Reapply "Remove MessageGraph (#4875)"

This reverts commit 3fa3a586b5.

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -1,29 +1,29 @@
 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 at forum.langchain.com.
+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: [pending,bug]
 body:
   - type: markdown
     attributes:
-      value: |
+      value: >
         Thank you for taking the time to file a bug report. 
         
-        Use this to report BUGS in LangGraph. For usage questions, feature requests and general design questions, please use the [LangChain Forum](https://forum.langchain.com/).
+        Use this to report BUGS in LangGraph. For usage questions, feature requests and general design questions, please use [GitHub Discussions](https://github.com/langchain-ai/langgraph/discussions).
         
         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:
         
-        * [LangChain Forum](https://forum.langchain.com/),
-        * [LangGraph Github Issues](https://github.com/langchain-ai/langgraph/issues),
-        * [LangGraph how-to guides](https://langchain-ai.github.io/langgraph/how-tos/).
-        * [LangChain documentation with the integrated search](https://python.langchain.com/docs/get_started/introduction),
-        * [GitHub search](https://github.com/langchain-ai/langgraph),
+        [LangGraph Github Discussions](https://github.com/langchain-ai/langgraph/discussions),
+        [LangGraph Github Issues](https://github.com/langchain-ai/langgraph/issues),
+        [LangGraph how-to guides](https://langchain-ai.github.io/langgraph/how-tos/).
+        [LangChain documentation with the integrated search](https://python.langchain.com/docs/get_started/introduction),
+        [GitHub search](https://github.com/langchain-ai/langgraph),
   - type: checkboxes
     id: checks
     attributes:
       label: Checked other resources
       description: Before submitting this issue, please confirm that you have completed all the steps below by checking each option. These steps help ensure your issue is well-defined, relevant, and actionable.
       options:
-        - label: This is a bug, not a usage question. For questions, please use the LangChain Forum (https://forum.langchain.com/).
+        - label: This is a bug, not a usage question. For questions, please use GitHub Discussions.
           required: true
         - label: I added a clear and detailed title that summarizes the issue.
           required: true
@@ -38,7 +38,7 @@ body:
     attributes:
       label: Example Code
       description: |
-        Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case. Replace this code with your own!
+        Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case.
       placeholder: |
         from langgraph.graph import StateGraph
 
@@ -78,7 +78,7 @@ body:
     attributes:
       label: System Info
       description: |
-        Run on your machine: `python -m langchain_core.sys_info`
+        python -m langchain_core.sys_info
       placeholder: |
         python -m langchain_core.sys_info
     validations:

.github/ISSUE_TEMPLATE/config.yml

@@ -1,6 +1,15 @@
-blank_issues_enabled: false
+blank_issues_enabled: true
 version: 2.1
 contact_links:
-  - name: LangChain Forum
-    url:  https://forum.langchain.com/
-    about: General community discussions, support, and feature requests
+  - 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/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://github.com/langchain-ai/langgraph/blob/main/CONTRIBUTING.md) 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/dependabot.yml

@@ -1,18 +1,11 @@
+# Please see the documentation for all configuration options:
+# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates
+# and
+# https://docs.github.com/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file
+
 version: 2
 updates:
   - package-ecosystem: "github-actions"
     directory: "/"
     schedule:
       interval: "weekly"
-
-  - package-ecosystem: "pip"
-    directories:
-      - "libs/checkpoint"
-      - "libs/checkpoint-postgres"
-      - "libs/checkpoint-sqlite"
-      - "libs/cli"
-      - "libs/langgraph"
-      - "libs/prebuilt"
-      - "libs/sdk-py"
-    schedule:
-      interval: "weekly"

.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

.github/scripts/run_langgraph_cli_test.py

@@ -1,145 +1,107 @@
+import asyncio
+import json
+import os
 import pathlib
 import sys
-import time
-from urllib import request, error
-
 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
+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."""
+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"]
+        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"]),
+                ]
+            )
+        else:
+            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",
+                ]
+            )
 
-        compose_cmd = ["docker", "compose"]
-        if capabilities.compose_type == "standalone":
-            compose_cmd = ["docker-compose"]
+        _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
 
-        set("Starting...")
         try:
             runner.run(
-                subp_exec(
-                    *compose_cmd,
-                    *args_up,
-                    input=stdin,
+                subp_exec_task(
+                    "docker",
+                    *args,
+                    tag,
                     verbose=verbose,
+                    on_stdout=on_stdout,
                 )
             )
-        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=False))
-            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=False,
-                    )
-                )
-            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"
-        print(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}")
-                        print(f"Unexpected status: {resp.status}")
-            except error.URLError as e:
-                last_err = e
-            except Exception as e:  # noqa: BLE001
-                last_err = e
-            time.sleep(0.5)
-        else:
-            # 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}"
-                )
-
-        # Clean up: bring compose stack down to free ports for next test
-        args_down = [*args, "down", "-v", "--remove-orphans"]
-        runner.run(
-            subp_exec(
-                *compose_cmd,
-                *args_down,
-                input=stdin,
-                verbose=verbose,
-            )
-        )
+        except asyncio.CancelledError:
+            pass
 
 
 if __name__ == "__main__":
@@ -148,6 +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()
     test(pathlib.Path(args.config), args.port, args.tag, verbose=True)

.github/workflows/_integration_test.yml

@@ -3,9 +3,6 @@ name: CLI integration test
 on:
   workflow_call:
 
-permissions:
-  contents: read
-
 jobs:
   build:
     runs-on: ubuntu-latest
@@ -43,43 +40,28 @@ jobs:
       - name: Build and test service A
         if: steps.changed-files.outputs.all
         working-directory: libs/cli/examples
-        env:
-          LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
         run: |
           # The build-arg isn't used; just testing that we accept other args
-          langgraph build -t langgraph-test-a
-          cp .env.example .env
-          if [ -n "${{ secrets.LANGSMITH_API_KEY }}" ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env; fi
+          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
-        env:
-          LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
         run: |
-          langgraph build -t langgraph-test-b
-          cp ../.env.example .env
-          if [ -n "${{ secrets.LANGSMITH_API_KEY }}" ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env; fi
+          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
-        env:
-          LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
         run: |
-          langgraph build -t langgraph-test-c
-          cp ../.env.example .env
-          if [ -n "${{ secrets.LANGSMITH_API_KEY }}" ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env; fi
+          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
-        env:
-          LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
         run: |
-          langgraph build -t langgraph-test-d
-          cp ../.env.example .env
-          if [ -n "${{ secrets.LANGSMITH_API_KEY }}" ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env; fi
+          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
@@ -87,27 +69,3 @@ jobs:
         working-directory: libs/cli/js-examples
         run: |
           langgraph build -t langgraph-test-e
-
-      - name: Build JS monorepo service
-        if: steps.changed-files.outputs.all
-        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
-        working-directory: libs/cli/python-monorepo-example
-        run: |
-          langgraph build -t langgraph-test-g -c apps/agent/langgraph.json
-          cp apps/agent/.env.example apps/agent/.env
-          if [ -n "${{ secrets.LANGSMITH_API_KEY }}" ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> apps/agent/.env; fi
-          timeout 60 python ../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-g -c apps/agent/langgraph.json
-
-      - name: Build and test prerelease reqs service
-        if: steps.changed-files.outputs.all
-        working-directory: libs/cli/examples/graph_prerelease_reqs
-        run: |
-          langgraph build -t langgraph-test-h
-          cp ../.env.example .env
-          if [ -n "${{ secrets.LANGSMITH_API_KEY }}" ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env; fi
-          timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-h

.github/workflows/_lint.yml

@@ -8,9 +8,6 @@ on:
         type: string
         description: "From which folder this pipeline executes"
 
-permissions:
-  contents: read
-
 env:
   # This env var allows us to get inline annotations when ruff has complaints.
   RUFF_OUTPUT_FORMAT: github

.github/workflows/_test.yml

@@ -8,9 +8,6 @@ on:
         type: string
         description: "From which folder this pipeline executes"
 
-permissions:
-  contents: read
-
 jobs:
   build:
     runs-on: ubuntu-latest

.github/workflows/_test_langgraph.yml

@@ -3,9 +3,6 @@ name: test
 on:
   workflow_call:
 
-permissions:
-  contents: read
-
 jobs:
   build:
     runs-on: ubuntu-latest

.github/workflows/_test_release.yml

@@ -11,9 +11,6 @@ on:
 env:
   PYTHON_VERSION: "3.10"
 
-permissions:
-  contents: read
-
 jobs:
   build:
     if: github.ref == 'refs/heads/main'

.github/workflows/baseline.yml

@@ -7,9 +7,6 @@ on:
     paths:
       - "libs/**"
 
-permissions:
-  contents: read
-
 jobs:
   benchmark:
     runs-on: ubuntu-latest

.github/workflows/bench.yml

@@ -5,9 +5,6 @@ on:
     paths:
       - "libs/**"
 
-permissions:
-  contents: read
-
 jobs:
   benchmark:
     runs-on: ubuntu-latest

.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.
 #
@@ -25,7 +21,7 @@ jobs:
     runs-on: ubuntu-latest
     outputs:
       python: ${{ steps.filter.outputs.python }}
-      deps: ${{ steps.filter.outputs.deps }}
+      sdk-js: ${{ steps.filter.outputs.sdk-js }}
     steps:
       - uses: actions/checkout@v4
       - uses: dorny/paths-filter@v3
@@ -40,9 +36,8 @@ jobs:
               - 'libs/checkpoint-sqlite/**'
               - 'libs/checkpoint-postgres/**'
               - 'libs/prebuilt/**'
-            deps:
-              - '**/pyproject.toml'
-              - '**/uv.lock'
+            sdk-js:
+              - 'libs/sdk-js/**'
 
   lint:
     needs: changes
@@ -60,7 +55,7 @@ jobs:
 
             "libs/prebuilt",
           ]
-    if: needs.changes.outputs.python == 'true' || needs.changes.outputs.deps == 'true'
+    if: needs.changes.outputs.python == 'true'
     uses: ./.github/workflows/_lint.yml
     with:
       working-directory: ${{ matrix.working-directory }}
@@ -78,9 +73,8 @@ jobs:
             "libs/checkpoint-sqlite",
             "libs/checkpoint-postgres",
             "libs/prebuilt",
-            "libs/sdk-py",
           ]
-    if: needs.changes.outputs.python == 'true' || needs.changes.outputs.deps == 'true'
+    if: needs.changes.outputs.python == 'true'
     uses: ./.github/workflows/_test.yml
     with:
       working-directory: ${{ matrix.working-directory }}
@@ -89,7 +83,7 @@ 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'
+    if: needs.changes.outputs.python == 'true'
     name: "cd libs/langgraph"
     uses: ./.github/workflows/_test_langgraph.yml
     secrets: inherit
@@ -146,21 +140,73 @@ jobs:
 
   integration-test:
     needs: changes
-    if: needs.changes.outputs.python == 'true' || needs.changes.outputs.deps == 'true'
+    if: needs.changes.outputs.python == 'true'
     name: CLI integration test
     uses: ./.github/workflows/_integration_test.yml
     secrets: inherit
 
+  lint-js:
+    needs: changes
+    if: needs.changes.outputs.sdk-js == 'true'
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        working-directory:
+          - "libs/sdk-js"
+    defaults:
+      run:
+        working-directory: ${{ matrix.working-directory }}
+    steps:
+      - uses: actions/checkout@v4
+      - name: Setup Node.js (LTS)
+        uses: actions/setup-node@v4
+        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
+
+  test-js:
+    needs: changes
+    if: needs.changes.outputs.sdk-js == 'true'
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        working-directory:
+          - "libs/sdk-js"
+    defaults:
+      run:
+        working-directory: ${{ matrix.working-directory }}
+    steps:
+      - uses: actions/checkout@v4
+      - name: Setup Node.js (LTS)
+        uses: actions/setup-node@v4
+        with:
+          node-version: "20"
+          cache: "yarn"
+          cache-dependency-path: ${{ matrix.working-directory }}/yarn.lock
+      - name: Install dependencies
+        run: yarn install
+      - name: Run tests
+        run: yarn test
+
   ci_success:
     name: "CI Success"
     needs:
       [
         lint,
+        lint-js,
         test,
         test-langgraph,
         check-sdk-methods,
         check-schema,
         integration-test,
+        test-js,
       ]
     if: |
       always()

.github/workflows/codespell-ignore-words.txt

@@ -1,11 +0,0 @@
-LangChain
-LangGraph
-LangSmith
-thead
-stdio
-nd
-jupyter
-lets
-lite
-uis
-deque

.github/workflows/codespell.yml

@@ -34,16 +34,10 @@
             id: extract_ignore_words
     
           - name: Codespell
-            uses: codespell-project/actions-codespell@v2.1
+            uses: codespell-project/actions-codespell@v2
             with:
-              skip: '*.ambr,*.lock,*.ipynb,*.yaml,*.zlib,*.css.map,*.js.map'
+              skip: '*.ambr,*.lock,*.ipynb,*.yaml,*.zlib,*.md'
               ignore_words_list: ${{ steps.extract_ignore_words.outputs.ignore_words_list }}
           # We do this to avoid spellchecking cell outputs
           - name: Codespell Notebooks
-            run: make codespell
-          
-          - name: Codespell LangGraph Library
-            run: |
-              # Change to root directory to check the main LangGraph library
-              cd ..
-              codespell --skip="*.ambr,*.lock,*.ipynb,*.yaml,*.zlib,*.css.map,*.js.map,*.pyc,__pycache__/*" --ignore-words-list="${{ steps.extract_ignore_words.outputs.ignore_words_list }}" libs/langgraph/langgraph/
+            run: make codespell

.github/workflows/deploy_docs.yml

@@ -35,7 +35,16 @@ jobs:
         with:
           filter: "docs/docs/**"
 
+  # TODO: Uncomment this to run on PRs
+  # 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
     timeout-minutes: 10 # Job will be cancelled if it runs for more than 10 minutes
     env:

.github/workflows/link_check.yml

@@ -11,9 +11,6 @@ on:
     - cron: "0 5 * * *"
   workflow_dispatch:
 
-permissions:
-  contents: read
-
 jobs:
   markdown-link-check:
     runs-on: ubuntu-latest

.github/workflows/pr_lint.yml

@@ -1,45 +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@v5
-        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
-          requireScope: false
-          ignoreLabels: |
-            ignore-lint-pr-title

.github/workflows/release.yml

@@ -8,9 +8,6 @@ on:
         type: string
         default: "libs/langgraph"
 
-permissions:
-  contents: read
-
 env:
   PYTHON_VERSION: "3.11"
 
@@ -62,13 +59,7 @@ jobs:
         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=$(grep -m 1 "^version = " pyproject.toml | cut -d '"' -f 2)
-          fi
+          VERSION=$(grep -m 1 "^version = " pyproject.toml | cut -d '"' -f 2)
           SHORT_PKG_NAME="$(echo "$PKG_NAME" | sed -e 's/langgraph//g' -e 's/-//g')"
           if [ -z $SHORT_PKG_NAME ]; then
             TAG="$VERSION"
@@ -143,9 +134,7 @@ 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 }}

.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@v4
+        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

@@ -11,9 +11,6 @@ on:
   schedule:
     - cron: "0 13 * * *"
 
-permissions:
-  contents: read
-
 defaults:
   run:
     working-directory: docs

.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@v4
-      
-      - name: Set up uv
-        uses: astral-sh/setup-uv@v6
-        with:
-          # use minimum supported Python version
-          python-version: "3.9"
-          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@v7
-        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

CONTRIBUTING.md

@@ -9,7 +9,7 @@ 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 tag a maintainer.
+  - 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.
@@ -20,7 +20,7 @@ For bug fixes, please open up an issue before proposing a fix to ensure the prop
 
 ### New features
 
-For new features, please start a new [discussion](https://forum.langchain.com/), where the maintainers will help with scoping out the necessary changes.
+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
 
@@ -60,7 +60,7 @@ In LangGraph, these are often higher level guides that show off end-to-end use c
 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/sql-agent/)
+- [Build a SQL Agent](https://langchain-ai.github.io/langgraph/tutorials/sql-agent/)
 
 Here are some high-level tips on writing a good tutorial:
 
@@ -111,6 +111,7 @@ in a more abstract way than how-to guides or tutorials, and should be geared tow
 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 the 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.
@@ -186,9 +187,9 @@ Be concise, including in code samples.
 
 ## Setup
 
-LangGraph documentation consists of two components:
+LangChain documentation consists of two components:
 
-1. Main Documentation: Hosted at [https://langchain-ai.github.io/langgraph/](https://langchain-ai.github.io/langgraph/),
+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.
@@ -249,17 +250,17 @@ make serve-docs
 
