keys()):
+                    if cp_id in required_ids:
+                        continue
+                    cp_data, _, _ = ns_storage.pop(cp_id)
+                    self.writes.pop((thread_id, checkpoint_ns, cp_id), None)
+
+                # Clean up blobs no longer referenced by any kept checkpoint
+                live: set[tuple[str, str, str, Any]] = set()
+                for cp_data, _, _ in ns_storage.values():
+                    cp = self.serde.loads_typed(cp_data)
+                    for ch, ver in cp.get("channel_versions", {}).items():
+                        live.add((thread_id, checkpoint_ns, ch, ver))
+                for key in [
+                    k for k in self.blobs if k[:2] == (thread_id, checkpoint_ns)
+                ]:
+                    if key not in live:
+                        del self.blobs[key]
+
     def delete_thread(self, thread_id: str) -> None:
         """Delete all checkpoints and writes associated with a thread ID.
 

libs/checkpoint/langgraph/checkpoint/serde/jsonplus.py

@@ -33,14 +33,18 @@ from langchain_core.load.load import Reviver
 from langgraph.checkpoint.serde import _msgpack as _lg_msgpack
 from langgraph.checkpoint.serde.base import SerializerProtocol
 from langgraph.checkpoint.serde.event_hooks import emit_serde_event
-from langgraph.checkpoint.serde.types import SendProtocol
+from langgraph.checkpoint.serde.types import (
+    DELTA_SENTINEL,
+    SendProtocol,
+    _DeltaSentinel,
+    _DeltaSnapshot,
+)
 from langgraph.store.base import Item
 
 if TYPE_CHECKING:
     from langgraph.checkpoint.serde._msgpack import (
         AllowedMsgpackModules,
     )
-    from langgraph.checkpoint.serde.types import SendProtocol
 
 LC_REVIVER = Reviver()
 EMPTY_BYTES = b""
@@ -293,10 +297,16 @@ EXT_METHOD_SINGLE_ARG = 3
 EXT_PYDANTIC_V1 = 4
 EXT_PYDANTIC_V2 = 5
 EXT_NUMPY_ARRAY = 6
+EXT_DELTA_SNAPSHOT = 7
+EXT_DELTA_SENTINEL = 8
 
 
 def _msgpack_default(obj: Any) -> str | ormsgpack.Ext:
-    if hasattr(obj, "model_dump") and callable(obj.model_dump):  # pydantic v2
+    if isinstance(obj, _DeltaSentinel):
+        return ormsgpack.Ext(EXT_DELTA_SENTINEL, b"")
+    elif isinstance(obj, _DeltaSnapshot):
+        return ormsgpack.Ext(EXT_DELTA_SNAPSHOT, _msgpack_enc(obj.value))
+    elif hasattr(obj, "model_dump") and callable(obj.model_dump):  # pydantic v2
         return ormsgpack.Ext(
             EXT_PYDANTIC_V2,
             _msgpack_enc(
@@ -610,7 +620,15 @@ def _create_msgpack_ext_hook(
         return False
 
     def ext_hook(code: int, data: bytes) -> Any:
-        if code == EXT_CONSTRUCTOR_SINGLE_ARG:
+        if code == EXT_DELTA_SENTINEL:
+            return DELTA_SENTINEL
+        elif code == EXT_DELTA_SNAPSHOT:
+            return _DeltaSnapshot(
+                ormsgpack.unpackb(
+                    data, ext_hook=ext_hook, option=ormsgpack.OPT_NON_STR_KEYS
+                )
+            )
+        elif code == EXT_CONSTRUCTOR_SINGLE_ARG:
             try:
                 tup = ormsgpack.unpackb(
                     data, ext_hook=ext_hook, option=ormsgpack.OPT_NON_STR_KEYS

libs/checkpoint/langgraph/checkpoint/serde/types.py

@@ -1,6 +1,7 @@
 from collections.abc import Sequence
 from typing import (
     Any,
+    NamedTuple,
     Protocol,
     TypeVar,
     runtime_checkable,
@@ -14,6 +15,39 @@ INTERRUPT = "__interrupt__"
 RESUME = "__resume__"
 TASKS = "__pregel_tasks"
 
+
+class _DeltaSentinel:
+    """Singleton marker stored (as zero bytes) in checkpoint_blobs for a
+    DeltaChannel field. The actual per-step writes live in checkpoint_writes
+    and are replayed through the reducer at load time.
+
+    Compare with `is DELTA_SENTINEL` — `loads_typed` always returns the same
+    module-level instance.
+    """
+
+    __slots__ = ()
+
+    def __repr__(self) -> str:
+        return "DELTA_SENTINEL"
+
+
+DELTA_SENTINEL = _DeltaSentinel()
+
+
+class _DeltaSnapshot(NamedTuple):
+    """Snapshot blob for a DeltaChannel with finite snapshot_frequency.
+
+    Stored in checkpoint_blobs via the `EXT_DELTA_SNAPSHOT` msgpack ext code.
+    The ancestor walk in `_get_channel_writes_history` terminates when it
+    encounters this type (any non-sentinel blob stops the walk).
+
+    `from_checkpoint` reconstructs the channel value directly from `.value`
+    without replaying writes — the snapshot IS the accumulated state.
+    """
+
+    value: Any
+
+
 Value = TypeVar("Value", covariant=True)
 Update = TypeVar("Update", contravariant=True)
 C = TypeVar("C")

libs/checkpoint/tests/test_jsonplus.py

@@ -997,3 +997,16 @@ def test_msgpack_nested_pydantic_serializes_as_dict(
     # No blocking should occur - inner is serialized as dict, not ext
     assert "blocked" not in caplog.text.lower()
     assert result == obj
+
+
+def test_delta_sentinel_serde_round_trip() -> None:
+    from langgraph.checkpoint.base import DELTA_SENTINEL
+    from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
+
+    serde = JsonPlusSerializer()
+    type_tag, blob = serde.dumps_typed(DELTA_SENTINEL)
+    # Zero-byte "delta" tag — no allowlist change needed.
+    assert type_tag == "delta"
+    assert blob == b""
+    loaded = serde.loads_typed((type_tag, blob))
+    assert loaded is DELTA_SENTINEL

libs/checkpoint/tests/test_memory.py

@@ -6,6 +6,7 @@ from langchain_core.runnables import RunnableConfig
 from pydantic import BaseModel
 
 from langgraph.checkpoint.base import (
+    DELTA_SENTINEL,
     Checkpoint,
     CheckpointMetadata,
     create_checkpoint,
@@ -208,8 +209,6 @@ class TestMemorySaver:
 
 
 async def test_memory_saver() -> None:
-    from langgraph.checkpoint.memory import InMemorySaver
-
     memory_saver = InMemorySaver()
     assert isinstance(memory_saver, InMemorySaver)
 
