ANN support testing

.claude/skills/build-checkpointer/SKILL.md

@@ -1,177 +0,0 @@
----
-name: build-checkpointer
-description: Build a LangGraph checkpoint saver implementation that passes all conformance tests. Use when creating a new checkpointer for any storage backend (Redis, DynamoDB, MongoDB, etc.) or wrapping an existing storage client.
-disable-model-invocation: true
-user-invocable: true
-argument-hint: [storage-backend]
----
-
-# Build a Conformant LangGraph Checkpointer
-
-You are building a LangGraph checkpoint saver for the **$ARGUMENTS** storage backend. Your goal is FULL conformance: all 82 tests across 8 capabilities must pass.
-
-Read [interface-reference.md](interface-reference.md) for method signatures, data structures, and the conformance test harness template.
-Read [critical-contracts.md](critical-contracts.md) for the 8 most common failure points.
-Read [sqlite-reference.md](sqlite-reference.md) for patterns from a working implementation.
-
-## Ground Rules
-
-**You are not done until ALL conformance tests pass.** Do not stop after writing code — you must run the tests, read failures, fix, and re-run in a loop until you see FULL conformance. If you hit a wall, try a different approach rather than giving up.
-
-**No hacks or shortcuts.** Specifically:
-- Do NOT skip or xfail tests to make the suite "pass"
-- Do NOT weaken assertions or modify the conformance test suite itself
-- Do NOT use `# type: ignore` to paper over real type mismatches
-- Do NOT store data in global/module-level dicts to fake persistence — use the actual storage backend
-- Do NOT disable serialization or store raw Python objects — use `self.serde.dumps_typed` / `loads_typed`
-- Do NOT catch and swallow exceptions to hide failures
-
-**Flag security concerns.** As you implement:
-- Ensure all queries use parameterized statements — never interpolate user-provided values (thread_id, checkpoint_id, etc.) into SQL or query strings
-- Check for injection risks in metadata filtering (JSON path queries, NoSQL operators, etc.)
-- Ensure connection credentials are not hardcoded in the implementation — accept them as constructor args
-- Flag any backend client library that has known CVEs or security advisories
-- If the backend requires TLS/auth, note it prominently in the constructor docstring
-
-**Ask the user for help when you need it.** Don't guess or assume — ask when:
-- You need database connection details, credentials, or access
-- You're unsure which client library or driver to use
-- You need the user to start/stop a database service
-- You're stuck on a test failure after multiple attempts
-- You're unsure about a design decision (e.g., schema layout, indexing strategy)
-
-**Safety checks — ask the user to confirm:**
-- "Is this database safe to use for testing? Please confirm it is NOT a production database." (before running any tests that create/delete tables)
-- "I'm about to create tables and run destructive test operations (INSERT, DELETE, DROP). Is this OK?" (before first test run)
-- "What connection string / credentials should I use?" (never assume defaults for non-local databases)
-
-## Step 1: Understand the target
-
-Determine the storage backend from the arguments. If no arguments were provided, ask the user:
-- What storage backend? (Redis, DynamoDB, MongoDB, Cassandra, etc.)
-- From scratch, or wrapping an existing client/library?
-- Any connection/authentication requirements?
-- How do I connect to a test instance? (Docker compose, local install, cloud sandbox, etc.)
-
-Install the backend's Python client library if needed.
-
-## Step 2: Scaffold the package
-
-Create `libs/checkpoint-<backend>/` with this structure:
-
-```
-libs/checkpoint-<backend>/
-  pyproject.toml
-  Makefile
-  langgraph/
-    checkpoint/
-      <backend>/
-        __init__.py      # Main implementation
-  tests/
-    test_conformance.py  # Conformance harness
-```
-
-The `pyproject.toml` should depend on:
-- `langgraph-checkpoint` (the base interfaces)
-- The backend's client library
-- `langgraph-checkpoint-conformance` as a test dependency
-
-Model the `Makefile` after `libs/checkpoint-sqlite/Makefile`.
-
-## Step 3: Implement the checkpointer
-
-Subclass `BaseCheckpointSaver` and implement ALL 8 async methods:
-
-**Required (5):** `aput`, `aget_tuple`, `alist`, `aput_writes`, `adelete_thread`
-**Extended (3):** `adelete_for_runs`, `acopy_thread`, `aprune`
-
-Key implementation guidance:
-
-1. **Storage layout depends on your backend.** Choose the layout that fits your backend's strengths:
-   - **SQL databases (Postgres, MySQL, SQLite):** Use 3 tables — checkpoints, checkpoint_blobs (channel values keyed by version), checkpoint_writes. The blobs table avoids re-serializing unchanged large values on every checkpoint write. Inline primitive channel values (str, int, float, bool, None) in the checkpoint JSON; store non-primitives as blobs keyed by `(thread_id, checkpoint_ns, channel, version)`.
-   - **Document stores (MongoDB, DynamoDB, Firestore):** Use 2 collections — checkpoints (with channel values embedded) and writes. Serialize the full checkpoint including all channel values. The blob optimization adds complexity without much benefit in document stores.
-   - **Key-value stores (Redis, etcd):** Use composite keys to namespace checkpoints and writes. Store serialized checkpoint + writes as values.
-
-   See `critical-contracts.md` for composite key design.
-
-3. **Serialize blobs and writes with `self.serde`** — use `self.serde.dumps_typed(value)` which returns `(type_str, bytes)` and `self.serde.loads_typed((type_str, bytes))` for deserialization. For CPU-bound serialization, use `asyncio.to_thread()` to avoid blocking the event loop.
-
-4. **Serialize metadata as JSON** — use `get_checkpoint_metadata(config, metadata)` to merge config metadata before storing, then `json.dumps()`. Deserialize with `json.loads()`. Metadata is small enough to store inline (no blob table needed).
-
-5. **Handle `new_versions` correctly** — this is the #1 source of failures. The checkpoint's `channel_values` contains ALL channels, but `new_versions` only lists CHANGED channels. If using a blob table (SQL pattern), only write blobs for channels in `new_versions` and reference all versions in the checkpoint JSON. If storing the full checkpoint (document/KV pattern), just serialize all of `checkpoint["channel_values"]` — simpler and correct.
-
-6. **Handle `WRITES_IDX_MAP`** — special channels (ERROR, INTERRUPT, SCHEDULED, RESUME) use fixed negative indices. Regular writes use their positional index. Special channel writes should UPSERT (replace on conflict); regular writes should be idempotent (ignore on conflict).
-
-7. **Return correct `parent_config`** — the `checkpoint_id` in the incoming config to `aput` is the parent. When returning `CheckpointTuple`, set `parent_config` to a config with that parent checkpoint_id, or None if there was no parent.
-
-### Production-quality patterns
-
-Go beyond "just passing tests" — build something that performs well at scale:
-
-- **Connection pooling.** Accept both a single connection and a connection pool in the constructor. Use a pool for production workloads. For Postgres, use `psycopg_pool.AsyncConnectionPool`. For Redis, use the client's built-in pool. Document which to use.
-- **Use native backend features.** Don't treat the backend as a dumb key-value store. Examples:
-  - Postgres: use JSONB containment (`@>`) for metadata filtering, `COPY FROM STDIN` for bulk inserts, `DISTINCT ON` for pruning, pipeline mode for batching
-  - Redis: use Lua scripts for atomic operations, sorted sets for ordering, hash fields for channel blobs
-  - DynamoDB: use query vs scan appropriately, batch write items, GSIs for metadata filtering
-  - MongoDB: use `$match` aggregation stages, bulk write operations, compound indexes
-- **Batch writes where possible.** In `aput`, group the checkpoint insert and blob upserts into a single round-trip (pipeline, transaction, or batch write). Don't make N separate calls for N blobs.
-- **Fetch writes alongside checkpoints in a single query.** Use subqueries, JOINs, or array aggregation to avoid N+1 patterns where you fetch N checkpoints then query writes for each one separately.
-- **Use `asyncio.to_thread()` for CPU-bound serialization** — `serde.dumps_typed` and `serde.loads_typed` can be expensive for large values. Offload to a thread to keep the event loop responsive.
-- **Add appropriate indexes.** At minimum: primary/unique keys on all collections, and an index on `thread_id` for `adelete_thread`. For `adelete_for_runs`, consider an index on the metadata `run_id` field if the backend supports it.
-
-## Step 4: Run conformance and iterate — DO NOT STOP UNTIL GREEN
-
-```bash
-cd libs/checkpoint-<backend>
-pip install -e ".[test]"
-python -m pytest tests/test_conformance.py -x -v
-```
-
-Or run via `make test` if your Makefile is set up.
-
-**This is the core of the task.** You MUST loop:
-
-1. Run the conformance tests
-2. Read the failure output carefully — it tells you exactly which contract was violated
-3. Understand WHY it failed — read the test source in `libs/checkpoint-conformance/langgraph/checkpoint/conformance/spec/` if the error message isn't clear
-4. Fix the implementation with a proper solution (not a hack — see Ground Rules)
-5. Re-run. Go back to step 1.
-
-**Do not stop until `report.passed_all()` returns True.** If you've been through 5+ iterations and are still failing, step back and re-read the critical-contracts.md and the failing test source code. The answer is always in the test — it specifies exactly what the contract requires.
-
-**If you're blocked, ask the user.** Common things to ask about:
-- "The database isn't reachable — can you check the connection / start the service?"
-- "I'm stuck on this test failure after N attempts — here's what I've tried, can you help?"
-- "I need to install this package / run this command — is that OK?"
-
-Common failure patterns:
-- `test_put_incremental_channel_update` fails → you're not storing all channel values, only the ones in `new_versions`
-- `test_put_writes_idempotent` fails → your write upsert logic is wrong, check `WRITES_IDX_MAP` handling
-- `test_list_global_search` fails → you're requiring a thread_id when config is None
-- `test_get_tuple_pending_writes` fails → writes not ordered by `(task_id, idx)` or missing `task_id` in tuple
-- `test_list_metadata_filter_*` fails → metadata filtering not checking all keys, or not handling custom keys
-
-## Step 5: Final verification and review
-
-Run the full suite one more time with verbose output:
-
-```bash
-python -m pytest tests/test_conformance.py -v
-```
-
-Confirm the output shows FULL conformance (all 82 tests pass). The report should show:
-- PUT: all pass
-- PUT_WRITES: all pass
-- GET_TUPLE: all pass
-- LIST: all pass
-- DELETE_THREAD: all pass
-- DELETE_FOR_RUNS: all pass
-- COPY_THREAD: all pass
-- PRUNE: all pass
-
-Then do a final review of your implementation:
-
-1. **Run `make lint` and `make format`** to clean up the code
-2. **Security review** — check for SQL/query injection, hardcoded credentials, unvalidated inputs
-3. **Performance review** — check for N+1 query patterns (fetching writes per checkpoint in a loop), missing indexes on frequently-filtered columns, unnecessary full-table scans
-4. **Report findings** — tell the user about any security concerns, performance considerations, or caveats about the implementation

.claude/skills/build-checkpointer/critical-contracts.md

@@ -1,86 +0,0 @@
-# Critical Contracts — Common Failure Points
-
-These are the 8 requirements most likely to cause test failures. Get these right and you'll pass.
-
-## 1. Store the FULL checkpoint, not just the diff
-
-`aput` receives `new_versions` which lists only CHANGED channels. But `checkpoint["channel_values"]` contains ALL channels. You must store all of them. The `new_versions` parameter is informational — some implementations use it to optimize blob storage by only writing changed blobs, but the simplest correct approach is to serialize and store the entire checkpoint.
-
-**Failing test:** `test_put_incremental_channel_update`, `test_put_new_channel_added`, `test_put_channel_removed`
-
-## 2. Write idempotency with WRITES_IDX_MAP
-
-The unique key for a write is `(thread_id, checkpoint_ns, checkpoint_id, task_id, idx)`.
-
-The `idx` comes from `WRITES_IDX_MAP.get(channel, positional_index)`:
-- Special channels: `__error__` → -1, `__interrupt__` → -3, `__scheduled__` → -2, `__resume__` → -4
-- Regular channels: use their positional index in the writes list (0, 1, 2, ...)
-
-For **special channels** (all writes are in WRITES_IDX_MAP): use UPSERT (replace on conflict) because these channels get updated in place.
-
-For **regular channels**: use INSERT-ignore-on-conflict to be idempotent — calling `aput_writes` twice with the same `(task_id, idx)` must not create duplicates.
-
-```python
-if all(w[0] in WRITES_IDX_MAP for w in writes):
-    # UPSERT — replace existing
-else:
-    # INSERT OR IGNORE — idempotent
-```
-
-**Failing test:** `test_put_writes_idempotent`, `test_put_writes_special_channels`
-
-## 3. Namespace isolation
-
-`checkpoint_ns` (from `config["configurable"].get("checkpoint_ns", "")`) is part of the composite key for BOTH checkpoints and writes. Default to empty string `""` if not present.
-
-Two checkpoints with the same `thread_id` and `checkpoint_id` but different `checkpoint_ns` are DIFFERENT checkpoints.
-
-**Failing test:** `test_put_child_namespace`, `test_put_writes_across_namespaces`, `test_get_tuple_respects_namespace`
-
-## 4. Metadata round-trip
-
-Before storing metadata, call `get_checkpoint_metadata(config, metadata)` which merges additional keys from config. Store the result as JSON. When loading, deserialize back to dict.
-
-ALL keys must survive — standard ones (`source`, `step`, `parents`, `run_id`) AND custom keys the caller added.
-
-**Failing test:** `test_put_preserves_metadata`, `test_list_metadata_custom_keys`
-
-## 5. Global search: `alist(None, filter=...)`
-
-When `config` is `None`, `alist` must search across ALL threads. Don't require `thread_id`. Filter by metadata keys if `filter` is provided.
-
-**Failing test:** `test_list_global_search`
-
-## 6. parent_config in CheckpointTuple
-
-When `aput(config, checkpoint, ...)` is called, `config["configurable"].get("checkpoint_id")` is the PARENT checkpoint ID. Store this as `parent_checkpoint_id`.
-
-When returning `CheckpointTuple`:
-- If `parent_checkpoint_id` exists: set `parent_config = {"configurable": {"thread_id": ..., "checkpoint_ns": ..., "checkpoint_id": parent_checkpoint_id}}`
-- If no parent: set `parent_config = None`
-
-**Failing test:** `test_put_parent_config`, `test_get_tuple_parent_config`
-
-## 7. Pending writes in CheckpointTuple
-
-`pending_writes` must be a list of `(task_id, channel, deserialized_value)` tuples, ordered by `(task_id, idx)`.
-
-Every `aget_tuple` and every tuple yielded by `alist` must include pending writes. Don't forget to query the writes table/collection.
-
-**Failing test:** `test_get_tuple_pending_writes`, `test_list_includes_pending_writes`
-
-## 8. Checkpoint ordering in alist
-
-`alist` must return checkpoints in descending order by `checkpoint_id` (newest first). Checkpoint IDs are UUID-like strings that sort chronologically. Use `ORDER BY checkpoint_id DESC` or equivalent.
-
-The `before` parameter means: only return checkpoints with `checkpoint_id < before_checkpoint_id`.
-
-**Failing test:** `test_list_ordering`, `test_list_before`, `test_list_limit_plus_before`
-
-## 9. Storage design principles
-
-All backends must key checkpoints by `(thread_id, checkpoint_ns, checkpoint_id)` and writes by `(thread_id, checkpoint_ns, checkpoint_id, task_id, idx)`.
-
-- **SQL backends:** Consider a 3rd blobs table keyed by `(thread_id, checkpoint_ns, channel, version)` to avoid re-serializing unchanged large channel values. Only write blobs for channels in `new_versions`; reconstruct all values on read via `channel_versions`.
-- **Document/KV backends:** Embed all channel values directly in the checkpoint document/value. Serialize the full checkpoint on every `aput` — simpler and correct.
-- **All backends need:** descending `checkpoint_id` ordering for `alist`, metadata field filtering for `alist(filter=...)`, delete by `thread_id` for `adelete_thread`, delete by `metadata.run_id` for `adelete_for_runs`.

.claude/skills/build-checkpointer/interface-reference.md

@@ -1,228 +0,0 @@
-# Checkpointer Interface Reference
-
-## Imports
-
-```python
-from langgraph.checkpoint.base import (
-    WRITES_IDX_MAP,
-    BaseCheckpointSaver,
-    ChannelVersions,
-    Checkpoint,
-    CheckpointMetadata,
-    CheckpointTuple,
-    SerializerProtocol,
-    get_checkpoint_id,
-    get_checkpoint_metadata,
-)
-from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
-from langchain_core.runnables import RunnableConfig
-```
-
-## Data Structures
-
-```python
-# RunnableConfig["configurable"] keys:
-#   thread_id: str        — identifies the conversation thread
-#   checkpoint_ns: str    — namespace (empty string for root, dotted path for subgraphs)
-#   checkpoint_id: str    — unique monotonically-increasing ID (UUID-like, sortable)
-
-# Checkpoint (TypedDict):
-#   v: int                — format version (currently 1)
-#   id: str               — unique checkpoint ID
-#   ts: str               — ISO 8601 timestamp
-#   channel_values: dict[str, Any]   — serialized state per channel
-#   channel_versions: ChannelVersions — version number per channel
-#   versions_seen: dict[str, ChannelVersions] — per-node version tracking
-
-# CheckpointMetadata (TypedDict):
-#   source: str           — "input" | "loop" | "update" | "fork"
-#   step: int             — -1 for input, 0+ for loop steps
-#   parents: dict[str, str] — parent checkpoint IDs
-#   (plus any custom keys the caller adds)
-
-# CheckpointTuple (NamedTuple):
-#   config: RunnableConfig
-#   checkpoint: Checkpoint
-#   metadata: CheckpointMetadata
-#   parent_config: RunnableConfig | None
-#   pending_writes: list[tuple[str, str, Any]] | None
-#     Each write is (task_id, channel, value)
-
-# ChannelVersions = dict[str, Any]  (typically str or int version numbers)
-
-# WRITES_IDX_MAP = {"__error__": -1, "__scheduled__": -2, "__interrupt__": -3, "__resume__": -4}
-```
-
-## Method Signatures
-
-### Required Methods
-
-```python
-async def aput(
-    self,
-    config: RunnableConfig,
-    checkpoint: Checkpoint,
-    metadata: CheckpointMetadata,
-    new_versions: ChannelVersions,
-) -> RunnableConfig:
-    """Store a checkpoint. Return config with checkpoint_id set to checkpoint["id"].
-
-    The incoming config["configurable"]["checkpoint_id"] is the PARENT checkpoint ID.
-    new_versions contains only the channels that changed — but checkpoint["channel_values"]
-    has ALL channels. Store the full checkpoint.
-    """
-
-async def aget_tuple(self, config: RunnableConfig) -> CheckpointTuple | None:
-    """Retrieve a checkpoint.
-
-    If config has checkpoint_id: return that exact checkpoint.
-    If no checkpoint_id: return the LATEST checkpoint for the thread+namespace.
-    Return None if not found.
-    Include pending_writes as list of (task_id, channel, value) ordered by (task_id, idx).
-    """
-
-async def alist(
-    self,
-    config: RunnableConfig | None,
-    *,
-    filter: dict[str, Any] | None = None,
-    before: RunnableConfig | None = None,
-    limit: int | None = None,
-) -> AsyncIterator[CheckpointTuple]:
-    """List checkpoints, newest first (descending checkpoint_id).
-
-    If config is None: search ALL threads (global search).
-    If config has thread_id: filter to that thread.
-    filter: dict of metadata key-value pairs (AND logic).
-    before: only return checkpoints before this checkpoint_id.
-    limit: max number to return.
-    Each yielded tuple must include pending_writes.
-    """
-
-async def aput_writes(
-    self,
-    config: RunnableConfig,
-    writes: Sequence[tuple[str, Any]],
-    task_id: str,
-    task_path: str = "",
-) -> None:
-    """Store pending writes for a checkpoint.
-
-    Each write is (channel, value). Use WRITES_IDX_MAP.get(channel, idx) for the index.
-    Special channels (in WRITES_IDX_MAP) should UPSERT (replace on conflict).
-    Regular channels should be idempotent (ignore on conflict).
-    """
-
-async def adelete_thread(self, thread_id: str) -> None:
-    """Delete ALL checkpoints and writes for a thread (all namespaces)."""
-```
-
-### Extended Methods
-
-```python
-async def adelete_for_runs(self, run_ids: Sequence[str]) -> None:
-    """Delete checkpoints+writes where metadata.run_id is in run_ids."""
-
-async def acopy_thread(self, source_thread_id: str, target_thread_id: str) -> None:
-    """Copy all checkpoints+writes from source thread to target thread."""
-
-async def aprune(
-    self,
-    thread_ids: Sequence[str],
-    *,
-    strategy: str = "keep_latest",
-) -> None:
-    """Prune checkpoints for given threads.
-    strategy="keep_latest": keep only the latest checkpoint per thread+namespace.
-    strategy="delete_all": delete everything for those threads.
-    """
-```
-
-## Conformance Test Harness Template
-
-Create `tests/test_conformance.py`:
-
-```python
-"""Conformance tests for <Backend>Saver."""
-from __future__ import annotations
-
-import pytest
-from langgraph.checkpoint.conformance import checkpointer_test, validate
-from langgraph.checkpoint.conformance.report import ProgressCallbacks
-
-# Import your checkpointer
-from langgraph.checkpoint.<backend> import <Backend>Saver
-
-
-# Optional: lifespan for one-time setup/teardown (database creation, etc.)
-# async def backend_lifespan():
-#     # setup
-#     yield
-#     # teardown
-
-
-@checkpointer_test(name="<Backend>Saver")  # add lifespan=backend_lifespan if needed
-async def backend_checkpointer():
-    # Create and yield a fresh checkpointer instance.
-    # Use async with if your saver needs connection management.
-    saver = <Backend>Saver(...)
-    yield saver
-    # cleanup (close connections, etc.)
-
-
-@pytest.mark.asyncio
-async def test_full_conformance():
-    """<Backend>Saver passes ALL conformance tests."""
-    report = await validate(
-        backend_checkpointer,
-        progress=ProgressCallbacks.verbose(),
-    )
-    report.print_report()
-    assert report.passed_all(), f"Conformance failed: {report.to_dict()}"
-```
-
-## Serialization Pattern
-
-```python
-# In __init__:
-super().__init__(serde=serde)
-
-# Storing metadata (use JSON, not serde):
-merged = get_checkpoint_metadata(config, metadata)
-serialized_md = json.dumps(merged).encode("utf-8")
-
-# Loading metadata:
-metadata = json.loads(serialized_md_bytes)
-
-# Storing/loading blob values and write values (use serde):
-type_, serialized = self.serde.dumps_typed(value)
-value = self.serde.loads_typed((type_, serialized_bytes))
-
-# For CPU-bound serde in async context, offload to thread:
-type_, serialized = await asyncio.to_thread(self.serde.dumps_typed, value)
-value = await asyncio.to_thread(self.serde.loads_typed, (type_, serialized_bytes))
-```
-
-## Schema Design by Backend Type
-
-All backends must store checkpoints keyed by `(thread_id, checkpoint_ns, checkpoint_id)` and writes keyed by `(thread_id, checkpoint_ns, checkpoint_id, task_id, idx)`.
-
-### SQL backends (Postgres, MySQL, SQLite)
-
-Use 3 tables: **checkpoints** (checkpoint JSON with primitive channel_values inlined + channel_versions for blob lookup, metadata JSON), **checkpoint_blobs** (non-primitive channel values keyed by `(thread_id, checkpoint_ns, channel, version)`), and **checkpoint_writes** (pending writes). The blobs table avoids re-serializing unchanged large values — only write blobs for channels in `new_versions`. On read, JOIN blobs via `channel_versions` to reconstruct all channel values. PKs on all three tables handle most access patterns; add an index on the metadata `run_id` field for `adelete_for_runs`. Use subqueries/JOINs to fetch writes alongside checkpoints in a single round-trip.
-
-### Document stores (MongoDB, Firestore, DynamoDB)
-
-Use 2 collections: **checkpoints** (full checkpoint with all channel_values embedded, metadata as top-level fields) and **writes**. Serialize the full checkpoint including all channel values on every `aput`. Use composite `_id` or PK/SK from the key parts. Required indexes:
-- `(thread_id, checkpoint_ns, checkpoint_id DESC)` — for `alist` ordering and `aget_tuple` latest-lookup
-- `(thread_id)` — for `adelete_thread`
-- `(metadata.run_id)` — for `adelete_for_runs`
-- Use native query operators (e.g. MongoDB `$match`, DynamoDB filter expressions) for metadata filtering in `alist(filter=...)`
-
-### Key-value stores (Redis, etcd)
-
-Use composite keys like `cp:{thread_id}:{ns}:{id}`. Use sorted sets or equivalent for descending-order listing. **Requires manual secondary indexes** maintained on every write:
-- Thread index (`thread:{thread_id}` → set of `{ns}:{checkpoint_id}`) — for `adelete_thread` and `alist`
-- Run ID index (`run:{run_id}` → set of checkpoint keys) — for `adelete_for_runs`
-- Write index (`writes:{thread_id}:{ns}:{checkpoint_id}` → set of `{task_id}:{idx}`) — for pending writes lookup
-- Metadata filtering for `alist(filter=...)` is the hardest: either scan+deserialize, or maintain per-field indexes. For small datasets scanning is acceptable; for large ones consider a search module.