 #### Linting
 
-To spell check the docs, run the following from the `docs` directory:
+The documentation is linted from the **monorepo root**. To lint it, run the following from there:
 
 ```bash
-codespell --skip="*.ambr,*.lock,*.ipynb,*.yaml,*.zlib,*.css.map,*.js.map" --ignore-words-list="infor,thead,stdio,nd,jupyter,lets,lite,uis,deque" .
+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 LangGraph because the API reference is the primary resource for developers to understand how to use the codebase.
+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.
 
@@ -290,4 +291,4 @@ def my_function(arg1: int, arg2: str) -> float:
         This is a description of the return value.
     """
     return 3.14
-```
+```

Makefile

@@ -47,16 +47,6 @@ lock:
 		fi; \
 	done
 
-# 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
-
 # Test all projects
 .PHONY: test
 test:

README.md

@@ -63,7 +63,7 @@ LangGraph provides low-level supporting infrastructure for *any* long-running, s
 While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents. To improve your LLM application development, pair LangGraph with:
 
 - [LangSmith](http://www.langchain.com/langsmith) — Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
-- [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/) — Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/).
+- [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform) — Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/).
 - [LangChain](https://python.langchain.com/docs/introduction/) – Provides integrations and composable components to streamline LLM application development.
 
 > [!NOTE]
@@ -73,12 +73,11 @@ While LangGraph can be used standalone, it also integrates seamlessly with any L
 
 - [Guides](https://langchain-ai.github.io/langgraph/how-tos/): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
 - [Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
-- [Examples](https://langchain-ai.github.io/langgraph/examples/): Guided examples on getting started with LangGraph.
-- [LangChain Forum](https://forum.langchain.com/): Connect with the community and share all of your technical questions, ideas, and feedback.
+- [Examples](https://langchain-ai.github.io/langgraph/tutorials/overview/): Guided examples on getting started with LangGraph.
 - [LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph): Learn the basics of LangGraph in our free, structured course.
 - [Templates](https://langchain-ai.github.io/langgraph/concepts/template_applications/): Pre-built reference apps for common agentic workflows (e.g. ReAct agent, memory, retrieval etc.) that can be cloned and adapted.
 - [Case studies](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship AI applications at scale.
 
 ## Acknowledgements
 
-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 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.

docs/.gitignore

@@ -1,3 +1,4 @@
 site/
+docs/cloud/reference/sdk/js_ts_sdk_ref.md
 
 .vercel

docs/Makefile

@@ -1,4 +1,10 @@
-.PHONY: lint-docs format-docs build-docs serve-docs serve-clean-docs clean-docs codespell llms-text build-prebuilt tests
+.PHONY: lint-docs format-docs build-docs serve-docs serve-clean-docs clean-docs codespell build-typedoc llms-text build-prebuilt tests
+
+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/cloud/reference/sdk/js_ts_sdk_ref.md > temp_file && mv temp_file docs/cloud/reference/sdk/js_ts_sdk_ref.md
 
 build-prebuilt:
 	# Use to create an update to date prebuilt page.
@@ -13,13 +19,10 @@ build-prebuilt:
 		uv run python -m _scripts.third_party_page.get_download_stats --fake stats.yml; \
 		set +x; \
 	fi
-	uv run python -m _scripts.third_party_page.create_third_party_page stats.yml docs/agents/prebuilt.md
+	uv run python -m _scripts.third_party_page.create_third_party_page stats.yml docs/agents/prebuilt.md --language python
 
-build-docs: build-prebuilt
-	TARGET_LANGUAGE=python uv run python -m mkdocs build --clean -f mkdocs.yml --strict
-
-build-docs-js: build-prebuilt
-	TARGET_LANGUAGE=js uv run python -m mkdocs build --clean -f mkdocs.yml --strict
+build-docs: build-typedoc build-prebuilt
+	uv run python -m mkdocs build --clean -f mkdocs.yml --strict
 
 llms-text:
 	uv run python -m _scripts.generate_llms_text docs/llms-full.txt
@@ -42,7 +45,7 @@ vercel-build-docs: install-vercel-deps
 serve-clean-docs: clean-docs
 	uv run python -m mkdocs serve -c -f mkdocs.yml --strict -w ../libs/langgraph
 
-serve-docs:
+serve-docs: build-typedoc
 	uv run python -m mkdocs serve -f mkdocs.yml -w ../libs/langgraph  -w ../libs/checkpoint -w ../libs/sdk-py --dirty
 
 clean-docs:

docs/_scripts/add_translation.py

@@ -4,11 +4,9 @@ import argparse
 
 import requests
 from langchain_anthropic import ChatAnthropic
-from textwrap import dedent
-
 
 # Load reference TypeScript snippets
-URL = "https://gist.githubusercontent.com/dqbd/b35d49e2ceec80e654fe1c5ab61ec477/raw/f4768aeedb67628190a4e06d063a938afc8e7672/snippets.md"
+URL = "https://gist.githubusercontent.com/eyurtsev/e7486731415463a9bc5b4682358859c8/raw/b5a5fda9c7e3387cfcb781f25082814d43675d50/gistfile1.txt"
 response = requests.get(URL)
 response.raise_for_status()
 reference_snippets = response.text
@@ -16,80 +14,6 @@ reference_snippets = response.text
 # Initialize model
 model = ChatAnthropic(model="claude-sonnet-4-0", max_tokens=64_000)
 
-
-FLUENT_INTERFACE_PROMPT = (
-    "CRITICAL: Always use method chaining (fluent interface) for StateGraph operations in TypeScript. "
-    "Never create separate variables for the graph builder or call methods individually. "
-    "The fluent interface provides better type safety and is the preferred pattern.\n\n"
-    "CORRECT examples with fluent interface:\n"
-    + dedent(
-        """
-        ```typescript
-        const graph = new StateGraph(MyState)
-          .addNode('node1', node1)
-          .addNode('node2', node2)
-          .addEdge(START, 'node1')
-          .addEdge('node1', 'node2')
-          .addEdge('node2', END)
-          .compile()
-        ```
-        
-        ```typescript
-        const graph = new StateGraph(MyState)
-          .addNode('chatbot', chatbot)
-          .addEdge(START, 'chatbot')
-          .addEdge('chatbot', END)
-          .compile()
-        ```
-        
-        ```typescript
-        const graph = new StateGraph(MyState)
-          .addNode('chatbot', chatbot)
-          .addEdge(START, 'chatbot')
-          .addEdge('chatbot', END)
-          .compile()
-        ```
-        """
-    )
-    + "\n"
-    + "INCORRECT examples to avoid:\n"
-    + dedent(
-        """
-        ```typescript
-        // WRONG: Creating separate builder variable
-        const graphBuilder = new StateGraph(MyState)
-        graphBuilder.addNode('node1', node1)
-        graphBuilder.addEdge(START, 'node1')
-        const graph = graphBuilder.compile()
-        ```
-        
-        ```typescript
-        // WRONG: Using Python-style method names
-        const workflow = new StateGraph(MyState)
-        workflow.add_node('node1', node1)
-        workflow.add_edge(START, 'node1')
-        const graph = workflow.compile()
-        ```
-        
-        ```typescript
-        // WRONG: Calling methods individually
-        const graphBuilder = new StateGraph(MyState)
-        graphBuilder.addNode('chatbot', chatbot)
-        graphBuilder.addEdge(START, 'chatbot')
-        graphBuilder.addEdge('chatbot', END)
-        const graph = graphBuilder.compile()
-        ```
-        """
-    )
-    + "\n"
-    + "Key rules:\n"
-    + "- Always chain methods directly on the StateGraph constructor\n"
-    + "- Use camelCase method names (addNode, addEdge, not add_node, add_edge)\n"
-    + "- Always end with .compile()\n"
-    + "- Never store the builder in a separate variable\n"
-)
-
-
 TRANSLATION_PROMPT = (
     "You are a helpful assistant that translates Python-based technical "
     "documentation written in Markdown to equivalent TypeScript-based documentation. "
@@ -108,12 +32,6 @@ TRANSLATION_PROMPT = (
     "the translation. "
     "Use the reference TypeScript snippets as guidance whenever possible to "
     "maintain alignment with existing conventions.\n\n"
-    "IMPORTANT REQUIREMENTS:\n"
-    "- Use Zod for state definition for StateGraph. Avoid using Annotation since it will be deprecated in the future.\n"
-    "- ALWAYS use fluent interface (method chaining) for StateGraph operations - this is CRITICAL\n"
-    "- Never create separate variables for graph builders\n"
-    "- Always chain methods directly on the StateGraph constructor and end with .compile()\n\n"
-    f"{FLUENT_INTERFACE_PROMPT}\n\n"
     f"Here are the reference TypeScript snippets:\n\n{reference_snippets}\n\n"
 )
 

docs/_scripts/generate_llms_text.py

@@ -14,7 +14,10 @@ from mkdocs.structure.pages import Page
 from pydantic import BaseModel, Field
 from yaml import SafeLoader
 
-from _scripts.notebook_hooks import _on_page_markdown_with_config
+from _scripts.notebook_hooks import (
+    _on_page_markdown_with_config,
+    _apply_conditional_rendering,
+)
 
 HERE = os.path.dirname(os.path.abspath(__file__))
 # Get source directory (parent of HERE / docs)

docs/_scripts/handle_auto_links.py

@@ -1,181 +0,0 @@
-"""Logic to identify and transform cross-reference links in markdown files.
-
-This module allows supporting custom markdown syntax for "autolinks". These are links
-that will be transformed based on the current scope context, such as "global", "python",
-or "js" into an appropriate markdown link format.
-
-For example,
-
-```markdown
-@[StateGraph]
-```
-
-May be transformed into:
-
-```markdown
-[StateGraph](some_path/api-reference/state-graph.md)
-```
-
-The transformation value depends on the scope in which the link is used.
-"""
-
-import logging
-import re
-from typing import Optional
-
-from _scripts.link_map import SCOPE_LINK_MAPS
-
-logger = logging.getLogger(__name__)
-
-
-def _transform_link(
-    link_name: str, scope: str, file_path: str, line_number: int, custom_title: Optional[str] = None
-) -> Optional[str]:
-    """Transform a cross-reference link based on the current scope.
-
-    Args:
-        link_name: The name of the link to transform (e.g., "StateGraph").
-        scope: The current scope context ("global", "python", "js", etc.).
-        file_path: The file path for error reporting.
-        line_number: The line number for error reporting.
-        custom_title: Optional custom title for the link. If None, uses link_name.
-
-    Returns:
-        A formatted markdown link if the link is found in the scope mapping,
-        None otherwise.
-
-    Example:
-        >>> _transform_link("StateGraph", "python", "file.md", 5)
-        "[StateGraph](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.StateGraph)"
-
-        >>> _transform_link("StateGraph", "python", "file.md", 5, "Custom Title")
-        "[Custom Title](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.StateGraph)"
-
-        >>> _transform_link("unknown-link", "python", "file.md", 5)
-        None
-    """
-    if scope == "global":
-        # Special scope that is composed of both Python and JS links
-        # For now, we will substitute in the python scope!
-        # But we need to add support for handling both scopes.
-        scope = "python"
-        logger.error(
-            "Encountered unhandled 'global' scope. Defaulting to 'python'."
-            "In file: %s, line %d, link_name: %s",
-            file_path,
-            line_number,
-            link_name,
-        )
-    link_map = SCOPE_LINK_MAPS.get(scope, {})
-    url = link_map.get(link_name)
-
-    if url:
-        title = custom_title if custom_title is not None else link_name
-        return f"[{title}]({url})"
-    else:
-        # Log error with file location information
-        logger.info(
-            # Using %s
-            "Link '%s' not found in scope '%s'. "
-            "In file: %s, line %d. Available links in scope: %s",
-            link_name,
-            scope,
-            file_path,
-            line_number,
-            list(link_map.keys() if link_map else []),
-        )
-        return None
-
-
-CONDITIONAL_FENCE_PATTERN = re.compile(
-    r"""
-    ^                       # Start of line
-    (?P<indent>[ \t]*)      # Optional indentation (spaces or tabs)
-    :::                     # Literal fence marker
-    (?P<language>\w+)?      # Optional language identifier (named group: language)
-    \s*                     # Optional trailing whitespace
-    $                       # End of line
-    """,
-    re.VERBOSE,
-)
-CROSS_REFERENCE_PATTERN = re.compile(
-    r"""
-    (?:                     # Non-capturing group for two possible formats:
-        @\[                 # @ symbol followed by opening bracket for title
-        (?P<title>[^\]]+)   # Custom title - one or more non-bracket characters
-        \]                  # Closing bracket for title
-        \[                  # Opening bracket for link name
-        (?P<link_name_with_title>[^\]]+)  # Link name - one or more non-bracket characters
-        \]                  # Closing bracket for link name
-        |                   # OR
-        @\[                 # @ symbol followed by opening bracket
-        (?P<link_name>[^\]]+)   # Link name - one or more non-bracket characters
-        \]                  # Closing bracket
-    )
-    """,
-    re.VERBOSE,
-)
-
-
-def _replace_autolinks(markdown: str, file_path: str, *, default_scope: str = "python") -> str:
-    """Preprocess markdown lines to handle @[links] with conditional fence scopes.
-
-    This function processes markdown content to transform @[link_name] references
-    based on the current conditional fence scope. Conditional fences use the
-    syntax :::language to define scope boundaries.
-
-    Args:
-        markdown: The markdown content to process.
-        file_path: The file path for error reporting.
-        default_scope: The default scope to use if no scope is matched.
-
-    Returns:
-        Processed markdown content with @[references] transformed to proper
-        markdown links or left unchanged if not found.
-
-    Example:
-        Input:
-            "@[StateGraph]\\n:::python\\n@[Command]\\n:::\\n"
-        Output:
-            "[StateGraph](url)\\n:::python\\n[Command](url)\\n:::\\n"
-    """
-    # Track the current scope context
-    current_scope = default_scope
-    lines = markdown.splitlines(keepends=True)
-    processed_lines = []
-
-    for line_number, line in enumerate(lines, 1):
-        line_stripped = line.strip()
-
-        # Check if this line defines a new conditional fence scope
-        fence_match = CONDITIONAL_FENCE_PATTERN.match(line_stripped)
-        if fence_match:
-            language = fence_match.group("language")
-            # Set scope to the specified language, or reset to global if no language
-            current_scope = language.lower() if language else default_scope
-            processed_lines.append(line)
-            continue
-
-        # Transform all @[link_name] references in this line based on current scope
-        def replace_cross_reference(match: re.Match[str]) -> str:
-            """Replace a single @[link_name] with the scoped equivalent."""
-            # Check if this is the @[title][ref] format or @[ref] format
-            title = match.group("title")
-            if title is not None:
-                # This is @[title][ref] format
-                link_name = match.group("link_name_with_title")
-                custom_title = title
-            else:
-                # This is @[ref] format
-                link_name = match.group("link_name")
-                custom_title = None
-            
-            transformed = _transform_link(
-                link_name, current_scope, file_path, line_number, custom_title
-            )
-            return transformed if transformed is not None else match.group(0)
-
-        transformed_line = CROSS_REFERENCE_PATTERN.sub(replace_cross_reference, line)
-        processed_lines.append(transformed_line)
-
-    return "".join(processed_lines)

docs/_scripts/js_translation/codeblocks/.gitkeep

@@ -1,6 +0,0 @@
-.prettierrc
-.eslint.config.mjs
-package.json
-README.md
-tsconfig.json
-yarn.lock

docs/_scripts/js_translation/codeblocks/.prettierrc

@@ -1,19 +0,0 @@
-{
-  "$schema": "https://json.schemastore.org/prettierrc",
-  "printWidth": 80,
-  "tabWidth": 2,
-  "useTabs": false,
-  "semi": true,
-  "singleQuote": false,
-  "quoteProps": "as-needed",
-  "jsxSingleQuote": false,
-  "trailingComma": "es5",
-  "bracketSpacing": true,
-  "arrowParens": "always",
-  "requirePragma": false,
-  "insertPragma": false,
-  "proseWrap": "preserve",
-  "htmlWhitespaceSensitivity": "css",
-  "vueIndentScriptAndStyle": false,
-  "endOfLine": "lf"
-}

docs/_scripts/js_translation/codeblocks/README.md

@@ -1 +0,0 @@
-# \_codeblocks

docs/_scripts/js_translation/codeblocks/eslint.config.mjs

@@ -1,14 +0,0 @@
-import js from "@eslint/js";
-import globals from "globals";
-import tseslint from "typescript-eslint";
-import { defineConfig } from "eslint/config";
-
-export default defineConfig([
-  {
-    files: ["**/*.{js,mjs,cjs,ts,mts,cts}"],
-    plugins: { js },
-    extends: ["js/recommended"],
-    languageOptions: { globals: globals.browser },
-  },
-  tseslint.configs.recommended,
-]);

docs/_scripts/js_translation/codeblocks/package.json

@@ -1,27 +0,0 @@
-{
-  "name": "_codeblocks",
-  "packageManager": "yarn@4.6.0",
-  "scripts": {
-    "lint": "eslint .",
-    "lint:fix": "eslint . --fix",
-    "format": "prettier --write .",
-    "format:fix": "prettier --write . --fix"
-  },
-  "dependencies": {
-    "@langchain/anthropic": "^0.3.24",
-    "@langchain/core": "^0.3.66",
-    "@langchain/langgraph": "^0.3.11",
-    "@langchain/langgraph-api": "^0.0.52",
-    "@langchain/langgraph-sdk": "^0.0.102",
-    "@langchain/openai": "^0.6.3",
-    "zod": "^4.0.10"
-  },
-  "devDependencies": {
-    "@eslint/js": "^9.32.0",
-    "eslint": "^9.32.0",
-    "globals": "^16.3.0",
-    "jiti": "^2.5.1",
-    "typescript": "^5.8.3",
-    "typescript-eslint": "^8.38.0"
-  }
-}

docs/_scripts/js_translation/codeblocks/tsconfig.json

@@ -1,114 +0,0 @@
-{
-  "compilerOptions": {
-    /* Visit https://aka.ms/tsconfig to read more about this file */
-
-    /* Projects */
-    // "incremental": true,                              /* Save .tsbuildinfo files to allow for incremental compilation of projects. */
-    // "composite": true,                                /* Enable constraints that allow a TypeScript project to be used with project references. */
-    // "tsBuildInfoFile": "./.tsbuildinfo",              /* Specify the path to .tsbuildinfo incremental compilation file. */
-    // "disableSourceOfProjectReferenceRedirect": true,  /* Disable preferring source files instead of declaration files when referencing composite projects. */
-    // "disableSolutionSearching": true,                 /* Opt a project out of multi-project reference checking when editing. */
-    // "disableReferencedProjectLoad": true,             /* Reduce the number of projects loaded automatically by TypeScript. */
-
-    /* Language and Environment */
-    "target": "esnext",                                  /* Set the JavaScript language version for emitted JavaScript and include compatible library declarations. */
-    // "lib": [],                                        /* Specify a set of bundled library declaration files that describe the target runtime environment. */
-    // "jsx": "preserve",                                /* Specify what JSX code is generated. */
-    // "libReplacement": true,                           /* Enable lib replacement. */
-    // "experimentalDecorators": true,                   /* Enable experimental support for legacy experimental decorators. */
-    // "emitDecoratorMetadata": true,                    /* Emit design-type metadata for decorated declarations in source files. */
-    // "jsxFactory": "",                                 /* Specify the JSX factory function used when targeting React JSX emit, e.g. 'React.createElement' or 'h'. */
-    // "jsxFragmentFactory": "",                         /* Specify the JSX Fragment reference used for fragments when targeting React JSX emit e.g. 'React.Fragment' or 'Fragment'. */
-    // "jsxImportSource": "",                            /* Specify module specifier used to import the JSX factory functions when using 'jsx: react-jsx*'. */
-    // "reactNamespace": "",                             /* Specify the object invoked for 'createElement'. This only applies when targeting 'react' JSX emit. */
-    // "noLib": true,                                    /* Disable including any library files, including the default lib.d.ts. */
-    // "useDefineForClassFields": true,                  /* Emit ECMAScript-standard-compliant class fields. */
-    // "moduleDetection": "auto",                        /* Control what method is used to detect module-format JS files. */
-
-    /* Modules */
-    "module": "nodenext",                                /* Specify what module code is generated. */
-    // "rootDir": "./",                                  /* Specify the root folder within your source files. */
-    "moduleResolution": "nodenext",                     /* Specify how TypeScript looks up a file from a given module specifier. */
-    // "baseUrl": "./",                                  /* Specify the base directory to resolve non-relative module names. */
-    // "paths": {},                                      /* Specify a set of entries that re-map imports to additional lookup locations. */
-    // "rootDirs": [],                                   /* Allow multiple folders to be treated as one when resolving modules. */
-    // "typeRoots": [],                                  /* Specify multiple folders that act like './node_modules/@types'. */
-    // "types": [],                                      /* Specify type package names to be included without being referenced in a source file. */
-    // "allowUmdGlobalAccess": true,                     /* Allow accessing UMD globals from modules. */
-    // "moduleSuffixes": [],                             /* List of file name suffixes to search when resolving a module. */
-    // "allowImportingTsExtensions": true,               /* Allow imports to include TypeScript file extensions. Requires '--moduleResolution bundler' and either '--noEmit' or '--emitDeclarationOnly' to be set. */
-    // "rewriteRelativeImportExtensions": true,          /* Rewrite '.ts', '.tsx', '.mts', and '.cts' file extensions in relative import paths to their JavaScript equivalent in output files. */
-    // "resolvePackageJsonExports": true,                /* Use the package.json 'exports' field when resolving package imports. */
-    // "resolvePackageJsonImports": true,                /* Use the package.json 'imports' field when resolving imports. */
-    // "customConditions": [],                           /* Conditions to set in addition to the resolver-specific defaults when resolving imports. */
-    // "noUncheckedSideEffectImports": true,             /* Check side effect imports. */
-    // "resolveJsonModule": true,                        /* Enable importing .json files. */
-    // "allowArbitraryExtensions": true,                 /* Enable importing files with any extension, provided a declaration file is present. */
-    // "noResolve": true,                                /* Disallow 'import's, 'require's or '<reference>'s from expanding the number of files TypeScript should add to a project. */
-
-    /* JavaScript Support */
-    // "allowJs": true,                                  /* Allow JavaScript files to be a part of your program. Use the 'checkJS' option to get errors from these files. */
-    // "checkJs": true,                                  /* Enable error reporting in type-checked JavaScript files. */
-    // "maxNodeModuleJsDepth": 1,                        /* Specify the maximum folder depth used for checking JavaScript files from 'node_modules'. Only applicable with 'allowJs'. */
-
-    /* Emit */
-    // "declaration": true,                              /* Generate .d.ts files from TypeScript and JavaScript files in your project. */
-    // "declarationMap": true,                           /* Create sourcemaps for d.ts files. */
-    // "emitDeclarationOnly": true,                      /* Only output d.ts files and not JavaScript files. */
-    // "sourceMap": true,                                /* Create source map files for emitted JavaScript files. */
-    // "inlineSourceMap": true,                          /* Include sourcemap files inside the emitted JavaScript. */
-    // "noEmit": true,                                   /* Disable emitting files from a compilation. */
-    // "outFile": "./",                                  /* Specify a file that bundles all outputs into one JavaScript file. If 'declaration' is true, also designates a file that bundles all .d.ts output. */
-    // "outDir": "./",                                   /* Specify an output folder for all emitted files. */
-    // "removeComments": true,                           /* Disable emitting comments. */
-    // "importHelpers": true,                            /* Allow importing helper functions from tslib once per project, instead of including them per-file. */
-    // "downlevelIteration": true,                       /* Emit more compliant, but verbose and less performant JavaScript for iteration. */
-    // "sourceRoot": "",                                 /* Specify the root path for debuggers to find the reference source code. */
-    // "mapRoot": "",                                    /* Specify the location where debugger should locate map files instead of generated locations. */
-    // "inlineSources": true,                            /* Include source code in the sourcemaps inside the emitted JavaScript. */
-    // "emitBOM": true,                                  /* Emit a UTF-8 Byte Order Mark (BOM) in the beginning of output files. */
-    // "newLine": "crlf",                                /* Set the newline character for emitting files. */
-    // "stripInternal": true,                            /* Disable emitting declarations that have '@internal' in their JSDoc comments. */
-    // "noEmitHelpers": true,                            /* Disable generating custom helper functions like '__extends' in compiled output. */
-    // "noEmitOnError": true,                            /* Disable emitting files if any type checking errors are reported. */
-    // "preserveConstEnums": true,                       /* Disable erasing 'const enum' declarations in generated code. */
-    // "declarationDir": "./",                           /* Specify the output directory for generated declaration files. */
-
-    /* Interop Constraints */
-    // "isolatedModules": true,                          /* Ensure that each file can be safely transpiled without relying on other imports. */
-    // "verbatimModuleSyntax": true,                     /* Do not transform or elide any imports or exports not marked as type-only, ensuring they are written in the output file's format based on the 'module' setting. */
-    // "isolatedDeclarations": true,                     /* Require sufficient annotation on exports so other tools can trivially generate declaration files. */
-    // "erasableSyntaxOnly": true,                       /* Do not allow runtime constructs that are not part of ECMAScript. */
-    // "allowSyntheticDefaultImports": true,             /* Allow 'import x from y' when a module doesn't have a default export. */
-    "esModuleInterop": true,                             /* Emit additional JavaScript to ease support for importing CommonJS modules. This enables 'allowSyntheticDefaultImports' for type compatibility. */
-    // "preserveSymlinks": true,                         /* Disable resolving symlinks to their realpath. This correlates to the same flag in node. */
-    "forceConsistentCasingInFileNames": true,            /* Ensure that casing is correct in imports. */
-
-    /* Type Checking */
-    "strict": false,                                      /* Enable all strict type-checking options. */
-    // "noImplicitAny": true,                            /* Enable error reporting for expressions and declarations with an implied 'any' type. */
-    // "strictNullChecks": true,                         /* When type checking, take into account 'null' and 'undefined'. */
-    // "strictFunctionTypes": true,                      /* When assigning functions, check to ensure parameters and the return values are subtype-compatible. */
-    // "strictBindCallApply": true,                      /* Check that the arguments for 'bind', 'call', and 'apply' methods match the original function. */
-    // "strictPropertyInitialization": true,             /* Check for class properties that are declared but not set in the constructor. */
-    // "strictBuiltinIteratorReturn": true,              /* Built-in iterators are instantiated with a 'TReturn' type of 'undefined' instead of 'any'. */
-    // "noImplicitThis": true,                           /* Enable error reporting when 'this' is given the type 'any'. */
-    // "useUnknownInCatchVariables": true,               /* Default catch clause variables as 'unknown' instead of 'any'. */
-    // "alwaysStrict": true,                             /* Ensure 'use strict' is always emitted. */
-    // "noUnusedLocals": true,                           /* Enable error reporting when local variables aren't read. */
-    // "noUnusedParameters": true,                       /* Raise an error when a function parameter isn't read. */
-    // "exactOptionalPropertyTypes": true,               /* Interpret optional property types as written, rather than adding 'undefined'. */
-    // "noImplicitReturns": true,                        /* Enable error reporting for codepaths that do not explicitly return in a function. */
-    // "noFallthroughCasesInSwitch": true,               /* Enable error reporting for fallthrough cases in switch statements. */
-    // "noUncheckedIndexedAccess": true,                 /* Add 'undefined' to a type when accessed using an index. */
-    // "noImplicitOverride": true,                       /* Ensure overriding members in derived classes are marked with an override modifier. */
-    // "noPropertyAccessFromIndexSignature": true,       /* Enforces using indexed accessors for keys declared using an indexed type. */
-    // "allowUnusedLabels": true,                        /* Disable error reporting for unused labels. */
-    // "allowUnreachableCode": true,                     /* Disable error reporting for unreachable code. */
-
-    /* Completeness */
-    // "skipDefaultLibCheck": true,                      /* Skip type checking .d.ts files that are included with TypeScript. */
-    "skipLibCheck": true                                 /* Skip type checking all .d.ts files. */
-    ""
-  }
-}

docs/_scripts/js_translation/extract_codeblocks.py

@@ -1,150 +0,0 @@
-#!/usr/bin/env python
-"""Extracts typescript code blocks from a markdown file."""
-
-import argparse
-import json
-import re
-import os
-from typing import List, TypedDict, Literal
-
-
-class CodeBlock(TypedDict):
-    """A code block extracted from a markdown file."""
-    starting_line: int
-    """The line number where the code block starts in the source file"""
-    ending_line: int
-    """The line number where the code block ends in the source file"""
-    indentation: int
-    """Number of spaces/tabs used for indentation of the code block"""
-    source_file: str
-    """Path to the markdown file containing this code block"""
-    frontmatter: str
-    """Any metadata or frontmatter specified after the opening code fence"""
-    code: str
-    """The actual code content within the code block"""
-    language: str
-    """The language of the code block (e.g. typescript, javascript)"""
-
-
-def extract_code_blocks(markdown_content: str, source_file: str) -> List[CodeBlock]:
-    """Extracts code blocks from a markdown file.
-
-    Args:
-        markdown_content: The content of the markdown file.
-        source_file: The path to the markdown file.
-
-    Returns:
-        A list of TypedDicts, where each dict represents a code block.
-    """
-    # Regex to find code blocks with specified languages, capturing indentation
-    # and frontmatter.
-    pattern = re.compile(
-        r"^(?P<indentation>\s*)```(?P<language>typescript|javascript|ts|js)(?P<frontmatter>[^\n]*)\n(?P<code>.*?)\n^(?P=indentation)```\s*$",
-        re.DOTALL | re.MULTILINE,
-    )
-
-    code_blocks: List[CodeBlock] = []
-    for match in pattern.finditer(markdown_content):
-        start_pos = match.start()
-
-        # Calculate line numbers
-        starting_line = markdown_content.count("\n", 0, start_pos) + 1
-        ending_line = starting_line + match.group(0).count("\n")
-
-        indentation_str = match.group("indentation")
-
-        code_block: CodeBlock = {
-            "starting_line": starting_line,
-            "ending_line": ending_line,
-            "indentation": len(indentation_str),
-            "source_file": source_file,
-            "frontmatter": match.group("frontmatter").strip(),
-            "code": match.group("code"),
-            "language": match.group("language"),
-        }
-        code_blocks.append(code_block)
-
-    return code_blocks
-
-def dump_code_blocks(input_file: str, output_file: str, format: Literal["json", "inline"]) -> None:
-    """Function to extract and save code blocks from a markdown file.
-
-    Args:
-        input_file: Path to the input markdown file.
-        output_file: Path to the output JSON file for the extracted code blocks.
-        format: Output format - either "json" or "inline"
-    """
-    with open(input_file, "r", encoding="utf-8") as f:
-        markdown_content = f.read()
-
-    extracted_code = extract_code_blocks(markdown_content, input_file)
-
-    if len(extracted_code) == 0:
-        print(f"No code blocks found in {input_file}")
-        return
-    
-    if format == "json":
-        with open(output_file, "w", encoding="utf-8") as f:
-            json.dump(extracted_code, f, indent=2)
-    elif format == "inline":
-        with open(output_file, "w", encoding="utf-8") as f:
-            for code_block in extracted_code:
-                f.write(f"// {json.dumps({k:v for k,v in code_block.items() if k != 'code'})}\n")
-                f.write("\n")
-                f.write(code_block["code"])
-                f.write("\n")
-    print(f"Extracted {len(extracted_code)} code blocks from {input_file} to {output_file}")
-
-def main(input_path: str, output_path: str, format: Literal["json", "inline"]) -> None:
-    """Main function to extract code blocks from a markdown file.
-
-    Args:
-        input_file: Path to the input markdown file.
-        output_file: Path to the output JSON file for the extracted code blocks.
-        format: Output format - either "json" or "inline"
-    """
-    # Check if input path is a directory
-    if os.path.isdir(input_path):
-        if os.path.isfile(output_path):
-            raise ValueError("If input_path is a directory, output_path must also be a directory")
-        if not os.path.isdir(output_path):
-            os.makedirs(output_path, exist_ok=True)
-        
-        # Process each markdown file in the directory recursively
-        for root, _, files in os.walk(input_path):
-            for filename in files:
-                if filename.endswith(".md"):
-                    # Get relative path to maintain directory structure
-                    rel_path = os.path.relpath(root, input_path)
-                    input_file = os.path.join(root, filename)
-                    # Create output directory if it doesn't exist
-                    output_dir = os.path.join(output_path, rel_path)
-                    os.makedirs(output_dir, exist_ok=True)
-                    output_file = os.path.join(output_dir, filename.replace(".md", ".ts"))
-                    dump_code_blocks(input_file, output_file, format)
-    else:
-        # Process single file
-        dump_code_blocks(input_path, output_path, format)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="Extract typescript code blocks from a markdown file."
-    )
-    parser.add_argument(
-        "input_file",
-        help="Path to the input markdown file.",
-    )
-    parser.add_argument(
-        "output_file",
-        help="Path to the output JSON file for the extracted code blocks.",
-    )
-    parser.add_argument(
-        "--format",
-        choices=["json", "inline"],
-        default="json",
-        help="Output format - either 'json' or 'inline'",
-    )
-    args = parser.parse_args()
-
-    main(args.input_file, args.output_file, args.format)

docs/_scripts/link_map.py

@@ -1,127 +1,5 @@
-"""Link mapping for cross-reference resolution across different scopes.
-
-This module provides link mappings for different language/framework scopes
-to resolve @[link_name] references to actual URLs.
-"""
-
-# Python-specific link mappings
-PYTHON_LINK_MAP = {
-    "StateGraph": "reference/graphs/#langgraph.graph.StateGraph",
-    "add_conditional_edges": "reference/graphs/#langgraph.graph.state.StateGraph.add_conditional_edges",
-    "add_edge": "reference/graphs/#langgraph.graph.state.StateGraph.add_edge",
-    "add_node": "reference/graphs/#langgraph.graph.state.StateGraph.add_node",
-    "add_messages": "reference/graphs/#langgraph.graph.message.add_messages",
-    "ToolNode": "reference/agents/#langgraph.prebuilt.tool_node.ToolNode",
-    "CompiledStateGraph.astream": "reference/graphs/#langgraph.graph.state.CompiledStateGraph.astream",
-    "Pregel.astream": "reference/pregel/#langgraph.pregel.Pregel.astream",
-    "AsyncPostgresSaver": "reference/checkpoints/#langgraph.checkpoint.postgres.aio.AsyncPostgresSaver",
-    "AsyncSqliteSaver": "reference/checkpoints/#langgraph.checkpoint.sqlite.aio.AsyncSqliteSaver",
-    "BaseCheckpointSaver": "reference/checkpoints/#langgraph.checkpoint.base.BaseCheckpointSaver",
-    "BaseStore": "reference/store/#langgraph.store.base.BaseStore",
-    "BaseStore.put": "reference/store/#langgraph.store.base.BaseStore.put",
-    "BinaryOperatorAggregate": "reference/pregel/#langgraph.pregel.Pregel--advanced-channels-context-and-binaryoperatoraggregate",
-    "CipherProtocol": "reference/checkpoints/#langgraph.checkpoint.serde.base.CipherProtocol",
-    "client.runs.stream": "cloud/reference/sdk/python_sdk_ref/#langgraph_sdk.client.RunsClient.stream",
-    "client.runs.wait": "cloud/reference/sdk/python_sdk_ref/#langgraph_sdk.client.RunsClient.wait",
-    "client.threads.get_history": "cloud/reference/sdk/python_sdk_ref/#langgraph_sdk.client.ThreadsClient.get_history",
-    "client.threads.update_state": "cloud/reference/sdk/python_sdk_ref/#langgraph_sdk.client.ThreadsClient.update_state",
-    "Command": "reference/types/#langgraph.types.Command",
-    "CompiledStateGraph": "reference/graphs/#langgraph.graph.state.CompiledStateGraph",
-    "create_react_agent": "reference/prebuilt/#langgraph.prebuilt.chat_agent_executor.create_react_agent",
-    "create_supervisor": "reference/supervisor/#langgraph_supervisor.supervisor.create_supervisor",
-    "EncryptedSerializer": "reference/checkpoints/#langgraph.checkpoint.serde.encrypted.EncryptedSerializer",
-    "entrypoint.final": "reference/func/#langgraph.func.entrypoint.final",
-    "entrypoint": "reference/func/#langgraph.func.entrypoint",
-    "from_pycryptodome_aes": "reference/checkpoints/#langgraph.checkpoint.serde.encrypted.EncryptedSerializer.from_pycryptodome_aes",
-    "get_state_history": "reference/graphs/#langgraph.graph.state.CompiledStateGraph.get_state_history",
-    "get_stream_writer": "reference/config/#langgraph.config.get_stream_writer",
-    "HumanInterrupt": "reference/prebuilt/#langgraph.prebuilt.interrupt.HumanInterrupt",
-    "InjectedState": "reference/agents/#langgraph.prebuilt.tool_node.InjectedState",
-    "InMemorySaver": "reference/checkpoints/#langgraph.checkpoint.memory.InMemorySaver",
-    "interrupt": "reference/types/#langgraph.types.Interrupt",
-    "CompiledStateGraph.invoke": "reference/graphs/#langgraph.graph.state.CompiledStateGraph.invoke",
-    "JsonPlusSerializer": "reference/checkpoints/#langgraph.checkpoint.serde.jsonplus.JsonPlusSerializer",
-    "langgraph.json": "cloud/reference/cli/#configuration-file",
-    "LastValue": "reference/channels/#langgraph.channels.LastValue",
-    "PostgresSaver": "reference/checkpoints/#langgraph.checkpoint.postgres.PostgresSaver",
-    "Pregel": "reference/pregel/",
-    "Pregel.stream": "reference/pregel/#langgraph.pregel.Pregel.stream",
-    "pre_model_hook": "reference/prebuilt/#langgraph.prebuilt.chat_agent_executor.create_react_agent",
-    "protocol": "reference/checkpoints/#langgraph.checkpoint.serde.base.SerializerProtocol",
-    "Send": "reference/types/#langgraph.types.Send",
-    "SerializerProtocol": "reference/checkpoints/#langgraph.checkpoint.serde.base.SerializerProtocol",
-    "SqliteSaver": "reference/checkpoints/#langgraph.checkpoint.sqlite.SqliteSaver",
-    "START": "reference/constants/#langgraph.constants.START",
-    "CompiledStateGraph.stream": "reference/graphs/#langgraph.graph.state.CompiledStateGraph.stream",
-    "task": "reference/func/#langgraph.func.task",
-    "Topic": "reference/channels/#langgraph.channels.Topic",
-    "update_state": "reference/graphs/#langgraph.graph.state.CompiledStateGraph.update_state",
-}
-
-# JavaScript-specific link mappings
 JS_LINK_MAP = {
-    "Auth": "reference/classes/sdk_auth.Auth.html",
-    "StateGraph": "reference/classes/langgraph.StateGraph.html",
-    "add_conditional_edges": "/reference/classes/langgraph.StateGraph.html#addConditionalEdges",
-    "add_edge": "reference/classes/langgraph.StateGraph.html#addEdge",
-    "add_node": "reference/classes/langgraph.StateGraph.html#addNode",
-    "add_messages": "reference/modules/langgraph.html#addMessages",
-    "ToolNode": "reference/classes/langgraph_prebuilt.ToolNode.html",
-    "BaseCheckpointSaver": "reference/classes/checkpoint.BaseCheckpointSaver.html",
-    "BaseStore": "reference/classes/checkpoint.BaseStore.html",
-    "BaseStore.put": "reference/classes/checkpoint.BaseStore.html#put",
-    "BinaryOperatorAggregate": "reference/classes/langgraph.BinaryOperatorAggregate.html",
-    "client.runs.stream": "reference/classes/sdk_client.RunsClient.html#stream",
-    "client.runs.wait": "reference/classes/sdk_client.RunsClient.html#wait",
-    "client.threads.get_history": "reference/classes/sdk_client.ThreadsClient.html#getHistory",
-    "client.threads.update_state": "reference/classes/sdk_client.ThreadsClient.html#updateState",
-    "Command": "reference/classes/langgraph.Command.html",
-    "CompiledStateGraph": "reference/classes/langgraph.CompiledStateGraph.html",
-    "create_react_agent": "reference/functions/langgraph_prebuilt.createReactAgent.html",
-    "create_supervisor": "reference/functions/langgraph_supervisor.createSupervisor.html",
-    "entrypoint.final": "reference/functions/langgraph.entrypoint.html#final",
-    "entrypoint": "reference/functions/langgraph.entrypoint.html",
-    "getContextVariable": "https://v03.api.js.langchain.com/functions/_langchain_core.context.getContextVariable.html",
-    "get_state_history": "reference/classes/langgraph.CompiledStateGraph.html#getStateHistory",
-    "HumanInterrupt": "reference/interfaces/langgraph_prebuilt.HumanInterrupt.html",
-    "interrupt": "reference/functions/langgraph.interrupt-2.html",
-    "CompiledStateGraph.invoke": "reference/classes/langgraph.CompiledStateGraph.html#invoke",
-    "langgraph.json": "cloud/reference/cli/#configuration-file",
-    "MemorySaver": "reference/classes/checkpoint.MemorySaver.html",
-    "messagesStateReducer": "reference/functions/langgraph.messagesStateReducer.html",
-    "PostgresSaver": "reference/classes/checkpoint_postgres.PostgresSaver.html",
-    "Pregel": "reference/classes/langgraph.Pregel.html",
-    "Pregel.stream": "reference/classes/langgraph.Pregel.html#stream",
-    "pre_model_hook": "reference/functions/langgraph_prebuilt.createReactAgent.html",
-    "protocol": "reference/interfaces/checkpoint.SerializerProtocol.html",
-    "Send": "reference/classes/langgraph.Send.html",
-    "SerializerProtocol": "reference/interfaces/checkpoint.SerializerProtocol.html",
-    "SqliteSaver": "reference/classes/checkpoint_sqlite.SqliteSaver.html",
-    "START": "reference/variables/langgraph.START.html",
-    "CompiledStateGraph.stream": "reference/classes/langgraph.CompiledStateGraph.html#stream",
-    "task": "reference/functions/langgraph.task.html",
-    ## TODO (hntrl): export Topic from langgraphjs
-    # "Topic": "reference/classes/langgraph_channels.Topic.html",
-    "update_state": "reference/classes/langgraph.CompiledStateGraph.html#updateState",
-}
-
-# TODO: Allow updating these to localhost for local development
-PY_REFERENCE_HOST = "https://langchain-ai.github.io/langgraph/"
-JS_REFERENCE_HOST = "https://langchain-ai.github.io/langgraphjs/"
-
-for key, value in PYTHON_LINK_MAP.items():
-    # Ensure the link is absolute
-    if not value.startswith("http"):
-        PYTHON_LINK_MAP[key] = f"{PY_REFERENCE_HOST}{value}"
-
-for key, value in JS_LINK_MAP.items():
-    # Ensure the link is absolute
-    if not value.startswith("http"):
-        JS_LINK_MAP[key] = f"{JS_REFERENCE_HOST}{value}"
-
-# Global scope is assembled from the Python and JS mappings
-# Combined mapping by scope
-SCOPE_LINK_MAPS = {
-    "python": PYTHON_LINK_MAP,
-    "js": JS_LINK_MAP,
+    "langgraph.types.interrupt": "https://langchain-ai.github.io/langgraphjs/reference/functions/langgraph.interrupt-2.html",
+    "create_react_agent": "https://langchain-ai.github.io/langgraphjs/reference/functions/langgraph_prebuilt.createReactAgent.html",
+    "langgraph.types.Command": "https://langchain-ai.github.io/langgraphjs/reference/classes/langgraph.Command.html",
 }

docs/_scripts/notebook_hooks.py

@@ -3,7 +3,6 @@
 Lifecycle events: https://www.mkdocs.org/dev-guide/plugins/#events
 """
 
-import json
 import logging
 import os
 import posixpath
@@ -16,8 +15,8 @@ from mkdocs.structure.files import Files, File
 from mkdocs.structure.pages import Page
 
 from _scripts.generate_api_reference_links import update_markdown_with_imports
-from _scripts.handle_auto_links import _replace_autolinks
 from _scripts.notebook_convert import convert_notebook
+from _scripts.link_map import JS_LINK_MAP
 
 logger = logging.getLogger(__name__)
 logging.basicConfig()
@@ -35,20 +34,20 @@ REDIRECT_MAP = {
     "how-tos/streaming-from-final-node.ipynb": "how-tos/streaming-specific-nodes.ipynb",
     "how-tos/streaming-events-from-within-tools-without-langchain.ipynb": "how-tos/streaming-events-from-within-tools.ipynb#example-without-langchain",
     # graph-api
-    "how-tos/state-reducers.ipynb": "how-tos/graph-api.md#define-and-update-state",
-    "how-tos/sequence.ipynb": "how-tos/graph-api.md#create-a-sequence-of-steps",
-    "how-tos/branching.ipynb": "how-tos/graph-api.md#create-branches",
-    "how-tos/recursion-limit.ipynb": "how-tos/graph-api.md#create-and-control-loops",
-    "how-tos/visualization.ipynb": "how-tos/graph-api.md#visualize-your-graph",
-    "how-tos/input_output_schema.ipynb": "how-tos/graph-api.md#define-input-and-output-schemas",
-    "how-tos/pass_private_state.ipynb": "how-tos/graph-api.md#pass-private-state-between-nodes",
-    "how-tos/state-model.ipynb": "how-tos/graph-api.md#use-pydantic-models-for-graph-state",
-    "how-tos/map-reduce.ipynb": "how-tos/graph-api.md#map-reduce-and-the-send-api",
-    "how-tos/command.ipynb": "how-tos/graph-api.md#combine-control-flow-and-state-updates-with-command",
-    "how-tos/configuration.ipynb": "how-tos/graph-api.md#add-runtime-configuration",
-    "how-tos/node-retries.ipynb": "how-tos/graph-api.md#add-retry-policies",
-    "how-tos/return-when-recursion-limit-hits.ipynb": "how-tos/graph-api.md#impose-a-recursion-limit",
-    "how-tos/async.ipynb": "how-tos/graph-api.md#async",
+    "how-tos/state-reducers.ipynb": "how-tos/graph-api#define-and-update-state",
+    "how-tos/sequence.ipynb": "how-tos/graph-api#create-a-sequence-of-steps",
+    "how-tos/branching.ipynb": "how-tos/graph-api#create-branches",
+    "how-tos/recursion-limit.ipynb": "how-tos/graph-api#create-and-control-loops",
+    "how-tos/visualization.ipynb": "how-tos/graph-api#visualize-your-graph",
+    "how-tos/input_output_schema.ipynb": "how-tos/graph-api#define-input-and-output-schemas",
+    "how-tos/pass_private_state.ipynb": "how-tos/graph-api#pass-private-state-between-nodes",
+    "how-tos/state-model.ipynb": "how-tos/graph-api#use-pydantic-models-for-graph-state",
+    "how-tos/map-reduce.ipynb": "how-tos/graph-api/#map-reduce-and-the-send-api",
+    "how-tos/command.ipynb": "how-tos/graph-api/#combine-control-flow-and-state-updates-with-command",
+    "how-tos/configuration.ipynb": "how-tos/graph-api/#add-runtime-configuration",
+    "how-tos/node-retries.ipynb": "how-tos/graph-api/#add-retry-policies",
+    "how-tos/return-when-recursion-limit-hits.ipynb": "how-tos/graph-api/#impose-a-recursion-limit",
+    "how-tos/async.ipynb": "how-tos/graph-api/#async",
     # memory how-tos
     "how-tos/memory/manage-conversation-history.ipynb": "how-tos/memory/add-memory.md",
     "how-tos/memory/delete-messages.ipynb": "how-tos/memory/add-memory.md#delete-messages",
@@ -56,8 +55,8 @@ REDIRECT_MAP = {
     "how-tos/memory.ipynb": "how-tos/memory/add-memory.md",
     "agents/memory.ipynb": "how-tos/memory/add-memory.md",
     # subgraph how-tos
-    "how-tos/subgraph-transform-state.ipynb": "how-tos/subgraph.md#different-state-schemas",
-    "how-tos/subgraphs-manage-state.ipynb": "how-tos/subgraph.md#add-persistence",
+    "how-tos/subgraph-transform-state.ipynb": "how-tos/subgraph.ipynb#different-state-schemas",
+    "how-tos/subgraphs-manage-state.ipynb": "how-tos/subgraph.ipynb#add-persistence",
     # persistence how-tos
     "how-tos/persistence_postgres.ipynb": "how-tos/memory/add-memory.md#use-in-production",
     "how-tos/persistence_mongodb.ipynb": "how-tos/memory/add-memory.md#use-in-production",
@@ -73,11 +72,10 @@ REDIRECT_MAP = {
     "how-tos/pass-config-to-tools.ipynb": "how-tos/tool-calling.ipynb#access-config",
     "how-tos/pass-run-time-values-to-tools.ipynb": "how-tos/tool-calling.ipynb#read-state",
     "how-tos/update-state-from-tools.ipynb": "how-tos/tool-calling.ipynb#update-state",
-    "agents/tools.md": "how-tos/tool-calling.md",
     # multi-agent how-tos
-    "how-tos/agent-handoffs.ipynb": "how-tos/multi_agent.md#handoffs",
-    "how-tos/multi-agent-network.ipynb": "how-tos/multi_agent.md#use-in-a-multi-agent-system",
-    "how-tos/multi-agent-multi-turn-convo.ipynb": "how-tos/multi_agent.md#multi-turn-conversation",
+    "how-tos/agent-handoffs.ipynb": "how-tos/multi_agent.ipynb#handoffs",
+    "how-tos/multi-agent-network.ipynb": "how-tos/multi_agent.ipynb#use-in-a-multi-agent-system",
+    "how-tos/multi-agent-multi-turn-convo.ipynb": "how-tos/multi_agent.ipynb#multi-turn-conversation",
     # cloud redirects
     "cloud/index.md": "index.md",
     "cloud/how-tos/index.md": "concepts/langgraph_platform",
@@ -88,12 +86,12 @@ REDIRECT_MAP = {
     "cloud/how-tos/human_in_the_loop_user_input.md": "cloud/how-tos/add-human-in-the-loop.md",
     "concepts/platform_architecture.md": "concepts/langgraph_cloud#architecture",
     # cloud streaming redirects
-    "cloud/how-tos/stream_values.md": "https://docs.langchain.com/langgraph-platform/streaming",
-    "cloud/how-tos/stream_updates.md": "https://docs.langchain.com/langgraph-platform/streaming",
-    "cloud/how-tos/stream_messages.md": "https://docs.langchain.com/langgraph-platform/streaming",
-    "cloud/how-tos/stream_events.md": "https://docs.langchain.com/langgraph-platform/streaming",
-    "cloud/how-tos/stream_debug.md": "https://docs.langchain.com/langgraph-platform/streaming",
-    "cloud/how-tos/stream_multiple.md": "https://docs.langchain.com/langgraph-platform/streaming",
+    "cloud/how-tos/stream_values.md": "cloud/how-tos/streaming.md#stream-graph-state",
+    "cloud/how-tos/stream_updates.md": "cloud/how-tos/streaming.md#stream-graph-state",
+    "cloud/how-tos/stream_messages.md": "cloud/how-tos/streaming.md#messages",
+    "cloud/how-tos/stream_events.md": "cloud/how-tos/streaming.md#stream-events",
+    "cloud/how-tos/stream_debug.md": "cloud/how-tos/streaming.md#debug",
+    "cloud/how-tos/stream_multiple.md": "cloud/how-tos/streaming.md#stream-multiple-modes",
     "cloud/concepts/streaming.md": "concepts/streaming.md",
     "agents/streaming.md": "how-tos/streaming.md",
     # prebuilt redirects
@@ -101,6 +99,10 @@ REDIRECT_MAP = {
     "how-tos/create-react-agent-memory.ipynb": "agents/memory.md",
     "how-tos/create-react-agent-system-prompt.ipynb": "agents/context.md#prompts",
     "how-tos/create-react-agent-structured-output.ipynb": "agents/agents.md#structured-output",
+    # Time-travel
+    "how-tos/human_in_the_loop/edit-graph-state.ipynb": "how-tos/human_in_the_loop/time-travel.md",
+    # breakpoints
+    "how-tos/human_in_the_loop/dynamic_breakpoints.ipynb": "how-tos/human_in_the_loop/breakpoints.md",
     # misc
     "prebuilt.md": "agents/prebuilt.md",
     "reference/prebuilt.md": "reference/agents.md",
@@ -122,90 +124,6 @@ REDIRECT_MAP = {
     "how-tos/review-tool-calls-functional.ipynb": "how-tos/use-functional-api.md",
     "how-tos/create-react-agent-hitl.ipynb": "how-tos/human_in_the_loop/add-human-in-the-loop.md",
     "agents/human-in-the-loop.md": "how-tos/human_in_the_loop/add-human-in-the-loop.md",
-    "how-tos/human_in_the_loop/dynamic_breakpoints.ipynb": "how-tos/human_in_the_loop/breakpoints.md",
-    "concepts/breakpoints.md": "concepts/human_in_the_loop.md",
-    "how-tos/human_in_the_loop/breakpoints.md": "how-tos/human_in_the_loop/add-human-in-the-loop.md",
-    "cloud/how-tos/human_in_the_loop_breakpoint.md": "cloud/how-tos/add-human-in-the-loop.md",
-    "how-tos/human_in_the_loop/edit-graph-state.ipynb": "how-tos/human_in_the_loop/time-travel.md",
-
-    # LGP mintlify migration redirects
-    "tutorials/auth/getting_started.md": "https://docs.langchain.com/langgraph-platform/auth",
-    "tutorials/auth/resource_auth.md": "https://docs.langchain.com/langgraph-platform/resource-auth",
-    "tutorials/auth/add_auth_server.md": "https://docs.langchain.com/langgraph-platform/add-auth-server",
-    "how-tos/use-remote-graph.md": "https://docs.langchain.com/langgraph-platform/use-remote-graph",
-    "how-tos/autogen-integration.md": "https://docs.langchain.com/langgraph-platform/autogen-integration",
-    "cloud/how-tos/use_stream_react.md": "https://docs.langchain.com/langgraph-platform/use-stream-react",
-    "cloud/how-tos/generative_ui_react.md": "https://docs.langchain.com/langgraph-platform/generative-ui-react",
-    "concepts/langgraph_platform.md": "https://docs.langchain.com/langgraph-platform/index",
-    "concepts/langgraph_components.md": "https://docs.langchain.com/langgraph-platform/components",
-    "concepts/langgraph_server.md": "https://docs.langchain.com/langgraph-platform/langgraph-server",
-    "concepts/langgraph_data_plane.md": "https://docs.langchain.com/langgraph-platform/data-plane",
-    "concepts/langgraph_control_plane.md": "https://docs.langchain.com/langgraph-platform/control-plane",
-    "concepts/langgraph_cli.md": "https://docs.langchain.com/langgraph-platform/langgraph-cli",
-    "concepts/langgraph_studio.md": "https://docs.langchain.com/langgraph-platform/langgraph-studio",
-    "cloud/how-tos/studio/quick_start.md": "https://docs.langchain.com/langgraph-platform/quick-start-studio",
-    "cloud/how-tos/invoke_studio.md": "https://docs.langchain.com/langgraph-platform/invoke-studio",
-    "cloud/how-tos/studio/manage_assistants.md": "https://docs.langchain.com/langgraph-platform/manage-assistants-studio",
-    "cloud/how-tos/threads_studio.md": "https://docs.langchain.com/langgraph-platform/threads-studio",
-    "cloud/how-tos/iterate_graph_studio.md": "https://docs.langchain.com/langgraph-platform/iterate-graph-studio",
-    "cloud/how-tos/studio/run_evals.md": "https://docs.langchain.com/langgraph-platform/run-evals-studio",
-    "cloud/how-tos/clone_traces_studio.md": "https://docs.langchain.com/langgraph-platform/clone-traces-studio",
-    "cloud/how-tos/datasets_studio.md": "https://docs.langchain.com/langgraph-platform/datasets-studio",
-    "concepts/sdk.md": "https://docs.langchain.com/langgraph-platform/sdk",
-    "concepts/plans.md": "https://docs.langchain.com/langgraph-platform/plans",
-    "concepts/application_structure.md": "https://docs.langchain.com/langgraph-platform/application-structure",
-    "concepts/scalability_and_resilience.md": "https://docs.langchain.com/langgraph-platform/scalability-and-resilience",
-    "concepts/auth.md": "https://docs.langchain.com/langgraph-platform/auth",
-    "how-tos/auth/custom_auth.md": "https://docs.langchain.com/langgraph-platform/custom-auth",
-    "how-tos/auth/openapi_security.md": "https://docs.langchain.com/langgraph-platform/openapi-security",
-    "concepts/assistants.md": "https://docs.langchain.com/langgraph-platform/assistants",
-    "cloud/how-tos/configuration_cloud.md": "https://docs.langchain.com/langgraph-platform/configuration-cloud",
-    "cloud/how-tos/use_threads.md": "https://docs.langchain.com/langgraph-platform/use-threads",
-    "cloud/how-tos/background_run.md": "https://docs.langchain.com/langgraph-platform/background-run",
-    "cloud/how-tos/same-thread.md": "https://docs.langchain.com/langgraph-platform/same-thread",
-    "cloud/how-tos/stateless_runs.md": "https://docs.langchain.com/langgraph-platform/stateless-runs",
-    "cloud/how-tos/configurable_headers.md": "https://docs.langchain.com/langgraph-platform/configurable-headers",
-    "concepts/double_texting.md": "https://docs.langchain.com/langgraph-platform/double-texting",
-    "cloud/how-tos/interrupt_concurrent.md": "https://docs.langchain.com/langgraph-platform/interrupt-concurrent",
-    "cloud/how-tos/rollback_concurrent.md": "https://docs.langchain.com/langgraph-platform/rollback-concurrent",
-    "cloud/how-tos/reject_concurrent.md": "https://docs.langchain.com/langgraph-platform/reject-concurrent",
-    "cloud/how-tos/enqueue_concurrent.md": "https://docs.langchain.com/langgraph-platform/enqueue-concurrent",
-    "cloud/concepts/webhooks.md": "https://docs.langchain.com/langgraph-platform/use-webhooks",
-    "cloud/how-tos/webhooks.md": "https://docs.langchain.com/langgraph-platform/use-webhooks",
-    "cloud/concepts/cron_jobs.md": "https://docs.langchain.com/langgraph-platform/cron-jobs",
-    "cloud/how-tos/cron_jobs.md": "https://docs.langchain.com/langgraph-platform/cron-jobs",
-    "how-tos/http/custom_lifespan.md": "https://docs.langchain.com/langgraph-platform/custom-lifespan",
-    "how-tos/http/custom_middleware.md": "https://docs.langchain.com/langgraph-platform/custom-middleware",
-    "how-tos/http/custom_routes.md": "https://docs.langchain.com/langgraph-platform/custom-routes",
-    "cloud/concepts/data_storage_and_privacy.md": "https://docs.langchain.com/langgraph-platform/data-storage-and-privacy",
-    "cloud/deployment/semantic_search.md": "https://docs.langchain.com/langgraph-platform/semantic-search",
-    "how-tos/ttl/configure_ttl.md": "https://docs.langchain.com/langgraph-platform/configure-ttl",
-    "concepts/deployment_options.md": "https://docs.langchain.com/langgraph-platform/deployment-options",
-    "cloud/quick_start.md": "https://docs.langchain.com/langgraph-platform/deployment-quickstart",
-    "cloud/deployment/setup.md": "https://docs.langchain.com/langgraph-platform/setup-app-requirements-txt",
-    "cloud/deployment/setup_pyproject.md": "https://docs.langchain.com/langgraph-platform/setup-pyproject",
-    "cloud/deployment/setup_javascript.md": "https://docs.langchain.com/langgraph-platform/setup-javascript",
-    "cloud/deployment/custom_docker.md": "https://docs.langchain.com/langgraph-platform/custom-docker",
-    "cloud/deployment/graph_rebuild.md": "https://docs.langchain.com/langgraph-platform/graph-rebuild",
-    "concepts/langgraph_cloud.md": "https://docs.langchain.com/langgraph-platform/cloud",
-    "concepts/langgraph_self_hosted_data_plane.md": "https://docs.langchain.com/langgraph-platform/hybrid",
-    "concepts/langgraph_self_hosted_control_plane.md": "https://docs.langchain.com/langgraph-platform/self-hosted",
-    "concepts/langgraph_standalone_container.md": "https://docs.langchain.com/langgraph-platform/self-hosted#standalone-server",
-    "cloud/deployment/cloud.md": "https://docs.langchain.com/langgraph-platform/cloud",
-    "cloud/deployment/self_hosted_data_plane.md": "https://docs.langchain.com/langgraph-platform/deploy-hybrid",
-    "cloud/deployment/self_hosted_control_plane.md": "https://docs.langchain.com/langgraph-platform/deploy-self-hosted-full-platform",
-    "cloud/deployment/standalone_container.md": "https://docs.langchain.com/langgraph-platform/deploy-standalone-server",
-    "concepts/server-mcp.md": "https://docs.langchain.com/langgraph-platform/server-mcp",
-    "cloud/how-tos/human_in_the_loop_time_travel.md": "https://docs.langchain.com/langgraph-platform/human-in-the-loop-time-travel",
-    "cloud/how-tos/add-human-in-the-loop.md": "https://docs.langchain.com/langgraph-platform/add-human-in-the-loop",
-    "cloud/deployment/egress.md": "https://docs.langchain.com/langgraph-platform/env-var",
-    "cloud/how-tos/streaming.md": "https://docs.langchain.com/langgraph-platform/streaming",
-    "cloud/reference/api/api_ref.md": "https://docs.langchain.com/langgraph-platform/server-api-ref",
-    "cloud/reference/langgraph_server_changelog.md": "https://docs.langchain.com/langgraph-platform/langgraph-server-changelog",
-    "cloud/reference/api/api_ref_control_plane.md": "https://docs.langchain.com/langgraph-platform/api-ref-control-plane",
-    "cloud/reference/cli.md": "https://docs.langchain.com/langgraph-platform/cli",
-    "cloud/reference/env_var.md": "https://docs.langchain.com/langgraph-platform/env-var",
-    "troubleshooting/studio.md": "https://docs.langchain.com/langgraph-platform/troubleshooting-studio",
 }
 
 
@@ -255,7 +173,31 @@ def _add_path_to_code_blocks(markdown: str, page: Page) -> str:
     return code_block_pattern.sub(replace_code_block_header, markdown)
 
 
-# Compiled regex patterns for better performance and readability
+def _resolve_cross_references(md_text: str, link_map: dict[str, str]) -> str:
+    """Replace [title][identifier] with [title](url) using language-specific link_map.
+
+    Args:
+        md_text: The markdown text to process.
+        link_map: mapping of identifier to URL.
+
+    Returns:
+        The processed markdown text with cross-references resolved.
+    """
+    # Pattern to match [title][identifier]
+    pattern = re.compile(r"\[([^\]]+)\]\[([^\]]+)\]")
+
+    def replace_reference(match: re.Match) -> str:
+        """Replace the matched reference with the corresponding URL."""
+        title, identifier = match.group(1), match.group(2)
+        url = link_map.get(identifier)
+
+        if url:
+            return f"[{title}]({url})"
+        else:
+            # Leave it unchanged if not found
+            return match.group(0)
+
+    return pattern.sub(replace_reference, md_text)
 
 
 def _apply_conditional_rendering(md_text: str, target_language: str) -> str:
@@ -265,7 +207,7 @@ def _apply_conditional_rendering(md_text: str, target_language: str) -> str:
     pattern = re.compile(
         r"(?P<indent>[ \t]*):::(?P<language>\w+)\s*\n"
         r"(?P<content>((?:.*\n)*?))"  # Capture the content inside the block
-        r"(?P=indent)[ \t]*:::"  # Match closing with the same indentation + any additional whitespace
+        r"(?P=indent):::"  # Match closing with the same indentation
     )
 
     def replace_conditional_blocks(match: re.Match) -> str:
@@ -350,7 +292,7 @@ def _highlight_code_blocks(markdown: str) -> str:
             opening_fence += f" {attributes}"
 
         if highlighted_lines:
-            opening_fence += f' hl_lines="{" ".join(highlighted_lines)}"'
+            opening_fence += f" hl_lines=\"{' '.join(highlighted_lines)}\""
 
         return (
             # The indent and opening fence
@@ -365,21 +307,6 @@ def _highlight_code_blocks(markdown: str) -> str:
     return markdown
 
 
-def _save_page_output(markdown: str, output_path: str):
-    """Save markdown content to a file, creating parent directories if needed.
-
-    Args:
-        markdown: The markdown content to save
-        output_path: The file path to save to
-    """
-    # Create parent directories recursively if they don't exist
-    os.makedirs(os.path.dirname(output_path), exist_ok=True)
-    
-    # Write the markdown content to the file
-    with open(output_path, "w", encoding="utf-8") as f:
-        f.write(markdown)
-
-
 def _on_page_markdown_with_config(
     markdown: str,
     page: Page,
@@ -395,14 +322,6 @@ def _on_page_markdown_with_config(
         # logger.info("Processing Jupyter notebook: %s", page.file.src_path)
         markdown = convert_notebook(page.file.abs_src_path)
 
-    target_language = kwargs.get(
-        "target_language",
-        os.environ.get("TARGET_LANGUAGE", "python")
-    )
-
-    # Apply cross-reference preprocessing to all markdown content
-    markdown = _replace_autolinks(markdown, page.file.src_path, default_scope=target_language)
-
     # Append API reference links to code blocks
     if add_api_references:
         markdown = update_markdown_with_imports(markdown, page.file.abs_src_path)
@@ -410,7 +329,18 @@ def _on_page_markdown_with_config(
     markdown = _highlight_code_blocks(markdown)
 
     # Apply conditional rendering for code blocks
+    target_language = kwargs.get("target_language", "python")
     markdown = _apply_conditional_rendering(markdown, target_language)
+    if target_language == "js":
+        markdown = _resolve_cross_references(markdown, JS_LINK_MAP)
+    elif target_language == "python":
+        # Via a dedicated plugin
+        pass
+    else:
+        raise ValueError(
+            f"Unsupported target language: {target_language}. "
+            "Supported languages are 'python' and 'js'."
+        )
 
     # Add file path as an attribute to code blocks that are executable.
     # This file path is used to associate fixtures with the executable code
@@ -425,20 +355,12 @@ def _on_page_markdown_with_config(
 
 
 def on_page_markdown(markdown: str, page: Page, **kwargs: Dict[str, Any]):
-    finalized_markdown = _on_page_markdown_with_config(
+    return _on_page_markdown_with_config(
         markdown,
         page,
         add_api_references=True,
         **kwargs,
     )
-    page.meta["original_markdown"] = finalized_markdown
-
-    output_path = os.environ.get("MD_OUTPUT_PATH")
-    if output_path:
-        file_path = os.path.join(output_path, page.file.src_path)
-        _save_page_output(finalized_markdown, file_path)
-
-    return finalized_markdown
 
 
 # redirects
@@ -509,103 +431,34 @@ height="0" width="0" style="display:none;visibility:hidden"></iframe></noscript>
         return html  # fallback if no <body> found
 
 
-def _inject_markdown_into_html(html: str, page: Page) -> str:
-    """Inject the original markdown content into the HTML page as JSON."""
-    original_markdown = page.meta.get("original_markdown", "")
-    if not original_markdown:
-        return html
-    markdown_data = {
-        "markdown": original_markdown,
-        "title": page.title or "Page Content",
-        "url": page.url or "",
-    }
-
-    # Properly escape the JSON for HTML
-    json_content = json.dumps(markdown_data, ensure_ascii=False)
-
-    json_content = (
-        json_content.replace("</", "\\u003c/")
-        .replace("<script", "\\u003cscript")
-        .replace("</script", "\\u003c/script")
-    )
-
-    script_content = (
-        f'<script id="page-markdown-content" '
-        f'type="application/json">{json_content}</script>'
-    )
-
-    # Insert before </head> if it exists, otherwise before </body>
-    if "</head>" not in html:
-        raise ValueError(
-            "HTML does not contain </head> tag. Cannot inject markdown content."
-        )
-    return html.replace("</head>", f"{script_content}</head>")
-
-
-def on_post_page(html: str, page: Page, config: MkDocsConfig) -> str:
+def on_post_page(output: str, page: Page, config: MkDocsConfig) -> str:
     """Inject Google Tag Manager noscript tag immediately after <body>.
 
     Args:
-        html: The HTML output of the page.
+        output: The HTML output of the page.
         page: The page instance.
         config: The MkDocs configuration object.
 
     Returns:
         modified HTML output with GTM code injected.
     """
-    html = _inject_markdown_into_html(html, page)
-    return _inject_gtm(html)
+    return _inject_gtm(output)
 
 
 # Create HTML files for redirects after site dir has been built
 def on_post_build(config):
     use_directory_urls = config.get("use_directory_urls")
     for page_old, page_new in REDIRECT_MAP.items():
-        # Convert .ipynb to .md for path calculation
         page_old = page_old.replace(".ipynb", ".md")
-        
-        # Calculate the HTML path for the old page (whether it exists or not)
-        if use_directory_urls:
-            # With directory URLs: /path/to/page/ becomes /path/to/page/index.html
-            if page_old.endswith(".md"):
-                old_html_path = page_old[:-3] + "/index.html"
-            else:
-                old_html_path = page_old + "/index.html"
-        else:
-            # Without directory URLs: /path/to/page.md becomes /path/to/page.html
-            if page_old.endswith(".md"):
-                old_html_path = page_old[:-3] + ".html"
-            else:
-                old_html_path = page_old + ".html"
-        
-        if isinstance(page_new, str) and page_new.startswith("http"):
-            # Handle external redirects
-            _write_html(config["site_dir"], old_html_path, page_new)
-        else:
-            # Handle internal redirects
-            page_new = page_new.replace(".ipynb", ".md")
-            page_new_before_hash, hash, suffix = page_new.partition("#")
-            
-            # Try to get the new path using File class, but fallback to manual calculation
-            try:
-                new_html_path = File(page_new_before_hash, "", "", True).url
-                new_html_path = (
-                    posixpath.relpath(new_html_path, start=posixpath.dirname(old_html_path))
-                    + hash
-                    + suffix
-                )
-            except:
-                # Fallback: calculate relative path manually
-                if use_directory_urls:
-                    if page_new_before_hash.endswith(".md"):
-                        new_html_path = page_new_before_hash[:-3] + "/"
-                    else:
-                        new_html_path = page_new_before_hash + "/"
-                else:
-                    if page_new_before_hash.endswith(".md"):
-                        new_html_path = page_new_before_hash[:-3] + ".html"
-                    else:
-                        new_html_path = page_new_before_hash + ".html"
-                new_html_path += hash + suffix
-            
-            _write_html(config["site_dir"], old_html_path, new_html_path)
+        page_new = page_new.replace(".ipynb", ".md")
+        page_new_before_hash, hash, suffix = page_new.partition("#")
+        old_html_path = File(page_old, "", "", use_directory_urls).dest_path.replace(
+            os.sep, "/"
+        )
+        new_html_path = File(page_new_before_hash, "", "", True).url
+        new_html_path = (
+            posixpath.relpath(new_html_path, start=posixpath.dirname(old_html_path))
+            + hash
+            + suffix
+        )
+        _write_html(config["site_dir"], old_html_path, new_html_path)

docs/_scripts/third_party_page/create_third_party_page.py

@@ -15,10 +15,9 @@ If you’re looking for other prebuilt libraries, explore the community-built op
 below. These libraries can extend LangGraph's functionality in various ways.
 
 ## 📚 Available Libraries
+
 [//]: # (This file is automatically generated using a script in docs/_scripts. Do not edit this file directly!)
-
-:::python
-{python_library_list}
+{library_list}
 
 ## ✨ Contributing Your Library
 
@@ -29,39 +28,16 @@ To share your project, simply open a Pull Request adding an entry for your packa
 
 **Guidelines**
 
-- Your repo must be distributed as an installable package on PyPI 📦
+- Your repo must be distributed as an installable package (e.g., PyPI for Python, npm 
+  for JavaScript/TypeScript, etc.) 📦
 - The repo should either use the Graph API (exposing a `StateGraph` instance) or 
   the Functional API (exposing an `entrypoint`).
 - The package must include documentation (e.g., a `README.md` or docs site) 
   explaining how to use it.
-
+  
 We'll review your contribution and merge it in!
 
 Thanks for contributing! 🚀
-:::
-
-:::js
-{js_library_list}
-
-## ✨ Contributing Your Library
-
-Have you built an awesome open-source library using LangGraph? We'd love to feature 
-your project on the official LangGraph documentation pages! 🏆
-
-To share your project, simply open a Pull Request adding an entry for your package in our [packages.yml]({langgraph_url}) file.
-
-**Guidelines**
-
-- Your repo must be distributed as an installable package on npm 📦
-- The repo should either use the Graph API (exposing a `StateGraph` instance) or 
-  the Functional API (exposing an `entrypoint`).
-- The package must include documentation (e.g., a `README.md` or docs site) 
-  explaining how to use it.
-
-We'll review your contribution and merge it in!
-
-Thanks for contributing! 🚀
-:::
 """
 
 
@@ -70,18 +46,36 @@ class ResolvedPackage(TypedDict):
     """The name of the package."""
     repo: str
     """Repository ID within github. Format is: [orgname]/[repo_name]."""
-    monorepo_path: str | None
-    """Optional: The path to the package in the monorepo. Must be relative to the root of the monorepo."""
-    language: str
-    """The language of the package. (either 'python' or 'js')"""
     weekly_downloads: int | None
     """The weekly download count of the package."""
     description: str
     """A brief description of what the package does."""
 
-def generate_package_table(resolved_packages: List[ResolvedPackage]) -> str:
-    """Generate the package table for the third party page.
+
+def generate_markdown(resolved_packages: List[ResolvedPackage], language: str) -> str:
+    """Generate the markdown content for the third party page.
+
+    Args:
+        resolved_packages: A list of resolved package information.
+        language: str
+
+    Returns:
+        The markdown content as a string.
     """
+    # Update the URL to the actual file once the initial version is merged
+    if language == "python":
+        langgraph_url = (
+            "https://github.com/langchain-ai/langgraph/blob/main/docs"
+            "/_scripts/third_party_page/packages.yml"
+        )
+    elif language == "js":
+        langgraph_url = (
+            "https://github.com/langchain-ai/langgraphjs/blob/main/docs"
+            "/_scripts/third_party/packages.yml"
+        )
+    else:
+        raise ValueError(f"Invalid language '{language}'. Expected 'python' or 'js'.")
+
     sorted_packages = sorted(
         resolved_packages, key=lambda p: p["weekly_downloads"] or 0, reverse=True
     )
@@ -91,15 +85,7 @@ def generate_package_table(resolved_packages: List[ResolvedPackage]) -> str:
     ]
     for package in sorted_packages:
         name = f"**{package['name']}**"
-        
-        monorepo_path = package.get("monorepo_path", "")
-        if monorepo_path:
-            monorepo_path = monorepo_path[1:] if monorepo_path.startswith('/') else monorepo_path
-            repo_url_suffix = f"/tree/main/{monorepo_path}"
-        else:
-            repo_url_suffix = ""
-        repo_url = f"https://github.com/{package['repo']}{repo_url_suffix}"
-
+        repo_url = f"[{package['repo']}](https://github.com/{package['repo']})"
         stars_badge = (
             f"https://img.shields.io/github/stars/{package['repo']}?style=social"
         )
@@ -107,39 +93,13 @@ def generate_package_table(resolved_packages: List[ResolvedPackage]) -> str:
         downloads = package["weekly_downloads"] or "-"
         row = f"| {name} | {repo_url} | {package['description']} | {downloads} | {stars}"
         rows.append(row)
-    return "\n".join(rows)
-
-def generate_markdown(resolved_packages: List[ResolvedPackage]) -> str:
-    """Generate the markdown content for the third party page.
-
-    Args:
-        resolved_packages: A list of resolved package information.
-
-    Returns:
-        The markdown content as a string.
-    """
-    # Update the URL to the actual file once the initial version is merged
-    langgraph_url = (
-        "https://github.com/langchain-ai/langgraph/blob/main/docs"
-        "/_scripts/third_party_page/packages.yml"
-    )
-
-    python_library_list = generate_package_table(
-        [p for p in resolved_packages if p["language"] == "python"]
-    )
-    js_library_list = generate_package_table(
-        [p for p in resolved_packages if p["language"] == "js"]
-    )
-    
     markdown_content = MARKDOWN.format(
-        python_library_list=python_library_list,
-        js_library_list=js_library_list,
-        langgraph_url=langgraph_url,
+        library_list="\n".join(rows), langgraph_url=langgraph_url
     )
     return markdown_content
 
 
-def main(input_file: str, output_file: str) -> None:
+def main(input_file: str, output_file: str, language: str) -> None:
     """Main function to create the third party page.
 
     Args:
@@ -151,7 +111,7 @@ def main(input_file: str, output_file: str) -> None:
     with open(input_file, "r") as f:
         resolved_packages: List[ResolvedPackage] = yaml.safe_load(f)
 
-    markdown_content = generate_markdown(resolved_packages)
+    markdown_content = generate_markdown(resolved_packages, language)
 
     # Write the markdown content to the output file
     with open(output_file, "w", encoding="utf-8") as f:
@@ -167,6 +127,12 @@ if __name__ == "__main__":
     parser.add_argument(
         "output_file", help="Path to the output file for the third party page."
     )
+    parser.add_argument(
+        "--language",
+        choices=["python", "js"],
+        default="python",
+        help="The language for which to generate the third party page. Defaults to 'python'.",
+    )
     args = parser.parse_args()
 
-    main(args.input_file, args.output_file)
+    main(args.input_file, args.output_file, args.language)

docs/_scripts/third_party_page/get_download_stats.py

@@ -11,146 +11,101 @@ import yaml
 
 
 class Package(TypedDict):
+    """A TypedDict representing a package"""
+
     name: str
     """The name of the package."""
     repo: str
     """Repository ID within github. Format is: [orgname]/[repo_name]."""
-    monorepo_path: str | None
-    """The path to the package in the monorepo. Only used for JS packages."""
     description: str
     """A brief description of what the package does."""
 
+
 class ResolvedPackage(Package):
     weekly_downloads: int | None
-    """The weekly download count of the package."""
-    language: str
-    """The language of the package. (either 'python' or 'js')"""
+
 
 HERE = pathlib.Path(__file__).parent
 PACKAGES_FILE = HERE / "packages.yml"
-PACKAGES = yaml.safe_load(PACKAGES_FILE.read_text())["packages"]
+PACKAGES = yaml.safe_load(PACKAGES_FILE.read_text())['packages']
 
-def _get_pypi_downloads(package: Package) -> int:
-    """Retrieve the weekly download count for a package from PyPIStats."""
 
-    # First check if package exists on PyPI
-    pypi_url = f"https://pypi.org/pypi/{package['name']}/json"
-    try:
-        pypi_response = requests.get(pypi_url)
-        pypi_response.raise_for_status()
-    except requests.exceptions.HTTPError:
-        raise AssertionError(f"Package {package['name']} does not exist on PyPI")
-
-    # Get first release date
-    pypi_data = pypi_response.json()
-    releases = pypi_data["releases"]
-    first_release_date = None
-    for version_releases in releases.values():
-        if version_releases:  # Some versions may be empty lists
-            upload_time = datetime.fromisoformat(version_releases[0]["upload_time"])
-            if first_release_date is None or upload_time < first_release_date:
-                first_release_date = upload_time
-
-    if first_release_date is None:
-        raise AssertionError(f"Package {package['name']} has no releases yet")
-
-    # If package was published in last 48 hours, skip download stats
-    if (datetime.now() - first_release_date).total_seconds() >= 48 * 3600:
-        url = f"https://pypistats.org/api/packages/{package['name']}/overall"
-
-        response = requests.get(url)
-        response.raise_for_status()
-        data = response.json()
-
-        sorted_data = sorted(
-            data["data"],
-            key=lambda x: datetime.strptime(x["date"], "%Y-%m-%d"),
-            reverse=True,
-        )
-
-        # Sum the last 7 days of downloads
-        return sum(entry["downloads"] for entry in sorted_data[:7])
-    else:
-        return None
-    
-def _get_npm_downloads(package: Package) -> int:
-    """Retrieve the weekly download count for a package on the npm registry."""
-
-    # Check if package exists on the npm registry
-    npm_url = f"https://registry.npmjs.org/{package['name']}"
-    try:
-        npm_response = requests.get(npm_url)
-        npm_response.raise_for_status()
-    except requests.exceptions.HTTPError:
-        raise AssertionError(f"Package {package['name']} does not exist on npm registry")
-
-    npm_data = npm_response.json()
-
-    # Retrieve the first publish date using the 'created' timestamp from the 'time' field.
-    created_str = npm_data.get("time", {}).get("created")
-    if created_str is None:
-        raise AssertionError(f"Package {package['name']} has no creation time in registry data")
-    # Remove the trailing 'Z' if present and parse the ISO format timestamp
-    first_publish_date = datetime.fromisoformat(created_str.rstrip("Z"))
-
-    # If package was published more than 48 hours ago, fetch download stats.
-    if (datetime.now() - first_publish_date).total_seconds() >= 48 * 3600:
-        stats_url = f"https://api.npmjs.org/downloads/point/last-week/{package['name']}"
-        stats_response = requests.get(stats_url)
-        stats_response.raise_for_status()
-        stats_data = stats_response.json()
-        return stats_data.get("downloads", None)
-    else:
-        return None
-
-def _get_weekly_downloads(packages: dict[str, list[Package]], fake: bool) -> list[ResolvedPackage]:
-    """Retrieve the weekly download count for a dictionary of python or js packages."""
+def _get_weekly_downloads(packages: list[Package], fake: bool) -> list[ResolvedPackage]:
+    """Retrieve the monthly download count for a list of packages from PyPIStats."""
     resolved_packages: list[ResolvedPackage] = []
 
     if fake:
         # To avoid making network requests during testing, return fake download counts
-        for language, package_list in packages.items():
-            for package in package_list:
-                resolved_packages.append(
-                    {
-                        "name": package["name"],
-                        "repo": package["repo"],
-                        "monorepo_path": package.get("monorepo_path", None),
-                        "language": language,
-                        "description": package["description"],
-                        "weekly_downloads": -12345,
-                    }
-                )
-        return resolved_packages
-
-    for language, package_list in packages.items():
-        for package in package_list:
-            if language == "python":
-                num_downloads = _get_pypi_downloads(package)
-            elif language == "js":
-                num_downloads = _get_npm_downloads(package)
-            else:
-                num_downloads = None
-            
+        for package in packages:
             resolved_packages.append(
                 {
                     "name": package["name"],
                     "repo": package["repo"],
-                    "monorepo_path": package.get("monorepo_path", None),
-                    "language": language,
+                    "weekly_downloads": -12345,
                     "description": package["description"],
-                    "weekly_downloads": num_downloads,
                 }
             )
+        return resolved_packages
+
+    for package in packages:
+        # First check if package exists on PyPI
+        pypi_url = f"https://pypi.org/pypi/{package['name']}/json"
+        try:
+            pypi_response = requests.get(pypi_url)
+            pypi_response.raise_for_status()
+        except requests.exceptions.HTTPError:
+            raise AssertionError(f"Package {package['name']} does not exist on PyPI")
+
+        # Get first release date
+        pypi_data = pypi_response.json()
+        releases = pypi_data["releases"]
+        first_release_date = None
+        for version_releases in releases.values():
+            if version_releases:  # Some versions may be empty lists
+                upload_time = datetime.fromisoformat(version_releases[0]["upload_time"])
+                if first_release_date is None or upload_time < first_release_date:
+                    first_release_date = upload_time
+
+        if first_release_date is None:
+            raise AssertionError(f"Package {package['name']} has no releases yet")
+
+        # If package was published in last 48 hours, skip download stats
+        if (datetime.now() - first_release_date).total_seconds() >= 48 * 3600:
+            url = f"https://pypistats.org/api/packages/{package['name']}/overall"
+
+            response = requests.get(url)
+            response.raise_for_status()
+            data = response.json()
+
+            sorted_data = sorted(
+                data["data"],
+                key=lambda x: datetime.strptime(x["date"], "%Y-%m-%d"),
+                reverse=True,
+            )
+
+            # Sum the last 7 days of downloads
+            num_downloads = sum(entry["downloads"] for entry in sorted_data[:7])
+        else:
+            num_downloads = None
+
+        resolved_packages.append(
+            {
+                "name": package["name"],
+                "repo": package["repo"],
+                "weekly_downloads": num_downloads,
+                "description": package["description"],
+            }
+        )
 
     return resolved_packages
 
+
+
 def main(output_file: str, fake: bool) -> None:
     """Main function to generate package download information.
 
     Args:
         output_file: Path to the output YAML file.
-        fake: If True, use fake download counts for testing purposes.
     """
     resolved_packages: list[ResolvedPackage] = _get_weekly_downloads(PACKAGES, fake)
 

docs/_scripts/third_party_page/packages.yml

@@ -1,58 +1,41 @@
 #A list of third-party packages to surface on the third-party page.
 packages:
-  python:
-    - name: "trustcall"
-      repo: "hinthornw/trustcall"
-      description: "Tenacious tool calling built on LangGraph."
-    - name: "breeze-agent"
-      repo: "andrestorres123/breeze-agent"
-      description: "A streamlined research system built inspired on STORM and built on LangGraph."
-    - name: "langgraph-supervisor"
-      repo: "langchain-ai/langgraph-supervisor-py"
-      description: "Build supervisor multi-agent systems with LangGraph."
-    - name: "langmem"
-      repo: "langchain-ai/langmem"
-      description: "Build agents that learn and adapt from interactions over time."
-    - name: "langchain-mcp-adapters"
-      repo: "langchain-ai/langchain-mcp-adapters"
-      description: "Make Anthropic Model Context Protocol (MCP) tools compatible with LangGraph agents."
-    - name: "open-deep-research"
-      repo: "langchain-ai/open_deep_research"
-      description: "Open source assistant for iterative web research and report writing."
-    - name: "langgraph-swarm"
-      repo: "langchain-ai/langgraph-swarm-py"
-      description: "Build swarm-style multi-agent systems using LangGraph."
-    - name: "delve-taxonomy-generator"
-      repo: "andrestorres123/delve"
-      description: "A taxonomy generator for unstructured data"
-    - name: "nodeology"
-      repo: "xyin-anl/Nodeology"
-      description: "Enable researcher to build scientific workflows easily with simplified interface."
-    - name: "langgraph-bigtool"
-      repo: "langchain-ai/langgraph-bigtool"
-      description: "Build LangGraph agents with large numbers of tools."
-    - name: "ai-data-science-team"
-      repo: "business-science/ai-data-science-team"
-      description: "An AI-powered data science team of agents to help you perform common data science tasks 10X faster."
-    - name: "langgraph-reflection"
-      repo: "langchain-ai/langgraph-reflection"
-      description: "LangGraph agent that runs a reflection step."
-    - name: "langgraph-codeact"
-      repo: "langchain-ai/langgraph-codeact"
-      description: "LangGraph implementation of CodeAct agent that generates and executes code instead of tool calling."
-  js:
-    - name: "@langchain/mcp-adapters"
-      repo: "langchain-ai/langchainjs"
-      description: "Make Anthropic Model Context Protocol (MCP) tools compatible with LangGraph agents."
-    - name: "@langchain/langgraph-supervisor"
-      repo: "langchain-ai/langgraphjs"
-      monorepo_path: "libs/langgraph-supervisor"
-      description: "Build supervisor multi-agent systems with LangGraph"
-    - name: "@langchain/langgraph-swarm"
-      repo: "langchain-ai/langgraphjs"
-      monorepo_path: "libs/langgraph-swarm"
-      description: "Build multi-agent swarms with LangGraph"
-    - name: "@langchain/langgraph-cua"
-      repo: "langchain-ai/langgraphjs"
-      monorepo_path: "libs/langgraph-cua"
-      description: "Build computer use agents with LangGraph"
+  - name: "trustcall"
+    repo: "hinthornw/trustcall"
+    description: "Tenacious tool calling built on LangGraph."
+  - name: "breeze-agent"
+    repo: "andrestorres123/breeze-agent"
+    description: "A streamlined research system built inspired on STORM and built on LangGraph."
+  - name: "langgraph-supervisor"
+    repo: "langchain-ai/langgraph-supervisor-py"
+    description: "Build supervisor multi-agent systems with LangGraph."
+  - name: "langmem"
+    repo: "langchain-ai/langmem"
+    description: "Build agents that learn and adapt from interactions over time."
+  - name: "langchain-mcp-adapters"
+    repo: "langchain-ai/langchain-mcp-adapters"
+    description: "Make Anthropic Model Context Protocol (MCP) tools compatible with LangGraph agents."
+  - name: "open-deep-research"
+    repo: "langchain-ai/open_deep_research"
+    description: "Open source assistant for iterative web research and report writing."
+  - name: "langgraph-swarm"
+    repo: "langchain-ai/langgraph-swarm-py"
+    description: "Build swarm-style multi-agent systems using LangGraph."
+  - name: "delve-taxonomy-generator"
+    repo: "andrestorres123/delve"
+    description: "A taxonomy generator for unstructured data"
+  - name: "nodeology"
+    repo: "xyin-anl/Nodeology"
+    description: "Enable researcher to build scientific workflows easily with simplified interface."
+  - name: "langgraph-bigtool"
+    repo: "langchain-ai/langgraph-bigtool"
+    description: "Build LangGraph agents with large numbers of tools."
+  - name: "ai-data-science-team"
+    repo: "business-science/ai-data-science-team"
+    description: "An AI-powered data science team of agents to help you perform common data science tasks 10X faster."
+  - name: "langgraph-reflection"
+    repo: "langchain-ai/langgraph-reflection"
+    description: "LangGraph agent that runs a reflection step."
+  - name: "langgraph-codeact"
+    repo: "langchain-ai/langgraph-codeact"
+    description: "LangGraph implementation of CodeAct agent that generates and executes code instead of tool calling."

docs/docs/additional-resources/index.md

@@ -1,11 +0,0 @@
-# Additional resources
-
-This section contains additional resources for LangGraph.
-
-- [Community agents](../agents/prebuilt.md): A collection of prebuilt libraries that you can use in your LangGraph applications.
-- [LangGraph Academy](https://academy.langchain.com/courses/intro-to-langgraph): A collection of courses that teach you how to use LangGraph.
-- [Case studies](../adopters.md): A collection of case studies that show how LangGraph is used in production.
-- [FAQ](../concepts/faq.md): A collection of frequently asked questions about LangGraph.
-- [llms.txt](../llms-txt-overview.md): A list of documentation files in the `llms.txt` format that allow LLMs and agents to access our documentation.
-- [LangChain Forum](https://forum.langchain.com/): A place to ask questions and get help from other LangGraph users.
-- [Troubleshooting](../troubleshooting/errors/index.md): A collection of troubleshooting guides for common issues.

docs/docs/adopters.md

@@ -8,41 +8,24 @@ This list of companies using LangGraph and their success stories is compiled fro
 | [AirTop](https://www.airtop.ai/) | Software & Technology (GenAI Native) | Browser automation for AI agents | [Case study, 2024](https://blog.langchain.dev/customers-airtop/) |
 | [AppFolio](https://www.appfolio.com/) | Real Estate | Copilot for domain-specific task | [Case study, 2024](https://blog.langchain.dev/customers-appfolio/) |
 | [Athena Intelligence](https://www.athenaintel.com/) | Software & Technology (GenAI Native) | Research & summarization | [Case study, 2024](https://blog.langchain.dev/customers-athena-intelligence/) |
-| [BlackRock](https://www.blackrock.com/) | Financial Services | Copilot for domain-specific task | [Interrupt talk, 2025](https://youtu.be/oyqeCHFM5U4?feature=shared) |
 | [Captide](https://www.captide.co/) | Software & Technology (GenAI Native) | Data extraction | [Case study, 2025](https://blog.langchain.dev/how-captide-is-redefining-equity-research-with-agentic-workflows-built-on-langgraph-and-langsmith/) |
-| [Cisco CX](https://www.cisco.com/site/us/en/services/modern-data-center/index.html?CCID=cc005911&DTID=eivtotr001480&OID=srwsas032775) | Software & Technology | Customer support | [Interrupt Talk, 2025](https://youtu.be/gPhyPRtIMn0?feature=shared) |
-| [Cisco Outshift](https://outshift.cisco.com/) | Software & Technology | DevOps | [Video story, 2025](https://www.youtube.com/watch?v=htcb-vGR_x0); [Case study, 2025](https://blog.langchain.com/cisco-outshift/); [Blog post, 2025](https://outshift.cisco.com/blog/build-react-agent-application-for-devops-tasks-using-rest-apis) |
-| [Cisco TAC](https://www.cisco.com/c/en/us/support/index.html) | Software & Technology | Customer support | [Video story, 2025](https://youtu.be/EAj0HBDGqaE?feature=shared) |
-| [City of Hope](https://www.cityofhope.org/) | Non-profit | Copilot for domain-specific task | [Video story, 2025](https://youtu.be/9ABwtK2gIZU?feature=shared) |
+| [Cisco Outshift](https://outshift.cisco.com/) | Software & Technology | DevOps | [Blog post, 2025](https://outshift.cisco.com/blog/build-react-agent-application-for-devops-tasks-using-rest-apis) |
 | [C.H. Robinson](https://www.chrobinson.com/en-us/) | Logistics | Automation | [Case study, 2025](https://blog.langchain.dev/customers-chrobinson/) |
-| [Definely](https://www.definely.com/) | Legal | Copilot for domain-specific task | [Case study, 2025](https://blog.langchain.com/customers-definely/) |
-| [Docent Pro](https://docentpro.com/) | Travel | GenAI embedded product experiences | [Case study, 2025](https://blog.langchain.com/customers-docentpro/) |
 | [Elastic](https://www.elastic.co/) | Software & Technology | Copilot for domain-specific task | [Blog post, 2025](https://www.elastic.co/blog/elastic-security-generative-ai-features) |
-| [Exa](https://exa.ai/) | Software & Technology (GenAI Native) | Search | [Case study, 2025](https://blog.langchain.com/exa/) |
 | [GitLab](https://about.gitlab.com/) | Software & Technology | Code generation | [Duo workflow docs](https://handbook.gitlab.com/handbook/engineering/architecture/design-documents/duo_workflow/) |
-| [Harmonic](https://harmonic.ai/) | Software & Technology | Search | [Case study, 2025](https://blog.langchain.com/customers-harmonic/) |
 | [Inconvo](https://inconvo.ai/?ref=blog.langchain.dev) | Software & Technology | Code generation | [Case study, 2025](https://blog.langchain.dev/customers-inconvo/) |
 | [Infor](https://infor.com/) | Software & Technology | GenAI embedded product experiences; customer support; copilot | [Case study, 2025](https://blog.langchain.dev/customers-infor/) |
-| [J.P. Morgan](https://www.jpmorganchase.com/) | Financial Services | Copilot for domain-specific task | [Interrupt talk, 2025](https://youtu.be/yMalr0jiOAc?feature=shared) |
 | [Klarna](https://www.klarna.com/) | Fintech | Copilot for domain-specific task | [Case study, 2025](https://blog.langchain.dev/customers-klarna/) |
 | [Komodo Health](https://www.komodohealth.com/) | Healthcare | Copilot for domain-specific task | [Blog post](https://www.komodohealth.com/perspectives/new-gen-ai-assistant-empowers-the-enterprise/) |
-| [LinkedIn](https://www.linkedin.com/) | Social Media | Code generation; Search & discovery | [Interrupt talk, 2025](https://youtu.be/NmblVxyBhi8?feature=shared); [Blog post, 2025](https://www.linkedin.com/blog/engineering/ai/practical-text-to-sql-for-data-analytics); [Blog post, 2024](https://www.linkedin.com/blog/engineering/generative-ai/behind-the-platform-the-journey-to-create-the-linkedin-genai-application-tech-stack) |
+| [LinkedIn](https://www.linkedin.com/) | Social Media | Code generation; Search & discovery | [Blog post, 2025](https://www.linkedin.com/blog/engineering/ai/practical-text-to-sql-for-data-analytics); [Blog post, 2024](https://www.linkedin.com/blog/engineering/generative-ai/behind-the-platform-the-journey-to-create-the-linkedin-genai-application-tech-stack) |
 | [Minimal](https://gominimal.ai/) | E-commerce | Customer support | [Case study, 2025](https://blog.langchain.dev/how-minimal-built-a-multi-agent-customer-support-system-with-langgraph-langsmith/) |
-| [Modern Treasury](https://www.moderntreasury.com/) | Fintech | GenAI embedded product experiences | [Video story, 2025](https://youtu.be/AwAiffXqaCU?feature=shared) |
-| [Monday](https://monday.com/) | Software & Technology | GenAI embedded product experiences | [Interrupt talk, 2025](https://blog.langchain.dev/how-minimal-built-a-multi-agent-customer-support-system-with-langgraph-langsmith/) |
-| [Morningstar](https://www.morningstar.com/) | Financial Services | Research & summarization | [Video story, 2025](https://youtu.be/6LidoFXCJPs?feature=shared) |
 | [OpenRecovery](https://www.openrecovery.com/) | Healthcare | Copilot for domain-specific task | [Case study, 2024](https://blog.langchain.dev/customers-openrecovery/) |
-| [Pigment](https://www.pigment.com/) | Fintech | GenAI embedded product experiences | [Video story, 2025](https://youtu.be/5JVSO2KYOmE?feature=shared) |
-| [Prosper](https://www.prosper.com/) | Fintech | Customer support | [Video story, 2025](https://youtu.be/9RFNOYtkwsc?feature=shared) |
 | [Qodo](https://www.qodo.ai/) | Software & Technology (GenAI Native) | Code generation | [Blog post, 2025](https://www.qodo.ai/blog/why-we-chose-langgraph-to-build-our-coding-agent/) |
-| [Rakuten](https://www.rakuten.com/) | E-commerce / Fintech | Copilot for domain-specific task | [Video story, 2025](https://youtu.be/gD1LIjCkuA8?feature=shared); [Blog post, 2025](https://rakuten.today/blog/from-ai-hype-to-real-world-tools-rakuten-teams-up-with-langchain.html) |
+| [Rakuten](https://www.rakuten.com/) | E-commerce / Fintech | Copilot for domain-specific task | [Blog post, 2025](https://rakuten.today/blog/from-ai-hype-to-real-world-tools-rakuten-teams-up-with-langchain.html) |
 | [Replit](https://replit.com/) | Software & Technology | Code generation | [Blog post, 2024](https://blog.langchain.dev/customers-replit/); [Breakout agent story, 2024](https://www.langchain.com/breakoutagents/replit); [Fireside chat video, 2024](https://www.youtube.com/watch?v=ViykMqljjxU) |
 | [Rexera](https://www.rexera.com/) | Real Estate (GenAI Native) | Copilot for domain-specific task | [Case study, 2024](https://blog.langchain.dev/customers-rexera/) |
-| [Abu Dhabi Government](https://www.tamm.abudhabi/) | Government | Search | [Case study, 2025](https://blog.langchain.com/customers-abu-dhabi-government/) |
 | [Tradestack](https://www.tradestack.uk/) | Software & Technology (GenAI Native) | Copilot for domain-specific task | [Case study, 2024](https://blog.langchain.dev/customers-tradestack/) |
-| [Uber](https://www.uber.com/) | Transportation | Developer productivity; Code generation | [Interrupt talk, 2025](https://youtu.be/Bugs0dVcNI8?feature=shared); [Presentation, 2024](https://dpe.org/sessions/ty-smith-adam-huda/this-year-in-ubers-ai-driven-developer-productivity-revolution/); [Video, 2024](https://www.youtube.com/watch?v=8rkA5vWUE4Y) |
-| [Unify](https://www.unifygtm.com/) | Software & Technology (GenAI Native) | Copilot for domain-specific task | [Interrupt talk, 2025](https://youtu.be/pKk-LfhujwI?feature=shared); [Blog post, 2024](https://blog.langchain.dev/unify-launches-agents-for-account-qualification-using-langgraph-and-langsmith/) |
-| [Vizient](https://www.vizientinc.com/) | Healthcare | Copilot for domain-specific task | [Video story, 2025](https://www.youtube.com/watch?v=vrjJ6NuyTWA); [Case study, 2025](https://blog.langchain.dev/p/3d2cd58c-13a5-4df9-bd84-7d54ed0ed82c/) |
+| [Uber](https://www.uber.com/) | Transportation | Developer productivity; Code generation | [Presentation, 2024](https://dpe.org/sessions/ty-smith-adam-huda/this-year-in-ubers-ai-driven-developer-productivity-revolution/); [Video, 2024](https://www.youtube.com/watch?v=8rkA5vWUE4Y) |
+| [Unify](https://www.unifygtm.com/) | Software & Technology (GenAI Native) | Copilot for domain-specific task | [Blog post, 2024](https://blog.langchain.dev/unify-launches-agents-for-account-qualification-using-langgraph-and-langsmith/) |
+| [Vizient](https://www.vizientinc.com/) | Healthcare | Copilot for domain-specific task | [Case study, 2025](https://blog.langchain.dev/p/3d2cd58c-13a5-4df9-bd84-7d54ed0ed82c/) |
 | [Vodafone](https://www.vodafone.com/) | Telecommunications | Code generation; internal search | [Case study, 2025](https://blog.langchain.dev/customers-vodafone/) |
-| [WebToon](https://www.webtoons.com/en/) | Media & Entertainment | Data extraction | [Case study, 2025](https://blog.langchain.com/customers-webtoon/) |
-| [11x](https://www.11x.ai/) | Software & Technology (GenAI Native) | Research & outreach | [Interrupt talk, 2025](https://youtu.be/fegwPmaAPQk?feature=shared) |

docs/docs/agents/agents.md

@@ -15,40 +15,23 @@ This guide shows you how to set up and use LangGraph's **prebuilt**, **reusable*
 
 Before you start this tutorial, ensure you have the following:
 
-- An [Anthropic](https://console.anthropic.com/settings/keys) API key
+- An [Anthropic](https://console.anthropic.com/settings/keys) API key 
 
 ## 1. Install dependencies
 
 If you haven't already, install LangGraph and LangChain:
 
-:::python
-
 ```
 pip install -U langgraph "langchain[anthropic]"
 ```
 
-!!! info
+!!! info 
 
-    `langchain[anthropic]` is installed so the agent can call the [model](https://python.langchain.com/docs/integrations/chat/).
-
-:::
-
-:::js
-
-```bash
-npm install @langchain/langgraph @langchain/core @langchain/anthropic
-```
-
-!!! info
-
-    `@langchain/core` `@langchain/anthropic` are installed so the agent can call the [model](https://js.langchain.com/docs/integrations/chat/).
-
-:::
+    LangChain is installed so the agent can call the [model](https://python.langchain.com/docs/integrations/chat/).
 
 ## 2. Create an agent
 
-:::python
-To create an agent, use @[`create_react_agent`][create_react_agent]:
+To create an agent, use [`create_react_agent`][langgraph.prebuilt.chat_agent_executor.create_react_agent]:
 