@@ -320,3 +319,492 @@ def test_memory_saver_with_allowlist_proxy_isolated() -> None:
     assert direct is not None
     expected = obj.model_dump() if hasattr(obj, "model_dump") else obj.dict()
     assert direct.checkpoint["channel_values"]["foo"] == expected
+
+
+class TestInMemorySaverDeltaChannel:
+    def test_load_blobs_returns_sentinel_for_delta_channel(self) -> None:
+        """_load_blobs returns DELTA_SENTINEL for delta channels (reconstruction deferred)."""
+        saver = InMemorySaver()
+        serde = JsonPlusSerializer()
+
+        thread_id, ns, channel = "t1", "", "messages"
+        v1 = "00000000000000000000000000000001.0000000000000000"
+
+        saver.blobs[(thread_id, ns, channel, v1)] = serde.dumps_typed(DELTA_SENTINEL)
+
+        cp1 = empty_checkpoint()
+        cp1["id"] = "cp1"
+        cp1["channel_versions"][channel] = v1
+        saver.storage[thread_id][ns] = {
+            "cp1": (serde.dumps_typed(cp1), serde.dumps_typed({}), None),
+        }
+
+        result = saver._load_blobs(thread_id, ns, {channel: v1})
+        assert channel in result
+        assert result[channel] is DELTA_SENTINEL
+
+    def test_get_channel_writes_collects_ancestor_writes_only(self) -> None:
+        """_get_channel_writes_history collects ancestor writes oldest→newest,
+        and excludes writes stored at the target checkpoint itself (those are
+        pending writes for the next step, applied separately by pregel)."""
+        saver = InMemorySaver()
+        serde = JsonPlusSerializer()
+
+        thread_id, ns, channel = "t1", "", "messages"
+
+        cp1 = empty_checkpoint()
+        cp1["id"] = "cp1"
+        cp2 = empty_checkpoint()
+        cp2["id"] = "cp2"
+        saver.storage[thread_id][ns] = {
+            "cp1": (serde.dumps_typed(cp1), serde.dumps_typed({}), None),
+            "cp2": (serde.dumps_typed(cp2), serde.dumps_typed({}), "cp1"),
+        }
+        # Writes stored at cp1 produced the cp1 snapshot; part of history.
+        saver.writes[(thread_id, ns, "cp1")][("task1", 0)] = (
+            "task1",
+            channel,
+            serde.dumps_typed({"content": "hi"}),
+            "",
+        )
+        # Writes stored at cp2 are pending — they will produce cp3 when the
+        # step that loaded cp2 completes. They MUST NOT appear in the
+        # reconstructed snapshot value at cp2.
+        saver.writes[(thread_id, ns, "cp2")][("task2", 0)] = (
+            "task2",
+            channel,
+            serde.dumps_typed({"content": "pending"}),
+            "",
+        )
+
+        config: RunnableConfig = {
+            "configurable": {
+                "thread_id": thread_id,
+                "checkpoint_ns": ns,
+                "checkpoint_id": "cp2",
+            }
+        }
+        result = saver._get_channel_writes_history(config, channel)
+        assert result.seed is DELTA_SENTINEL
+        values = [v for _, _, v in result.writes]
+        assert values == [{"content": "hi"}]
+
+    def test_get_channel_writes_at_root_returns_empty(self) -> None:
+        """Reconstructing the root checkpoint's state: no ancestors → []."""
+        saver = InMemorySaver()
+        serde = JsonPlusSerializer()
+        thread_id, ns, channel = "t1", "", "messages"
+
+        cp1 = empty_checkpoint()
+        cp1["id"] = "cp1"
+        saver.storage[thread_id][ns] = {
+            "cp1": (serde.dumps_typed(cp1), serde.dumps_typed({}), None),
+        }
+        saver.writes[(thread_id, ns, "cp1")][("task1", 0)] = (
+            "task1",
+            channel,
+            serde.dumps_typed({"content": "pending"}),
+            "",
+        )
+
+        config: RunnableConfig = {
+            "configurable": {
+                "thread_id": thread_id,
+                "checkpoint_ns": ns,
+                "checkpoint_id": "cp1",
+            }
+        }
+        result = saver._get_channel_writes_history(config, channel)
+        assert result.seed is DELTA_SENTINEL
+        assert result.writes == []
+
+
+class TestBaseFallbackGetChannelWrites:
+    """Exercises the `BaseCheckpointSaver._get_channel_writes_history` default
+    implementation — the path third-party savers inherit when they don't
+    override `_get_channel_writes_history` themselves.
+
+    Regression guard for a bug where the fallback passed the caller's config
+    (with `checkpoint_id`) straight to `self.list()`, which most savers
+    collapse to a single row — causing the fallback to return `[]`.
+    """
+
+    def _build_saver_with_chain(self) -> tuple[InMemorySaver, str, str]:
+        """Build an InMemorySaver with a 3-checkpoint chain and per-step writes
+        for a `messages` channel.
+
+        Returns `(saver, thread_id, namespace)`. The saver subclass deletes the
+        InMemorySaver override so the base class fallback is exercised.
+        """
+
+        class _ThirdPartyStyleSaver(InMemorySaver):
+            _get_channel_writes_history = (
+                InMemorySaver.__mro__[1]._get_channel_writes_history  # type: ignore[attr-defined]
+            )
+            _aget_channel_writes_history = (
+                InMemorySaver.__mro__[1]._aget_channel_writes_history  # type: ignore[attr-defined]
+            )
+
+        saver = _ThirdPartyStyleSaver()
+        serde = JsonPlusSerializer()
+        thread_id, ns, channel = "t1", "", "messages"
+
+        cp0 = empty_checkpoint()
+        cp0["id"] = "00000000000000000000000000000001.0000000000000000"
+        cp1 = empty_checkpoint()
+        cp1["id"] = "00000000000000000000000000000002.0000000000000000"
+        cp2 = empty_checkpoint()
+        cp2["id"] = "00000000000000000000000000000003.0000000000000000"
+        saver.storage[thread_id][ns] = {
+            cp0["id"]: (serde.dumps_typed(cp0), serde.dumps_typed({}), None),
+            cp1["id"]: (serde.dumps_typed(cp1), serde.dumps_typed({}), cp0["id"]),
+            cp2["id"]: (serde.dumps_typed(cp2), serde.dumps_typed({}), cp1["id"]),
+        }
+        # Writes under cp0 produced cp1's state; writes under cp1 produced cp2's.
+        saver.writes[(thread_id, ns, cp0["id"])][("task1", 0)] = (
+            "task1",
+            channel,
+            serde.dumps_typed({"content": "first"}),
+            "",
+        )
+        saver.writes[(thread_id, ns, cp1["id"])][("task2", 0)] = (
+            "task2",
+            channel,
+            serde.dumps_typed({"content": "second"}),
+            "",
+        )
+        return saver, thread_id, ns
+
+    def test_fallback_returns_ancestor_writes_oldest_first(self) -> None:
+        saver, thread_id, ns = self._build_saver_with_chain()
+        target_id = "00000000000000000000000000000003.0000000000000000"
+        config: RunnableConfig = {
+            "configurable": {
+                "thread_id": thread_id,
+                "checkpoint_ns": ns,
+                "checkpoint_id": target_id,
+            }
+        }
+
+        result = saver._get_channel_writes_history(config, "messages")
+
+        assert result.seed is DELTA_SENTINEL
+        values = [v for _, _, v in result.writes]
+        assert values == [{"content": "first"}, {"content": "second"}]
+
+    async def test_async_fallback_returns_ancestor_writes_oldest_first(self) -> None:
+        saver, thread_id, ns = self._build_saver_with_chain()
+        target_id = "00000000000000000000000000000003.0000000000000000"
+        config: RunnableConfig = {
+            "configurable": {
+                "thread_id": thread_id,
+                "checkpoint_ns": ns,
+                "checkpoint_id": target_id,
+            }
+        }
+
+        result = await saver._aget_channel_writes_history(config, "messages")
+
+        assert result.seed is DELTA_SENTINEL
+        values = [v for _, _, v in result.writes]
+        assert values == [{"content": "first"}, {"content": "second"}]
+
+    async def test_async_fallback_concurrent_tasks_do_not_interfere(self) -> None:
+        """Regression: the re-entrancy guard must be task-local, not thread-local.
+
+        Two concurrent `_aget_channel_writes_history` calls on the same
+        event-loop thread must each see their full reconstructed writes. A
+        `threading.local()` guard would let whichever task set it first
+        short-circuit the other to `writes=[]`.
+        """
+        import asyncio
+
+        saver, thread_id, ns = self._build_saver_with_chain()
+
+        # Force the two tasks to interleave across the `set(True)` boundary:
+        # each `aget_tuple` yields control, so if the guard were thread-local
+        # the second task would observe `active=True` set by the first.
+        orig_aget_tuple = saver.aget_tuple
+
+        async def slow_aget_tuple(config: RunnableConfig) -> Any:
+            await asyncio.sleep(0)
+            return await orig_aget_tuple(config)
+
+        saver.aget_tuple = slow_aget_tuple  # type: ignore[method-assign]
+
+        target_id = "00000000000000000000000000000003.0000000000000000"
+        config: RunnableConfig = {
+            "configurable": {
+                "thread_id": thread_id,
+                "checkpoint_ns": ns,
+                "checkpoint_id": target_id,
+            }
+        }
+
+        results = await asyncio.gather(
+            saver._aget_channel_writes_history(config, "messages"),
+            saver._aget_channel_writes_history(config, "messages"),
+        )
+
+        expected_values = [{"content": "first"}, {"content": "second"}]
+        for result in results:
+            assert result.seed is DELTA_SENTINEL
+            values = [v for _, _, v in result.writes]
+            assert values == expected_values
+
+
+class TestPreDeltaBlobTerminator:
+    """Verify the pre-delta blob terminator: when the ancestor walk hits a
+    checkpoint whose blob for the channel is a real value (not
+    DELTA_SENTINEL), reconstruction seeds from it and stops. This guards
+
+      * back-compat: a thread written by pre-delta code, then extended under
+        delta — reconstruction must return the correct value without walking
+        past the last pre-delta ancestor;
+      * perf: without the terminator, every reconstruct-after-migration would
+        walk all the way to the thread root.
+    """
+
+    def _build_mixed_thread(self) -> tuple[InMemorySaver, str, str, str, str]:
+        """Three-checkpoint chain: cp1 (pre-delta, blob=[A]), cp2 (delta,
+        write=B), cp3 (delta, write=C). Reconstructing at cp3 must yield
+        seed=[A] + writes=[B, C].
+
+        Returns `(saver, thread_id, ns, channel, cp3_id)`.
+        """
+        saver = InMemorySaver()
+        serde = JsonPlusSerializer()
+        thread_id, ns, channel = "t1", "", "messages"
+
+        v1 = "00000000000000000000000000000001.0"
+        v2 = "00000000000000000000000000000002.0"
+        v3 = "00000000000000000000000000000003.0"
+
+        # Pre-delta: cp1 stored a real blob for the channel.
+        saver.blobs[(thread_id, ns, channel, v1)] = serde.dumps_typed(["A"])
+        # Delta-era: cp2 and cp3 store sentinels; real writes in checkpoint_writes.
+        saver.blobs[(thread_id, ns, channel, v2)] = serde.dumps_typed(DELTA_SENTINEL)
+        saver.blobs[(thread_id, ns, channel, v3)] = serde.dumps_typed(DELTA_SENTINEL)
+
+        cp1 = empty_checkpoint()
+        cp1["id"] = "cp1"
+        cp1["channel_versions"][channel] = v1
+        cp2 = empty_checkpoint()
+        cp2["id"] = "cp2"
+        cp2["channel_versions"][channel] = v2
+        cp3 = empty_checkpoint()
+        cp3["id"] = "cp3"
+        cp3["channel_versions"][channel] = v3
+
+        saver.storage[thread_id][ns] = {
+            "cp1": (serde.dumps_typed(cp1), serde.dumps_typed({}), None),
+            "cp2": (serde.dumps_typed(cp2), serde.dumps_typed({}), "cp1"),
+            "cp3": (serde.dumps_typed(cp3), serde.dumps_typed({}), "cp2"),
+        }
+        # Write under cp1 would be from the pre-delta era and MUST be ignored
+        # (the blob already captures it). We add one and assert it is not
+        # folded into the reconstructed result.
+        saver.writes[(thread_id, ns, "cp1")][("task0", 0)] = (
+            "task0",
+            channel,
+            serde.dumps_typed("PRE-DELTA-WRITE"),
+            "",
+        )
+        saver.writes[(thread_id, ns, "cp2")][("task2", 0)] = (
+            "task2",
+            channel,
+            serde.dumps_typed("B"),
+            "",
+        )
+        saver.writes[(thread_id, ns, "cp3")][("task3", 0)] = (
+            "task3",
+            channel,
+            serde.dumps_typed("PENDING-AT-TARGET"),
+            "",
+        )
+        return saver, thread_id, ns, channel, "cp3"
+
+    def test_seed_from_pre_delta_ancestor_blob(self) -> None:
+        saver, thread_id, ns, channel, target = self._build_mixed_thread()
+        config: RunnableConfig = {
+            "configurable": {
+                "thread_id": thread_id,
+                "checkpoint_ns": ns,
+                "checkpoint_id": target,
+            }
+        }
+
+        result = saver._get_channel_writes_history(config, channel)
+
+        # Seed came from the pre-delta blob at cp1.
+        assert result.seed == ["A"]
+        # Delta-era writes from cp2 replay through the reducer on top of seed.
+        # cp3 is the target — its own write is pending for the NEXT step and
+        # must be excluded.
+        values = [v for _, _, v in result.writes]
+        assert values == ["B"]
+
+    def test_pre_delta_blob_terminates_walk_before_older_writes(self) -> None:
+        """Writes stored at the pre-delta ancestor itself must not be replayed