.claude/skills/build-checkpointer/sqlite-reference.md

@@ -1,174 +0,0 @@
-# SQLite Implementation Reference
-
-Working patterns from `libs/checkpoint-sqlite/langgraph/checkpoint/sqlite/aio.py`.
-
-## Schema
-
-```sql
-CREATE TABLE IF NOT EXISTS checkpoints (
-    thread_id TEXT NOT NULL,
-    checkpoint_ns TEXT NOT NULL DEFAULT '',
-    checkpoint_id TEXT NOT NULL,
-    parent_checkpoint_id TEXT,
-    type TEXT,
-    checkpoint BLOB,
-    metadata BLOB,
-    PRIMARY KEY (thread_id, checkpoint_ns, checkpoint_id)
-);
-
-CREATE TABLE IF NOT EXISTS writes (
-    thread_id TEXT NOT NULL,
-    checkpoint_ns TEXT NOT NULL DEFAULT '',
-    checkpoint_id TEXT NOT NULL,
-    task_id TEXT NOT NULL,
-    idx INTEGER NOT NULL,
-    channel TEXT NOT NULL,
-    type TEXT,
-    value BLOB,
-    PRIMARY KEY (thread_id, checkpoint_ns, checkpoint_id, task_id, idx)
-);
-```
-
-## aput pattern
-
-```python
-async def aput(self, config, checkpoint, metadata, new_versions):
-    thread_id = config["configurable"]["thread_id"]
-    checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
-    parent_checkpoint_id = config["configurable"].get("checkpoint_id")
-
-    type_, serialized_checkpoint = self.serde.dumps_typed(checkpoint)
-    serialized_metadata = json.dumps(
-        get_checkpoint_metadata(config, metadata), ensure_ascii=False
-    ).encode("utf-8", "ignore")
-
-    # UPSERT checkpoint row
-    await db.execute(
-        "INSERT OR REPLACE INTO checkpoints (...) VALUES (...)",
-        (thread_id, checkpoint_ns, checkpoint["id"], parent_checkpoint_id,
-         type_, serialized_checkpoint, serialized_metadata),
-    )
-
-    return {
-        "configurable": {
-            "thread_id": thread_id,
-            "checkpoint_ns": checkpoint_ns,
-            "checkpoint_id": checkpoint["id"],
-        }
-    }
-```
-
-## aget_tuple pattern
-
-```python
-async def aget_tuple(self, config):
-    checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
-
-    if checkpoint_id := get_checkpoint_id(config):
-        # Fetch specific checkpoint
-        query = "... WHERE thread_id = ? AND checkpoint_ns = ? AND checkpoint_id = ?"
-    else:
-        # Fetch latest
-        query = "... WHERE thread_id = ? AND checkpoint_ns = ? ORDER BY checkpoint_id DESC LIMIT 1"
-
-    row = await fetch_one(query, ...)
-    if not row:
-        return None
-
-    # Fetch pending writes for this checkpoint
-    writes = await fetch_all(
-        "SELECT task_id, channel, type, value FROM writes "
-        "WHERE thread_id = ? AND checkpoint_ns = ? AND checkpoint_id = ? "
-        "ORDER BY task_id, idx",
-        ...
-    )
-
-    return CheckpointTuple(
-        config={"configurable": {"thread_id": ..., "checkpoint_ns": ..., "checkpoint_id": ...}},
-        checkpoint=self.serde.loads_typed((type_, blob)),
-        metadata=json.loads(metadata_blob),
-        parent_config=(
-            {"configurable": {"thread_id": ..., "checkpoint_ns": ..., "checkpoint_id": parent_id}}
-            if parent_id else None
-        ),
-        pending_writes=[
-            (task_id, channel, self.serde.loads_typed((type_, value)))
-            for task_id, channel, type_, value in writes
-        ],
-    )
-```
-
-## alist pattern
-
-```python
-async def alist(self, config, *, filter=None, before=None, limit=None):
-    # Build WHERE clause dynamically
-    where_clauses = []
-    params = []
-
-    if config is not None:
-        where_clauses.append("thread_id = ?")
-        params.append(config["configurable"]["thread_id"])
-        if checkpoint_ns := config["configurable"].get("checkpoint_ns"):
-            where_clauses.append("checkpoint_ns = ?")
-            params.append(checkpoint_ns)
-
-    if filter:
-        # Filter on metadata JSON — for each key-value pair:
-        for key, value in filter.items():
-            where_clauses.append(f"json_extract(metadata, '$.{key}') = ?")
-            params.append(json.dumps(value) if not isinstance(value, (str, int, float)) else value)
-
-    if before:
-        before_id = before["configurable"]["checkpoint_id"]
-        where_clauses.append("checkpoint_id < ?")
-        params.append(before_id)
-
-    where = "WHERE " + " AND ".join(where_clauses) if where_clauses else ""
-    query = f"SELECT ... FROM checkpoints {where} ORDER BY checkpoint_id DESC"
-    if limit:
-        query += " LIMIT ?"
-        params.append(limit)
-
-    # For each checkpoint row, also fetch its writes (same as aget_tuple)
-```
-
-## aput_writes pattern
-
-```python
-async def aput_writes(self, config, writes, task_id, task_path=""):
-    thread_id = config["configurable"]["thread_id"]
-    checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
-    checkpoint_id = config["configurable"]["checkpoint_id"]
-
-    # Choose UPSERT vs INSERT-ignore based on channel types
-    if all(w[0] in WRITES_IDX_MAP for w in writes):
-        query = "INSERT OR REPLACE INTO writes (...) VALUES (...)"
-    else:
-        query = "INSERT OR IGNORE INTO writes (...) VALUES (...)"
-
-    rows = [
-        (thread_id, checkpoint_ns, checkpoint_id, task_id,
-         WRITES_IDX_MAP.get(channel, idx), channel,
-         *self.serde.dumps_typed(value))
-        for idx, (channel, value) in enumerate(writes)
-    ]
-    await executemany(query, rows)
-```
-
-## adelete_thread pattern
-
-```python
-async def adelete_thread(self, thread_id):
-    await execute("DELETE FROM checkpoints WHERE thread_id = ?", (thread_id,))
-    await execute("DELETE FROM writes WHERE thread_id = ?", (thread_id,))
-```
-
-## Key takeaway
-
-The SQLite implementation is ~300 lines and is the simplest correct reference. It uses 2 tables and serializes the full checkpoint as a single blob.
-
-- SQLite patterns show the simplest correct implementation of every contract
-- SQL backends can add a 3rd blobs table for performance (see `interface-reference.md` Schema Design section)
-- NoSQL backends should adapt the contracts to native idioms — focus on `critical-contracts.md`
-- Don't port SQL patterns to NoSQL; use your backend's native features (document embedding, sorted sets, composite keys, etc.)

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -1,60 +1,58 @@
 name: "\U0001F41B Bug Report"
-description: Report a bug in LangGraph. To report a security issue, please instead use the security option (below). For questions, please use the LangChain forum (below).
-labels: ["bug"]
-type: bug
+description: Report a bug in LangGraph. To report a security issue, please instead use the security option below. For questions, please use the GitHub Discussions.
+labels: ["02 Bug Report"]
 body:
   - type: markdown
     attributes:
-      value: |
-        Thank you for taking the time to file a bug report.
-
-        For usage questions, feature requests and general design questions, please use the [LangChain Forum](https://forum.langchain.com/).
-
-        Check these before submitting to see if your issue has already been reported, fixed or if there's another way to solve your problem:
-
-        * [Documentation](https://docs.langchain.com/oss/python/langgraph/overview),
-        * [API Reference Documentation](https://reference.langchain.com/python/),
-        * [LangChain ChatBot](https://chat.langchain.com/)
-        * [GitHub search](https://github.com/langchain-ai/langgraph),
-        * [LangChain Forum](https://forum.langchain.com/),
+      value: >
+        Thank you for taking the time to file a bug report. 
+        
+        Use this to report bugs in LangChain. 
+        
+        If you're not certain that your issue is due to a bug in LangChain, please use [GitHub Discussions](https://github.com/langchain-ai/langchain/discussions)
+        to ask for help with your issue.
+        
+        Relevant links to check before filing a bug report to see if your issue has already been reported, fixed or
+        if there's another way to solve your problem:
+        
+        [LangGraph documentation](https://langchain-ai.github.io/langgraph/).
+        [LangChain documentation with the integrated search](https://python.langchain.com/docs/get_started/introduction),
+        [GitHub search](https://github.com/langchain-ai/langgraph),
+        [LangChain Github Discussions](https://github.com/langchain-ai/langgraph/discussions),
+        [LangChain Github Issues](https://github.com/langchain-ai/langgraph/issues),
+        [LangChain ChatBot](https://chat.langchain.com/)
   - type: checkboxes
     id: checks
     attributes:
       label: Checked other resources
       description: Please confirm and check all the following options.
       options:
-        - label: This is a bug, not a usage question.
+        - label: I added a very descriptive title to this issue.
           required: true
-        - label: I added a clear and descriptive title that summarizes this issue.
+        - label: I searched the [LangGraph](https://langchain-ai.github.io/langgraph/)/LangChain documentation with the integrated search.
           required: true
         - label: I used the GitHub search to find a similar question and didn't find it.
           required: true
-        - label: I am sure that this is a bug in LangGraph rather than my code.
+        - label: I am sure that this is a bug in LangGraph/LangChain rather than my code.
           required: true
-        - label: The bug is not resolved by updating to the latest stable version of LangGraph (or the specific integration package).
-          required: true
-        - label: This is not related to the langchain-community package.
-          required: true
-        - label: I posted a self-contained, minimal, reproducible example. A maintainer can copy it and run it AS IS.
+        - label: I am sure this is better as an issue [rather than a GitHub discussion](https://github.com/langchain-ai/langgraph/discussions/new/choose), since this is a LangGraph bug and not a design question.
           required: true
   - type: textarea
     id: reproduction
     validations:
       required: true
     attributes:
-      label: Reproduction Steps / Example Code (Python)
+      label: Example Code
       description: |
         Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case.
-
+        
         If a maintainer can copy it, run it, and see it right away, there's a much higher chance that you'll be able to get help.
+        
+        **Important!** 
+        
+        * Reduce your code to the minimum required to reproduce the issue if possible. This makes it much easier for others to help you.
+        * Avoid screenshots when possible, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
 
-        **Important!**
-
-        * Avoid screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
-        * Reduce your code to the minimum required to reproduce the issue if possible.
-
-        (This will be automatically formatted into code, so no need for backticks.)
-      render: python
       placeholder: |
         from langgraph.graph import StateGraph
 
@@ -63,13 +61,17 @@ body:
           
         chain = StateGraph(list)
         chain.invoke('Hello!')
+      render: python
   - type: textarea
+    id: error
+    validations:
+      required: false
     attributes:
       label: Error Message and Stack Trace (if applicable)
       description: |
-        If you are reporting an error, please copy and paste the full error message and
-        stack trace.
-        (This will be automatically formatted into code, so no need for backticks.)
+        If you are reporting an error, please include the full error message and stack trace.
+      placeholder: |
+        Exception + full stack trace
       render: shell
   - type: textarea
     id: description
@@ -90,19 +92,25 @@ body:
     attributes:
       label: System Info
       description: |
-        Please share your system info with us.
-
-        Run the following command in your terminal and paste the output here:
-
-        `python -m langchain_core.sys_info`
-
-        or if you have an existing python interpreter running:
-
-        ```python
-        from langchain_core import sys_info
-        sys_info.print_sys_info()
-        ```
-      placeholder: |
+        Please share your system info with us. 
+        
+        "pip freeze | grep langchain" 
+        platform (windows / linux / mac)
+        python version
+        
+        OR if you're on a recent version of langchain-core you can paste the output of:
+        
         python -m langchain_core.sys_info
+      placeholder: |
+        "pip freeze | grep langgraph"
+        platform
+        python version
+        
+        Alternatively, if you're on a recent version of langchain-core you can paste the output of:
+        
+        python -m langchain_core.sys_info
+        
+        These will only surface LangChain packages, don't forget to include any other relevant
+        packages you're using (if you're not sure what's relevant, you can paste the entire output of `pip freeze`).
     validations:
       required: true

.github/ISSUE_TEMPLATE/config.yml

@@ -1,15 +1,15 @@
 blank_issues_enabled: false
 version: 2.1
 contact_links:
-  - name: 💬 LangChain Forum
-    url:  https://forum.langchain.com/
-    about: General community discussions and support
-  - name: 📚 LangGraph Documentation
-    url: https://docs.langchain.com/oss/python/langgraph/overview
-    about: View the official LangGraph documentation
-  - name: 📚 API Reference Documentation
-    url: https://reference.langchain.com/python/
-    about: View the official LangGraph API reference documentation
-  - name: 📚 Documentation issue
-    url: https://github.com/langchain-ai/docs/issues/new?template=02-langgraph.yml
-    about: Report an issue related to the LangGraph documentation
+  - name: 🤔 Question or Problem
+    about: Ask a question or ask about a problem in GitHub Discussions.
+    url: https://github.com/langchain-ai/langgraph/discussions/categories/q-a
+  - name: Feature Request
+    url: https://github.com/langchain-ai/langgraph/discussions/categories/ideas
+    about: Suggest a feature or an idea
+  - name: Show and tell
+    about: Show what you built with LangChain
+    url: https://github.com/langchain-ai/langgraph/discussions/categories/show-and-tell
+  - name: Slack
+    url:  https://www.langchain.com/join-community
+    about: General community discussions

.github/ISSUE_TEMPLATE/documentation.yml

@@ -0,0 +1,19 @@
+name: Documentation
+description: Report an issue related to the LangGraph documentation.
+title: "DOC: <Please write a comprehensive title after the 'DOC: ' prefix>"
+labels: [03 - Documentation]
+
+body:
+- type: textarea
+  attributes: 
+    label: "Issue with current documentation:"
+    description: >
+      Please make sure to leave a reference to the document/code you're
+      referring to.
+
+- type: textarea
+  attributes:
+    label: "Idea or request for content:"
+    description: >
+      Please describe as clearly as possible what topics you think are missing
+      from the current documentation.

.github/ISSUE_TEMPLATE/privileged.yml

@@ -1,29 +1,25 @@
 name: 🔒 Privileged
-description: You are a LangGraph maintainer, or was asked directly by a maintainer to create an issue here. If not, check the other options.
+description: You are a LangChain maintainer, or was asked directly by a maintainer to create an issue here. If not, check the other options.
 body:
   - type: markdown
     attributes:
       value: |
-        Thanks for your interest in LangGraph! 🚀
-
-        If you are not a LangGraph maintainer or were not asked directly by a maintainer to create an issue, then please start the conversation on the [LangChain Forum](https://forum.langchain.com/) instead.
-
-        You are a LangGraph maintainer if you maintain any of the packages inside of the LangGraph repository 
-        or are a regular contributor to LangGraph with previous merged merged pull requests.
+        Thanks for your interest in LangChain! 🚀
+        
+        If you are not a LangChain maintainer or were not asked directly by a maintainer to create an issue, then please start the conversation in a [Question in GitHub Discussions](https://github.com/langchain-ai/langchain/discussions/categories/q-a) instead.
+        
+        You are a LangChain maintainer if you maintain any of the packages inside of the LangChain repository 
+        or are a regular contributor to LangChain with previous merged merged pull requests.
   - type: checkboxes
     id: privileged
     attributes:
       label: Privileged issue
       description: Confirm that you are allowed to create an issue here.
       options:
-        - label: I am a LangGraph maintainer, or was asked directly by a LangGraph maintainer to create an issue here.
+        - label: I am a LangChain maintainer, or was asked directly by a LangChain maintainer to create an issue here.
           required: true
   - type: textarea
     id: content
     attributes:
       label: Issue Content
       description: Add the content of the issue here.
-  - type: markdown
-    attributes:
-      value: |
-        Community members should **NOT** work on Privileged issues unless these issues have been explicitly marked with a "help-wanted" tag.

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,31 +0,0 @@
-Thank you for contributing to LangGraph! Follow these steps to mark your pull request as ready for review. **If any of these steps are not completed, your PR will not be considered for review.**
-
-- [ ] **PR title**: Follows the format: {TYPE}({SCOPE}): {DESCRIPTION}
-  - Examples:
-    - feat(core): add multi-tenant support
-    - fix(cli): resolve flag parsing error
-    - docs(openai): update API usage examples
-  - Allowed `{TYPE}` values:
-    - feat, fix, docs, style, refactor, perf, test, build, ci, chore, revert, release
-  - Allowed `{SCOPE}` values (optional):
-    - langgraph, docs, cli, checkpoint, checkpoint-postgres, checkpoint-sqlite, prebuilt, scheduler-kafka, sdk-py
-  - Once you've written the title, please delete this checklist item; do not include it in the PR.
-
-- [ ] **PR message**: ***Delete this entire checklist*** and replace with
-  - **Description:** a description of the change. Include a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/using-issues/linking-a-pull-request-to-an-issue#linking-a-pull-request-to-an-issue-using-a-keyword) if applicable.
-  - **Issue:** the issue # it fixes, if applicable
-  - **Dependencies:** any dependencies required for this change
-  - **Twitter handle:** if your PR gets announced, and you'd like a mention, we'll gladly shout you out!
-
-- [ ] **Add tests and docs**: If you're adding a new integration, you must include:
-  1. A test for the integration, preferably unit tests that do not rely on network access,
-  2. An example notebook showing its use. It lives in `docs/docs/integrations` directory.
-
-- [ ] **Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. We will not consider a PR unless these three are passing in CI. See [contribution guidelines](https://docs.langchain.com/oss/python/contributing/overview) for more.
-
-Additional guidelines:
-
-- Make sure optional dependencies are imported within a function.
-- Please do not add dependencies to `pyproject.toml` files (even optional ones) unless they are **required** for unit tests.
-- Most PRs should not touch more than one package.
-- Changes should be backwards compatible.