 ```python
 from langgraph.prebuilt import create_react_agent
@@ -69,56 +52,13 @@ agent.invoke(
 )
 ```
 
-1. Define a tool for the agent to use. Tools can be defined as vanilla Python functions. For more advanced tool usage and customization, check the [tools](../how-tos/tool-calling.md) page.
+1. Define a tool for the agent to use. Tools can be defined as vanilla Python functions. For more advanced tool usage and customization, check the [tools](./tools.md) page.
 2. Provide a language model for the agent to use. To learn more about configuring language models for the agents, check the [models](./models.md) page.
 3. Provide a list of tools for the model to use.
 4. Provide a system prompt (instructions) to the language model used by the agent.
-   :::
-
-:::js
-To create an agent, use [`createReactAgent`](https://langchain-ai.github.io/langgraphjs/reference/functions/langgraph_prebuilt.createReactAgent.html):
-
-```typescript
-import { ChatAnthropic } from "@langchain/anthropic";
-import { createReactAgent } from "@langchain/langgraph/prebuilt";
-import { tool } from "@langchain/core/tools";
-import { z } from "zod";
-
-const getWeather = tool(
-  // (1)!
-  async ({ city }) => {
-    return `It's always sunny in ${city}!`;
-  },
-  {
-    name: "get_weather",
-    description: "Get weather for a given city.",
-    schema: z.object({
-      city: z.string().describe("The city to get weather for"),
-    }),
-  }
-);
-
-const agent = createReactAgent({
-  llm: new ChatAnthropic({ model: "anthropic:claude-3-5-sonnet-latest" }), // (2)!
-  tools: [getWeather], // (3)!
-  stateModifier: "You are a helpful assistant", // (4)!
-});
-
-// Run the agent
-await agent.invoke({
-  messages: [{ role: "user", content: "what is the weather in sf" }],
-});
-```
-
-1. Define a tool for the agent to use. Tools can be defined using the `tool` function. For more advanced tool usage and customization, check the [tools](./tools.md) page.
-2. Provide a language model for the agent to use. To learn more about configuring language models for the agents, check the [models](./models.md) page.
-3. Provide a list of tools for the model to use.
-4. Provide a system prompt (instructions) to the language model used by the agent.
-   :::
 
 ## 3. Configure an LLM
 
-:::python
 To configure an LLM with specific parameters, such as temperature, use [init_chat_model](https://python.langchain.com/api_reference/langchain/chat_models/langchain.chat_models.base.init_chat_model.html):
 
 ```python
@@ -139,45 +79,19 @@ agent = create_react_agent(
 )
 ```
 
-:::
-
-:::js
-To configure an LLM with specific parameters, such as temperature, use a model instance:
-
-```typescript
-import { ChatAnthropic } from "@langchain/anthropic";
-import { createReactAgent } from "@langchain/langgraph/prebuilt";
-
-// highlight-next-line
-const model = new ChatAnthropic({
-  model: "claude-3-5-sonnet-latest",
-  // highlight-next-line
-  temperature: 0,
-});
-
-const agent = createReactAgent({
-  // highlight-next-line
-  llm: model,
-  tools: [getWeather],
-});
-```
-
-:::
-
 For more information on how to configure LLMs, see [Models](./models.md).
 
 ## 4. Add a custom prompt
 
 Prompts instruct the LLM how to behave. Add one of the following types of prompts:
 
-- **Static**: A string is interpreted as a **system message**.
-- **Dynamic**: A list of messages generated at **runtime**, based on input or configuration.
+* **Static**: A string is interpreted as a **system message**.
+* **Dynamic**: A list of messages generated at **runtime**, based on input or configuration.
 
 === "Static prompt"
 
     Define a fixed prompt string or list of messages:
 
-    :::python
     ```python
     from langgraph.prebuilt import create_react_agent
 
@@ -193,30 +107,9 @@ Prompts instruct the LLM how to behave. Add one of the following types of prompt
         {"messages": [{"role": "user", "content": "what is the weather in sf"}]}
     )
     ```
-    :::
-
-    :::js
-    ```typescript
-    import { createReactAgent } from "@langchain/langgraph/prebuilt";
-    import { ChatAnthropic } from "@langchain/anthropic";
-
-    const agent = createReactAgent({
-      llm: new ChatAnthropic({ model: "anthropic:claude-3-5-sonnet-latest" }),
-      tools: [getWeather],
-      // A static prompt that never changes
-      // highlight-next-line
-      stateModifier: "Never answer questions about the weather."
-    });
-
-    await agent.invoke({
-      messages: [{ role: "user", content: "what is the weather in sf" }]
-    });
-    ```
-    :::
 
 === "Dynamic prompt"
 
-    :::python
     Define a function that returns a message list based on the agent's state and configuration:
 
     ```python
@@ -251,52 +144,12 @@ Prompts instruct the LLM how to behave. Add one of the following types of prompt
         - Internal agent state updated during a multi-step reasoning process (using `state`).
 
         Dynamic prompts can be defined as functions that take `state` and `config` and return a list of messages to send to the LLM.
-    :::
-
-    :::js
-    Define a function that returns messages based on the agent's state and configuration:
-
-    ```typescript
-    import { type BaseMessageLike } from "@langchain/core/messages";
-    import { type RunnableConfig } from "@langchain/core/runnables";
-    import { createReactAgent } from "@langchain/langgraph/prebuilt";
-
-    // highlight-next-line
-    const dynamicPrompt = (state: { messages: BaseMessageLike[] }, config: RunnableConfig): BaseMessageLike[] => {  // (1)!
-      const userName = config.configurable?.user_name;
-      const systemMsg = `You are a helpful assistant. Address the user as ${userName}.`;
-      return [{ role: "system", content: systemMsg }, ...state.messages];
-    };
-
-    const agent = createReactAgent({
-      llm: "anthropic:claude-3-5-sonnet-latest",
-      tools: [getWeather],
-      // highlight-next-line
-      stateModifier: dynamicPrompt
-    });
-
-    await agent.invoke(
-      { messages: [{ role: "user", content: "what is the weather in sf" }] },
-      // highlight-next-line
-      { configurable: { user_name: "John Smith" } }
-    );
-    ```
-
-    1. Dynamic prompts allow including non-message [context](./context.md) when constructing an input to the LLM, such as:
-
-        - Information passed at runtime, like a `user_id` or API credentials (using `config`).
-        - Internal agent state updated during a multi-step reasoning process (using `state`).
-
-        Dynamic prompts can be defined as functions that take `state` and `config` and return a list of messages to send to the LLM.
-    :::
 
 For more information, see [Context](./context.md).
 
 ## 5. Add memory
 
-To allow multi-turn conversations with an agent, you need to enable [persistence](../concepts/persistence.md) by providing a checkpointer when creating an agent. At runtime, you need to provide a config containing `thread_id` — a unique identifier for the conversation (session):
-
-:::python
+To allow multi-turn conversations with an agent, you need to enable [persistence](../concepts/persistence.md) by providing a `checkpointer` when creating an agent. At runtime, you need to provide a config containing `thread_id` — a unique identifier for the conversation (session):
 
 ```python
 from langgraph.prebuilt import create_react_agent
@@ -329,50 +182,8 @@ ny_response = agent.invoke(
 
 1. `checkpointer` allows the agent to store its state at every step in the tool calling loop. This enables [short-term memory](../how-tos/memory/add-memory.md#add-short-term-memory) and [human-in-the-loop](../concepts/human_in_the_loop.md) capabilities.
 2. Pass configuration with `thread_id` to be able to resume the same conversation on future agent invocations.
-   :::
 
-:::js
-
-```typescript
-import { createReactAgent } from "@langchain/langgraph/prebuilt";
-import { MemorySaver } from "@langchain/langgraph";
-
-// highlight-next-line
-const checkpointer = new MemorySaver();
-
-const agent = createReactAgent({
-  llm: "anthropic:claude-3-5-sonnet-latest",
-  tools: [getWeather],
-  // highlight-next-line
-  checkpointSaver: checkpointer, // (1)!
-});
-
-// Run the agent
-// highlight-next-line
-const config = { configurable: { thread_id: "1" } };
-const sfResponse = await agent.invoke(
-  { messages: [{ role: "user", content: "what is the weather in sf" }] },
-  // highlight-next-line
-  config // (2)!
-);
-const nyResponse = await agent.invoke(
-  { messages: [{ role: "user", content: "what about new york?" }] },
-  // highlight-next-line
-  config
-);
-```
-
-1. `checkpointSaver` allows the agent to store its state at every step in the tool calling loop. This enables [short-term memory](../how-tos/memory/add-memory.md#add-short-term-memory) and [human-in-the-loop](../concepts/human_in_the_loop.md) capabilities.
-2. Pass configuration with `thread_id` to be able to resume the same conversation on future agent invocations.
-   :::
-
-:::python
 When you enable the checkpointer, it stores agent state at every step in the provided checkpointer database (or in memory, if using `InMemorySaver`).
-:::
-
-:::js
-When you enable the checkpointer, it stores agent state at every step in the provided checkpointer database (or in memory, if using `MemorySaver`).
-:::
 
 Note that in the above example, when the agent is invoked the second time with the same `thread_id`, the original message history from the first conversation is automatically included, together with the new user input.
 
@@ -380,7 +191,6 @@ For more information, see [Memory](../how-tos/memory/add-memory.md).
 
 ## 6. Configure structured output
 
-:::python
 To produce structured responses conforming to a schema, use the `response_format` parameter. The schema can be defined with a `Pydantic` model or `TypedDict`. The result will be accessible via the `structured_response` field.
 
 ```python
@@ -405,43 +215,9 @@ response = agent.invoke(
 response["structured_response"]
 ```
 
-1.  When `response_format` is provided, a separate step is added at the end of the agent loop: agent message history is passed to an LLM with structured output to generate a structured response.
+1. When `response_format` is provided, a separate step is added at the end of the agent loop: agent message history is passed to an LLM with structured output to generate a structured response.
 
-        To provide a system prompt to this LLM, use a tuple `(prompt, schema)`, e.g., `response_format=(prompt, WeatherResponse)`.
-
-    :::
-
-:::js
-To produce structured responses conforming to a schema, use the `responseFormat` parameter. The schema can be defined with a `Zod` schema. The result will be accessible via the `structuredResponse` field.
-
-```typescript
-import { z } from "zod";
-import { createReactAgent } from "@langchain/langgraph/prebuilt";
-
-const WeatherResponse = z.object({
-  conditions: z.string(),
-});
-
-const agent = createReactAgent({
-  llm: "anthropic:claude-3-5-sonnet-latest",
-  tools: [getWeather],
-  // highlight-next-line
-  responseFormat: WeatherResponse, // (1)!
-});
-
-const response = await agent.invoke({
-  messages: [{ role: "user", content: "what is the weather in sf" }],
-});
-
-// highlight-next-line
-response.structuredResponse;
-```
-
-1.  When `responseFormat` is provided, a separate step is added at the end of the agent loop: agent message history is passed to an LLM with structured output to generate a structured response.
-
-        To provide a system prompt to this LLM, use an object `{ prompt, schema }`, e.g., `responseFormat: { prompt, schema: WeatherResponse }`.
-
-    :::
+    To provide a system prompt to this LLM, use a tuple `(prompt, schema)`, e.g., `response_format=(prompt, WeatherResponse)`.
 
 !!! Note "LLM post-processing"
 

docs/docs/agents/context.md

@@ -1,84 +1,67 @@
 # Context
 
-**Context engineering** is the practice of building dynamic systems that provide the right information and tools, in the right format, so that an AI application can accomplish a task. Context can be characterized along two key dimensions:
+**Context engineering** is the practice of building dynamic systems that provide the right information and tools, in the right format, so that a language model can plausibly accomplish a task.
 
-1. By **mutability**:
-    - **Static context**: Immutable data that doesn't change during execution (e.g., user metadata, database connections, tools)
-    - **Dynamic context**: Mutable data that evolves as the application runs (e.g., conversation history, intermediate results, tool call observations)
-2. By **lifetime**:
-    - **Runtime context**: Data scoped to a single run or invocation
-    - **Cross-conversation context**: Data that persists across multiple conversations or sessions
+Context includes *any* data outside the message list that can shape behavior. This can be:
 
-!!! tip "Runtime context vs LLM context"
+- Information passed at runtime, like a `user_id` or API credentials.
+- Internal state updated during a multi-step reasoning process.
+- Persistent memory or facts from previous interactions.
 
-    Runtime context refers to local context: data and dependencies your code needs to run. It does **not** refer to:
+LangGraph provides **three** primary ways to supply context:
 
-    * The LLM context, which is the data passed into the LLM's prompt.
-    * The "context window", which is the maximum number of tokens that can be passed to the LLM.
+| Type                                                                         | Description                                   | Mutable? | Lifetime                |
+|------------------------------------------------------------------------------|-----------------------------------------------|----------|-------------------------|
+| [**Config**](#config-static-context)                                         | data passed at the start of a run             | ❌        | per run                 |
+| [**Short-term memory (State)**](#short-term-memory-mutable-context)          | dynamic data that can change during execution | ✅        | per run or conversation |
+| [**Long-term memory (Store)**](#long-term-memory-cross-conversation-context) | data that can be shared between conversations | ✅        | across conversations    |
 
-    Runtime context can be used to optimize the LLM context. For example, you can use user metadata
-    in the runtime context to fetch user preferences and feed them into the context window.
+## Provide runtime context
 
-LangGraph provides three ways to manage context, which combines the mutability and lifetime dimensions:
+### Config (static context)
 
-:::python
+Config is for immutable data like user metadata or API keys. Use
+when you have values that don't change mid-run.
 
-| Context type                                                                                | Description                                            | Mutability | Lifetime           | Access method                           |
-| ------------------------------------------------------------------------------------------- | ------------------------------------------------------ | ---------- | ------------------ | --------------------------------------- |
-| [**Static runtime context**](#static-runtime-context)                                       | User metadata, tools, db connections passed at startup | Static     | Single run         | `context` argument to `invoke`/`stream` |
-| [**Dynamic runtime context (state)**](#dynamic-runtime-context-state)                       | Mutable data that evolves during a single run          | Dynamic    | Single run         | LangGraph state object                  |
-| [**Dynamic cross-conversation context (store)**](#dynamic-cross-conversation-context-store) | Persistent data shared across conversations            | Dynamic    | Cross-conversation | LangGraph store                         |
-
-## Static runtime context
-
-**Static runtime context** represents immutable data like user metadata, tools, and database connections that are passed to an application at the start of a run via the `context` argument to `invoke`/`stream`. This data does not change during execution.
-
-!!! version-added "New in LangGraph v0.6: `context` replaces `config['configurable']`"
-
-    Runtime context is now passed to the `context` argument of `invoke`/`stream`,
-    which replaces the previous pattern of passing application configuration to `config['configurable']`.
+Specify configuration using a key called **"configurable"** which is reserved
+for this purpose:
 
 ```python
-@dataclass
-class ContextSchema:
-    user_name: str
-
 graph.invoke( # (1)!
     {"messages": [{"role": "user", "content": "hi!"}]}, # (2)!
     # highlight-next-line
-    context={"user_name": "John Smith"} # (3)!
+    config={"configurable": {"user_id": "user_123"}} # (3)!
 )
 ```
 
 1. This is the invocation of the agent or graph. The `invoke` method runs the underlying graph with the provided input.
 2. This example uses messages as an input, which is common, but your application may use different input structures.
-3. This is where you pass the runtime data. The `context` parameter allows you to provide additional dependencies that the agent can use during its execution.
+3. This is where you pass the configuration data. The `config` parameter allows you to provide additional context that the agent can use during its execution.
 
 === "Agent prompt"
 
     ```python
     from langchain_core.messages import AnyMessage
-    from langgraph.runtime import get_runtime
+    from langchain_core.runnables import RunnableConfig
     from langgraph.prebuilt.chat_agent_executor import AgentState
     from langgraph.prebuilt import create_react_agent
 
     # highlight-next-line
-    def prompt(state: AgentState) -> list[AnyMessage]:
-        runtime = get_runtime(ContextSchema)
-        system_msg = f"You are a helpful assistant. Address the user as {runtime.context.user_name}."
+    def prompt(state: AgentState, config: RunnableConfig) -> list[AnyMessage]:
+        user_name = config["configurable"].get("user_name")
+        system_msg = f"You are a helpful assistant. Address the user as {user_name}."
         return [{"role": "system", "content": system_msg}] + state["messages"]
 
     agent = create_react_agent(
         model="anthropic:claude-3-7-sonnet-latest",
         tools=[get_weather],
-        prompt=prompt,
-        context_schema=ContextSchema
+        prompt=prompt
     )
 
     agent.invoke(
         {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
         # highlight-next-line
-        context={"user_name": "John Smith"}
+        config={"configurable": {"user_name": "John Smith"}}
     )
     ```
 
@@ -87,11 +70,11 @@ graph.invoke( # (1)!
 === "Workflow node"
 
     ```python
-    from langgraph.runtime import Runtime
+    from langchain_core.runnables import RunnableConfig
 
     # highlight-next-line
-    def node(state: State, config: Runtime[ContextSchema]):
-        user_name = runtime.context.user_name
+    def node(state: State, config: RunnableConfig):
+        user_name = config["configurable"].get("user_name")
         ...
     ```
 
@@ -100,55 +83,21 @@ graph.invoke( # (1)!
 === "In a tool"
 
     ```python
-    from langgraph.runtime import get_runtime
+    from langchain_core.runnables import RunnableConfig
 
     @tool
     # highlight-next-line
-    def get_user_email() -> str:
+    def get_user_info(config: RunnableConfig) -> str:
         """Retrieve user information based on user ID."""
-        # simulate fetching user info from a database
-        runtime = get_runtime(ContextSchema)
-        email = get_user_email_from_db(runtime.context.user_name)
-        return email
+        user_id = config["configurable"].get("user_id")
+        return "User is John Smith" if user_id == "user_123" else "Unknown user"
     ```
 
     See the [tool calling guide](../how-tos/tool-calling.md#configuration) for details.
 
-!!! tip
+### Short-term memory (mutable context)
 
-    The `Runtime` object can be used to access static context and other utilities like the active store and stream writer.
-    See the [Runtime][langgraph.runtime.Runtime] documentation for details.
-
-:::
-
-:::js
-
-| Context type                                                                                | Description                                   | Mutability | Lifetime           |
-| ------------------------------------------------------------------------------------------- | --------------------------------------------- | ---------- | ------------------ |
-| [**Config**](#config-static-context)                                                        | data passed at the start of a run             | Static     | Single run         |
-| [**Dynamic runtime context (state)**](#dynamic-runtime-context-state)                       | Mutable data that evolves during a single run | Dynamic    | Single run         |
-| [**Dynamic cross-conversation context (store)**](#dynamic-cross-conversation-context-store) | Persistent data shared across conversations   | Dynamic    | Cross-conversation |
-
-## Config (static context)
-
-Config is for immutable data like user metadata or API keys. Use this when you have values that don't change mid-run.
-
-Specify configuration using a key called **"configurable"** which is reserved for this purpose.
-
-```typescript
-await graph.invoke(
-  // (1)!
-  { messages: [{ role: "user", content: "hi!" }] }, // (2)!
-  // highlight-next-line
-  { configurable: { user_id: "user_123" } } // (3)!
-);
-```
-
-:::
-
-## Dynamic runtime context (state)
-
-**Dynamic runtime context** represents mutable data that can evolve during a single run and is managed through the LangGraph state object. This includes conversation history, intermediate results, and values derived from tools or LLM outputs. In LangGraph, the state object acts as [short-term memory](../concepts/memory.md) during a run.
+State acts as [short-term memory](../concepts/memory.md) during a run. It holds dynamic data that can evolve during execution, such as values derived from tools or LLM outputs.
 
 === "In an agent"
 
@@ -156,7 +105,6 @@ await graph.invoke(
 
     State can also be accessed by the agent's **tools**, which can read or update the state as needed. See [tool calling guide](../how-tos/tool-calling.md#short-term-memory) for details.
 
-    :::python
     ```python
     from langchain_core.messages import AnyMessage
     from langchain_core.runnables import RunnableConfig
@@ -191,51 +139,10 @@ await graph.invoke(
 
     1. Define a custom state schema that extends `AgentState` or `MessagesState`.
     2. Pass the custom state schema to the agent. This allows the agent to access and modify the state during execution.
-    :::
 
-    :::js
-    ```typescript
-    import type { BaseMessage } from "@langchain/core/messages";
-    import { createReactAgent } from "@langchain/langgraph/prebuilt";
-    import { MessagesZodState } from "@langchain/langgraph";
-    import { z } from "zod";
-
-    // highlight-next-line
-    const CustomState = z.object({ // (1)!
-      messages: MessagesZodState.shape.messages,
-      userName: z.string(),
-    });
-
-    const prompt = (
-      // highlight-next-line
-      state: z.infer<typeof CustomState>
-    ): BaseMessage[] => {
-      const userName = state.userName;
-      const systemMsg = `You are a helpful assistant. User's name is ${userName}`;
-      return [{ role: "system", content: systemMsg }, ...state.messages];
-    };
-
-    const agent = createReactAgent({
-      llm: model,
-      tools: [...],
-      // highlight-next-line
-      stateSchema: CustomState, // (2)!
-      stateModifier: prompt,
-    });
-
-    await agent.invoke({
-      messages: [{ role: "user", content: "hi!" }],
-      userName: "John Smith",
-    });
-    ```
-
-    1. Define a custom state schema that extends `MessagesZodState` or creates a new schema.
-    2. Pass the custom state schema to the agent. This allows the agent to access and modify the state during execution.
-    :::
 
 === "In a workflow"
 
-    :::python
     ```python
     from typing_extensions import TypedDict
     from langchain_core.messages import AnyMessage
@@ -260,49 +167,18 @@ await graph.invoke(
     builder.set_entry_point("node")
     graph = builder.compile()
     ```
-
+    
     1. Define a custom state
     2. Access the state in any node or tool
     3. The Graph API is designed to work as easily as possible with state. The return value of a node represents a requested update to the state.
-    :::
 
-    :::js
-    ```typescript
-    import type { BaseMessage } from "@langchain/core/messages";
-    import { StateGraph, MessagesZodState, START } from "@langchain/langgraph";
-    import { z } from "zod";
-
-    // highlight-next-line
-    const CustomState = z.object({ // (1)!
-      messages: MessagesZodState.shape.messages,
-      extraField: z.number(),
-    });
-
-    const builder = new StateGraph(CustomState)
-      .addNode("node", async (state) => { // (2)!
-        const messages = state.messages;
-        // ...
-        return { // (3)!
-          // highlight-next-line
-          extraField: state.extraField + 1,
-        };
-      })
-      .addEdge(START, "node");
-
-    const graph = builder.compile();
-    ```
-
-    1. Define a custom state
-    2. Access the state in any node or tool
-    3. The Graph API is designed to work as easily as possible with state. The return value of a node represents a requested update to the state.
-    :::
 
 !!! tip "Turning on memory"
 
     Please see the [memory guide](../how-tos/memory/add-memory.md) for more details on how to enable memory. This is a powerful feature that allows you to persist the agent's state across multiple invocations. Otherwise, the state is scoped only to a single run.
 
-## Dynamic cross-conversation context (store)
+### Long-term memory (cross-conversation context)
 
-**Dynamic cross-conversation context** represents persistent, mutable data that spans across multiple conversations or sessions and is managed through the LangGraph store. This includes user profiles, preferences, and historical interactions. The LangGraph store acts as [long-term memory](../concepts/memory.md#long-term-memory) across multiple runs. This can be used to read or update persistent facts (e.g., user profiles, preferences, prior interactions).
+For context that spans *across* conversations or sessions, LangGraph allows access to **long-term memory** via a `store`. This can be used to read or update persistent facts (e.g., user profiles, preferences, prior interactions). 
 
-For more information, see the [Memory guide](../how-tos/memory/add-memory.md).
+For more information, see the [Memory guide](../how-tos/memory/add-memory.md).

docs/docs/agents/evals.md

@@ -11,62 +11,25 @@ hide:
 
 To evaluate your agent's performance you can use `LangSmith` [evaluations](https://docs.smith.langchain.com/evaluation). You would need to first define an evaluator function to judge the results from an agent, such as final outputs or trajectory. Depending on your evaluation technique, this may or may not involve a reference output:
 
-:::python
-
 ```python
 def evaluator(*, outputs: dict, reference_outputs: dict):
     # compare agent outputs against reference outputs
     output_messages = outputs["messages"]
-    reference_messages = reference_outputs["messages"]
+    reference_messages = reference["messages"]
     score = compare_messages(output_messages, reference_messages)
     return {"key": "evaluator_score", "score": score}
 ```
 
-:::
-
-:::js
-
-```typescript
-type EvaluatorParams = {
-  outputs: Record<string, any>;
-  referenceOutputs: Record<string, any>;
-};
-
-function evaluator({ outputs, referenceOutputs }: EvaluatorParams) {
-  // compare agent outputs against reference outputs
-  const outputMessages = outputs.messages;
-  const referenceMessages = referenceOutputs.messages;
-  const score = compareMessages(outputMessages, referenceMessages);
-  return { key: "evaluator_score", score: score };
-}
-```
-
-:::
-
 To get started, you can use prebuilt evaluators from `AgentEvals` package:
 
-:::python
-
 ```bash
 pip install -U agentevals
 ```
 
-:::
-
-:::js
-
-```bash
-npm install agentevals
-```
-
-:::
-
 ## Create evaluator
 
 A common way to evaluate agent performance is by comparing its trajectory (the order in which it calls its tools) against a reference trajectory:
 
-:::python
-
 ```python
 import json
 # highlight-next-line
@@ -117,72 +80,15 @@ result = evaluator(
 )
 ```
 
-:::
-
-:::js
-
-```typescript
-import { createTrajectoryMatchEvaluator } from "agentevals/trajectory/match";
-
-const outputs = [
-  {
-    role: "assistant",
-    tool_calls: [
-      {
-        function: {
-          name: "get_weather",
-          arguments: JSON.stringify({ city: "san francisco" }),
-        },
-      },
-      {
-        function: {
-          name: "get_directions",
-          arguments: JSON.stringify({ destination: "presidio" }),
-        },
-      },
-    ],
-  },
-];
-
-const referenceOutputs = [
-  {
-    role: "assistant",
-    tool_calls: [
-      {
-        function: {
-          name: "get_weather",
-          arguments: JSON.stringify({ city: "san francisco" }),
-        },
-      },
-    ],
-  },
-];
-
-// Create the evaluator
-const evaluator = createTrajectoryMatchEvaluator({
-  // Specify how the trajectories will be compared. `superset` will accept output trajectory as valid if it's a superset of the reference one. Other options include: strict, unordered and subset
-  trajectoryMatchMode: "superset", // (1)!
-});
-
-// Run the evaluator
-const result = evaluator({
-  outputs: outputs,
-  referenceOutputs: referenceOutputs,
-});
-```
-
-:::
-
 1. Specify how the trajectories will be compared. `superset` will accept output trajectory as valid if it's a superset of the reference one. Other options include: [strict](https://github.com/langchain-ai/agentevals?tab=readme-ov-file#strict-match), [unordered](https://github.com/langchain-ai/agentevals?tab=readme-ov-file#unordered-match) and [subset](https://github.com/langchain-ai/agentevals?tab=readme-ov-file#subset-and-superset-match)
 
+
 As a next step, learn more about how to [customize trajectory match evaluator](https://github.com/langchain-ai/agentevals?tab=readme-ov-file#agent-trajectory-match).
 
 ### LLM-as-a-judge
 
 You can use LLM-as-a-judge evaluator that uses an LLM to compare the trajectory against the reference outputs and output a score:
 
-:::python
-
 ```python
 import json
 from agentevals.trajectory.llm import (
@@ -197,24 +103,6 @@ evaluator = create_trajectory_llm_as_judge(
 )
 ```
 
-:::
-
-:::js
-
-```typescript
-import {
-  createTrajectoryLlmAsJudge,
-  TRAJECTORY_ACCURACY_PROMPT_WITH_REFERENCE,
-} from "agentevals/trajectory/llm";
-
-const evaluator = createTrajectoryLlmAsJudge({
-  prompt: TRAJECTORY_ACCURACY_PROMPT_WITH_REFERENCE,
-  model: "openai:o3-mini",
-});
-```
-
-:::
-
 ## Run evaluator
 
 To run an evaluator, you will first need to create a [LangSmith dataset](https://docs.smith.langchain.com/evaluation/concepts#datasets). To use the prebuilt AgentEvals evaluators, you will need a dataset with the following schema:
@@ -222,8 +110,6 @@ To run an evaluator, you will first need to create a [LangSmith dataset](https:/
 - **input**: `{"messages": [...]}` input messages to call the agent with.
 - **output**: `{"messages": [...]}` expected message history in the agent output. For trajectory evaluation, you can choose to keep only assistant messages.
 