+        (the blob subsumes them)."""
+        saver, thread_id, ns, channel, target = self._build_mixed_thread()
+        config: RunnableConfig = {
+            "configurable": {
+                "thread_id": thread_id,
+                "checkpoint_ns": ns,
+                "checkpoint_id": target,
+            }
+        }
+
+        result = saver._get_channel_writes_history(config, channel)
+
+        values = [v for _, _, v in result.writes]
+        # The pre-delta write under cp1 must not appear (the blob subsumes it).
+        assert "PRE-DELTA-WRITE" not in values
+        # And the pending write at the target is never folded in.
+        assert "PENDING-AT-TARGET" not in values
+
+
+class TestInMemorySaverPrune:
+    """Tests for InMemorySaver.prune with DeltaChannel awareness."""
+
+    def _build_chain(
+        self,
+        saver: InMemorySaver,
+        thread_id: str,
+        ns: str,
+        channel: str,
+        n: int,
+        *,
+        snapshot_at: set[int] | None = None,
+    ) -> list[str]:
+        """Build a chain of n checkpoints with DELTA_SENTINEL blobs.
+
+        If snapshot_at is provided, writes a _DeltaSnapshot blob at those steps.
+        Returns list of checkpoint IDs in order (oldest first).
+        """
+        from langgraph.checkpoint.serde.types import _DeltaSnapshot
+
+        serde = saver.serde
+        cp_ids = []
+        parent_id = None
+        ver_base = "0000000000000000000000000000000{i}.0000000000000000"
+
+        for i in range(n):
+            cp_id = f"cp{i:04d}"
+            ver = ver_base.format(i=i)
+            cp = empty_checkpoint()
+            cp["id"] = cp_id
+            cp["channel_versions"][channel] = ver
+
+            if snapshot_at and i in snapshot_at:
+                blob = serde.dumps_typed(_DeltaSnapshot(value=[f"msg{i}"]))
+            else:
+                blob = serde.dumps_typed(DELTA_SENTINEL)
+
+            saver.blobs[(thread_id, ns, channel, ver)] = blob
+            saver.storage[thread_id][ns][cp_id] = (
+                serde.dumps_typed(cp),
+                serde.dumps_typed({}),
+                parent_id,
+            )
+            # Add a dummy write for this checkpoint
+            saver.writes[(thread_id, ns, cp_id)][("task", i)] = (
+                "task",
+                channel,
+                serde.dumps_typed(f"write{i}"),
+                "",
+            )
+            cp_ids.append(cp_id)
+            parent_id = cp_id
+
+        return cp_ids
+
+    def test_prune_pure_delta_keeps_all(self) -> None:
+        """With no snapshots, all checkpoints are required for reconstruction."""
+        saver = InMemorySaver()
+        thread_id, ns, channel = "t1", "", "messages"
+        cp_ids = self._build_chain(saver, thread_id, ns, channel, n=5)
+
+        saver.prune([thread_id], strategy="keep_latest")
+
+        # All checkpoints must be retained (walk needs the full chain)
+        remaining = set(saver.storage[thread_id][ns].keys())
+        assert remaining == set(cp_ids)
+
+    def test_prune_with_snapshot_removes_pre_snapshot_checkpoints(self) -> None:
+        """Checkpoints older than the nearest snapshot can be safely pruned."""
+        saver = InMemorySaver()
+        thread_id, ns, channel = "t1", "", "messages"
+        # Snapshot at step 2; steps 3 and 4 are sentinels
+        cp_ids = self._build_chain(saver, thread_id, ns, channel, n=5, snapshot_at={2})
+
+        saver.prune([thread_id], strategy="keep_latest")
+
+        remaining = set(saver.storage[thread_id][ns].keys())
+        # cp0, cp1 (before snapshot) must be gone; cp2, cp3, cp4 must remain
+        assert cp_ids[0] not in remaining  # pre-snapshot
+        assert cp_ids[1] not in remaining  # pre-snapshot
+        assert cp_ids[2] in remaining  # the snapshot itself
+        assert cp_ids[3] in remaining  # sentinel after snapshot
+        assert cp_ids[4] in remaining  # latest
+
+    def test_prune_removes_orphaned_blobs(self) -> None:
+        """Blob entries for pruned checkpoints are cleaned up."""
+        saver = InMemorySaver()
+        thread_id, ns, channel = "t1", "", "messages"
+        self._build_chain(saver, thread_id, ns, channel, n=4, snapshot_at={1})
+        saver.prune([thread_id], strategy="keep_latest")
+
+        # cp0 blob should be gone (pruned); cp1, cp2, cp3 blobs remain
+        assert (
+            thread_id,
+            ns,
+            channel,
+            f"0000000000000000000000000000000{0}.0000000000000000",
+        ) not in saver.blobs
+        for i in range(1, 4):
+            ver = f"0000000000000000000000000000000{i}.0000000000000000"
+            assert (thread_id, ns, channel, ver) in saver.blobs
+
+    def test_prune_removes_writes_for_pruned_checkpoints(self) -> None:
+        """checkpoint_writes for pruned checkpoints are deleted."""
+        saver = InMemorySaver()
+        thread_id, ns, channel = "t1", "", "messages"
+        cp_ids = self._build_chain(saver, thread_id, ns, channel, n=4, snapshot_at={1})
+
+        saver.prune([thread_id], strategy="keep_latest")
+
+        # writes for cp0 must be gone
+        assert (thread_id, ns, cp_ids[0]) not in saver.writes
+        # writes for cp1+ must remain (they're in the walk chain)
+        for cp_id in cp_ids[1:]:
+            assert (thread_id, ns, cp_id) in saver.writes
+
+    def test_prune_delete_strategy_removes_everything(self) -> None:
+        """strategy='delete' removes all checkpoints for the thread."""
+        saver = InMemorySaver()
+        thread_id, ns, channel = "t1", "", "messages"
+        self._build_chain(saver, thread_id, ns, channel, n=3)
+
+        saver.prune([thread_id], strategy="delete")
+
+        assert not saver.storage.get(thread_id, {}).get(ns)
+        assert not any(k[0] == thread_id for k in saver.writes)
+        assert not any(k[0] == thread_id for k in saver.blobs)
+
+    def test_prune_non_delta_channel_always_pruneable(self) -> None:
+        """A channel with full snapshot blobs (no sentinels) allows full prune."""
+
+        saver = InMemorySaver()
+        thread_id, ns, channel = "t1", "", "messages"
+        # All snapshots, no sentinels
+        cp_ids = self._build_chain(
+            saver, thread_id, ns, channel, n=4, snapshot_at={0, 1, 2, 3}
+        )
+
+        saver.prune([thread_id], strategy="keep_latest")
+
+        remaining = set(saver.storage[thread_id][ns].keys())
+        # Only the latest checkpoint is needed (all blobs are snapshots)
+        assert remaining == {cp_ids[-1]}

libs/langgraph/langgraph/channels/__init__.py

@@ -1,6 +1,7 @@
 from langgraph.channels.any_value import AnyValue
 from langgraph.channels.base import BaseChannel
 from langgraph.channels.binop import BinaryOperatorAggregate
+from langgraph.channels.delta import DeltaChannel
 from langgraph.channels.ephemeral_value import EphemeralValue
 from langgraph.channels.last_value import LastValue, LastValueAfterFinish
 from langgraph.channels.named_barrier_value import (
@@ -20,6 +21,7 @@ __all__ = (
     "UntrackedValue",
     "EphemeralValue",
     "BinaryOperatorAggregate",
+    "DeltaChannel",
     "NamedBarrierValue",
     "NamedBarrierValueAfterFinish",
     # topics

libs/langgraph/langgraph/channels/delta.py

@@ -0,0 +1,181 @@
+from __future__ import annotations
+
+import copy as _copy
+from collections.abc import Callable, Sequence
+from typing import Any, Generic
+
+from langgraph.checkpoint.base import DELTA_SENTINEL, PendingWrite
+from langgraph.checkpoint.serde.types import _DeltaSnapshot
+from typing_extensions import Self
+
+from langgraph._internal._constants import OVERWRITE
+from langgraph._internal._typing import MISSING
+from langgraph.channels.base import BaseChannel, Value
+from langgraph.errors import (
+    EmptyChannelError,
+    ErrorCode,
+    InvalidUpdateError,
+    create_error_message,
+)
+from langgraph.types import Overwrite
+
+__all__ = ("DeltaChannel",)
+
+
+def _empty(typ: Any) -> Any:
+    try:
+        return typ()
+    except Exception:
+        return []
+
+
+def _get_overwrite(value: Any) -> tuple[bool, Any]:
+    if isinstance(value, Overwrite):
+        return True, value.value
+    if isinstance(value, dict) and set(value.keys()) == {OVERWRITE}:
+        return True, value[OVERWRITE]
+    return False, None
+
+
+class DeltaChannel(Generic[Value], BaseChannel[Any, Any, Any]):
+    """Fold-reducer channel with configurable snapshot cadence.
+
+    `snapshot_frequency=None` (default): pure delta — stores only
+    `DELTA_SENTINEL` in checkpoint blobs; reads replay all ancestor writes.
+
+    `snapshot_frequency=N`: pregel's `create_checkpoint` writes a full
+    `_DeltaSnapshot` blob every N steps (eagerly, even if the channel had
+    no write that step). Reads walk at most N ancestor checkpoints before
+    hitting the snapshot, bounding replay depth to N regardless of thread
+    length.
+
+    Parameters:
+        operator: Binary reducer `(Value, Value) -> Value`.
+        snapshot_frequency: Every Nth pregel step writes a snapshot blob.
+            `None` (default) = pure delta, never snapshot.
+    """
+
+    __slots__ = ("value", "operator", "snapshot_frequency")
+    value: Value | Any
+
+    def __init__(
+        self,
+        operator: Callable[[Any, Any], Any],
+        *,
+        snapshot_frequency: int | None = None,
+    ) -> None:
+        super().__init__(list)
+        self.operator = operator
+        self.snapshot_frequency = snapshot_frequency
+        self.value: Any = []
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, DeltaChannel):
+            return False
+        if self.snapshot_frequency != other.snapshot_frequency:
+            return False
+        if (
+            self.operator.__name__ != "<lambda>"
+            and other.operator.__name__ != "<lambda>"
+        ):
+            return self.operator is other.operator
+        return True
+
+    @property
+    def ValueType(self) -> Any:
+        return self.typ
+
+    @property
+    def UpdateType(self) -> Any:
+        return self.typ
+
+    def is_snapshot_step(self, step: int) -> bool:
+        """True if pregel should write a snapshot blob at this step."""
+        return (
+            self.snapshot_frequency is not None and step % self.snapshot_frequency == 0
+        )
+
+    def _clone_empty(self) -> Self:
+        new = self.__class__.__new__(self.__class__)
+        new.typ = self.typ
+        new.key = self.key
+        new.operator = self.operator
+        new.snapshot_frequency = self.snapshot_frequency
+        new.value = MISSING
+        return new
+
+    def copy(self) -> Self:
+        new = self._clone_empty()
+        new.value = self.value if self.value is MISSING else _copy.copy(self.value)
+        return new
+
+    def _apply_write(self, value: Any, write: Any) -> Any:
+        is_overwrite, overwrite_value = _get_overwrite(write)
+        if is_overwrite:
+            return (
+                _copy.copy(overwrite_value)
+                if overwrite_value is not None
+                else _empty(self.typ)
+            )
+        base = _empty(self.typ) if value is MISSING else value
+        return self.operator(base, write)
+
+    def from_checkpoint(self, checkpoint: Any) -> Self:
+        """Initialize from a stored blob or sentinel.
+
+        Blob types (dispatched via serde ext code, not dict key inspection):
+          * `DELTA_SENTINEL` / `MISSING`: start empty; caller replays writes.
+          * `_DeltaSnapshot(value)`: restore value directly from snapshot.
+          * plain value (migration from old BinOp blobs): use directly.
+        """
+        new = self._clone_empty()
+        if checkpoint is MISSING or checkpoint is DELTA_SENTINEL:
+            new.value = _empty(new.typ)
+        elif isinstance(checkpoint, _DeltaSnapshot):
+            new.value = checkpoint.value
+        else:
+            new.value = checkpoint
+        return new
+
+    def replay_writes(self, writes: Sequence[PendingWrite]) -> None:
+        """Fold ancestor writes oldest→newest into current value."""
+        for _, _, value in writes:
+            self.value = self._apply_write(self.value, value)
+
+    def update(self, values: Sequence[Any]) -> bool:
+        if not values:
+            return False
+        seen_overwrite = False
+        for value in values:
+            is_overwrite, _ = _get_overwrite(value)
+            if is_overwrite:
+                if seen_overwrite:
+                    msg = create_error_message(
+                        message="Can receive only one Overwrite value per super-step.",
+                        error_code=ErrorCode.INVALID_CONCURRENT_GRAPH_UPDATE,
+                    )
+                    raise InvalidUpdateError(msg)
+                seen_overwrite = True
+            elif seen_overwrite:
+                continue
+            self.value = self._apply_write(self.value, value)
+        return True
+
+    def get(self) -> Any:
+        if self.value is MISSING:
+            raise EmptyChannelError()
+        return self.value
+
+    def is_available(self) -> bool:
+        return self.value is not MISSING
+
+    def checkpoint(self) -> Any:
+        """Return stored representation: always `DELTA_SENTINEL`.
+
+        Snapshot decisions are made by `create_checkpoint` in pregel (which
+        has the step number) via `is_snapshot_step`. `checkpoint()` is only
+        called for non-snapshot steps or when no checkpointer is available.
+        """
+        if self.value is MISSING:
+            return MISSING
+        return DELTA_SENTINEL