.github/actions/poetry_setup/action.yml

@@ -0,0 +1,88 @@
+# An action for setting up poetry install with caching.
+# Using a custom action since the default action does not
+# take poetry install groups into account.
+# Action code from:
+# https://github.com/actions/setup-python/issues/505#issuecomment-1273013236
+name: poetry-install-with-caching
+description: Poetry install with support for caching of dependency groups.
+
+inputs:
+  python-version:
+    description: Python version, supporting MAJOR.MINOR only
+    required: true
+
+  poetry-version:
+    description: Poetry version
+    required: true
+
+  cache-key:
+    description: Cache key to use for manual handling of caching
+    required: true
+
+runs:
+  using: composite
+  steps:
+    - uses: actions/setup-python@v5
+      name: Setup python ${{ inputs.python-version }}
+      id: setup-python
+      with:
+        python-version: ${{ inputs.python-version }}
+
+    - uses: actions/cache@v3
+      id: cache-bin-poetry
+      name: Cache Poetry binary - Python ${{ inputs.python-version }}
+      env:
+        SEGMENT_DOWNLOAD_TIMEOUT_MIN: "1"
+      with:
+        path: |
+          /opt/pipx/venvs/poetry
+        # This step caches the poetry installation, so make sure it's keyed on the poetry version as well.
+        key: bin-poetry-${{ runner.os }}-${{ runner.arch }}-py-${{ inputs.python-version }}-${{ inputs.poetry-version }}
+
+    - name: Refresh shell hashtable and fixup softlinks
+      if: steps.cache-bin-poetry.outputs.cache-hit == 'true'
+      shell: bash
+      env:
+        POETRY_VERSION: ${{ inputs.poetry-version }}
+        PYTHON_VERSION: ${{ inputs.python-version }}
+      run: |
+        set -eux
+
+        # Refresh the shell hashtable, to ensure correct `which` output.
+        hash -r
+
+        # `actions/cache@v3` doesn't always seem able to correctly unpack softlinks.
+        # Delete and recreate the softlinks pipx expects to have.
+        rm /opt/pipx/venvs/poetry/bin/python
+        cd /opt/pipx/venvs/poetry/bin
+        ln -s "$(which "python$PYTHON_VERSION")" python
+        chmod +x python
+        cd /opt/pipx_bin/
+        ln -s /opt/pipx/venvs/poetry/bin/poetry poetry
+        chmod +x poetry
+
+        # Ensure everything got set up correctly.
+        /opt/pipx/venvs/poetry/bin/python --version
+        /opt/pipx_bin/poetry --version
+
+    - name: Install poetry
+      if: steps.cache-bin-poetry.outputs.cache-hit != 'true'
+      shell: bash
+      env:
+        POETRY_VERSION: ${{ inputs.poetry-version }}
+        PYTHON_VERSION: ${{ inputs.python-version }}
+      # Install poetry using the python version installed by setup-python step.
+      run: pipx install "poetry==$POETRY_VERSION" --python '${{ steps.setup-python.outputs.python-path }}' --verbose
+
+    - name: Restore pip and poetry cached dependencies
+      uses: actions/cache@v3
+      env:
+        SEGMENT_DOWNLOAD_TIMEOUT_MIN: "4"
+      with:
+        path: |
+          ~/.cache/pip
+          ~/.cache/pypoetry/virtualenvs
+          ~/.cache/pypoetry/cache
+          ~/.cache/pypoetry/artifacts
+          ./.venv
+        key: py-deps-${{ runner.os }}-${{ runner.arch }}-py-${{ inputs.python-version }}-poetry-${{ inputs.poetry-version }}-${{ inputs.cache-key }}-${{ hashFiles('./poetry.lock') }}

.github/dependabot.yml

@@ -1,111 +0,0 @@
-version: 2
-updates:
-  - package-ecosystem: "github-actions"
-    directory: "/"
-    schedule:
-      interval: "weekly"
-      day: "monday"
-    groups:
-      all-dependencies:
-        patterns:
-          - "*"
-
-  - package-ecosystem: "uv"
-    directory: "/libs/checkpoint"
-    schedule:
-      interval: "weekly"
-      day: "monday"
-    groups:
-      all-dependencies:
-        patterns:
-          - "*"
-  - package-ecosystem: "uv"
-    directory: "/libs/checkpoint-conformance"
-    schedule:
-      interval: "weekly"
-      day: "monday"
-    groups:
-      all-dependencies:
-        patterns:
-          - "*"
-
-
-  - package-ecosystem: "uv"
-    directory: "/libs/checkpoint-postgres"
-    schedule:
-      interval: "weekly"
-      day: "monday"
-    groups:
-      all-dependencies:
-        patterns:
-          - "*"
-
-  - package-ecosystem: "uv"
-    directory: "/libs/checkpoint-sqlite"
-    schedule:
-      interval: "weekly"
-      day: "monday"
-    groups:
-      all-dependencies:
-        patterns:
-          - "*"
-
-  - package-ecosystem: "uv"
-    directory: "/libs/cli"
-    schedule:
-      interval: "weekly"
-      day: "monday"
-    groups:
-      all-dependencies:
-        patterns:
-          - "*"
-
-  - package-ecosystem: "uv"
-    directory: "/libs/langgraph"
-    schedule:
-      interval: "weekly"
-      day: "monday"
-    groups:
-      all-dependencies:
-        patterns:
-          - "*"
-
-  - package-ecosystem: "uv"
-    directory: "/libs/prebuilt"
-    schedule:
-      interval: "weekly"
-      day: "monday"
-    groups:
-      all-dependencies:
-        patterns:
-          - "*"
-
-  - package-ecosystem: "uv"
-    directory: "/libs/sdk-py"
-    schedule:
-      interval: "weekly"
-      day: "monday"
-    groups:
-      all-dependencies:
-        patterns:
-          - "*"
-
-  - package-ecosystem: "npm"
-    directory: "/libs/cli/js-examples"
-    schedule:
-      interval: "weekly"
-      day: "monday"
-    groups:
-      all-dependencies:
-        patterns:
-          - "*"
-
-  - package-ecosystem: "npm"
-    directory: "/libs/cli/js-monorepo-example"
-    schedule:
-      interval: "weekly"
-      day: "monday"
-    groups:
-      all-dependencies:
-        patterns:
-          - "*"

.github/scripts/check_sdk_methods.py

@@ -1,15 +1,10 @@
 import ast
 import os
 from itertools import filterfalse
-from typing import Dict, List, Tuple
+from typing import List, Tuple
 
 ROOT_PATH = os.path.abspath(os.path.join(__file__, "..", "..", ".."))
 CLIENT_PATH = os.path.join(ROOT_PATH, "libs", "sdk-py", "langgraph_sdk", "client.py")
-ASYNC_TO_SYNC_METHOD_MAP: Dict[str, str] = {
-    "aclose": "close",
-    "__aenter__": "__enter__",
-    "__aexit__": "__exit__",
-}
 
 
 def get_class_methods(node: ast.ClassDef) -> List[str]:
@@ -27,7 +22,7 @@ def find_classes(tree: ast.AST) -> List[Tuple[str, List[str]]]:
 
 def compare_sync_async_methods(sync_methods: List[str], async_methods: List[str]) -> List[str]:
     sync_set = set(sync_methods)
-    async_set = {ASYNC_TO_SYNC_METHOD_MAP.get(async_method, async_method) for async_method in async_methods}
+    async_set = set(async_methods)
     missing_in_sync = list(async_set - sync_set)
     missing_in_async = list(sync_set - async_set)
     return missing_in_sync + missing_in_async
@@ -38,7 +33,7 @@ def main():
         tree = ast.parse(file.read())
 
     classes = find_classes(tree)
-
+  
     def is_sync(class_spec: Tuple[str, List[str]]) -> bool:
         return class_spec[0].startswith("Sync")
 

.github/scripts/run_langgraph_cli_test.py

@@ -1,164 +1,108 @@
-import logging
+import asyncio
+import json
+import os
 import pathlib
 import sys
-import time
-from urllib import error, request
-
 import langgraph_cli
-import langgraph_cli.config
 import langgraph_cli.docker
-from langgraph_cli.cli import prepare_args_and_stdin
-from langgraph_cli.constants import DEFAULT_PORT
+import langgraph_cli.config
+
 from langgraph_cli.exec import Runner, subp_exec
 from langgraph_cli.progress import Progress
-
-logger = logging.getLogger(__name__)
-logging.basicConfig(level=logging.INFO)
+from langgraph_cli.constants import DEFAULT_PORT
 
 
-def test(config: pathlib.Path, port: int, tag: str, verbose: bool):
-    """Spin up API with Postgres/Redis via docker compose and wait until ready."""
-    logger.info("Starting test...")
+def test(
+    config: pathlib.Path,
+    port: int,
+    tag: str,
+    verbose: bool,
+):
     with Runner() as runner, Progress(message="Pulling...") as set:
-        # Detect docker/compose capabilities
+        # check docker available
         capabilities = langgraph_cli.docker.check_capabilities(runner)
-
-        # Validate config and prepare compose stdin/args using built image
+        # open config
         config_json = langgraph_cli.config.validate_config_file(config)
-        args, stdin = prepare_args_and_stdin(
-            capabilities=capabilities,
-            config_path=config,
-            config=config_json,
-            docker_compose=None,
-            port=port,
-            watch=False,
-            debugger_port=None,
-            debugger_base_url=f"http://127.0.0.1:{port}",
-            postgres_uri=None,
-            api_version=None,
-            image=tag,
-            base_image=None,
-        )
 
-        # Compose up with wait (implies detach), similar to `langgraph up --wait`
-        args_up = [*args, "up", "--remove-orphans", "--wait"]
-
-        compose_cmd = ["docker", "compose"]
-        if capabilities.compose_type == "standalone":
-            compose_cmd = ["docker-compose"]
-
-        set("Starting...")
-        try:
-            runner.run(
-                subp_exec(
-                    *compose_cmd,
-                    *args_up,
-                    input=stdin,
-                    verbose=verbose,
-                )
+        set("Running...")
+        args = [
+            "run",
+            "--rm",
+            "-p",
+            f"{port}:8000",
+        ]
+        if isinstance(config_json["env"], str):
+            args.extend(
+                [
+                    "--env-file",
+                    str(config.parent / config_json["env"]),
+                ]
             )
-        except Exception as e:  # noqa: BLE001
-            # On failure, show diagnostics then ensure clean teardown
-            sys.stderr.write(f"docker compose up failed: {e}\n")
-            try:
-                sys.stderr.write("\n== docker compose ps ==\n")
-                runner.run(
-                    subp_exec(*compose_cmd, *args, "ps", input=stdin, verbose=True)
-                )
-            except Exception:
-                pass
-            try:
-                sys.stderr.write("\n== docker compose logs (api) ==\n")
-                runner.run(
-                    subp_exec(
-                        *compose_cmd,
-                        *args,
-                        "logs",
-                        "langgraph-api",
-                        input=stdin,
-                        verbose=True,
-                    )
-                )
-            except Exception:
-                pass
-            finally:
-                try:
-                    runner.run(
-                        subp_exec(
-                            *compose_cmd,
-                            *args,
-                            "down",
-                            "-v",
-                            "--remove-orphans",
-                            input=stdin,
-                            verbose=False,
-                        )
-                    )
-                finally:
-                    raise
-
-        set("")
-        base_url = f"http://localhost:{port}"
-        ok_url = f"{base_url}/ok"
-        logger.info(f"Waiting for {ok_url} to respond with 200...")
-        deadline = time.time() + 30
-        last_err: Exception | None = None
-        while time.time() < deadline:
-            try:
-                with request.urlopen(ok_url, timeout=2) as resp:
-                    if resp.status == 200:
-                        sys.stdout.write(
-                            f"""Ready!\n- API: {base_url}\n- /ok: 200 OK\n"""
-                        )
-                        sys.stdout.flush()
-                        break
-                    else:
-                        last_err = RuntimeError(f"Unexpected status: {resp.status}")
-                        logger.error(f"Unexpected status: {resp.status}")
-            except error.URLError as e:
-                logger.error(f"URLError: {e}")
-                last_err = e
-            except Exception as e:  # noqa: BLE001
-                logger.error(f"Exception: {e}")
-                last_err = e
-            time.sleep(0.5)
         else:
-            logger.error("Timeout waiting for /ok to return 200")
-            # Bring stack down before raising
-            args_down = [*args, "down", "-v", "--remove-orphans"]
-            try:
-                runner.run(
-                    subp_exec(
-                        *compose_cmd,
-                        *args_down,
-                        input=stdin,
-                        verbose=verbose,
-                    )
-                )
-            finally:
-                raise SystemExit(
-                    f"/ok did not return 202 within timeout. Last error: {last_err}"
+            for k, v in config_json["env"].items():
+                args.extend(
+                    [
+                        "-e",
+                        f"{k}={v}",
+                    ]
                 )
+        if capabilities.healthcheck_start_interval:
+            args.extend(
+                [
+                    "--health-interval",
+                    "5s",
+                    "--health-retries",
+                    "1",
+                    "--health-start-period",
+                    "10s",
+                    "--health-start-interval",
+                    "1s",
+                ]
+            )
+        else:
+            args.extend(
+                [
+                    "--health-interval",
+                    "5s",
+                    "--health-retries",
+                    "2",
+                ]
+            )
+
+        _task = None
+
+        def on_stdout(line: str):
+            nonlocal _task
+            if "GET /ok" in line or "Uvicorn running on" in line:
+                set("")
+                sys.stdout.write(
+                    f"""Ready!
+- API: http://localhost:{port}
+"""
+                )
+                sys.stdout.flush()
+                _task.cancel()
+                return True
+            return False
+
+        async def subp_exec_task(*args, **kwargs):
+            nonlocal _task
+            _task = asyncio.create_task(subp_exec(*args, **kwargs))
+            await _task
 
-        # Clean up: bring compose stack down to free ports for next test
-        logger.info("Test succeeded. Bringing down compose stack...")
         try:
-            args_down = [*args, "down", "-v", "--remove-orphans"]
             runner.run(
-                subp_exec(
-                    *compose_cmd,
-                    *args_down,
-                    input=stdin,
+                subp_exec_task(
+                    "docker",
+                    *args,
+                    tag,
                     verbose=verbose,
+                    on_stdout=on_stdout,
                 )
             )
-            logger.info("Compose stack down. Finishing...")
-        except Exception:
-            logger.exception("Failed to bring down compose stack")
+        except asyncio.CancelledError:
             pass
 
-    logger.info("Test finished")
-
 
 if __name__ == "__main__":
     import argparse
@@ -166,12 +110,6 @@ if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("-t", "--tag", type=str)
     parser.add_argument("-c", "--config", type=str, default="./langgraph.json")
-    parser.add_argument("-p", "--port", type=int, default=DEFAULT_PORT)
+    parser.add_argument("-p", "--port", default=DEFAULT_PORT)
     args = parser.parse_args()
-    try:
-        test(pathlib.Path(args.config), args.port, args.tag, verbose=True)
-    except BaseException:
-        logger.exception("Test failed")
-        raise
-    
-    logger.info("Test execution finished")
+    test(pathlib.Path(args.config), args.port, args.tag, verbose=True)

.github/workflows/_integration_test.yml

@@ -2,12 +2,9 @@ name: CLI integration test
 
 on:
   workflow_call:
-    secrets:
-      LANGSMITH_API_KEY:
-        required: false
 
-permissions:
-  contents: read
+env:
+  POETRY_VERSION: "1.7.1"
 
 jobs:
   build:
@@ -16,123 +13,62 @@ jobs:
       matrix:
         python-version:
           - "3.10"
-          - "3.14"
-        example:
-          - name: A
-            workdir: libs/cli/examples
-            tag: langgraph-test-a
-          - name: B
-            workdir: libs/cli/examples/graphs
-            tag: langgraph-test-b
-          - name: C
-            workdir: libs/cli/examples/graphs_reqs_a
-            tag: langgraph-test-c
-          - name: D
-            workdir: libs/cli/examples/graphs_reqs_b
-            tag: langgraph-test-d
+          - "3.11"
     name: "CLI integration test"
-    env:
-      HAS_LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY != '' }}
     defaults:
       run:
         working-directory: libs/cli
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v4
       - name: Get changed files
         id: changed-files
         uses: Ana06/get-changed-files@v2.3.0
         with:
           filter: "libs/cli/**"
-      - name: Set up Python ${{ matrix.python-version }}
+      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
         if: steps.changed-files.outputs.all
-        uses: astral-sh/setup-uv@v7
+        uses: "./.github/actions/poetry_setup"
         with:
           python-version: ${{ matrix.python-version }}
-          enable-cache: true
-          cache-suffix: "cli-integration-test"
-          ignore-nothing-to-cache: true
+          poetry-version: ${{ env.POETRY_VERSION }}
+          cache-key: integration-test-cli
+      - name: Setup env
+        if: steps.changed-files.outputs.all
+        working-directory: libs/cli/examples
+        run: cat .env.example > .env
       - name: Install cli globally
         if: steps.changed-files.outputs.all
         run: pip install -e .
-      - name: Build service ${{ matrix.example.name }}
+      - name: Build and test service A
         if: steps.changed-files.outputs.all
-        working-directory: ${{ matrix.example.workdir }}
+        working-directory: libs/cli/examples
         run: |
-          langgraph build -t ${{ matrix.example.tag }}
-      - name: Test service ${{ matrix.example.name }}
-        if: ${{ steps.changed-files.outputs.all && env.HAS_LANGSMITH_API_KEY == 'true' }}
-        working-directory: ${{ matrix.example.workdir }}
-        env:
-          LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
+          # The build-arg isn't used; just testing that we accept other args
+          langgraph build -t langgraph-test-a  --base-image "langchain/langgraph-trial"
+          cp .env.example .envg
+          timeout 60 python ../../../.github/scripts/run_langgraph_cli_test.py -c langgraph.json -t langgraph-test-a
+      - name: Build and test service B
+        if: steps.changed-files.outputs.all
+        working-directory: libs/cli/examples/graphs
         run: |
-          # Prepare environment file from local or parent example directory
-          if [ -f .env.example ]; then cp .env.example .env; elif [ -f ../.env.example ]; then cp ../.env.example .env && cp ../.env.example ../.env; fi
-          echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env
-          if [ -f ../.env ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> ../.env; fi
-          # Run the integration test using the built tag
-          REPO_ROOT=$(git rev-parse --show-toplevel)
-          timeout 60 python "$REPO_ROOT/.github/scripts/run_langgraph_cli_test.py" -t ${{ matrix.example.tag }}
+          langgraph build -t langgraph-test-b  --base-image "langchain/langgraph-trial"
+          timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-b
+      - name: Build and test service C
+        if: steps.changed-files.outputs.all
+        working-directory: libs/cli/examples/graphs_reqs_a
+        run: |
+          langgraph build -t langgraph-test-c  --base-image "langchain/langgraph-trial"
+          timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-c
+      - name: Build and test service D
+        if: steps.changed-files.outputs.all
+        working-directory: libs/cli/examples/graphs_reqs_b
+        run: |
+          langgraph build -t langgraph-test-d  --base-image "langchain/langgraph-trial"
+          timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-d
 
       - name: Build JS service
-        if: ${{ steps.changed-files.outputs.all && matrix.example.name == 'A' }}
+        if: steps.changed-files.outputs.all
         working-directory: libs/cli/js-examples
         run: |
           langgraph build -t langgraph-test-e
-
-      - name: Build JS monorepo service
-        if: ${{ steps.changed-files.outputs.all && matrix.example.name == 'A' }}
-        working-directory: libs/cli/js-monorepo-example
-        run: |
-          langgraph build -t langgraph-test-f -c apps/agent/langgraph.json --build-command "yarn run turbo build" --install-command "yarn install"
-
-      - name: Build Python monorepo service
-        if: ${{ steps.changed-files.outputs.all && matrix.example.name == 'A' }}
-        working-directory: libs/cli/python-monorepo-example
-        run: |
-          langgraph build -t langgraph-test-g -c apps/agent/langgraph.json
-      - name: Test Python monorepo service
-        if: ${{ steps.changed-files.outputs.all && matrix.example.name == 'A' && env.HAS_LANGSMITH_API_KEY == 'true' }}
-        working-directory: libs/cli/python-monorepo-example
-        env:
-          LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
-        run: |
-          cp apps/agent/.env.example apps/agent/.env
-          echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> apps/agent/.env
-          timeout 60 python ../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-g -c apps/agent/langgraph.json
-
-      - name: Build prerelease reqs service
-        if: ${{ steps.changed-files.outputs.all && matrix.example.name == 'A' }}
-        working-directory: libs/cli/examples/graph_prerelease_reqs
-        run: |
-          langgraph build -t langgraph-test-h
-      - name: Test prerelease reqs service
-        if: ${{ steps.changed-files.outputs.all && matrix.example.name == 'A' && env.HAS_LANGSMITH_API_KEY == 'true' }}
-        working-directory: libs/cli/examples/graph_prerelease_reqs
-        env:
-          LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
-        run: |
-          cp ../.env.example .env
-          echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env
-          timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-h
-          echo "Finished starting up langgraph-test-h"
-          LANGGRAPH_VERSION=$(docker run --rm --entrypoint "" langgraph-test-h python -c "import sys; from importlib.metadata import version; v = version('langgraph'); print(v);")
-          if [ "$LANGGRAPH_VERSION" != "1.0.8" ]; then
-            echo "LANGGRAPH_VERSION != 1.0.8; $LANGGRAPH_VERSION"
-            exit 1
-          fi
-          LANGCHAIN_OPENAI_VERSION=$(docker run --rm --entrypoint "" langgraph-test-h python -c "import sys; from importlib.metadata import version; v = version('langchain-openai'); print(v);")
-          if [ "$LANGCHAIN_OPENAI_VERSION" != "1.0.1" ]; then
-            echo "LANGCHAIN_OPENAI_VERSION != 1.0.1; $LANGCHAIN_OPENAI_VERSION"
-            exit 1
-          fi
-          LANGCHAIN_ANTHROPIC_VERSION=$(docker run --rm --entrypoint "" langgraph-test-h python -c "import sys; from importlib.metadata import version; v = version('langchain-anthropic'); print(v);")
-          if [ "$LANGCHAIN_ANTHROPIC_VERSION" != "1.0.0a5" ]; then
-            echo "LANGCHAIN_ANTHROPIC_VERSION != 1.0.0a5; $LANGCHAIN_ANTHROPIC_VERSION"
-            exit 1
-          fi
-
-      - name: Build and test prerelease reqs fail service
-        if: ${{ steps.changed-files.outputs.all && matrix.example.name == 'A' }}
-        working-directory: libs/cli/examples/graph_prerelease_reqs_fail
-        run: |
-          langgraph build -t langgraph-test-i || [ $? -eq 1 ]
+  