-:::python
-
 ```python
 from langsmith import Client
 from langgraph.prebuilt import create_react_agent
@@ -239,27 +125,4 @@ experiment_results = client.evaluate(
     data="<Name of your dataset>",
     evaluators=[evaluator]
 )
-```
-
-:::
-
-:::js
-
-```typescript
-import { Client } from "langsmith";
-import { createReactAgent } from "@langchain/langgraph/prebuilt";
-import { createTrajectoryMatchEvaluator } from "agentevals/trajectory/match";
-
-const client = new Client();
-const agent = createReactAgent({...});
-const evaluator = createTrajectoryMatchEvaluator({...});
-
-const experimentResults = await client.evaluate(
-    (inputs) => agent.invoke(inputs),
-    // replace with your dataset name
-    { data: "<Name of your dataset>" },
-    { evaluators: [evaluator] }
-);
-```
-
-:::
+```

docs/docs/agents/mcp.md

@@ -13,29 +13,17 @@ hide:
 
 ![MCP](./assets/mcp.png)
 
-:::python
 Install the `langchain-mcp-adapters` library to use MCP tools in LangGraph:
 
 ```bash
 pip install langchain-mcp-adapters
 ```
 
-:::
-
-:::js
-Install the `@langchain/mcp-adapters` library to use MCP tools in LangGraph:
-
-```bash
-npm install langchain-mcp-adapters
-```
-
-:::
-
 ## Use MCP tools
 
-:::python
 The `langchain-mcp-adapters` package enables agents to use tools defined across one or more MCP servers.
 
+
 === "In an agent"
 
     ```python title="Agent using tools defined on MCP servers"
@@ -76,16 +64,14 @@ The `langchain-mcp-adapters` package enables agents to use tools defined across
 
 === "In a workflow"
 
-    ```python title="Workflow using MCP tools with ToolNode"
+    ```python
     from langchain_mcp_adapters.client import MultiServerMCPClient
+    from langgraph.graph import StateGraph, MessagesState, START
+    from langgraph.prebuilt import ToolNode, tools_condition
+
     from langchain.chat_models import init_chat_model
-    from langgraph.graph import StateGraph, MessagesState, START, END
-    from langgraph.prebuilt import ToolNode
+    model = init_chat_model("openai:gpt-4.1")
 
-    # Initialize the model
-    model = init_chat_model("anthropic:claude-3-5-sonnet-latest")
-
-    # Set up MCP client
     client = MultiServerMCPClient(
         {
             "math": {
@@ -103,154 +89,28 @@ The `langchain-mcp-adapters` package enables agents to use tools defined across
     )
     tools = await client.get_tools()
 
-    # Bind tools to model
-    model_with_tools = model.bind_tools(tools)
+    def call_model(state: MessagesState):
+        response = model.bind_tools(tools).invoke(state["messages"])
+        return {"messages": response}
 
-    # Create ToolNode
-    tool_node = ToolNode(tools)
-
-    def should_continue(state: MessagesState):
-        messages = state["messages"]
-        last_message = messages[-1]
-        if last_message.tool_calls:
-            return "tools"
-        return END
-
-    # Define call_model function
-    async def call_model(state: MessagesState):
-        messages = state["messages"]
-        response = await model_with_tools.ainvoke(messages)
-        return {"messages": [response]}
-
-    # Build the graph
     builder = StateGraph(MessagesState)
-    builder.add_node("call_model", call_model)
-    builder.add_node("tools", tool_node)
-
+    builder.add_node(call_model)
+    builder.add_node(ToolNode(tools))
     builder.add_edge(START, "call_model")
     builder.add_conditional_edges(
         "call_model",
-        should_continue,
+        tools_condition,
     )
     builder.add_edge("tools", "call_model")
-
-    # Compile the graph
     graph = builder.compile()
-
-    # Test the graph
-    math_response = await graph.ainvoke(
-        {"messages": [{"role": "user", "content": "what's (3 + 5) x 12?"}]}
-    )
-    weather_response = await graph.ainvoke(
-        {"messages": [{"role": "user", "content": "what is the weather in nyc?"}]}
-    )
+    math_response = await graph.ainvoke({"messages": "what's (3 + 5) x 12?"})
+    weather_response = await graph.ainvoke({"messages": "what is the weather in nyc?"})
     ```
 
-:::
 
-:::js
-The `@langchain/mcp-adapters` package enables agents to use tools defined across one or more MCP servers.
-
-=== "In an agent"
-
-    ```typescript title="Agent using tools defined on MCP servers"
-    // highlight-next-line
-    import { MultiServerMCPClient } from "langchain-mcp-adapters/client";
-    import { ChatAnthropic } from "@langchain/langgraph/prebuilt";
-    import { createReactAgent } from "@langchain/langgraph/prebuilt";
-
-    // highlight-next-line
-    const client = new MultiServerMCPClient({
-      math: {
-        command: "node",
-        // Replace with absolute path to your math_server.js file
-        args: ["/path/to/math_server.js"],
-        transport: "stdio",
-      },
-      weather: {
-        // Ensure you start your weather server on port 8000
-        url: "http://localhost:8000/mcp",
-        transport: "streamable_http",
-      },
-    });
-
-    // highlight-next-line
-    const tools = await client.getTools();
-    const agent = createReactAgent({
-      llm: new ChatAnthropic({ model: "claude-3-7-sonnet-latest" }),
-      // highlight-next-line
-      tools,
-    });
-
-    const mathResponse = await agent.invoke({
-      messages: [{ role: "user", content: "what's (3 + 5) x 12?" }],
-    });
-
-    const weatherResponse = await agent.invoke({
-      messages: [{ role: "user", content: "what is the weather in nyc?" }],
-    });
-    ```
-
-=== "In a workflow"
-
-    ```typescript
-    import { MultiServerMCPClient } from "langchain-mcp-adapters/client";
-    import { StateGraph, MessagesZodState, START } from "@langchain/langgraph";
-    import { ToolNode } from "@langchain/langgraph/prebuilt";
-    import { ChatOpenAI } from "@langchain/openai";
-    import { AIMessage } from "@langchain/core/messages";
-    import { z } from "zod";
-
-    const model = new ChatOpenAI({ model: "gpt-4" });
-
-    const client = new MultiServerMCPClient({
-      math: {
-        command: "node",
-        // Make sure to update to the full absolute path to your math_server.js file
-        args: ["./examples/math_server.js"],
-        transport: "stdio",
-      },
-      weather: {
-        // make sure you start your weather server on port 8000
-        url: "http://localhost:8000/mcp/",
-        transport: "streamable_http",
-      },
-    });
-
-    const tools = await client.getTools();
-
-    const builder = new StateGraph(MessagesZodState)
-      .addNode("callModel", async (state) => {
-        const response = await model.bindTools(tools).invoke(state.messages);
-        return { messages: [response] };
-      })
-      .addNode("tools", new ToolNode(tools))
-      .addEdge(START, "callModel")
-      .addConditionalEdges("callModel", (state) => {
-        const lastMessage = state.messages.at(-1) as AIMessage | undefined;
-        if (!lastMessage?.tool_calls?.length) {
-          return "__end__";
-        }
-        return "tools";
-      })
-      .addEdge("tools", "callModel");
-
-    const graph = builder.compile();
-
-    const mathResponse = await graph.invoke({
-      messages: [{ role: "user", content: "what's (3 + 5) x 12?" }],
-    });
-
-    const weatherResponse = await graph.invoke({
-      messages: [{ role: "user", content: "what is the weather in nyc?" }],
-    });
-    ```
-
-:::
 
 ## Custom MCP servers
 
-:::python
 To create your own MCP servers, you can use the `mcp` library. This library provides a simple way to define tools and run them as servers.
 
 Install the MCP library:
@@ -258,24 +118,8 @@ Install the MCP library:
 ```bash
 pip install mcp
 ```
-
-:::
-
-:::js
-To create your own MCP servers, you can use the `@modelcontextprotocol/sdk` library. This library provides a simple way to define tools and run them as servers.
-
-Install the MCP SDK:
-
-```bash
-npm install @modelcontextprotocol/sdk
-```
-
-:::
-
 Use the following reference implementations to test your agent with MCP tool servers.
 
-:::python
-
 ```python title="Example Math Server (stdio transport)"
 from mcp.server.fastmcp import FastMCP
 
@@ -295,115 +139,6 @@ if __name__ == "__main__":
     mcp.run(transport="stdio")
 ```
 
-:::
-
-:::js
-
-```typescript title="Example Math Server (stdio transport)"
-import { Server } from "@modelcontextprotocol/sdk/server/index.js";
-import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
-import {
-  CallToolRequestSchema,
-  ListToolsRequestSchema,
-} from "@modelcontextprotocol/sdk/types.js";
-
-const server = new Server(
-  {
-    name: "math-server",
-    version: "0.1.0",
-  },
-  {
-    capabilities: {
-      tools: {},
-    },
-  }
-);
-
-server.setRequestHandler(ListToolsRequestSchema, async () => {
-  return {
-    tools: [
-      {
-        name: "add",
-        description: "Add two numbers",
-        inputSchema: {
-          type: "object",
-          properties: {
-            a: {
-              type: "number",
-              description: "First number",
-            },
-            b: {
-              type: "number",
-              description: "Second number",
-            },
-          },
-          required: ["a", "b"],
-        },
-      },
-      {
-        name: "multiply",
-        description: "Multiply two numbers",
-        inputSchema: {
-          type: "object",
-          properties: {
-            a: {
-              type: "number",
-              description: "First number",
-            },
-            b: {
-              type: "number",
-              description: "Second number",
-            },
-          },
-          required: ["a", "b"],
-        },
-      },
-    ],
-  };
-});
-
-server.setRequestHandler(CallToolRequestSchema, async (request) => {
-  switch (request.params.name) {
-    case "add": {
-      const { a, b } = request.params.arguments as { a: number; b: number };
-      return {
-        content: [
-          {
-            type: "text",
-            text: String(a + b),
-          },
-        ],
-      };
-    }
-    case "multiply": {
-      const { a, b } = request.params.arguments as { a: number; b: number };
-      return {
-        content: [
-          {
-            type: "text",
-            text: String(a * b),
-          },
-        ],
-      };
-    }
-    default:
-      throw new Error(`Unknown tool: ${request.params.name}`);
-  }
-});
-
-async function main() {
-  const transport = new StdioServerTransport();
-  await server.connect(transport);
-  console.error("Math MCP server running on stdio");
-}
-
-main();
-```
-
-:::
-
-:::python
-
 ```python title="Example Weather Server (Streamable HTTP transport)"
 from mcp.server.fastmcp import FastMCP
 
@@ -418,100 +153,8 @@ if __name__ == "__main__":
     mcp.run(transport="streamable-http")
 ```
 
-:::
-
-:::js
-
-```typescript title="Example Weather Server (HTTP transport)"
-import { Server } from "@modelcontextprotocol/sdk/server/index.js";
-import { SSEServerTransport } from "@modelcontextprotocol/sdk/server/sse.js";
-import {
-  CallToolRequestSchema,
-  ListToolsRequestSchema,
-} from "@modelcontextprotocol/sdk/types.js";
-import express from "express";
-
-const app = express();
-app.use(express.json());
-
-const server = new Server(
-  {
-    name: "weather-server",
-    version: "0.1.0",
-  },
-  {
-    capabilities: {
-      tools: {},
-    },
-  }
-);
-
-server.setRequestHandler(ListToolsRequestSchema, async () => {
-  return {
-    tools: [
-      {
-        name: "get_weather",
-        description: "Get weather for location",
-        inputSchema: {
-          type: "object",
-          properties: {
-            location: {
-              type: "string",
-              description: "Location to get weather for",
-            },
-          },
-          required: ["location"],
-        },
-      },
-    ],
-  };
-});
-
-server.setRequestHandler(CallToolRequestSchema, async (request) => {
-  switch (request.params.name) {
-    case "get_weather": {
-      const { location } = request.params.arguments as { location: string };
-      return {
-        content: [
-          {
-            type: "text",
-            text: `It's always sunny in ${location}`,
-          },
-        ],
-      };
-    }
-    default:
-      throw new Error(`Unknown tool: ${request.params.name}`);
-  }
-});
-
-app.post("/mcp", async (req, res) => {
-  const transport = new SSEServerTransport("/mcp", res);
-  await server.connect(transport);
-});
-
-const PORT = process.env.PORT || 8000;
-app.listen(PORT, () => {
-  console.log(`Weather MCP server running on port ${PORT}`);
-});
-```
-
-:::
-
-:::python
-
 ## Additional resources
 
 - [MCP documentation](https://modelcontextprotocol.io/introduction)
 - [MCP Transport documentation](https://modelcontextprotocol.io/docs/concepts/transports)
-- [langchain_mcp_adapters](https://github.com/langchain-ai/langchain-mcp-adapters)
-  :::
-
-:::js
-
-## Additional resources
-
-- [MCP documentation](https://modelcontextprotocol.io/introduction)
-- [MCP Transport documentation](https://modelcontextprotocol.io/docs/concepts/transports)
-- [`@langchain/mcp-adapters`](https://npmjs.com/package/@langchain/mcp-adapters)
-  :::
+- [langchain_mcp_adapters](https://github.com/langchain-ai/langchain-mcp-adapters)

docs/docs/agents/models.md

@@ -2,70 +2,18 @@
 
 LangGraph provides built-in support for [LLMs (language models)](https://python.langchain.com/docs/concepts/chat_models/) via the LangChain library. This makes it easy to integrate various LLMs into your agents and workflows.
 
+
 ## Initialize a model
 
-:::python
 Use [`init_chat_model`](https://python.langchain.com/docs/how_to/chat_models_universal_init/) to initialize models:
 
-{% include-markdown "../../snippets/chat_model_tabs.md" %}
-:::
-
-:::js
-Use model provider classes to initialize models:
-
-=== "OpenAI"
-
-    ```typescript
-    import { ChatOpenAI } from "@langchain/openai";
-
-    const model = new ChatOpenAI({
-      model: "gpt-4o",
-      temperature: 0,
-    });
-    ```
-
-=== "Anthropic"
-
-    ```typescript
-    import { ChatAnthropic } from "@langchain/anthropic";
-
-    const model = new ChatAnthropic({
-      model: "claude-3-5-sonnet-20240620",
-      temperature: 0,
-      maxTokens: 2048,
-    });
-    ```
-
-=== "Google"
-
-    ```typescript
-    import { ChatGoogleGenerativeAI } from "@langchain/google-genai";
-
-    const model = new ChatGoogleGenerativeAI({
-      model: "gemini-1.5-pro",
-      temperature: 0,
-    });
-    ```
-
-=== "Groq"
-
-    ```typescript
-    import { ChatGroq } from "@langchain/groq";
-
-    const model = new ChatGroq({
-      model: "llama-3.1-70b-versatile",
-      temperature: 0,
-    });
-    ```
-
-:::
-
-:::python
+{!snippets/chat_model_tabs.md!}
 
 ### Instantiate a model directly
 
 If a model provider is not available via `init_chat_model`, you can instantiate the provider's model class directly. The model must implement the [BaseChatModel interface](https://python.langchain.com/api_reference/core/language_models/langchain_core.language_models.chat_models.BaseChatModel.html) and support tool calling:
 
+
 ```python
 # Anthropic is already supported by `init_chat_model`,
 # but you can also instantiate it directly.
@@ -78,20 +26,19 @@ model = ChatAnthropic(
 )
 ```
 
-:::
-
 !!! important "Tool calling support"
 
     If you are building an agent or workflow that requires the model to call external tools, ensure that the underlying
     language model supports [tool calling](../concepts/tools.md). Compatible models can be found in the [LangChain integrations directory](https://python.langchain.com/docs/integrations/chat/).
 
+
 ## Use in an agent
 
-:::python
 When using `create_react_agent` you can specify the model by its name string, which is a shorthand for initializing the model using `init_chat_model`. This allows you to use the model without needing to import or instantiate it directly.
 
 === "model name"
 
+
       ```python
       from langgraph.prebuilt import create_react_agent
 
@@ -123,103 +70,10 @@ When using `create_react_agent` you can specify the model by its name string, wh
       )
       ```
 
-:::
-
-:::js
-When using `createReactAgent` you can pass the model instance directly:
-
-```typescript
-import { ChatOpenAI } from "@langchain/openai";
-import { createReactAgent } from "@langchain/langgraph/prebuilt";
-
-const model = new ChatOpenAI({
-  model: "gpt-4o",
-  temperature: 0,
-});
-
-const agent = createReactAgent({
-  llm: model,
-  tools: tools,
-});
-```
-
-:::
-
-:::python
-
-### Dynamic model selection
-
-Pass a callable function to `create_react_agent` to dynamically select the model at runtime. This is useful for scenarios where you want to choose a model based on user input, configuration settings, or other runtime conditions.
-
-The selector function must return a chat model. If you're using tools, you must bind the tools to the model within the selector function.
-
-  ```python
-from dataclasses import dataclass
-from typing import Literal
-from langchain.chat_models import init_chat_model
-from langchain_core.language_models import BaseChatModel
-from langchain_core.tools import tool
-from langgraph.prebuilt import create_react_agent
-from langgraph.prebuilt.chat_agent_executor import AgentState
-from langgraph.runtime import Runtime
-
-@tool
-def weather() -> str:
-    """Returns the current weather conditions."""
-    return "It's nice and sunny."
-
-
-# Define the runtime context
-@dataclass
-class CustomContext:
-    provider: Literal["anthropic", "openai"]
-
-# Initialize models
-openai_model = init_chat_model("openai:gpt-4o")
-anthropic_model = init_chat_model("anthropic:claude-sonnet-4-20250514")
-
-
-# Selector function for model choice
-def select_model(state: AgentState, runtime: Runtime[CustomContext]) -> BaseChatModel:
-    if runtime.context.provider == "anthropic":
-        model = anthropic_model
-    elif runtime.context.provider == "openai":
-        model = openai_model
-    else:
-        raise ValueError(f"Unsupported provider: {runtime.context.provider}")
-
-    # With dynamic model selection, you must bind tools explicitly
-    return model.bind_tools([weather])
-
-
-# Create agent with dynamic model selection
-agent = create_react_agent(select_model, tools=[weather])
-
-# Invoke with context to select model
-output = agent.invoke(
-    {
-        "messages": [
-            {
-                "role": "user",
-                "content": "Which model is handling this?",
-            }
-        ]
-    },
-    context=CustomContext(provider="openai"),
-)
-
-print(output["messages"][-1].text())
-```
-
-!!! version-added "New in LangGraph v0.6"
-
-:::
-
 ## Advanced model configuration
 
 ### Disable streaming
 
-:::python
 To disable streaming of the individual LLM tokens, set `disable_streaming=True` when initializing the model:
 
 === "`init_chat_model`"
@@ -247,25 +101,9 @@ To disable streaming of the individual LLM tokens, set `disable_streaming=True`
     ```
 
 Refer to the [API reference](https://python.langchain.com/api_reference/core/language_models/langchain_core.language_models.chat_models.BaseChatModel.html#langchain_core.language_models.chat_models.BaseChatModel.disable_streaming) for more information on `disable_streaming`
-:::
-
-:::js
-To disable streaming of the individual LLM tokens, set `streaming: false` when initializing the model:
-
-```typescript
-import { ChatOpenAI } from "@langchain/openai";
-
-const model = new ChatOpenAI({
-  model: "gpt-4o",
-  streaming: false,
-});
-```
-
-:::
 
 ### Add model fallbacks
 
-:::python
 You can add a fallback to a different model or a different LLM provider using `model.with_fallbacks([...])`:
 
 === "`init_chat_model`"
@@ -298,28 +136,6 @@ You can add a fallback to a different model or a different LLM provider using `m
     ```
 
 See this [guide](https://python.langchain.com/docs/how_to/fallbacks/#fallback-to-better-model) for more information on model fallbacks.
-:::
-
-:::js
-You can add a fallback to a different model or a different LLM provider using `model.withFallbacks([...])`:
-
-```typescript
-import { ChatOpenAI } from "@langchain/openai";
-import { ChatAnthropic } from "@langchain/anthropic";
-
-const modelWithFallbacks = new ChatOpenAI({
-  model: "gpt-4o",
-}).withFallbacks([
-  new ChatAnthropic({
-    model: "claude-3-5-sonnet-20240620",
-  }),
-]);
-```
-
-See this [guide](https://js.langchain.com/docs/how_to/fallbacks/#fallback-to-better-model) for more information on model fallbacks.
-:::
-
-:::python
 
 ### Use the built-in rate limiter
 
@@ -336,49 +152,28 @@ rate_limiter = InMemoryRateLimiter(
 )
 
 model = ChatAnthropic(
-   model_name="claude-3-opus-20240229",
+   model_name="claude-3-opus-20240229", 
    rate_limiter=rate_limiter
 )
 ```
 
 See the LangChain docs for more information on how to [handle rate limiting](https://python.langchain.com/docs/how_to/chat_model_rate_limiting/).
-:::
 
 ## Bring your own model
 
 If your desired LLM isn't officially supported by LangChain, consider these options:
 
-:::python
-
 1. **Implement a custom LangChain chat model**: Create a model conforming to the [LangChain chat model interface](https://python.langchain.com/docs/how_to/custom_chat_model/). This enables full compatibility with LangGraph's agents and workflows but requires understanding of the LangChain framework.
-   :::
-
-:::js
-
-1. **Implement a custom LangChain chat model**: Create a model conforming to the [LangChain chat model interface](https://js.langchain.com/docs/how_to/custom_chat/). This enables full compatibility with LangGraph's agents and workflows but requires understanding of the LangChain framework.
-   :::
 
 2. **Direct invocation with custom streaming**: Use your model directly by [adding custom streaming logic](../how-tos/streaming.md#use-with-any-llm) with `StreamWriter`.
    Refer to the [custom streaming documentation](../how-tos/streaming.md#use-with-any-llm) for guidance. This approach suits custom workflows where prebuilt agent integration is not necessary.
 
+ 
 ## Additional resources
 
-:::python
-
 - [Multimodal inputs](https://python.langchain.com/docs/how_to/multimodal_inputs/)
 - [Structured outputs](https://python.langchain.com/docs/how_to/structured_output/)
 - [Model integration directory](https://python.langchain.com/docs/integrations/chat/)
 - [Force model to call a specific tool](https://python.langchain.com/docs/how_to/tool_choice/)
 - [All chat model how-to guides](https://python.langchain.com/docs/how_to/#chat-models)
 - [Chat model integrations](https://python.langchain.com/docs/integrations/chat/)
-  :::
-
-:::js
-
-- [Multimodal inputs](https://js.langchain.com/docs/how_to/multimodal_inputs/)
-- [Structured outputs](https://js.langchain.com/docs/how_to/structured_output/)
-- [Model integration directory](https://js.langchain.com/docs/integrations/chat/)
-- [Force model to call a specific tool](https://js.langchain.com/docs/how_to/tool_choice/)
-- [All chat model how-to guides](https://js.langchain.com/docs/how_to/#chat-models)
-- [Chat model integrations](https://js.langchain.com/docs/integrations/chat/)
-  :::

docs/docs/agents/multi-agent.md

@@ -22,7 +22,6 @@ Two of the most popular multi-agent architectures are:
 
 ![Supervisor](./assets/supervisor.png)
 
-:::python
 Use [`langgraph-supervisor`](https://github.com/langchain-ai/langgraph-supervisor-py) library to create a supervisor multi-agent system:
 
 ```bash
@@ -83,76 +82,10 @@ for chunk in supervisor.stream(
     print("\n")
 ```
 
-:::
-
-:::js
-Use [`@langchain/langgraph-supervisor`](https://github.com/langchain-ai/langgraphjs/tree/main/libs/langgraph-supervisor) library to create a supervisor multi-agent system:
-
-```bash
-npm install @langchain/langgraph-supervisor
-```
-
-```typescript
-import { ChatOpenAI } from "@langchain/openai";
-import { createReactAgent } from "@langchain/langgraph/prebuilt";
-// highlight-next-line
-import { createSupervisor } from "langgraph-supervisor";
-
-function bookHotel(hotelName: string) {
-  /**Book a hotel*/
-  return `Successfully booked a stay at ${hotelName}.`;
-}
-
-function bookFlight(fromAirport: string, toAirport: string) {
-  /**Book a flight*/
-  return `Successfully booked a flight from ${fromAirport} to ${toAirport}.`;
-}
-
-const flightAssistant = createReactAgent({
-  llm: "openai:gpt-4o",
-  tools: [bookFlight],
-  stateModifier: "You are a flight booking assistant",
-  // highlight-next-line
-  name: "flight_assistant",
-});
-
-const hotelAssistant = createReactAgent({
-  llm: "openai:gpt-4o",
-  tools: [bookHotel],
-  stateModifier: "You are a hotel booking assistant",
-  // highlight-next-line
-  name: "hotel_assistant",
-});
-
-// highlight-next-line
-const supervisor = createSupervisor({
-  agents: [flightAssistant, hotelAssistant],
-  llm: new ChatOpenAI({ model: "gpt-4o" }),
-  systemPrompt:
-    "You manage a hotel booking assistant and a " +
-    "flight booking assistant. Assign work to them.",
-});
-
-for await (const chunk of supervisor.stream({
-  messages: [
-    {
-      role: "user",
-      content: "book a flight from BOS to JFK and a stay at McKittrick Hotel",
-    },
-  ],
-})) {
-  console.log(chunk);
-  console.log("\n");
-}
-```
-
-:::
-
 ## Swarm
 
 ![Swarm](./assets/swarm.png)
 
-:::python
 Use [`langgraph-swarm`](https://github.com/langchain-ai/langgraph-swarm-py) library to create a swarm multi-agent system:
 
 ```bash
@@ -210,82 +143,18 @@ for chunk in swarm.stream(
     print("\n")
 ```
 
-:::
-
-:::js
-Use [`@langchain/langgraph-swarm`](https://github.com/langchain-ai/langgraphjs/tree/main/libs/langgraph-swarm) library to create a swarm multi-agent system:
-
-```bash
-npm install @langchain/langgraph-swarm
-```
-
-```typescript
-import { createReactAgent } from "@langchain/langgraph/prebuilt";
-// highlight-next-line
-import { createSwarm, createHandoffTool } from "@langchain/langgraph-swarm";
-
-const transferToHotelAssistant = createHandoffTool({
-  agentName: "hotel_assistant",
-  description: "Transfer user to the hotel-booking assistant.",
-});
-
-const transferToFlightAssistant = createHandoffTool({
-  agentName: "flight_assistant",
-  description: "Transfer user to the flight-booking assistant.",
-});
-
-const flightAssistant = createReactAgent({
-  llm: "anthropic:claude-3-5-sonnet-latest",
-  // highlight-next-line
-  tools: [bookFlight, transferToHotelAssistant],
-  stateModifier: "You are a flight booking assistant",
-  // highlight-next-line
-  name: "flight_assistant",
-});
-
-const hotelAssistant = createReactAgent({
-  llm: "anthropic:claude-3-5-sonnet-latest",
-  // highlight-next-line
-  tools: [bookHotel, transferToFlightAssistant],
-  stateModifier: "You are a hotel booking assistant",
-  // highlight-next-line
-  name: "hotel_assistant",
-});
-
-// highlight-next-line
-const swarm = createSwarm({
-  agents: [flightAssistant, hotelAssistant],
-  defaultActiveAgent: "flight_assistant",
-});
-
-for await (const chunk of swarm.stream({
-  messages: [
-    {
-      role: "user",
-      content: "book a flight from BOS to JFK and a stay at McKittrick Hotel",
-    },
-  ],
-})) {
-  console.log(chunk);
-  console.log("\n");
-}
-```
-
-:::
-
 ## Handoffs
 
-A common pattern in multi-agent interactions is **handoffs**, where one agent _hands off_ control to another. Handoffs allow you to specify:
+A common pattern in multi-agent interactions is **handoffs**, where one agent *hands off* control to another. Handoffs allow you to specify:
 
 - **destination**: target agent to navigate to
 - **payload**: information to pass to that agent
 
-:::python
 This is used both by `langgraph-supervisor` (supervisor hands off to individual agents) and `langgraph-swarm` (an individual agent can hand off to other agents).
 
 To implement handoffs with `create_react_agent`, you need to:
 
-1.  Create a special tool that can transfer control to a different agent
+1. Create a special tool that can transfer control to a different agent
 
     ```python
     def transfer_to_bob():
@@ -304,7 +173,7 @@ To implement handoffs with `create_react_agent`, you need to:
         )
     ```
 
-2.  Create individual agents that have access to handoff tools:
+1. Create individual agents that have access to handoff tools:
 
     ```python
     flight_assistant = create_react_agent(
@@ -315,7 +184,7 @@ To implement handoffs with `create_react_agent`, you need to:
     )
     ```
 
-3.  Define a parent graph that contains individual agents as nodes:
+1. Define a parent graph that contains individual agents as nodes:
 
     ```python
     from langgraph.graph import StateGraph, MessagesState
@@ -327,60 +196,8 @@ To implement handoffs with `create_react_agent`, you need to:
     )
     ```
 
-:::
-
-:::js
-This is used both by `@langchain/langgraph-supervisor` (supervisor hands off to individual agents) and `@langchain/langgraph-swarm` (an individual agent can hand off to other agents).
-
-To implement handoffs with `createReactAgent`, you need to:
-
-1.  Create a special tool that can transfer control to a different agent
-
-    ```typescript
-    function transferToBob() {
-      /**Transfer to bob.*/
-      return new Command({
-        // name of the agent (node) to go to
-        // highlight-next-line
-        goto: "bob",
-        // data to send to the agent
-        // highlight-next-line
-        update: { messages: [...] },
-        // indicate to LangGraph that we need to navigate to
-        // agent node in a parent graph
-        // highlight-next-line
-        graph: Command.PARENT,
-      });
-    }
-    ```
-
-2.  Create individual agents that have access to handoff tools:
-
-    ```typescript
-    const flightAssistant = createReactAgent({
-      ..., tools: [bookFlight, transferToHotelAssistant]
-    });
-    const hotelAssistant = createReactAgent({
-      ..., tools: [bookHotel, transferToFlightAssistant]
-    });
-    ```
-
-3.  Define a parent graph that contains individual agents as nodes:
-
-    ```typescript
-    import { StateGraph, MessagesZodState } from "@langchain/langgraph";
-    const multiAgentGraph = new StateGraph(MessagesZodState)
-      .addNode("flight_assistant", flightAssistant)
-      .addNode("hotel_assistant", hotelAssistant)
-      // ...
-    ```
-
-    :::
-
 Putting this together, here is how you can implement a simple multi-agent system with two agents — a flight booking assistant and a hotel booking assistant:
 
-:::python
-
 ```python
 from typing import Annotated
 from langchain_core.tools import tool, InjectedToolCallId
@@ -481,158 +298,11 @@ for chunk in multi_agent_graph.stream(
 3. Name of the agent or node to hand off to.
 4. Take the agent's messages and **add** them to the parent's **state** as part of the handoff. The next agent will see the parent state.
 5. Indicate to LangGraph that we need to navigate to agent node in a **parent** multi-agent graph.
-   :::
-
-:::js
-
-```typescript
-import { tool } from "@langchain/core/tools";
-import { ChatAnthropic } from "@langchain/anthropic";
-import { createReactAgent } from "@langchain/langgraph/prebuilt";
-import {
-  StateGraph,
-  START,
-  MessagesZodState,
-  Command,
-} from "@langchain/langgraph";
-import { z } from "zod";
-
-function createHandoffTool({
-  agentName,
-  description,
-}: {
-  agentName: string;
-  description?: string;
-}) {
-  const name = `transfer_to_${agentName}`;
-  const toolDescription = description || `Transfer to ${agentName}`;
-
-  return tool(
-    async (_, config) => {
-      const toolMessage = {
-        role: "tool" as const,
-        content: `Successfully transferred to ${agentName}`,
-        name: name,
-        tool_call_id: config.toolCall?.id!,
-      };
-      return new Command({
-        // (2)!
-        // highlight-next-line
-        goto: agentName, // (3)!
-        // highlight-next-line
-        update: { messages: [toolMessage] }, // (4)!
-        // highlight-next-line
-        graph: Command.PARENT, // (5)!
-      });
-    },
-    {
-      name,
-      description: toolDescription,
-      schema: z.object({}),
-    }
-  );
-}
-
-// Handoffs
-const transferToHotelAssistant = createHandoffTool({
-  agentName: "hotel_assistant",
-  description: "Transfer user to the hotel-booking assistant.",
-});
-
-const transferToFlightAssistant = createHandoffTool({
-  agentName: "flight_assistant",
-  description: "Transfer user to the flight-booking assistant.",
-});
-
-// Simple agent tools
-const bookHotel = tool(
-  async ({ hotelName }) => {
-    /**Book a hotel*/
-    return `Successfully booked a stay at ${hotelName}.`;
-  },
-  {
-    name: "book_hotel",
-    description: "Book a hotel",
-    schema: z.object({
-      hotelName: z.string().describe("Name of the hotel to book"),
-    }),
-  }
-);
-
-const bookFlight = tool(
-  async ({ fromAirport, toAirport }) => {
-    /**Book a flight*/
-    return `Successfully booked a flight from ${fromAirport} to ${toAirport}.`;
-  },
-  {
-    name: "book_flight",
-    description: "Book a flight",
-    schema: z.object({
-      fromAirport: z.string().describe("Departure airport code"),
-      toAirport: z.string().describe("Arrival airport code"),
-    }),
-  }
-);
-
-// Define agents
-const flightAssistant = createReactAgent({
-  llm: new ChatAnthropic({ model: "anthropic:claude-3-5-sonnet-latest" }),
-  // highlight-next-line
-  tools: [bookFlight, transferToHotelAssistant],
-  stateModifier: "You are a flight booking assistant",
-  // highlight-next-line
-  name: "flight_assistant",
-});
-
-const hotelAssistant = createReactAgent({
-  llm: new ChatAnthropic({ model: "anthropic:claude-3-5-sonnet-latest" }),
-  // highlight-next-line
-  tools: [bookHotel, transferToFlightAssistant],
-  stateModifier: "You are a hotel booking assistant",
-  // highlight-next-line
-  name: "hotel_assistant",
-});
-
-// Define multi-agent graph
-const multiAgentGraph = new StateGraph(MessagesZodState)
-  .addNode("flight_assistant", flightAssistant)
-  .addNode("hotel_assistant", hotelAssistant)
-  .addEdge(START, "flight_assistant")
-  .compile();
-
-// Run the multi-agent graph
-for await (const chunk of multiAgentGraph.stream({
-  messages: [
-    {
-      role: "user",
-      content: "book a flight from BOS to JFK and a stay at McKittrick Hotel",
-    },
-  ],
-})) {
-  console.log(chunk);
-  console.log("\n");
-}
-```
-
-1. Access agent's state
-2. The `Command` primitive allows specifying a state update and a node transition as a single operation, making it useful for implementing handoffs.
-3. Name of the agent or node to hand off to.
-4. Take the agent's messages and **add** them to the parent's **state** as part of the handoff. The next agent will see the parent state.
-5. Indicate to LangGraph that we need to navigate to agent node in a **parent** multi-agent graph.
-
-:::
 
 !!! Note
-
     This handoff implementation assumes that:
 
     - each agent receives overall message history (across all agents) in the multi-agent system as its input
     - each agent outputs its internal messages history to the overall message history of the multi-agent system
 
-:::python
-Check out LangGraph [supervisor](https://github.com/langchain-ai/langgraph-supervisor-py#customizing-handoff-tools) and [swarm](https://github.com/langchain-ai/langgraph-swarm-py#customizing-handoff-tools) documentation to learn how to customize handoffs.
-:::
-
-:::js
-Check out LangGraph [supervisor](https://github.com/langchain-ai/langgraphjs/tree/main/libs/langgraph-supervisor#customizing-handoff-tools) and [swarm](https://github.com/langchain-ai/langgraphjs/tree/main/libs/langgraph-swarm#customizing-handoff-tools) documentation to learn how to customize handoffs.
-:::
+    Check out LangGraph [supervisor](https://github.com/langchain-ai/langgraph-supervisor-py#customizing-handoff-tools) and [swarm](https://github.com/langchain-ai/langgraph-swarm-py#customizing-handoff-tools) documentation to learn how to customize handoffs.

docs/docs/agents/overview.md

@@ -14,7 +14,7 @@ LangGraph provides both low-level primitives and high-level prebuilt components
 
 ## What is an agent?
 
-An _agent_ consists of three components: a **large language model (LLM)**, a set of **tools** it can use, and a **prompt** that provides instructions.
+An *agent* consists of three components: a **large language model (LLM)**, a set of **tools** it can use, and a **prompt** that provides instructions.
 
 The LLM operates in a loop. In each iteration, it selects a tool to invoke, provides input, receives the result (an observation), and uses that observation to inform the next action. The loop continues until a stopping condition is met — typically when the agent has gathered enough information to respond to the user.
 
@@ -27,12 +27,12 @@ The LLM operates in a loop. In each iteration, it selects a tool to invoke, prov
 
 LangGraph includes several capabilities essential for building robust, production-ready agentic systems:
 
-- [**Memory integration**](../how-tos/memory/add-memory.md): Native support for _short-term_ (session-based) and _long-term_ (persistent across sessions) memory, enabling stateful behaviors in chatbots and assistants.
-- [**Human-in-the-loop control**](../concepts/human_in_the_loop.md): Execution can pause _indefinitely_ to await human feedback—unlike websocket-based solutions limited to real-time interaction. This enables asynchronous approval, correction, or intervention at any point in the workflow.
+- [**Memory integration**](../how-tos/memory/add-memory.md): Native support for *short-term* (session-based) and *long-term* (persistent across sessions) memory, enabling stateful behaviors in chatbots and assistants.
+- [**Human-in-the-loop control**](../concepts/human_in_the_loop.md): Execution can pause *indefinitely* to await human feedback—unlike websocket-based solutions limited to real-time interaction. This enables asynchronous approval, correction, or intervention at any point in the workflow.
 - [**Streaming support**](../how-tos/streaming.md): Real-time streaming of agent state, model tokens, tool outputs, or combined streams.
-- [**Deployment tooling**](../tutorials/langgraph-platform/local-server.md): Includes infrastructure-free deployment tools. [**LangGraph Platform**](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/) supports testing, debugging, and deployment.
-  - **[Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/)**: A visual IDE for inspecting and debugging workflows.
-  - Supports multiple [**deployment options**](https://langchain-ai.github.io/langgraph/concepts/deployment_options.md) for production.
+- [**Deployment tooling**](./deployment.md): Includes infrastructure-free deployment tools. [**LangGraph Platform**](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/) supports testing, debugging, and deployment.
+    - **[Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/)**: A visual IDE for inspecting and debugging workflows.
+    - Supports multiple [**deployment options**](https://langchain-ai.github.io/langgraph/concepts/deployment_options.md) for production.
 
 ## High-level building blocks
 
@@ -40,32 +40,30 @@ LangGraph comes with a set of prebuilt components that implement common agent be
 
 Using LangGraph for agent development allows you to focus on your application's logic and behavior, instead of building and maintaining the supporting infrastructure for state, memory, and human feedback.
 
-:::python
-
 ## Package ecosystem
 
 The high-level components are organized into several packages, each with a specific focus.
 
-| Package                                    | Description                                                                              | Installation                            |
-| ------------------------------------------ | ---------------------------------------------------------------------------------------- | --------------------------------------- |
-| `langgraph-prebuilt` (part of `langgraph`) | Prebuilt components to [**create agents**](./agents.md)                                  | `pip install -U langgraph langchain`    |
-| `langgraph-supervisor`                     | Tools for building [**supervisor**](./multi-agent.md#supervisor) agents                  | `pip install -U langgraph-supervisor`   |
-| `langgraph-swarm`                          | Tools for building a [**swarm**](./multi-agent.md#swarm) multi-agent system              | `pip install -U langgraph-swarm`        |
-| `langchain-mcp-adapters`                   | Interfaces to [**MCP servers**](./mcp.md) for tool and resource integration              | `pip install -U langchain-mcp-adapters` |
-| `langmem`                                  | Agent memory management: [**short-term and long-term**](../how-tos/memory/add-memory.md) | `pip install -U langmem`                |
-| `agentevals`                               | Utilities to [**evaluate agent performance**](./evals.md)                                | `pip install -U agentevals`             |
+| Package                                    | Description                                                                 | Installation                            |
+|--------------------------------------------|-----------------------------------------------------------------------------|-----------------------------------------|
+| `langgraph-prebuilt` (part of `langgraph`) | Prebuilt components to [**create agents**](./agents.md)                     | `pip install -U langgraph langchain`    |
+| `langgraph-supervisor`                     | Tools for building [**supervisor**](./multi-agent.md#supervisor) agents     | `pip install -U langgraph-supervisor`   |
+| `langgraph-swarm`                          | Tools for building a [**swarm**](./multi-agent.md#swarm) multi-agent system | `pip install -U langgraph-swarm`        |
+| `langchain-mcp-adapters`                   | Interfaces to [**MCP servers**](./mcp.md) for tool and resource integration | `pip install -U langchain-mcp-adapters` |
+| `langmem`                                  | Agent memory management: [**short-term and long-term**](../how-tos/memory/add-memory.md)        | `pip install -U langmem`                |
+| `agentevals`                               | Utilities to [**evaluate agent performance**](./evals.md)                   | `pip install -U agentevals`             |
 
 ## Visualize an agent graph
 
 Use the following tool to visualize the graph generated by
-@[`create_react_agent`][create_react_agent]
+[`create_react_agent`][langgraph.prebuilt.chat_agent_executor.create_react_agent]
 and to view an outline of the corresponding code.
 It allows you to explore the infrastructure of the agent as defined by the presence of:
 
-- [`tools`](../how-tos/tool-calling.md): A list of tools (functions, APIs, or other callable objects) that the agent can use to perform tasks.
-- [`pre_model_hook`](../how-tos/create-react-agent-manage-message-history.ipynb): A function that is called before the model is invoked. It can be used to condense messages or perform other preprocessing tasks.
-- `post_model_hook`: A function that is called after the model is invoked. It can be used to implement guardrails, human-in-the-loop flows, or other postprocessing tasks.
-- [`response_format`](../agents/agents.md#6-configure-structured-output): A data structure used to constrain the type of the final output, e.g., a `pydantic` `BaseModel`.
+* [`tools`](../agents/tools.md): A list of tools (functions, APIs, or other callable objects) that the agent can use to perform tasks.
+* [`pre_model_hook`](../how-tos/create-react-agent-manage-message-history.ipynb): A function that is called before the model is invoked. It can be used to condense messages or perform other preprocessing tasks.
+* `post_model_hook`: A function that is called after the model is invoked. It can be used to implement guardrails, human-in-the-loop flows, or other postprocessing tasks.
+* [`response_format`](../agents/agents.md#6-configure-structured-output): A data structure used to constrain the type of the final output, e.g., a `pydantic` `BaseModel`.
 
 <div class="agent-layout">
   <div class="agent-graph-features-container">
@@ -84,13 +82,15 @@ It allows you to explore the infrastructure of the agent as defined by the prese
   </div>
 </div>
 
+
 The following code snippet shows how to create the above agent (and underlying graph) with
-@[`create_react_agent`][create_react_agent]:
+[`create_react_agent`][langgraph.prebuilt.chat_agent_executor.create_react_agent]:
 
 <div class="language-python">
   <pre><code id="agent-code" class="language-python"></code></pre>
 </div>
 
+
 <script>
 function getCheckedValue(id) {
   return document.getElementById(id).checked ? "1" : "0";
@@ -159,7 +159,7 @@ function generateCodeSnippet({ tools, pre, post, response }) {
   if (post) lines.push("    post_model_hook=post_model_hook,");
   if (response) lines.push("    response_format=ResponseFormat,");
 
-  lines.push(")", "", "# Visualize the graph", "# For Jupyter or GUI environments:", "agent.get_graph().draw_mermaid_png()", "", "# To save PNG to file:", "png_data = agent.get_graph().draw_mermaid_png()", "with open(\"graph.png\", \"wb\") as f:", "    f.write(png_data)", "", "# For terminal/ASCII output:", "agent.get_graph().draw_ascii()");
+  lines.push(")", "", "agent.get_graph().draw_mermaid_png()");
 
   return lines.join("\n");
 }
@@ -189,159 +189,3 @@ function initializeWidget() {
 window.addEventListener("DOMContentLoaded", initializeWidget);
 document$.subscribe(initializeWidget);
 </script>
-
-:::
-
-:::js
-
-## Package ecosystem
-
-The high-level components are organized into several packages, each with a specific focus.
-
-| Package                  | Description                                                                 | Installation                                       |
-| ------------------------ | --------------------------------------------------------------------------- | -------------------------------------------------- |
-| `langgraph`              | Prebuilt components to [**create agents**](./agents.md)                     | `npm install @langchain/langgraph @langchain/core` |
-| `langgraph-supervisor`   | Tools for building [**supervisor**](./multi-agent.md#supervisor) agents     | `npm install @langchain/langgraph-supervisor`      |
-| `langgraph-swarm`        | Tools for building a [**swarm**](./multi-agent.md#swarm) multi-agent system | `npm install @langchain/langgraph-swarm`           |
-| `langchain-mcp-adapters` | Interfaces to [**MCP servers**](./mcp.md) for tool and resource integration | `npm install @langchain/mcp-adapters`              |
-| `agentevals`             | Utilities to [**evaluate agent performance**](./evals.md)                   | `npm install agentevals`                           |
-
-## Visualize an agent graph
-
-Use the following tool to visualize the graph generated by @[`createReactAgent`][create_react_agent] and to view an outline of the corresponding code. It allows you to explore the infrastructure of the agent as defined by the presence of:
-
-- [`tools`](./tools.md): A list of tools (functions, APIs, or other callable objects) that the agent can use to perform tasks.
-- `preModelHook`: A function that is called before the model is invoked. It can be used to condense messages or perform other preprocessing tasks.
-- `postModelHook`: A function that is called after the model is invoked. It can be used to implement guardrails, human-in-the-loop flows, or other postprocessing tasks.
-- [`responseFormat`](./agents.md#6-configure-structured-output): A data structure used to constrain the type of the final output (via Zod schemas).
-
-<div class="agent-layout">
-  <div class="agent-graph-features-container">
-    <div class="agent-graph-features">
-      <h3 class="agent-section-title">Features</h3>
-      <label><input type="checkbox" id="tools" checked> <code>tools</code></label>
-      <label><input type="checkbox" id="preModelHook"> <code>preModelHook</code></label>
-      <label><input type="checkbox" id="postModelHook"> <code>postModelHook</code></label>
-      <label><input type="checkbox" id="responseFormat"> <code>responseFormat</code></label>
-    </div>
-  </div>
-
-  <div class="agent-graph-container">
-    <h3 class="agent-section-title">Graph</h3>
-    <img id="agent-graph-img" src="../assets/react_agent_graphs/0001.svg" alt="graph image" style="max-width: 100%;"/>
-  </div>
-</div>
-
-The following code snippet shows how to create the above agent (and underlying graph) with @[`createReactAgent`][create_react_agent]:
-
-<div class="language-typescript">
-  <pre><code id="agent-code" class="language-typescript"></code></pre>
-</div>
-
-<script>
-function getCheckedValue(id) {
-  return document.getElementById(id).checked ? "1" : "0";
-}
-
-function getKey() {
-  return [
-    getCheckedValue("responseFormat"),
-    getCheckedValue("postModelHook"),
-    getCheckedValue("preModelHook"),
-    getCheckedValue("tools")
-  ].join("");
-}
-
-function dedent(strings, ...values) {
-  const str = String.raw({ raw: strings }, ...values)
-  const [space] = str.split("\n").filter(Boolean).at(0).match(/^(\s*)/)
-  const spaceLen = space.length
-  return str.split("\n").map(line => line.slice(spaceLen)).join("\n").trim()
-}
-
-Object.assign(dedent, {
-  offset: (size) => (strings, ...values) => {
-    return dedent(strings, ...values).split("\n").map(line => " ".repeat(size) + line).join("\n")
-  }
-})
-
-
-
-
-function generateCodeSnippet({ tools, pre, post, response }) {
-  const lines = []
-
-  lines.push(dedent`
-    import { createReactAgent } from "@langchain/langgraph/prebuilt";
-    import { ChatOpenAI } from "@langchain/openai";
-  `)
-
-  if (tools) lines.push(`import { tool } from "@langchain/core/tools";`);  
-  if (response || tools) lines.push(`import { z } from "zod";`);
-
-  lines.push("", dedent`
-    const agent = createReactAgent({
-      llm: new ChatOpenAI({ model: "o4-mini" }),
-  `)
-
-  if (tools) {
-    lines.push(dedent.offset(2)`
-      tools: [
-        tool(() => "Sample tool output", {
-          name: "sampleTool",
-          schema: z.object({}),
-        }),
-      ],
-    `)
-  }
-
-  if (pre) {
-    lines.push(dedent.offset(2)`
-      preModelHook: (state) => ({ llmInputMessages: state.messages }),
-    `)
-  }
-
-  if (post) {
-    lines.push(dedent.offset(2)`
-      postModelHook: (state) => state,
-    `)
-  }
-
-  if (response) {
-    lines.push(dedent.offset(2)`
-      responseFormat: z.object({ result: z.string() }),
-    `)
-  }
-
-  lines.push(`});`);
-
-  return lines.join("\n");
-}
-
-function render() {
-  const key = getKey();
-  document.getElementById("agent-graph-img").src = `../assets/react_agent_graphs/${key}.svg`;
-
-  const state = {
-    tools: document.getElementById("tools").checked,
-    pre: document.getElementById("preModelHook").checked,
-    post: document.getElementById("postModelHook").checked,
-    response: document.getElementById("responseFormat").checked
-  };
-
-  document.getElementById("agent-code").textContent = generateCodeSnippet(state);
-}
-
-function initializeWidget() {
-  render(); // no need for `await` here
-  document.querySelectorAll(".agent-graph-features input").forEach((input) => {
-    input.addEventListener("change", render);
-  });
-}
-
-// Init for both full reload and SPA nav (used by MkDocs Material)
-window.addEventListener("DOMContentLoaded", initializeWidget);
-document$.subscribe(initializeWidget);
-</script>
-
-:::

docs/docs/agents/prebuilt.md

@@ -5,24 +5,23 @@ If you’re looking for other prebuilt libraries, explore the community-built op
 below. These libraries can extend LangGraph's functionality in various ways.
 
 ## 📚 Available Libraries
+
 [//]: # (This file is automatically generated using a script in docs/_scripts. Do not edit this file directly!)
-
-:::python
 | Name | GitHub URL | Description | Weekly Downloads | Stars |
 | --- | --- | --- | --- | --- |
-| **trustcall** | https://github.com/hinthornw/trustcall | Tenacious tool calling built on LangGraph. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/hinthornw/trustcall?style=social)
-| **breeze-agent** | https://github.com/andrestorres123/breeze-agent | A streamlined research system built inspired on STORM and built on LangGraph. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/andrestorres123/breeze-agent?style=social)
-| **langgraph-supervisor** | https://github.com/langchain-ai/langgraph-supervisor-py | Build supervisor multi-agent systems with LangGraph. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/langgraph-supervisor-py?style=social)
-| **langmem** | https://github.com/langchain-ai/langmem | Build agents that learn and adapt from interactions over time. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/langmem?style=social)
-| **langchain-mcp-adapters** | https://github.com/langchain-ai/langchain-mcp-adapters | Make Anthropic Model Context Protocol (MCP) tools compatible with LangGraph agents. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/langchain-mcp-adapters?style=social)
-| **open-deep-research** | https://github.com/langchain-ai/open_deep_research | Open source assistant for iterative web research and report writing. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/open_deep_research?style=social)
-| **langgraph-swarm** | https://github.com/langchain-ai/langgraph-swarm-py | Build swarm-style multi-agent systems using LangGraph. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/langgraph-swarm-py?style=social)
-| **delve-taxonomy-generator** | https://github.com/andrestorres123/delve | A taxonomy generator for unstructured data | -12345 | ![GitHub stars](https://img.shields.io/github/stars/andrestorres123/delve?style=social)
-| **nodeology** | https://github.com/xyin-anl/Nodeology | Enable researcher to build scientific workflows easily with simplified interface. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/xyin-anl/Nodeology?style=social)
-| **langgraph-bigtool** | https://github.com/langchain-ai/langgraph-bigtool | Build LangGraph agents with large numbers of tools. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/langgraph-bigtool?style=social)
-| **ai-data-science-team** | https://github.com/business-science/ai-data-science-team | An AI-powered data science team of agents to help you perform common data science tasks 10X faster. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/business-science/ai-data-science-team?style=social)
-| **langgraph-reflection** | https://github.com/langchain-ai/langgraph-reflection | LangGraph agent that runs a reflection step. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/langgraph-reflection?style=social)
-| **langgraph-codeact** | https://github.com/langchain-ai/langgraph-codeact | LangGraph implementation of CodeAct agent that generates and executes code instead of tool calling. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/langgraph-codeact?style=social)
+| **trustcall** | [hinthornw/trustcall](https://github.com/hinthornw/trustcall) | Tenacious tool calling built on LangGraph. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/hinthornw/trustcall?style=social)
+| **breeze-agent** | [andrestorres123/breeze-agent](https://github.com/andrestorres123/breeze-agent) | A streamlined research system built inspired on STORM and built on LangGraph. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/andrestorres123/breeze-agent?style=social)
+| **langgraph-supervisor** | [langchain-ai/langgraph-supervisor-py](https://github.com/langchain-ai/langgraph-supervisor-py) | Build supervisor multi-agent systems with LangGraph. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/langgraph-supervisor-py?style=social)
+| **langmem** | [langchain-ai/langmem](https://github.com/langchain-ai/langmem) | Build agents that learn and adapt from interactions over time. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/langmem?style=social)
+| **langchain-mcp-adapters** | [langchain-ai/langchain-mcp-adapters](https://github.com/langchain-ai/langchain-mcp-adapters) | Make Anthropic Model Context Protocol (MCP) tools compatible with LangGraph agents. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/langchain-mcp-adapters?style=social)
+| **open-deep-research** | [langchain-ai/open_deep_research](https://github.com/langchain-ai/open_deep_research) | Open source assistant for iterative web research and report writing. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/open_deep_research?style=social)
+| **langgraph-swarm** | [langchain-ai/langgraph-swarm-py](https://github.com/langchain-ai/langgraph-swarm-py) | Build swarm-style multi-agent systems using LangGraph. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/langgraph-swarm-py?style=social)
+| **delve-taxonomy-generator** | [andrestorres123/delve](https://github.com/andrestorres123/delve) | A taxonomy generator for unstructured data | -12345 | ![GitHub stars](https://img.shields.io/github/stars/andrestorres123/delve?style=social)
+| **nodeology** | [xyin-anl/Nodeology](https://github.com/xyin-anl/Nodeology) | Enable researcher to build scientific workflows easily with simplified interface. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/xyin-anl/Nodeology?style=social)
+| **langgraph-bigtool** | [langchain-ai/langgraph-bigtool](https://github.com/langchain-ai/langgraph-bigtool) | Build LangGraph agents with large numbers of tools. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/langgraph-bigtool?style=social)
+| **ai-data-science-team** | [business-science/ai-data-science-team](https://github.com/business-science/ai-data-science-team) | An AI-powered data science team of agents to help you perform common data science tasks 10X faster. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/business-science/ai-data-science-team?style=social)
+| **langgraph-reflection** | [langchain-ai/langgraph-reflection](https://github.com/langchain-ai/langgraph-reflection) | LangGraph agent that runs a reflection step. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/langgraph-reflection?style=social)
+| **langgraph-codeact** | [langchain-ai/langgraph-codeact](https://github.com/langchain-ai/langgraph-codeact) | LangGraph implementation of CodeAct agent that generates and executes code instead of tool calling. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/langgraph-codeact?style=social)
 
 ## ✨ Contributing Your Library
 
@@ -33,41 +32,13 @@ To share your project, simply open a Pull Request adding an entry for your packa
 
 **Guidelines**
 
-- Your repo must be distributed as an installable package on PyPI 📦
+- Your repo must be distributed as an installable package (e.g., PyPI for Python, npm 
+  for JavaScript/TypeScript, etc.) 📦
 - The repo should either use the Graph API (exposing a `StateGraph` instance) or 
   the Functional API (exposing an `entrypoint`).
 - The package must include documentation (e.g., a `README.md` or docs site) 
   explaining how to use it.
-
+  
 We'll review your contribution and merge it in!
 
 Thanks for contributing! 🚀
-:::
-
-:::js
-| Name | GitHub URL | Description | Weekly Downloads | Stars |
-| --- | --- | --- | --- | --- |
-| **@langchain/mcp-adapters** | https://github.com/langchain-ai/langchainjs | Make Anthropic Model Context Protocol (MCP) tools compatible with LangGraph agents. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/langchainjs?style=social)
-| **@langchain/langgraph-supervisor** | https://github.com/langchain-ai/langgraphjs/tree/main/libs/langgraph-supervisor | Build supervisor multi-agent systems with LangGraph | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/langgraphjs?style=social)
-| **@langchain/langgraph-swarm** | https://github.com/langchain-ai/langgraphjs/tree/main/libs/langgraph-swarm | Build multi-agent swarms with LangGraph | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/langgraphjs?style=social)
-| **@langchain/langgraph-cua** | https://github.com/langchain-ai/langgraphjs/tree/main/libs/langgraph-cua | Build computer use agents with LangGraph | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/langgraphjs?style=social)
-
-## ✨ Contributing Your Library
-
-Have you built an awesome open-source library using LangGraph? We'd love to feature 
-your project on the official LangGraph documentation pages! 🏆
-
-To share your project, simply open a Pull Request adding an entry for your package in our [packages.yml](https://github.com/langchain-ai/langgraph/blob/main/docs/_scripts/third_party_page/packages.yml) file.
-
-**Guidelines**
-
-- Your repo must be distributed as an installable package on npm 📦
-- The repo should either use the Graph API (exposing a `StateGraph` instance) or 
-  the Functional API (exposing an `entrypoint`).
-- The package must include documentation (e.g., a `README.md` or docs site) 
-  explaining how to use it.
-
-We'll review your contribution and merge it in!
-
-Thanks for contributing! 🚀
-:::

docs/docs/agents/run_agents.md

@@ -9,27 +9,18 @@ hide:
 
 # Running agents
 
+
 Agents support both synchronous and asynchronous execution using either `.invoke()` / `await .ainvoke()` for full responses, or `.stream()` / `.astream()` for **incremental** [streaming](../how-tos/streaming.md) output. This section explains how to provide input, interpret output, enable streaming, and control execution limits.
 
+
 ## Basic usage
 
 Agents can be executed in two primary modes:
 
-:::python
-
 - **Synchronous** using `.invoke()` or `.stream()`
 - **Asynchronous** using `await .ainvoke()` or `async for` with `.astream()`
-  :::
 
-:::js
-
-- **Synchronous** using `.invoke()` or `.stream()`
-- **Asynchronous** using `await .invoke()` or `for await` with `.stream()`
-  :::
-
-:::python
 === "Sync invocation"
-
     ```python
     from langgraph.prebuilt import create_react_agent
 