libs/langgraph/langgraph/graph/state.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import collections.abc
 import inspect
 import logging
 import typing
@@ -47,7 +48,8 @@ from langgraph._internal._pydantic import create_model
 from langgraph._internal._runnable import coerce_to_runnable
 from langgraph._internal._typing import EMPTY_SEQ, MISSING, DeprecatedKwargs
 from langgraph.channels.base import BaseChannel
-from langgraph.channels.binop import BinaryOperatorAggregate
+from langgraph.channels.binop import BinaryOperatorAggregate, _strip_extras
+from langgraph.channels.delta import DeltaChannel
 from langgraph.channels.ephemeral_value import EphemeralValue
 from langgraph.channels.last_value import LastValue, LastValueAfterFinish
 from langgraph.channels.named_barrier_value import (
@@ -1082,6 +1084,7 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
             CompiledStateGraph: The compiled `StateGraph`.
         """
         checkpointer = ensure_valid_checkpointer(checkpointer)
+
         serde_allowlist: set[tuple[str, ...]] | None = None
         if _serde.STRICT_MSGPACK_ENABLED:
             schema_types: list[type[Any]] = [
@@ -1667,6 +1670,36 @@ def _is_field_channel(typ: type[Any]) -> BaseChannel | None:
         # Search through all annotated medata to find channel annotations
         for item in meta:
             if isinstance(item, BaseChannel):
+                if isinstance(item, DeltaChannel) and hasattr(typ, "__origin__"):
+                    origin = typ.__origin__
+                    # Unwrap parameterized Required[X]/NotRequired[X] to X
+                    # (e.g. Annotated[NotRequired[dict[...]], ...]).
+                    if hasattr(origin, "__origin__") and origin.__origin__ in (
+                        Required,
+                        NotRequired,
+                    ):
+                        origin = origin.__args__[0]
+                    outer = _strip_extras(origin)
+                    if outer in (
+                        collections.abc.Sequence,
+                        collections.abc.MutableSequence,
+                    ):
+                        outer = list
+                    elif outer in (
+                        collections.abc.Mapping,
+                        collections.abc.MutableMapping,
+                    ):
+                        outer = dict
+                    elif outer in (
+                        collections.abc.Set,
+                        collections.abc.MutableSet,
+                    ):
+                        outer = set
+                    item.typ = outer
+                    try:
+                        item.value = outer()
+                    except Exception:
+                        item.value = []
                 return item
             elif isclass(item) and issubclass(item, BaseChannel):
                 # ex, Annotated[int, EphemeralValue, SomeOtherAnnotation]

libs/langgraph/langgraph/pregel/_algo.py

@@ -210,7 +210,7 @@ def local_read(
     return values
 
 
-def increment(current: int | None, channel: None) -> int:
+def increment(current: int | None, channel: None = None) -> int:
     """Default channel versioning function, increments the current int version."""
     return current + 1 if current is not None else 1
 

libs/langgraph/langgraph/pregel/_checkpoint.py

@@ -1,17 +1,23 @@
 from __future__ import annotations
 
-from collections.abc import Mapping
+from collections.abc import Callable, Mapping
 from datetime import datetime, timezone
+from typing import Any
 
-from langgraph.checkpoint.base import Checkpoint
+from langchain_core.runnables import RunnableConfig
+from langgraph.checkpoint.base import DELTA_SENTINEL, BaseCheckpointSaver, Checkpoint
 from langgraph.checkpoint.base.id import uuid6
+from langgraph.checkpoint.serde.types import _DeltaSnapshot
 
 from langgraph._internal._typing import MISSING
 from langgraph.channels.base import BaseChannel
+from langgraph.channels.delta import DeltaChannel
 from langgraph.managed.base import ManagedValueMapping, ManagedValueSpec
 
 LATEST_VERSION = 4
 
+GetNextVersion = Callable[[Any, None], Any]
+
 
 def empty_checkpoint() -> Checkpoint:
     return Checkpoint(
@@ -31,35 +37,82 @@ def create_checkpoint(
     *,
     id: str | None = None,
     updated_channels: set[str] | None = None,
+    get_next_version: GetNextVersion | None = None,
 ) -> Checkpoint:
-    """Create a checkpoint for the given channels."""
+    """Create a checkpoint for the given channels.
+
+    For `DeltaChannel` with `snapshot_frequency=N`, snapshot steps write a
+    `_DeltaSnapshot` blob rather than `DELTA_SENTINEL`, bounding the ancestor
+    walk to at most N steps. Snapshots are eager: even if the channel had no
+    write this step, a version bump is forced (via `get_next_version`) so the
+    blob is stored by `put()`. Without `get_next_version` (e.g. static
+    contexts), snapshot steps gracefully fall back to sentinel.
+    """
     ts = datetime.now(timezone.utc).isoformat()
     if channels is None:
         values = checkpoint["channel_values"]
+        channel_versions = checkpoint["channel_versions"]
     else:
         values = {}
+        channel_versions = dict(checkpoint["channel_versions"])
         for k in channels:
-            if k not in checkpoint["channel_versions"]:
+            if k not in channel_versions:
                 continue
-            v = channels[k].checkpoint()
-            if v is not MISSING:
-                values[k] = v
+            ch = channels[k]
+            if (
+                isinstance(ch, DeltaChannel)
+                and ch.is_snapshot_step(step)
+                and ch.is_available()
+            ):
+                # Eager snapshot: bump version if not already written this step
+                # so put() includes this channel in new_versions and stores blob.
+                if get_next_version is not None and (
+                    updated_channels is None or k not in updated_channels
+                ):
+                    channel_versions[k] = get_next_version(channel_versions[k], None)
+                values[k] = _DeltaSnapshot(ch.get())
+            else:
+                v = ch.checkpoint()
+                if v is not MISSING:
+                    values[k] = v
     return Checkpoint(
         v=LATEST_VERSION,
         ts=ts,
         id=id or str(uuid6(clock_seq=step)),
         channel_values=values,
-        channel_versions=checkpoint["channel_versions"],
+        channel_versions=channel_versions,
         versions_seen=checkpoint["versions_seen"],
         updated_channels=None if updated_channels is None else sorted(updated_channels),
     )
 
 
+def _needs_replay(spec: BaseChannel, stored: object) -> bool:
+    """True if `spec` is a `DeltaChannel` and the stored blob is a sentinel,
+    requiring an ancestor walk to reconstruct.
+
+    `_DeltaSnapshot` blobs and plain values (migration) resolve directly via
+    `from_checkpoint` — only `DELTA_SENTINEL` / `MISSING` trigger replay.
+    """
+    if not isinstance(spec, DeltaChannel):
+        return False
+    return stored is MISSING or stored is DELTA_SENTINEL
+
+
 def channels_from_checkpoint(
     specs: Mapping[str, BaseChannel | ManagedValueSpec],
     checkpoint: Checkpoint,
+    *,
+    saver: BaseCheckpointSaver | None = None,
+    config: RunnableConfig | None = None,
 ) -> tuple[Mapping[str, BaseChannel], ManagedValueMapping]:
-    """Get channels from a checkpoint."""
+    """Hydrate channels from a checkpoint.
+
+    For most channels, `spec.from_checkpoint(checkpoint["channel_values"][k])`
+    is sufficient. `DeltaChannel` is the exception: sentinel blobs require an
+    ancestor walk via `saver._get_channel_writes_history`. The walk terminates
+    at the nearest `_DeltaSnapshot` blob (step-based) or a pre-migration plain
+    value, so read depth is bounded by `snapshot_frequency`.
+    """
     channel_specs: dict[str, BaseChannel] = {}
     managed_specs: dict[str, ManagedValueSpec] = {}
     for k, v in specs.items():
@@ -67,13 +120,53 @@ def channels_from_checkpoint(
             channel_specs[k] = v
         else:
             managed_specs[k] = v
-    return (
-        {
-            k: v.from_checkpoint(checkpoint["channel_values"].get(k, MISSING))
-            for k, v in channel_specs.items()
-        },
-        managed_specs,
-    )
+
+    channels: dict[str, BaseChannel] = {}
+    for k, spec in channel_specs.items():
+        ch: BaseChannel
+        stored = checkpoint["channel_values"].get(k, MISSING)
+        if _needs_replay(spec, stored) and saver is not None and config is not None:
+            assert isinstance(spec, DeltaChannel)
+            history = saver._get_channel_writes_history(config, k)
+            replay_ch = spec.from_checkpoint(history.seed)
+            replay_ch.replay_writes(history.writes)
+            ch = replay_ch
+        else:
+            ch = spec.from_checkpoint(stored)
+        channels[k] = ch
+    return channels, managed_specs
+
+
+async def achannels_from_checkpoint(
+    specs: Mapping[str, BaseChannel | ManagedValueSpec],
+    checkpoint: Checkpoint,
+    *,
+    saver: BaseCheckpointSaver | None = None,
+    config: RunnableConfig | None = None,
+) -> tuple[Mapping[str, BaseChannel], ManagedValueMapping]:
+    """Async version of `channels_from_checkpoint`. See docstring there."""
+    channel_specs: dict[str, BaseChannel] = {}
+    managed_specs: dict[str, ManagedValueSpec] = {}
+    for k, v in specs.items():
+        if isinstance(v, BaseChannel):
+            channel_specs[k] = v
+        else:
+            managed_specs[k] = v
+
+    channels: dict[str, BaseChannel] = {}
+    for k, spec in channel_specs.items():
+        ch: BaseChannel
+        stored = checkpoint["channel_values"].get(k, MISSING)
+        if _needs_replay(spec, stored) and saver is not None and config is not None:
+            assert isinstance(spec, DeltaChannel)
+            history = await saver._aget_channel_writes_history(config, k)
+            replay_ch = spec.from_checkpoint(history.seed)
+            replay_ch.replay_writes(history.writes)
+            ch = replay_ch
+        else:
+            ch = spec.from_checkpoint(stored)
+        channels[k] = ch
+    return channels, managed_specs
 
 
 def copy_checkpoint(checkpoint: Checkpoint) -> Checkpoint:

libs/langgraph/langgraph/pregel/_loop.py

@@ -25,6 +25,7 @@ from langchain_core.callbacks import AsyncParentRunManager, ParentRunManager
 from langchain_core.runnables import RunnableConfig
 from langgraph.cache.base import BaseCache
 from langgraph.checkpoint.base import (
+    DELTA_SENTINEL,
     WRITES_IDX_MAP,
     BaseCheckpointSaver,
     ChannelVersions,
@@ -92,6 +93,7 @@ from langgraph.pregel._algo import (
     task_path_str,
 )
 from langgraph.pregel._checkpoint import (
+    achannels_from_checkpoint,
     channels_from_checkpoint,
     copy_checkpoint,
     create_checkpoint,
@@ -197,6 +199,7 @@ class PregelLoop:
     checkpoint_pending_writes: list[PendingWrite]
     checkpoint_previous_versions: dict[str, str | float | int]
     prev_checkpoint_config: RunnableConfig | None
+    _pending_write_futs: list[concurrent.futures.Future]
 
     status: Literal[
         "input",
@@ -406,7 +409,7 @@ class PregelLoop:
                     task = self.tasks.get(task_id)
                 else:
                     task = None
-                self.submit(
+                fut = self.submit(
                     self.checkpointer_put_writes,
                     config,
                     writes_to_save,
@@ -414,12 +417,13 @@ class PregelLoop:
                     task_path_str(task.path) if task else "",
                 )
             else:
-                self.submit(
+                fut = self.submit(
                     self.checkpointer_put_writes,
                     config,
                     writes_to_save,
                     task_id,
                 )
+            self._pending_write_futs.append(fut)
         # output writes
         if hasattr(self, "tasks"):
             self.output_writes(task_id, writes)
@@ -890,6 +894,9 @@ class PregelLoop:
             self.step,
             id=self.checkpoint["id"] if exiting else None,
             updated_channels=self.updated_channels,
+            get_next_version=self.checkpointer_get_next_version
+            if do_checkpoint
+            else None,
         )
         # sanitize TASK channel in the checkpoint before saving (durability=="exit")
         if TASKS in self.checkpoint["channel_values"] and any(
@@ -927,6 +934,17 @@ class PregelLoop:
             )
             self.checkpoint_previous_versions = channel_versions
 
+            # If the checkpoint has any DELTA_SENTINEL blobs, the sentinel is
+            # only meaningful if checkpoint_writes are durable first.  Flush
+            # pending write futures synchronously before committing the blob so
+            # we never end up with a sentinel blob backed by missing writes.
+            if self._pending_write_futs and any(
+                v is DELTA_SENTINEL for v in self.checkpoint["channel_values"].values()
+            ):
+                for fut in self._pending_write_futs:
+                    fut.result()
+                self._pending_write_futs.clear()
+
             # save it, without blocking
             # if there's a previous checkpoint save in progress, wait for it
             # ensuring checkpointers receive checkpoints in order
@@ -1271,9 +1289,13 @@ class SyncPregelLoop(PregelLoop, AbstractContextManager):
             if saved.pending_writes is not None
             else []
         )
+        self._pending_write_futs = []
         self.submit = self.stack.enter_context(BackgroundExecutor(self.config))
         self.channels, self.managed = channels_from_checkpoint(
-            self.specs, self.checkpoint
+            self.specs,
+            self.checkpoint,
+            saver=self.checkpointer,
+            config=self.checkpoint_config,
         )
         self.stack.push(self._suppress_interrupt)
         self.status = "input"
@@ -1473,11 +1495,15 @@ class AsyncPregelLoop(PregelLoop, AbstractAsyncContextManager):
             if saved.pending_writes is not None
             else []
         )
+        self._pending_write_futs = []
         self.submit = await self.stack.enter_async_context(
             AsyncBackgroundExecutor(self.config)
         )
-        self.channels, self.managed = channels_from_checkpoint(
-            self.specs, self.checkpoint
+        self.channels, self.managed = await achannels_from_checkpoint(
+            self.specs,
+            self.checkpoint,
+            saver=self.checkpointer,
+            config=self.checkpoint_config,
         )
         self.stack.push(self._suppress_interrupt)
         self.status = "input"

libs/langgraph/langgraph/pregel/main.py

@@ -122,6 +122,7 @@ from langgraph.pregel._algo import (
 )
 from langgraph.pregel._call import identifier
 from langgraph.pregel._checkpoint import (
+    achannels_from_checkpoint,
     channels_from_checkpoint,
     copy_checkpoint,
     create_checkpoint,
@@ -1052,6 +1053,10 @@ class Pregel(
         channels, managed = channels_from_checkpoint(
             self.channels,
             saved.checkpoint,
+            saver=self.checkpointer
+            if isinstance(self.checkpointer, BaseCheckpointSaver)
+            else None,
+            config=saved.config,
         )
         # tasks for this checkpoint
         next_tasks = prepare_next_tasks(
@@ -1168,9 +1173,13 @@ class Pregel(
 
         step = saved.metadata.get("step", -1) + 1
         stop = step + 2
-        channels, managed = channels_from_checkpoint(
+        channels, managed = await achannels_from_checkpoint(
             self.channels,
             saved.checkpoint,
+            saver=self.checkpointer
+            if isinstance(self.checkpointer, BaseCheckpointSaver)
+            else None,
+            config=saved.config,
         )
         # tasks for this checkpoint
         next_tasks = prepare_next_tasks(
@@ -1541,6 +1550,11 @@ class Pregel(
             channels, managed = channels_from_checkpoint(
                 self.channels,
                 checkpoint,
+                saver=self.checkpointer
+                if saved is not None
+                and isinstance(self.checkpointer, BaseCheckpointSaver)
+                else None,
+                config=saved.config if saved is not None else None,
             )
             values, as_node = updates[0][:2]
 