.github/workflows/_lint.yml

@@ -8,10 +8,9 @@ on:
         type: string
         description: "From which folder this pipeline executes"
 
-permissions:
-  contents: read
-
 env:
+  POETRY_VERSION: "1.7.1"
+
   # This env var allows us to get inline annotations when ruff has complaints.
   RUFF_OUTPUT_FORMAT: github
 
@@ -31,34 +30,55 @@ jobs:
           - "3.12"
     name: "lint #${{ matrix.python-version }}"
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v4
       - name: Get changed files
         id: changed-files
         uses: Ana06/get-changed-files@v2.3.0
         with:
           filter: "${{ inputs.working-directory }}/**"
-      - name: Set up Python ${{ matrix.python-version }}
+      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
         if: steps.changed-files.outputs.all
-        uses: astral-sh/setup-uv@v7
+        uses: "./.github/actions/poetry_setup"
         with:
           python-version: ${{ matrix.python-version }}
-          enable-cache: true
-          cache-suffix: lint-${{ inputs.working-directory }}
+          poetry-version: ${{ env.POETRY_VERSION }}
+          working-directory: ${{ inputs.working-directory }}
+          cache-key: lint-${{ inputs.working-directory }}
+
+      - name: Check Poetry File
+        if: steps.changed-files.outputs.all
+        shell: bash
+        working-directory: ${{ inputs.working-directory }}
+        run: poetry check
+
+      - name: Check lock file
+        if: steps.changed-files.outputs.all
+        shell: bash
+        working-directory: ${{ inputs.working-directory }}
+        run: poetry lock --check
 
       - name: Install dependencies
         if: steps.changed-files.outputs.all
+        # Also installs dev/lint/test/typing dependencies, to ensure we have
+        # type hints for as many of our libraries as possible.
+        # This helps catch errors that require dependencies to be spotted, for example:
+        # https://github.com/langchain-ai/langchain/pull/10249/files#diff-935185cd488d015f026dcd9e19616ff62863e8cde8c0bee70318d3ccbca98341
+        #
+        # If you change this configuration, make sure to change the `cache-key`
+        # in the `poetry_setup` action above to stop using the old cache.
+        # It doesn't matter how you change it, any change will cause a cache-bust.
         working-directory: ${{ inputs.working-directory }}
-        run: uv sync --frozen --group lint
+        run: poetry install --with dev
 
       - name: Get .mypy_cache to speed up mypy
         if: steps.changed-files.outputs.all
-        uses: actions/cache@v5
+        uses: actions/cache@v3
         env:
           SEGMENT_DOWNLOAD_TIMEOUT_MIN: "2"
         with:
           path: |
             ${{ inputs.working-directory }}/.mypy_cache
-          key: mypy-lint-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/uv.lock', inputs.working-directory)) }}
+          key: mypy-lint-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', inputs.working-directory)) }}
 
       - name: Analysing package code with our lint
         if: steps.changed-files.outputs.all
@@ -73,18 +93,27 @@ jobs:
 
       - name: Install test dependencies
         if: steps.changed-files.outputs.all
+        # Also installs dev/lint/test/typing dependencies, to ensure we have
+        # type hints for as many of our libraries as possible.
+        # This helps catch errors that require dependencies to be spotted, for example:
+        # https://github.com/langchain-ai/langchain/pull/10249/files#diff-935185cd488d015f026dcd9e19616ff62863e8cde8c0bee70318d3ccbca98341
+        #
+        # If you change this configuration, make sure to change the `cache-key`
+        # in the `poetry_setup` action above to stop using the old cache.
+        # It doesn't matter how you change it, any change will cause a cache-bust.
         working-directory: ${{ inputs.working-directory }}
-        run: uv sync --group lint
+        run: |
+          poetry install --with dev
 
       - name: Get .mypy_cache_test to speed up mypy
         if: steps.changed-files.outputs.all
-        uses: actions/cache@v5
+        uses: actions/cache@v3
         env:
           SEGMENT_DOWNLOAD_TIMEOUT_MIN: "2"
         with:
           path: |
             ${{ inputs.working-directory }}/.mypy_cache_test
-          key: mypy-test-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/uv.lock', inputs.working-directory)) }}
+          key: mypy-test-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', inputs.working-directory)) }}
 
       - name: Analysing tests with our lint
         if: steps.changed-files.outputs.all

.github/workflows/_test.yml

@@ -8,8 +8,8 @@ on:
         type: string
         description: "From which folder this pipeline executes"
 
-permissions:
-  contents: read
+env:
+  POETRY_VERSION: "1.7.1"
 
 jobs:
   build:
@@ -17,21 +17,22 @@ jobs:
     strategy:
       matrix:
         python-version:
+          - "3.9"
           - "3.10"
           - "3.11"
           - "3.12"
           - "3.13"
-          - "3.14"
 
     name: "test #${{ matrix.python-version }}"
     steps:
-      - uses: actions/checkout@v6
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: astral-sh/setup-uv@v7
+      - uses: actions/checkout@v4
+      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
         with:
           python-version: ${{ matrix.python-version }}
-          enable-cache: true
-          cache-suffix: test-${{ inputs.working-directory }}
+          poetry-version: ${{ env.POETRY_VERSION }}
+          working-directory: ${{ inputs.working-directory }}
+          cache-key: test-${{ inputs.working-directory }}
       - name: Login to Docker Hub
         uses: docker/login-action@v3
         if: ${{ !github.event.pull_request.head.repo.fork }}
@@ -42,12 +43,14 @@ jobs:
       - name: Install dependencies
         shell: bash
         working-directory: ${{ inputs.working-directory }}
-        run: uv sync --frozen --group test --no-dev
+        run: |
+          poetry install --with dev
 
       - name: Run tests
         shell: bash
         working-directory: ${{ inputs.working-directory }}
-        run: make test
+        run: |
+          make test
 
       - name: Ensure the tests did not create any additional files
         shell: bash

.github/workflows/_test_langgraph.yml

@@ -3,8 +3,8 @@ name: test
 on:
   workflow_call:
 
-permissions:
-  contents: read
+env:
+  POETRY_VERSION: "1.7.1"
 
 jobs:
   build:
@@ -12,24 +12,34 @@ jobs:
     strategy:
       matrix:
         python-version:
+          - "3.9"
           - "3.10"
           - "3.11"
           - "3.12"
           - "3.13"
-          - "3.14"
+        core-version:
+          - "latest"
+        ff-send-v2:
+          - "false"
+        include:
+          - python-version: "3.11"
+            core-version: ">=0.2.42,<0.3.0"
+          - python-version: "3.11"
+            core-version: "latest"
+            ff-send-v2: "true"
 
     defaults:
       run:
         working-directory: libs/langgraph
-    name: "test #${{ matrix.python-version }}"
+    name: "test #${{ matrix.python-version }} (langchain-core: ${{ matrix.core-version }}, ff-send-v2: ${{ matrix.ff-send-v2 }})"
     steps:
-      - uses: actions/checkout@v6
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: astral-sh/setup-uv@v7
+      - uses: actions/checkout@v4
+      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
         with:
           python-version: ${{ matrix.python-version }}
-          enable-cache: true
-          cache-suffix: "test-langgraph"
+          poetry-version: ${{ env.POETRY_VERSION }}
+          cache-key: test-langgraph
       - name: Login to Docker Hub
         uses: docker/login-action@v3
         if: ${{ !github.event.pull_request.head.repo.fork }}
@@ -39,11 +49,18 @@ jobs:
 
       - name: Install dependencies
         shell: bash
-        run: uv sync --frozen --group test --no-dev
+        run: |
+          poetry install --with dev
+          if [ "${{ matrix.core-version }}" != "latest" ]; then
+            poetry run pip install "langchain-core${{ matrix.core-version }}"
+          fi
 
       - name: Run tests
         shell: bash
-        run: make test_parallel
+        env:
+          LANGGRAPH_FF_SEND_V2: ${{ matrix.ff-send-v2 }}
+        run: |
+          make test
 
       - name: Ensure the tests did not create any additional files
         shell: bash

.github/workflows/_test_release.yml

@@ -9,13 +9,12 @@ on:
         description: "From which folder this pipeline executes"
 
 env:
+  POETRY_VERSION: "1.7.1"
   PYTHON_VERSION: "3.10"
 
-permissions:
-  contents: read
-
 jobs:
   build:
+    if: github.ref == 'refs/heads/main'
     runs-on: ubuntu-latest
 
     outputs:
@@ -23,14 +22,15 @@ jobs:
       version: ${{ steps.check-version.outputs.version }}
 
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v4
 
-      - name: Set up Python $${ env.PYTHON_VERSION }}
-        uses: astral-sh/setup-uv@v7
+      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
         with:
           python-version: ${{ env.PYTHON_VERSION }}
-          enable-cache: true
-          cache-suffix: "release"
+          poetry-version: ${{ env.POETRY_VERSION }}
+          working-directory: ${{ inputs.working-directory }}
+          cache-key: release
 
       # We want to keep this build stage *separate* from the release stage,
       # so that there's no sharing of permissions between them.
@@ -44,11 +44,11 @@ jobs:
       # > from the publish job.
       # https://github.com/pypa/gh-action-pypi-publish#non-goals
       - name: Build project for distribution
-        run: uv build
+        run: poetry build
         working-directory: ${{ inputs.working-directory }}
 
       - name: Upload build
-        uses: actions/upload-artifact@v6
+        uses: actions/upload-artifact@v4
         with:
           name: test-dist
           path: ${{ inputs.working-directory }}/dist/
@@ -58,8 +58,8 @@ jobs:
         shell: bash
         working-directory: ${{ inputs.working-directory }}
         run: |
-          echo pkg-name=$(grep -m 1 "^name = " pyproject.toml | cut -d '"' -f 2)
-          echo version=$(grep -m 1 "^version = " pyproject.toml | cut -d '"' -f 2)
+          echo pkg-name="$(poetry version | cut -d ' ' -f 1)" >> $GITHUB_OUTPUT
+          echo version="$(poetry version --short)" >> $GITHUB_OUTPUT
 
   publish:
     needs:
@@ -74,9 +74,9 @@ jobs:
       id-token: write
 
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v4
 
-      - uses: actions/download-artifact@v7
+      - uses: actions/download-artifact@v4
         with:
           name: test-dist
           path: ${{ inputs.working-directory }}/dist/

.github/workflows/_test_scheduler_kafka.yml

@@ -0,0 +1,57 @@
+name: test
+
+on:
+  workflow_call:
+
+env:
+  POETRY_VERSION: "1.7.1"
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version:
+          - "3.11"
+          - "3.12"
+
+    defaults:
+      run:
+        working-directory: libs/scheduler-kafka
+    name: "test #${{ matrix.python-version }}"
+    steps:
+      - uses: actions/checkout@v4
+      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
+        with:
+          python-version: ${{ matrix.python-version }}
+          poetry-version: ${{ env.POETRY_VERSION }}
+          cache-key: test-scheduler-kafka
+      - name: Login to Docker Hub
+        uses: docker/login-action@v3
+        if: ${{ !github.event.pull_request.head.repo.fork }}
+        with:
+          username: ${{ secrets.DOCKERHUB_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_RO_TOKEN }}
+
+      - name: Install dependencies
+        shell: bash
+        run: |
+          poetry install --with dev
+
+      - name: Run tests
+        shell: bash
+        run: |
+          make test
+
+      - name: Ensure the tests did not create any additional files
+        shell: bash
+        run: |
+          set -eu
+
+          STATUS="$(git status)"
+          echo "$STATUS"
+
+          # grep will exit non-zero if the target message isn't found,
+          # and `set -e` above will cause the step to fail.
+          echo "$STATUS" | grep 'nothing to commit, working tree clean'

.github/workflows/baseline.yml

@@ -7,8 +7,8 @@ on:
     paths:
       - "libs/**"
 
-permissions:
-  contents: read
+env:
+  POETRY_VERSION: "1.7.1"
 
 jobs:
   benchmark:
@@ -17,20 +17,20 @@ jobs:
       run:
         working-directory: libs/langgraph
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v4
       - run: SHA=$(git rev-parse HEAD) && echo "SHA=$SHA" >> $GITHUB_ENV
-      - name: Set up Python 3.11
-        uses: astral-sh/setup-uv@v7
+      - name: Set up Python 3.11 + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
         with:
           python-version: "3.11"
-          enable-cache: true
-          cache-suffix: "bench"
+          poetry-version: ${{ env.POETRY_VERSION }}
+          cache-key: bench
       - name: Install dependencies
-        run: uv sync --group test
+        run: poetry install --with dev
       - name: Run benchmarks
         run: OUTPUT=out/benchmark-baseline.json make -s benchmark
       - name: Save outputs
-        uses: actions/cache/save@v5
+        uses: actions/cache/save@v4
         with:
           key: ${{ runner.os }}-benchmark-baseline-${{ env.SHA }}
           path: |

.github/workflows/bench.yml

@@ -5,8 +5,8 @@ on:
     paths:
       - "libs/**"
 
-permissions:
-  contents: read
+env:
+  POETRY_VERSION: "1.7.1"
 
 jobs:
   benchmark:
@@ -15,22 +15,22 @@ jobs:
       run:
         working-directory: libs/langgraph
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v4
       - id: files
         name: Get changed files
         uses: Ana06/get-changed-files@v2.3.0
         with:
           format: json
-      - name: Set up Python 3.11
-        uses: astral-sh/setup-uv@v7
+      - name: Set up Python 3.11 + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
         with:
           python-version: "3.11"
-          enable-cache: true
-          cache-suffix: "bench"
+          poetry-version: ${{ env.POETRY_VERSION }}
+          cache-key: bench
       - name: Install dependencies
-        run: uv sync --group test
+        run: poetry install --with dev
       - name: Download baseline
-        uses: actions/cache/restore@v5
+        uses: actions/cache/restore@v4
         with:
           key: ${{ runner.os }}-benchmark-baseline
           restore-keys: |
@@ -43,7 +43,7 @@ jobs:
         run: |
           {
             echo 'OUTPUT<<EOF'
-            make -s benchmark-fast
+            make -s benchmark
             echo EOF
           } >> "$GITHUB_OUTPUT"
       - name: Compare benchmarks
@@ -53,11 +53,11 @@ jobs:
             echo 'OUTPUT<<EOF'
             mv out/benchmark-baseline.json out/main.json
             mv out/benchmark.json out/changes.json
-            uv run pyperf compare_to out/main.json out/changes.json --table --group-by-speed
+            poetry run pyperf compare_to out/main.json out/changes.json --table --group-by-speed
             echo EOF
           } >> "$GITHUB_OUTPUT"
       - name: Annotation
-        uses: actions/github-script@v8
+        uses: actions/github-script@v7
         with:
           script: |
             const file = JSON.parse(`${{ steps.files.outputs.added_modified_renamed }}`)[0]

.github/workflows/ci.yml

@@ -3,13 +3,9 @@ name: CI
 
 on:
   push:
-    branches:
-      - main
+    branches: [main]
   pull_request:
 
-permissions:
-  contents: read
-
 # If another push to the same PR or branch happens while this workflow is still running,
 # cancel the earlier run in favor of the next run.
 #
@@ -20,33 +16,11 @@ concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}
   cancel-in-progress: true
 
-jobs:
-  changes:
-    runs-on: ubuntu-latest
-    outputs:
-      python: ${{ steps.filter.outputs.python }}
-      deps: ${{ steps.filter.outputs.deps }}
-    steps:
-      - uses: actions/checkout@v6
-      - uses: dorny/paths-filter@v3
-        id: filter
-        with:
-          filters: |
-            python:
-              - 'libs/langgraph/**'
-              - 'libs/sdk-py/**'
-              - 'libs/cli/**'
-              - 'libs/checkpoint/**'
-              - 'libs/checkpoint-sqlite/**'
-              - 'libs/checkpoint-postgres/**'
-              - 'libs/checkpoint-conformance/**'
-              - 'libs/prebuilt/**'
-            deps:
-              - '**/pyproject.toml'
-              - '**/uv.lock'
+env:
+  POETRY_VERSION: "1.7.1"
 
+jobs:
   lint:
-    needs: changes
     name: cd ${{ matrix.working-directory }}
     strategy:
       matrix:
@@ -57,32 +31,26 @@ jobs:
             "libs/cli",
             "libs/checkpoint",
             "libs/checkpoint-sqlite",
+            "libs/checkpoint-duckdb",
             "libs/checkpoint-postgres",
-            "libs/checkpoint-conformance",
-            "libs/prebuilt",
+            "libs/scheduler-kafka",
           ]
-    if: needs.changes.outputs.python == 'true' || needs.changes.outputs.deps == 'true'
     uses: ./.github/workflows/_lint.yml
     with:
       working-directory: ${{ matrix.working-directory }}
     secrets: inherit
 
   test:
-    needs: changes
     name: cd ${{ matrix.working-directory }}
     strategy:
       matrix:
-        working-directory:
-          [
+        working-directory: [
             "libs/cli",
             "libs/checkpoint",
             "libs/checkpoint-sqlite",
-            "libs/checkpoint-postgres",
-            "libs/checkpoint-conformance",
-            "libs/prebuilt",
-            "libs/sdk-py",
+            "libs/checkpoint-duckdb",
+            "libs/checkpoint-postgres"
           ]
-    if: needs.changes.outputs.python == 'true' || needs.changes.outputs.deps == 'true'
     uses: ./.github/workflows/_test.yml
     with:
       working-directory: ${{ matrix.working-directory }}
@@ -90,80 +58,60 @@ jobs:
 
   # NOTE: we're testing langgraph separately because it requires a different matrix
   test-langgraph:
-    needs: changes
-    if: needs.changes.outputs.python == 'true' || needs.changes.outputs.deps == 'true'
     name: "cd libs/langgraph"
     uses: ./.github/workflows/_test_langgraph.yml
     secrets: inherit
 
+  # NOTE: we're testing scheduler-kafka separately because it requires a different matrix
+  test-scheduler-kafka:
+    name: "cd libs/scheduler-kafka"
+    uses: ./.github/workflows/_test_scheduler_kafka.yml
+    secrets: inherit
+
   check-sdk-methods:
-    needs: changes
-    if: needs.changes.outputs.python == 'true'
     name: "Check SDK methods matching"
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v4
       - name: Set up Python
-        uses: actions/setup-python@v6
+        uses: actions/setup-python@v5
         with:
-          python-version: "3.11"
+          python-version: '3.11'
       - name: Run check_sdk_methods script
         run: python .github/scripts/check_sdk_methods.py
 
-  check-schema:
-    needs: changes
-    if: needs.changes.outputs.python == 'true'
-    name: "Check CLI schema hasn't changed #${{ matrix.python-version }}"
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        python-version:
-          - "3.13"
-    steps:
-      - uses: actions/checkout@v6
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: astral-sh/setup-uv@v7
-        with:
-          python-version: "3.13"
-          enable-cache: true
-          cache-suffix: "schema-check-cli"
-      - name: Install CLI dependencies
-        run: |
-          cd libs/cli
-          uv sync
-      - name: Generate schema and check for changes
-        run: |
-          cd libs/cli
-          # Create a temporary copy of the current schema
-          cp schemas/schema.json schemas/schema.current.json
-          # Generate new schema
-          uv run python generate_schema.py
-          # Compare the new schema with the original
-          if ! diff -q schemas/schema.json schemas/schema.current.json > /dev/null; then
-            echo "Error: Langgraph.json configuration schema has changed. Please run 'uv run python generate_schema.py' in the libs/cli directory and commit the changes."
-            diff schemas/schema.json schemas/schema.current.json
-            exit 1
-          fi
-          echo "Schema check passed - no changes detected"
-
   integration-test:
-    needs: changes
-    if: needs.changes.outputs.python == 'true' || needs.changes.outputs.deps == 'true'
     name: CLI integration test
     uses: ./.github/workflows/_integration_test.yml
     secrets: inherit
 
+  lint-js:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        working-directory:
+          - "libs/sdk-js"
+    defaults:
+      run:
+        working-directory: ${{ matrix.working-directory }}
+    steps:
+      - uses: actions/checkout@v3
+      - name: Setup Node.js (LTS)
+        uses: actions/setup-node@v3
+        with:
+          node-version: "20"
+          cache: "yarn"
+          cache-dependency-path: ${{ matrix.working-directory }}/yarn.lock
+      - name: Install dependencies
+        run: yarn install
+      - name: Run lint
+        run: yarn lint
+      - name: Build
+        run: yarn build
+
   ci_success:
     name: "CI Success"
-    needs:
-      [
-        lint,
-        test,
-        test-langgraph,
-        check-sdk-methods,
-        check-schema,
-        integration-test,
-      ]
+    needs: [lint, lint-js, test, test-langgraph, test-scheduler-kafka, integration-test]
     if: |
       always()
     runs-on: ubuntu-latest

.github/workflows/codespell.yml

@@ -0,0 +1,39 @@
+---
+    name: CI / cd . / make spell_check
+    
+    on:
+      push:
+        branches: [main]
+      pull_request:
+        branches: [main]
+    
+    permissions:
+      contents: read
+    
+    jobs:
+      codespell:
+        name: (Check for spelling errors)
+        runs-on: ubuntu-latest
+    
+        steps:
+          - name: Checkout
+            uses: actions/checkout@v4
+    
+          - name: Install Dependencies
+            run: |
+              pip install toml codespell jupytext
+    
+          - name: Extract Ignore Words List
+            run: |
+              # Use a Python script to extract the ignore words list from pyproject.toml
+              python .github/workflows/extract_ignored_words_list.py
+            id: extract_ignore_words
+    
+          - name: Codespell
+            uses: codespell-project/actions-codespell@v2
+            with:
+              skip: '*.ambr,*.lock,*.ipynb,*.yaml,*.zlib'
+              ignore_words_list: ${{ steps.extract_ignore_words.outputs.ignore_words_list }}
+          # We do this to avoid spellchecking cell outputs
+          - name: Codespell Notebooks
+            run: make codespell