@@ -40,7 +31,6 @@ Agents can be executed in two primary modes:
     ```
 
 === "Async invocation"
-
     ```python
     from langgraph.prebuilt import create_react_agent
 
@@ -49,24 +39,6 @@ Agents can be executed in two primary modes:
     response = await agent.ainvoke({"messages": [{"role": "user", "content": "what is the weather in sf"}]})
     ```
 
-:::
-
-:::js
-
-```typescript
-import { createReactAgent } from "@langchain/langgraph/prebuilt";
-
-const agent = createReactAgent(...);
-// highlight-next-line
-const response = await agent.invoke({
-    "messages": [
-        { "role": "user", "content": "what is the weather in sf" }
-    ]
-});
-```
-
-:::
-
 ## Inputs and outputs
 
 Agents use a language model that expects a list of `messages` as an input. Therefore, agent inputs and outputs are stored as a list of `messages` under the `messages` key in the agent [state](../concepts/low_level.md#working-with-messages-in-graph-state).
@@ -75,73 +47,33 @@ Agents use a language model that expects a list of `messages` as an input. There
 
 Agent input must be a dictionary with a `messages` key. Supported formats are:
 
-:::python
-| Format | Example |
+| Format             | Example                                                                                                                       |
 |--------------------|-------------------------------------------------------------------------------------------------------------------------------|
-| String | `{"messages": "Hello"}` — Interpreted as a [HumanMessage](https://python.langchain.com/docs/concepts/messages/#humanmessage) |
-| Message dictionary | `{"messages": {"role": "user", "content": "Hello"}}` |
-| List of messages | `{"messages": [{"role": "user", "content": "Hello"}]}` |
-| With custom state | `{"messages": [{"role": "user", "content": "Hello"}], "user_name": "Alice"}` — If using a custom `state_schema` |
-:::
+| String             | `{"messages": "Hello"}`  — Interpreted as a [HumanMessage](https://python.langchain.com/docs/concepts/messages/#humanmessage) |
+| Message dictionary | `{"messages": {"role": "user", "content": "Hello"}}`                                                                          |
+| List of messages   | `{"messages": [{"role": "user", "content": "Hello"}]}`                                                                        |
+| With custom state  | `{"messages": [{"role": "user", "content": "Hello"}], "user_name": "Alice"}` — If using a custom `state_schema`               |
 
-:::js
-| Format | Example |
-|--------------------|-------------------------------------------------------------------------------------------------------------------------------|
-| String | `{"messages": "Hello"}` — Interpreted as a [HumanMessage](https://js.langchain.com/docs/concepts/messages/#humanmessage) |
-| Message dictionary | `{"messages": {"role": "user", "content": "Hello"}}` |
-| List of messages | `{"messages": [{"role": "user", "content": "Hello"}]}` |
-| With custom state | `{"messages": [{"role": "user", "content": "Hello"}], "user_name": "Alice"}` — If using a custom state definition |
-:::
-
-:::python
 Messages are automatically converted into LangChain's internal message format. You can read
 more about [LangChain messages](https://python.langchain.com/docs/concepts/messages/#langchain-messages) in the LangChain documentation.
-:::
-
-:::js
-Messages are automatically converted into LangChain's internal message format. You can read
-more about [LangChain messages](https://js.langchain.com/docs/concepts/messages/#langchain-messages) in the LangChain documentation.
-:::
 
 !!! tip "Using custom agent state"
 
-    :::python
-    You can provide additional fields defined in your agent's state schema directly in the input dictionary. This allows dynamic behavior based on runtime data or prior tool outputs.
+    You can provide additional fields defined in your agent’s state schema directly in the input dictionary. This allows dynamic behavior based on runtime data or prior tool outputs.  
     See the [context guide](./context.md) for full details.
-    :::
-
-    :::js
-    You can provide additional fields defined in your agent's state directly in the state definition. This allows dynamic behavior based on runtime data or prior tool outputs.
-    See the [context guide](./context.md) for full details.
-    :::
 
 !!! note
 
-    :::python
     A string input for `messages` is converted to a [HumanMessage](https://python.langchain.com/docs/concepts/messages/#humanmessage). This behavior differs from the `prompt` parameter in `create_react_agent`, which is interpreted as a [SystemMessage](https://python.langchain.com/docs/concepts/messages/#systemmessage) when passed as a string.
-    :::
 
-    :::js
-    A string input for `messages` is converted to a [HumanMessage](https://js.langchain.com/docs/concepts/messages/#humanmessage). This behavior differs from the `prompt` parameter in `createReactAgent`, which is interpreted as a [SystemMessage](https://js.langchain.com/docs/concepts/messages/#systemmessage) when passed as a string.
-    :::
 
 ## Output format
 
-:::python
 Agent output is a dictionary containing:
 
 - `messages`: A list of all messages exchanged during execution (user input, assistant replies, tool invocations).
 - Optionally, `structured_response` if [structured output](./agents.md#6-configure-structured-output) is configured.
 - If using a custom `state_schema`, additional keys corresponding to your defined fields may also be present in the output. These can hold updated state values from tool execution or prompt logic.
-:::
-
-:::js
-Agent output is a dictionary containing:
-
-- `messages`: A list of all messages exchanged during execution (user input, assistant replies, tool invocations).
-- Optionally, `structuredResponse` if [structured output](./agents.md#6-configure-structured-output) is configured.
-- If using a custom state definition, additional keys corresponding to your defined fields may also be present in the output. These can hold updated state values from tool execution or prompt logic.
-:::
 
 See the [context guide](./context.md) for more details on working with custom state schemas and accessing context.
 
@@ -155,7 +87,6 @@ Agents support streaming responses for more responsive applications. This includ
 
 Streaming is available in both sync and async modes:
 
-:::python
 === "Sync streaming"
 
     ```python
@@ -176,36 +107,14 @@ Streaming is available in both sync and async modes:
         print(chunk)
     ```
 
-:::
-
-:::js
-
-```typescript
-for await (const chunk of agent.stream(
-  { messages: [{ role: "user", content: "what is the weather in sf" }] },
-  { streamMode: "updates" }
-)) {
-  console.log(chunk);
-}
-```
-
-:::
-
 !!! tip
 
     For full details, see the [streaming guide](../how-tos/streaming.md).
 
 ## Max iterations
 
-:::python
 To control agent execution and avoid infinite loops, set a recursion limit. This defines the maximum number of steps the agent can take before raising a `GraphRecursionError`. You can configure `recursion_limit` at runtime or when defining agent via `.with_config()`:
-:::
 
-:::js
-To control agent execution and avoid infinite loops, set a recursion limit. This defines the maximum number of steps the agent can take before raising a `GraphRecursionError`. You can configure `recursionLimit` at runtime or when defining agent via `.withConfig()`:
-:::
-
-:::python
 === "Runtime"
 
     ```python
@@ -254,70 +163,6 @@ To control agent execution and avoid infinite loops, set a recursion limit. This
         print("Agent stopped due to max iterations.")
     ```
 
-:::
-
-:::js
-=== "Runtime"
-
-    ```typescript
-    import { GraphRecursionError } from "@langchain/langgraph";
-    import { ChatAnthropic } from "@langchain/langgraph/prebuilt";
-    import { createReactAgent } from "@langchain/langgraph/prebuilt";
-
-    const maxIterations = 3;
-    // highlight-next-line
-    const recursionLimit = 2 * maxIterations + 1;
-    const agent = createReactAgent({
-        llm: new ChatAnthropic({ model: "claude-3-5-haiku-latest" }),
-        tools: [getWeather]
-    });
-
-    try {
-        const response = await agent.invoke(
-            {"messages": [{"role": "user", "content": "what's the weather in sf"}]},
-            // highlight-next-line
-            { recursionLimit }
-        );
-    } catch (error) {
-        if (error instanceof GraphRecursionError) {
-            console.log("Agent stopped due to max iterations.");
-        }
-    }
-    ```
-
-=== "`.withConfig()`"
-
-    ```typescript
-    import { GraphRecursionError } from "@langchain/langgraph";
-    import { ChatAnthropic } from "@langchain/langgraph/prebuilt";
-    import { createReactAgent } from "@langchain/langgraph/prebuilt";
-
-    const maxIterations = 3;
-    // highlight-next-line
-    const recursionLimit = 2 * maxIterations + 1;
-    const agent = createReactAgent({
-        llm: new ChatAnthropic({ model: "claude-3-5-haiku-latest" }),
-        tools: [getWeather]
-    });
-    // highlight-next-line
-    const agentWithRecursionLimit = agent.withConfig({ recursionLimit });
-
-    try {
-        const response = await agentWithRecursionLimit.invoke(
-            {"messages": [{"role": "user", "content": "what's the weather in sf"}]},
-        );
-    } catch (error) {
-        if (error instanceof GraphRecursionError) {
-            console.log("Agent stopped due to max iterations.");
-        }
-    }
-    ```
-
-:::
-
-:::python
-
 ## Additional Resources
 
-- [Async programming in LangChain](https://python.langchain.com/docs/concepts/async)
-  :::
+* [Async programming in LangChain](https://python.langchain.com/docs/concepts/async)

docs/docs/agents/tools.md

@@ -0,0 +1,310 @@
+---
+search:
+  boost: 2
+tags:
+  - agent
+hide:
+  - tags
+---
+
+# Tools
+
+[Tools](https://python.langchain.com/docs/concepts/tools/) are a way to encapsulate a function and its input schema in a way that can be passed to a chat model that supports tool calling. This allows the model to request the execution of this function with specific inputs.
+
+You can either [define your own tools](#define-simple-tools) or use [prebuilt integrations](#prebuilt-tools) that LangChain provides.
+
+## Define simple tools
+
+You can pass a vanilla function to `create_react_agent` to use as a tool:
+
+```python
+from langgraph.prebuilt import create_react_agent
+
+def multiply(a: int, b: int) -> int:
+    """Multiply two numbers."""
+    return a * b
+
+create_react_agent(
+    model="anthropic:claude-3-7-sonnet",
+    tools=[multiply]
+)
+```
+
+`create_react_agent` automatically converts vanilla functions to [LangChain tools](https://python.langchain.com/docs/concepts/tools/#tool-interface).
+
+## Customize tools
+
+For more control over tool behavior, use the `@tool` decorator:
+
+```python
+# highlight-next-line
+from langchain_core.tools import tool
+
+# highlight-next-line
+@tool("multiply_tool", parse_docstring=True)
+def multiply(a: int, b: int) -> int:
+    """Multiply two numbers.
+
+    Args:
+        a: First operand
+        b: Second operand
+    """
+    return a * b
+```
+
+You can also define a custom input schema using Pydantic:
+
+```python
+from pydantic import BaseModel, Field
+
+class MultiplyInputSchema(BaseModel):
+    """Multiply two numbers"""
+    a: int = Field(description="First operand")
+    b: int = Field(description="Second operand")
+
+# highlight-next-line
+@tool("multiply_tool", args_schema=MultiplyInputSchema)
+def multiply(a: int, b: int) -> int:
+    return a * b
+```
+
+For additional customization, refer to the [custom tools guide](https://python.langchain.com/docs/how_to/custom_tools/).
+
+## Hide arguments from the model
+
+Some tools require runtime-only arguments (e.g., user ID or session context) that should not be controllable by the model.
+
+You can put these arguments in the `state` or `config` of the agent, and access
+this information inside the tool:
+
+```python
+from langgraph.prebuilt import InjectedState
+from langgraph.prebuilt.chat_agent_executor import AgentState
+from langchain_core.runnables import RunnableConfig
+
+def my_tool(
+    # This will be populated by an LLM
+    tool_arg: str,
+    # access information that's dynamically updated inside the agent
+    # highlight-next-line
+    state: Annotated[AgentState, InjectedState],
+    # access static data that is passed at agent invocation
+    # highlight-next-line
+    config: RunnableConfig,
+) -> str:
+    """My tool."""
+    do_something_with_state(state["messages"])
+    do_something_with_config(config)
+    ...
+```
+
+## Disable parallel tool calling
+
+Some model providers support executing multiple tools in parallel, but
+allow users to disable this feature.
+
+For supported providers, you can disable parallel tool calling by setting `parallel_tool_calls=False` via the `model.bind_tools()` method:
+
+```python
+from langchain.chat_models import init_chat_model
+
+def add(a: int, b: int) -> int:
+    """Add two numbers"""
+    return a + b
+
+def multiply(a: int, b: int) -> int:
+    """Multiply two numbers."""
+    return a * b
+
+model = init_chat_model("anthropic:claude-3-5-sonnet-latest", temperature=0)
+tools = [add, multiply]
+agent = create_react_agent(
+    # disable parallel tool calls
+    # highlight-next-line
+    model=model.bind_tools(tools, parallel_tool_calls=False),
+    tools=tools
+)
+
+agent.invoke(
+    {"messages": [{"role": "user", "content": "what's 3 + 5 and 4 * 7?"}]}
+)
+```
+
+## Return tool results directly
+
+Use `return_direct=True` to return tool results immediately and stop the agent loop:
+
+```python
+from langchain_core.tools import tool
+
+# highlight-next-line
+@tool(return_direct=True)
+def add(a: int, b: int) -> int:
+    """Add two numbers"""
+    return a + b
+
+agent = create_react_agent(
+    model="anthropic:claude-3-7-sonnet-latest",
+    tools=[add]
+)
+
+agent.invoke(
+    {"messages": [{"role": "user", "content": "what's 3 + 5?"}]}
+)
+```
+
+## Force tool use
+
+To force the agent to use specific tools, you can set the `tool_choice` option in `model.bind_tools()`:
+
+```python
+from langchain_core.tools import tool
+
+# highlight-next-line
+@tool(return_direct=True)
+def greet(user_name: str) -> int:
+    """Greet user."""
+    return f"Hello {user_name}!"
+
+tools = [greet]
+
+agent = create_react_agent(
+    # highlight-next-line
+    model=model.bind_tools(tools, tool_choice={"type": "tool", "name": "greet"}),
+    tools=tools
+)
+
+agent.invoke(
+    {"messages": [{"role": "user", "content": "Hi, I am Bob"}]}
+)
+```
+
+!!! Warning "Avoid infinite loops"
+
+    Forcing tool usage without stopping conditions can create infinite loops. Use one of the following safeguards:
+
+    - Mark the tool with [`return_direct=True`](#return-tool-results-directly) to end the loop after execution.
+    - Set [`recursion_limit`](../concepts/low_level.md#recursion-limit) to restrict the number of execution steps.
+
+## Handle tool errors
+
+By default, the agent will catch all exceptions raised during tool calls and will pass those as tool messages to the LLM. To control how the errors are handled, you can use the prebuilt [`ToolNode`][langgraph.prebuilt.tool_node.ToolNode] — the node that executes tools inside `create_react_agent` — via its `handle_tool_errors` parameter:
+
+=== "Enable error handling (default)"
+
+    ```python
+    from langgraph.prebuilt import create_react_agent
+
+    def multiply(a: int, b: int) -> int:
+        """Multiply two numbers."""
+        if a == 42:
+            raise ValueError("The ultimate error")
+        return a * b
+
+    # Run with error handling (default)
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[multiply]
+    )
+    agent.invoke(
+        {"messages": [{"role": "user", "content": "what's 42 x 7?"}]}
+    )
+    ```
+
+=== "Disable error handling"
+
+    ```python
+    from langgraph.prebuilt import create_react_agent, ToolNode
+
+    def multiply(a: int, b: int) -> int:
+        """Multiply two numbers."""
+        if a == 42:
+            raise ValueError("The ultimate error")
+        return a * b
+
+    # highlight-next-line
+    tool_node = ToolNode(
+        [multiply],
+        # highlight-next-line
+        handle_tool_errors=False  # (1)!
+    )
+    agent_no_error_handling = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=tool_node
+    )
+    agent_no_error_handling.invoke(
+        {"messages": [{"role": "user", "content": "what's 42 x 7?"}]}
+    )
+    ```
+
+    1. This disables error handling (enabled by default). See all available strategies in the [API reference][langgraph.prebuilt.tool_node.ToolNode].
+
+=== "Custom error handling"
+
+    ```python
+    from langgraph.prebuilt import create_react_agent, ToolNode
+
+    def multiply(a: int, b: int) -> int:
+        """Multiply two numbers."""
+        if a == 42:
+            raise ValueError("The ultimate error")
+        return a * b
+
+    # highlight-next-line
+    tool_node = ToolNode(
+        [multiply],
+        # highlight-next-line
+        handle_tool_errors=(
+            "Can't use 42 as a first operand, you must switch operands!"  # (1)!
+        )
+    )
+    agent_custom_error_handling = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=tool_node
+    )
+    agent_custom_error_handling.invoke(
+        {"messages": [{"role": "user", "content": "what's 42 x 7?"}]}
+    )
+    ```
+
+    1. This provides a custom message to send to the LLM in case of an exception. See all available strategies in the [API reference][langgraph.prebuilt.tool_node.ToolNode].
+
+See [API reference][langgraph.prebuilt.tool_node.ToolNode] for more information on different tool error handling options.
+
+## Working with memory
+
+LangGraph allows access to short-term and long-term memory from tools. See [Memory](../how-tos/memory/add-memory.md) guide for more information on:
+
+* how to [read](../how-tos/memory/add-memory.md#read-short-term) from and [write](../how-tos/memory/add-memory.md#write-short-term) to **short-term** memory
+* how to [read](../how-tos/memory/add-memory.md#read-long-term) from and [write](../how-tos/memory/add-memory.md#write-long-term) to **long-term** memory
+
+## Prebuilt tools
+
+You can use prebuilt tools from model providers by passing a dictionary with tool specs to the `tools` parameter of `create_react_agent`. For example, to use the `web_search_preview` tool from OpenAI:
+
+```python
+from langgraph.prebuilt import create_react_agent
+
+agent = create_react_agent(
+    model="openai:gpt-4o-mini", 
+    tools=[{"type": "web_search_preview"}]
+)
+response = agent.invoke(
+    {"messages": ["What was a positive news story from today?"]}
+)
+```
+
+Additionally, LangChain supports a wide range of prebuilt tool integrations for interacting with APIs, databases, file systems, web data, and more. These tools extend the functionality of agents and enable rapid development.
+
+You can browse the full list of available integrations in the [LangChain integrations directory](https://python.langchain.com/docs/integrations/tools/).
+
+Some commonly used tool categories include:
+
+- **Search**: Bing, SerpAPI, Tavily
+- **Code interpreters**: Python REPL, Node.js REPL
+- **Databases**: SQL, MongoDB, Redis
+- **Web data**: Web scraping and browsing
+- **APIs**: OpenWeatherMap, NewsAPI, and others
+
+These integrations can be configured and added to your agents using the same `tools` parameter shown in the examples above.
+

docs/docs/agents/ui.md

@@ -13,7 +13,7 @@ You can use a prebuilt chat UI for interacting with any LangGraph agent through
 
 ## Run agent in UI
 
-First, set up LangGraph API server [locally](../tutorials/langgraph-platform/local-server.md) or deploy your agent on [LangGraph Platform](https://langchain-ai.github.io/langgraph/cloud/quick_start/).
+First, set up LangGraph API server [locally](./deployment.md#launch-langgraph-server-locally) or deploy your agent on [LangGraph Platform](https://langchain-ai.github.io/langgraph/cloud/quick_start/).
 
 Then, navigate to [Agent Chat UI](https://agentchat.vercel.app), or clone the repository and [run the dev server locally](https://github.com/langchain-ai/agent-chat-ui?tab=readme-ov-file#setup):
 
@@ -25,13 +25,13 @@ Then, navigate to [Agent Chat UI](https://agentchat.vercel.app), or clone the re
 
 ## Add human-in-the-loop
 
-Agent Chat UI has full support for [human-in-the-loop](../concepts/human_in_the_loop.md) workflows. To try it out, replace the agent code in `src/agent/graph.py` (from the [deployment](../tutorials/langgraph-platform/local-server.md) guide) with this [agent implementation](../how-tos/human_in_the_loop/add-human-in-the-loop.md#add-interrupts-to-any-tool):
+Agent Chat UI has full support for [human-in-the-loop](../concepts/human_in_the_loop.md) workflows. To try it out, replace the agent code in `src/agent/graph.py` (from the [deployment](./deployment.md) guide) with this [agent implementation](../how-tos/human_in_the_loop/add-human-in-the-loop.md#add-interrupts-to-any-tool):
 
 <video controls src="../assets/interrupt-chat-ui.mp4" type="video/mp4"></video>
 
 !!! Important
 
-    Agent Chat UI works best if your LangGraph agent interrupts using the @[`HumanInterrupt` schema][HumanInterrupt]. If you do not use that schema, the Agent Chat UI will be able to render the input passed to the `interrupt` function, but it will not have full support for resuming your graph.
+    Agent Chat UI works best if your LangGraph agent interrupts using the [`HumanInterrupt` schema][langgraph.prebuilt.interrupt.HumanInterrupt]. If you do not use that schema, the Agent Chat UI will be able to render the input passed to the `interrupt` function, but it will not have full support for resuming your graph.
 
 ## Generative UI
 

docs/docs/cloud/concepts/data_storage_and_privacy.md

@@ -1,54 +0,0 @@
-# Data Storage and Privacy
-
-This document describes how data is processed in the LangGraph CLI and the LangGraph Server for both the in-memory server (`langgraph dev`) and the local Docker server (`langgraph up`). It also describes what data is tracked when interacting with the hosted LangGraph Studio frontend.
-
-## CLI
-
-LangGraph **CLI** is the command-line interface for building and running LangGraph applications; see the [CLI guide](../../concepts/langgraph_cli.md) to learn more.
-
-By default, calls to most CLI commands log a single analytics event upon invocation. This helps us better prioritize improvements to the CLI experience. Each telemetry event contains the calling process's OS, OS version, Python version, the CLI version, the command name (`dev`, `up`, `run`, etc.), and booleans representing whether a flag was passed to the command. You can see the full analytics logic [here](https://github.com/langchain-ai/langgraph/blob/main/libs/cli/langgraph_cli/analytics.py). 
-
-You can disable all CLI telemetry by setting `LANGGRAPH_CLI_NO_ANALYTICS=1`.
-
-## LangGraph Server (in-memory & docker)
-
-The [LangGraph Server](../../concepts/langgraph_server.md) provides a durable execution runtime that relies on persisting checkpoints of your application state, long-term memories, thread metadata, assistants, and similar resources to the local file system or a database. Unless you have deliberately customized the storage location, this information is either written to local disk (for `langgraph dev`) or a PostgreSQL database (for `langgraph up` and in all deployments).
-
-### LangSmith Tracing
-
-When running the LangGraph server (either in-memory or in Docker), LangSmith tracing may be enabled to facilitate faster debugging and offer observability of graph state and LLM prompts in production. You can always disable tracing by setting `LANGSMITH_TRACING=false` in your server's runtime environment.
-
-### In-memory development server (`langgraph dev`)
-
-`langgraph dev` runs an [in-memory development server](../../tutorials/langgraph-platform/local-server.md) as a single Python process, designed for quick development and testing. It saves all checkpointing and memory data to disk within a `.langgraph_api` directory in the current working directory. Apart from the telemetry data described in the [CLI](#cli) section, no data leaves the machine unless you have enabled tracing or your graph code explicitly contacts an external service.
-
-### Standalone Container (`langgraph up`)
-
-`langgraph up` builds your local package into a Docker image and runs the server as a [standalone container](../../concepts/deployment_options.md#standalone-container) consisting of three containers: the API server, a PostgreSQL container, and a Redis container. All persistent data (checkpoints, assistants, etc.) are stored in the PostgreSQL database. Redis is used as a pubsub connection for real-time streaming of events. You can encrypt all checkpoints before saving to the database by setting a valid `LANGGRAPH_AES_KEY` environment variable. You can also specify [TTLs](../../how-tos/ttl/configure_ttl.md) for checkpoints and cross-thread memories in `langgraph.json` to control how long data is stored. All persisted threads, memories, and other data can be deleted via the relevant API endpoints.
-
-Additional API calls are made to confirm that the server has a valid license and to track the number of executed runs and tasks. Periodically, the API server validates the provided license key (or API key).
-
-If you've disabled [tracing](#langsmith-tracing), no user data is persisted externally unless your graph code explicitly contacts an external service.
-
-## Studio
-
-[LangGraph Studio](../../concepts/langgraph_studio.md) is a graphical interface for interacting with your LangGraph server. It does not persist any private data (the data you send to your server is not sent to LangSmith). Though the studio interface is served at [smith.langchain.com](https://smith.langchain.com), it is run in your browser and connects directly to your local LangGraph server so that no data needs to be sent to LangSmith.
-
-If you are logged in, LangSmith does collect some usage analytics to help improve studio's user experience. This includes:
-
-- Page visits and navigation patterns
-- User actions (button clicks)
-- Browser type and version
-- Screen resolution and viewport size
-
-Importantly, no application data or code (or other sensitive configuration details) are collected. All of that is stored in the persistence layer of your LangGraph server. When using Studio anonymously, no account creation is required and usage analytics are not collected.
-
-## Quick reference
-
-In summary, you can opt-out of server-side telemetry by turning off CLI analytics and disabling tracing.
-
-| Variable                       | Purpose                   | Default                          |
-| ------------------------------ | ------------------------- | -------------------------------- |
-| `LANGGRAPH_CLI_NO_ANALYTICS=1` | Disable CLI analytics     | Analytics enabled                |
-| `LANGSMITH_API_KEY`            | Enable LangSmith tracing  | Tracing disabled                 |
-| `LANGSMITH_TRACING=false`      | Disable LangSmith tracing | Depends on environment           |

docs/docs/cloud/concepts/threads.md

@@ -0,0 +1,12 @@
+# Threads
+
+A thread contains the accumulated state of a sequence of [runs](../../concepts/assistants.md#execution). When a run is executed, the [state](../../concepts/low_level.md#state) of the underlying graph of the assistant will be persisted to the thread.
+
+A thread's current and historical state can be retrieved. To persist state, a thread must be created prior to executing a run.
+
+The state of a thread at a particular point in time is called a [checkpoint](../../concepts/persistence.md#checkpoints). Checkpoints are persisted and can be used to restore the state of a thread at a later time.
+
+## Learn more
+
+* For more on threads and checkpoints, see this section of the [LangGraph conceptual guide](../../concepts/persistence.md).
+* The LangGraph Platform API provides several endpoints for creating and managing threads and thread state. See the [API reference](../../cloud/reference/api/api_ref.html#tag/threads) for more details.

docs/docs/cloud/deployment/cloud.md

@@ -99,8 +99,8 @@ Starting from the `LangGraph Platform` view...
 1. In the top-right corner, select the gear icon (`Deployment Settings`).
 1. Update the `Git Branch` to the desired branch.
 1. Check/uncheck checkbox to `Automatically update deployment on push to branch`.
-1. Branch creation/deletion and tag creation/deletion events will not trigger an update. Only pushes to an existing branch will trigger an update.
-1. Pushes in quick succession to a branch will queue subsequent updates. Once a build completes, the most recent commit will begin building and the other queued builds will be skipped.
+    1. Branch creation/deletion and tag creation/deletion events will not trigger an update. Only pushes to an existing branch will trigger an update.
+    1. Pushes in quick succession to a branch will not trigger subsequent updates. In the future, this functionality may be changed/improved.
 
 ## Add or Remove GitHub Repositories
 

docs/docs/cloud/deployment/egress.md

@@ -1,119 +0,0 @@
-# Egress for Subscription Metrics and Operational Metadata
-
-> **Important: Self Hosted Only** 
-> This section only applies to customers who are not running in offline mode and assumes you are using a self-hosted LangGraph Platform instance.
-> This does not apply to SaaS or Hybrid deployments.
-
-Self-Hosted LangGraph Platform instances store all information locally and will never send sensitive information outside of your network. We currently only track platform usage for billing purposes according to the entitlements in your order. In order to better remotely support our customers, we do require egress to `https://beacon.langchain.com`.
-
-In the future, we will be introducing support diagnostics to help us ensure that the LangGraph Platform is running at an optimal level within your environment.
-
-> **Warning**  
-> **This will require egress to `https://beacon.langchain.com` from your network.**
-> **If using an API key, you will also need to allow egress to `https://api.smith.langchain.com` or `https://eu.api.smith.langchain.com` for API key verification.**
-
-Generally, data that we send to Beacon can be categorized as follows:
-
-- **Subscription Metrics**
-  - Subscription metrics are used to determine level of access and utilization of LangSmith. This includes, but are not limited to:
-    - Nodes Executed
-    - Runs Executed
-    - License Key Verification
-- **Operational Metadata**
-  - This metadata will contain and collect the above subscription metrics to assist with remote support, allowing the LangChain team to diagnose and troubleshoot performance issues more effectively and proactively.
-
-## Example Payloads
-
-In an effort to maximize transparency, we provide sample payloads here:
-
-### License Verification (If using an Enterprise License)
-
-**Endpoint:**
-
-`POST beacon.langchain.com/v1/beacon/verify`
-
-**Request:**
-
-```json
-{
-  "license": "<YOUR_LICENSE_KEY>"
-}
-```
-
-**Response:**
-
-```json
-{
-  "token": "Valid JWT" // Short-lived JWT token to avoid repeated license checks
-}
-```
-
-### Api Key Verification (If using a LangSmith API Key)
-
-**Endpoint:**
-`POST api.smith.langchain.com/auth`
-
-**Request:**
-
-```json
-"Headers": {
-  X-Api-Key: <YOUR_API_KEY>
-}
-```
-
-**Response:**
-
-```json
-{
-  "org_config": {
-    "org_id": "3a1c2b6f-4430-4b92-8a5b-79b8b567bbc1",
-    ... // Additional organization details
-  }
-}
-```
-
-### Usage Reporting
-
-**Endpoint:**
-
-`POST beacon.langchain.com/v1/metadata/submit`
-
-**Request:**
-
-```json
-{
-  "license": "<YOUR_LICENSE_KEY>",
-  "from_timestamp": "2025-01-06T09:00:00Z",
-  "to_timestamp": "2025-01-06T10:00:00Z",
-  "tags": {
-    "langgraph.python.version": "0.1.0",
-    "langgraph_api.version": "0.2.0",
-    "langgraph.platform.revision": "abc123",
-    "langgraph.platform.variant": "standard",
-    "langgraph.platform.host": "host-1",
-    "langgraph.platform.tenant_id": "3a1c2b6f-4430-4b92-8a5b-79b8b567bbc1",
-    "langgraph.platform.project_id": "c5b5f53a-4716-4326-8967-d4f7f7799735",
-    "langgraph.platform.plan": "enterprise",
-    "user_app.uses_indexing": "true",
-    "user_app.uses_custom_app": "false",
-    "user_app.uses_custom_auth": "true",
-    "user_app.uses_thread_ttl": "true",
-    "user_app.uses_store_ttl": "false"
-  },
-  "measures": {
-    "langgraph.platform.runs": 150,
-    "langgraph.platform.nodes": 450
-  },
-  "logs": []
-}
-```
-
-**Response:**
-
-```json
-"204 No Content"
-```
-
-## Our Commitment
-
-LangChain will not store any sensitive information in the Subscription Metrics or Operational Metadata. Any data collected will not be shared with a third party. If you have any concerns about the data being sent, please reach out to your account team.

docs/docs/cloud/deployment/graph_rebuild.md

@@ -20,7 +20,7 @@ my-app/
 |-- openai_agent.py     # code for your graph
 ```
 
-where the graph is defined in `openai_agent.py`. 
+where the graph is defined in `openai_agent.py`.
 
 ### No rebuild
 
@@ -28,11 +28,11 @@ In the standard LangGraph API configuration, the server uses the compiled graph
 
 ```python
 from langchain_openai import ChatOpenAI
-from langgraph.graph import END, START, MessageGraph
+from langgraph.graph import END, START, StateGraph, MessagesState
 
 model = ChatOpenAI(temperature=0)
 
-graph_workflow = MessageGraph()
+graph_workflow = StateGraph(MessagesState)
 
 graph_workflow.add_node("agent", model)
 graph_workflow.add_edge("agent", END)
@@ -61,7 +61,7 @@ To make your graph rebuild on each new run with custom configuration, you need t
 from typing import Annotated
 from typing_extensions import TypedDict
 from langchain_openai import ChatOpenAI
-from langgraph.graph import END, START, MessageGraph
+from langgraph.graph import END, START
 from langgraph.graph.state import StateGraph
 from langgraph.graph.message import add_messages
 from langgraph.prebuilt import ToolNode
@@ -144,4 +144,4 @@ Finally, you need to specify the path to your graph-making function (`make_graph
 }
 ```
 
-See more info on LangGraph API configuration file [here](../reference/cli.md#configuration-file)
+See more info on LangGraph API configuration file [here](../reference/cli.md#configuration-file)

docs/docs/cloud/deployment/self_hosted_control_plane.md

@@ -3,7 +3,7 @@
 Before deploying, review the [conceptual guide for the Self-Hosted Control Plane](../../concepts/langgraph_self_hosted_control_plane.md) deployment option.
 
 !!! info "Important"
-    The Self-Hosted Control Plane deployment option requires an [Enterprise](../../concepts/plans.md) plan.
+    The Self-Hosted Control Plane deployment option is currently in beta stage and requires an [Enterprise](../../concepts/plans.md) plan.
 
 ## Prerequisites
 
@@ -23,8 +23,6 @@ Before deploying, review the [conceptual guide for the Self-Hosted Control Plane
 
         kubectl get storageclass
 
-1. Egress to `https://beacon.langchain.com` from your network. This is required for license verification and usage reporting if not running in air-gapped mode. See the [Egress documentation](../../cloud/deployment/egress.md) for more details.
-
 ## Setup
 
 1. As part of configuring your Self-Hosted LangSmith instance, you enable the `langgraphPlatform` option. This will provision a few key resources.

docs/docs/cloud/deployment/self_hosted_data_plane.md

@@ -3,7 +3,7 @@
 Before deploying, review the [conceptual guide for the Self-Hosted Data Plane](../../concepts/langgraph_self_hosted_data_plane.md) deployment option.
 
 !!! info "Important"
-    The Self-Hosted Data Plane deployment option requires an [Enterprise](../../concepts/plans.md) plan.
+    The Self-Hosted Data Plane deployment option is currently in beta stage and requires an [Enterprise](../../concepts/plans.md) plan.
 
 ## Prerequisites
 
@@ -35,6 +35,7 @@ Before deploying, review the [conceptual guide for the Self-Hosted Data Plane](.
 1. Configure your `langgraph-dataplane-values.yaml` file.
 
         config:
+          langgraphPlatformLicenseKey: "" # Your LangGraph Platform license key
           langsmithApiKey: "" # API Key of your Workspace
           langsmithWorkspaceId: "" # Workspace ID
           hostBackendUrl: "https://api.host.langchain.com" # Only override this if on EU

docs/docs/cloud/deployment/setup.md

@@ -95,7 +95,7 @@ my-app/
 
 ## Define Graphs
 
-Implement your graphs! Graphs can be defined in a single file or multiple files. Make note of the variable names of each @[CompiledStateGraph][CompiledStateGraph] to be included in the LangGraph application. The variable names will be used later when creating the [LangGraph configuration file](../reference/cli.md#configuration-file).
+Implement your graphs! Graphs can be defined in a single file or multiple files. Make note of the variable names of each [CompiledStateGraph][langgraph.graph.state.CompiledStateGraph] to be included in the LangGraph application. The variable names will be used later when creating the [LangGraph configuration file](../reference/cli.md#configuration-file).
 
 Example `agent.py` file, which shows how to import from other modules you define (code for the modules is not shown here, please see [this repository](https://github.com/langchain-ai/langgraph-example) to see their implementation):
 
@@ -108,11 +108,11 @@ from langgraph.graph import StateGraph, END, START
 from my_agent.utils.nodes import call_model, should_continue, tool_node # import nodes
 from my_agent.utils.state import AgentState # import state
 
-# Define the runtime context
-class GraphContext(TypedDict):
+# Define the config
+class GraphConfig(TypedDict):
     model_name: Literal["anthropic", "openai"]
 
-workflow = StateGraph(AgentState, context_schema=GraphContext)
+workflow = StateGraph(AgentState, config_schema=GraphConfig)
 workflow.add_node("agent", call_model)
 workflow.add_node("action", tool_node)
 workflow.add_edge(START, "agent")

docs/docs/cloud/deployment/setup_pyproject.md

@@ -108,7 +108,7 @@ my-app/
 
 ## Define Graphs
 
-Implement your graphs! Graphs can be defined in a single file or multiple files. Make note of the variable names of each @[CompiledStateGraph][CompiledStateGraph] to be included in the LangGraph application. The variable names will be used later when creating the [LangGraph configuration file](../reference/cli.md#configuration-file).
+Implement your graphs! Graphs can be defined in a single file or multiple files. Make note of the variable names of each [CompiledStateGraph][langgraph.graph.state.CompiledStateGraph] to be included in the LangGraph application. The variable names will be used later when creating the [LangGraph configuration file](../reference/cli.md#configuration-file).
 
 Example `agent.py` file, which shows how to import from other modules you define (code for the modules is not shown here, please see [this repository](https://github.com/langchain-ai/langgraph-example-pyproject) to see their implementation):
 
@@ -121,11 +121,11 @@ from langgraph.graph import StateGraph, END, START
 from my_agent.utils.nodes import call_model, should_continue, tool_node # import nodes
 from my_agent.utils.state import AgentState # import state
 
-# Define the runtime context
-class GraphContext(TypedDict):
+# Define the config
+class GraphConfig(TypedDict):
     model_name: Literal["anthropic", "openai"]
 
-workflow = StateGraph(AgentState, context_schema=GraphContext)
+workflow = StateGraph(AgentState, config_schema=GraphConfig)
 workflow.add_node("agent", call_model)
 workflow.add_node("action", tool_node)
 workflow.add_edge(START, "agent")

docs/docs/cloud/deployment/standalone_container.md

@@ -21,9 +21,9 @@ Before deploying, review the [conceptual guide for the Standalone Container](../
 
             `<database_name_1>` and `database_name_2` are different databases within the same instance, but `<hostname_1>` is shared. **The same database cannot be used for separate deployments**.
 
+    1. `LANGSMITH_API_KEY`: (if using [Lite](../../concepts/langgraph_server.md#server-versions)) LangSmith API key. This will be used to authenticate ONCE at server start up.
     1. `LANGGRAPH_CLOUD_LICENSE_KEY`: (if using [Enterprise](../../concepts/langgraph_data_plane.md#licensing)) LangGraph Platform license key. This will be used to authenticate ONCE at server start up.
     1. `LANGSMITH_ENDPOINT`: To send traces to a [self-hosted LangSmith](https://docs.smith.langchain.com/self_hosting) instance, set `LANGSMITH_ENDPOINT` to the hostname of the self-hosted LangSmith instance.
-1. Egress to `https://beacon.langchain.com` from your network. This is required for license verification and usage reporting if not running in air-gapped mode. See the [Egress documentation](../../cloud/deployment/egress.md) for more details.
 
 ## Kubernetes (Helm)
 

docs/docs/cloud/how-tos/add-human-in-the-loop.md

@@ -2,7 +2,7 @@
 
 To review, edit, and approve tool calls in an agent or workflow, use LangGraph's [human-in-the-loop](../../concepts/human_in_the_loop.md) features.
 
-## Dynamic interrupts
+## LangGraph API invoke & resume
 
 === "Python"
 
@@ -30,7 +30,9 @@ To review, edit, and approve tool calls in an agent or workflow, use LangGraph's
     # > [
     # >     {
     # >         'value': {'text_to_revise': 'original text'},
-    # >         'id': '...',
+    # >         'resumable': True,
+    # >         'ns': ['human_node:fc722478-2f21-0578-c572-d9fc4dd07c3b'],
+    # >         'when': 'during'
     # >     }
     # > ]
 
@@ -201,7 +203,9 @@ To review, edit, and approve tool calls in an agent or workflow, use LangGraph's
         # > [
         # >     {
         # >         'value': {'text_to_revise': 'original text'},
-        # >         'id': '...',
+        # >         'resumable': True,
+        # >         'ns': ['human_node:fc722478-2f21-0578-c572-d9fc4dd07c3b'],
+        # >         'when': 'during'
         # >     }
         # > ]
 
@@ -301,185 +305,6 @@ To review, edit, and approve tool calls in an agent or workflow, use LangGraph's
         }"
         ```
 
-## Static interrupts
-
-Static interrupts (also known as static breakpoints) are triggered either before or after a node executes. 
-
-!!! warning
-
-    Static interrupts are **not** recommended for human-in-the-loop workflows. They are best used for debugging and testing.
-
-You can set static interrupts by specifying `interrupt_before` and `interrupt_after` at compile time:
-
-```python
-# highlight-next-line
-graph = graph_builder.compile( # (1)!
-    # highlight-next-line
-    interrupt_before=["node_a"], # (2)!
-    # highlight-next-line
-    interrupt_after=["node_b", "node_c"], # (3)!
-)
-```
-
-1. The breakpoints are set during `compile` time.
-2. `interrupt_before` specifies the nodes where execution should pause before the node is executed.
-3. `interrupt_after` specifies the nodes where execution should pause after the node is executed.
-
-Alternatively, you can set static interrupts at run time:
-
-=== "Python"
-
-    ```python
-    # highlight-next-line
-    await client.runs.wait( # (1)!
-        thread_id,
-        assistant_id,
-        inputs=inputs,
-        # highlight-next-line
-        interrupt_before=["node_a"], # (2)!
-        # highlight-next-line
-        interrupt_after=["node_b", "node_c"] # (3)!
-    )
-    ```
-
-    1. `client.runs.wait` is called with the `interrupt_before` and `interrupt_after` parameters. This is a run-time configuration and can be changed for every invocation.
-    2. `interrupt_before` specifies the nodes where execution should pause before the node is executed.
-    3. `interrupt_after` specifies the nodes where execution should pause after the node is executed.
-
-=== "JavaScript"
-
-    ```js
-    // highlight-next-line
-    await client.runs.wait( // (1)!
-        threadID,
-        assistantID,
-        {
-        input: input,
-        // highlight-next-line
-        interruptBefore: ["node_a"], // (2)!
-        // highlight-next-line
-        interruptAfter: ["node_b", "node_c"] // (3)!
-        }
-    )
-    ```
-
-    1. `client.runs.wait` is called with the `interruptBefore` and `interruptAfter` parameters. This is a run-time configuration and can be changed for every invocation.
-    2. `interruptBefore` specifies the nodes where execution should pause before the node is executed.
-    3. `interruptAfter` specifies the nodes where execution should pause after the node is executed.
-
-=== "cURL"
-
-    ```bash
-    curl --request POST \
-    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
-    --header 'Content-Type: application/json' \
-    --data "{
-        \"assistant_id\": \"agent\",
-        \"interrupt_before\": [\"node_a\"],
-        \"interrupt_after\": [\"node_b\", \"node_c\"],
-        \"input\": <INPUT>
-    }"
-    ```
-
-The following example shows how to add static interrupts:
-
-=== "Python"
-
-    ```python
-    from langgraph_sdk import get_client
-    client = get_client(url=<DEPLOYMENT_URL>)
-
-    # Using the graph deployed with the name "agent"
-    assistant_id = "agent"
-
-    # create a thread
-    thread = await client.threads.create()
-    thread_id = thread["thread_id"]
-
-    # Run the graph until the breakpoint
-    result = await client.runs.wait(
-        thread_id,
-        assistant_id,
-        input=inputs   # (1)!
-    )
-
-    # Resume the graph
-    await client.runs.wait(
-        thread_id,
-        assistant_id,
-        input=None   # (2)!
-    )
-    ```
-
-    1. The graph is run until the first breakpoint is hit.
-    2. The graph is resumed by passing in `None` for the input. This will run the graph until the next breakpoint is hit.
-
-=== "JavaScript"
-
-    ```js
-    import { Client } from "@langchain/langgraph-sdk";
-    const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
-
-    // Using the graph deployed with the name "agent"
-    const assistantID = "agent";
-
-    // create a thread
-    const thread = await client.threads.create();
-    const threadID = thread["thread_id"];
-
-    // Run the graph until the breakpoint
-    const result = await client.runs.wait(
-      threadID,
-      assistantID,
-      { input: input }   // (1)!
-    );
-
-    // Resume the graph
-    await client.runs.wait(
-      threadID,
-      assistantID,
-      { input: null }   // (2)!
-    );
-    ```
-
-    1. The graph is run until the first breakpoint is hit.
-    2. The graph is resumed by passing in `null` for the input. This will run the graph until the next breakpoint is hit.
-
-=== "cURL"
-
-    Create a thread:
-
-    ```bash
-    curl --request POST \
-    --url <DEPLOYMENT_URL>/threads \
-    --header 'Content-Type: application/json' \
-    --data '{}'
-    ```
-
-    Run the graph until the breakpoint:
-
-    ```bash
-    curl --request POST \
-    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
-    --header 'Content-Type: application/json' \
-    --data "{
-      \"assistant_id\": \"agent\",
-      \"input\": <INPUT>
-    }"
-    ```
-
-    Resume the graph:
-
-    ```bash
-    curl --request POST \
-    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
-    --header 'Content-Type: application/json' \
-    --data "{
-      \"assistant_id\": \"agent\"
-    }"
-    ```
-
-
 ## Learn more
 
 - [Human-in-the-loop conceptual guide](../../concepts/human_in_the_loop.md): learn more about LangGraph human-in-the-loop features. 

docs/docs/cloud/how-tos/configuration_cloud.md

@@ -2,20 +2,21 @@
 
 In this guide we will show how to create, configure, and manage an [assistant](../../concepts/assistants.md).
 
-First, as a brief refresher on the concept of runtime context, consider the following simple `call_model` node and context schema. Observe that this node tries to read and use the `model_provider` as defined by the `Runtime` object's `context` property.
+First, as a brief refresher on the concept of configurations, consider the following simple `call_model` node and configuration schema. Observe that this node tries to read and use the `model_name` as defined by the `config` object's `configurable`.
 