@@ -1984,9 +1998,14 @@ class Pregel(
             )
             if saved:
                 checkpoint_config = patch_configurable(config, saved.config[CONF])
-            channels, managed = channels_from_checkpoint(
+            channels, managed = await achannels_from_checkpoint(
                 self.channels,
                 checkpoint,
+                saver=self.checkpointer
+                if saved is not None
+                and isinstance(self.checkpointer, BaseCheckpointSaver)
+                else None,
+                config=saved.config if saved is not None else None,
             )
             values, as_node = updates[0][:2]
             # no values, just clear all tasks

libs/langgraph/tests/test_channels.py

@@ -2,13 +2,17 @@ import operator
 from collections.abc import Sequence
 
 import pytest
+from langchain_core.messages import AIMessage, HumanMessage
+from langgraph.checkpoint.base import DELTA_SENTINEL
 
 from langgraph._internal._typing import MISSING
 from langgraph.channels.binop import BinaryOperatorAggregate
+from langgraph.channels.delta import DeltaChannel
 from langgraph.channels.last_value import LastValue
 from langgraph.channels.topic import Topic
 from langgraph.channels.untracked_value import UntrackedValue
 from langgraph.errors import EmptyChannelError, InvalidUpdateError
+from langgraph.graph.message import add_messages
 
 pytestmark = pytest.mark.anyio
 
@@ -117,3 +121,594 @@ def test_untracked_value() -> None:
     new_channel = UntrackedValue(dict).from_checkpoint(checkpoint)
     with pytest.raises(EmptyChannelError):
         new_channel.get()
+
+
+def test_delta_channel_basic_two_steps() -> None:
+    from langchain_core.messages import AIMessage, HumanMessage
+    from langgraph.checkpoint.base import DELTA_SENTINEL
+
+    from langgraph.graph.message import add_messages
+
+    ch = DeltaChannel(add_messages).from_checkpoint(MISSING)
+
+    # Step 1: one message added
+    ch.update([HumanMessage(content="hi", id="h1")])
+    d1 = ch.checkpoint()
+    assert d1 is DELTA_SENTINEL
+
+    # Step 2: another message
+    ch.update([AIMessage(content="hello", id="a1")])
+    d2 = ch.checkpoint()
+    assert d2 is DELTA_SENTINEL
+
+    # Full accumulated value is preserved in memory
+    assert len(ch.get()) == 2
+    assert ch.get()[0].content == "hi"
+    assert ch.get()[1].content == "hello"
+
+
+def test_delta_channel_from_checkpoint_writes_list() -> None:
+    """replay_writes on a fresh channel replays through the operator."""
+    from langchain_core.messages import AIMessage, HumanMessage
+
+    from langgraph.graph.message import add_messages
+
+    spec = DeltaChannel(add_messages)
+    ch = spec.from_checkpoint(DELTA_SENTINEL)
+    ch.replay_writes(
+        [
+            ("t0", "messages", HumanMessage(content="hi", id="h1")),
+            ("t1", "messages", AIMessage(content="hello", id="a1")),
+            ("t2", "messages", HumanMessage(content="bye", id="h2")),
+        ]
+    )
+    msgs = ch.get()
+    assert len(msgs) == 3
+    assert msgs[0].content == "hi"
+    assert msgs[1].content == "hello"
+    assert msgs[2].content == "bye"
+
+
+def test_delta_channel_from_checkpoint_backwards_compat() -> None:
+    from langchain_core.messages import HumanMessage
+
+    from langgraph.graph.message import add_messages
+
+    # Old BinaryOperatorAggregate checkpoint: plain list treated as backward compat
+    spec = DeltaChannel(add_messages)
+    old_value = [HumanMessage(content="old", id="h1")]
+    ch = spec.from_checkpoint(old_value)
+    assert ch.get() == old_value
+
+
+def test_delta_channel_overwrite() -> None:
+    from langchain_core.messages import HumanMessage
+    from langgraph.checkpoint.base import DELTA_SENTINEL
+
+    from langgraph.graph.message import add_messages
+    from langgraph.types import Overwrite
+
+    ch = DeltaChannel(add_messages).from_checkpoint(MISSING)
+    ch.update([HumanMessage(content="old", id="h1")])
+
+    ch.update([Overwrite([HumanMessage(content="new", id="h2")])])
+    d = ch.checkpoint()
+    assert d is DELTA_SENTINEL
+    # After overwrite, value is reset to only the new message
+    assert len(ch.get()) == 1
+    assert ch.get()[0].content == "new"
+
+
+def test_delta_channel_remove_message_and_replay() -> None:
+    """RemoveMessage must round-trip correctly when writes are replayed."""
+    from langchain_core.messages import AIMessage, HumanMessage, RemoveMessage
+
+    from langgraph.graph.message import add_messages
+
+    spec = DeltaChannel(add_messages)
+    ch = spec.from_checkpoint(MISSING)
+
+    # Step 1: add two messages
+    ch.update([HumanMessage(content="hi", id="h1")])
+    ch.update([AIMessage(content="hello", id="a1")])
+    assert ch.get() == [
+        HumanMessage(content="hi", id="h1"),
+        AIMessage(content="hello", id="a1"),
+    ]
+
+    # Step 2: remove the AI message
+    ch.update([RemoveMessage(id="a1")])
+    assert ch.get() == [HumanMessage(content="hi", id="h1")]
+
+    # Replay the writes list from scratch — must reproduce the post-remove state
+    ch2 = spec.from_checkpoint(DELTA_SENTINEL)
+    ch2.replay_writes(
+        [
+            ("t0", "messages", HumanMessage(content="hi", id="h1")),
+            ("t1", "messages", AIMessage(content="hello", id="a1")),
+            ("t2", "messages", RemoveMessage(id="a1")),
+        ]
+    )
+    assert ch2.get() == [HumanMessage(content="hi", id="h1")]
+
+
+def test_delta_channel_update_by_id_and_replay() -> None:
+    """Updating a message by ID must round-trip correctly through writes replay."""
+    from langchain_core.messages import HumanMessage
+
+    from langgraph.graph.message import add_messages
+
+    spec = DeltaChannel(add_messages)
+    ch = spec.from_checkpoint(MISSING)
+
+    # Step 1: add a message
+    ch.update([HumanMessage(content="original", id="h1")])
+
+    # Step 2: update the same message by ID
+    ch.update([HumanMessage(content="updated", id="h1")])
+    assert ch.get() == [HumanMessage(content="updated", id="h1")]
+
+    # Replay writes — must produce the updated message, not the original
+    ch2 = spec.from_checkpoint(DELTA_SENTINEL)
+    ch2.replay_writes(
+        [
+            ("t0", "messages", HumanMessage(content="original", id="h1")),
+            ("t1", "messages", HumanMessage(content="updated", id="h1")),
+        ]
+    )
+    assert len(ch2.get()) == 1
+    assert ch2.get()[0].content == "updated"
+
+
+def test_delta_channel_checkpoint_returns_sentinel() -> None:
+    """checkpoint() always returns DELTA_SENTINEL regardless of state."""
+    from langgraph.checkpoint.base import DELTA_SENTINEL
+
+    from langgraph.graph.message import add_messages
+
+    ch = DeltaChannel(add_messages).from_checkpoint(MISSING)
+    assert ch.checkpoint() is DELTA_SENTINEL
+
+    from langchain_core.messages import HumanMessage
+
+    ch.update([HumanMessage(content="hi", id="h1")])
+    assert ch.checkpoint() is DELTA_SENTINEL
+
+
+def test_delta_channel_snapshot_step_based() -> None:
+    """Snapshots fire on every Nth step regardless of whether the channel was written.
+
+    With snapshot_frequency=N, every Nth pregel step produces a _DeltaSnapshot
+    blob — even if the channel had no write that step (eager snapshot). This
+    bounds the ancestor walk to at most N steps on any read.
+    """
+    from typing import Annotated
+
+    from langchain_core.messages import AIMessage, HumanMessage
+    from langgraph.checkpoint.memory import InMemorySaver
+    from langgraph.checkpoint.serde.types import _DeltaSnapshot
+    from typing_extensions import TypedDict
+
+    from langgraph.graph import START, StateGraph
+    from langgraph.graph.message import add_messages
+
+    # snapshot_frequency=5: snapshot every 5 pregel steps
+    class State(TypedDict):
+        messages: Annotated[list, DeltaChannel(add_messages, snapshot_frequency=5)]
+        other: str
+
+    def node_a(state: State) -> dict:
+        # writes to messages
+        i = len(state["messages"]) // 2
+        return {"messages": [AIMessage(content=f"a{i}", id=f"a{i}")]}
+
+    def node_b(state: State) -> dict:
+        # writes ONLY to other, not messages — snapshot must still fire at step N
+        return {"other": "y"}
+
+    g = StateGraph(State)
+    g.add_node("a", node_a)
+    g.add_node("b", node_b)
+    g.add_edge(START, "a")
+    g.add_edge("a", "b")
+    saver = InMemorySaver()
+    graph = g.compile(checkpointer=saver)
+
+    config = {"configurable": {"thread_id": "t1"}}
+    for i in range(6):
+        graph.invoke(
+            {"messages": [HumanMessage(content=f"h{i}", id=f"h{i}")], "other": ""},
+            config,
+        )
+
+    # Confirm at least one snapshot blob exists for messages
+    msg_blob_values = [
+        saver.serde.loads_typed((type_tag, blob))
+        for k, (type_tag, blob) in saver.blobs.items()
+        if k[2] == "messages" and type_tag == "msgpack" and blob
+    ]
+    snapshots = [v for v in msg_blob_values if isinstance(v, _DeltaSnapshot)]
+    assert snapshots, "expected at least one _DeltaSnapshot blob for messages"
+
+    # Final state must be correct regardless of snapshot cadence
+    state = graph.get_state(config)
+    assert len(state.values["messages"]) == 12  # 6 human + 6 AI
+
+
+def test_delta_channel_snapshot_fires_even_when_not_written() -> None:
+    """Eager snapshot: _DeltaSnapshot stored at snapshot step even when the
+    channel had no write that step (node_b doesn't touch messages).
+    """
+    from typing import Annotated
+
+    from langchain_core.messages import AIMessage, HumanMessage
+    from langgraph.checkpoint.memory import InMemorySaver
+    from langgraph.checkpoint.serde.types import _DeltaSnapshot
+    from typing_extensions import TypedDict
+
+    from langgraph.graph import START, StateGraph
+    from langgraph.graph.message import add_messages
+
+    class State(TypedDict):
+        messages: Annotated[list, DeltaChannel(add_messages, snapshot_frequency=3)]
+        tick: int
+
+    def writer(state: State) -> dict:
+        i = len(state["messages"]) // 2
+        return {"messages": [AIMessage(content=f"a{i}", id=f"a{i}")]}
+
+    def ticker(state: State) -> dict:
+        # never writes messages
+        return {"tick": state["tick"] + 1}
+
+    g = StateGraph(State)
+    g.add_node("writer", writer)
+    g.add_node("ticker", ticker)
+    g.add_edge(START, "writer")
+    g.add_edge("writer", "ticker")
+    saver = InMemorySaver()
+    graph = g.compile(checkpointer=saver)
+
+    config = {"configurable": {"thread_id": "t1"}}
+    for i in range(5):
+        graph.invoke(
+            {"messages": [HumanMessage(content=f"h{i}", id=f"h{i}")], "tick": 0},
+            config,
+        )
+
+    # Count distinct message channel blob versions
+    msg_blobs = {
+        k: saver.serde.loads_typed((t, b))
+        for k, (t, b) in saver.blobs.items()
+        if k[2] == "messages" and t == "msgpack" and b
+    }
+    snapshots = {k: v for k, v in msg_blobs.items() if isinstance(v, _DeltaSnapshot)}
+    # There must be snapshots (ticker steps are snapshot steps too)
+    assert snapshots, (
+        "eager snapshots must fire even on steps where messages wasn't written"
+    )
+
+    # All get_state calls must return the correct accumulated value
+    state = graph.get_state(config)
+    assert len(state.values["messages"]) == 10  # 5 human + 5 AI
+
+
+def test_delta_channel_inmemory_saver_assembles_writes() -> None:
+    """InMemorySaver assembles writes from checkpoint_writes inside get_tuple."""
+    from typing import Annotated
+
+    from langchain_core.messages import AIMessage, HumanMessage
+    from langgraph.checkpoint.memory import InMemorySaver
+    from typing_extensions import TypedDict
+
+    from langgraph.graph import START, StateGraph
+    from langgraph.graph.message import add_messages
+
+    class State(TypedDict):
+        messages: Annotated[list, DeltaChannel(add_messages)]
+
+    n = {"v": 0}
+
+    def respond(state: State) -> dict:
+        n["v"] += 1
+        return {"messages": [AIMessage(content=f"ok{n['v']}", id=f"ai{n['v']}")]}
+
+    builder = StateGraph(State)
+    builder.add_node("respond", respond)
+    builder.add_edge(START, "respond")
+    saver = InMemorySaver()
+    graph = builder.compile(checkpointer=saver)
+    config = {"configurable": {"thread_id": "t1"}}
+
+    graph.invoke({"messages": [HumanMessage(content="hi", id="h1")]}, config)
+    graph.invoke({"messages": [HumanMessage(content="bye", id="h2")]}, config)
+
+    # get_tuple returns raw storage shape — channel_values stores DELTA_SENTINEL
+    # for delta channels; the reconstructed writes flow separately via
+    # saver._get_channel_writes_history.
+    saved = saver.get_tuple(config)
+    assert saved is not None
+    assert "messages" in saved.checkpoint["channel_values"]
+    assert saved.checkpoint["channel_values"]["messages"] is DELTA_SENTINEL
+
+    state = graph.get_state(config)
+    assert len(state.values["messages"]) == 4  # 2 human + 2 AI
+
+
+# ---------------------------------------------------------------------------
+# Dict-reducer tests
+# ---------------------------------------------------------------------------
+
+
+def _delta_channel_with_type(operator, typ):
+    """Build a DeltaChannel with an explicit type via the Annotated injection path."""
+    from typing import Annotated
+
+    from langgraph.channels.delta import DeltaChannel
+    from langgraph.graph.state import _get_channel
+
+    return _get_channel("_test", Annotated[typ, DeltaChannel(operator)])
+
+
+def test_delta_channel_dict_reducer_fresh_channel() -> None:
+    """DeltaChannel with a dict reducer starts as empty dict on MISSING checkpoint."""
+
+    def merge_dicts(left: dict, right: dict) -> dict:
+        return {**left, **right}
+
+    ch = _delta_channel_with_type(merge_dicts, dict).from_checkpoint(MISSING)
+    assert ch.is_available()
+    assert ch.get() == {}
+
+
+def test_delta_channel_dict_reducer_basic_updates() -> None:
+    """DeltaChannel with a dict reducer accumulates key/value pairs across steps."""
+
+    def merge_dicts(left: dict, right: dict) -> dict:
+        return {**left, **right}
+
+    ch = _delta_channel_with_type(merge_dicts, dict).from_checkpoint(MISSING)
+
+    ch.update([{"a": 1}])
+    d1 = ch.checkpoint()
+    assert d1 is DELTA_SENTINEL
+
+    ch.update([{"b": 2}])
+    d2 = ch.checkpoint()
+    assert d2 is DELTA_SENTINEL
+
+    assert ch.get() == {"a": 1, "b": 2}
+
+
+def test_delta_channel_dict_reducer_writes_reconstruction() -> None:
+    """replay_writes on a fresh channel replays through a dict merge reducer."""
+
+    def merge_dicts(left: dict, right: dict) -> dict:
+        return {**left, **right}
+
+    spec = _delta_channel_with_type(merge_dicts, dict)
+    ch = spec.from_checkpoint(DELTA_SENTINEL)
+    ch.replay_writes(
+        [
+            ("t0", "files", {"a": 1}),
+            ("t1", "files", {"b": 2}),
+            ("t2", "files", {"c": 3}),
+        ]
+    )
+    assert ch.get() == {"a": 1, "b": 2, "c": 3}
+
+
+def test_delta_channel_dict_reducer_with_deletions() -> None:
+    """Dict reducer that treats None values as deletions works end-to-end."""
+
+    def merge_files(left: dict | None, right: dict) -> dict:
+        if left is None:
+            return {k: v for k, v in right.items() if v is not None}
+        result = {**left}
+        for k, v in right.items():
+            if v is None:
+                result.pop(k, None)
+            else:
+                result[k] = v
+        return result
+
+    ch = _delta_channel_with_type(merge_files, dict).from_checkpoint(MISSING)
+    ch.update([{"file1.py": "content1", "file2.py": "content2"}])
+    ch.update([{"file1.py": None, "file3.py": "content3"}])
+    assert ch.get() == {"file2.py": "content2", "file3.py": "content3"}
+
+    spec = _delta_channel_with_type(merge_files, dict)
+    ch2 = spec.from_checkpoint(DELTA_SENTINEL)
+    ch2.replay_writes(
+        [
+            ("t0", "files", {"file1.py": "content1", "file2.py": "content2"}),
+            ("t1", "files", {"file1.py": None, "file3.py": "content3"}),
+        ]
+    )
+    assert ch2.get() == {"file2.py": "content2", "file3.py": "content3"}
+
+
+def test_delta_channel_dict_reducer_overwrite_in_update() -> None:
+    """Overwrite(dict) in update() must preserve dict shape, not coerce to list."""
+    from langgraph.types import Overwrite
+
+    def merge_dicts(left: dict, right: dict) -> dict:
+        return {**left, **right}
+
+    ch = _delta_channel_with_type(merge_dicts, dict).from_checkpoint(MISSING)
+    ch.update([{"a": 1}])
+    ch.update([Overwrite({"b": 2, "c": 3})])
+    assert ch.get() == {"b": 2, "c": 3}
+
+
+def test_delta_channel_dict_reducer_overwrite_in_writes_replay() -> None:
+    """Overwrite(dict) embedded in replayed writes must reconstruct as dict."""
+    from langgraph.types import Overwrite
+
+    def merge_dicts(left: dict, right: dict) -> dict:
+        return {**left, **right}
+
+    spec = _delta_channel_with_type(merge_dicts, dict)
+    ch = spec.from_checkpoint(DELTA_SENTINEL)
+    ch.replay_writes(
+        [
+            ("t0", "files", {"a": 1}),
+            ("t1", "files", Overwrite({"x": 10, "y": 20})),
+            ("t2", "files", {"z": 30}),
+        ]
+    )
+    assert ch.get() == {"x": 10, "y": 20, "z": 30}
+
+
+def test_delta_channel_dict_reducer_with_notrequired_annotation() -> None:
+    """DeltaChannel infers dict type through `Annotated[NotRequired[dict[...]], ch]`."""
+    from typing import Annotated
+
+    from typing_extensions import NotRequired
+
+    from langgraph.channels.delta import DeltaChannel
+    from langgraph.graph.state import _get_channel
+
+    def merge_dicts(left: dict | None, right: dict) -> dict:
+        if left is None:
+            return dict(right)
+        return {**left, **right}
+
+    annotation = Annotated[NotRequired[dict[str, int]], DeltaChannel(merge_dicts)]
+    ch = _get_channel("files", annotation).from_checkpoint(MISSING)
+    assert ch.get() == {}
+    ch.update([{"a": 1}])
+    ch.update([{"b": 2}])
+    assert ch.get() == {"a": 1, "b": 2}
+
+
+def test_delta_channel_dict_reducer_end_to_end_filesystem() -> None:
+    """End-to-end: graph with dict-reducer (filesystem-style) channel wrapped in DeltaChannel."""
+    from typing import Annotated
+
+    from langgraph.checkpoint.memory import InMemorySaver
+    from typing_extensions import TypedDict
+
+    from langgraph.channels.delta import DeltaChannel
+    from langgraph.graph import START, StateGraph
+
+    def merge_files(left: dict | None, right: dict) -> dict:
+        if left is None:
+            return {k: v for k, v in right.items() if v is not None}
+        result = {**left}
+        for k, v in right.items():
+            if v is None:
+                result.pop(k, None)
+            else:
+                result[k] = v
+        return result
+
+    class State(TypedDict):
+        files: Annotated[dict[str, str], DeltaChannel(merge_files)]
+
+    turn = {"v": 0}
+
+    def write_file(state: State) -> dict:
+        turn["v"] += 1
+        n = turn["v"]
+        return {"files": {f"/doc_{n}.txt": f"content for turn {n}"}}
+
+    builder = StateGraph(State)
+    builder.add_node("write_file", write_file)
+    builder.add_edge(START, "write_file")
+    saver = InMemorySaver()
+    graph = builder.compile(checkpointer=saver)
+    config = {"configurable": {"thread_id": "fs"}}
+
+    for _ in range(3):
+        graph.invoke({"files": {}}, config)
+
+    saved = saver.get_tuple(config)
+    assert saved is not None
+    assert saved.checkpoint["channel_values"]["files"] is DELTA_SENTINEL
+    state = graph.get_state(config)
+    assert state.values["files"] == {
+        "/doc_1.txt": "content for turn 1",
+        "/doc_2.txt": "content for turn 2",
+        "/doc_3.txt": "content for turn 3",
+    }
+
+    def delete_file(state: State) -> dict:
+        return {"files": {"/doc_1.txt": None}}
+
+    builder2 = StateGraph(State)
+    builder2.add_node("write_file", write_file)
+    builder2.add_node("delete_file", delete_file)
+    builder2.add_edge(START, "write_file")
+    builder2.add_edge("write_file", "delete_file")
+    turn["v"] = 0
+    saver2 = InMemorySaver()
+    graph2 = builder2.compile(checkpointer=saver2)
+    config2 = {"configurable": {"thread_id": "fs2"}}
+    graph2.invoke({"files": {}}, config2)
+    state2 = graph2.get_state(config2)
+    assert state2.values["files"] == {}
+
+
+def test_delta_channel_dict_reducer_backwards_compat() -> None:
+    """A pre-DeltaChannel dict checkpoint must load as a dict, not be listified."""
+
+    def merge_dicts(left: dict, right: dict) -> dict:
+        return {**left, **right}
+
+    spec = _delta_channel_with_type(merge_dicts, dict)
+    old_value = {"a": 1, "b": 2}
+    ch = spec.from_checkpoint(old_value)
+    assert ch.get() == {"a": 1, "b": 2}
+
+
+# ---------------------------------------------------------------------------
+# seed / pre-delta migration
+# ---------------------------------------------------------------------------
+
+
+def test_delta_channel_from_checkpoint_honors_seed() -> None:
+    """A non-sentinel value to from_checkpoint is used as the pre-delta seed.
+
+    Guards the pre-delta migration path: when the saver's ancestor walk hits
+    a pre-DeltaChannel blob it passes it as `seed` so replay reconstructs
+    the post-migration state correctly rather than replaying from empty.
+    """
+    spec = DeltaChannel(add_messages)
+    seed = [HumanMessage(content="pre-delta", id="p1")]
+    ch = spec.from_checkpoint(seed)
+    ch.replay_writes(
+        [
+            ("t0", "messages", AIMessage(content="delta-1", id="d1")),
+            ("t1", "messages", HumanMessage(content="delta-2", id="d2")),
+        ]
+    )
+    msgs = ch.get()
+    assert [m.content for m in msgs] == ["pre-delta", "delta-1", "delta-2"]
+
+
+def test_delta_channel_from_checkpoint_seed_without_writes() -> None:
+    """Reconstruction at a pre-delta ancestor with no newer deltas returns
+    just the seed — the saver's terminator fired immediately."""
+    spec = DeltaChannel(add_messages)
+    seed = [HumanMessage(content="only-snap", id="s1")]
+    ch = spec.from_checkpoint(seed)
+    ch.replay_writes([])
+    assert ch.get() == seed
+
+
+def test_delta_channel_from_checkpoint_seed_none_is_distinct_from_sentinel() -> None:
+    """`seed=None` must start replay from None, not from an empty channel.
+
+    The DELTA_SENTINEL / MISSING sentinels mean 'no seed'; passing `None`
+    explicitly should feed None to the reducer as the left operand.
+    """
+
+    def replace(left, right):
+        return right
+
+    spec = DeltaChannel(replace)
+    ch = spec.from_checkpoint(None)
+    ch.replay_writes([("t0", "x", "after")])
+    # Reducer replaces; seed=None → first write produces "after".
+    assert ch.get() == "after"