.github/workflows/deploy-redirects.yml

@@ -1,49 +0,0 @@
-name: Deploy Redirects to GitHub Pages
-
-on:
-  push:
-    branches:
-      - main
-    paths:
-      - 'docs/**'
-      - '.github/workflows/deploy-redirects.yml'
-  workflow_dispatch:
-
-permissions:
-  contents: read
-  pages: write
-  id-token: write
-
-concurrency:
-  group: "pages"
-  cancel-in-progress: false
-
-jobs:
-  deploy:
-    environment:
-      name: github-pages
-      url: ${{ steps.deployment.outputs.page_url }}
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-
-      - name: Setup Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: '3.11'
-
-      - name: Generate redirect files
-        run: python docs/generate_redirects.py
-
-      - name: Setup Pages
-        uses: actions/configure-pages@v4
-
-      - name: Upload artifact
-        uses: actions/upload-pages-artifact@v3
-        with:
-          path: 'docs/_site'
-
-      - name: Deploy to GitHub Pages
-        id: deployment
-        uses: actions/deploy-pages@v4

.github/workflows/deploy_docs.yml

@@ -0,0 +1,132 @@
+name: Deploy Docs
+
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+    branches:
+      - main
+  workflow_dispatch:
+
+env:
+  POETRY_VERSION: "1.7.1"
+
+permissions:
+  contents: read
+  pages: write
+  id-token: write
+
+concurrency:
+  group: "pages"
+  cancel-in-progress: false
+
+jobs:
+  get-changed-files:
+    runs-on: ubuntu-latest
+    outputs:
+      changed-files: ${{ steps.changed-files.outputs.added_modified }}
+    steps:
+      - uses: actions/checkout@v4
+      - name: Get changed files
+        id: changed-files
+        uses: Ana06/get-changed-files@v2.3.0
+        with:
+          filter: "docs/docs/**"
+
+  run-changed-notebooks:
+    needs: get-changed-files
+    uses: ./.github/workflows/run_notebooks.yml
+    secrets: inherit
+    with:
+      changed-files: ${{ needs.get-changed-files.outputs.changed-files }}
+
+  deploy:
+    # needs: run-changed-notebooks
+    runs-on: ubuntu-latest
+    env:
+      GITHUB_TOKEN: ${{ secrets.MKDOCS_GITHUB_TOKEN }}
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
+        with:
+          python-version: "3.12"
+          poetry-version: ${{ env.POETRY_VERSION }}
+          cache-key: docs
+
+      - name: Install dependencies
+        run: |
+          poetry install --with test --no-root
+          poetry run pip install -U \
+            pytest \
+            pytest-check-links \
+            langsmith \
+            langchain \
+            GitPython \
+            "git+https://${GITHUB_TOKEN}@github.com/langchain-ai/mkdocs-material-insiders.git"
+
+      - name: Lint Docs
+        # This step lints the docs using the existing linting set up.
+        # It should be very fast and should not require any external services.
+        run: make lint-docs
+      - name: Build site
+        run: make build-docs
+        env:
+          MKDOCS_GIT_COMMITTERS_APIKEY: ${{ secrets.MKDOCS_GIT_COMMITTERS_APIKEY }}
+
+      - name: Check links in notebooks
+        env:
+          LANGCHAIN_API_KEY: test
+        run: |
+          if [ "${{ github.event_name }}" == "schedule" ] || [ "${{ github.event_name }}" == "workflow_dispatch" ] || ([ "${{ github.event_name }}" == "push" ] && [ "${{ github.ref }}" == "refs/heads/main" ]); then
+            echo "Running link check on all HTML files matching notebooks in docs directory..."
+            poetry run pytest -v \
+              --check-links-ignore "https://(api|web|docs)\.smith\.langchain\.com/.*" \
+              --check-links-ignore "https://x.com/.*" \
+              --check-links-ignore "https://github\.com/.*" \
+              --check-links-ignore "http://localhost:8123/.*" \
+              --check-links-ignore "/.*\.(ipynb|html)$" \
+              --check-links-ignore "https://python\.langchain\.com/.*" \
+              --check-links-ignore "https://openai\.com/.*" \
+              --check-links-ignore "https://pepy\.tech/.*" \
+              --check-links $(find docs/site -name "index.html" | grep -v 'storm/index.html') 
+              
+          else
+            echo "Fetching changes from origin/main..."
+            git fetch origin main
+            echo "Checking for changed notebook files..."
+            CHANGED_FILES=$(git diff --name-only --diff-filter=d origin/main | grep 'docs/docs/.*\.ipynb$' | grep -v 'storm.ipynb' | sed -E 's|^docs/docs/|docs/site/|; s/\.ipynb$/\/index.html/' || true)
+            echo "Changed files: ${CHANGED_FILES}"
+            if [ -n "${CHANGED_FILES}" ]; then
+              echo "Running link check on HTML files matching changed notebook files..."
+              poetry run pytest -v \
+                --check-links-ignore "https://(api|web|docs)\.smith\.langchain\.com/.*" \
+                --check-links-ignore "http://localhost:8123/.*" \
+                --check-links-ignore "https://x.com/.*" \
+                --check-links-ignore "https://github\.com/.*" \
+                --check-links-ignore "/.*\.(ipynb|html)$" \
+                --check-links ${CHANGED_FILES} \
+                || ([ $? = 5 ] && exit 0 || exit $?)
+            else
+              echo "No notebook files changed."
+            fi
+          fi
+
+      - name: Configure GitHub Pages
+        if: github.ref == 'refs/heads/main'
+        uses: actions/configure-pages@v4
+
+      - name: Upload Pages Artifact
+        if: github.ref == 'refs/heads/main'
+        uses: actions/upload-pages-artifact@v3
+        with:
+          path: ./docs/site/
+
+      - name: Deploy to GitHub Pages
+        if: github.ref == 'refs/heads/main'
+        id: deployment
+        uses: actions/deploy-pages@v4

.github/workflows/extract_ignored_words_list.py

@@ -0,0 +1,10 @@
+import toml
+
+pyproject_toml = toml.load("libs/langgraph/pyproject.toml")
+
+# Extract the ignore words list (adjust the key as per your TOML structure)
+ignore_words_list = (
+    pyproject_toml.get("tool", {}).get("codespell", {}).get("ignore-words-list")
+)
+
+print(f"::set-output name=ignore_words_list::{ignore_words_list}")  # noqa: T201

.github/workflows/link_check.yml

@@ -0,0 +1,49 @@
+name: Check Docs & Links
+
+on:
+  pull_request:
+    branches:
+      - main
+  push:
+    branches:
+      - main
+  schedule:
+    - cron: "0 5 * * *"
+  workflow_dispatch:
+
+env:
+  POETRY_VERSION: "1.7.1"
+
+jobs:
+  markdown-link-check:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Check links in Markdown files
+        uses: gaurav-nelson/github-action-markdown-link-check@v1
+        with:
+          folder-path: "docs/"
+          check-modified-files-only: ${{ github.event_name != 'schedule' }}
+          file-path: "./README.md"
+          config-file: "./.markdown-link-check.config.json"
+
+  check-readmes-synced:
+    # This checks that the repo README.md is identical to the libs/langgraph/README.md
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 1
+
+      - name: Check README.md is in sync
+        run: |
+            if ! diff -q README.md libs/langgraph/README.md >/dev/null; then
+            echo "README.md is out of sync with libs/langgraph/README.md"
+            diff -C 3 README.md libs/langgraph/README.md
+            exit 1
+            fi

.github/workflows/pr_lint.yml

@@ -1,46 +0,0 @@
-name: PR Title Lint
-
-permissions:
-  pull-requests: read
-
-on:
-  pull_request:
-    types: [opened, edited, synchronize]
-
-jobs:
-  lint-pr-title:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Validate PR Title
-        uses: amannn/action-semantic-pull-request@v6
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-        with:
-          types: |
-            feat
-            fix
-            docs
-            style
-            refactor
-            perf
-            test
-            build
-            ci
-            chore
-            revert
-            release
-          scopes: |
-            checkpoint
-            checkpoint-postgres
-            checkpoint-sqlite
-            cli
-            langgraph
-            prebuilt
-            scheduler-kafka
-            sdk-py
-            docs
-            ci
-            deps
-          requireScope: false
-          ignoreLabels: |
-            ignore-lint-pr-title

.github/workflows/release.yml

@@ -8,14 +8,13 @@ on:
         type: string
         default: "libs/langgraph"
 
-permissions:
-  contents: read
-
 env:
   PYTHON_VERSION: "3.11"
+  POETRY_VERSION: "1.7.1"
 
 jobs:
   build:
+    if: github.ref == 'refs/heads/main'
     runs-on: ubuntu-latest
 
     outputs:
@@ -25,14 +24,15 @@ jobs:
       tag: ${{ steps.check-version.outputs.tag }}
 
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v4
 
-      - name: Set up Python
-        uses: astral-sh/setup-uv@v7
+      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
         with:
           python-version: ${{ env.PYTHON_VERSION }}
-          enable-cache: true
-          cache-suffix: "release"
+          poetry-version: ${{ env.POETRY_VERSION }}
+          working-directory: ${{ inputs.working-directory }}
+          cache-key: release
 
       # We want to keep this build stage *separate* from the release stage,
       # so that there's no sharing of permissions between them.
@@ -46,11 +46,11 @@ jobs:
       # > from the publish job.
       # https://github.com/pypa/gh-action-pypi-publish#non-goals
       - name: Build project for distribution
-        run: uv build
+        run: poetry build
         working-directory: ${{ inputs.working-directory }}
 
       - name: Upload build
-        uses: actions/upload-artifact@v6
+        uses: actions/upload-artifact@v4
         with:
           name: dist
           path: ${{ inputs.working-directory }}/dist/
@@ -60,14 +60,8 @@ jobs:
         shell: bash
         working-directory: ${{ inputs.working-directory }}
         run: |
-          PKG_NAME=$(grep -m 1 "^name = " pyproject.toml | cut -d '"' -f 2)
-          if grep -q 'dynamic.*=.*\[.*"version".*\]' pyproject.toml; then
-            # handle dynamic versioning
-            DIR_NAME=$(echo "$PKG_NAME" | tr '-' '_')
-            VERSION=$(grep -m 1 '^__version__' "${DIR_NAME}/__init__.py" | cut -d '"' -f 2)
-          else
-            VERSION=$(grep -m 1 "^version = " pyproject.toml | cut -d '"' -f 2)
-          fi
+          PKG_NAME="$(poetry version | cut -d ' ' -f 1)"
+          VERSION="$(poetry version --short)"
           SHORT_PKG_NAME="$(echo "$PKG_NAME" | sed -e 's/langgraph//g' -e 's/-//g')"
           if [ -z $SHORT_PKG_NAME ]; then
             TAG="$VERSION"
@@ -86,7 +80,7 @@ jobs:
     outputs:
       release-body: ${{ steps.generate-release-body.outputs.release-body }}
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v4
         with:
           repository: langchain-ai/langgraph
           path: langgraph
@@ -142,9 +136,7 @@ jobs:
     needs:
       - build
       - release-notes
-    permissions:
-      contents: read
-      id-token: write
+    permissions: write-all
     uses: ./.github/workflows/_test_release.yml
     with:
       working-directory: ${{ inputs.working-directory }}
@@ -157,7 +149,7 @@ jobs:
       - test-pypi-publish
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v4
 
       # We explicitly *don't* set up caching here. This ensures our tests are
       # maximally sensitive to catching breakage.
@@ -172,11 +164,12 @@ jobs:
       # - The package is published, and it breaks on the missing dependency when
       #   used in the real world.
 
-      - name: Set up Python
-        uses: astral-sh/setup-uv@v7
+      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
         with:
           python-version: ${{ env.PYTHON_VERSION }}
-          enable-cache: true
+          poetry-version: ${{ env.POETRY_VERSION }}
+          working-directory: ${{ inputs.working-directory }}
 
       - name: Import published package
         shell: bash
@@ -194,21 +187,17 @@ jobs:
         # - attempt install again after 5 seconds if it fails because there is
         #   sometimes a delay in availability on test pypi
         run: |
-          uv run pip install \
+          poetry run pip install \
             --extra-index-url https://test.pypi.org/simple/ \
             "$PKG_NAME==$VERSION" || \
           ( \
             sleep 5 && \
-            uv run pip install \
+            poetry run pip install \
               --extra-index-url https://test.pypi.org/simple/ \
               "$PKG_NAME==$VERSION" \
           )
 
-          if [[ "$PKG_NAME" == *prebuilt* ]]; then
-            uv run pip install langgraph
-          fi
-
-          if [[ "$PKG_NAME" == *checkpoint* || "$PKG_NAME" == *prebuilt* ]]; then
+          if [[ "$PKG_NAME" == *checkpoint* ]]; then
             # since checkpoint packages are namespace packages, import them with . convention
             # i.e. import langgraph.checkpoint or langgraph.checkpoint.sqlite
             IMPORT_NAME="$(echo "$PKG_NAME" | sed s/-/./g)"
@@ -218,10 +207,10 @@ jobs:
             IMPORT_NAME="$(echo "$PKG_NAME" | sed s/-/_/g)"
           fi
 
-          uv run python -c "import $IMPORT_NAME; print(dir($IMPORT_NAME))"
+          poetry run python -c "import $IMPORT_NAME; print(dir($IMPORT_NAME))"
 
       - name: Import test dependencies
-        run: uv sync --group test
+        run: poetry install --with dev
         working-directory: ${{ inputs.working-directory }}
 
       # Overwrite the local version of the package with the test PyPI version.
@@ -232,7 +221,7 @@ jobs:
           PKG_NAME: ${{ needs.build.outputs.pkg-name }}
           VERSION: ${{ needs.build.outputs.version }}
         run: |
-          uv run pip install \
+          poetry run pip install \
             --extra-index-url https://test.pypi.org/simple/ \
             "$PKG_NAME==$VERSION"
 
@@ -260,16 +249,17 @@ jobs:
         working-directory: ${{ inputs.working-directory }}
 
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v4
 
-      - name: Set up Python
-        uses: astral-sh/setup-uv@v7
+      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
         with:
           python-version: ${{ env.PYTHON_VERSION }}
-          enable-cache: true
-          cache-suffix: "release"
+          poetry-version: ${{ env.POETRY_VERSION }}
+          working-directory: ${{ inputs.working-directory }}
+          cache-key: release
 
-      - uses: actions/download-artifact@v7
+      - uses: actions/download-artifact@v4
         with:
           name: dist
           path: ${{ inputs.working-directory }}/dist/
@@ -301,16 +291,17 @@ jobs:
         working-directory: ${{ inputs.working-directory }}
 
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v4
 
-      - name: Set up Python
-        uses: astral-sh/setup-uv@v7
+      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
         with:
           python-version: ${{ env.PYTHON_VERSION }}
-          enable-cache: true
-          cache-suffix: "release"
+          poetry-version: ${{ env.POETRY_VERSION }}
+          working-directory: ${{ inputs.working-directory }}
+          cache-key: release
 
-      - uses: actions/download-artifact@v7
+      - uses: actions/download-artifact@v4
         with:
           name: dist
           path: ${{ inputs.working-directory }}/dist/
@@ -322,6 +313,5 @@ jobs:
           token: ${{ secrets.GITHUB_TOKEN }}
           generateReleaseNotes: false
           tag: ${{needs.build.outputs.tag}}
-          name: ${{ needs.build.outputs.pkg-name }}==${{ needs.build.outputs.version }}
           body: ${{ needs.release-notes.outputs.release-body }}
           commit: ${{ github.sha }}

.github/workflows/release_js.yml

@@ -0,0 +1,38 @@
+name: JS Release
+
+on:
+  workflow_dispatch:
+
+jobs:
+  publish:
+    # Disallow publishing from branches that aren't `main`.
+    if: github.ref == 'refs/heads/main'
+    runs-on: ubuntu-latest
+
+    strategy:
+      matrix:
+        working-directory:
+          - "libs/sdk-js"
+
+    defaults:
+      run:
+        working-directory: ${{ matrix.working-directory }}
+
+    steps:
+      - uses: actions/checkout@v4
+      # JS Build
+      - name: Use Node.js
+        uses: actions/setup-node@v3
+        with:
+          node-version: "20"
+          cache: "yarn"
+          cache-dependency-path: ${{ matrix.working-directory }}/yarn.lock
+
+      - name: Install dependencies
+        run: yarn install
+      - name: Build
+        run: yarn build
+      - name: Publish package to NPM
+        run: |
+          echo "//registry.npmjs.org/:_authToken=${{ secrets.NPM_TOKEN }}" > .npmrc
+          npm publish

.github/workflows/run_notebooks.yml

@@ -0,0 +1,78 @@
+name: Run notebooks
+
+on:
+  workflow_dispatch:
+  workflow_call:
+    inputs:
+      changed-files:
+        required: false
+        type: string
+        description: "JSON string of changed files"
+  schedule:
+    - cron: '0 13 * * *'
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        lib-version:
+          - "development"
+          - "latest"
+
+    steps:
+      - uses: actions/checkout@v4
+      - name: Set up Python + Poetry
+        uses: "./.github/actions/poetry_setup"
+        with:
+          python-version: 3.11
+          poetry-version: 1.7.1
+          cache-key: test-langgraph-notebooks
+
+      - name: Install dependencies
+        run: |
+          poetry install --with test
+          poetry run pip install jupyter
+
+      - name: Start services
+        run: make start-services
+
+      - name: Pre-download tiktoken files
+        run: |
+          poetry run python docs/_scripts/download_tiktoken.py
+
+      - name: Prepare notebooks
+        run: |
+          if [ "${{ matrix.lib-version }}" = "development" ]; then
+            poetry run python docs/_scripts/prepare_notebooks_for_ci.py --comment-install-cells
+          else
+            poetry run python docs/_scripts/prepare_notebooks_for_ci.py
+          fi
+
+      - name: Run notebooks
+        env:
+          # these won't actually be used because of the VCR cassettes
+          # but need to set them to avoid triggering getpass()
+          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
+          TAVILY_API_KEY: ${{ secrets.TAVILY_API_KEY }}
+          LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
+          NOMIC_API_KEY: ${{ secrets.NOMIC_API_KEY }}
+          COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }}
+          FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }}
+        run: |
+          if [ "${{ github.event_name }}" = "workflow_dispatch" ] || [ "${{ github.event_name }}" = "schedule" ]; then
+            echo "Running all notebooks"
+            ./docs/_scripts/execute_notebooks.sh
+          else
+            CHANGED_FILES=$(echo '${{ inputs.changed-files }}' | tr ' ' '\n' | grep '\.ipynb$' || true)
+            if [ -n "$CHANGED_FILES" ]; then
+              echo "Running changed notebooks: $CHANGED_FILES"
+              ./docs/_scripts/execute_notebooks.sh $CHANGED_FILES
+            else
+              echo "No notebook files changed, skipping execution"
+            fi
+          fi
+
+      - name: Stop services
+        run: make stop-services

.github/workflows/size.yml

@@ -0,0 +1,29 @@
+name: Check File Size
+
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+    branches:
+      - main
+  workflow_dispatch:
+
+jobs:
+  file-size-check:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - name: Get changed files
+        id: changed-files
+        uses: tj-actions/changed-files@v44
+      - name: Filter by size
+        # TODO: roll back the web voyager hack
+        run: |
+          large_added_files=$(find ${{ steps.changed-files.outputs.added_files }} -maxdepth 0 -size +1M | grep -v "web_voyager" || true)
+          if [ -n "$large_added_files" ]; then
+            echo "Large files added: $large_added_files"
+            echo "# Large files added:" >> $GITHUB_STEP_SUMMARY
+            echo "$large_added_files" >> $GITHUB_STEP_SUMMARY
+            exit 1
+          fi

.github/workflows/uv_lock_ugprade.yml

@@ -1,45 +0,0 @@
-name: UV Lock Upgrade
-
-on:
-  schedule:
-    # run at midnight every Sunday
-    - cron: '0 0 * * 0'
-  # allow manual triggering
-  workflow_dispatch:
-
-permissions:
-  contents: write
-  pull-requests: write
-
-jobs:
-  upgrade-dependencies:
-    runs-on: ubuntu-latest
-    
-    steps:
-      - uses: actions/checkout@v6
-      
-      - name: Set up uv
-        uses: astral-sh/setup-uv@v7
-        with:
-          # use minimum supported Python version
-          python-version: "3.10"
-          enable-cache: true
-          cache-suffix: "uv-lock-upgrade"
-          
-      - name: Run uv lock --upgrade in all Python packages
-        run: make lock-upgrade
-          
-      - name: Create Pull Request
-        uses: peter-evans/create-pull-request@v8
-        with:
-          token: ${{ secrets.GITHUB_TOKEN }}
-          commit-message: "chore(deps): upgrade dependencies with `uv lock --upgrade`"
-          title: "chore(deps): upgrade dependencies with `uv lock --upgrade`"
-          body: |
-            This PR updates the dependencies in all Python packages using `uv lock --upgrade`.
-            
-            This is an automated PR created by the UV Lock Upgrade workflow.
-          branch: deps/uv-lock-upgrade
-          delete-branch: true
-          labels: |
-            dependencies

.gitignore

@@ -6,6 +6,9 @@ __pycache__/
 *.py[cod]
 *$py.class
 
+# C extensions
+*.so
+
 # Distribution / packaging
 .Python
 build/
@@ -51,12 +54,27 @@ coverage.xml
 .hypothesis/
 .pytest_cache/
 
+# Translations
+*.mo
+*.pot
+
 # Django stuff:
 *.log
 local_settings.py
 db.sqlite3
 db.sqlite3-journal
 
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+docs/docs/_build/
+
 # PyBuilder
 target/
 
@@ -71,9 +89,23 @@ ipython_config.py
 # pyenv
 .python-version
 
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
 # PEP 582; used by e.g. github.com/David-OConnor/pyflow
 __pypackages__/
 
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
 # Environments
 .env
 .envrc
@@ -85,6 +117,16 @@ ENV/
 env.bak/
 venv.bak/
 
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
 # mypy
 .mypy_cache/
 .dmypy.json
@@ -96,7 +138,44 @@ dmypy.json
 # macOS display setting files
 .DS_Store
 
-.vercel
-.turbo
-.editorconfig
-.scratch
+# Wandb directory
+wandb/
+
+# asdf tool versions
+.tool-versions
+/.ruff_cache/
+
+*.pkl
+*.bin
+
+# integration test artifacts
+data_map*
+\[('_type', 'fake'), ('stop', None)]
+
+# Replit files
+*replit*
+
+node_modules
+docs/.yarn/
+docs/node_modules/
+docs/.docusaurus/
+docs/.cache-loader/
+docs/_dist
+docs/api_reference/api_reference.rst
+docs/api_reference/experimental_api_reference.rst
+docs/api_reference/_build
+docs/api_reference/*/
+!docs/api_reference/_static/
+!docs/api_reference/templates/
+!docs/api_reference/themes/
+docs/docs_skeleton/build
+docs/docs_skeleton/node_modules
+docs/docs_skeleton/yarn.lock
+
+# Any new jupyter notebooks
+# not intended for the repo
+Untitled*.ipynb
+
+Chinook.db
+
+libs/langgraph/out

.markdown-link-check.config.json

@@ -0,0 +1,4 @@
+{
+  "aliveStatusCodes": [200, 206, 402],
+  "ignorePatterns": ["*dcbadge.vercel.app*"]
+}

AGENTS.md

@@ -1,57 +0,0 @@
-# AGENTS Instructions
-
-This repository is a monorepo. Each library lives in a subdirectory under `libs/`.
-
-When you modify code in any library, run the following commands in that library's directory before creating a pull request:
-
-- `make format` – run code formatters
-- `make lint` – run the linter
-- `make test` – execute the test suite
-
-To run a particular test file or to pass additional pytest options you can specify the `TEST` variable:
-
-```txt
-TEST=path/to/test.py make test
-```
-
-Other pytest arguments can also be supplied inside the `TEST` variable.
-
-## Libraries
-
-The repository contains several Python and JavaScript/TypeScript libraries.
-Below is a high-level overview:
-
-- **checkpoint** – base interfaces for LangGraph checkpointers.
-- **checkpoint-postgres** – Postgres implementation of the checkpoint saver.
-- **checkpoint-sqlite** – SQLite implementation of the checkpoint saver.
-- **cli** – official command-line interface for LangGraph.
-- **langgraph** – core framework for building stateful, multi-actor agents.
-- **prebuilt** – high-level APIs for creating and running agents and tools.
-- **sdk-js** – JS/TS SDK for interacting with the LangGraph REST API.
-- **sdk-py** – Python SDK for the LangGraph Server API.
-
-### Dependency map
-
-The diagram below lists downstream libraries for each production dependency as
-declared in that library's `pyproject.toml` (or `package.json`).
-
-```text
-checkpoint
-├── checkpoint-postgres
-├── checkpoint-sqlite
-├── prebuilt
-└── langgraph
-
-prebuilt
-└── langgraph
-
-sdk-py
-├── langgraph
-└── cli
-
-sdk-js (standalone)
-```
-
-Changes to a library may impact all of its dependents shown above.
-
-- Do NOT use Sphinx-style double backtick formatting (` ``code`` `). Use single backticks (`` `code` ``) for inline code references in docstrings and comments.