 === "Python"
 
     ```python
-    @dataclass
-    class ContextSchema:
-        llm_provider: str = "anthropic"
 
-    builder = StateGraph(AgentState, context_schema=ContextSchema)
+    class ConfigSchema(TypedDict):
+        model_name: str
 
-    def call_model(state, runtime: Runtime[ContextSchema]):
+    builder = StateGraph(AgentState, config_schema=ConfigSchema)
+
+    def call_model(state, config):
         messages = state["messages"]
-        model = _get_model(runtime.context.llm_provider)
+        model_name = config.get('configurable', {}).get("model_name", "anthropic")
+        model = _get_model(model_name)
         response = model.invoke(messages)
         # We return a list, because this will get added to the existing list
         return {"messages": [response]}
@@ -43,9 +44,7 @@ First, as a brief refresher on the concept of runtime context, consider the foll
     }
     ```
 
-:::python
-For more information on runtime context, [see here](../../concepts/low_level.md#runtime-context).
-:::
+For more information on configurations, [see here](../../concepts/low_level.md#configuration).
 
 ## Create an assistant
 
@@ -329,4 +328,4 @@ If you now run your graph and pass in this assistant id, it will use the first v
 If using LangGraph Studio, to set the active version of your assistant, click the "Manage Assistants" button and locate the assistant you would like to use. Select the assistant and the version, and then click the "Active" toggle. This will update the assistant to make the selected version active.
 
 !!! warning "Deleting Assistants"
-Deleting as assistant will delete ALL of its versions. There is currently no way to delete a single version, but by pointing your assistant to the correct version you can skip any versions that you don't wish to use.
+    Deleting as assistant will delete ALL of its versions. There is currently no way to delete a single version, but by pointing your assistant to the correct version you can skip any versions that you don't wish to use.

docs/docs/cloud/how-tos/generative_ui_react.md

@@ -30,33 +30,17 @@ export default {
 
 Next, define your UI components in your `langgraph.json` configuration:
 
-=== "Python agent"
-
-    ```json title="langgraph.json"
-    {
-      "node_version": "20",
-      "graphs": {
-        "agent": "./src/agent.py:graph"
-      },
-      "ui": {
-        "agent": "./src/agent/ui.tsx"
-      }
-    }
-    ```
-
-=== "JS agent"
-
-    ```json title="langgraph.json"
-    {
-      "node_version": "20",
-      "graphs": {
-        "agent": "./src/agent/index.ts:graph"
-      },
-      "ui": {
-        "agent": "./src/agent/ui.tsx"
-      }
-    }
-    ```
+```json
+{
+  "node_version": "20",
+  "graphs": {
+    "agent": "./src/agent/index.ts:graph"
+  },
+  "ui": {
+    "agent": "./src/agent/ui.tsx"
+  }
+}
+```
 
 The `ui` section points to the UI components that will be used by graphs. By default, we recommend using the same key as the graph name, but you can split out the components however you like, see [Customise the namespace of UI components](#customise-the-namespace-of-ui-components) for more details.
 

docs/docs/cloud/how-tos/human_in_the_loop_breakpoint.md

@@ -0,0 +1,185 @@
+# Set breakpoints using Server API
+
+[Breakpoints](../../concepts/breakpoints.md) pause graph execution at defined points and let you step through each stage. They use LangGraph's [**persistence layer**](../../concepts/persistence.md), which saves the graph state after each step.
+
+With breakpoints, you can inspect the graph's state and node inputs at any point. Execution pauses indefinitely until you resume, as the checkpointer preserves the state.
+
+!!! tip
+
+    For conceptual information on breakpoints, see [Breakpoints](../../concepts/breakpoints.md).
+
+## Set static breakpoints
+
+Static breakpoints are triggered either before or after a node executes. You can set static breakpoints by specifying `interrupt_before` and `interrupt_after` at compile time or run time.
+
+=== "Compile time"
+
+    ```python
+    # highlight-next-line
+    graph = graph_builder.compile( # (1)!
+        # highlight-next-line
+        interrupt_before=["node_a"], # (2)!
+        # highlight-next-line
+        interrupt_after=["node_b", "node_c"], # (3)!
+    )
+    ```
+
+    1. The breakpoints are set during `compile` time.
+    2. `interrupt_before` specifies the nodes where execution should pause before the node is executed.
+    3. `interrupt_after` specifies the nodes where execution should pause after the node is executed.
+
+=== "Run time"
+
+    === "Python"
+
+        ```python
+        # highlight-next-line
+        await client.runs.wait( # (1)!
+            thread_id,
+            assistant_id,
+            inputs=inputs,
+            # highlight-next-line
+            interrupt_before=["node_a"], # (2)!
+            # highlight-next-line
+            interrupt_after=["node_b", "node_c"] # (3)!
+        )
+        ```
+
+        1. `client.runs.wait` is called with the `interrupt_before` and `interrupt_after` parameters. This is a run-time configuration and can be changed for every invocation.
+        2. `interrupt_before` specifies the nodes where execution should pause before the node is executed.
+        3. `interrupt_after` specifies the nodes where execution should pause after the node is executed.
+
+    === "JavaScript"
+
+        ```js
+        // highlight-next-line
+        await client.runs.wait( // (1)!
+          threadID,
+          assistantID,
+          {
+            input: input,
+            // highlight-next-line
+            interruptBefore: ["node_a"], // (2)!
+            // highlight-next-line
+            interruptAfter: ["node_b", "node_c"] // (3)!
+          }
+        )
+        ```
+
+        1. `client.runs.wait` is called with the `interruptBefore` and `interruptAfter` parameters. This is a run-time configuration and can be changed for every invocation.
+        2. `interruptBefore` specifies the nodes where execution should pause before the node is executed.
+        3. `interruptAfter` specifies the nodes where execution should pause after the node is executed.
+
+    === "cURL"
+
+        ```bash
+        curl --request POST \
+        --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
+        --header 'Content-Type: application/json' \
+        --data "{
+          \"assistant_id\": \"agent\",
+          \"interrupt_before\": [\"node_a\"],
+          \"interrupt_after\": [\"node_b\", \"node_c\"],
+          \"input\": <INPUT>
+        }"
+        ```
+
+## Example
+
+This example shows how to add **static** breakpoints. See [Use breakpoints](../../how-tos/human_in_the_loop/breakpoints.md) for more options on adding breakpoints.
+
+=== "Python"
+
+    ```python
+    from langgraph_sdk import get_client
+    client = get_client(url=<DEPLOYMENT_URL>)
+
+    # Using the graph deployed with the name "agent"
+    assistant_id = "agent"
+
+    # create a thread
+    thread = await client.threads.create()
+    thread_id = thread["thread_id"]
+
+    # Run the graph until the breakpoint
+    result = await client.runs.wait(
+        thread_id,
+        assistant_id,
+        input=inputs   # (1)!
+    )
+
+    # Resume the graph
+    await client.runs.wait(
+        thread_id,
+        assistant_id,
+        input=None   # (2)!
+    )
+    ```
+
+    1. The graph is run until the first breakpoint is hit.
+    2. The graph is resumed by passing in `None` for the input. This will run the graph until the next breakpoint is hit.
+
+=== "JavaScript"
+
+    ```js
+    import { Client } from "@langchain/langgraph-sdk";
+    const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
+
+    // Using the graph deployed with the name "agent"
+    const assistantID = "agent";
+
+    // create a thread
+    const thread = await client.threads.create();
+    const threadID = thread["thread_id"];
+
+    // Run the graph until the breakpoint
+    const result = await client.runs.wait(
+      threadID,
+      assistantID,
+      { input: input }   // (1)!
+    );
+
+    // Resume the graph
+    await client.runs.wait(
+      threadID,
+      assistantID,
+      { input: null }   // (2)!
+    );
+    ```
+
+    1. The graph is run until the first breakpoint is hit.
+    2. The graph is resumed by passing in `null` for the input. This will run the graph until the next breakpoint is hit.
+
+=== "cURL"
+
+    Create a thread:
+
+    ```bash
+    curl --request POST \
+    --url <DEPLOYMENT_URL>/threads \
+    --header 'Content-Type: application/json' \
+    --data '{}'
+    ```
+
+    Run the graph until the breakpoint:
+
+    ```bash
+    curl --request POST \
+    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
+    --header 'Content-Type: application/json' \
+    --data "{
+      \"assistant_id\": \"agent\",
+      \"input\": <INPUT>
+    }"
+    ```
+
+    Resume the graph:
+
+    ```bash
+    curl --request POST \
+    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
+    --header 'Content-Type: application/json' \
+    --data "{
+      \"assistant_id\": \"agent\"
+    }"
+    ```

docs/docs/cloud/how-tos/human_in_the_loop_time_travel.md

@@ -4,11 +4,11 @@ LangGraph provides the [**time travel**](../../concepts/time-travel.md) function
 
 To time travel using the LangGraph Server API (via the LangGraph SDK):
 
-1. **Run the graph** with initial inputs using [LangGraph SDK](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/)'s @[`client.runs.wait`][client.runs.wait] or @[`client.runs.stream`][client.runs.stream] APIs.
-2. **Identify a checkpoint in an existing thread**: Use @[`client.threads.get_history`][client.threads.get_history] method to retrieve the execution history for a specific `thread_id` and locate the desired `checkpoint_id`.
+1. **Run the graph** with initial inputs using [LangGraph SDK](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/)'s [`client.runs.wait`][langgraph_sdk.client.RunsClient.wait] or [`client.runs.stream`][langgraph_sdk.client.RunsClient.stream] APIs.
+2. **Identify a checkpoint in an existing thread**: Use [`client.threads.get_history`][langgraph_sdk.client.ThreadsClient.get_history] method to retrieve the execution history for a specific `thread_id` and locate the desired `checkpoint_id`.
    Alternatively, set a [breakpoint](./human_in_the_loop_breakpoint.md) before the node(s) where you want execution to pause. You can then find the most recent checkpoint recorded up to that breakpoint.
-3. **(Optional) modify the graph state**: Use the @[`client.threads.update_state`][client.threads.update_state] method to modify the graph’s state at the checkpoint and resume execution from alternative state.
-4. **Resume execution from the checkpoint**: Use the @[`client.runs.wait`][client.runs.wait] or @[`client.runs.stream`][client.runs.stream] APIs with an input of `None` and the appropriate `thread_id` and `checkpoint_id`.
+3. **(Optional) modify the graph state**: Use the [`client.threads.update_state`][langgraph_sdk.client.ThreadsClient.update_state] method to modify the graph’s state at the checkpoint and resume execution from alternative state.
+4. **Resume execution from the checkpoint**: Use the [`client.runs.wait`][langgraph_sdk.client.RunsClient.wait] or [`client.runs.stream`][langgraph_sdk.client.RunsClient.stream] APIs with an input of `None` and the appropriate `thread_id` and `checkpoint_id`.
 
 ## Use time travel in a workflow
 

docs/docs/cloud/how-tos/invoke_studio.md

@@ -29,7 +29,7 @@ Click the dropdown next to "Submit" and click the toggle to enable/disable strea
 To run your graph with breakpoints, click the "Interrupt" button. Select a node and whether to pause before and/or after that node has executed. Click "Continue" in the thread log to resume execution.
 
 
-For more information on breakpoints see [here](../../concepts/human_in_the_loop.md).
+For more information on breakpoints see [here](../../concepts/breakpoints.md).
 
 ### Submit run
 

docs/docs/cloud/how-tos/use_stream_react.md

@@ -1,4 +1,4 @@
-# How to integrate LangGraph into your React application
+How to integrate LangGraph into your React application# How to integrate LangGraph into your React application
 
 !!! info "Prerequisites"
 
@@ -137,7 +137,7 @@ const thread = useStream<{ messages: Message[] }>({
 
 You can also manually manage the resuming process by using the run callbacks to persist the run metadata and the `joinStream` function to resume the stream. Make sure to pass `streamResumable: true` when creating the run; otherwise some events might be lost.
 
-```tsx
+````tsx
 import type { Message } from "@langchain/langgraph-sdk";
 import { useStream } from "@langchain/langgraph-sdk/react";
 import { useCallback, useState, useEffect, useRef } from "react";
@@ -236,7 +236,7 @@ const thread = useStream<{ messages: Message[] }>({
   threadId: threadId,
   onThreadId: setThreadId,
 });
-```
+````
 
 We recommend storing the `threadId` in your URL's query parameters to let users resume conversations after page refreshes.
 
@@ -503,74 +503,6 @@ const handleSubmit = (text: string) => {
 };
 ```
 
-### Cached Thread Display
-
-Use the `initialValues` option to display cached thread data immediately while the history is being loaded from the server. This improves user experience by showing cached data instantly when navigating to existing threads.
-
-```tsx
-import { useStream } from "@langchain/langgraph-sdk/react";
-
-const CachedThreadExample = ({ threadId, cachedThreadData }) => {
-  const stream = useStream({
-    apiUrl: "http://localhost:2024",
-    assistantId: "agent",
-    threadId,
-    // Show cached data immediately while history loads
-    initialValues: cachedThreadData?.values,
-    messagesKey: "messages",
-  });
-
-  return (
-    <div>
-      {stream.messages.map((message) => (
-        <div key={message.id}>{message.content as string}</div>
-      ))}
-    </div>
-  );
-};
-```
-
-### Optimistic Thread Creation
-
-Use the `threadId` option in `submit` function to enable optimistic UI patterns where you need to know the thread ID before the thread is actually created.
-
-```tsx
-import { useState } from "react";
-import { useStream } from "@langchain/langgraph-sdk/react";
-
-const OptimisticThreadExample = () => {
-  const [threadId, setThreadId] = useState<string | null>(null);
-  const [optimisticThreadId] = useState(() => crypto.randomUUID());
-
-  const stream = useStream({
-    apiUrl: "http://localhost:2024",
-    assistantId: "agent",
-    threadId,
-    onThreadId: setThreadId, // (3) Updated after thread has been created.
-    messagesKey: "messages",
-  });
-
-  const handleSubmit = (text: string) => {
-    // (1) Perform a soft navigation to /threads/${optimisticThreadId}
-    // without waiting for thread creation.
-    window.history.pushState({}, "", `/threads/${optimisticThreadId}`);
-
-    // (2) Submit message to create thread with the predetermined ID.
-    stream.submit(
-      { messages: [{ type: "human", content: text }] },
-      { threadId: optimisticThreadId }
-    );
-  };
-
-  return (
-    <div>
-      <p>Thread ID: {threadId ?? optimisticThreadId}</p>
-      {/* Rest of component */}
-    </div>
-  );
-};
-```
-
 ### TypeScript
 
 The `useStream()` hook is friendly for apps written in TypeScript and you can specify types for the state to get better type safety and IDE support.

docs/docs/cloud/how-tos/webhooks.md

@@ -140,22 +140,6 @@ https://my-server.app/my-webhook-endpoint?token=YOUR_SECRET_TOKEN
 
 Your server should extract and validate this token before processing requests.
 
-## Disable webhooks
-
-As of `langgraph-api>=0.2.78`, developers can disable webhooks in the `langgraph.json` file:
-
-```json
-{
-  "http": {
-    "disable_webhooks": true
-  }
-}
-```
-
-This feature is primarily intended for self-hosted deployments, where platform administrators or developers may prefer to disable webhooks to simplify their security posture—especially if they are not configuring firewall rules or other network controls. Disabling webhooks helps prevent untrusted payloads from being sent to internal endpoints.
-
-For full configuration details, refer to the [configuration file reference](https://langchain-ai.github.io/langgraph/cloud/reference/cli/?h=disable_webhooks#configuration-file).
-
 ## Test webhooks
 
 You can test your webhook using online services like:

docs/docs/cloud/quick_start.md

@@ -154,9 +154,8 @@ You can now test the API:
 
     ```bash
     curl -s --request POST \
-        --url <DEPLOYMENT_URL>/runs/stream \
+        --url <DEPLOYMENT_URL> \
         --header 'Content-Type: application/json' \
-        --header "X-Api-Key: <LANGSMITH API KEY> \
         --data "{
             \"assistant_id\": \"agent\",
             \"input\": {

docs/docs/cloud/reference/api/api_ref.md

@@ -1,12 +1,12 @@
-# LangGraph Server API Reference
+# API Reference
 
-The LangGraph Server API reference is available within each deployment at the `/docs` endpoint (e.g. `http://localhost:8124/docs`).
+The LangGraph Platform API reference is available with each deployment at the `/docs` URL path (e.g. `http://localhost:8124/docs`).
 
 Click <a href="/langgraph/cloud/reference/api/api_ref.html" target="_blank">here</a> to view the API reference.
 
 ## Authentication
 
-For deployments to LangGraph Platform, authentication is required. Pass the `X-Api-Key` header with each request to the LangGraph Server. The value of the header should be set to a valid LangSmith API key for the organization where the LangGraph Server is deployed.
+For deployments to LangGraph Platform, authentication is required. Pass the `X-Api-Key` header with each request to the LangGraph Platform API. The value of the header should be set to a valid LangSmith API key for the organization where the API is deployed.
 
 Example `curl` command:
 ```shell
@@ -18,5 +18,5 @@ curl --request POST \
   "metadata": {},
   "limit": 10,
   "offset": 0
-}'
+}'  
 ```

docs/docs/cloud/reference/api/api_ref_control_plane.md

@@ -1,247 +0,0 @@
-# LangGraph Control Plane API Reference
-
-The LangGraph Control Plane API is used to programmatically create and manage LangGraph Server deployments. For example, the APIs can be orchestrated to create custom CI/CD workflows.
-
-Click <a href="https://api.host.langchain.com/docs" target="_blank">here</a> to view the API reference.
-
-## Host
-
-LangGraph Control Plane hosts for Cloud SaaS data regions:
-
-| US | EU |
-|----|----|
-| `https://api.host.langchain.com` | `https://eu.api.host.langchain.com` |
-
-**Note**: Self-hosted deployments of LangGraph Platform will have a custom host for the LangGraph Control Plane.
-
-## Authentication
-
-To authenticate with the LangGraph Control Plane API, set the `X-Api-Key` header to a valid LangSmith API key.
-
-Example `curl` command:
-```shell
-curl --request GET \
-  --url http://localhost:8124/v2/deployments \
-  --header 'X-Api-Key: LANGSMITH_API_KEY'
-```
-
-## Versioning
-
-Each endpoint path is prefixed with a version (e.g. `v1`, `v2`).
-
-## Quick Start
-
-1. Call `POST /v2/deployments` to create a new Deployment. The response body contains the Deployment ID (`id`) and the ID of the latest (and first) revision (`latest_revision_id`).
-1. Call `GET /v2/deployments/{deployment_id}` to retrieve the Deployment. Set `deployment_id` in the URL to the value of Deployment ID (`id`).
-1. Poll for revision `status` until `status` is `DEPLOYED` by calling `GET /v2/deployments/{deployment_id}/revisions/{latest_revision_id}`.
-1. Call `PATCH /v2/deployments/{deployment_id}` to update the deployment.
-
-## Example Code
-Below is example Python code that demonstrates how to orchestrate the LangGraph Control Plane APIs to create a deployment, update the deployment, and delete the deployment.
-```python
-import os
-import time
-
-import requests
-from dotenv import load_dotenv
-
-
-load_dotenv()
-
-# required environment variables
-CONTROL_PLANE_HOST = os.getenv("CONTROL_PLANE_HOST")
-LANGSMITH_API_KEY = os.getenv("LANGSMITH_API_KEY")
-INTEGRATION_ID = os.getenv("INTEGRATION_ID")
-MAX_WAIT_TIME = 1800  # 30 mins
-
-
-def get_headers() -> dict:
-    """Return common headers for requests to LangGraph Control Plane API."""
-    return {
-        "X-Api-Key": LANGSMITH_API_KEY,
-    }
-
-
-def create_deployment() -> str:
-    """Create deployment. Return deployment ID."""
-    headers = get_headers()
-    headers["Content-Type"] = "application/json"
-
-    deployment_name = "my_deployment"
-
-    request_body = {
-        "name": deployment_name,
-        "source": "github",
-        "source_config": {
-            "integration_id": INTEGRATION_ID,
-            "repo_url": "https://github.com/langchain-ai/langgraph-example",
-            "deployment_type": "dev",
-            "build_on_push": False,
-            "custom_url": None,
-            "resource_spec": None,
-        },
-        "source_revision_config": {
-            "repo_ref": "main",
-            "langgraph_config_path": "langgraph.json",
-            "image_uri": None,
-        },
-        "secrets": [
-            {
-                "name": "OPENAI_API_KEY",
-                "value": "test_openai_api_key",
-            },
-            {
-                "name": "ANTHROPIC_API_KEY",
-                "value": "test_anthropic_api_key",
-            },
-            {
-                "name": "TAVILY_API_KEY",
-                "value": "test_tavily_api_key",
-            },
-        ],
-    }
-
-    response = requests.post(
-        url=f"{CONTROL_PLANE_HOST}/v2/deployments",
-        headers=headers,
-        json=request_body,
-    )
-
-    if response.status_code != 201:
-        raise Exception(f"Failed to create deployment: {response.text}")
-
-    deployment_id = response.json()["id"]
-    print(f"Created deployment {deployment_name} ({deployment_id})")
-    return deployment_id
-
-
-def get_deployment(deployment_id: str) -> dict:
-    """Get deployment."""
-    response = requests.get(
-        url=f"{CONTROL_PLANE_HOST}/v2/deployments/{deployment_id}",
-        headers=get_headers(),
-    )
-
-    if response.status_code != 200:
-        raise Exception(f"Failed to get deployment ID {deployment_id}: {response.text}")
-
-    return response.json()
-
-
-def list_revisions(deployment_id: str) -> list[dict]:
-    """List revisions.
-
-    Return list is sorted by created_at in descending order (latest first).
-    """
-    response = requests.get(
-        url=f"{CONTROL_PLANE_HOST}/v2/deployments/{deployment_id}/revisions",
-        headers=get_headers(),
-    )
-
-    if response.status_code != 200:
-        raise Exception(
-            f"Failed to list revisions for deployment ID {deployment_id}: {response.text}"
-        )
-
-    return response.json()
-
-
-def get_revision(
-    deployment_id: str,
-    revision_id: str,
-) -> dict:
-    """Get revision."""
-    response = requests.get(
-        url=f"{CONTROL_PLANE_HOST}/v2/deployments/{deployment_id}/revisions/{revision_id}",
-        headers=get_headers(),
-    )
-
-    if response.status_code != 200:
-        raise Exception(f"Failed to get revision ID {revision_id}: {response.text}")
-
-    return response.json()
-
-
-def patch_deployment(deployment_id: str) -> None:
-    """Patch deployment."""
-    headers = get_headers()
-    headers["Content-Type"] = "application/json"
-
-    response = requests.patch(
-        url=f"{CONTROL_PLANE_HOST}/v2/deployments/{deployment_id}",
-        headers=headers,
-        json={
-            "source_config": {
-                "build_on_push": True,
-            },
-            "source_revision_config": {
-                "repo_ref": "main",
-                "langgraph_config_path": "langgraph.json",
-            },
-        },
-    )
-
-    if response.status_code != 200:
-        raise Exception(f"Failed to patch deployment: {response.text}")
-
-    print(f"Patched deployment ID {deployment_id}")
-
-
-def wait_for_deployment(deployment_id: str, revision_id: str) -> None:
-    """Wait for revision status to be DEPLOYED."""
-    start_time = time.time()
-    revision, status = None, None
-    while time.time() - start_time < MAX_WAIT_TIME:
-        revision = get_revision(deployment_id, revision_id)
-        status = revision["status"]
-        if status == "DEPLOYED":
-            break
-        elif "FAILED" in status:
-            raise Exception(f"Revision ID {revision_id} failed: {revision}")
-
-        print(f"Waiting for revision ID {revision_id} to be DEPLOYED...")
-        time.sleep(60)
-
-    if status != "DEPLOYED":
-        raise Exception(
-            f"Timeout waiting for revision ID {revision_id} to be DEPLOYED: {revision}"
-        )
-
-
-def delete_deployment(deployment_id: str) -> None:
-    """Delete deployment."""
-    response = requests.delete(
-        url=f"{CONTROL_PLANE_HOST}/v2/deployments/{deployment_id}",
-        headers=get_headers(),
-    )
-
-    if response.status_code != 204:
-        raise Exception(
-            f"Failed to delete deployment ID {deployment_id}: {response.text}"
-        )
-
-    print(f"Deployment ID {deployment_id} deleted")
-
-
-if __name__ == "__main__":
-    # create deployment and get the latest revision
-    deployment_id = create_deployment()
-    revisions = list_revisions(deployment_id)
-    latest_revision = revisions["resources"][0]
-    latest_revision_id = latest_revision["id"]
-
-    # wait for latest revision to be DEPLOYED
-    wait_for_deployment(deployment_id, latest_revision_id)
-
-    # patch the deployment and get the latest revision
-    patch_deployment(deployment_id)
-    revisions = list_revisions(deployment_id)
-    latest_revision = revisions["resources"][0]
-    latest_revision_id = latest_revision["id"]
-
-    # wait for latest revision to be DEPLOYED
-    wait_for_deployment(deployment_id, latest_revision_id)
-
-    # delete the deployment
-    delete_deployment(deployment_id)
-```

docs/docs/cloud/reference/cli.md

@@ -51,10 +51,9 @@ The LangGraph CLI requires a JSON configuration file that follows this [schema](
     | <span style="white-space: nowrap;">`node_version`</span>     | Specify `node_version: 20` to use LangGraph.js.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
     | <span style="white-space: nowrap;">`pip_config_file`</span>  | Path to `pip` config file.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
     | <span style="white-space: nowrap;">`pip_installer`</span> | _(Added in v0.3)_ Optional. Python package installer selector. It can be set to `"auto"`, `"pip"`, or `"uv"`. From version&nbsp;0.3 onward the default strategy is to run `uv pip`, which typically delivers faster builds while remaining a drop-in replacement. In the uncommon situation where `uv` cannot handle your dependency graph or the structure of your `pyproject.toml`, specify `"pip"` here to revert to the earlier behaviour. |
-    | <span style="white-space: nowrap;">`keep_pkg_tools`</span> | _(Added in v0.3.4)_ Optional. Control whether to retain Python packaging tools (`pip`, `setuptools`, `wheel`) in the final image. Accepted values: <ul><li><code>true</code> : Keep all three tools (skip uninstall).</li><li><code>false</code> / omitted : Uninstall all three tools (default behaviour).</li><li><code>list[str]</code> : Names of tools <strong>to retain</strong>. Each value must be one of "pip", "setuptools", "wheel".</li></ul>. By default, all three tools are uninstalled. |
     | <span style="white-space: nowrap;">`dockerfile_lines`</span> | Array of additional lines to add to Dockerfile following the import from parent image.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
     | <span style="white-space: nowrap;">`checkpointer`</span>   | Configuration for the checkpointer. Contains a `ttl` field which is an object with the following keys: <ul><li>`strategy`: How to handle expired checkpoints (e.g., `"delete"`).</li><li>`sweep_interval_minutes`: How often to check for expired checkpoints (integer).</li><li>`default_ttl`: Default time-to-live for checkpoints in **minutes** (integer). Defines how long checkpoints are kept before the specified strategy is applied.</li></ul> |
-    | <span style="white-space: nowrap;">`http`</span>            | HTTP server configuration with the following fields: <ul><li>`app`: Path to custom Starlette/FastAPI app (e.g., `"./src/agent/webapp.py:app"`). See [custom routes guide](../../how-tos/http/custom_routes.md).</li><li>`cors`: CORS configuration with fields for `allow_origins`, `allow_methods`, `allow_headers`, etc.</li><li>`configurable_headers`: Define which request headers to exclude or include as a run's configurable values.</li><li>`disable_assistants`: Disable `/assistants` routes</li><li>`disable_mcp`: Disable `/mcp` routes</li><li>`disable_meta`: Disable `/ok`, `/info`, `/metrics`, and `/docs` routes</li><li>`disable_runs`: Disable `/runs` routes</li><li>`disable_store`: Disable `/store` routes</li><li>`disable_threads`: Disable `/threads` routes</li><li>`disable_ui`: Disable `/ui` routes</li><li>`disable_webhooks`: Disable webhooks calls on run completion in all routes</li><li>`mount_prefix`: Prefix for mounted routes (e.g., "/my-deployment/api")</li></ul> |
+    | <span style="white-space: nowrap;">`http`</span>            | HTTP server configuration with the following fields: <ul><li>`app`: Path to custom Starlette/FastAPI app (e.g., `"./src/agent/webapp.py:app"`). See [custom routes guide](../../how-tos/http/custom_routes.md).</li><li>`disable_assistants`: Disable `/assistants` routes</li><li>`disable_threads`: Disable `/threads` routes</li><li>`disable_runs`: Disable `/runs` routes</li><li>`disable_store`: Disable `/store` routes</li><li>`disable_meta`: Disable `/ok`, `/info`, `/metrics`, and `/docs` routes</li><li>`disable_mcp`: Disable `/mcp` routes</li><li>`cors`: CORS configuration with fields for `allow_origins`, `allow_methods`, `allow_headers`, etc.</li><li>`configurable_headers`: Define which request headers to exclude or include as a run's configurable values.</li></ul> |
 
 === "JS"
 
@@ -396,7 +395,7 @@ The LangGraph CLI requires a JSON configuration file that follows this [schema](
 
 === "Python"
 
-    Start LangGraph API server. For local testing, requires a LangSmith API key with access to LangGraph Platform. Requires a license key for production use.
+    Start LangGraph API server. For local testing, requires a LangSmith API key with access to LangGraph Platform closed beta. Requires a license key for production use.
 
     **Usage**
 
@@ -409,8 +408,6 @@ The LangGraph CLI requires a JSON configuration file that follows this [schema](
     | Option                       | Default                   | Description                                                                                                             |
     | ---------------------------- | ------------------------- | ----------------------------------------------------------------------------------------------------------------------- |
     | `--wait`                     |                           | Wait for services to start before returning. Implies --detach                                                           |
-    | `--base-image TEXT`          | `langchain/langgraph-api`  | Base image to use for the LangGraph API server. Pin to specific versions using version tags.                            |
-    | `--image TEXT`               |                           | Docker image to use for the langgraph-api service. If specified, skips building and uses this image directly.           |
     | `--postgres-uri TEXT`        | Local database            | Postgres URI to use for the database.                                                                                   |
     | `--watch`                    |                           | Restart on file changes                                                                                                 |
     | `--debugger-base-url TEXT`   | `http://127.0.0.1:[PORT]` | URL used by the debugger to access LangGraph API.                                                                       |
@@ -425,7 +422,7 @@ The LangGraph CLI requires a JSON configuration file that follows this [schema](
 
 === "JS"
 
-    Start LangGraph API server. For local testing, requires a LangSmith API key with access to LangGraph Platform. Requires a license key for production use.
+    Start LangGraph API server. For local testing, requires a LangSmith API key with access to LangGraph Platform closed beta. Requires a license key for production use.
 
     **Usage**
 
@@ -438,8 +435,6 @@ The LangGraph CLI requires a JSON configuration file that follows this [schema](
     | Option                                                                 | Default                   | Description                                                                                                             |
     | ---------------------------------------------------------------------- | ------------------------- | ----------------------------------------------------------------------------------------------------------------------- |
     | <span style="white-space: nowrap;">`--wait`</span>                     |                           | Wait for services to start before returning. Implies --detach                                                           |
-    | <span style="white-space: nowrap;">`--base-image TEXT`</span>          | <span style="white-space: nowrap;">`langchain/langgraph-api`</span> | Base image to use for the LangGraph API server. Pin to specific versions using version tags. |
-    | <span style="white-space: nowrap;">`--image TEXT`</span>               |                           | Docker image to use for the langgraph-api service. If specified, skips building and uses this image directly. |
     | <span style="white-space: nowrap;">`--postgres-uri TEXT`</span>        | Local database            | Postgres URI to use for the database.                                                                                   |
     | <span style="white-space: nowrap;">`--watch`</span>                    |                           | Restart on file changes                                                                                                 |
     | <span style="white-space: nowrap;">`-c, --config FILE`</span>          | `langgraph.json`          | Path to configuration file declaring dependencies, graphs and environment variables.                                    |
@@ -483,19 +478,19 @@ The LangGraph CLI requires a JSON configuration file that follows this [schema](
 
     RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt langchain_community langchain_anthropic langchain_openai wikipedia scikit-learn
 
-    ADD ./graphs /deps/outer-graphs/src
+    ADD ./graphs /deps/__outer_graphs/src
     RUN set -ex && \
         for line in '[project]' \
                     'name = "graphs"' \
                     'version = "0.1"' \
                     '[tool.setuptools.package-data]' \
                     '"*" = ["**/*"]'; do \
-            echo "$line" >> /deps/outer-graphs/pyproject.toml; \
+            echo "$line" >> /deps/__outer_graphs/pyproject.toml; \
         done
 
     RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*
 
-    ENV LANGSERVE_GRAPHS='{"agent": "/deps/outer-graphs/src/agent.py:graph", "storm": "/deps/outer-graphs/src/storm.py:graph"}'
+    ENV LANGSERVE_GRAPHS='{"agent": "/deps/__outer_graphs/src/agent.py:graph", "storm": "/deps/__outer_graphs/src/storm.py:graph"}'
     ```
 
     ???+ note "Updating your langgraph.json file"

docs/docs/cloud/reference/env_var.md

@@ -10,10 +10,6 @@ This environment variable should be set to `True` if the implementation of a gra
 
 Defaults to `False`.
 
-## `BG_JOB_SHUTDOWN_GRACE_PERIOD_SECS`
-
-Specifies, in seconds, how long the server will wait for background jobs to finish after the queue receives a shutdown signal. After this period, the server will force termination. Defaults to `180` seconds. Set this to ensure jobs have enough time to complete cleanly during shutdown. Added in `langgraph-api==0.2.16`.
-
 ## `BG_JOB_TIMEOUT_SECS`
 
 The timeout of a background run can be increased. However, the infrastructure for a Cloud SaaS deployment enforces a 1 hour timeout limit for API requests. This means the connection between client and server will timeout after 1 hour. This is not configurable.
@@ -22,15 +18,16 @@ A background run can execute for longer than 1 hour, but a client must reconnect
 
 Defaults to `3600`.
 
+## `BG_JOB_SHUTDOWN_GRACE_PERIOD_SECS`
+
+Specifies, in seconds, how long the server will wait for background jobs to finish after the queue receives a shutdown signal. After this period, the server will force termination. Defaults to `3600` seconds. Set this to ensure jobs have enough time to complete cleanly during shutdown. Added in `langgraph-api==0.2.16`.
+
 ## `DD_API_KEY`
 
 Specify `DD_API_KEY` (your [Datadog API Key](https://docs.datadoghq.com/account_management/api-app-keys/)) to automatically enable Datadog tracing for the deployment. Specify other [`DD_*` environment variables](https://ddtrace.readthedocs.io/en/stable/configuration.html) to configure the tracing instrumentation.
 
 If `DD_API_KEY` is specified, the application process is wrapped in the [`ddtrace-run` command](https://ddtrace.readthedocs.io/en/stable/installation_quickstart.html). Other `DD_*` environment variables (e.g. `DD_SITE`, `DD_ENV`, `DD_SERVICE`, `DD_TRACE_ENABLED`) are typically needed to properly configure the tracing instrumentation. See [`DD_*` environment variables](https://ddtrace.readthedocs.io/en/stable/configuration.html) for more details.
 
-!!! note 
-    Enabling `DD_API_KEY` (and thus `ddtrace-run`) can override or interfere with other auto-instrumentation solutions (such as OpenTelemetry) that you may have instrumented into your application code.
-
 ## `LANGCHAIN_TRACING_SAMPLING_RATE`
 
 Sampling rate for traces sent to LangSmith. Valid values: Any float between `0` and `1`.
@@ -43,14 +40,6 @@ Type of authentication for the LangGraph Server deployment. Valid values: `langs
 
 For deployments to LangGraph Platform, this environment variable is set automatically. For local development or deployments where authentication is handled externally (e.g. self-hosted), set this environment variable to `noop`.
 
-## `LANGGRAPH_POSTGRES_POOL_MAX_SIZE`
-
-Beginning with langgraph-api version `0.2.12`, the maximum size of the Postgres connection pool (per replica) can be controlled using the `LANGGRAPH_POSTGRES_POOL_MAX_SIZE` environment variable. By setting this variable, you can determine the upper bound on the number of simultaneous connections the server will establish with the Postgres database.
-
-For example, if a deployment is scaled up to 10 replicas and `LANGGRAPH_POSTGRES_POOL_MAX_SIZE` is configured to `150`, then up to `1500` connections to Postgres can be established. This is particularly useful for deployments where database resources are limited (or more available) or where you need to tune connection behavior for performance or scaling reasons.
-
-Defaults to `150` connections.
-
 ## `LANGSMITH_RUNS_ENDPOINTS`
 
 For deployments with [self-hosted LangSmith](https://docs.smith.langchain.com/self_hosting) only.
@@ -65,10 +54,6 @@ Set `LANGSMITH_TRACING` to `false` to disable tracing to LangSmith.
 
 Defaults to `true`.
 
-## `LOG_COLOR`
-
-This is mainly relevant in the context of using the dev server via the `langgraph dev` command. Set `LOG_COLOR` to `true` to enable ANSI-colored console output when using the default console renderer. Disabling color output by setting this variable to `false` produces monochrome logs. Defaults to `true`.
-
 ## `LOG_LEVEL`
 
 Configure [log level](https://docs.python.org/3/library/logging.html#logging-levels). Defaults to `INFO`.
@@ -77,14 +62,9 @@ Configure [log level](https://docs.python.org/3/library/logging.html#logging-lev
 
 Set `LOG_JSON` to `true` to render all log messages as JSON objects using the configured `JSONRenderer`. This produces structured logs that can be easily parsed or ingested by log management systems. Defaults to `false`.
 
-## `MOUNT_PREFIX`
+## `LOG_COLOR`
 
-!!! info "Only Allowed in Self-Hosted Deployments"
-    The `MOUNT_PREFIX` environment variable is only allowed in Self-Hosted Deployment models, LangGraph Platform SaaS will not allow this environment variable.
-
-Set `MOUNT_PREFIX` to serve the LangGraph Server under a specific path prefix. This is useful for deployments where the server is behind a reverse proxy or load balancer that requires a specific path prefix.
-
-For example, if the server is to be served under `https://example.com/langgraph`, set `MOUNT_PREFIX` to `/langgraph`.
+This is mainly relevant in the context of using the dev server via the `langgraph dev` command. Set `LOG_COLOR` to `true` to enable ANSI-colored console output when using the default console renderer. Disabling color output by setting this variable to `false` produces monochrome logs. Defaults to `true`.
 
 ## `N_JOBS_PER_WORKER`
 
@@ -114,14 +94,16 @@ Database Connectivity:
 
 - The custom Postgres instance must be accessible by the LangGraph Server. The user is responsible for ensuring connectivity.
 
-## `REDIS_CLUSTER`
+## `LANGGRAPH_POSTGRES_POOL_MAX_SIZE`
 
-!!! info "Only Allowed in Self-Hosted Deployments"
-    Redis Cluster mode is only available in Self-Hosted Deployment models, LangGraph Platform SaaS will provision a redis instance for you by default.
+Beginning with langgraph-api version `0.2.12`, the maximum size of the Postgres connection pool can be controlled using the `LANGGRAPH_POSTGRES_POOL_MAX_SIZE` environment variable. By setting this variable, you can determine the upper bound on the number of simultaneous connections the server will establish with the Postgres database. This is particularly useful for deployments where database resources are limited (or more available) or where you need to tune connection behavior for performance or scaling reasons. If not specified, the pool size defaults to 150 connections.
 
-Set `REDIS_CLUSTER` to `True` to enable Redis Cluster mode. When enabled, the system will connect to Redis using cluster mode. This is useful when connecting to a Redis Cluster deployment.
+## `REDIS_URI_CUSTOM`
 
-Defaults to `False`.
+!!! info "Only for Self-Hosted Data Plane and Self-Hosted Control Plane"
+    Custom Redis instances are only available for [Self-Hosted Data Plane](../../concepts/langgraph_self_hosted_data_plane.md) and [Self-Hosted Control Plane](../../concepts/langgraph_self_hosted_control_plane.md) deployments.
+
+Specify `REDIS_URI_CUSTOM` to use a custom Redis instance. The value of `REDIS_URI_CUSTOM` must be a valid [Redis connection URI](https://redis-py.readthedocs.io/en/stable/connections.html#redis.Redis.from_url).
 
 ## `REDIS_KEY_PREFIX`
 
@@ -132,19 +114,20 @@ Specify a prefix for Redis keys. This allows multiple LangGraph Server instances
 
 Defaults to `''`.
 
-## `REDIS_URI_CUSTOM`
+## `REDIS_CLUSTER`
 
-!!! info "Only for Self-Hosted Data Plane and Self-Hosted Control Plane"
-    Custom Redis instances are only available for [Self-Hosted Data Plane](../../concepts/langgraph_self_hosted_data_plane.md) and [Self-Hosted Control Plane](../../concepts/langgraph_self_hosted_control_plane.md) deployments.
+!!! info "Only Allowed in Self-Hosted Deployments"
+    Redis Cluster mode is only available in Self-Hosted Deployment models, LangGraph Platform SaaS will provision a redis instance for you by default.
 
-Specify `REDIS_URI_CUSTOM` to use a custom Redis instance. The value of `REDIS_URI_CUSTOM` must be a valid [Redis connection URI](https://redis-py.readthedocs.io/en/stable/connections.html#redis.Redis.from_url).
+Set `REDIS_CLUSTER` to `True` to enable Redis Cluster mode. When enabled, the system will connect to Redis using cluster mode. This is useful when connecting to a Redis Cluster deployment.
 
-## `RESUMABLE_STREAM_TTL_SECONDS`
+Defaults to `False`.
 
-Time-to-live in seconds for resumable stream data in Redis.
+## `MOUNT_PREFIX`
 
-When a run is created and the output is streamed, the stream can be configured to be resumable (e.g. `stream_resumable=True`). If a stream is resumable, output from the stream is temporarily stored in Redis. The TTL for this data can be configured by setting `RESUMABLE_STREAM_TTL_SECONDS`.
+!!! info "Only Allowed in Self-Hosted Deployments"
+    The `MOUNT_PREFIX` environment variable is only allowed in Self-Hosted Deployment models, LangGraph Platform SaaS will not allow this environment variable.
 
-See the [Python](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/#langgraph_sdk.client.RunsClient.stream) and [JS/TS](https://langchain-ai.github.io/langgraphjs/reference/classes/sdk_client.RunsClient.html#stream) SDKs for more details on how to implement resumable streams.
+Set `MOUNT_PREFIX` to serve the LangGraph Server under a specific path prefix. This is useful for deployments where the server is behind a reverse proxy or load balancer that requires a specific path prefix.
 
-Defaults to `120` seconds.
+For example, if the server is to be served under `https://example.com/langgraph`, set `MOUNT_PREFIX` to `/langgraph`.

docs/docs/cloud/reference/langgraph_server_changelog.md

@@ -1,233 +0,0 @@
-# LangGraph Server Changelog
-
-> **Note:** This changelog is no longer actively maintained. For the most up-to-date LangGraph Server changelog, please visit our new documentation site: [LangGraph Server Changelog](https://docs.langchain.com/langgraph-platform/langgraph-server-changelog#langgraph-server-changelog)
-
-[LangGraph Server](../../concepts/langgraph_server.md) is an API platform for creating and managing agent-based applications. It provides built-in persistence, a task queue, and supports deploying, configuring, and running assistants (agentic workflows) at scale. This changelog documents all notable updates, features, and fixes to LangGraph Server releases.
-
----
-
-## v0.2.111 (2025-07-29)
-- Started the heartbeat immediately upon connection to prevent JS graph streaming errors during long startups.
-
-## v0.2.110 (2025-07-29)
-- Added interrupts as default values for all operations except streams to maintain consistent behavior.
-
-## v0.2.109 (2025-07-28)
-- Fixed an issue where missing config schema occurred when `config_type` was not set.
-
-## v0.2.108 (2025-07-28)
-- Added compatibility for langgraph v0.6, including new context API support and a migration to enhance context handling in assistant operations.
-
-## v0.2.107 (2025-07-27)
-- Implemented caching for authentication processes to improve performance.
-- Merged count and select queries to improve database query efficiency.
-
-## v0.2.106 (2025-07-27)
-- Log whether run uses resumable streams.
-
-## v0.2.105 (2025-07-27)
-- Added a `/heapdump` endpoint to capture and save JS process heap data.
-
-## v0.2.103 (2025-07-25)
-- Corrected the metadata endpoint to ensure accurate data retrieval.
-
-## v0.2.102 (2025-07-24)
-- Captured interrupt events in the wait method to preserve legacy behavior and stream updates by default.
-- Added support for SDK structlog in the JavaScript environment, enhancing logging capabilities.
-
-## v0.2.101 (2025-07-24)
-- Used the correct metadata endpoint for self-hosted environments, resolving an access issue.
-
-## v0.2.99 (2025-07-22)
-- Improved license validation by adding an in-memory cache and handling Redis connection errors more effectively.
-- Automatically remove agents from memory that are removed from `langgraph.json` to prevent persistence issues.
-- Ensured the UI namespace for generated UI is a valid JavaScript property name to prevent errors.
-- Raised a 422 error for improved request validation feedback.
-
-## v0.2.98 (2025-07-19)
-- Added langgraph node context for improved log filtering and trace visibility.
-
-## v0.2.97 (2025-07-19)
-- Fixed scheduling issue with ckpt ingestion worker that occurred on isolated background loops.
-- Ensured queue worker starts only after all migrations have completed.
-- Added more detailed error messages for thread state issues and improved response handling when state updates fail.
-- Exposed interrupt ID while retrieving thread state for enhanced API response details.
-
-## v0.2.96 (2025-07-17)
-- Added a fallback mechanism for configurable header patterns to handle exclude/include settings more effectively.
-
-## v0.2.95 (2025-07-17)
-- Avoided setting the future if it is already done to prevent redundant operations.
-- Resolved compatibility errors in CI by switching from `typing.TypedDict` to `typing_extensions.TypedDict` for Python versions below 3.12.
-
-## v0.2.94 (2025-07-16)
-- Improved performance by omitting pending sends for langgraph versions 0.5 and above.
-- Improved server startup logs to provide clearer warnings when the DD_API_KEY environment variable is set.
-
-## v0.2.93 (2025-07-16)
-- Removed the GIN index for run metadata to improve performance.
-
-## v0.2.92 (2025-07-16)
-- Enabled copying functionality for blobs and checkpoints, improving data management flexibility.
-
-## v0.2.91 (2025-07-16)
-- Reduced writes to the `checkpoint_blobs` table by inlining small values (null, numeric, str, etc.). This means we don't need to store extra values for channels that haven't been updated.
-
-## v0.2.90 (2025-07-16)
-- Improve checkpoint writes via node-local background queueing.
-
-
-## v0.2.89 (2025-07-15)
-- Decoupled checkpoint writing from thread/run state by removing foreign keys and updated logger to prevent timeout-related failures.
-
-## v0.2.88 (2025-07-14)
-- Removed the foreign key constraint for `thread` in the `run` table to simplify database schema.
-
-## v0.2.87 (2025-07-14)
-- Added more detailed logs for Redis worker signaling to improve debugging.
-
-## v0.2.86 (2025-07-11)
-- Honored tool descriptions in the `/mcp` endpoint to align with expected functionality.
-
-## v0.2.85 (2025-07-10)
-- Added support for the `on_disconnect` field to `runs/wait` and included disconnect logs for better debugging.
-
-## v0.2.84 (2025-07-09)
-- Removed unnecessary status updates to streamline thread handling and updated version to 0.2.84.
-
-## v0.2.83 (2025-07-09)
-- Reduced the default time-to-live for resumable streams to 2 minutes.
-- Enhanced data submission logic to send data to both Beacon and LangSmith instance based on license configuration.
-- Enabled submission of self-hosted data to a Langsmith instance when the endpoint is configured.
-
-## v0.2.82 (2025-07-03)
-- Addressed a race condition in background runs by implementing a lock using join, ensuring reliable execution across CTEs.
-
-## v0.2.81 (2025-07-03)
-- Optimized run streams by reducing initial wait time to improve responsiveness for older or non-existent runs.
-
-## v0.2.80 (2025-07-03)
-- Corrected parameter passing in the `logger.ainfo()` API call to resolve a TypeError.
-
-## v0.2.79 (2025-07-02)
-- Fixed a JsonDecodeError in checkpointing with remote graph by correcting JSON serialization to handle trailing slashes properly.
-- Introduced a configuration flag to disable webhooks globally across all routes.
-
-## v0.2.78 (2025-07-02)
-- Added timeout retries to webhook calls to improve reliability.
-- Added HTTP request metrics, including a request count and latency histogram, for enhanced monitoring capabilities.
-
-## v0.2.77 (2025-07-02)
-- Added HTTP metrics to improve performance monitoring.
-- Changed the Redis cache delimiter to reduce conflicts with subgraph message names and updated caching behavior.
-
-## v0.2.76 (2025-07-01)
-- Updated Redis cache delimiter to prevent conflicts with subgraph messages.
-
-## v0.2.74 (2025-06-30)
-- Scheduled webhooks in an isolated loop to ensure thread-safe operations and prevent errors with PYTHONASYNCIODEBUG=1.
-
-## v0.2.73 (2025-06-27)
-- Fixed an infinite frame loop issue and removed the dict_parser due to structlog's unexpected behavior.
-- Throw a 409 error on deadlock occurrence during run cancellations to handle lock conflicts gracefully.
-
-## v0.2.72 (2025-06-27)
-- Ensured compatibility with future langgraph versions.
-- Implemented a 409 response status to handle deadlock issues during cancellation.
-
-## v0.2.71 (2025-06-26)
-- Improved logging for better clarity and detail regarding log types.
-
-## v0.2.70 (2025-06-26)
-- Improved error handling to better distinguish and log TimeoutErrors caused by users from internal run timeouts.
-
-## v0.2.69 (2025-06-26)
-- Added sorting and pagination to the crons API and updated schema definitions for improved accuracy.
-
-## v0.2.66 (2025-06-26)
-- Fixed a 404 error when creating multiple runs with the same thread_id using `on_not_exist="create"`.
-
-## v0.2.65 (2025-06-25)
-- Ensured that only fields from `assistant_versions` are returned when necessary.
-- Ensured consistent data types for in-memory and PostgreSQL users, improving internal authentication handling.
-
-## v0.2.64 (2025-06-24)
-- Added descriptions to version entries for better clarity.
-
-## v0.2.62 (2025-06-23)
-- Improved user handling for custom authentication in the JS Studio.
-- Added Prometheus-format run statistics to the metrics endpoint for better monitoring.
-- Added run statistics in Prometheus format to the metrics endpoint.
-
-## v0.2.61 (2025-06-20)
-- Set a maximum idle time for Redis connections to prevent unnecessary open connections.
-
-## v0.2.60 (2025-06-20)
-- Enhanced error logging to include traceback details for dictionary operations.
-- Added a `/metrics` endpoint to expose queue worker metrics for monitoring.
-
-## v0.2.57 (2025-06-18)
-- Removed CancelledError from retriable exceptions to allow local interrupts while maintaining retriability for workers.
-- Introduced middleware to gracefully shut down the server after completing in-flight requests upon receiving a SIGINT.
-- Reduced metadata stored in checkpoint to only include necessary information.
-- Improved error handling in join runs to return error details when present.
-
-## v0.2.56 (2025-06-17)
-- Improved application stability by adding a handler for SIGTERM signals.
-
-## v0.2.55 (2025-06-17)
-- Improved the handling of cancellations in the queue entrypoint.
-- Improved cancellation handling in the queue entry point.
-
-## v0.2.54 (2025-06-16)
-- Enhanced error message for LuaLock timeout during license validation.
-- Fixed the $contains filter in custom auth by requiring an explicit ::text cast and updated tests accordingly.
-- Ensured project and tenant IDs are formatted as UUIDs for consistency.
-
-## v0.2.53 (2025-06-13)
-- Resolved a timing issue to ensure the queue starts only after the graph is registered.
-- Improved performance by setting thread and run status in a single query and enhanced error handling during checkpoint writes.
-- Reduced the default background grace period to 3 minutes.
-
-## v0.2.52 (2025-06-12)
-- Now logging expected graphs when one is omitted to improve traceability.
-- Implemented a time-to-live (TTL) feature for resumable streams.
-- Improved query efficiency and consistency by adding a unique index and optimizing row locking.
-
-## v0.2.51 (2025-06-12)
-- Handled `CancelledError` by marking tasks as ready to retry, improving error management in worker processes.
-- Added LG API version and request ID to metadata and logs for better tracking.
-- Added LG API version and request ID to metadata and logs to improve traceability.
-- Improved database performance by creating indexes concurrently.
-- Ensured postgres write is committed only after the Redis running marker is set to prevent race conditions.
-- Enhanced query efficiency and reliability by adding a unique index on thread_id/running, optimizing row locks, and ensuring deterministic run selection.
-- Resolved a race condition by ensuring Postgres updates only occur after the Redis running marker is set.
-
-## v0.2.46 (2025-06-07)
-- Introduced a new connection for each operation while preserving transaction characteristics in Threads state `update()` and `bulk()` commands.
-
-## v0.2.45 (2025-06-05)
-- Enhanced streaming feature by incorporating tracing contexts.
-- Removed an unnecessary query from the Crons.search function.
-- Resolved connection reuse issue when scheduling next run for multiple cron jobs.
-- Removed an unnecessary query in the Crons.search function to improve efficiency.
-- Resolved an issue with scheduling the next cron run by improving connection reuse.
-
-## v0.2.44 (2025-06-04)
-- Enhanced the worker logic to exit the pipeline before continuing when the Redis message limit is reached.
-- Introduced a ceiling for Redis message size with an option to skip messages larger than 128 MB for improved performance.
-- Ensured the pipeline always closes properly to prevent resource leaks.
-
-## v0.2.43 (2025-06-04)
-- Improved performance by omitting logs in metadata calls and ensuring output schema compliance in value streaming.
-- Ensured the connection is properly closed after use.
-- Aligned output format to strictly adhere to the specified schema.
-- Stopped sending internal logs in metadata requests to improve privacy.
-
-## v0.2.42 (2025-06-04)
-- Added timestamps to track the start and end of a request's run.
-- Added tracer information to the configuration settings.
-- Added support for streaming with tracing contexts.
-
-## v0.2.41 (2025-06-03)
-- Added locking mechanism to prevent errors in pipelined executions.

docs/docs/concepts/agentic_concepts.md

@@ -97,7 +97,7 @@ Parallel processing is vital for efficient multi-agent systems and complex tasks
 - Implementation of map-reduce-like operations
 - Efficient handling of independent subtasks
 
-For practical implementation, see our [map-reduce tutorial](../how-tos/graph-api.md#map-reduce-and-the-send-api)
+For practical implementation, see our [map-reduce tutorial](../how-tos/graph-api.ipynb#map-reduce-and-the-send-api)
 
 ### Subgraphs
 
@@ -107,7 +107,7 @@ For practical implementation, see our [map-reduce tutorial](../how-tos/graph-api
 - Hierarchical organization of agent teams
 - Controlled communication between agents and the main system
 
-Subgraphs communicate with the parent graph through overlapping keys in the state schema. This enables flexible, modular agent design. For implementation details, refer to our [subgraph how-to guide](../how-tos/subgraph.md).
+Subgraphs communicate with the parent graph through overlapping keys in the state schema. This enables flexible, modular agent design. For implementation details, refer to our [subgraph how-to guide](../how-tos/subgraph.ipynb).
 
 ### Reflection
 

docs/docs/concepts/application_structure.md

@@ -22,9 +22,8 @@ To deploy using the LangGraph Platform, the following information should be prov
 
 ## File Structure
 
-Below are examples of directory structures for applications:
+Below are examples of directory structures for Python and JavaScript applications:
 
-:::python
 === "Python (requirements.txt)"
 
     ```plaintext
@@ -41,7 +40,6 @@ Below are examples of directory structures for applications:
     ├── requirements.txt # package dependencies
     └── langgraph.json # configuration file for LangGraph
     ```
-
 === "Python (pyproject.toml)"
 
     ```plaintext
@@ -50,7 +48,7 @@ Below are examples of directory structures for applications:
     │   ├── utils # utilities for your graph
     │   │   ├── __init__.py
     │   │   ├── tools.py # tools for your graph
-    │   │   ├── nodes.py # node functions for your graph
+    │   │   ├── nodes.py # node functions for you graph
     │   │   └── state.py # state definition of your graph
     │   ├── __init__.py
     │   └── agent.py # code for constructing your graph
@@ -59,24 +57,20 @@ Below are examples of directory structures for applications:
     └── pyproject.toml # dependencies for your project
     ```
 
-:::
+=== "JS (package.json)"
 
-:::js
-
-```plaintext
-my-app/
-├── src # all project code lies within here
-│   ├── utils # optional utilities for your graph
-│   │   ├── tools.ts # tools for your graph
-│   │   ├── nodes.ts # node functions for your graph
-│   │   └── state.ts # state definition of your graph
-│   └── agent.ts # code for constructing your graph
-├── package.json # package dependencies
-├── .env # environment variables
-└── langgraph.json # configuration file for LangGraph
-```
-
-:::
+    ```plaintext
+    my-app/
+    ├── src # all project code lies within here
+    │   ├── utils # optional utilities for your graph
+    │   │   ├── tools.ts # tools for your graph
+    │   │   ├── nodes.ts # node functions for you graph
+    │   │   └── state.ts # state definition of your graph
+    │   └── agent.ts # code for constructing your graph
+    ├── package.json # package dependencies
+    ├── .env # environment variables
+    └── langgraph.json # configuration file for LangGraph
+    ```
 
 !!! note
 
@@ -94,66 +88,52 @@ See the [LangGraph configuration file reference](../cloud/reference/cli.md#confi
 
 ### Examples
 
-:::python
+=== "Python"
 
-- The dependencies involve a custom local package and the `langchain_openai` package.
-- A single graph will be loaded from the file `./your_package/your_file.py` with the variable `variable`.
-- The environment variables are loaded from the `.env` file.
+    * The dependencies involve a custom local package and the `langchain_openai` package.
+    * A single graph will be loaded from the file `./your_package/your_file.py` with the variable `variable`.
+    * The environment variables are loaded from the `.env` file.
 
-```json
-{
-  "dependencies": ["langchain_openai", "./your_package"],
-  "graphs": {
-    "my_agent": "./your_package/your_file.py:agent"
-  },
-  "env": "./.env"
-}
-```
+    ```json
+    {
+        "dependencies": [
+            "langchain_openai",
+            "./your_package"
+        ],
+        "graphs": {
+            "my_agent": "./your_package/your_file.py:agent"
+        },
+        "env": "./.env"
+    }
+    ```
 
-:::
+=== "JavaScript"
 
-:::js
+    * The dependencies will be loaded from a dependency file in the local directory (e.g., `package.json`).
+    * A single graph will be loaded from the file `./your_package/your_file.js` with the function `agent`.
+    * The environment variable `OPENAI_API_KEY` is set inline.
 
-- The dependencies will be loaded from a dependency file in the local directory (e.g., `package.json`).
-- A single graph will be loaded from the file `./your_package/your_file.js` with the function `agent`.
-- The environment variable `OPENAI_API_KEY` is set inline.
-
-```json
-{
-  "dependencies": ["."],
-  "graphs": {
-    "my_agent": "./your_package/your_file.js:agent"
-  },
-  "env": {
-    "OPENAI_API_KEY": "secret-key"
-  }
-}
-```
-
-:::
+    ```json
+    {
+        "dependencies": [
+            "."
+        ],
+        "graphs": {
+            "my_agent": "./your_package/your_file.js:agent"
+        },
+        "env": {
+            "OPENAI_API_KEY": "secret-key"
+        }
+    }
+    ```
 
 ## Dependencies
 
-:::python
-A LangGraph application may depend on other Python packages.
-:::
-
-:::js
-A LangGraph application may depend on other TypeScript/JavaScript libraries.
-:::
+A LangGraph application may depend on other Python packages or JavaScript libraries (depending on the programming language in which the application is written).
 
 You will generally need to specify the following information for dependencies to be set up correctly:
 
-:::python
-
 1. A file in the directory that specifies the dependencies (e.g. `requirements.txt`, `pyproject.toml`, or `package.json`).
-   :::
-
-:::js
-
-1. A file in the directory that specifies the dependencies (e.g. `package.json`).
-   :::
-
 2. A `dependencies` key in the [LangGraph configuration file](#configuration-file-concepts) that specifies the dependencies required to run the LangGraph application.
 3. Any additional binaries or system libraries can be specified using `dockerfile_lines` key in the [LangGraph configuration file](#configuration-file-concepts).
 

docs/docs/concepts/assistants.md

@@ -1,6 +1,6 @@
 # Assistants
 
-**Assistants** allow you to manage configurations (like prompts, LLM selection, tools) separately from your graph's core logic, enabling rapid changes that don't alter the graph architecture. It is a way to create multiple specialized versions of the same graph architecture, each optimized for different use cases through context/configuration variations rather than structural changes.
+**Assistants** allow you to manage configurations (like prompts, LLM selection, tools) separately from your graph's core logic, enabling rapid changes that don't alter the graph architecture. It is a way to create multiple specialized versions of the same graph architecture, each optimized for different use cases through configuration variations rather than structural changes.
 
 For example, imagine a general-purpose writing agent built on a common graph architecture. While the structure remains the same, different writing styles—such as blog posts and tweets—require tailored configurations to optimize performance. To support these variations, you can create multiple assistants (e.g., one for blogs and another for tweets) that share the underlying graph but differ in model selection and system prompt.
 
@@ -14,11 +14,8 @@ The LangGraph Cloud API provides several endpoints for creating and managing ass
 
 ## Configuration
 
-:::python
-Assistants build on the LangGraph open source concepts of configuration and [runtime context](low_level.md#runtime-context).
-:::
-
-While these features are available in the open source LangGraph library, assistants are only present in [LangGraph Platform](langgraph_platform.md). This is due to the fact that assistants are tightly coupled to your deployed graph. Upon deployment, LangGraph Server will automatically create a default assistant for each graph using the graph's default context and configuration settings.
+Assistants build on the LangGraph open source concept of [configuration](low_level.md#configuration).
+While configuration is available in the open source LangGraph library, assistants are only present in [LangGraph Platform](langgraph_platform.md). This is due to the fact that assistants are tightly coupled to your deployed graph. Upon deployment, LangGraph Server will automatically create a default assistant for each graph using the graph's default configuration settings.
 
 In practice, an assistant is just an _instance_ of a graph with a specific configuration. Therefore, multiple assistants can reference the same graph but can contain different configurations (e.g. prompts, models, tools). The LangGraph Server API provides several endpoints for creating and managing assistants. See the [API reference](../cloud/reference/api/api_ref.html) and [this how-to](../cloud/how-tos/configuration_cloud.md) for more details on how to create assistants.
 
@@ -29,6 +26,6 @@ Once you've created an assistant, subsequent edits to that assistant will create
 
 ## Execution
 
-A **run** is an invocation of an assistant. Each run may have its own input, configuration, context, and metadata, which may affect execution and output of the underlying graph. A run can optionally be executed on a [thread](./persistence.md#threads).
+A **run** is an invocation of an assistant. Each run may have its own input, configuration, and metadata, which may affect execution and output of the underlying graph. A run can optionally be executed on a [thread](./persistence.md#threads).
 
 The LangGraph Platform API provides several endpoints for creating and managing runs. See the [API reference](../cloud/reference/api/api_ref.html#tag/thread-runs/) for more details.

docs/docs/concepts/auth.md

@@ -16,13 +16,7 @@ While often used interchangeably, these terms represent distinct security concep
 - [**Authentication**](#authentication) ("AuthN") verifies _who_ you are. This runs as middleware for every request.
 - [**Authorization**](#authorization) ("AuthZ") determines _what you can do_. This validates the user's privileges and roles on a per-resource basis.
 
-:::python
 In LangGraph Platform, authentication is handled by your [`@auth.authenticate`](../cloud/reference/sdk/python_sdk_ref.md#langgraph_sdk.auth.Auth.authenticate) handler, and authorization is handled by your [`@auth.on`](../cloud/reference/sdk/python_sdk_ref.md#langgraph_sdk.auth.Auth.on) handlers.
-:::
-
-:::js
-In LangGraph Platform, authentication is handled by your [`@auth.authenticate`](../cloud/reference/sdk/typescript_sdk_ref.md#auth.authenticate) handler, and authorization is handled by your [`@auth.on`](../cloud/reference/sdk/typescript_sdk_ref.md#auth.on) handlers.
-:::
 
 ## Default Security Models
 
@@ -35,8 +29,7 @@ LangGraph Platform provides different security defaults:
 - Can be customized with your auth handler
 
 !!! note "Custom auth"
-
-   Custom auth **is supported** for all plans in LangGraph Platform.
+    Custom auth **is supported** for all plans in LangGraph Platform.
 
 ### Self-Hosted
 
@@ -44,30 +37,34 @@ LangGraph Platform provides different security defaults:
 - Complete flexibility to implement your security model
 - You control all aspects of authentication and authorization
 
+!!! note "Custom auth"
+    Custom auth is supported for **Enterprise** self-hosted deployments.
+    Standalone Container (Lite) deployments do not support custom auth natively.
+
 ## System Architecture
 
 A typical authentication setup involves three main components:
 
 1. **Authentication Provider** (Identity Provider/IdP)
 
-   - A dedicated service that manages user identities and credentials
-   - Handles user registration, login, password resets, etc.
-   - Issues tokens (JWT, session tokens, etc.) after successful authentication
-   - Examples: Auth0, Supabase Auth, Okta, or your own auth server
+    * A dedicated service that manages user identities and credentials
+    * Handles user registration, login, password resets, etc.
+    * Issues tokens (JWT, session tokens, etc.) after successful authentication
+    * Examples: Auth0, Supabase Auth, Okta, or your own auth server
 
 2. **LangGraph Backend** (Resource Server)
 
-   - Your LangGraph application that contains business logic and protected resources
-   - Validates tokens with the auth provider
-   - Enforces access control based on user identity and permissions
-   - Doesn't store user credentials directly
+    * Your LangGraph application that contains business logic and protected resources
+    * Validates tokens with the auth provider
+    * Enforces access control based on user identity and permissions
+    * Doesn't store user credentials directly
 
 3. **Client Application** (Frontend)
 
-   - Web app, mobile app, or API client
-   - Collects time-sensitive user credentials and sends to auth provider
-   - Receives tokens from auth provider
-   - Includes these tokens in requests to LangGraph backend
+    * Web app, mobile app, or API client
+    * Collects time-sensitive user credentials and sends to auth provider
+    * Receives tokens from auth provider
+    * Includes these tokens in requests to LangGraph backend
 
 Here's how these components typically interact:
 
@@ -87,22 +84,15 @@ sequenceDiagram
     LG-->>Client: 8. Return resources
 ```
 