libs/langgraph/tests/test_delta_channel_benchmark.py

@@ -0,0 +1,472 @@
+"""Benchmark: DeltaChannel snapshot_frequency — storage vs. read-depth tradeoff.
+
+Run directly:  python tests/test_delta_channel_benchmark.py
+Run via pytest: pytest tests/test_delta_channel_benchmark.py -s
+
+Part 1 — baseline (original): DeltaChannel(inf) vs add_messages (BinOp).
+Part 2 — snapshot_frequency sweep: shows the storage/read-latency tradeoff
+  across frequencies [1, 5, 10, 50, inf] at scale.
+
+Key insight:
+  snapshot_frequency=inf  → O(N) storage, O(N) read depth (pure delta)
+  snapshot_frequency=N    → O(N²/N) storage, O(N) read depth bounded by freq
+  snapshot_frequency=1    → O(N²) storage, O(1) read depth (full snapshot)
+"""
+
+from __future__ import annotations
+
+import contextlib
+import math
+import sys
+import time
+from typing import Annotated, Any
+
+import pytest
+from langchain_core.messages import AIMessage, HumanMessage
+from langgraph.checkpoint.memory import MemorySaver
+from typing_extensions import TypedDict
+
+from langgraph.channels.delta import DeltaChannel
+from langgraph.graph import END, StateGraph
+from langgraph.graph.message import add_messages
+
+try:
+    from langgraph.checkpoint.postgres import PostgresSaver
+
+    _POSTGRES_AVAILABLE = True
+    _POSTGRES_URI = "postgres://sydney_runkle@localhost:5441/postgres?sslmode=disable"
+except ImportError:
+    _POSTGRES_AVAILABLE = False
+
+# ---------------------------------------------------------------------------
+# Realistic message payload (~100 tokens / ~400 chars each)
+# ---------------------------------------------------------------------------
+
+_HUMAN_TEMPLATE = (
+    "I need help understanding the implications of {topic} on our system architecture. "
+    "Specifically, I'm concerned about how this interacts with our existing {concern} "
+    "and whether we need to refactor the {component} layer before proceeding. "
+    "We've had prior incidents in this area and want to be deliberate. "
+    "What should we prioritize first, and are there known failure modes we should design around from the start?"
+)
+
+_AI_TEMPLATE = (
+    "Great question about {topic}. The key insight here is that {concern} introduces "
+    "a subtle ordering dependency that most teams overlook until they hit it in production. "
+    "For your {component} layer specifically, I'd recommend starting with a careful audit "
+    "of the interface boundaries before making any structural changes. This will give you "
+    "a clear picture of the blast radius and let you sequence the migration safely."
+)
+
+_TOPICS = [
+    "distributed tracing",
+    "eventual consistency",
+    "schema migration",
+    "backpressure handling",
+    "idempotency guarantees",
+    "cache invalidation",
+    "connection pooling",
+    "rate limiting",
+    "circuit breaking",
+    "observability pipelines",
+]
+
+_CONCERNS = [
+    "concurrency model",
+    "retry semantics",
+    "state management",
+    "error propagation",
+    "latency budget",
+]
+
+_COMPONENTS = [
+    "persistence",
+    "routing",
+    "ingestion",
+    "aggregation",
+    "serialization",
+]
+
+
+def _human_content(i: int) -> str:
+    return _HUMAN_TEMPLATE.format(
+        topic=_TOPICS[i % len(_TOPICS)],
+        concern=_CONCERNS[i % len(_CONCERNS)],
+        component=_COMPONENTS[i % len(_COMPONENTS)],
+    )
+
+
+def _ai_content(i: int) -> str:
+    return _AI_TEMPLATE.format(
+        topic=_TOPICS[i % len(_TOPICS)],
+        concern=_CONCERNS[i % len(_CONCERNS)],
+        component=_COMPONENTS[i % len(_COMPONENTS)],
+    )
+
+
+# ---------------------------------------------------------------------------
+# State definitions
+# ---------------------------------------------------------------------------
+
+
+class BinaryState(TypedDict):
+    messages: Annotated[list, add_messages]
+
+
+class DeltaState(TypedDict):
+    messages: Annotated[list, DeltaChannel(add_messages)]
+
+
+def _make_delta_state(snapshot_frequency: int | float) -> type:
+    """Create a TypedDict with DeltaChannel at the given snapshot_frequency."""
+    channel = DeltaChannel(add_messages, snapshot_frequency=snapshot_frequency)
+    # Use the functional TypedDict form so the Annotated type is stored as an
+    # already-evaluated object rather than a forward-reference string (which
+    # would fail when get_type_hints tries to resolve 'snapshot_frequency').
+    return TypedDict(  # type: ignore[return-value]
+        f"DeltaState_freq{snapshot_frequency}",
+        {"messages": Annotated[list, channel]},
+    )
+
+
+# ---------------------------------------------------------------------------
+# Graph factory
+# ---------------------------------------------------------------------------
+
+
+def _make_graph(state_cls: type, checkpointer: Any = None) -> Any:
+    def human_node(state: Any) -> dict:
+        return {}
+
+    def ai_node(state: Any) -> dict:
+        i = len(state["messages"]) // 2
+        return {"messages": [AIMessage(content=_ai_content(i), id=f"a{i}")]}
+
+    g = StateGraph(state_cls)
+    g.add_node("human", human_node)
+    g.add_node("ai", ai_node)
+    g.add_edge("human", "ai")
+    g.add_edge("ai", END)
+    g.set_entry_point("human")
+    return g.compile(checkpointer=checkpointer or MemorySaver())
+
+
+# ---------------------------------------------------------------------------
+# Measurement helpers
+# ---------------------------------------------------------------------------
+
+
+def _total_blob_bytes(saver: MemorySaver) -> int:
+    total = 0
+    for (_, _, _, _), (type_tag, blob) in saver.blobs.items():
+        if blob is not None:
+            total += len(blob)
+    return total
+
+
+def _run_turns(
+    n_turns: int,
+    state_cls: type,
+    checkpointer: Any = None,
+) -> tuple[float, float, int]:
+    """Run n_turns conversation turns.
+
+    Returns (write_elapsed_s, read_elapsed_s, total_blob_bytes).
+    Read latency is the average of 5 get_state calls after the full history
+    is built — forces state rehydration including ancestor replay if needed.
+    """
+    graph = _make_graph(state_cls, checkpointer)
+    config = {"configurable": {"thread_id": "bench"}}
+
+    t0 = time.perf_counter()
+    for i in range(n_turns):
+        graph.invoke(
+            {"messages": [HumanMessage(content=_human_content(i), id=f"h{i}")]},
+            config,
+        )
+    write_elapsed = time.perf_counter() - t0
+
+    t1 = time.perf_counter()
+    for _ in range(5):
+        graph.get_state(config)
+    read_elapsed = (time.perf_counter() - t1) / 5
+
+    blob_bytes = (
+        _total_blob_bytes(graph.checkpointer)
+        if isinstance(graph.checkpointer, MemorySaver)
+        else -1
+    )
+    return write_elapsed, read_elapsed, blob_bytes
+
+
+def _fmt_bytes(n: int) -> str:
+    if n >= 1_000_000:
+        return f"{n / 1_000_000:.1f} MB"
+    if n >= 1_000:
+        return f"{n / 1_000:.1f} KB"
+    return f"{n} B"
+
+
+def _approx_tokens(n_turns: int) -> str:
+    tokens = n_turns * 200
+    if tokens >= 1_000_000:
+        return f"~{tokens / 1_000_000:.1f}M tok"
+    if tokens >= 1_000:
+        return f"~{tokens / 1_000:.0f}K tok"
+    return f"~{tokens} tok"
+
+
+# ---------------------------------------------------------------------------
+# Checkpointer factories
+# ---------------------------------------------------------------------------
+
+
+@contextlib.contextmanager
+def _pg_saver(thread_id: str = "bench"):
+    """Context manager that yields a fresh PostgresSaver and cleans up after."""
+    with PostgresSaver.from_conn_string(_POSTGRES_URI) as saver:
+        saver.setup()
+        with saver._cursor() as cur:
+            for tbl in ("checkpoints", "checkpoint_blobs", "checkpoint_writes"):
+                cur.execute(f"DELETE FROM {tbl} WHERE thread_id = %s", (thread_id,))
+        yield saver
+        with saver._cursor() as cur:
+            for tbl in ("checkpoints", "checkpoint_blobs", "checkpoint_writes"):
+                cur.execute(f"DELETE FROM {tbl} WHERE thread_id = %s", (thread_id,))
+
+
+def _checkpointers() -> list[tuple[str, Any]]:
+    """Return (label, saver_or_None) pairs for available checkpointers."""
+    result: list[tuple[str, Any]] = [("InMemory", None)]
+    if _POSTGRES_AVAILABLE:
+        try:
+            import psycopg
+
+            psycopg.connect(_POSTGRES_URI).close()
+            result.append(("Postgres", "postgres"))
+        except Exception:
+            pass
+    return result
+
+
+# ---------------------------------------------------------------------------
+# Part 1: baseline DeltaChannel(inf) vs add_messages
+# ---------------------------------------------------------------------------
+
+BASELINE_TURN_COUNTS = [10, 25, 50, 100, 500]
+DELTA_ONLY_TURN_COUNTS = [1000]
+
+
+def _run_baseline_for_checkpointer(cp_label: str, cp_hint: Any) -> None:
+    W = 72
+
+    def _make_saver():
+        if cp_hint is None:
+            return contextlib.nullcontext(None)
+        return _pg_saver()
+
+    rows: list[tuple[int, Any, Any, Any, Any, Any, Any]] = []
+    for turns in BASELINE_TURN_COUNTS:
+        with _make_saver() as saver:
+            b_wt, b_rt, b_bytes = _run_turns(turns, BinaryState, saver)
+        with _make_saver() as saver:
+            d_wt, d_rt, d_bytes = _run_turns(turns, DeltaState, saver)
+        rows.append((turns, b_bytes, d_bytes, b_rt, d_rt, b_wt, d_wt))
+    for turns in DELTA_ONLY_TURN_COUNTS:
+        with _make_saver() as saver:
+            d_wt, d_rt, d_bytes = _run_turns(turns, DeltaState, saver)
+        rows.append((turns, None, d_bytes, None, d_rt, None, d_wt))
+
+    def _bytes_or_na(v: Any) -> str:
+        if v is None or v < 0:
+            return "n/a"
+        return _fmt_bytes(v)
+
+    def _ms_or_na(v: Any) -> str:
+        return "n/a" if v is None else f"{v * 1000:.1f}ms"
+
+    print(f"\n  [{cp_label}] Storage (blob bytes)")
+    print(
+        f"  {'turns':>6}  {'ctx':>10}  {'add_msgs':>12}  {'delta(inf)':>12}  {'savings':>8}"
+    )
+    print("  " + "-" * (W - 2))
+    for turns, b_bytes, d_bytes, b_rt, d_rt, b_wt, d_wt in rows:
+        if b_bytes is None or b_bytes < 0 or d_bytes is None or d_bytes < 0:
+            ratio_str = "n/a"
+        else:
+            ratio = b_bytes / d_bytes if d_bytes else float("inf")
+            ratio_str = f"{ratio:.0f}x"
+        print(
+            f"  {turns:>6}  {_approx_tokens(turns):>10}  "
+            f"{_bytes_or_na(b_bytes):>12}  {_bytes_or_na(d_bytes):>12}  {ratio_str:>8}"
+        )
+
+    print(f"\n  [{cp_label}] Read latency (avg of 5 get_state calls)")
+    print(f"  {'turns':>6}  {'ctx':>10}  {'add_msgs':>12}  {'delta(inf)':>12}")
+    print("  " + "-" * (W - 2))
+    for turns, b_bytes, d_bytes, b_rt, d_rt, b_wt, d_wt in rows:
+        print(
+            f"  {turns:>6}  {_approx_tokens(turns):>10}  "
+            f"{_ms_or_na(b_rt):>12}  {_ms_or_na(d_rt):>12}"
+        )
+
+
+def run_baseline_benchmark() -> None:
+    print()
+    print("Part 1 — DeltaChannel(inf) vs add_messages: storage & latency")
+    print("=" * 72)
+    for cp_label, cp_hint in _checkpointers():
+        _run_baseline_for_checkpointer(cp_label, cp_hint)
+    print()
+
+
+# ---------------------------------------------------------------------------
+# Part 2: snapshot_frequency sweep
+# ---------------------------------------------------------------------------
+
+# Frequencies to test. 1 = always snapshot (like BinOp), inf = pure delta.
+SNAPSHOT_FREQUENCIES: list[int | float] = [1, 5, 10, 50, math.inf]
+
+# Turn counts for the sweep — high enough to show storage divergence.
+SWEEP_TURN_COUNTS = [50, 100, 500]
+
+
+def _freq_label(freq: int | float) -> str:
+    if freq == math.inf:
+        return "inf"
+    return str(int(freq))
+
+
+def _run_sweep_for_checkpointer(cp_label: str, cp_hint: Any) -> None:
+    def _make_saver():
+        if cp_hint is None:
+            return contextlib.nullcontext(None)
+        return _pg_saver()
+
+    # Collect results: {turns: {freq_label: (write_s, read_s, bytes)}}
+    results: dict[int, dict[str, tuple[float, float, int]]] = {}
+    for turns in SWEEP_TURN_COUNTS:
+        results[turns] = {}
+        for freq in SNAPSHOT_FREQUENCIES:
+            state_cls = _make_delta_state(freq)
+            with _make_saver() as saver:
+                wt, rt, bb = _run_turns(turns, state_cls, saver)
+            results[turns][_freq_label(freq)] = (wt, rt, bb)
+
+    freq_labels = [_freq_label(f) for f in SNAPSHOT_FREQUENCIES]
+    col_w = 12
+
+    header = f"  {'turns':>6}  {'ctx':>10}" + "".join(
+        f"  {f'freq={freq_label}':>{col_w}}" for freq_label in freq_labels
+    )
+
+    print(f"\n  [{cp_label}] Storage (blob bytes) — lower is better")
+    print(header)
+    print("  " + "-" * (len(header) - 2))
+    for turns in SWEEP_TURN_COUNTS:
+        row = f"  {turns:>6}  {_approx_tokens(turns):>10}"
+        for label in freq_labels:
+            _, _, bb = results[turns][label]
+            row += f"  {_fmt_bytes(bb) if bb >= 0 else 'n/a':>{col_w}}"
+        print(row)
+
+    print(f"\n  [{cp_label}] Read latency (avg of 5 get_state) — lower is better")
+    print(header)
+    print("  " + "-" * (len(header) - 2))
+    for turns in SWEEP_TURN_COUNTS:
+        row = f"  {turns:>6}  {_approx_tokens(turns):>10}"
+        for label in freq_labels:
+            _, rt, _ = results[turns][label]
+            row += f"  {f'{rt * 1000:.1f}ms':>{col_w}}"
+        print(row)
+
+    print(
+        f"\n  [{cp_label}] Per-invoke write latency (total / turns) — lower is better"
+    )
+    print(header)
+    print("  " + "-" * (len(header) - 2))
+    for turns in SWEEP_TURN_COUNTS:
+        row = f"  {turns:>6}  {_approx_tokens(turns):>10}"
+        for label in freq_labels:
+            wt, _, _ = results[turns][label]
+            row += f"  {f'{(wt / turns) * 1000:.1f}ms':>{col_w}}"
+        print(row)
+
+
+def run_snapshot_freq_benchmark() -> None:
+    print()
+    print("Part 2 — DeltaChannel snapshot_frequency sweep")
+    print("Lower freq → fewer snapshots → less storage but deeper read replay")
+    print("=" * 80)
+    for cp_label, cp_hint in _checkpointers():
+        _run_sweep_for_checkpointer(cp_label, cp_hint)
+    print()
+    print("Legend:")
+    print(
+        "  freq=1    snapshot every write (full blob always — same as add_messages / BinOp)"
+    )
+    print("  freq=N    snapshot every N writes; read walks at most N ancestor writes")
+    print("  freq=inf  pure delta; read walks entire ancestor chain")
+    print()
+
+
+# ---------------------------------------------------------------------------
+# Pytest entry points
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.skip(
+    reason="slow benchmark — run manually with: python tests/test_delta_channel_benchmark.py"
+)
+def test_delta_channel_baseline_benchmark(capsys: Any) -> None:
+    """DeltaChannel(inf) uses less storage than add_messages at scale."""
+    with capsys.disabled():
+        run_baseline_benchmark()
+
+    for turns in [25, 50]:
+        _, _, b_bytes = _run_turns(turns, BinaryState)
+        _, _, d_bytes = _run_turns(turns, DeltaState)
+        assert d_bytes < b_bytes, (
+            f"DeltaChannel should use less storage at {turns} turns, "
+            f"got delta={d_bytes} binary={b_bytes}"
+        )
+
+
+@pytest.mark.skip(
+    reason="slow benchmark — run manually with: python tests/test_delta_channel_benchmark.py"
+)
+def test_snapshot_freq_benchmark(capsys: Any) -> None:
+    """snapshot_frequency trades storage for bounded read depth."""
+    with capsys.disabled():
+        run_snapshot_freq_benchmark()
+
+    # Correctness: results at all frequencies should agree on final state.
+    n_turns = 20
+    states: dict[str, list] = {}
+    for freq in SNAPSHOT_FREQUENCIES:
+        state_cls = _make_delta_state(freq)
+        graph = _make_graph(state_cls)
+        config = {"configurable": {"thread_id": "correctness"}}
+        for i in range(n_turns):
+            graph.invoke(
+                {"messages": [HumanMessage(content=_human_content(i), id=f"h{i}")]},
+                config,
+            )
+        state = graph.get_state(config)
+        states[_freq_label(freq)] = [m.id for m in state.values["messages"]]
+
+    ref = states["inf"]
+    for label, msg_ids in states.items():
+        assert msg_ids == ref, (
+            f"freq={label} produced different message IDs than freq=inf"
+        )
+
+
+# ---------------------------------------------------------------------------
+# Script entry point
+# ---------------------------------------------------------------------------
+
+if __name__ == "__main__":
+    run_baseline_benchmark()
+    run_snapshot_freq_benchmark()
+    sys.exit(0)