CLAUDE.md

@@ -1,57 +0,0 @@
-# AGENTS Instructions
-
-This repository is a monorepo. Each library lives in a subdirectory under `libs/`.
-
-When you modify code in any library, run the following commands in that library's directory before creating a pull request:
-
-- `make format` – run code formatters
-- `make lint` – run the linter
-- `make test` – execute the test suite
-
-To run a particular test file or to pass additional pytest options you can specify the `TEST` variable:
-
-```
-TEST=path/to/test.py make test
-```
-
-Other pytest arguments can also be supplied inside the `TEST` variable.
-
-## Libraries
-
-The repository contains several Python and JavaScript/TypeScript libraries.
-Below is a high-level overview:
-
-- **checkpoint** – base interfaces for LangGraph checkpointers.
-- **checkpoint-postgres** – Postgres implementation of the checkpoint saver.
-- **checkpoint-sqlite** – SQLite implementation of the checkpoint saver.
-- **cli** – official command-line interface for LangGraph.
-- **langgraph** – core framework for building stateful, multi-actor agents.
-- **prebuilt** – high-level APIs for creating and running agents and tools.
-- **sdk-js** – JS/TS SDK for interacting with the LangGraph REST API.
-- **sdk-py** – Python SDK for the LangGraph Server API.
-
-### Dependency map
-
-The diagram below lists downstream libraries for each production dependency as
-declared in that library's `pyproject.toml` (or `package.json`).
-
-```text
-checkpoint
-├── checkpoint-postgres
-├── checkpoint-sqlite
-├── prebuilt
-└── langgraph
-
-prebuilt
-└── langgraph
-
-sdk-py
-├── langgraph
-└── cli
-
-sdk-js (standalone)
-```
-
-Changes to a library may impact all of its dependents shown above.
-
-- Do NOT use Sphinx-style double backtick formatting (` ``code`` `). Use single backticks (`` `code` ``) for inline code references in docstrings and comments.

CONTRIBUTING.md

@@ -0,0 +1,293 @@
+# Contributing to LangGraph
+
+Thank you for being interested in contributing to LangGraph!
+
+## General guidelines
+
+Here are some things to keep in mind for all types of contributions:
+
+- Follow the ["fork and pull request"](https://docs.github.com/en/get-started/exploring-projects-on-github/contributing-to-a-project) workflow.
+- Fill out the checked-in pull request template when opening pull requests. Note related issues and tag relevant maintainers.
+- Ensure your PR passes formatting, linting, and testing checks before requesting a review.
+  - If you would like comments or feedback, please open an issue or discussion and tag a maintainer.
+- Backwards compatibility is key. Your changes must not be breaking, except in case of critical bug and security fixes.
+- Look for duplicate PRs or issues that have already been opened before opening a new one.
+- Keep scope as isolated as possible. As a general rule, your changes should not affect more than one package at a time.
+
+### Bugfixes
+
+For bug fixes, please open up an issue before proposing a fix to ensure the proposal properly addresses the underlying problem. In general, bug fixes should all have an accompanying unit test that fails before the fix.
+
+### New features
+
+For new features, please start a new [discussion](https://github.com/langchain-ai/langgraph/discussions), where the maintainers will help with scoping out the necessary changes.
+
+## Contribute Documentation
+
+Documentation is a vital part of LangGraph. We welcome both new documentation for new features and
+community improvements to our current documentation. Please read the resources below before getting started:
+
+- [Documentation style guide](#documentation-style-guide)
+- [Documentation setup](#setup)
+
+## Documentation Style Guide
+
+As LangGraph continues to grow, the surface area of documentation required to cover it continues to grow too.
+This page provides guidelines for anyone writing documentation for LangGraph, as well as some of our philosophies around organization and structure.
+
+## Philosophy
+
+LangGraph's documentation follows the [Diataxis framework](https://diataxis.fr).
+Under this framework, all documentation falls under one of four categories: [Tutorials](#tutorials),
+[How-to guides](#how-to-guides),
+[References](#references), and [Explanations (aka conceptual guides)](#conceptual-guide).
+
+### Tutorials
+
+Tutorials are lessons that take the reader through a practical activity. Their purpose is to help the user
+gain understanding of concepts and how they interact by showing one way to achieve some goal in a hands-on way.
+
+They should **avoid** giving
+multiple permutations of ways to achieve that goal in-depth. Choice is burdensome. Instead, they should guide a new user through a recommended path to accomplishing a concrete goal. While the end result of a tutorial does not necessarily need to
+be completely production-ready, it should be useful and practically satisfy the the goal that you clearly stated in the tutorial's introduction.
+
+To quote the Diataxis website:
+
+> A tutorial serves the user’s *acquisition* of skills and knowledge - their study. Its purpose is not to help the user get something done, but to help them learn.
+
+In LangGraph, these are often higher level guides that show off end-to-end use cases.
+
+Some examples include:
+
+- [Build a Customer Support Bot](https://langchain-ai.github.io/langgraph/tutorials/customer-support/customer-support/)
+- [Build a SQL Agent](https://langchain-ai.github.io/langgraph/tutorials/sql-agent/)
+
+Here are some high-level tips on writing a good tutorial:
+
+- Focus on guiding the user to get something done, but keep in mind the end-goal is more to impart principles than to create a perfect production system.
+- Be specific, not abstract and follow one path.
+  - No need to go deeply into alternative approaches, but it’s ok to reference them, ideally with a link to an appropriate how-to guide.
+- Get "a point on the board" as soon as possible - something the user can run that outputs something.
+  - You can iterate and expand afterwards.
+  - Try to frequently checkpoint at given steps where the user can run code and see progress.
+- Focus on results, not technical explanation.
+  - Crosslink heavily to appropriate conceptual/reference pages
+- The first time you mention a LangGraph concept, use its full name (e.g. "human-in-the-loop"), and link to its conceptual/other documentation page.
+  - It's also helpful to add a prerequisite callout that links to any pages with necessary background information.
+- End with a recap/next steps section summarizing what the tutorial covered and future reading, such as related how-to guides.
+- Use phrases like "Next we can run X & Y. We will expect Z.". Then afterwards, use language like "Notice Z" that recalls our expectations and directs the reader's attention to the topic we are trying to teach.
+- Do not shy away from repetition.
+
+### How-to guides
+
+A how-to guide, as the name implies, demonstrates how to do something discrete and specific.
+It should assume that the user is already familiar with underlying concepts, and is trying to solve an immediate problem, but
+should still give some background or list the scenarios where the information contained within can be relevant.
+They can and should discuss alternatives if one approach may be better than another in certain cases.
+
+To quote the Diataxis website:
+
+> A how-to guide serves the work of the already-competent user, whom you can assume to know what they want to do, and to be able to follow your instructions correctly.
+
+Some examples include:
+
+- [How to add persistence to your graph](https://langchain-ai.github.io/langgraph/how-tos/persistence/)
+- [How to view and update past graph state](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/time-travel/)
+
+Here are some high-level tips on writing a good how-to guide:
+
+- Clearly explain what you are guiding the user through at the start
+- Assume higher intent than a tutorial and show what the user needs to do to get that task done
+- Assume familiarity of concepts, but explain why suggested actions are helpful
+  - Crosslink heavily to conceptual/reference pages
+- Discuss alternatives and responses to real-world tradeoffs that may arise when solving a problem
+- Use lots of example code, ideally within complete code blocks that the reader can copy and run.
+- End with a recap/next steps section summarizing what the tutorial covered and future reading, such as other related how-to guides
+
+### Conceptual guides
+
+LangGraph's conceptual guides fall under the **Explanation** quadrant of Diataxis. They should cover LangChain terms and concepts
+in a more abstract way than how-to guides or tutorials, and should be geared towards curious users interested in
+gaining a deeper understanding of the framework. Try to avoid excessively large code examples. The goal here is to
+impart perspective to the user rather than to finish a practical project. These guides should cover **why** things work they way they do.
+
+
+To quote the Diataxis website:
+
+> The perspective of explanation is higher and wider than that of the other types. It does not take the user’s eye-level view, as in a how-to guide, or a close-up view of the machinery, like reference material. Its scope in each case is a topic - “an area of knowledge”, that somehow has to be bounded in a reasonable, meaningful way.
+
+Some examples include:
+
+- [What does it mean to be agentic?](https://langchain-ai.github.io/langgraph/concepts/high_level/)
+- [Tool calling](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#tool-calling)
+
+Here are some high-level tips on writing a good conceptual guide:
+
+- Explain design decisions. Why does concept X exist and why was it designed this way?
+- Use analogies and reference other concepts and alternatives
+- Avoid blending in too much reference content
+- You can and should reference content covered in other guides, but make sure to link to them
+
+### References
+
+References contain detailed, low-level information that describes exactly what functionality exists and how to use it.
+In LangGraph, this is mainly our API reference pages, which are populated from docstrings within code.
+References pages are generally not read end-to-end, but are consulted as necessary when a user needs to know
+how to use something specific.
+
+To quote the Diataxis website:
+
+> The only purpose of a reference guide is to describe, as succinctly as possible, and in an orderly way. Whereas the content of tutorials and how-to guides are led by needs of the user, reference material is led by the product it describes.
+
+Many of the reference pages in LangChain are automatically generated from code,
+but here are some high-level tips on writing a good docstring:
+
+- Be concise
+- Discuss special cases and deviations from a user's expectations
+- Go into detail on required inputs and outputs
+- Light details on when one might use the feature are fine, but in-depth details belong in other sections.
+
+Each category serves a distinct purpose and requires a specific approach to writing and structuring the content.
+
+## General guidelines
+
+Here are some other guidelines you should think about when writing and organizing documentation.
+
+We generally do not merge new tutorials from outside contributors without an actue need.
+We welcome updates as well as new integration docs, how-tos, and references.
+
+### Avoid duplication
+
+Multiple pages that cover the same material in depth are difficult to maintain and cause confusion. There should
+be only one (very rarely two), canonical pages for a given concept or feature. Instead, you should link to other guides.
+
+### Link to other sections
+
+Because sections of the docs do not exist in a vacuum, it is important to link to other sections as often as possible
+to allow a developer to learn more about an unfamiliar topic inline.
+
+This includes linking to the API references as well as conceptual sections!
+
+### Be concise
+
+In general, take a less-is-more approach. If a section with a good explanation of a concept already exists, you should link to it rather than
+re-explain it, unless the concept you are documenting presents some new wrinkle.
+
+Be concise, including in code samples.
+
+### General style
+
+- Use active voice and present tense whenever possible
+- Use examples and code snippets to illustrate concepts and usage
+- Use appropriate header levels (`#`, `##`, `###`, etc.) to organize the content hierarchically
+- Use fewer cells with more code to make copy/paste easier
+- Use bullet points and numbered lists to break down information into easily digestible chunks
+- Use tables (especially for **Reference** sections) and diagrams often to present information visually
+- Include the table of contents for longer documentation pages to help readers navigate the content, but hide it for shorter pages
+
+## Setup
+
+LangChain documentation consists of two components:
+
+1. Main Documentation: Hosted at [https://langchain-ai.github.io](https://langchain-ai.github.io/langgraph/),
+this comprehensive resource serves as the primary user-facing documentation.
+It covers a wide array of topics, including tutorials, use cases, integrations,
+and more, offering extensive guidance on building with LangGraph.
+The content for this documentation lives in the `/docs` directory of the monorepo.
+2. In-code Documentation: This is documentation of the codebase itself, which is also
+used to generate the externally facing [API Reference](https://langchain-ai.github.io/langgraph/reference/graphs/).
+The content for the API reference is autogenerated by scanning the docstrings in the codebase. For this reason we ask that developers document their code well.
+
+We appreciate all contributions to the documentation, whether it be fixing a typo,
+adding a new tutorial or example and whether it be in the main documentation or the API Reference.
+
+### 📜 Main Documentation
+
+The content for the main documentation is located in the `/docs` directory of the monorepo.
+
+The documentation is written using a combination of ipython notebooks (`.ipynb` files)
+and markdown (`.md` files). The notebooks are converted to markdown
+and then built using [MkDocs](https://www.mkdocs.org/).
+
+Feel free to make contributions to the main documentation! 🥰
+
+After modifying the documentation:
+
+1. Run the linting and formatting commands (see below) to ensure that the documentation is well-formatted and free of errors.
+2. Optionally build the documentation locally to verify that the changes look good.
+3. Make a pull request with the changes.
+
+### ⚒️ Linting and Building Documentation Locally
+
+After writing up the documentation, you may want to lint and build the documentation
+locally to ensure that it looks good and is free of errors.
+
+If you're unable to build it locally that's okay as well, as you will be able to
+see a preview of the documentation on the pull request page.
+
+From the **monorepo root**, run the following command to install the dependencies:
+
+```bash
+poetry install --with docs --no-root
+```
+
+#### Building
+
+The code that builds the documentation is located in the `/docs` directory of the monorepo.
+
+Before building the documentation, it is always a good idea to clean the build directory:
+
+```bash
+make clean-docs
+```
+
+You can build and preview the documentation as outlined below:
+
+```bash
+make serve-docs
+```
+
+#### Linting
+
+The documentation is linted from the **monorepo root**. To lint it, run the following from there:
+
+```bash
+make spellcheck
+```
+
+### ️In-code Documentation
+
+The in-code documentation is autogenerated from docstrings.
+
+For the API reference to be useful, the codebase must be well-documented. This means that all functions, classes, and methods should have a docstring that explains what they do, what the arguments are, and what the return value is. This is a good practice in general, but it is especially important for LangChain because the API reference is the primary resource for developers to understand how to use the codebase.
+
+We generally follow the [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings) for docstrings.
+
+Here is an example of a well-documented function:
+
+```python
+
+def my_function(arg1: int, arg2: str) -> float:
+    """This is a short description of the function. (It should be a single sentence.)
+
+    This is a longer description of the function. It should explain what
+    the function does, what the arguments are, and what the return value is.
+    It should wrap at 88 characters.
+
+    Examples:
+        This is a section for examples of how to use the function.
+
+        .. code-block:: python
+
+            my_function(1, "hello")
+
+    Args:
+        arg1: This is a description of arg1. We do not need to specify the type since
+            it is already specified in the function signature.
+        arg2: This is a description of arg2.
+
+    Returns:
+        This is a description of the return value.
+    """
+    return 3.14
+```

Makefile

@@ -1,68 +1,39 @@
-# Define the directories containing projects
-LIBS_DIRS := $(wildcard libs/*)
+.PHONY: lint-docs format-docs build-docs serve-docs serve-clean-docs clean-docs codespell build-typedoc
 
-# Default target
-.PHONY: all
-all: lint format lock test
+build-typedoc:
+	cd libs/sdk-js && yarn install --include-dev && yarn typedoc
+	cd libs/sdk-js && yarn --silent concat-md --decrease-title-levels --ignore=js_ts_sdk_ref.md --start-title-level-at 2 docs > ../../docs/docs/cloud/reference/sdk/js_ts_sdk_ref.md 2>/dev/null
+	#  Add links to the monorepo
+	sed -e '1,10s|@langchain/langgraph-sdk|[@langchain/langgraph-sdk](https://github.com/langchain-ai/langgraph/tree/main/libs/sdk-js)|g' docs/docs/cloud/reference/sdk/js_ts_sdk_ref.md > temp_file && mv temp_file docs/docs/cloud/reference/sdk/js_ts_sdk_ref.md
 
-# Install dependencies for all projects
-.PHONY: install
-install:
-	@echo "Creating virtual environment..."
-	@uv venv
-	@for dir in $(LIBS_DIRS); do \
-		if [ -f $$dir/pyproject.toml ]; then \
-			echo "Installing dependencies for $$dir"; \
-			uv pip install -e $$dir; \
-		fi; \
-	done
+build-docs: build-typedoc
+	poetry run python -m mkdocs build --clean -f docs/mkdocs.yml --strict
 
-# Lint all projects
-.PHONY: lint
-lint:
-	@for dir in $(LIBS_DIRS); do \
-		if [ -f $$dir/Makefile ]; then \
-			echo "Running lint in $$dir"; \
-			$(MAKE) -C $$dir lint; \
-		fi; \
-	done
+serve-clean-docs: clean-docs
+	poetry run python -m mkdocs serve -c -f docs/mkdocs.yml --strict -w ./libs/langgraph
 
-# Format all projects
-.PHONY: format
-format:
-	@for dir in $(LIBS_DIRS); do \
-		if [ -f $$dir/Makefile ]; then \
-			echo "Running format in $$dir"; \
-			$(MAKE) -C $$dir format; \
-		fi; \
-	done
+serve-docs: build-typedoc
+	poetry run python -m mkdocs serve -f docs/mkdocs.yml -w ./libs/langgraph --dirty
 
-# Lock all projects
-.PHONY: lock
-lock:
-	@for dir in $(LIBS_DIRS); do \
-		if [ -f $$dir/Makefile ]; then \
-			echo "Running lock in $$dir"; \
-			(cd $$dir && uv lock); \
-		fi; \
-	done
+clean-docs:
+	find ./docs/docs -name "*.ipynb" -type f -delete
+	rm -rf docs/site
 
-# Lock all projects and upgrade dependencies
-.PHONY: lock-upgrade
-lock-upgrade:
-	@for dir in $(LIBS_DIRS); do \
-		if [ -f $$dir/Makefile ]; then \
-			echo "Running lock-upgrade in $$dir"; \
-			(cd $$dir && uv lock --upgrade); \
-		fi; \
-	done
+## Run format against the project documentation.
+format-docs:
+	poetry run ruff format docs/docs
+	poetry run ruff check --fix docs/docs
 
-# Test all projects
-.PHONY: test
-test:
-	@for dir in $(LIBS_DIRS); do \
-		if [ -f $$dir/Makefile ]; then \
-			echo "Running test in $$dir"; \
-			$(MAKE) -C $$dir test; \
-		fi; \
-	done
+# Check the docs for linting violations
+lint-docs:
+	poetry run ruff format --check docs/docs
+	poetry run ruff check docs/docs
+
+codespell:
+	./docs/codespell_notebooks.sh .
+
+start-services:
+	docker compose -f docs/test-compose.yml up -V --force-recreate --wait --remove-orphans
+
+stop-services:
+	docker compose -f docs/test-compose.yml down

README.md

@@ -1,91 +1,245 @@
-<picture class="github-only">
-  <source media="(prefers-color-scheme: light)" srcset="https://langchain-ai.github.io/langgraph/static/wordmark_dark.svg">
-  <source media="(prefers-color-scheme: dark)" srcset="https://langchain-ai.github.io/langgraph/static/wordmark_light.svg">
-  <img alt="LangGraph Logo" src="https://langchain-ai.github.io/langgraph/static/wordmark_dark.svg" width="80%">
-</picture>
+# 🦜🕸️LangGraph
 
-<div>
-<br>
-</div>
-
-[![Version](https://img.shields.io/pypi/v/langgraph.svg)](https://pypi.org/project/langgraph/)
+![Version](https://img.shields.io/pypi/v/langgraph)
 [![Downloads](https://static.pepy.tech/badge/langgraph/month)](https://pepy.tech/project/langgraph)
 [![Open Issues](https://img.shields.io/github/issues-raw/langchain-ai/langgraph)](https://github.com/langchain-ai/langgraph/issues)
-[![Docs](https://img.shields.io/badge/docs-latest-blue)](https://docs.langchain.com/oss/python/langgraph/overview)
+[![Docs](https://img.shields.io/badge/docs-latest-blue)](https://langchain-ai.github.io/langgraph/)
 
-Trusted by companies shaping the future of agents – including Klarna, Replit, Elastic, and more – LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents.
+⚡ Building language agents as graphs ⚡
 
-## Get started
+> [!NOTE]
+> Looking for the JS version? Click [here](https://github.com/langchain-ai/langgraphjs) ([JS docs](https://langchain-ai.github.io/langgraphjs/)).
 