-:::python
 Your [`@auth.authenticate`](../cloud/reference/sdk/python_sdk_ref.md#langgraph_sdk.auth.Auth.authenticate) handler in LangGraph handles steps 4-6, while your [`@auth.on`](../cloud/reference/sdk/python_sdk_ref.md#langgraph_sdk.auth.Auth.on) handlers implement step 7.
-:::
-
-:::js
-Your [`auth.authenticate`](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/#authenticate) handler in LangGraph handles steps 4-6, while your [`auth.on`](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/#on>) handlers implement step 7.
-:::
 
 ## Authentication
 
-:::python
 Authentication in LangGraph runs as middleware on every request. Your [`@auth.authenticate`](../cloud/reference/sdk/python_sdk_ref.md#langgraph_sdk.auth.Auth.authenticate) handler receives request information and should:
 
 1. Validate the credentials
 2. Return [user info](../cloud/reference/sdk/python_sdk_ref.md#langgraph_sdk.auth.types.MinimalUserDict) containing the user's identity and user information if valid
-3. Raise an [HTTPException](../cloud/reference/sdk/python_sdk_ref.md#langgraph_sdk.auth.exceptions.HTTPException) or AssertionError if invalid
+3. Raise an [HTTP exception](../cloud/reference/sdk/python_sdk_ref.md#langgraph_sdk.auth.exceptions.HTTPException) or AssertionError if invalid
 
 ```python
 from langgraph_sdk import Auth
@@ -136,49 +126,9 @@ The returned user information is available:
 
 - To your authorization handlers via [`ctx.user`](../cloud/reference/sdk/python_sdk_ref.md#langgraph_sdk.auth.types.AuthContext)
 - In your application via `config["configuration"]["langgraph_auth_user"]`
-  :::
-
-:::js
-Authentication in LangGraph runs as middleware on every request. Your [`authenticate`](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/#authenticate>) handler receives request information and should:
-
-1. Validate the credentials
-2. Return user information containing the user's identity and user information if valid
-3. Raise an [HTTPException](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/#class-httpexception>) if invalid
-
-```typescript
-import { Auth, HTTPException } from "@langchain/langgraph-sdk";
-
-export const auth = new Auth();
-
-auth.authenticate(async (request) => {
-  // Validate credentials (e.g., API key, JWT token)
-  const apiKey = request.headers.get("x-api-key");
-  if (!apiKey || !isValidKey(apiKey)) {
-    throw new HTTPException(401, "Invalid API key");
-  }
-
-  // Return user info - only identity and isAuthenticated are required
-  // Add any additional fields you need for authorization
-  return {
-    identity: "user-123", // Required: unique user identifier
-    isAuthenticated: true, // Optional: assumed true by default
-    permissions: ["read", "write"], // Optional: for permission-based auth
-    // You can add more custom fields if you want to implement other auth patterns
-    role: "admin",
-    orgId: "org-456",
-  };
-});
-```
-
-The returned user information is available:
-
-- To your authorization handlers via the `user` property in a [callback handler](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/#on)
-- In your application via `config.configurable.langgraph_auth_user`
-  :::
 
 ??? tip "Supported Parameters"
 
-    :::python
     The [`@auth.authenticate`](../cloud/reference/sdk/python_sdk_ref.md#langgraph_sdk.auth.Auth.authenticate) handler can accept any of the following parameters by name:
 
     * request (Request): The raw ASGI request object
@@ -189,84 +139,19 @@ The returned user information is available:
     * query_params (dict[str, str]): URL query parameters, e.g., {"stream": "true"}
     * headers (dict[bytes, bytes]): Request headers
     * authorization (str | None): The Authorization header value (e.g., "Bearer <token>")
-    :::
-
-    :::js
-    The [`authenticate`](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/#authenticate) handler can accept any of the following parameters:
-
-    * request (Request): The raw request object
-    * body (object): The parsed request body
-    * path (string): The request path, e.g., "/threads/abcd-1234-abcd-1234/runs/abcd-1234-abcd-1234/stream"
-    * method (string): The HTTP method, e.g., "GET"
-    * pathParams (Record<string, string>): URL path parameters, e.g., {"threadId": "abcd-1234-abcd-1234", "runId": "abcd-1234-abcd-1234"}
-    * queryParams (Record<string, string>): URL query parameters, e.g., {"stream": "true"}
-    * headers (Record<string, string>): Request headers
-    * authorization (string | null): The Authorization header value (e.g., "Bearer <token>")
-    :::
-
+    
     In many of our tutorials, we will just show the "authorization" parameter to be concise, but you can opt to accept more information as needed
     to implement your custom authentication scheme.
 
-### Agent authentication
-
-Custom authentication permits delegated access. The values you return in `@auth.authenticate` are added to the run context, giving agents user-scoped credentials lets them access resources on the user’s behalf.
-
-```mermaid
-sequenceDiagram
-  %% Actors
-  participant ClientApp as Client
-  participant AuthProv  as Auth Provider
-  participant LangGraph as LangGraph Backend
-  participant SecretStore as Secret Store
-  participant ExternalService as External Service
-
-  %% Platform login / AuthN
-  ClientApp  ->> AuthProv: 1. Login (username / password)
-  AuthProv   -->> ClientApp: 2. Return token
-  ClientApp  ->> LangGraph: 3. Request with token
-
-  Note over LangGraph: 4. Validate token (@auth.authenticate)
-  LangGraph  -->> AuthProv: 5. Fetch user info
-  AuthProv   -->> LangGraph: 6. Confirm validity
-
-  %% Fetch user tokens from secret store
-  LangGraph  ->> SecretStore: 6a. Fetch user tokens
-  SecretStore -->> LangGraph: 6b. Return tokens
-
-  Note over LangGraph: 7. Apply access control (@auth.on.*)
-
-  %% External Service round-trip
-  LangGraph  ->> ExternalService: 8. Call external service (with header)
-  Note over ExternalService: 9. External service validates header and executes action
-  ExternalService  -->> LangGraph: 10. Service response
-
-  %% Return to caller
-  LangGraph  -->> ClientApp: 11. Return resources
-```
-
-After authentication, the platform creates a special configuration object that is passed to your graph and all nodes via the configurable context.
-This object contains information about the current user, including any custom fields you return from your [`@auth.authenticate`](../cloud/reference/sdk/python_sdk_ref.md#langgraph_sdk.auth.Auth.authenticate) handler.
-
-To enable an agent to act on behalf of the user, use [custom authentication middleware](../how-tos/auth/custom_auth.md). This will allow the agent to interact with external systems like MCP servers, external databases, and even other agents on behalf of the user.
-
-For more information, see the [Use custom auth](../how-tos/auth/custom_auth.md#enable-agent-authentication) guide.
-
-### Agent authentication with MCP
-
-For information on how to authenticate an agent to an MCP server, see the [MCP conceptual guide](../concepts/mcp.md).
-
 ## Authorization
 
-After authentication, LangGraph calls your authorization handlers to control access to specific resources (e.g., threads, assistants, crons). These handlers can:
+After authentication, LangGraph calls your [`@auth.on`](../cloud/reference/sdk/python_sdk_ref.md#langgraph_sdk.auth.Auth.on) handlers to control access to specific resources (e.g., threads, assistants, crons). These handlers can:
 
-1. Add metadata to be saved during resource creation by mutating the metadata. See the [supported actions table](#supported-actions) for the list of types the value can take for each action.
-2. Filter resources by metadata during search/list or read operations by returning a [filter](#filter-operations).
+1. Add metadata to be saved during resource creation by mutating the `value["metadata"]` dictionary directly. See the [supported actions table](#supported-actions) for the list of types the value can take for each action.
+2. Filter resources by metadata during search/list or read operations by returning a [filter dictionary](#filter-operations).
 3. Raise an HTTP exception if access is denied.
 
-If you want to just implement simple user-scoped access control, you can use a single authorization handler for all resources and actions. If you want to have different control depending on the resource and action, you can use [resource-specific handlers](#resource-specific-handlers). See the [Supported Resources](#supported-resources) section for a full list of the resources that support access control.
-
-:::python
-Your [`@auth.on`](../cloud/reference/sdk/python_sdk_ref.md#langgraph_sdk.auth.Auth.on) handlers control access by mutating the `value["metadata"]` dictionary directly and returning a [filter dictionary](#filter-operations).
+If you want to just implement simple user-scoped access control, you can use a single [`@auth.on`](../cloud/reference/sdk/python_sdk_ref.md#langgraph_sdk.auth.Auth.on) handler for all resources and actions. If you want to have different control depending on the resource and action, you can use [resource-specific handlers](#resource-specific-handlers). See the [Supported Resources](#supported-resources) section for a full list of the resources that support access control.
 
 ```python
 @auth.on
@@ -308,42 +193,9 @@ async def add_owner(
     return filters
 ```
 
-:::
-
-:::js
-You can granularly control access by mutating the `value.metadata` object directly and returning a [filter object](#filter-operations) when registering an [`on()`](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/#on) handler.
-
-```typescript
-import { Auth, HTTPException } from "@langchain/langgraph-sdk/auth";
-
-export const auth = new Auth()
-  .authenticate(async (request: Request) => ({
-    identity: "user-123",
-    permissions: [],
-  }))
-  .on("*", ({ value, user }) => {
-    // Create filter to restrict access to just this user's resources
-    const filters = { owner: user.identity };
-
-    // If the operation supports metadata, add the user identity
-    // as metadata to the resource.
-    if ("metadata" in value) {
-      value.metadata ??= {};
-      value.metadata.owner = user.identity;
-    }
-
-    // Return filters to restrict access
-    // These filters are applied to ALL operations (create, read, update, search, etc.)
-    // to ensure users can only access their own resources
-    return filters;
-  });
-```
-
-:::
-
 ### Resource-Specific Handlers {#resource-specific-handlers}
 
-You can register handlers for specific resources and actions by chaining the resource and action names together with the authorization decorator.
+You can register handlers for specific resources and actions by chaining the resource and action names together with the [`@auth.on`](../cloud/reference/sdk/python_sdk_ref.md#langgraph_sdk.auth.Auth.on) decorator.
 When a request is made, the most specific handler that matches that resource and action is called. Below is an example of how to register handlers for specific resources and actions. For the following setup:
 
 1. Authenticated users are able to create threads, read threads, and create runs on threads
@@ -354,8 +206,6 @@ When a request is made, the most specific handler that matches that resource and
 
     For a full list of supported resources and actions, see the [Supported Resources](#supported-resources) section below.
 
-:::python
-
 ```python
 # Generic / global handler catches calls that aren't handled by more specific handlers
 @auth.on
@@ -440,104 +290,11 @@ async def on_assistant_create(
         )
 ```
 
-:::
-
-:::js
-
-```typescript
-import { Auth, HTTPException } from "@langchain/langgraph-sdk/auth";
-
-export const auth = new Auth()
-  .authenticate(async (request: Request) => ({
-    identity: "user-123",
-    permissions: ["threads:write", "threads:read"],
-  }))
-  .on("*", ({ event, user }) => {
-    console.log(`Request for ${event} by ${user.identity}`);
-    throw new HTTPException(403, { message: "Forbidden" });
-  })
-
-  // Matches the "threads" resource and all actions - create, read, update, delete, search
-  // Since this is **more specific** than the generic `on("*")` handler, it will take precedence over the generic handler for all actions on the "threads" resource
-  .on("threads", ({ permissions, value, user }) => {
-    if (!permissions.includes("write")) {
-      throw new HTTPException(403, {
-        message: "User lacks the required permissions.",
-      });
-    }
-
-    // Not all events do include `metadata` property in `value`.
-    // So we need to add this type guard.
-    if ("metadata" in value) {
-      value.metadata ??= {};
-      value.metadata.owner = user.identity;
-    }
-
-    return { owner: user.identity };
-  })
-
-  // Thread creation. This will match only on thread create actions.
-  // Since this is **more specific** than both the generic `on("*")` handler and the `on("threads")` handler, it will take precedence for any "create" actions on the "threads" resources
-  .on("threads:create", ({ value, user, permissions }) => {
-    if (!permissions.includes("write")) {
-      throw new HTTPException(403, {
-        message: "User lacks the required permissions.",
-      });
-    }
-
-    // Setting metadata on the thread being created will ensure that the resource contains an "owner" field
-    // Then any time a user tries to access this thread or runs within the thread,
-    // we can filter by owner
-    value.metadata ??= {};
-    value.metadata.owner = user.identity;
-
-    return { owner: user.identity };
-  })
-
-  // Reading a thread. Since this is also more specific than the generic `on("*")` handler, and the `on("threads")` handler,
-  .on("threads:read", ({ user }) => {
-    // Since we are reading (and not creating) a thread,
-    // we don't need to set metadata. We just need to
-    // return a filter to ensure users can only see their own threads.
-    return { owner: user.identity };
-  })
-
-  // Run creation, streaming, updates, etc.
-  // This takes precedence over the generic `on("*")` handler and the `on("threads")` handler
-  .on("threads:create_run", ({ value, user }) => {
-    value.metadata ??= {};
-    value.metadata.owner = user.identity;
-
-    return { owner: user.identity };
-  })
-
-  // Assistant creation. This will match only on assistant create actions.
-  // Since this is **more specific** than both the generic `on("*")` handler and the `on("assistants")` handler, it will take precedence for any "create" actions on the "assistants" resources
-  .on("assistants:create", ({ value, user, permissions }) => {
-    if (!permissions.includes("assistants:create")) {
-      throw new HTTPException(403, {
-        message: "User lacks the required permissions.",
-      });
-    }
-
-    // Setting metadata on the assistant being created will ensure that the resource contains an "owner" field.
-    // Then any time a user tries to access this assistant, we can filter by owner
-    value.metadata ??= {};
-    value.metadata.owner = user.identity;
-
-    return { owner: user.identity };
-  });
-```
-
-:::
-
 Notice that we are mixing global and resource-specific handlers in the above example. Since each request is handled by the most specific handler, a request to create a `thread` would match the `on_thread_create` handler but NOT the `reject_unhandled_requests` handler. A request to `update` a thread, however would be handled by the global handler, since we don't have a more specific handler for that resource and action.
 
 ### Filter Operations {#filter-operations}
 
-:::python
-Authorization handlers can return different types of values:
-
+Authorization handlers can return `None`, a boolean, or a filter dictionary.
 - `None` and `True` mean "authorize access to all underling resources"
 - `False` means "deny access to all underling resources (raises a 403 exception)"
 - A metadata filter dictionary will restrict access to resources
@@ -550,24 +307,6 @@ A filter dictionary is a dictionary with keys that match the resource metadata.
 
 A dictionary with multiple keys is treated using a logical `AND` filter. For example, `{"owner": org_id, "allowed_users": {"$contains": user_id}}` will only match resources with metadata whose "owner" is `org_id` and whose "allowed_users" list contains `user_id`.
 See the reference [here](../cloud/reference/sdk/python_sdk_ref.md#langgraph_sdk.auth.types.FilterType) for more information.
-:::
-
-:::js
-Authorization handlers can return different types of values:
-
-- `null` and `true` mean "authorize access to all underling resources"
-- `false` means "deny access to all underling resources (raises a 403 exception)"
-- A metadata filter object will restrict access to resources
-
-A filter object is an object with keys that match the resource metadata. It supports three operators:
-
-- The default value is a shorthand for exact match, or "$eq", below. For example, `{ owner: userId}` will include only resources with metadata containing `{ owner: userId }`
-- `$eq`: Exact match (e.g., `{ owner: { $eq: userId } }`) - this is equivalent to the shorthand above, `{ owner: userId }`
-- `$contains`: List membership (e.g., `{ allowedUsers: { $contains: userId} }`) The value here must be an element of the list. The metadata in the stored resource must be a list/container type.
-
-An object with multiple keys is treated using a logical `AND` filter. For example, `{ owner: orgId, allowedUsers: { $contains: userId} }` will only match resources with metadata whose "owner" is `orgId` and whose "allowedUsers" list contains `userId`.
-See the reference [here](../cloud/reference/sdk/typescript_sdk_ref.md#auth.types.FilterType) for more information.
-:::
 
 ## Common Access Patterns
 
@@ -577,8 +316,6 @@ Here are some typical authorization patterns:
 
 This common pattern lets you scope all threads, assistants, crons, and runs to a single user. It's useful for common single-user use cases like regular chatbot-style apps.
 
-:::python
-
 ```python
 @auth.on
 async def owner_only(ctx: Auth.types.AuthContext, value: dict):
@@ -587,33 +324,10 @@ async def owner_only(ctx: Auth.types.AuthContext, value: dict):
     return {"owner": ctx.user.identity}
 ```
 
-:::
-
-:::js
-
-```typescript
-export const auth = new Auth()
-  .authenticate(async (request: Request) => ({
-    identity: "user-123",
-    permissions: ["threads:write", "threads:read"],
-  }))
-  .on("*", ({ value, user }) => {
-    if ("metadata" in value) {
-      value.metadata ??= {};
-      value.metadata.owner = user.identity;
-    }
-    return { owner: user.identity };
-  });
-```
-
-:::
-
 ### Permission-based Access
 
 This pattern lets you control access based on **permissions**. It's useful if you want certain roles to have broader or more restricted access to resources.
 
-:::python
-
 ```python
 # In your auth handler:
 @auth.authenticate
@@ -650,72 +364,19 @@ async def rbac_create(ctx: Auth.types.AuthContext, value: dict):
     return _default(ctx, value)
 ```
 
-:::
-
-:::js
-
-```typescript
-import { Auth, HTTPException } from "@langchain/langgraph-sdk/auth";
-
-export const auth = new Auth()
-  .authenticate(async (request: Request) => ({
-    identity: "user-123",
-    // Define permissions in auth
-    permissions: ["threads:write", "threads:read"],
-  }))
-  .on("threads:create", ({ value, user, permissions }) => {
-    if (!permissions.includes("threads:write")) {
-      throw new HTTPException(403, { message: "Unauthorized" });
-    }
-
-    if ("metadata" in value) {
-      value.metadata ??= {};
-      value.metadata.owner = user.identity;
-    }
-    return { owner: user.identity };
-  })
-  .on("threads:read", ({ user, permissions }) => {
-    if (
-      !permissions.includes("threads:read") &&
-      !permissions.includes("threads:write")
-    ) {
-      throw new HTTPException(403, { message: "Unauthorized" });
-    }
-
-    return { owner: user.identity };
-  });
-```
-
-:::
-
 ## Supported Resources
 
 LangGraph provides three levels of authorization handlers, from most general to most specific:
 
-:::python
-
 1. **Global Handler** (`@auth.on`): Matches all resources and actions
 2. **Resource Handler** (e.g., `@auth.on.threads`, `@auth.on.assistants`, `@auth.on.crons`): Matches all actions for a specific resource
 3. **Action Handler** (e.g., `@auth.on.threads.create`, `@auth.on.threads.read`): Matches a specific action on a specific resource
 
 The most specific matching handler will be used. For example, `@auth.on.threads.create` takes precedence over `@auth.on.threads` for thread creation.
 If a more specific handler is registered, the more general handler will not be called for that resource and action.
-:::
 
-:::js
-
-1. **Global Handler** (`on("*")`): Matches all resources and actions
-2. **Resource Handler** (e.g., `on("threads")`, `on("assistants")`, `on("crons")`): Matches all actions for a specific resource
-3. **Action Handler** (e.g., `on("threads:create")`, `on("threads:read")`): Matches a specific action on a specific resource
-
-The most specific matching handler will be used. For example, `on("threads:create")` takes precedence over `on("threads")` for thread creation.
-If a more specific handler is registered, the more general handler will not be called for that resource and action.
-:::
-
-:::python
 ???+ tip "Type Safety"
-Each handler has type hints available for its `value` parameter. For example:
-
+    Each handler has type hints available for its `value` parameter at `Auth.types.on.<resource>.<action>.value`. For example:
     ```python
     @auth.on.threads.create
     async def on_thread_create(
@@ -723,14 +384,14 @@ Each handler has type hints available for its `value` parameter. For example:
         value: Auth.types.on.threads.create.value  # Specific type for thread creation
     ):
         ...
-
+    
     @auth.on.threads
     async def on_threads(
         ctx: Auth.types.AuthContext,
         value: Auth.types.on.threads.value  # Union type of all thread actions
     ):
         ...
-
+    
     @auth.on
     async def on_all(
         ctx: Auth.types.AuthContext,
@@ -738,16 +399,11 @@ Each handler has type hints available for its `value` parameter. For example:
     ):
         ...
     ```
-
     More specific handlers provide better type hints since they handle fewer action types.
 
-:::
-
 #### Supported actions and types {#supported-actions}
-
 Here are all the supported action handlers:
 
-:::python
 | Resource | Handler | Description | Value Type |
 |----------|---------|-------------|------------|
 | **Threads** | `@auth.on.threads.create` | Thread creation | [`ThreadsCreate`](../cloud/reference/sdk/python_sdk_ref.md#langgraph_sdk.auth.types.ThreadsCreate) |
@@ -766,40 +422,12 @@ Here are all the supported action handlers:
 | | `@auth.on.crons.update` | Cron job updates | [`CronsUpdate`](../cloud/reference/sdk/python_sdk_ref.md#langgraph_sdk.auth.types.CronsUpdate) |
 | | `@auth.on.crons.delete` | Cron job deletion | [`CronsDelete`](../cloud/reference/sdk/python_sdk_ref.md#langgraph_sdk.auth.types.CronsDelete) |
 | | `@auth.on.crons.search` | Listing cron jobs | [`CronsSearch`](../cloud/reference/sdk/python_sdk_ref.md#langgraph_sdk.auth.types.CronsSearch) |
-:::
-
-:::js
-| Resource | Event | Description | Value Type |
-| -------------- | -------------------- | -------------------------- | ------------------------------------------------------------------------------------------------------------------ |
-| **Threads** | `threads:create` | Thread creation | [`ThreadsCreate`](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/#threadscreate) |
-| | `threads:read` | Thread retrieval | [`ThreadsRead`](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/#threadsread) |
-| | `threads:update` | Thread updates | [`ThreadsUpdate`](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/#threadsupdate) |
-| | `threads:delete` | Thread deletion | [`ThreadsDelete`](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/#threadsdelete) |
-| | `threads:search` | Listing threads | [`ThreadsSearch`](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/#threadssearch) |
-| | `threads:create_run` | Creating or updating a run | [`RunsCreate`](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/#threadscreate_run) |
-| **Assistants** | `assistants:create` | Assistant creation | [`AssistantsCreate`](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/#assistantscreate) |
-| | `assistants:read` | Assistant retrieval | [`AssistantsRead`](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/#assistantsread) |
-| | `assistants:update` | Assistant updates | [`AssistantsUpdate`](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/#assistantsupdate) |
-| | `assistants:delete` | Assistant deletion | [`AssistantsDelete`](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/#assistantsdelete) |
-| | `assistants:search` | Listing assistants | [`AssistantsSearch`](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/#assistantssearch) |
-| **Crons** | `crons:create` | Cron job creation | [`CronsCreate`](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/#cronscreate) |
-| | `crons:read` | Cron job retrieval | [`CronsRead`](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/#cronsread) |
-| | `crons:update` | Cron job updates | [`CronsUpdate`](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/#cronsupdate) |
-| | `crons:delete` | Cron job deletion | [`CronsDelete`](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/#cronsdelete) |
-| | `crons:search` | Listing cron jobs | [`CronsSearch`](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/#cronssearch) |
-:::
 
 ???+ note "About Runs"
 
     Runs are scoped to their parent thread for access control. This means permissions are typically inherited from the thread, reflecting the conversational nature of the data model. All run operations (reading, listing) except creation are controlled by the thread's handlers.
-
-    :::python
     There is a specific `create_run` handler for creating new runs because it had more arguments that you can view in the handler.
-    :::
 
-    :::js
-    There is a specific `threads:create_run` handler for creating new runs because it had more arguments that you can view in the handler.
-    :::
 
 ## Next Steps
 

docs/docs/concepts/breakpoints.md

@@ -0,0 +1,18 @@
+---
+search:
+  boost: 2
+---
+
+# Breakpoints
+
+[Breakpoints](../how-tos/human_in_the_loop/breakpoints.md) pause graph execution at defined points and let you step through each stage. They use LangGraph's [**persistence layer**](./persistence.md), which saves the graph state after each step.
+
+With breakpoints, you can inspect the graph's state and node inputs at any point. Execution pauses **indefinitely** until you resume, as the checkpointer preserves the state.
+
+<figure markdown="1">
+![image](img/breakpoints.png){: style="max-height:400px"}
+<figcaption>An example graph consisting of 3 sequential steps with a breakpoint before step_3. </figcaption> </figure>
+
+!!! tip
+
+    For information on how to use breakpoints, see [Set breakpoints](../how-tos/human_in_the_loop/breakpoints.md) and [Set breakpoints using Server API](../cloud/how-tos/human_in_the_loop_breakpoint.md).

docs/docs/concepts/deployment_options.md

@@ -7,7 +7,10 @@ search:
 
 ## Free deployment
 
-[Local](../tutorials/langgraph-platform/local-server.md): Deploy for local testing and development.
+There are two free options for deploying LangGraph applications via the LangGraph Server:
+
+1. [Local](../tutorials/langgraph-platform/local-server.md): Deploy for local testing and development. 
+1. [Standalone Container (Lite)](../concepts/langgraph_standalone_container.md): A limited version of Standalone Container for deployments unlikely to see more that 1 million node executions per year and that do not need crons and other enterprise features. Standalone Container (Lite) deployment option is free with a LangSmith API key.
 
 ## Production deployment
 
@@ -15,9 +18,9 @@ There are 4 main options for deploying with the [LangGraph Platform](langgraph_p
 
 1. [Cloud SaaS](#cloud-saas)
 
-1. [Self-Hosted Data Plane](#self-hosted-data-plane)
+1. [Self-Hosted Data Plane<sup>(Beta)</sup>](#self-hosted-data-plane)
 
-1. [Self-Hosted Control Plane](#self-hosted-control-plane)
+1. [Self-Hosted Control Plane<sup>(Beta)</sup>](#self-hosted-control-plane)
 
 1. [Standalone Container](#standalone-container)
 
@@ -30,7 +33,8 @@ A quick comparison:
 | **CI/CD** | Managed internally by platform | Managed externally by you | Managed externally by you | Managed externally by you |
 | **Data/compute residency** | LangChain's cloud | Your cloud | Your cloud | Your cloud |
 | **LangSmith compatibility** | Trace to LangSmith SaaS | Trace to LangSmith SaaS | Trace to Self-Hosted LangSmith | Optional tracing |
-| **[Pricing](https://www.langchain.com/pricing-langgraph-platform)** | Plus | Enterprise | Enterprise | Enterprise |
+| **[Server version compatibility](../concepts/langgraph_server.md#server-versions)** | Enterprise | Enterprise | Enterprise | Lite, Enterprise |
+| **[Pricing](https://www.langchain.com/pricing-langgraph-platform)** | Plus | Enterprise | Enterprise | Developer |
 
 ## Cloud SaaS
 
@@ -46,7 +50,7 @@ For more information, please see:
 ## Self-Hosted Data Plane
 
 !!! info "Important"
-    The Self-Hosted Data Plane deployment option requires an [Enterprise](../concepts/plans.md) plan.
+    The Self-Hosted Data Plane deployment option is currently in beta stage and requires an [Enterprise](../concepts/plans.md) plan.
 
 The [Self-Hosted Data Plane](./langgraph_self_hosted_data_plane.md) deployment option is a "hybrid" model for deployment where we manage the [control plane](./langgraph_control_plane.md) in our cloud and you manage the [data plane](./langgraph_data_plane.md) in your cloud. This option provides a way to securely manage your data plane infrastructure, while offloading control plane management to us.
 
@@ -62,7 +66,7 @@ For more information, please see:
 ## Self-Hosted Control Plane
 
 !!! info "Important"
-    The Self-Hosted Control Plane deployment option requires an [Enterprise](../concepts/plans.md) plan.
+    The Self-Hosted Control Plane deployment option is currently in beta stage and requires an [Enterprise](../concepts/plans.md) plan.
 
 The [Self-Hosted Control Plane](./langgraph_self_hosted_control_plane.md) deployment option is a fully self-hosted model for deployment where you manage the [control plane](./langgraph_control_plane.md) and [data plane](./langgraph_data_plane.md) in your cloud. This option gives you full control and responsibility of the control plane and data plane infrastructure.
 

docs/docs/concepts/durable_execution.md

@@ -5,7 +5,7 @@ search:
 
 # Durable Execution
 
-**Durable execution** is a technique in which a process or workflow saves its progress at key points, allowing it to pause and later resume exactly where it left off. This is particularly useful in scenarios that require [human-in-the-loop](./human_in_the_loop.md), where users can inspect, validate, or modify the process before continuing, and in long-running tasks that might encounter interruptions or errors (e.g., calls to an LLM timing out). By preserving completed work, durable execution enables a process to resume without reprocessing previous steps -- even after a significant delay (e.g., a week later).
+**Durable execution** is a technique in which a process or workflow saves its progress at key points, allowing it to pause and later resume exactly where it left off. This is particularly useful in scenarios that require [human-in-the-loop](./human_in_the_loop.md), where users can inspect, validate, or modify the process before continuing, and in long-running tasks that might encounter interruptions or errors (e.g., calls to an LLM timing out). By preserving completed work, durable execution enables a process to resume without reprocessing previous steps -- even after a significant delay (e.g., a week later). 
 
 LangGraph's built-in [persistence](./persistence.md) layer provides durable execution for workflows, ensuring that the state of each execution step is saved to a durable store. This capability guarantees that if a workflow is interrupted -- whether by a system failure or for [human-in-the-loop](./human_in_the_loop.md) interactions -- it can be resumed from its last recorded state.
 
@@ -20,14 +20,7 @@ To leverage durable execution in LangGraph, you need to:
 
 1. Enable [persistence](./persistence.md) in your workflow by specifying a [checkpointer](./persistence.md#checkpointer-libraries) that will save workflow progress.
 2. Specify a [thread identifier](./persistence.md#threads) when executing a workflow. This will track the execution history for a particular instance of the workflow.
-
-:::python 
-3. Wrap any non-deterministic operations (e.g., random number generation) or operations with side effects (e.g., file writes, API calls) inside @[tasks][task] to ensure that when a workflow is resumed, these operations are not repeated for the particular run, and instead their results are retrieved from the persistence layer. For more information, see [Determinism and Consistent Replay](#determinism-and-consistent-replay).
-:::
-
-:::js 
-3. Wrap any non-deterministic operations (e.g., random number generation) or operations with side effects (e.g., file writes, API calls) inside @[tasks][task] to ensure that when a workflow is resumed, these operations are not repeated for the particular run, and instead their results are retrieved from the persistence layer. For more information, see [Determinism and Consistent Replay](#determinism-and-consistent-replay).
-:::
+3. Wrap any non-deterministic operations (e.g., random number generation) or operations with side effects (e.g., file writes, API calls) inside [tasks][langgraph.func.task] to ensure that when a workflow is resumed, these operations are not repeated for the particular run, and instead their results are retrieved from the persistence layer. For more information, see [Determinism and Consistent Replay](#determinism-and-consistent-replay).
 
 ## Determinism and Consistent Replay
 
@@ -37,70 +30,17 @@ As a result, when you are writing a workflow for durable execution, you must wra
 
 To ensure that your workflow is deterministic and can be consistently replayed, follow these guidelines:
 
-- **Avoid Repeating Work**: If a [node](./low_level.md#nodes) contains multiple operations with side effects (e.g., logging, file writes, or network calls), wrap each operation in a separate **task**. This ensures that when the workflow is resumed, the operations are not repeated, and their results are retrieved from the persistence layer.
-- **Encapsulate Non-Deterministic Operations:** Wrap any code that might yield non-deterministic results (e.g., random number generation) inside **tasks** or **nodes**. This ensures that, upon resumption, the workflow follows the exact recorded sequence of steps with the same outcomes.
+- **Avoid Repeating Work**:  If a [node](./low_level.md#nodes) contains multiple operations with side effects (e.g., logging, file writes, or network calls), wrap each operation in a separate **task**. This ensures that when the workflow is resumed, the operations are not repeated, and their results are retrieved from the persistence layer.
+- **Encapsulate Non-Deterministic Operations:**  Wrap any code that might yield non-deterministic results (e.g., random number generation) inside **tasks** or **nodes**. This ensures that, upon resumption, the workflow follows the exact recorded sequence of steps with the same outcomes.
 - **Use Idempotent Operations**: When possible ensure that side effects (e.g., API calls, file writes) are idempotent. This means that if an operation is retried after a failure in the workflow, it will have the same effect as the first time it was executed. This is particularly important for operations that result in data writes. In the event that a **task** starts but fails to complete successfully, the workflow's resumption will re-run the **task**, relying on recorded outcomes to maintain consistency. Use idempotency keys or verify existing results to avoid unintended duplication, ensuring a smooth and predictable workflow execution.
 
-:::python
 For some examples of pitfalls to avoid, see the [Common Pitfalls](./functional_api.md#common-pitfalls) section in the functional API, which shows
-how to structure your code using **tasks** to avoid these issues. The same principles apply to the @[StateGraph (Graph API)][StateGraph].
-:::
-
-:::js
-For some examples of pitfalls to avoid, see the [Common Pitfalls](./functional_api.md#common-pitfalls) section in the functional API, which shows
-how to structure your code using **tasks** to avoid these issues. The same principles apply to the @[StateGraph (Graph API)][StateGraph].
-:::
-
-## Durability modes
-
-LangGraph supports three durability modes that allow you to balance performance and data consistency based on your application's requirements. The durability modes, from least to most durable, are as follows:
-
-- [`"exit"`](#exit)
-- [`"async"`](#async)
-- [`"sync"`](#sync)
-
-A higher durability mode add more overhead to the workflow execution.
-
-!!! version-added "Added in v0.6.0"
-
-    Use the `durability` parameter instead of `checkpoint_during` (deprecated in v0.6.0) for persistence policy management:
-    
-    * `durability="async"` replaces `checkpoint_during=True`
-    * `durability="exit"` replaces `checkpoint_during=False`
-    
-    for persistence policy management, with the following mapping:
-
-    * `checkpoint_during=True` -> `durability="async"`
-    * `checkpoint_during=False` -> `durability="exit"`
-
-
-### `"exit"`
-Changes are persisted only when graph execution completes (either successfully or with an error). This provides the best performance for long-running graphs but means intermediate state is not saved, so you cannot recover from mid-execution failures or interrupt the graph execution.
-
-### `"async"`
-Changes are persisted asynchronously while the next step executes. This provides good performance and durability, but there's a small risk that checkpoints might not be written if the process crashes during execution.
-
-### `"sync"`
-Changes are persisted synchronously before the next step starts. This ensures that every checkpoint is written before continuing execution, providing high durability at the cost of some performance overhead.
-
-You can specify the durability mode when calling any graph execution method:
-
-:::python
-
-```python
-graph.stream(
-    {"input": "test"}, 
-    durability="sync"
-)
-```
-
-:::
+how to structure your code using **tasks** to avoid these issues. The same principles apply to the [StateGraph (Graph API)][langgraph.graph.state.StateGraph].
 
 ## Using tasks in nodes
 
 If a [node](./low_level.md#nodes) contains multiple operations, you may find it easier to convert each operation into a **task** rather than refactor the operations into individual nodes.
 
-:::python
 === "Original"
 
     ```python
@@ -108,7 +48,7 @@ If a [node](./low_level.md#nodes) contains multiple operations, you may find it
     from typing_extensions import TypedDict
     import uuid
 
-    from langgraph.checkpoint.memory import InMemorySaver
+    from langgraph.checkpoint.memory import MemorySaver
     from langgraph.graph import StateGraph, START, END
     import requests
 
@@ -134,7 +74,7 @@ If a [node](./low_level.md#nodes) contains multiple operations, you may find it
     builder.add_edge("call_api", END)
 
     # Specify a checkpointer
-    checkpointer = InMemorySaver()
+    checkpointer = MemorySaver()
 
     # Compile the graph with the checkpointer
     graph = builder.compile(checkpointer=checkpointer)
@@ -154,7 +94,7 @@ If a [node](./low_level.md#nodes) contains multiple operations, you may find it
     from typing_extensions import TypedDict
     import uuid
 
-    from langgraph.checkpoint.memory import InMemorySaver
+    from langgraph.checkpoint.memory import MemorySaver
     from langgraph.func import task
     from langgraph.graph import StateGraph, START, END
     import requests
@@ -189,7 +129,7 @@ If a [node](./low_level.md#nodes) contains multiple operations, you may find it
     builder.add_edge("call_api", END)
 
     # Specify a checkpointer
-    checkpointer = InMemorySaver()
+    checkpointer = MemorySaver()
 
     # Compile the graph with the checkpointer
     graph = builder.compile(checkpointer=checkpointer)
@@ -202,136 +142,16 @@ If a [node](./low_level.md#nodes) contains multiple operations, you may find it
     graph.invoke({"urls": ["https://www.example.com"]}, config)
     ```
 
-:::
-
-:::js
-=== "Original"
-
-    ```typescript
-    import { StateGraph, START, END } from "@langchain/langgraph";
-    import { MemorySaver } from "@langchain/langgraph";
-    import { v4 as uuidv4 } from "uuid";
-    import { z } from "zod";
-
-    // Define a Zod schema to represent the state
-    const State = z.object({
-      url: z.string(),
-      result: z.string().optional(),
-    });
-
-    const callApi = async (state: z.infer<typeof State>) => {
-      // highlight-next-line
-      const response = await fetch(state.url);
-      const text = await response.text();
-      const result = text.slice(0, 100); // Side-effect
-      return {
-        result,
-      };
-    };
-
-    // Create a StateGraph builder and add a node for the callApi function
-    const builder = new StateGraph(State)
-      .addNode("callApi", callApi)
-      .addEdge(START, "callApi")
-      .addEdge("callApi", END);
-
-    // Specify a checkpointer
-    const checkpointer = new MemorySaver();
-
-    // Compile the graph with the checkpointer
-    const graph = builder.compile({ checkpointer });
-
-    // Define a config with a thread ID.
-    const threadId = uuidv4();
-    const config = { configurable: { thread_id: threadId } };
-
-    // Invoke the graph
-    await graph.invoke({ url: "https://www.example.com" }, config);
-    ```
-
-=== "With task"
-
-    ```typescript
-    import { StateGraph, START, END } from "@langchain/langgraph";
-    import { MemorySaver } from "@langchain/langgraph";
-    import { task } from "@langchain/langgraph";
-    import { v4 as uuidv4 } from "uuid";
-    import { z } from "zod";
-
-    // Define a Zod schema to represent the state
-    const State = z.object({
-      urls: z.array(z.string()),
-      results: z.array(z.string()).optional(),
-    });
-
-    const makeRequest = task("makeRequest", async (url: string) => {
-      // highlight-next-line
-      const response = await fetch(url);
-      const text = await response.text();
-      return text.slice(0, 100);
-    });
-
-    const callApi = async (state: z.infer<typeof State>) => {
-      // highlight-next-line
-      const requests = state.urls.map((url) => makeRequest(url));
-      const results = await Promise.all(requests);
-      return {
-        results,
-      };
-    };
-
-    // Create a StateGraph builder and add a node for the callApi function
-    const builder = new StateGraph(State)
-      .addNode("callApi", callApi)
-      .addEdge(START, "callApi")
-      .addEdge("callApi", END);
-
-    // Specify a checkpointer
-    const checkpointer = new MemorySaver();
-
-    // Compile the graph with the checkpointer
-    const graph = builder.compile({ checkpointer });
-
-    // Define a config with a thread ID.
-    const threadId = uuidv4();
-    const config = { configurable: { thread_id: threadId } };
-
-    // Invoke the graph
-    await graph.invoke({ urls: ["https://www.example.com"] }, config);
-    ```
-
-:::
-
 ## Resuming Workflows
 
 Once you have enabled durable execution in your workflow, you can resume execution for the following scenarios:
 
-:::python
-
-- **Pausing and Resuming Workflows:** Use the @[interrupt][interrupt] function to pause a workflow at specific points and the @[Command] primitive to resume it with updated state. See [**Human-in-the-Loop**](./human_in_the_loop.md) for more details.
+- **Pausing and Resuming Workflows:** Use the [interrupt][langgraph.types.interrupt] function to pause a workflow at specific points and the [Command][langgraph.types.Command] primitive to resume it with updated state. See [**Human-in-the-Loop**](./human_in_the_loop.md) for more details.
 - **Recovering from Failures:** Automatically resume workflows from the last successful checkpoint after an exception (e.g., LLM provider outage). This involves executing the workflow with the same thread identifier by providing it with a `None` as the input value (see this [example](../how-tos/use-functional-api.md#resuming-after-an-error) with the functional API).
-  :::
-
-:::js
-
-- **Pausing and Resuming Workflows:** Use the @[interrupt][interrupt] function to pause a workflow at specific points and the @[Command] primitive to resume it with updated state. See [**Human-in-the-Loop**](./human_in_the_loop.md) for more details.
-- **Recovering from Failures:** Automatically resume workflows from the last successful checkpoint after an exception (e.g., LLM provider outage). This involves executing the workflow with the same thread identifier by providing it with a `null` as the input value (see this [example](../how-tos/use-functional-api.md#resuming-after-an-error) with the functional API).
-  :::
 
 ## Starting Points for Resuming Workflows
 
-:::python
-
-- If you're using a @[StateGraph (Graph API)][StateGraph], the starting point is the beginning of the [**node**](./low_level.md#nodes) where execution stopped.
-- If you're making a subgraph call inside a node, the starting point will be the **parent** node that called the subgraph that was halted.
-  Inside the subgraph, the starting point will be the specific [**node**](./low_level.md#nodes) where execution stopped.
-- If you're using the Functional API, the starting point is the beginning of the [**entrypoint**](./functional_api.md#entrypoint) where execution stopped.
-  :::
-
-:::js
-
-- If you're using a [StateGraph (Graph API)](./low_level.md), the starting point is the beginning of the [**node**](./low_level.md#nodes) where execution stopped.
-- If you're making a subgraph call inside a node, the starting point will be the **parent** node that called the subgraph that was halted.
-  Inside the subgraph, the starting point will be the specific [**node**](./low_level.md#nodes) where execution stopped.
-- If you're using the Functional API, the starting point is the beginning of the [**entrypoint**](./functional_api.md#entrypoint) where execution stopped.
-  :::
+* If you're using a [StateGraph (Graph API)][langgraph.graph.state.StateGraph], the starting point is the beginning of the [**node**](./low_level.md#nodes) where execution stopped. 
+* If you're making a subgraph call inside a node, the starting point will be the **parent** node that called the subgraph that was halted.
+Inside the subgraph, the starting point will be the specific [**node**](./low_level.md#nodes) where execution stopped.
+* If you're using the Functional API, the starting point is the beginning of the [**entrypoint**](./functional_api.md#entrypoint) where execution stopped.

docs/docs/concepts/faq.md

@@ -13,7 +13,7 @@ No. LangGraph is an orchestration framework for complex agentic systems and is m
 
 ## How is LangGraph different from other agent frameworks?
 
-Other agentic frameworks can work for simple, generic tasks but fall short for complex tasks. LangGraph provides a more expressive framework to handle your unique tasks without restricting you to a single black-box cognitive architecture.
+Other agentic frameworks can work for simple, generic tasks but fall short for complex tasks bespoke to a company’s needs. LangGraph provides a more expressive framework to handle companies’ unique tasks without restricting users to a single black-box cognitive architecture.
 
 ## Does LangGraph impact the performance of my app?
 
@@ -28,14 +28,14 @@ Yes. LangGraph is an MIT-licensed open-source library and is free to use.
 LangGraph is a stateful, orchestration framework that brings added control to agent workflows. LangGraph Platform is a service for deploying and scaling LangGraph applications, with an opinionated API for building agent UXs, plus an integrated developer studio.
 
 | Features            | LangGraph (open source)                                   | LangGraph Platform                                                                                     |
-| ------------------- | --------------------------------------------------------- | ------------------------------------------------------------------------------------------------------ |
+|---------------------|-----------------------------------------------------------|--------------------------------------------------------------------------------------------------------|
 | Description         | Stateful orchestration framework for agentic applications | Scalable infrastructure for deploying LangGraph applications                                           |
 | SDKs                | Python and JavaScript                                     | Python and JavaScript                                                                                  |
 | HTTP APIs           | None                                                      | Yes - useful for retrieving & updating state or long-term memory, or creating a configurable assistant |
 | Streaming           | Basic                                                     | Dedicated mode for token-by-token messages                                                             |
 | Checkpointer        | Community contributed                                     | Supported out-of-the-box                                                                               |
 | Persistence Layer   | Self-managed                                              | Managed Postgres with efficient storage                                                                |
-| Deployment          | Self-managed                                              | • Cloud SaaS <br> • Free self-hosted <br> • Enterprise (paid self-hosted)                              |
+| Deployment          | Self-managed                                              | • Cloud SaaS <br> • Free self-hosted <br> • Enterprise (paid self-hosted)                      |
 | Scalability         | Self-managed                                              | Auto-scaling of task queues and servers                                                                |
 | Fault-tolerance     | Self-managed                                              | Automated retries                                                                                      |
 | Concurrency Control | Simple threading                                          | Supports double-texting                                                                                |
@@ -47,7 +47,7 @@ LangGraph is a stateful, orchestration framework that brings added control to ag
 
 No. LangGraph Platform is proprietary software.
 
-There is a free, self-hosted version of LangGraph Platform with access to basic features. The Cloud SaaS deployment option and the Self-Hosted deployment options are paid services. [Contact our sales team](https://www.langchain.com/contact-sales) to learn more.
+There is a free, self-hosted version of LangGraph Platform with access to basic features. The Cloud SaaS deployment option is free while in beta, but will eventually be a paid service. We will always give ample notice before charging for a service and reward our early adopters with preferential pricing. The Self-Hosted deployment options are paid services. [Contact our sales team](https://www.langchain.com/contact-sales) to learn more.
 
 For more information, see our [LangGraph Platform pricing page](https://www.langchain.com/pricing-langgraph-platform).
 
@@ -67,4 +67,4 @@ If you set an environment variable of `LANGSMITH_TRACING=false`, then no traces
 
 ## What does "nodes executed" mean for LangGraph Platform usage?
 
-**Nodes Executed** is the aggregate number of nodes in a LangGraph application that are called and completed successfully during an invocation of the application. If a node in the graph is not called during execution or ends in an error state, these nodes will not be counted. If a node is called and completes successfully multiple times, each occurrence will be counted.
+**Nodes Executed** is the aggregate number of nodes in a LangGraph application that are called and completed successfully during an invocation of the application. If a node in the graph is not called during execution or ends in an error state, these nodes will not be counted. If a node is called and completes successfully multiple times, each occurrence will be counted.

docs/docs/concepts/functional_api.md

@@ -9,21 +9,12 @@ search:
 
 The **Functional API** allows you to add LangGraph's key features — [persistence](./persistence.md), [memory](../how-tos/memory/add-memory.md), [human-in-the-loop](./human_in_the_loop.md), and [streaming](./streaming.md) — to your applications with minimal changes to your existing code.
 
-It is designed to integrate these features into existing code that may use standard language primitives for branching and control flow, such as `if` statements, `for` loops, and function calls. Unlike many data orchestration frameworks that require restructuring code into an explicit pipeline or DAG, the Functional API allows you to incorporate these capabilities without enforcing a rigid execution model.
+It is designed to integrate these features into existing code that may use standard language primitives for branching and control flow, such as `if` statements, `for` loops, and function calls. Unlike many data orchestration frameworks that require restructuring code into an explicit pipeline or DAG, the Functional API allows you to incorporate these capabilities without enforcing a rigid execution model.  
 
-The Functional API uses two key building blocks:
+The Functional API uses two key building blocks:  
 
-:::python
-
-- **`@entrypoint`** – Marks a function as the starting point of a workflow, encapsulating logic and managing execution flow, including handling long-running tasks and interrupts.
+- **`@entrypoint`** – Marks a function as the starting point of a workflow, encapsulating logic and managing execution flow, including handling long-running tasks and interrupts.  
 - **`@task`** – Represents a discrete unit of work, such as an API call or data processing step, that can be executed asynchronously within an entrypoint. Tasks return a future-like object that can be awaited or resolved synchronously.  
-  :::
-
-:::js
-
-- **`entrypoint`** – An entrypoint encapsulates workflow logic and manages execution flow, including handling long-running tasks and interrupts.
-- **`task`** – Represents a discrete unit of work, such as an API call or data processing step, that can be executed asynchronously within an entrypoint. Tasks return a future-like object that can be awaited or resolved synchronously.  
-  :::
 
 This provides a minimal abstraction for building workflows with state management and streaming.
 
@@ -42,24 +33,24 @@ Here are some key differences:
 - **Checkpointing**: Both APIs generate and use checkpoints. In the **Graph API** a new checkpoint is generated after every [superstep](./low_level.md). In the **Functional API**, when tasks are executed, their results are saved to an existing checkpoint associated with the given entrypoint instead of creating a new checkpoint.
 - **Visualization**: The Graph API makes it easy to visualize the workflow as a graph which can be useful for debugging, understanding the workflow, and sharing with others. The Functional API does not support visualization as the graph is dynamically generated during runtime.
 
+
 ## Example
 
 Below we demonstrate a simple application that writes an essay and [interrupts](human_in_the_loop.md) to request human review.
 
-:::python
-
 ```python
-from langgraph.checkpoint.memory import InMemorySaver
+from langgraph.checkpoint.memory import MemorySaver
 from langgraph.func import entrypoint, task
 from langgraph.types import interrupt
 