libs/langgraph/tests/test_delta_channel_migration.py

@@ -0,0 +1,504 @@
+"""Tests for the BinaryOperatorAggregate -> DeltaChannel migration path.
+
+A thread written under `BinaryOperatorAggregate(...)` must keep working
+after its annotation is swapped to `DeltaChannel(...)` on the same
+checkpointer — pre-migration state visible at each *settled* ancestor
+checkpoint is preserved, and post-migration writes fold on top through
+the reducer.
+
+Mechanism under test: the saver's `_get_channel_writes_history(config,
+channel)` walks the parent chain; when it encounters an ancestor whose
+`channel_values[channel]` is a real value (not `DELTA_SENTINEL`), it
+returns that as the `seed`. `DeltaChannel.from_checkpoint(seed)` uses
+it as the base value, and `replay_writes(writes)` folds on-path deltas.
+
+Scenarios covered:
+
+1. **Basic migration (sync + async)**: build pre-migration state with
+   `BinaryOperatorAggregate`, swap the annotation to `DeltaChannel` on
+   the same checkpointer, and verify that every settled pre-migration
+   super-step boundary (`next=('__start__',)`) round-trips exactly
+   under the delta-channel view.
+2. **Time travel into a pre-migration checkpoint** after migration —
+   `graph.get_state(pre_migration_config)` at a settled ancestor
+   returns the same state as under the binop channel.
+3. **Continuing a migrated thread**: driving one more super-step after
+   migration produces a state that includes the pre-migration settled
+   prefix plus the new delta write — proving `from_checkpoint(seed)` +
+   `replay_writes` correctly fold post-migration deltas onto the
+   pre-migration seed.
+4. **Base-saver fallback path**: a third-party-style subclass that
+   removes the optimized `InMemorySaver` override and falls back to
+   `BaseCheckpointSaver._get_channel_writes_history` must produce the
+   same result as the optimized path.
+5. **Channel-type isolation across threads**: two threads on the same
+   checkpointer under the delta-channel graph — one freshly-started,
+   one migrated from pre-migration state — don't cross-contaminate.
+   The parent-chain walk is scoped to the thread.
+
+TODO: add postgres variants in the existing `libs/checkpoint-postgres`
+test files (different fixture setup; not this file).
+"""
+
+from __future__ import annotations
+
+import operator
+from typing import Annotated, Any
+
+import pytest
+from langgraph.checkpoint.memory import InMemorySaver
+from typing_extensions import TypedDict
+
+from langgraph.channels.binop import BinaryOperatorAggregate
+from langgraph.channels.delta import DeltaChannel
+from langgraph.graph import END, START, StateGraph
+
+pytestmark = pytest.mark.anyio
+
+
+# ---------------------------------------------------------------------------
+# Graph factories
+#
+# A minimal reducer (`operator.add` on lists of str) with a noop node keeps
+# state change localized to the HumanMessage-like payload passed through
+# `invoke`. That isolates the pre/post-migration parity assertions to
+# channel-hydration semantics.
+# ---------------------------------------------------------------------------
+
+
+def _noop(_state: Any) -> dict:
+    return {}
+
+
+def _binop_graph(checkpointer: Any) -> Any:
+    class BinopState(TypedDict):
+        items: Annotated[list, BinaryOperatorAggregate(list, operator.add)]
+
+    return (
+        StateGraph(BinopState)
+        .add_node("noop", _noop)
+        .add_edge(START, "noop")
+        .add_edge("noop", END)
+        .compile(checkpointer=checkpointer)
+    )
+
+
+def _delta_graph(checkpointer: Any) -> Any:
+    class DeltaState(TypedDict):
+        items: Annotated[list, DeltaChannel(operator.add)]
+
+    return (
+        StateGraph(DeltaState)
+        .add_node("noop", _noop)
+        .add_edge(START, "noop")
+        .add_edge("noop", END)
+        .compile(checkpointer=checkpointer)
+    )
+
+
+def _drive(graph: Any, config: dict, tag: str, n: int) -> None:
+    for i in range(n):
+        graph.invoke({"items": [f"{tag}{i}"]}, config)
+
+
+async def _adrive(graph: Any, config: dict, tag: str, n: int) -> None:
+    for i in range(n):
+        await graph.ainvoke({"items": [f"{tag}{i}"]}, config)
+
+
+def _settled_boundaries(history: list) -> list[tuple[dict, list]]:
+    """Return `[(config, items), ...]` for every checkpoint in `history`
+    whose `next == ('__start__',)` — the stable boundaries between invokes.
+    """
+    return [
+        (s.config, list(s.values.get("items", [])))
+        for s in history
+        if s.next == ("__start__",)
+    ]
+
+
+# ---------------------------------------------------------------------------
+# 1. Basic migration (sync + async)
+# ---------------------------------------------------------------------------
+
+
+def test_basic_migration_preserves_pre_migration_state() -> None:
+    """Build state under `BinaryOperatorAggregate`, migrate to
+    `DeltaChannel` on the same checkpointer, and verify that every
+    settled pre-migration super-step boundary round-trips exactly.
+
+    Settled boundaries (`next=('__start__',)`) are the stable hydration
+    targets for the migration path: writes that produced the NEXT
+    super-step are kept as `pending_writes` on the ancestor, so walking
+    from a descendant finds the ancestor's blob as the seed and
+    reconstructs the correct state.
+    """
+
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "basic-sync"}}
+
+    # Pre-migration: accumulate items across 3 invokes.
+    binop = _binop_graph(checkpointer)
+    _drive(binop, config, "u", 3)
+
+    pre_boundaries = _settled_boundaries(list(binop.get_state_history(config)))
+    assert len(pre_boundaries) >= 2, "expected multiple settled boundaries"
+
+    # Migrate: swap the annotation on the same checkpointer.
+    delta = _delta_graph(checkpointer)
+
+    for cfg, items in pre_boundaries:
+        snap = delta.get_state(cfg)
+        assert list(snap.values.get("items", [])) == items, (
+            f"snapshot mismatch at {cfg['configurable']['checkpoint_id']}: "
+            f"expected {items}, got {snap.values.get('items', [])}"
+        )
+
+
+async def test_basic_migration_preserves_pre_migration_state_async() -> None:
+    """Async variant of the basic migration scenario."""
+
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "basic-async"}}
+
+    binop = _binop_graph(checkpointer)
+    await _adrive(binop, config, "u", 3)
+
+    pre_history = [s async for s in binop.aget_state_history(config)]
+    pre_boundaries = _settled_boundaries(pre_history)
+    assert len(pre_boundaries) >= 2
+
+    delta = _delta_graph(checkpointer)
+
+    for cfg, items in pre_boundaries:
+        snap = await delta.aget_state(cfg)
+        assert list(snap.values.get("items", [])) == items, (
+            f"async snapshot mismatch at {cfg['configurable']['checkpoint_id']}"
+        )
+
+
+# ---------------------------------------------------------------------------
+# 2. Time travel into a pre-migration checkpoint after migration
+# ---------------------------------------------------------------------------
+
+
+def test_time_travel_into_pre_migration_checkpoint() -> None:
+    """After migration, `graph.get_state(pre_migration_config)` at a
+    settled ancestor returns the state as stored at that point."""
+
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "time-travel"}}
+
+    binop = _binop_graph(checkpointer)
+    _drive(binop, config, "u", 3)
+
+    pre_boundaries = _settled_boundaries(list(binop.get_state_history(config)))
+    assert pre_boundaries, "no settled ancestors to time-travel to"
+
+    delta = _delta_graph(checkpointer)
+
+    # Pick the oldest non-empty boundary — a long distance to walk back.
+    non_empty = [(cfg, items) for cfg, items in pre_boundaries if items]
+    assert non_empty, "expected at least one non-empty boundary"
+    target_cfg, expected_items = non_empty[-1]
+
+    snap = delta.get_state(target_cfg)
+    assert list(snap.values.get("items", [])) == expected_items
+
+
+# ---------------------------------------------------------------------------
+# 3. Continuing a migrated thread: deltas fold onto pre-migration seed
+# ---------------------------------------------------------------------------
+
+
+def test_continuing_migrated_thread_folds_deltas_on_seed() -> None:
+    """Resume a pre-migration settled ancestor via `invoke(None, cfg)`
+    under the delta-channel graph. Since the pre-migration checkpoint
+    has an existing `pending_writes` entry (the input for the NEXT
+    super-step), re-running from that ancestor reproduces the same
+    post-ancestor state as the original binop run.
+
+    This proves the seed-terminator + write-replay pipeline works
+    end-to-end across the migration boundary.
+    """
+
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "continue"}}
+
+    binop = _binop_graph(checkpointer)
+    _drive(binop, config, "u", 2)
+
+    # Pick the oldest settled boundary with non-empty state.
+    pre_boundaries = _settled_boundaries(list(binop.get_state_history(config)))
+    target_cfg, seed_items = next(
+        (cfg, items) for cfg, items in reversed(pre_boundaries) if items
+    )
+    assert seed_items, "need a non-empty seed boundary"
+
+    # Migrate and resume from the pre-migration ancestor. `invoke(None,
+    # cfg)` replays the pending writes staged at `cfg` under the new
+    # channel; the reducer folds those deltas onto the seed.
+    delta = _delta_graph(checkpointer)
+    result = delta.invoke(None, target_cfg)
+
+    # The resumed state must include the pre-migration seed items in order.
+    result_items = list(result.get("items", []))
+    for idx, prefix_item in enumerate(seed_items):
+        assert result_items[idx] == prefix_item, (
+            f"pre-migration seed item at {idx} not preserved: "
+            f"got {result_items[: idx + 1]}, expected {seed_items}"
+        )
+
+
+# ---------------------------------------------------------------------------
+# 4. Base-saver fallback path
+# ---------------------------------------------------------------------------
+
+
+class _ThirdPartyStyleSaver(InMemorySaver):
+    """Simulates a third-party saver that inherits the reference
+    `_get_channel_writes_history` implementation from
+    `BaseCheckpointSaver` rather than overriding it.
+
+    We rebind the two methods to the base-class versions (via MRO) so
+    the fallback path is exercised even though the storage layer is
+    still the in-memory one.
+    """
+
+    # MRO: [_ThirdPartyStyleSaver, InMemorySaver, BaseCheckpointSaver, ...]
+    _get_channel_writes_history = (  # type: ignore[assignment]
+        InMemorySaver.__mro__[1]._get_channel_writes_history  # type: ignore[attr-defined]
+    )
+    _aget_channel_writes_history = (  # type: ignore[assignment]
+        InMemorySaver.__mro__[1]._aget_channel_writes_history  # type: ignore[attr-defined]
+    )
+
+
+def test_base_saver_fallback_matches_optimized_override() -> None:
+    """The reference `BaseCheckpointSaver` implementation must produce
+    the same migration behavior as the optimized `InMemorySaver`
+    override. We drive the same migration scenario through both savers
+    and assert per-snapshot parity in the delta-channel view."""
+
+    # Fast path: optimized InMemorySaver override.
+    fast_saver = InMemorySaver()
+    fast_config = {"configurable": {"thread_id": "fast"}}
+    fast_binop = _binop_graph(fast_saver)
+    _drive(fast_binop, fast_config, "u", 3)
+    fast_delta = _delta_graph(fast_saver)
+    fast_history = [
+        (s.next, list(s.values.get("items", [])))
+        for s in fast_delta.get_state_history(fast_config)
+    ]
+
+    # Slow path: base-class fallback.
+    slow_saver = _ThirdPartyStyleSaver()
+    slow_config = {"configurable": {"thread_id": "slow"}}
+    slow_binop = _binop_graph(slow_saver)
+    _drive(slow_binop, slow_config, "u", 3)
+    slow_delta = _delta_graph(slow_saver)
+    slow_history = [
+        (s.next, list(s.values.get("items", [])))
+        for s in slow_delta.get_state_history(slow_config)
+    ]
+
+    assert slow_history == fast_history, (
+        "base-saver fallback should match optimized-override behavior; "
+        f"fast={fast_history}, slow={slow_history}"
+    )
+
+
+# ---------------------------------------------------------------------------
+# 5. Thread isolation under mixed-generation storage
+# ---------------------------------------------------------------------------
+
+
+def test_delta_and_migrated_threads_do_not_cross_contaminate() -> None:
+    """Two threads sharing a checkpointer — one migrated from
+    pre-migration state, one freshly-started under DeltaChannel — must
+    maintain independent state. The parent-chain walk in
+    `_get_channel_writes_history` must be scoped to the target thread.
+    """
+
+    checkpointer = InMemorySaver()
+    migrated_cfg = {"configurable": {"thread_id": "migrated"}}
+    fresh_cfg = {"configurable": {"thread_id": "fresh"}}
+
+    # Thread A: pre-migration build-up.
+    binop = _binop_graph(checkpointer)
+    _drive(binop, migrated_cfg, "m", 2)
+
+    # Thread B: fresh delta-channel run.
+    delta = _delta_graph(checkpointer)
+    _drive(delta, fresh_cfg, "f", 2)
+
+    # Thread A: migrate and confirm its state is anchored in its own
+    # thread's pre-migration history (tag 'm'), never mixing in tag 'f'.
+    migrated_boundaries = _settled_boundaries(
+        list(delta.get_state_history(migrated_cfg))
+    )
+    assert migrated_boundaries, "migrated thread has no settled boundaries"
+    for _, items in migrated_boundaries:
+        for it in items:
+            assert it.startswith("m"), (
+                f"migrated thread leaked item from other thread: {it}"
+            )
+
+    # Thread B: settled boundaries must only contain 'f' tags.
+    fresh_boundaries = _settled_boundaries(list(delta.get_state_history(fresh_cfg)))
+    assert fresh_boundaries, "fresh thread has no settled boundaries"
+    for _, items in fresh_boundaries:
+        for it in items:
+            assert it.startswith("f"), (
+                f"fresh thread leaked item from migrated thread: {it}"
+            )
+
+
+# ---------------------------------------------------------------------------
+# 6. Tip-of-pre-migration hydration: the latest checkpoint from a binop-run
+# thread has a real accumulated value in its own `channel_values["items"]`.
+# When hydrated under the delta-channel graph via `get_state(config)` with no
+# `checkpoint_id`, the short-circuit must use that value directly instead of
+# walking ancestors (which would skip the tip's own blob).
+# ---------------------------------------------------------------------------
+
+
+def test_tip_of_pre_migration_hydrates_directly() -> None:
+    """`graph.get_state(config)` at the latest (pre-migration) checkpoint
+    returns the full accumulated list stored in that checkpoint's own
+    `channel_values`. The hydration must not walk ancestors past it."""
+
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "tip-sync"}}
+
+    binop = _binop_graph(checkpointer)
+    _drive(binop, config, "u", 3)
+
+    binop_tip = binop.get_state(config)
+    expected_items = list(binop_tip.values.get("items", []))
+    assert expected_items == ["u0", "u1", "u2"], (
+        f"sanity: pre-migration tip should accumulate all 3 items, got {expected_items}"
+    )
+
+    delta = _delta_graph(checkpointer)
+
+    snap = delta.get_state(config)
+    assert list(snap.values.get("items", [])) == expected_items, (
+        f"tip hydration mismatch: expected {expected_items}, "
+        f"got {snap.values.get('items', [])}"
+    )
+
+
+async def test_tip_of_pre_migration_hydrates_directly_async() -> None:
+    """Async variant of the tip-of-pre-migration hydration scenario."""
+
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "tip-async"}}
+
+    binop = _binop_graph(checkpointer)
+    await _adrive(binop, config, "u", 3)
+
+    binop_tip = await binop.aget_state(config)
+    expected_items = list(binop_tip.values.get("items", []))
+    assert expected_items == ["u0", "u1", "u2"]
+
+    delta = _delta_graph(checkpointer)
+
+    snap = await delta.aget_state(config)
+    assert list(snap.values.get("items", [])) == expected_items, (
+        f"async tip hydration mismatch: expected {expected_items}, "
+        f"got {snap.values.get('items', [])}"
+    )
+
+
+# ---------------------------------------------------------------------------
+# 7. `update_state` after migration writes a real value to the new
+# checkpoint's `channel_values` (not a sentinel). Hydration must use it
+# directly — the ancestor walk would skip this blob and return stale state.
+# ---------------------------------------------------------------------------
+
+
+def test_update_state_after_migration_uses_written_value() -> None:
+    """After migrating and running at least one post-migration super-step
+    (so the thread's tip has a `DELTA_SENTINEL`), `update_state` writes a
+    concrete value to a new checkpoint's `channel_values`. `get_state`
+    must reflect that concrete value."""
+
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "update-state"}}
+
+    # Pre-migration: accumulate a little state.
+    binop = _binop_graph(checkpointer)
+    _drive(binop, config, "u", 2)
+
+    # Migrate and run one more super-step so the tip is a post-migration
+    # checkpoint with `DELTA_SENTINEL` in its own `channel_values`.
+    delta = _delta_graph(checkpointer)
+    delta.invoke({"items": ["post"]}, config)
+
+    # `update_state` writes a concrete value into a new checkpoint's blob
+    # via the reducer against the hydrated prior state.
+    delta.update_state(config, {"items": ["x", "y"]})
+
+    snap = delta.get_state(config)
+    updated_items = list(snap.values.get("items", []))
+    # Must include the "x","y" update; without the hydration fix, the
+    # update_state-written blob would be skipped in favor of an ancestor
+    # walk, and the update values would disappear.
+    assert "x" in updated_items and "y" in updated_items, (
+        f"update_state values missing from snapshot: {updated_items}"
+    )
+    # The "x","y" items should be folded onto the prior accumulated state,
+    # not stand alone. This verifies the update-written blob is used
+    # directly by `get_state` (no ancestor walk past it).
+    assert len(updated_items) >= 4, (
+        f"update_state snapshot should preserve pre-update state, got {updated_items}"
+    )
+    assert updated_items[-2:] == ["x", "y"], (
+        f"update_state deltas should be at the tail, got {updated_items}"
+    )
+
+
+# ---------------------------------------------------------------------------
+# 8. Fork from an `update_state` checkpoint: a new run branched off the
+# update_state-produced checkpoint must see that checkpoint's concrete
+# `channel_values` as its base, with new deltas folded on top.
+# ---------------------------------------------------------------------------
+
+
+def test_fork_from_update_state_checkpoint() -> None:
+    """Branching a new run from the checkpoint produced by `update_state`
+    must use that checkpoint's concrete blob as the base. Additional
+    deltas from the forked run fold onto it through the reducer."""
+
+    checkpointer = InMemorySaver()
+    config = {"configurable": {"thread_id": "fork"}}
+
+    # Pre-migration build-up, then migrate and add one post-migration step.
+    binop = _binop_graph(checkpointer)
+    _drive(binop, config, "u", 2)
+    delta = _delta_graph(checkpointer)
+    delta.invoke({"items": ["post"]}, config)
+
+    # Apply `update_state` and capture the returned config (references
+    # the new checkpoint produced by the update).
+    update_cfg = delta.update_state(config, {"items": ["x", "y"]})
+
+    update_snap = delta.get_state(update_cfg)
+    base_items = list(update_snap.values.get("items", []))
+    assert "x" in base_items and "y" in base_items, (
+        f"update_state values missing from snapshot: {base_items}"
+    )
+    assert base_items[-2:] == ["x", "y"], (
+        f"sanity: update_state deltas should be at the tail, got {base_items}"
+    )
+
+    # Fork: invoke from the update_state checkpoint with a new delta.
+    forked = delta.invoke({"items": ["fork0"]}, update_cfg)
+    forked_items = list(forked.get("items", []))
+    # The fork must see the update_state-written blob as its base (not
+    # walk past it), and the new delta must fold on top of it.
+    assert forked_items[: len(base_items)] == base_items, (
+        f"fork lost update_state base: base={base_items}, forked={forked_items}"
+    )
+    assert forked_items[-1] == "fork0", f"fork delta not appended: {forked_items}"

libs/langgraph/tests/test_pregel.py

@@ -41,6 +41,7 @@ from typing_extensions import NotRequired, TypedDict
 
 from langgraph._internal._constants import CONFIG_KEY_NODE_FINISHED, ERROR, PULL
 from langgraph.channels.binop import BinaryOperatorAggregate
+from langgraph.channels.delta import DeltaChannel
 from langgraph.channels.ephemeral_value import EphemeralValue
 from langgraph.channels.last_value import LastValue
 from langgraph.channels.topic import Topic
@@ -9400,3 +9401,273 @@ def test_fork_does_not_apply_pending_writes(
 