-Install LangGraph:
+## Overview
 
-```
+[LangGraph](https://langchain-ai.github.io/langgraph/) is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures, differentiating it from DAG-based solutions. As a very low-level framework, it provides fine-grained control over both the flow and state of your application, crucial for creating reliable agents. Additionally, LangGraph includes built-in persistence, enabling advanced human-in-the-loop and memory features.
+
+LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.
+
+[LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform) is infrastructure for deploying LangGraph agents. It is a commercial solution for deploying agentic applications to production, built on the open-source LangGraph framework. The LangGraph Platform consists of several components that work together to support the development, deployment, debugging, and monitoring of LangGraph applications: [LangGraph Server](https://langchain-ai.github.io/langgraph/concepts/langgraph_server) (APIs), [LangGraph SDKs](https://langchain-ai.github.io/langgraph/concepts/sdk) (clients for the APIs), [LangGraph CLI](https://langchain-ai.github.io/langgraph/concepts/langgraph_cli) (command line tool for building the server), [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio) (UI/debugger),
+
+To learn more about LangGraph, check out our first LangChain Academy course, *Introduction to LangGraph*, available for free [here](https://academy.langchain.com/courses/intro-to-langgraph).
+
+### Key Features
+
+- **Cycles and Branching**: Implement loops and conditionals in your apps.
+- **Persistence**: Automatically save state after each step in the graph. Pause and resume the graph execution at any point to support error recovery, human-in-the-loop workflows, time travel and more.
+- **Human-in-the-Loop**: Interrupt graph execution to approve or edit next action planned by the agent.
+- **Streaming Support**: Stream outputs as they are produced by each node (including token streaming).
+- **Integration with LangChain**: LangGraph integrates seamlessly with [LangChain](https://github.com/langchain-ai/langchain/) and [LangSmith](https://docs.smith.langchain.com/) (but does not require them).
+
+### LangGraph Platform
+
+LangGraph Platform is a commercial solution for deploying agentic applications to production, built on the open-source LangGraph framework.
+Here are some common issues that arise in complex deployments, which LangGraph Platform addresses:
+
+- **Streaming support**: LangGraph Server provides [multiple streaming modes](https://langchain-ai.github.io/langgraph/concepts/streaming) optimized for various application needs
+- **Background runs**: Runs agents asynchronously in the background
+- **Support for long running agents**: Infrastructure that can handle long running processes
+- **[Double texting](https://langchain-ai.github.io/langgraph/concepts/double_texting)**: Handle the case where you get two messages from the user before the agent can respond
+- **Handle burstiness**: Task queue for ensuring requests are handled consistently without loss, even under heavy loads
+
+## Installation
+
+```shell
 pip install -U langgraph
 ```
 
-Create a simple workflow:
+## Example
 
-```python
-from langgraph.graph import START, StateGraph
-from typing_extensions import TypedDict
+One of the central concepts of LangGraph is state. Each graph execution creates a state that is passed between nodes in the graph as they execute, and each node updates this internal state with its return value after it executes. The way that the graph updates its internal state is defined by either the type of graph chosen or a custom function.
 
+Let's take a look at a simple example of an agent that can use a search tool.
 
-class State(TypedDict):
-    text: str
-
-
-def node_a(state: State) -> dict:
-    return {"text": state["text"] + "a"}
-
-
-def node_b(state: State) -> dict:
-    return {"text": state["text"] + "b"}
-
-
-graph = StateGraph(State)
-graph.add_node("node_a", node_a)
-graph.add_node("node_b", node_b)
-graph.add_edge(START, "node_a")
-graph.add_edge("node_a", "node_b")
-
-print(graph.compile().invoke({"text": ""}))
-# {'text': 'ab'}
+```shell
+pip install langchain-anthropic
 ```
 
-Get started with the [LangGraph Quickstart](https://docs.langchain.com/oss/python/langgraph/quickstart).
+```shell
+export ANTHROPIC_API_KEY=sk-...
+```
 
-To quickly build agents with LangChain's `create_agent` (built on LangGraph), see the [LangChain Agents documentation](https://docs.langchain.com/oss/python/langchain/agents).
+Optionally, we can set up [LangSmith](https://docs.smith.langchain.com/) for best-in-class observability.
 
-## Core benefits
+```shell
+export LANGSMITH_TRACING=true
+export LANGSMITH_API_KEY=lsv2_sk_...
+```
 
-LangGraph provides low-level supporting infrastructure for *any* long-running, stateful workflow or agent. LangGraph does not abstract prompts or architecture, and provides the following central benefits:
+```python
+from typing import Annotated, Literal, TypedDict
 
-- [Durable execution](https://docs.langchain.com/oss/python/langgraph/durable-execution): Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off.
-- [Human-in-the-loop](https://docs.langchain.com/oss/python/langgraph/interrupts): Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution.
-- [Comprehensive memory](https://docs.langchain.com/oss/python/langgraph/memory): Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions.
-- [Debugging with LangSmith](http://www.langchain.com/langsmith): Gain deep visibility into complex agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics.
-- [Production-ready deployment](https://docs.langchain.com/langsmith/app-development): Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows.
+from langchain_core.messages import HumanMessage
+from langchain_anthropic import ChatAnthropic
+from langchain_core.tools import tool
+from langgraph.checkpoint.memory import MemorySaver
+from langgraph.graph import END, START, StateGraph, MessagesState
+from langgraph.prebuilt import ToolNode
 
-## LangGraph’s ecosystem
 
-While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents. To improve your LLM application development, pair LangGraph with:
+# Define the tools for the agent to use
+@tool
+def search(query: str):
+    """Call to surf the web."""
+    # This is a placeholder, but don't tell the LLM that...
+    if "sf" in query.lower() or "san francisco" in query.lower():
+        return "It's 60 degrees and foggy."
+    return "It's 90 degrees and sunny."
 
-- [LangSmith](http://www.langchain.com/langsmith) — Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
-- [LangSmith Deployment](https://docs.langchain.com/langsmith/deployments) — Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangGraph Studio](https://docs.langchain.com/oss/python/langgraph/studio).
-- [LangChain](https://docs.langchain.com/oss/python/langchain/overview) – Provides integrations and composable components to streamline LLM application development.
 
-> [!NOTE]
-> Looking for the JS version of LangGraph? See the [JS repo](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://docs.langchain.com/oss/javascript/langgraph/overview).
+tools = [search]
 
-## Additional resources
+tool_node = ToolNode(tools)
 
-- [Guides](https://docs.langchain.com/oss/python/langgraph/overview): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
-- [Reference](https://reference.langchain.com/python/langgraph/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
-- [Examples](https://docs.langchain.com/oss/python/langgraph/agentic-rag): Guided examples on getting started with LangGraph.
-- [LangChain Forum](https://forum.langchain.com/): Connect with the community and share all of your technical questions, ideas, and feedback.
-- [LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph): Learn the basics of LangGraph in our free, structured course.
-- [Case studies](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship AI applications at scale.
+model = ChatAnthropic(model="claude-3-5-sonnet-20240620", temperature=0).bind_tools(tools)
 
-## Acknowledgements
+# Define the function that determines whether to continue or not
+def should_continue(state: MessagesState) -> Literal["tools", END]:
+    messages = state['messages']
+    last_message = messages[-1]
+    # If the LLM makes a tool call, then we route to the "tools" node
+    if last_message.tool_calls:
+        return "tools"
+    # Otherwise, we stop (reply to the user)
+    return END
 
-LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.
+
+# Define the function that calls the model
+def call_model(state: MessagesState):
+    messages = state['messages']
+    response = model.invoke(messages)
+    # We return a list, because this will get added to the existing list
+    return {"messages": [response]}
+
+
+# Define a new graph
+workflow = StateGraph(MessagesState)
+
+# Define the two nodes we will cycle between
+workflow.add_node("agent", call_model)
+workflow.add_node("tools", tool_node)
+
+# Set the entrypoint as `agent`
+# This means that this node is the first one called
+workflow.add_edge(START, "agent")
+
+# We now add a conditional edge
+workflow.add_conditional_edges(
+    # First, we define the start node. We use `agent`.
+    # This means these are the edges taken after the `agent` node is called.
+    "agent",
+    # Next, we pass in the function that will determine which node is called next.
+    should_continue,
+)
+
+# We now add a normal edge from `tools` to `agent`.
+# This means that after `tools` is called, `agent` node is called next.
+workflow.add_edge("tools", 'agent')
+
+# Initialize memory to persist state between graph runs
+checkpointer = MemorySaver()
+
+# Finally, we compile it!
+# This compiles it into a LangChain Runnable,
+# meaning you can use it as you would any other runnable.
+# Note that we're (optionally) passing the memory when compiling the graph
+app = workflow.compile(checkpointer=checkpointer)
+
+# Use the Runnable
+final_state = app.invoke(
+    {"messages": [HumanMessage(content="what is the weather in sf")]},
+    config={"configurable": {"thread_id": 42}}
+)
+final_state["messages"][-1].content
+```
+
+```
+"Based on the search results, I can tell you that the current weather in San Francisco is:\n\nTemperature: 60 degrees Fahrenheit\nConditions: Foggy\n\nSan Francisco is known for its microclimates and frequent fog, especially during the summer months. The temperature of 60°F (about 15.5°C) is quite typical for the city, which tends to have mild temperatures year-round. The fog, often referred to as "Karl the Fog" by locals, is a characteristic feature of San Francisco\'s weather, particularly in the mornings and evenings.\n\nIs there anything else you\'d like to know about the weather in San Francisco or any other location?"
+```
+
+Now when we pass the same `"thread_id"`, the conversation context is retained via the saved state (i.e. stored list of messages)
+
+```python
+final_state = app.invoke(
+    {"messages": [HumanMessage(content="what about ny")]},
+    config={"configurable": {"thread_id": 42}}
+)
+final_state["messages"][-1].content
+```
+
+```
+"Based on the search results, I can tell you that the current weather in New York City is:\n\nTemperature: 90 degrees Fahrenheit (approximately 32.2 degrees Celsius)\nConditions: Sunny\n\nThis weather is quite different from what we just saw in San Francisco. New York is experiencing much warmer temperatures right now. Here are a few points to note:\n\n1. The temperature of 90°F is quite hot, typical of summer weather in New York City.\n2. The sunny conditions suggest clear skies, which is great for outdoor activities but also means it might feel even hotter due to direct sunlight.\n3. This kind of weather in New York often comes with high humidity, which can make it feel even warmer than the actual temperature suggests.\n\nIt's interesting to see the stark contrast between San Francisco's mild, foggy weather and New York's hot, sunny conditions. This difference illustrates how varied weather can be across different parts of the United States, even on the same day.\n\nIs there anything else you'd like to know about the weather in New York or any other location?"
+```
+
+### Step-by-step Breakdown
+
+1. <details>
+    <summary>Initialize the model and tools.</summary>
+
+    - we use `ChatAnthropic` as our LLM. **NOTE:** we need make sure the model knows that it has these tools available to call. We can do this by converting the LangChain tools into the format for OpenAI tool calling using the `.bind_tools()` method.
+    - we define the tools we want to use - a search tool in our case. It is really easy to create your own tools - see documentation here on how to do that [here](https://python.langchain.com/docs/modules/agents/tools/custom_tools).
+   </details>
+
+2. <details>
+    <summary>Initialize graph with state.</summary>
+
+    - we initialize graph (`StateGraph`) by passing state schema (in our case `MessagesState`)
+    - `MessagesState` is a prebuilt state schema that has one attribute -- a list of LangChain `Message` objects, as well as logic for merging the updates from each node into the state
+   </details>
+
+3. <details>
+    <summary>Define graph nodes.</summary>
+
+    There are two main nodes we need:
+
+      - The `agent` node: responsible for deciding what (if any) actions to take.
+      - The `tools` node that invokes tools: if the agent decides to take an action, this node will then execute that action.
+   </details>
+
+4. <details>
+    <summary>Define entry point and graph edges.</summary>
+
+      First, we need to set the entry point for graph execution - `agent` node.
+
+      Then we define one normal and one conditional edge. Conditional edge means that the destination depends on the contents of the graph's state (`MessageState`). In our case, the destination is not known until the agent (LLM) decides.
+
+      - Conditional edge: after the agent is called, we should either:
+        - a. Run tools if the agent said to take an action, OR
+        - b. Finish (respond to the user) if the agent did not ask to run tools
+      - Normal edge: after the tools are invoked, the graph should always return to the agent to decide what to do next
+   </details>
+
+5. <details>
+    <summary>Compile the graph.</summary>
+
+    - When we compile the graph, we turn it into a LangChain [Runnable](https://python.langchain.com/v0.2/docs/concepts/#runnable-interface), which automatically enables calling `.invoke()`, `.stream()` and `.batch()` with your inputs
+    - We can also optionally pass checkpointer object for persisting state between graph runs, and enabling memory, human-in-the-loop workflows, time travel and more. In our case we use `MemorySaver` - a simple in-memory checkpointer
+    </details>
+
+6. <details>
+   <summary>Execute the graph.</summary>
+
+    1. LangGraph adds the input message to the internal state, then passes the state to the entrypoint node, `"agent"`.
+    2. The `"agent"` node executes, invoking the chat model.
+    3. The chat model returns an `AIMessage`. LangGraph adds this to the state.
+    4. Graph cycles the following steps until there are no more `tool_calls` on `AIMessage`:
+
+        - If `AIMessage` has `tool_calls`, `"tools"` node executes
+        - The `"agent"` node executes again and returns `AIMessage`
+
+    5. Execution progresses to the special `END` value and outputs the final state.
+    And as a result, we get a list of all our chat messages as output.
+   </details>
+
+
+## Documentation
+
+* [Tutorials](https://langchain-ai.github.io/langgraph/tutorials/): Learn to build with LangGraph through guided examples.
+* [How-to Guides](https://langchain-ai.github.io/langgraph/how-tos/): Accomplish specific things within LangGraph, from streaming, to adding memory & persistence, to common design patterns (branching, subgraphs, etc.), these are the place to go if you want to copy and run a specific code snippet.
+* [Conceptual Guides](https://langchain-ai.github.io/langgraph/concepts/high_level/): In-depth explanations of the key concepts and principles behind LangGraph, such as nodes, edges, state and more.
+* [API Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Review important classes and methods, simple examples of how to use the graph and checkpointing APIs, higher-level prebuilt components and more.
+* [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform): LangGraph Platform is a commercial solution for deploying agentic applications in production, built on the open-source LangGraph framework.
+
+## Contributing
+
+For more information on how to contribute, see [here](https://github.com/langchain-ai/langgraph/blob/main/CONTRIBUTING.md).

docs/.gitignore

@@ -1 +1,2 @@
-_site/
+site/
+docs/cloud/reference/sdk/js_ts_sdk_ref.md

docs/README.md

@@ -0,0 +1,61 @@
+# Setup
+
+To setup requirements for building docs you can run:
+
+```bash
+poetry install --with test
+```
+
+## Serving documentation locally
+
+To run the documentation server locally you can run:
+
+```bash
+make serve-docs
+```
+
+## Execute notebooks
+
+If you would like to automatically execute all of the notebooks, to mimic the "Run notebooks" GHA, you can run:
+
+```bash
+python docs/_scripts/prepare_notebooks_for_ci.py
+./docs/_scripts/execute_notebooks.sh
+```
+
+**Note**: if you want to run the notebooks without `%pip install` cells, you can run:
+
+```bash
+python docs/_scripts/prepare_notebooks_for_ci.py --comment-install-cells
+./docs/_scripts/execute_notebooks.sh
+```
+
+`prepare_notebooks_for_ci.py` script will add VCR cassette context manager for each cell in the notebook, so that:
+* when the notebook is run for the first time, cells with network requests will be recorded to a VCR cassette file
+* when the notebook is run subsequently, the cells with network requests will be replayed from the cassettes
+
+**Note**: this is currently limited only to the notebooks in `docs/docs/how-tos`
+
+## Adding new notebooks
+
+If you are adding a notebook with API requests, it's **recommended** to record network requests so that they can be subsequently replayed. If this is not done, the notebook runner will make API requests every time the notebook is run, which can be costly and slow.
+
+To record network requests, please make sure to first run `prepare_notebooks_for_ci.py` script.
+
+Then, run
+
+```bash
+jupyter execute <path_to_notebook>
+```
+
+Once the notebook is executed, you should see the new VCR cassettes recorded in `docs/cassettes` directory and discard the updated notebook.
+
+## Updating existing notebooks
+
+If you are updating an existing notebook, please make sure to remove any existing cassettes for the notebook in `docs/cassettes` directory (each cassette is prefixed with the notebook name), and then run the steps from the "Adding new notebooks" section above.
+
+To delete cassettes for a notebook, you can run:
+
+```bash
+rm docs/cassettes/<notebook_name>*
+```

docs/_scripts/download_tiktoken.py

@@ -0,0 +1,5 @@
+import tiktoken
+
+# This will trigger the download and caching of the necessary files
+for encoding in ("gpt2", "gpt-3.5"):
+    tiktoken.encoding_for_model(encoding)

docs/_scripts/execute_notebooks.sh

@@ -0,0 +1,36 @@
+#!/bin/bash
+
+# Read the list of notebooks to skip from the JSON file
+SKIP_NOTEBOOKS=$(python -c "import json; print('\n'.join(json.load(open('docs/notebooks_no_execution.json'))))")
+
+# Function to execute a single notebook
+execute_notebook() {
+    file="$1"
+    echo "Starting execution of $file"
+    start_time=$(date +%s)
+    if ! output=$(time poetry run jupyter execute "$file" 2>&1); then
+        end_time=$(date +%s)
+        execution_time=$((end_time - start_time))
+        echo "Error in $file. Execution time: $execution_time seconds"
+        echo "Error details: $output"
+        exit 1
+    fi
+    end_time=$(date +%s)
+    execution_time=$((end_time - start_time))
+    echo "Finished $file. Execution time: $execution_time seconds"
+}
+
+export -f execute_notebook
+
+# Check if custom notebook paths are provided
+if [ $# -gt 0 ]; then
+    notebooks=$(echo "$@" | tr ' ' '\n' | grep -vFf <(echo "$SKIP_NOTEBOOKS"))
+else
+    # Find all notebooks and filter out those in the skip list
+    notebooks=$(find docs/docs/tutorials docs/docs/how-tos -name "*.ipynb" | grep -v ".ipynb_checkpoints" | grep -vFf <(echo "$SKIP_NOTEBOOKS"))
+fi
+
+# Execute notebooks sequentially
+for file in $notebooks; do
+    execute_notebook "$file"
+done