     # Should be: 1 (input) + 20 (forked node_a) + 100 (node_b) = 121
     assert result == {"value": 121}
+
+
+async def test_delta_channel_end_to_end_inmemory() -> None:
+    """Full graph run: DeltaChannel accumulates correctly across multiple turns."""
+    from langchain_core.messages import AIMessage, HumanMessage
+    from langgraph.checkpoint.memory import InMemorySaver
+
+    from langgraph.graph import START, StateGraph
+    from langgraph.graph.message import add_messages
+
+    class State(TypedDict):
+        messages: Annotated[list, DeltaChannel(add_messages)]
+
+    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")
+    graph = builder.compile(checkpointer=InMemorySaver())
+
+    config = {"configurable": {"thread_id": "diff-test-1"}}
+
+    # Turn 1
+    graph.invoke({"messages": [HumanMessage(content="hello", id="h1")]}, config)
+    # Turn 2
+    graph.invoke({"messages": [HumanMessage(content="world", id="h2")]}, config)
+    # Turn 3
+    graph.invoke({"messages": [HumanMessage(content="bye", id="h3")]}, config)
+
+    state = graph.get_state(config)
+    msgs = state.values["messages"]
+    # 3 human + 3 AI = 6 total
+    assert len(msgs) == 6, f"expected 6 messages, got {len(msgs)}: {msgs}"
+    assert msgs[0].content == "hello"
+    assert msgs[2].content == "world"
+    assert msgs[4].content == "bye"
+    assert msgs[1].content == "reply-1"
+    assert msgs[3].content == "reply-3"
+    assert msgs[5].content == "reply-5"
+
+
+async def test_delta_channel_time_travel() -> None:
+    """Time-travel back to turn-1 checkpoint and resume; continuation must not include turn-2 deltas."""
+    from langchain_core.messages import AIMessage, HumanMessage
+    from langgraph.checkpoint.memory import InMemorySaver
+
+    from langgraph.graph import START, StateGraph
+    from langgraph.graph.message import add_messages
+
+    class State(TypedDict):
+        messages: Annotated[list, DeltaChannel(add_messages)]
+
+    counter = {"n": 0}
+
+    def respond(state: State) -> dict:
+        counter["n"] += 1
+        return {
+            "messages": [
+                AIMessage(content=f"ai-{counter['n']}", id=f"ai-{counter['n']}")
+            ]
+        }
+
+    builder = StateGraph(State)
+    builder.add_node("respond", respond)
+    builder.add_edge(START, "respond")
+    saver = InMemorySaver()
+    graph = builder.compile(checkpointer=saver)
+
+    config = {"configurable": {"thread_id": "diff-time-travel"}}
+
+    # Run 2 turns: h1→ai-1, h2→ai-2
+    graph.invoke({"messages": [HumanMessage(content="h1", id="h1")]}, config)
+    graph.invoke({"messages": [HumanMessage(content="h2", id="h2")]}, config)
+
+    # Find the checkpoint after turn 1 (2 messages: h1 + ai-1)
+    history = list(graph.get_state_history(config))
+    after_turn1 = next(h for h in history if len(h.values.get("messages", [])) == 2)
+
+    assert len(after_turn1.values["messages"]) == 2
+    assert after_turn1.values["messages"][0].content == "h1"
+    assert after_turn1.values["messages"][1].content == "ai-1"
+
+    # Resume from turn-1 checkpoint: inject h3, expect 3 messages total (h1, ai-1, ai-N)
+    # NOT 5 messages (turn-2 deltas must not bleed into the resumed run)
+    result = graph.invoke(
+        {"messages": [HumanMessage(content="h3", id="h3")]},
+        after_turn1.config,
+    )
+    msgs = result["messages"]
+    # Should be: h1, ai-1, h3, ai-N — 4 messages total
+    assert len(msgs) == 4, (
+        f"expected 4 messages after time-travel resume, got {len(msgs)}: {msgs}"
+    )
+    assert msgs[0].content == "h1"
+    assert msgs[1].content == "ai-1"
+    assert msgs[2].content == "h3"
+
+
+async def test_delta_channel_remove_message_end_to_end() -> None:
+    """RemoveMessage inside a DeltaChannel graph must persist and reload correctly."""
+    from langchain_core.messages import AIMessage, HumanMessage, RemoveMessage
+    from langgraph.checkpoint.memory import InMemorySaver
+
+    from langgraph.graph import START, StateGraph
+    from langgraph.graph.message import add_messages
+
+    class State(TypedDict):
+        messages: Annotated[list, DeltaChannel(add_messages)]
+
+    def respond(state: State) -> dict:
+        return {"messages": [AIMessage(content="reply", id="ai-1")]}
+
+    def delete_first(state: State) -> dict:
+        # removes the first message
+        return {"messages": [RemoveMessage(id=state["messages"][0].id)]}
+
+    builder = StateGraph(State)
+    builder.add_node("respond", respond)
+    builder.add_node("delete_first", delete_first)
+    builder.add_edge(START, "respond")
+    builder.add_edge("respond", "delete_first")
+    graph = builder.compile(checkpointer=InMemorySaver())
+
+    config = {"configurable": {"thread_id": "diff-remove-test"}}
+    graph.invoke({"messages": [HumanMessage(content="hello", id="h1")]}, config)
+
+    state = graph.get_state(config)
+    msgs = state.values["messages"]
+    # h1 was removed, only ai-1 should remain
+    assert len(msgs) == 1, f"expected 1 message, got {len(msgs)}: {msgs}"
+    assert msgs[0].id == "ai-1"
+
+    # A subsequent turn must reconstruct from the checkpoint correctly
+    graph.invoke({"messages": [HumanMessage(content="again", id="h2")]}, config)
+    state = graph.get_state(config)
+    msgs = state.values["messages"]
+    # ai-1 + h2 + ai-1(second reply, same id overwrites) + h2 removed
+    # more simply: after second run we expect ai-1 updated + h2 remaining minus deleted h2
+    # just assert h1 is still gone
+    assert all(m.id != "h1" for m in msgs), (
+        "h1 should still be absent after second turn"
+    )
+
+
+async def test_delta_channel_update_by_id_end_to_end() -> None:
+    """Updating a message by ID via DeltaChannel must persist and reload correctly."""
+    from langchain_core.messages import HumanMessage
+    from langgraph.checkpoint.memory import InMemorySaver
+
+    from langgraph.graph import START, StateGraph
+    from langgraph.graph.message import add_messages
+
+    class State(TypedDict):
+        messages: Annotated[list, DeltaChannel(add_messages)]
+
+    def update_msg(state: State) -> dict:
+        # re-send h1 with updated content
+        return {"messages": [HumanMessage(content="updated", id="h1")]}
+
+    builder = StateGraph(State)
+    builder.add_node("update_msg", update_msg)
+    builder.add_edge(START, "update_msg")
+    graph = builder.compile(checkpointer=InMemorySaver())
+
+    config = {"configurable": {"thread_id": "diff-update-id-test"}}
+    graph.invoke({"messages": [HumanMessage(content="original", id="h1")]}, config)
+
+    state = graph.get_state(config)
+    msgs = state.values["messages"]
+    assert len(msgs) == 1, f"expected 1 message, got {len(msgs)}: {msgs}"
+    assert msgs[0].content == "updated"
+    assert msgs[0].id == "h1"
+
+    # Second turn: verify the updated state is the base for further accumulation
+    graph.invoke({"messages": [HumanMessage(content="new", id="h2")]}, config)
+    state = graph.get_state(config)
+    msgs = state.values["messages"]
+    ids = [m.id for m in msgs]
+    assert "h1" in ids  # h1 persists (updated, not duplicated)
+    assert "h2" in ids
+    assert ids.count("h1") == 1, "h1 must not be duplicated"
+
+
+async def test_delta_channel_write_flushed_before_put() -> None:
+    """checkpoint_writes are flushed synchronously before put when DELTA_SENTINEL
+    is present, ensuring writes are durable before the sentinel blob is committed.
+
+    We verify this by intercepting put_writes and put calls and confirming
+    put_writes always completes before put is called for sentinel checkpoints.
+    """
+    import threading
+    from typing import Annotated
+
+    from langchain_core.messages import AIMessage, HumanMessage
+    from langgraph.checkpoint.base import DELTA_SENTINEL
+    from langgraph.checkpoint.memory import InMemorySaver
+
+    from langgraph.graph import START, StateGraph
+    from langgraph.graph.message import add_messages
+
+    class State(TypedDict):
+        messages: Annotated[list, DeltaChannel(add_messages)]
+
+    def respond(state: State) -> dict:
+        i = len(state["messages"])
+        return {"messages": [AIMessage(content=f"r{i}", id=f"ai{i}")]}
+
+    order: list[str] = []
+    lock = threading.Lock()
+    original_put_writes = InMemorySaver.put_writes
+    original_put = InMemorySaver.put
+
+    def tracked_put_writes(self, config, writes, task_id, task_path=""):
+        result = original_put_writes(self, config, writes, task_id, task_path)
+        with lock:
+            order.append("put_writes")
+        return result
+
+    def tracked_put(self, config, checkpoint, metadata, new_versions):
+        # Check if this checkpoint has any DELTA_SENTINEL blobs
+        has_sentinel = any(
+            v is DELTA_SENTINEL for v in checkpoint.get("channel_values", {}).values()
+        )
+        if has_sentinel:
+            with lock:
+                order.append("put_sentinel")
+        else:
+            with lock:
+                order.append("put_snapshot")
+        return original_put(self, config, checkpoint, metadata, new_versions)
+
+    InMemorySaver.put_writes = tracked_put_writes
+    InMemorySaver.put = tracked_put
+    try:
+        builder = StateGraph(State)
+        builder.add_node("respond", respond)
+        builder.add_edge(START, "respond")
+        saver = InMemorySaver()
+        graph = builder.compile(checkpointer=saver)
+        config = {"configurable": {"thread_id": "flush-test"}}
+
+        for i in range(3):
+            graph.invoke(
+                {"messages": [HumanMessage(content=f"h{i}", id=f"h{i}")]}, config
+            )
+
+        # For every sentinel put, all preceding put_writes must already be in order
+        for i, event in enumerate(order):
+            if event == "put_sentinel":
+                # All put_writes before this index must appear before this sentinel
+                preceding = order[:i]
+                assert "put_writes" in preceding, (
+                    f"put_sentinel at index {i} had no preceding put_writes: {order}"
+                )
+                # And the most recent put_writes must come before this sentinel
+                last_write_idx = max(
+                    j for j, e in enumerate(order[:i]) if e == "put_writes"
+                )
+                assert last_write_idx < i, (
+                    f"put_writes at {last_write_idx} not before put_sentinel at {i}"
+                )
+    finally:
+        InMemorySaver.put_writes = original_put_writes
+        InMemorySaver.put = original_put
+
+    # Final state must still be correct
+    state = graph.get_state(config)
+    assert len(state.values["messages"]) == 6  # 3 human + 3 AI

libs/langgraph/tests/test_time_travel.py

@@ -1161,9 +1161,7 @@ def test_subgraph_interrupt_resume_with_explicit_head_checkpoint_id(
     assert called == ["step_a", "ask_human"]
 
     # Resume with explicit head checkpoint_id in config
-    head_checkpoint_id = graph.get_state(config).config["configurable"][
-        "checkpoint_id"
-    ]
+    head_checkpoint_id = graph.get_state(config).config["configurable"]["checkpoint_id"]
     called.clear()
     resume_config = {
         "configurable": {

notes/delta_channel_query_bench.md

@@ -0,0 +1,137 @@
+# Delta-channel reconstruction: query strategy benchmark
+
+**Branch:** `delta-channel-writes-based`
+**Question (Nuno):** Is the recursive CTE the right query shape for reconstructing a delta channel inside `get_tuple`, or would a plain `SELECT WHERE` be cheaper even though it returns more rows?
+**Answer:** Plain `SELECT WHERE` wins at every realistic depth. The recursion isn't the problem — the JSON-expression join inside the CTE is.
+
+## Setup
+
+- Postgres 16 on `localhost:5441` (the `compose-postgres.yml` instance, run directly without docker for this round)
+- Single delta channel `messages`, one write per checkpoint, `DELTA_SENTINEL` blob per checkpoint
+- Linear chain (`branch=1`) and 5-way branching at every step (`branch=5`) — branching is the case where plain over-fetches sibling rows
+- Median of 20 timed runs after 3 warmups, fresh psycopg cursor per strategy
+- Bench script: `bench_get_tuple_strategies.py` at repo root
+
+Three strategies compared:
+
+| name | roundtrips | shape |
+|------|-----------|-------|
+| `cte` | 1 | Current prod: recursive CTE walks ancestors, LEFT JOINs writes + blobs |
+| `plain` | 3 | Nuno's suggestion: thread-wide `SELECT WHERE` per table, Python walks parent chain and filters |
+| `cte+narrow` | 2 | CTE returns ancestor IDs only, then one `UNION ALL` of writes + blobs filtered by `ANY(ids)` |
+
+## Results (ms per get_tuple, median of 20)
+
+```
+ depth  branch       cte       plain    cte+narrow   rows_cte   rows_plain   plain/cte
+    10       1     0.14ms      0.26ms       0.24ms          9           30      1.89x
+    10       5     0.21ms      0.23ms       0.17ms          9          110      1.11x
+    50       1     0.89ms      0.27ms       0.33ms         49          150      0.31x
+    50       5     2.35ms      0.66ms       0.51ms         49          550      0.28x
+   200       1    11.61ms      0.78ms       1.30ms        199          600      0.07x
+   200       5    34.79ms      2.33ms       3.07ms        199         2200      0.07x
+  1000       1   274.60ms      2.59ms      13.29ms        999         3000      0.01x
+  1000       5   856.01ms     10.14ms      15.31ms        999        11000      0.01x
+```
+
+Lower is better. `plain/cte < 1` means plain is faster.
+
+### Headline numbers
+
+- depth 50: plain is **3x** faster
+- depth 200: plain is **15x** faster
+- depth 1000: plain is **~100x** faster
+- Branching makes plain over-fetch (3000 rows → 11000 rows at d=1000), but it remains ~85x faster than the CTE
+
+## Why the CTE collapses
+
+`EXPLAIN (ANALYZE, BUFFERS)` of the CTE at depth 1000 (linear). Excerpt with the load-bearing nodes:
+
+```
+Sort  ... actual time=137.798..137.827 rows=999
+  CTE ancestors
+    ->  Recursive Union  ... actual time=0.005..2.443 rows=999
+                                                 ^^^^^^
+                                                 recursion is 2.4 ms — fine
+  ->  Nested Loop Left Join  ... actual time=2.676..137.529 rows=999
+        Join Filter: (cw.checkpoint_id = a.cid)
+        Rows Removed by Join Filter: 998001
+                                     ^^^^^^^
+                                     999 ancestors x ~1000 writes
+        ->  Nested Loop Left Join  ... actual time=2.669..85.061 rows=999
+              Join Filter: (bl.version = ((c.checkpoint -> 'channel_versions'::text) ->> bl.channel))
+              Rows Removed by Join Filter: 998001
+                                           ^^^^^^^
+                                           same quadratic blow-up on the blob join
+```
+
+Two pathological things are happening:
+
+1. **The blob join filter is on a JSON expression**: `bl.version = (c.checkpoint -> 'channel_versions' ->> bl.channel)`. The planner cannot push this into an index lookup, so it materializes `checkpoint_blobs` for the thread and does a nested-loop comparison against every ancestor — a Cartesian product that grows as `O(ancestors × blobs_in_thread)`.
+2. **The writes join is similar**: writes for the thread are materialized once, then nested-loop joined against ancestors with a `Join Filter` rather than a hash/merge join over the indexed `checkpoint_id`.
+
+At depth 1000 that's **~2 million rows evaluated, 99.9% of them discarded**. The recursion itself is a rounding error.
+
+For comparison, the plain Q1 (`SELECT … FROM checkpoints WHERE thread_id=? AND checkpoint_ns=?`) at depth 1000:
+
+```
+Seq Scan on checkpoints  ... actual time=0.012..0.121 rows=1000
+Execution Time: 0.140 ms
+```
+
+A simple seq scan over 57 buffers. Q2 and Q3 follow the same shape and complete in well under 1 ms each.
+
+## Crossover and remote-DB reasoning
+
+- Pure local Postgres: plain wins from depth ~30 onward; CTE wins by fractions of a ms below that
+- Remote Postgres at ~5 ms RTT adds ~10 ms to plain (3 roundtrips vs 1). Crossover shifts to ~depth 30. Above that, the CTE's quadratic SQL cost still dominates the RTT savings.
+
+There is no realistic conversation depth where the CTE wins on a remote DB. At depth 200+ (anything resembling a real multi-turn agent run) plain is faster regardless of network.
+
+## Recommendation
+
+**Switch to plain SELECT WHERE, one delta channel at a time.**
+
+Three indexed queries per delta channel:
+
+```sql
+-- Q1: parent chain + per-checkpoint version of this channel
+SELECT checkpoint_id,
+       parent_checkpoint_id,
+       checkpoint -> 'channel_versions' ->> 'channel_name' AS ver
+FROM checkpoints
+WHERE thread_id = ? AND checkpoint_ns = ?;
+
+-- Q2: writes for this channel, anywhere in the thread
+SELECT checkpoint_id, type, blob, task_id, idx
+FROM checkpoint_writes
+WHERE thread_id = ? AND checkpoint_ns = ? AND channel = ?;
+
+-- Q3: blobs for this channel, anywhere in the thread
+SELECT version, type, blob
+FROM checkpoint_blobs
+WHERE thread_id = ? AND checkpoint_ns = ? AND channel = ?;
+```
+
+Python then:
+- Builds `parent_of: dict[cid, parent_cid]` from Q1
+- Walks from target's parent newest → oldest
+- Filters Q2 rows by `ancestor_set`, processes oldest → newest, applies overwrite-terminator
+- Picks seed blob via the per-ancestor `ver` map, terminates at first non-sentinel blob
+
+All O(n) on n = thread checkpoints, with tight constants (dict lookups). No recursion, no JSON-expression joins, no quadratic plans.
+
+If the 3-roundtrip cost ever shows up on remote-DB benchmarks, fold Q2 + Q3 into one `UNION ALL` to get back to 2 roundtrips. Bench says it isn't worth the SQL complexity right now.
+
+## Bonus: code simplification from single-channel scope
+
+Multi-channel reconstruction in the current `_reconstruct_delta_channels_cur` carries:
+
+- `rows_by_cid` nested dicts, keyed by cid then channel
+- `seen_blob: set[(cid, channel)]` and `seen_write: set[(cid, channel, task_id, idx)]` dedup
+- `collected: dict[channel, list]`, `done: set[channel]`, `seeds: dict[channel, value]`
+- Inner `for ch in channels_list` loops and an early-exit `if len(done) == len(channels_list)`
+
+Single-channel collapses these to a single list, a single bool, and one `Optional[Any]`. Roughly half the Python in that function, plus an obvious shape for splitting pure post-processing into `base.py` so sync and async stop duplicating it.
+
+If multi-channel coalescing turns out to matter later, it can come back as a SQL-level optimization without re-introducing the bookkeeping in Python.