docs/_scripts/generate_api_reference_links.py

@@ -0,0 +1,246 @@
+import importlib
+import inspect
+import logging
+import os
+import re
+from typing import List, Literal, Optional
+from typing_extensions import TypedDict
+
+import nbformat
+from nbconvert.preprocessors import Preprocessor
+
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+# Base URL for all class documentation
+_LANGCHAIN_API_REFERENCE = "https://python.langchain.com/api_reference/"
+_LANGGRAPH_API_REFERENCE = "https://langchain-ai.github.io/langgraph/reference/"
+
+
+# (alias/re-exported modules, source module, class, docs namespace)
+MANUAL_API_REFERENCES_LANGGRAPH = [
+    (
+        ["langgraph.prebuilt"],
+        "langgraph.prebuilt.chat_agent_executor",
+        "create_react_agent",
+        "prebuilt",
+    ),
+    (["langgraph.prebuilt"], "langgraph.prebuilt.tool_node", "ToolNode", "prebuilt"),
+    (
+        ["langgraph.prebuilt"],
+        "langgraph.prebuilt.tool_node",
+        "tools_condition",
+        "prebuilt",
+    ),
+    (
+        ["langgraph.prebuilt"],
+        "langgraph.prebuilt.tool_node",
+        "InjectedState",
+        "prebuilt",
+    ),
+    # Graph
+    (["langgraph.graph"], "langgraph.graph.message", "add_messages", "graphs"),
+    (["langgraph.graph"], "langgraph.graph.state", "StateGraph", "graphs"),
+    (["langgraph.graph"], "langgraph.graph.state", "CompiledStateGraph", "graphs"),
+    ([], "langgraph.types", "StreamMode", "types"),
+    (["langgraph.graph"], "langgraph.constants", "START", "constants"),
+    (["langgraph.graph"], "langgraph.constants", "END", "constants"),
+    (["langgraph.constants"], "langgraph.types", "Send", "types"),
+    (["langgraph.constants"], "langgraph.types", "Interrupt", "types"),
+    ([], "langgraph.types", "RetryPolicy", "types"),
+    ([], "langgraph.checkpoint.base", "Checkpoint", "checkpoints"),
+    ([], "langgraph.checkpoint.base", "CheckpointMetadata", "checkpoints"),
+    ([], "langgraph.checkpoint.base", "BaseCheckpointSaver", "checkpoints"),
+    ([], "langgraph.checkpoint.base", "SerializerProtocol", "checkpoints"),
+    ([], "langgraph.checkpoint.serde.jsonplus", "JsonPlusSerializer", "checkpoints"),
+    ([], "langgraph.checkpoint.memory", "MemorySaver", "checkpoints"),
+    ([], "langgraph.checkpoint.sqlite.aio", "AsyncSqliteSaver", "checkpoints"),
+    ([], "langgraph.checkpoint.sqlite", "SqliteSaver", "checkpoints"),
+    ([], "langgraph.checkpoint.postgres.aio", "AsyncPostgresSaver", "checkpoints"),
+    ([], "langgraph.checkpoint.postgres", "PostgresSaver", "checkpoints"),
+]
+
+WELL_KNOWN_LANGGRAPH_OBJECTS = {
+    (module_, class_): (source_module, namespace)
+    for (modules, source_module, class_, namespace) in MANUAL_API_REFERENCES_LANGGRAPH
+    for module_ in modules + [source_module]
+}
+
+
+def _make_regular_expression(pkg_prefix: str) -> re.Pattern:
+    if not pkg_prefix.isidentifier():
+        raise ValueError(f"Invalid package prefix: {pkg_prefix}")
+    return re.compile(
+        r"from\s+(" + pkg_prefix + "(?:_\w+)?(?:\.\w+)*?)\s+import\s+"
+        r"((?:\w+(?:,\s*)?)*"  # Match zero or more words separated by a comma+optional ws
+        r"(?:\s*\(.*?\))?)",  # Match optional parentheses block
+        re.DOTALL,  # Match newlines as well
+    )
+
+
+# Regular expression to match langchain import lines
+_IMPORT_LANGCHAIN_RE = _make_regular_expression("langchain")
+_IMPORT_LANGGRAPH_RE = _make_regular_expression("langgraph")
+
+
+def _get_full_module_name(module_path, class_name) -> Optional[str]:
+    """Get full module name using inspect"""
+    try:
+        module = importlib.import_module(module_path)
+        class_ = getattr(module, class_name)
+        module = inspect.getmodule(class_)
+        if module is None:
+            # For constants, inspect.getmodule() might return None
+            # In this case, we'll return the original module_path
+            return module_path
+        return module.__name__
+    except AttributeError as e:
+        logger.warning(f"Could not find module for {class_name}, {e}")
+        return None
+    except ImportError as e:
+        logger.warning(f"Failed to load for class {class_name}, {e}")
+        return None
+
+
+def _get_doc_title(data: str, file_name: str) -> str:
+    try:
+        return re.findall(r"^#\s*(.*)", data, re.MULTILINE)[0]
+    except IndexError:
+        pass
+    # Parse the rst-style titles
+    try:
+        return re.findall(r"^(.*)\n=+\n", data, re.MULTILINE)[0]
+    except IndexError:
+        return file_name
+
+
+class ImportInformation(TypedDict):
+    imported: str  # imported class name
+    source: str  # module path
+    docs: str  # URL to the documentation
+    title: str  # Title of the document
+
+
+def _get_imports(
+    code: str, doc_title: str, package_ecosystem: Literal["langchain", "langgraph"]
+) -> List[ImportInformation]:
+    """Get imports from the given code block.
+
+    Args:
+        code: Python code block from which to extract imports
+        doc_title: Title of the document
+        package_ecosystem: "langchain" or "langgraph". The two live in different
+            repositories and have separate documentation sites.
+
+    Returns:
+        List of import information for the given code block
+    """
+    imports = []
+
+    if package_ecosystem == "langchain":
+        pattern = _IMPORT_LANGCHAIN_RE
+    elif package_ecosystem == "langgraph":
+        pattern = _IMPORT_LANGGRAPH_RE
+    else:
+        raise ValueError(f"Invalid package ecosystem: {package_ecosystem}")
+
+    for import_match in pattern.finditer(code):
+        module = import_match.group(1)
+        if "pydantic_v1" in module:
+            continue
+        imports_str = (
+            import_match.group(2).replace("(\n", "").replace("\n)", "")
+        )  # Handle newlines within parentheses
+        # remove any newline and spaces, then split by comma
+        imported_classes = [
+            imp.strip()
+            for imp in re.split(r",\s*", imports_str.replace("\n", ""))
+            if imp.strip()
+        ]
+        for class_name in imported_classes:
+            module_path = _get_full_module_name(module, class_name)
+            if not module_path:
+                continue
+            if len(module_path.split(".")) < 2:
+                continue
+
+            if package_ecosystem == "langchain":
+                pkg = module_path.split(".")[0].replace("langchain_", "")
+                top_level_mod = module_path.split(".")[1]
+
+                url = (
+                    _LANGCHAIN_API_REFERENCE
+                    + pkg
+                    + "/"
+                    + top_level_mod
+                    + "/"
+                    + module_path
+                    + "."
+                    + class_name
+                    + ".html"
+                )
+            elif package_ecosystem == "langgraph":
+                if (module, class_name) not in WELL_KNOWN_LANGGRAPH_OBJECTS:
+                    # Likely not documented yet
+                    continue
+
+                source_module, namespace = WELL_KNOWN_LANGGRAPH_OBJECTS[
+                    (module, class_name)
+                ]
+                url = (
+                    _LANGGRAPH_API_REFERENCE
+                    + namespace
+                    + "/#"
+                    + source_module
+                    + "."
+                    + class_name
+                )
+            else:
+                raise ValueError(f"Invalid package ecosystem: {package_ecosystem}")
+
+            # Add the import information to our list
+            imports.append(
+                {
+                    "imported": class_name,
+                    "source": module,
+                    "docs": url,
+                    "title": doc_title,
+                }
+            )
+
+    return imports
+
+
+class ImportPreprocessor(Preprocessor):
+    """A preprocessor to replace imports in each Python code cell with links to their
+    documentation and append the import info in a comment."""
+
+    def preprocess(self, nb, resources):
+        self.all_imports = []
+        file_name = os.path.basename(resources.get("metadata", {}).get("name", ""))
+        _DOC_TITLE = _get_doc_title(nb.cells[0].source, file_name)
+
+        cells = []
+        for cell in nb.cells:
+            if cell.cell_type == "code":
+                cells.append(cell)
+                imports = _get_imports(
+                    cell.source, _DOC_TITLE, "langchain"
+                ) + _get_imports(cell.source, _DOC_TITLE, "langgraph")
+                if not imports:
+                    continue
+
+                cells.append(
+                    nbformat.v4.new_markdown_cell(
+                        source=f"""
+<div>
+<b>API Reference:</b>
+{' | '.join(f'<a href="{imp["docs"]}">{imp["imported"]}</a>' for imp in imports)}
+</div>
+                    """
+                    )
+                )
+            else:
+                cells.append(cell)
+        nb.cells = cells
+        return nb, resources

docs/_scripts/notebook_convert.py

@@ -0,0 +1,126 @@
+import os
+import re
+from pathlib import Path
+
+import nbformat
+from nbconvert.exporters import MarkdownExporter
+from nbconvert.preprocessors import Preprocessor
+
+from generate_api_reference_links import ImportPreprocessor
+
+
+class EscapePreprocessor(Preprocessor):
+    def preprocess_cell(self, cell, resources, cell_index):
+        if cell.cell_type == "markdown":
+            # rewrite markdown links to html links (excluding image links)
+            cell.source = re.sub(
+                r"(?<!!)\[([^\]]*)\]\((?![^\)]*//)([^)]*)(?:\.ipynb)?\)",
+                r'<a href="\2">\1</a>',
+                cell.source,
+            )
+            # Fix image paths in <img> tags
+            cell.source = re.sub(
+                r'<img\s+src="\.?/img/([^"]+)"', r'<img src="../img/\1"', cell.source
+            )
+
+        elif cell.cell_type == "code":
+            # escape ``` in code
+            cell.source = cell.source.replace("```", r"\`\`\`")
+            # escape ``` in output
+            if "outputs" in cell:
+                filter_out = set()
+                for i, output in enumerate(cell["outputs"]):
+                    if "text" in output:
+                        if not output["text"].strip():
+                            filter_out.add(i)
+                            continue
+
+                        value = output["text"].replace("```", r"\`\`\`")
+                        # handle a funky case w/ references in text
+                        value = re.sub(r"\[(\d+)\](?=\[(\d+)\])", r"[\1]\\", value)
+                        output["text"] = value
+                    elif "data" in output:
+                        for key, value in output["data"].items():
+                            if isinstance(value, str):
+                                value = value.replace("```", r"\`\`\`")
+                                # handle a funky case w/ references in text
+                                output["data"][key] = re.sub(
+                                    r"\[(\d+)\](?=\[(\d+)\])", r"[\1]\\", value
+                                )
+                cell["outputs"] = [
+                    output
+                    for i, output in enumerate(cell["outputs"])
+                    if i not in filter_out
+                ]
+
+        return cell, resources
+
+
+class ExtractAttachmentsPreprocessor(Preprocessor):
+    """
+    Extracts all of the outputs from the notebook file.  The extracted
+    outputs are returned in the 'resources' dictionary.
+    """
+
+    def preprocess_cell(self, cell, resources, cell_index):
+        """
+        Apply a transformation on each cell,
+        Parameters
+        ----------
+        cell : NotebookNode cell
+            Notebook cell being processed
+        resources : dictionary
+            Additional resources used in the conversion process.  Allows
+            preprocessors to pass variables into the Jinja engine.
+        cell_index : int
+            Index of the cell being processed (see base.py)
+        """
+
+        # Get files directory if it has been specified
+
+        # Make sure outputs key exists
+        if not isinstance(resources["outputs"], dict):
+            resources["outputs"] = {}
+
+        # Loop through all of the attachments in the cell
+        for name, attach in cell.get("attachments", {}).items():
+            for mime, data in attach.items():
+                if mime not in {
+                    "image/png",
+                    "image/jpeg",
+                    "image/svg+xml",
+                    "application/pdf",
+                }:
+                    continue
+
+                # attachments are pre-rendered. Only replace markdown-formatted
+                # images with the following logic
+                attach_str = f"({name})"
+                if attach_str in cell.source:
+                    data = f"(data:{mime};base64,{data})"
+                    cell.source = cell.source.replace(attach_str, data)
+
+        return cell, resources
+
+
+exporter = MarkdownExporter(
+    preprocessors=[
+        EscapePreprocessor,
+        ExtractAttachmentsPreprocessor,
+        ImportPreprocessor,
+    ],
+    template_name="mdoutput",
+    extra_template_basedirs=[
+        os.path.join(os.path.dirname(__file__), "notebook_convert_templates")
+    ],
+)
+
+
+def convert_notebook(
+    notebook_path: Path,
+) -> Path:
+    with open(notebook_path) as f:
+        nb = nbformat.read(f, as_version=4)
+
+    body, _ = exporter.from_notebook_node(nb)
+    return body

docs/_scripts/notebook_convert_templates/mdoutput/conf.json

@@ -0,0 +1,5 @@
+{
+  "mimetypes": {
+    "text/markdown": true
+  }
+}

docs/_scripts/notebook_convert_templates/mdoutput/index.md.j2

@@ -0,0 +1,33 @@
+{% extends 'markdown/index.md.j2' %}
+
+{%- block traceback_line -%}
+```output
+{{ line.rstrip() | strip_ansi }}
+```
+{%- endblock traceback_line -%}
+
+{%- block stream -%}
+```output
+{{ output.text.rstrip() }}
+```
+{%- endblock stream -%}
+
+{%- block data_text scoped -%}
+```output
+{{ output.data['text/plain'].rstrip() }}
+```
+{%- endblock data_text -%}
+
+{%- block data_html scoped -%}
+```html
+{{ output.data['text/html'] | safe }} 
+```
+{%- endblock data_html -%}
+
+{%- block data_jpg scoped -%}
+![](data:image/jpg;base64,{{ output.data['image/jpeg'] }})
+{%- endblock data_jpg -%}
+
+{%- block data_png scoped -%}
+![](data:image/png;base64,{{ output.data['image/png'] }})
+{%- endblock data_png -%}

docs/_scripts/notebook_hooks.py

@@ -0,0 +1,40 @@
+import logging
+from typing import Any, Dict
+
+from mkdocs.structure.pages import Page
+from mkdocs.structure.files import Files, File
+from notebook_convert import convert_notebook
+
+logger = logging.getLogger(__name__)
+logging.basicConfig()
+logger.setLevel(logging.INFO)
+
+
+class NotebookFile(File):
+    def is_documentation_page(self):
+        return True
+
+
+def on_files(files: Files, **kwargs: Dict[str, Any]):
+    new_files = Files([])
+    for file in files:
+        if file.src_path.endswith(".ipynb"):
+            new_file = NotebookFile(
+                path=file.src_path,
+                src_dir=file.src_dir,
+                dest_dir=file.dest_dir,
+                use_directory_urls=file.use_directory_urls,
+            )
+            new_files.append(new_file)
+        else:
+            new_files.append(file)
+    return new_files
+
+
+def on_page_markdown(markdown: str, page: Page, **kwargs: Dict[str, Any]):
+    if page.file.src_path.endswith(".ipynb"):
+        logger.info("Processing Jupyter notebook: %s", page.file.src_path)
+        body = convert_notebook(page.file.abs_src_path)
+        return body
+
+    return markdown

docs/_scripts/prepare_notebooks_for_ci.py

@@ -0,0 +1,215 @@
+"""Preprocess notebooks for CI. Currently adds VCR cassettes and optionally removes pip install cells."""
+
+import logging
+import os
+import json
+import click
+import nbformat
+
+logger = logging.getLogger(__name__)
+NOTEBOOK_DIRS = ("docs/docs/how-tos","docs/docs/tutorials")
+DOCS_PATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+CASSETTES_PATH = os.path.join(DOCS_PATH, "cassettes")
+
+BLOCKLIST_COMMANDS = (
+    # skip if has WebBaseLoader to avoid caching web pages
+    "WebBaseLoader",
+    # skip if has draw_mermaid_png to avoid generating mermaid images via API
+    "draw_mermaid_png",
+)
+
+NOTEBOOKS_NO_CASSETTES = (
+    "docs/docs/how-tos/visualization.ipynb",
+    "docs/docs/how-tos/many-tools.ipynb"
+)
+
+NOTEBOOKS_NO_EXECUTION = [
+    # this uses a user provided project name for langsmith
+    "docs/docs/tutorials/tnt-llm/tnt-llm.ipynb",
+    # this uses langsmith datasets
+    "docs/docs/tutorials/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb",
+    # this uses browser APIs
+    "docs/docs/tutorials/web-navigation/web_voyager.ipynb",
+    # these RAG guides use an ollama model
+    "docs/docs/tutorials/rag/langgraph_adaptive_rag_local.ipynb",
+    "docs/docs/tutorials/rag/langgraph_crag_local.ipynb",
+    "docs/docs/tutorials/rag/langgraph_self_rag_local.ipynb",
+    # this loads a massive dataset from gcp
+    "docs/docs/tutorials/usaco/usaco.ipynb",
+    # TODO: figure out why autogen notebook is not runnable (they are just hanging. possible due to code execution?)
+    "docs/docs/how-tos/autogen-integration.ipynb",
+    # TODO: need to update these notebooks to make sure they are runnable in CI
+    "docs/docs/tutorials/storm/storm.ipynb",  # issues only when running with VCR
+    "docs/docs/tutorials/lats/lats.ipynb",  # issues only when running with VCR
+    "docs/docs/tutorials/rag/langgraph_crag.ipynb",  # flakiness from tavily
+    "docs/docs/tutorials/rag/langgraph_adaptive_rag.ipynb",  # Cannot create a consistent method resolution error from VCR
+    "docs/docs/how-tos/map-reduce.ipynb"  # flakiness from structured output, only when running with VCR
+]
+
+
+def comment_install_cells(notebook: nbformat.NotebookNode) -> nbformat.NotebookNode:
+    for cell in notebook.cells:
+        if cell.cell_type != "code":
+            continue
+
+        if "pip install" in cell.source:
+            # Comment out the lines in cells containing "pip install"
+            cell.source = "\n".join(
+                f"# {line}" if line.strip() else line
+                for line in cell.source.splitlines()
+            )
+
+    return notebook
+
+
+def is_magic_command(code: str) -> bool:
+    return code.strip().startswith("%") or code.strip().startswith("!")
+
+
+def is_comment(code: str) -> bool:
+    return code.strip().startswith("#")
+
+
+def has_blocklisted_command(code: str, metadata: dict) -> bool:
+    if 'hide_from_vcr' in metadata:
+        return True
+    
+    code = code.strip()
+    for blocklisted_pattern in BLOCKLIST_COMMANDS:
+        if blocklisted_pattern in code:
+            return True
+    return False
+
+
+def add_vcr_to_notebook(
+    notebook: nbformat.NotebookNode, cassette_prefix: str
+) -> nbformat.NotebookNode:
+    """Inject `with vcr.cassette` into each code cell of the notebook."""
+
+    # Inject VCR context manager into each code cell
+    for idx, cell in enumerate(notebook.cells):
+        if cell.cell_type != "code":
+            continue
+
+        lines = cell.source.splitlines()
+        # skip if empty cell
+        if not lines:
+            continue
+
+        are_magic_lines = [is_magic_command(line) for line in lines]
+
+        # skip if all magic
+        if all(are_magic_lines):
+            continue
+
+        if any(are_magic_lines):
+            raise ValueError(
+                "Cannot process code cells with mixed magic and non-magic code."
+            )
+
+        # skip if just comments
+        if all(is_comment(line) or not line.strip() for line in lines):
+            continue
+
+        if has_blocklisted_command(cell.source, cell.metadata):
+            continue
+
+        cell_id = cell.get("id", idx)
+        cassette_name = f"{cassette_prefix}_{cell_id}.msgpack.zlib"
+        cell.source = f"with custom_vcr.use_cassette('{cassette_name}', filter_headers=['x-api-key', 'authorization'], record_mode='once', serializer='advanced_compressed'):\n" + "\n".join(
+            f"    {line}" for line in lines
+        )
+
+    # Add import statement
+    vcr_import_lines = [
+        "import nest_asyncio",
+        "nest_asyncio.apply()",
+        "import vcr",
+        "import msgpack",
+        "import base64",
+        "import zlib",
+        "import os",
+        "os.environ.pop(\"LANGCHAIN_TRACING_V2\", None)",
+        "custom_vcr = vcr.VCR()",
+        "",
+        "def compress_data(data, compression_level=9):",
+        "    packed = msgpack.packb(data, use_bin_type=True)",
+        "    compressed = zlib.compress(packed, level=compression_level)",
+        "    return base64.b64encode(compressed).decode('utf-8')",
+        "",
+        "def decompress_data(compressed_string):",
+        "    decoded = base64.b64decode(compressed_string)",
+        "    decompressed = zlib.decompress(decoded)",
+        "    return msgpack.unpackb(decompressed, raw=False)",
+        "",
+        "class AdvancedCompressedSerializer:",
+        "    def serialize(self, cassette_dict):",
+        "        return compress_data(cassette_dict)",
+        "",
+        "    def deserialize(self, cassette_string):",
+        "        return decompress_data(cassette_string)",
+        "",
+        "custom_vcr.register_serializer('advanced_compressed', AdvancedCompressedSerializer())",
+        "custom_vcr.serializer = 'advanced_compressed'",
+    ]
+    import_cell = nbformat.v4.new_code_cell(source="\n".join(vcr_import_lines))
+    import_cell.pop("id", None)
+    notebook.cells.insert(0, import_cell)
+    return notebook
+
+
+def process_notebooks(should_comment_install_cells: bool) -> None:
+    for directory in NOTEBOOK_DIRS:
+        for root, _, files in os.walk(directory):
+            for file in files:
+                if not file.endswith(".ipynb") or "ipynb_checkpoints" in root:
+                    continue
+
+                notebook_path = os.path.join(root, file)
+                try:
+                    notebook = nbformat.read(notebook_path, as_version=4)
+
+                    if should_comment_install_cells:
+                        notebook = comment_install_cells(notebook)
+
+                    base_filename = os.path.splitext(os.path.basename(file))[0]
+                    cassette_prefix = os.path.join(CASSETTES_PATH, base_filename)
+                    if notebook_path not in NOTEBOOKS_NO_CASSETTES:
+                        notebook = add_vcr_to_notebook(
+                            notebook, cassette_prefix=cassette_prefix
+                        )
+
+                    if notebook_path in NOTEBOOKS_NO_EXECUTION:
+                        # Add a cell at the beginning to indicate that this notebook should not be executed
+                        warning_cell = nbformat.v4.new_markdown_cell(
+                            source="**Warning:** This notebook is not meant to be executed automatically."
+                        )
+                        notebook.cells.insert(0, warning_cell)
+
+                        # Add a special tag to the first code cell
+                        if notebook.cells and notebook.cells[1].cell_type == "code":
+                            notebook.cells[1].metadata["tags"] = notebook.cells[1].metadata.get("tags", []) + ["no_execution"]
+
+                    nbformat.write(notebook, notebook_path)
+                    logger.info(f"Processed: {notebook_path}")
+                except Exception as e:
+                    logger.error(f"Error processing {notebook_path}: {e}")
+    
+    with open(os.path.join(DOCS_PATH, "notebooks_no_execution.json"), "w") as f:
+        json.dump(NOTEBOOKS_NO_EXECUTION, f)
+
+
+@click.command()
+@click.option(
+    "--comment-install-cells",
+    is_flag=True,
+    default=False,
+    help="Whether to comment out install cells",
+)
+def main(comment_install_cells):
+    process_notebooks(should_comment_install_cells=comment_install_cells)
+    logger.info("All notebooks processed successfully.")
+
+
+if __name__ == "__main__":
+    main()

docs/cassettes/LLMCompiler_15dd9639-691f-4906-9012-83fd6e9ac126.msgpack.zlib

@@ -0,0 +1 @@
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

docs/cassettes/LLMCompiler_31854dfd-b82f-4c24-9b58-6bae66777909.msgpack.zlib

@@ -0,0 +1 @@
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docs/cassettes/agent_supervisor_56ba78e9-d9c1-457c-a073-d606d5d3e013.msgpack.zlib

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docs/cassettes/async_8edb04b9-40b6-46f1-a7a8-4b2d8aba7752.msgpack.zlib

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docs/cassettes/async_cfd140f0-a5a6-4697-8115-322242f197b5.msgpack.zlib

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docs/cassettes/create-react-agent-memory_187479f9-32fa-4611-9487-cf816ba2e147.msgpack.zlib

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