+
 @task
 def write_essay(topic: str) -> str:
     """Write an essay about the given topic."""
     time.sleep(1) # A placeholder for a long-running task.
     return f"An essay about topic: {topic}"
 
-@entrypoint(checkpointer=InMemorySaver())
+@entrypoint(checkpointer=MemorySaver())
 def workflow(topic: str) -> dict:
     """A simple workflow that writes an essay and asks for a review."""
     essay = write_essay("cat").result()
@@ -79,101 +70,60 @@ def workflow(topic: str) -> dict:
     }
 ```
 
-:::
-
-:::js
-
-```typescript
-import { MemorySaver, entrypoint, task, interrupt } from "@langchain/langgraph";
-
-const writeEssay = task("writeEssay", async (topic: string) => {
-  // A placeholder for a long-running task.
-  await new Promise((resolve) => setTimeout(resolve, 1000));
-  return `An essay about topic: ${topic}`;
-});
-
-const workflow = entrypoint(
-  { checkpointer: new MemorySaver(), name: "workflow" },
-  async (topic: string) => {
-    const essay = await writeEssay(topic);
-    const isApproved = interrupt({
-      // Any json-serializable payload provided to interrupt as argument.
-      // It will be surfaced on the client side as an Interrupt when streaming data
-      // from the workflow.
-      essay, // The essay we want reviewed.
-      // We can add any additional information that we need.
-      // For example, introduce a key called "action" with some instructions.
-      action: "Please approve/reject the essay",
-    });
-
-    return {
-      essay, // The essay that was generated
-      isApproved, // Response from HIL
-    };
-  }
-);
-```
-
-:::
-
 ??? example "Detailed Explanation"
 
     This workflow will write an essay about the topic "cat" and then pause to get a review from a human. The workflow can be interrupted for an indefinite amount of time until a review is provided.
 
-    When the workflow is resumed, it executes from the very start, but because the result of the `writeEssay` task was already saved, the task result will be loaded from the checkpoint instead of being recomputed.
+    When the workflow is resumed, it executes from the very start, but because the result of the `write_essay` task was already saved, the task result will be loaded from the checkpoint instead of being recomputed.
 
-    :::python
     ```python
     import time
     import uuid
+
     from langgraph.func import entrypoint, task
     from langgraph.types import interrupt
-    from langgraph.checkpoint.memory import InMemorySaver
-
+    from langgraph.checkpoint.memory import MemorySaver
 
     @task
     def write_essay(topic: str) -> str:
         """Write an essay about the given topic."""
-        time.sleep(1)  # This is a placeholder for a long-running task.
+        time.sleep(1) # This is a placeholder for a long-running task.
         return f"An essay about topic: {topic}"
 
-    @entrypoint(checkpointer=InMemorySaver())
+    @entrypoint(checkpointer=MemorySaver())
     def workflow(topic: str) -> dict:
         """A simple workflow that writes an essay and asks for a review."""
         essay = write_essay("cat").result()
-        is_approved = interrupt(
-            {
-                # Any json-serializable payload provided to interrupt as argument.
-                # It will be surfaced on the client side as an Interrupt when streaming data
-                # from the workflow.
-                "essay": essay,  # The essay we want reviewed.
-                # We can add any additional information that we need.
-                # For example, introduce a key called "action" with some instructions.
-                "action": "Please approve/reject the essay",
-            }
-        )
+        is_approved = interrupt({
+            # Any json-serializable payload provided to interrupt as argument.
+            # It will be surfaced on the client side as an Interrupt when streaming data
+            # from the workflow.
+            "essay": essay, # The essay we want reviewed.
+            # We can add any additional information that we need.
+            # For example, introduce a key called "action" with some instructions.
+            "action": "Please approve/reject the essay",
+        })
+
         return {
-            "essay": essay,  # The essay that was generated
-            "is_approved": is_approved,  # Response from HIL
+            "essay": essay, # The essay that was generated
+            "is_approved": is_approved, # Response from HIL
         }
 
-
     thread_id = str(uuid.uuid4())
-    config = {"configurable": {"thread_id": thread_id}}
+
+    config = {
+        "configurable": {
+            "thread_id": thread_id
+        }
+    }
+
     for item in workflow.stream("cat", config):
         print(item)
-    # > {'write_essay': 'An essay about topic: cat'}
-    # > {
-    # >     '__interrupt__': (
-    # >        Interrupt(
-    # >            value={
-    # >                'essay': 'An essay about topic: cat',
-    # >                'action': 'Please approve/reject the essay'
-    # >            },
-    # >            id='b9b2b9d788f482663ced6dc755c9e981'
-    # >        ),
-    # >    )
-    # > }
+    ```
+
+    ```pycon
+    {'write_essay': 'An essay about topic: cat'}
+    {'__interrupt__': (Interrupt(value={'essay': 'An essay about topic: cat', 'action': 'Please approve/reject the essay'}, resumable=True, ns=['workflow:f7b8508b-21c0-8b4c-5958-4e8de74d2684'], when='during'),)}
     ```
 
     An essay has been written and is ready for review. Once the review is provided, we can resume the workflow:
@@ -194,104 +144,18 @@ const workflow = entrypoint(
     ```
 
     The workflow has been completed and the review has been added to the essay.
-    :::
-
-    :::js
-    ```typescript
-    import { v4 as uuidv4 } from "uuid";
-    import { MemorySaver, entrypoint, task, interrupt } from "@langchain/langgraph";
-
-    const writeEssay = task("writeEssay", async (topic: string) => {
-      // This is a placeholder for a long-running task.
-      await new Promise(resolve => setTimeout(resolve, 1000));
-      return `An essay about topic: ${topic}`;
-    });
-
-    const workflow = entrypoint(
-      { checkpointer: new MemorySaver(), name: "workflow" },
-      async (topic: string) => {
-        const essay = await writeEssay(topic);
-        const isApproved = interrupt({
-          // Any json-serializable payload provided to interrupt as argument.
-          // It will be surfaced on the client side as an Interrupt when streaming data
-          // from the workflow.
-          essay, // The essay we want reviewed.
-          // We can add any additional information that we need.
-          // For example, introduce a key called "action" with some instructions.
-          action: "Please approve/reject the essay",
-        });
-
-        return {
-          essay, // The essay that was generated
-          isApproved, // Response from HIL
-        };
-      }
-    );
-
-    const threadId = uuidv4();
-
-    const config = {
-      configurable: {
-        thread_id: threadId
-      }
-    };
-
-    for await (const item of workflow.stream("cat", config)) {
-      console.log(item);
-    }
-    ```
-
-    ```console
-    { writeEssay: 'An essay about topic: cat' }
-    {
-      __interrupt__: [{
-        value: { essay: 'An essay about topic: cat', action: 'Please approve/reject the essay' },
-        resumable: true,
-        ns: ['workflow:f7b8508b-21c0-8b4c-5958-4e8de74d2684'],
-        when: 'during'
-      }]
-    }
-    ```
-
-    An essay has been written and is ready for review. Once the review is provided, we can resume the workflow:
-
-    ```typescript
-    import { Command } from "@langchain/langgraph";
-
-    // Get review from a user (e.g., via a UI)
-    // In this case, we're using a bool, but this can be any json-serializable value.
-    const humanReview = true;
-
-    for await (const item of workflow.stream(new Command({ resume: humanReview }), config)) {
-      console.log(item);
-    }
-    ```
-
-    ```console
-    { workflow: { essay: 'An essay about topic: cat', isApproved: true } }
-    ```
-
-    The workflow has been completed and the review has been added to the essay.
-    :::
 
 ## Entrypoint
 
-:::python
-The @[`@entrypoint`][entrypoint] decorator can be used to create a workflow from a function. It encapsulates workflow logic and manages execution flow, including handling _long-running tasks_ and [interrupts](./human_in_the_loop.md).
-:::
-
-:::js
-The @[`entrypoint`][entrypoint] function can be used to create a workflow from a function. It encapsulates workflow logic and manages execution flow, including handling _long-running tasks_ and [interrupts](./human_in_the_loop.md).
-:::
+The [`@entrypoint`][langgraph.func.entrypoint] decorator can be used to create a workflow from a function. It encapsulates workflow logic and manages execution flow, including handling *long-running tasks* and [interrupts](./human_in_the_loop.md).
 
 ### Definition
 
-:::python
-An **entrypoint** is defined by decorating a function with the `@entrypoint` decorator.
+An **entrypoint** is defined by decorating a function with the `@entrypoint` decorator. 
 
 The function **must accept a single positional argument**, which serves as the workflow input. If you need to pass multiple pieces of data, use a dictionary as the input type for the first argument.
 
-Decorating a function with an `entrypoint` produces a @[`Pregel`][Pregel.stream] instance which helps to manage the execution of the workflow (e.g., handles streaming, resumption, and checkpointing).
+Decorating a function with an `entrypoint` produces a [`Pregel`][langgraph.pregel.Pregel.stream] instance which helps to manage the execution of the workflow (e.g., handles streaming, resumption, and checkpointing).
 
 You will usually want to pass a **checkpointer** to the `@entrypoint` decorator to enable persistence and use features like **human-in-the-loop**.
 
@@ -318,48 +182,22 @@ You will usually want to pass a **checkpointer** to the `@entrypoint` decorator
         # some logic that may involve long-running tasks like API calls,
         # and may be interrupted for human-in-the-loop
         ...
-        return result
+        return result 
     ```
 
-:::
-
-:::js
-An **entrypoint** is defined by calling the `entrypoint` function with configuration and a function.
-
-The function **must accept a single positional argument**, which serves as the workflow input. If you need to pass multiple pieces of data, use an object as the input type for the first argument.
-
-Creating an entrypoint with a function produces a workflow instance which helps to manage the execution of the workflow (e.g., handles streaming, resumption, and checkpointing).
-
-You will often want to pass a **checkpointer** to the `entrypoint` function to enable persistence and use features like **human-in-the-loop**.
-
-```typescript
-import { entrypoint } from "@langchain/langgraph";
-
-const myWorkflow = entrypoint(
-  { checkpointer, name: "workflow" },
-  async (someInput: Record<string, any>): Promise<number> => {
-    // some logic that may involve long-running tasks like API calls,
-    // and may be interrupted for human-in-the-loop
-    return result;
-  }
-);
-```
-
-:::
-
 !!! important "Serialization"
 
     The **inputs** and **outputs** of entrypoints must be JSON-serializable to support checkpointing. Please see the [serialization](#serialization) section for more details.
 
-:::python
 
 ### Injectable parameters
 
 When declaring an `entrypoint`, you can request access to additional parameters that will be injected automatically at run time. These parameters include:
 
+
 | Parameter    | Description                                                                                                                                                        |
-| ------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| **previous** | Access the state associated with the previous `checkpoint` for the given thread. See [short-term-memory](#short-term-memory).                                      |
+|--------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| **previous** | Access the state associated with the previous `checkpoint` for the given thread. See [short-term-memory](#short-term-memory).                                  |
 | **store**    | An instance of [BaseStore][langgraph.store.base.BaseStore]. Useful for [long-term memory](../how-tos/use-functional-api.md#long-term-memory).                      |
 | **writer**   | Use to access the StreamWriter when working with Async Python < 3.11. See [streaming with functional API for details](../how-tos/use-functional-api.md#streaming). |
 | **config**   | For accessing run time configuration. See [RunnableConfig](https://python.langchain.com/docs/concepts/runnables/#runnableconfig) for information.                  |
@@ -381,7 +219,7 @@ When declaring an `entrypoint`, you can request access to additional parameters
     @entrypoint(
         checkpointer=checkpointer,  # Specify the checkpointer
         store=in_memory_store  # Specify the store
-    )
+    )  
     def my_workflow(
         some_input: dict,  # The input (e.g., passed via `invoke`)
         *,
@@ -392,12 +230,9 @@ When declaring an `entrypoint`, you can request access to additional parameters
     ) -> ...:
     ```
 
-:::
-
 ### Executing
 
-:::python
-Using the [`@entrypoint`](#entrypoint) yields a @[`Pregel`][Pregel.stream] object that can be executed using the `invoke`, `ainvoke`, `stream`, and `astream` methods.
+Using the [`@entrypoint`](#entrypoint) yields a [`Pregel`][langgraph.pregel.Pregel.stream] object that can be executed using the `invoke`, `ainvoke`, `stream`, and `astream` methods.
 
 === "Invoke"
 
@@ -422,7 +257,7 @@ Using the [`@entrypoint`](#entrypoint) yields a @[`Pregel`][Pregel.stream] objec
     ```
 
 === "Stream"
-
+    
     ```python
     config = {
         "configurable": {
@@ -447,42 +282,9 @@ Using the [`@entrypoint`](#entrypoint) yields a @[`Pregel`][Pregel.stream] objec
         print(chunk)
     ```
 
-:::
-
-:::js
-Using the [`entrypoint`](#entrypoint) function will return an object that can be executed using the `invoke` and `stream` methods.
-
-=== "Invoke"
-
-    ```typescript
-    const config = {
-      configurable: {
-        thread_id: "some_thread_id"
-      }
-    };
-    await myWorkflow.invoke(someInput, config); // Wait for the result
-    ```
-
-=== "Stream"
-
-    ```typescript
-    const config = {
-      configurable: {
-        thread_id: "some_thread_id"
-      }
-    };
-
-    for await (const chunk of myWorkflow.stream(someInput, config)) {
-      console.log(chunk);
-    }
-    ```
-
-:::
-
 ### Resuming
 
-:::python
-Resuming an execution after an @[interrupt][interrupt] can be done by passing a **resume** value to the @[Command] primitive.
+Resuming an execution after an [interrupt][langgraph.types.interrupt] can be done by passing a **resume** value to the [Command][langgraph.types.Command] primitive.
 
 === "Invoke"
 
@@ -494,7 +296,7 @@ Resuming an execution after an @[interrupt][interrupt] can be done by passing a
             "thread_id": "some_thread_id"
         }
     }
-
+    
     my_workflow.invoke(Command(resume=some_resume_value), config)
     ```
 
@@ -508,7 +310,7 @@ Resuming an execution after an @[interrupt][interrupt] can be done by passing a
             "thread_id": "some_thread_id"
         }
     }
-
+    
     await my_workflow.ainvoke(Command(resume=some_resume_value), config)
     ```
 
@@ -522,7 +324,7 @@ Resuming an execution after an @[interrupt][interrupt] can be done by passing a
             "thread_id": "some_thread_id"
         }
     }
-
+    
     for chunk in my_workflow.stream(Command(resume=some_resume_value), config):
         print(chunk)
     ```
@@ -542,52 +344,9 @@ Resuming an execution after an @[interrupt][interrupt] can be done by passing a
         print(chunk)
     ```
 
-:::
-
-:::js
-Resuming an execution after an @[interrupt][interrupt] can be done by passing a **resume** value to the @[`Command`][Command] primitive.
-
-=== "Invoke"
-
-    ```typescript
-    import { Command } from "@langchain/langgraph";
-
-    const config = {
-      configurable: {
-        thread_id: "some_thread_id"
-      }
-    };
-
-    await myWorkflow.invoke(new Command({ resume: someResumeValue }), config);
-    ```
-
-=== "Stream"
-
-    ```typescript
-    import { Command } from "@langchain/langgraph";
-
-    const config = {
-      configurable: {
-        thread_id: "some_thread_id"
-      }
-    };
-
-    const stream = await myWorkflow.stream(
-      new Command({ resume: someResumableValue }),
-      config,
-    )
-
-    for await (const chunk of stream) {
-      console.log(chunk);
-    }
-    ```
-
-:::
-
-:::python
-
 **Resuming after an error**
 
+
 To resume after an error, run the `entrypoint` with a `None` and the same **thread id** (config).
 
 This assumes that the underlying **error** has been resolved and execution can proceed successfully.
@@ -601,7 +360,7 @@ This assumes that the underlying **error** has been resolved and execution can p
             "thread_id": "some_thread_id"
         }
     }
-
+    
     my_workflow.invoke(None, config)
     ```
 
@@ -614,7 +373,7 @@ This assumes that the underlying **error** has been resolved and execution can p
             "thread_id": "some_thread_id"
         }
     }
-
+    
     await my_workflow.ainvoke(None, config)
     ```
 
@@ -627,7 +386,7 @@ This assumes that the underlying **error** has been resolved and execution can p
             "thread_id": "some_thread_id"
         }
     }
-
+    
     for chunk in my_workflow.stream(None, config):
         print(chunk)
     ```
@@ -646,49 +405,10 @@ This assumes that the underlying **error** has been resolved and execution can p
         print(chunk)
     ```
 
-:::
-
-:::js
-
-**Resuming after an error**
-
-To resume after an error, run the `entrypoint` with `null` and the same **thread id** (config).
-
-This assumes that the underlying **error** has been resolved and execution can proceed successfully.
-
-=== "Invoke"
-
-    ```typescript
-    const config = {
-      configurable: {
-        thread_id: "some_thread_id"
-      }
-    };
-
-    await myWorkflow.invoke(null, config);
-    ```
-
-=== "Stream"
-
-    ```typescript
-    const config = {
-      configurable: {
-        thread_id: "some_thread_id"
-      }
-    };
-
-    for await (const chunk of myWorkflow.stream(null, config)) {
-      console.log(chunk);
-    }
-    ```
-
-:::
-
 ### Short-term memory
 
-When an `entrypoint` is defined with a `checkpointer`, it stores information between successive invocations on the same **thread id** in [checkpoints](persistence.md#checkpoints).
+When an `entrypoint` is defined with a `checkpointer`, it stores information between successive invocations on the same **thread id** in [checkpoints](persistence.md#checkpoints). 
 
-:::python
 This allows accessing the state from the previous invocation using the `previous` parameter.
 
 By default, the `previous` parameter is the return value of the previous invocation.
@@ -709,40 +429,9 @@ my_workflow.invoke(1, config)  # 1 (previous was None)
 my_workflow.invoke(2, config)  # 3 (previous was 1 from the previous invocation)
 ```
 
-:::
-
-:::js
-This allows accessing the state from the previous invocation using the `getPreviousState` function.
-
-By default, the `getPreviousState` function returns the return value of the previous invocation.
-
-```typescript
-import { entrypoint, getPreviousState } from "@langchain/langgraph";
-
-const myWorkflow = entrypoint(
-  { checkpointer, name: "workflow" },
-  async (number: number) => {
-    const previous = getPreviousState<number>() ?? 0;
-    return number + previous;
-  }
-);
-
-const config = {
-  configurable: {
-    thread_id: "some_thread_id",
-  },
-};
-
-await myWorkflow.invoke(1, config); // 1 (previous was undefined)
-await myWorkflow.invoke(2, config); // 3 (previous was 1 from the previous invocation)
-```
-
-:::
-
 #### `entrypoint.final`
 
-:::python
-@[`entrypoint.final`][entrypoint.final] is a special primitive that can be returned from an entrypoint and allows **decoupling** the value that is **saved in the checkpoint** from the **return value of the entrypoint**.
+[entrypoint.final][langgraph.func.entrypoint.final] is a special primitive that can be returned from an entrypoint and allows **decoupling** the value that is **saved in the checkpoint** from the **return value of the entrypoint**.
 
 The first value is the return value of the entrypoint, and the second value is the value that will be saved in the checkpoint. The type annotation is `entrypoint.final[return_type, save_type]`.
 
@@ -751,7 +440,7 @@ The first value is the return value of the entrypoint, and the second value is t
 def my_workflow(number: int, *, previous: Any = None) -> entrypoint.final[int, int]:
     previous = previous or 0
     # This will return the previous value to the caller, saving
-    # 2 * number to the checkpoint, which will be used in the next invocation
+    # 2 * number to the checkpoint, which will be used in the next invocation 
     # for the `previous` parameter.
     return entrypoint.final(value=previous, save=2 * number)
 
@@ -765,52 +454,15 @@ my_workflow.invoke(3, config)  # 0 (previous was None)
 my_workflow.invoke(1, config)  # 6 (previous was 3 * 2 from the previous invocation)
 ```
 
-:::
-
-:::js
-@[`entrypoint.final`][entrypoint.final] is a special primitive that can be returned from an entrypoint and allows **decoupling** the value that is **saved in the checkpoint** from the **return value of the entrypoint**.
-
-The first value is the return value of the entrypoint, and the second value is the value that will be saved in the checkpoint.
-
-```typescript
-import { entrypoint, getPreviousState } from "@langchain/langgraph";
-
-const myWorkflow = entrypoint(
-  { checkpointer, name: "workflow" },
-  async (number: number) => {
-    const previous = getPreviousState<number>() ?? 0;
-    // This will return the previous value to the caller, saving
-    // 2 * number to the checkpoint, which will be used in the next invocation
-    // for the `previous` parameter.
-    return entrypoint.final({
-      value: previous,
-      save: 2 * number,
-    });
-  }
-);
-
-const config = {
-  configurable: {
-    thread_id: "1",
-  },
-};
-
-await myWorkflow.invoke(3, config); // 0 (previous was undefined)
-await myWorkflow.invoke(1, config); // 6 (previous was 3 * 2 from the previous invocation)
-```
-
-:::
-
 ## Task
 
 A **task** represents a discrete unit of work, such as an API call or data processing step. It has two key characteristics:
 
-- **Asynchronous Execution**: Tasks are designed to be executed asynchronously, allowing multiple operations to run concurrently without blocking.
-- **Checkpointing**: Task results are saved to a checkpoint, enabling resumption of the workflow from the last saved state. (See [persistence](persistence.md) for more details).
+* **Asynchronous Execution**: Tasks are designed to be executed asynchronously, allowing multiple operations to run concurrently without blocking.
+* **Checkpointing**: Task results are saved to a checkpoint, enabling resumption of the workflow from the last saved state. (See [persistence](persistence.md) for more details).
 
 ### Definition
 
-:::python
 Tasks are defined using the `@task` decorator, which wraps a regular Python function.
 
 ```python
@@ -823,37 +475,21 @@ def slow_computation(input_value):
     return result
 ```
 
-:::
-
-:::js
-Tasks are defined using the `task` function, which wraps a regular function.
-
-```typescript
-import { task } from "@langchain/langgraph";
-
-const slowComputation = task("slowComputation", async (inputValue: any) => {
-  // Simulate a long-running operation
-  return result;
-});
-```
-
-:::
-
 !!! important "Serialization"
 
     The **outputs** of tasks must be JSON-serializable to support checkpointing.
 
 ### Execution
 
-**Tasks** can only be called from within an **entrypoint**, another **task**, or a [state graph node](./low_level.md#nodes).
+**Tasks** can only be called from within an **entrypoint**, another **task**, or a [state graph node](./low_level.md#nodes). 
 
-Tasks _cannot_ be called directly from the main application code.
+Tasks *cannot* be called directly from the main application code. 
 
-:::python
-When you call a **task**, it returns _immediately_ with a future object. A future is a placeholder for a result that will be available later.
+When you call a **task**, it returns *immediately* with a future object. A future is a placeholder for a result that will be available later.
 
 To obtain the result of a **task**, you can either wait for it synchronously (using `result()`) or await it asynchronously (using `await`).
 
+
 === "Synchronous Invocation"
 
     ```python
@@ -871,22 +507,6 @@ To obtain the result of a **task**, you can either wait for it synchronously (us
         return await slow_computation(some_input)  # Await result asynchronously
     ```
 
-:::
-
-:::js
-When you call a **task**, it returns a Promise that can be awaited.
-
-```typescript
-const myWorkflow = entrypoint(
-  { checkpointer, name: "workflow" },
-  async (someInput: number): Promise<number> => {
-    return await slowComputation(someInput);
-  }
-);
-```
-
-:::
-
 ## When to use a task
 
 **Tasks** are useful in the following scenarios:
@@ -896,21 +516,16 @@ const myWorkflow = entrypoint(
 - **Parallel Execution**: For I/O-bound tasks, **tasks** enable parallel execution, allowing multiple operations to run concurrently without blocking (e.g., calling multiple APIs).
 - **Observability**: Wrapping operations in **tasks** provides a way to track the progress of the workflow and monitor the execution of individual operations using [LangSmith](https://docs.smith.langchain.com/).
 - **Retryable Work**: When work needs to be retried to handle failures or inconsistencies, **tasks** provide a way to encapsulate and manage the retry logic.
-
+ 
 ## Serialization
 
 There are two key aspects to serialization in LangGraph:
 
-1. `entrypoint` inputs and outputs must be JSON-serializable.
-2. `task` outputs must be JSON-serializable.
+1. `@entrypoint` inputs and outputs must be JSON-serializable.
+2. `@task` outputs must be JSON-serializable.
 
-:::python
-These requirements are necessary for enabling checkpointing and workflow resumption. Use python primitives like dictionaries, lists, strings, numbers, and booleans to ensure that your inputs and outputs are serializable.
-:::
-
-:::js
-These requirements are necessary for enabling checkpointing and workflow resumption. Use primitives like objects, arrays, strings, numbers, and booleans to ensure that your inputs and outputs are serializable.
-:::
+These requirements are necessary for enabling checkpointing and workflow resumption. Use python primitives
+like dictionaries, lists, strings, numbers, and booleans to ensure that your inputs and outputs are serializable.
 
 Serialization ensures that workflow state, such as task results and intermediate values, can be reliably saved and restored. This is critical for enabling human-in-the-loop interactions, fault tolerance, and parallel execution.
 
@@ -918,9 +533,9 @@ Providing non-serializable inputs or outputs will result in a runtime error when
 
 ## Determinism
 
-To utilize features like **human-in-the-loop**, any randomness should be encapsulated inside of **tasks**. This guarantees that when execution is halted (e.g., for human in the loop) and then resumed, it will follow the same _sequence of steps_, even if **task** results are non-deterministic.
+To utilize features like **human-in-the-loop**, any randomness should be encapsulated inside of **tasks**. This guarantees that when execution is halted (e.g., for human in the loop) and then resumed, it will follow the same *sequence of steps*, even if **task** results are non-deterministic.
 
-LangGraph achieves this behavior by persisting **task** and [**subgraph**](./subgraphs.md) results as they execute. A well-designed workflow ensures that resuming execution follows the _same sequence of steps_, allowing previously computed results to be retrieved correctly without having to re-execute them. This is particularly useful for long-running **tasks** or **tasks** with non-deterministic results, as it avoids repeating previously done work and allows resuming from essentially the same.
+LangGraph achieves this behavior by persisting **task** and [**subgraph**](./subgraphs.md) results as they execute. A well-designed workflow ensures that resuming execution follows the *same sequence of steps*, allowing previously computed results to be retrieved correctly without having to re-execute them. This is particularly useful for long-running **tasks** or **tasks** with non-deterministic results, as it avoids repeating previously done work and allows resuming from essentially the same.
 
 While different runs of a workflow can produce different results, resuming a **specific** run should always follow the same sequence of recorded steps. This allows LangGraph to efficiently look up **task** and **subgraph** results that were executed prior to the graph being interrupted and avoid recomputing them.
 
@@ -938,7 +553,6 @@ Encapsulate side effects (e.g., writing to a file, sending an email) in tasks to
 
     In this example, a side effect (writing to a file) is directly included in the workflow, so it will be executed a second time when resuming the workflow.
 
-    :::python
     ```python
     @entrypoint(checkpointer=checkpointer)
     def my_workflow(inputs: dict) -> int:
@@ -951,31 +565,11 @@ Encapsulate side effects (e.g., writing to a file, sending an email) in tasks to
         value = interrupt("question")
         return value
     ```
-    :::
-
-    :::js
-    ```typescript
-    import { entrypoint, interrupt } from "@langchain/langgraph";
-    import fs from "fs";
-
-    const myWorkflow = entrypoint(
-      { checkpointer, name: "workflow },
-      async (inputs: Record<string, any>) => {
-        // This code will be executed a second time when resuming the workflow.
-        // Which is likely not what you want.
-        fs.writeFileSync("output.txt", "Side effect executed");
-        const value = interrupt("question");
-        return value;
-      }
-    );
-    ```
-    :::
 
 === "Correct"
 
     In this example, the side effect is encapsulated in a task, ensuring consistent execution upon resumption.
 
-    :::python
     ```python
     from langgraph.func import task
 
@@ -993,43 +587,17 @@ Encapsulate side effects (e.g., writing to a file, sending an email) in tasks to
         value = interrupt("question")
         return value
     ```
-    :::
-
-    :::js
-    ```typescript
-    import { entrypoint, task, interrupt } from "@langchain/langgraph";
-    import * as fs from "fs";
-
-    const writeToFile = task("writeToFile", async () => {
-      fs.writeFileSync("output.txt", "Side effect executed");
-    });
-
-    const myWorkflow = entrypoint(
-      { checkpointer, name: "workflow" },
-      async (inputs: Record<string, any>) => {
-        // The side effect is now encapsulated in a task.
-        await writeToFile();
-        const value = interrupt("question");
-        return value;
-      }
-    );
-    ```
-    :::
 
 ### Non-deterministic control flow
 
 Operations that might give different results each time (like getting current time or random numbers) should be encapsulated in tasks to ensure that on resume, the same result is returned.
 
-- In a task: Get random number (5) → interrupt → resume → (returns 5 again) → ...
-- Not in a task: Get random number (5) → interrupt → resume → get new random number (7) → ...
+* In a task: Get random number (5) → interrupt → resume → (returns 5 again) → ...
+* Not in a task: Get random number (5) → interrupt → resume → get new random number (7) → ...
 
-:::python
-This is especially important when using **human-in-the-loop** workflows with multiple interrupts calls. LangGraph keeps a list of resume values for each task/entrypoint. When an interrupt is encountered, it's matched with the corresponding resume value. This matching is strictly **index-based**, so the order of the resume values should match the order of the interrupts.
-:::
-
-:::js
-This is especially important when using **human-in-the-loop** workflows with multiple interrupt calls. LangGraph keeps a list of resume values for each task/entrypoint. When an interrupt is encountered, it's matched with the corresponding resume value. This matching is strictly **index-based**, so the order of the resume values should match the order of the interrupts.
-:::
+This is especially important when using **human-in-the-loop** workflows with multiple interrupts calls. LangGraph keeps a list
+of resume values for each task/entrypoint. When an interrupt is encountered, it's matched with the corresponding resume value.
+This matching is strictly **index-based**, so the order of the resume values should match the order of the interrupts.
 
 If order of execution is not maintained when resuming, one `interrupt` call may be matched with the wrong `resume` value, leading to incorrect results.
 
@@ -1039,7 +607,6 @@ Please read the section on [determinism](#determinism) for more details.
 
     In this example, the workflow uses the current time to determine which task to execute. This is non-deterministic because the result of the workflow depends on the time at which it is executed.
 
-    :::python
     ```python
     from langgraph.func import entrypoint
 
@@ -1048,51 +615,24 @@ Please read the section on [determinism](#determinism) for more details.
         t0 = inputs["t0"]
         # highlight-next-line
         t1 = time.time()
-
+        
         delta_t = t1 - t0
-
+        
         if delta_t > 1:
             result = slow_task(1).result()
             value = interrupt("question")
         else:
             result = slow_task(2).result()
             value = interrupt("question")
-
+            
         return {
             "result": result,
             "value": value
         }
     ```
-    :::
-
-    :::js
-    ```typescript
-    import { entrypoint, interrupt } from "@langchain/langgraph";
-
-    const myWorkflow = entrypoint(
-      { checkpointer, name: "workflow" },
-      async (inputs: { t0: number }) => {
-        const t1 = Date.now();
-
-        const deltaT = t1 - inputs.t0;
-
-        if (deltaT > 1000) {
-          const result = await slowTask(1);
-          const value = interrupt("question");
-          return { result, value };
-        } else {
-          const result = await slowTask(2);
-          const value = interrupt("question");
-          return { result, value };
-        }
-      }
-    );
-    ```
-    :::
 
 === "Correct"
 
-    :::python
     In this example, the workflow uses the input `t0` to determine which task to execute. This is deterministic because the result of the workflow depends only on the input.
 
     ```python
@@ -1111,48 +651,19 @@ Please read the section on [determinism](#determinism) for more details.
         t0 = inputs["t0"]
         # highlight-next-line
         t1 = get_time().result()
-
+        
         delta_t = t1 - t0
-
+        
         if delta_t > 1:
             result = slow_task(1).result()
             value = interrupt("question")
         else:
             result = slow_task(2).result()
             value = interrupt("question")
-
+            
         return {
             "result": result,
             "value": value
         }
     ```
-    :::
 
-    :::js
-    In this example, the workflow uses the input `t0` to determine which task to execute. This is deterministic because the result of the workflow depends only on the input.
-
-    ```typescript
-    import { entrypoint, task, interrupt } from "@langchain/langgraph";
-
-    const getTime = task("getTime", () => Date.now());
-
-    const myWorkflow = entrypoint(
-      { checkpointer, name: "workflow" },
-      async (inputs: { t0: number }): Promise<any> => {
-        const t1 = await getTime();
-
-        const deltaT = t1 - inputs.t0;
-
-        if (deltaT > 1000) {
-          const result = await slowTask(1);
-          const value = interrupt("question");
-          return { result, value };
-        } else {
-          const result = await slowTask(2);
-          const value = interrupt("question");
-          return { result, value };
-        }
-      }
-    );
-    ```
-    :::

docs/docs/concepts/human_in_the_loop.md

@@ -23,18 +23,9 @@ To review, edit, and approve tool calls in an agent or workflow, [use LangGraph'
 
 ## Key capabilities
 
-* **Persistent execution state**: Interrupts use LangGraph's [persistence](./persistence.md) layer, which saves the graph state, to indefinitely pause graph execution until you resume. This is possible because LangGraph checkpoints the graph state after each step, which allows the system to persist execution context and later resume the workflow, continuing from where it left off. This supports asynchronous human review or input without time constraints.
+* **Persistent execution state**: LangGraph allows you to pause execution **indefinitely** — for minutes, hours, or even days—until human input is received. This is possible because LangGraph checkpoints the graph state after each step, which allows the system to persist execution context and later resume the workflow, continuing from where it left off. This supports asynchronous human review or input without time constraints.
 
-    There are two ways to pause a graph:
-
-    - [Dynamic interrupts](../how-tos/human_in_the_loop/add-human-in-the-loop.md#pause-using-interrupt): Use `interrupt` to pause a graph from inside a specific node, based on the current state of the graph.
-    - [Static interrupts](../how-tos/human_in_the_loop/add-human-in-the-loop.md#debug-with-interrupts): Use `interrupt_before` and `interrupt_after` to pause the graph at pre-defined points, either before or after a node executes.
-
-    <figure markdown="1">
-    ![image](./img/breakpoints.png){: style="max-height:400px"}
-    <figcaption>An example graph consisting of 3 sequential steps with a breakpoint before step_3. </figcaption> </figure>
-
-* **Flexible integration points**: Human-in-the-loop logic can be introduced at any point in the workflow. This allows targeted human involvement, such as approving API calls, correcting outputs, or guiding conversations.
+* **Flexible integration points**: HIL logic can be introduced at any point in the workflow. This allows targeted human involvement, such as approving API calls, correcting outputs, or guiding conversations.
 
 ## Patterns
 

docs/docs/concepts/langgraph_cli.md

@@ -7,66 +7,29 @@ search:
 
 **LangGraph CLI** is a multi-platform command-line tool for building and running the [LangGraph API server](./langgraph_server.md) locally. The resulting server includes all API endpoints for your graph's runs, threads, assistants, etc. as well as the other services required to run your agent, including a managed database for checkpointing and storage.
 
-:::python
-
 ## Installation
 
 The LangGraph CLI can be installed via pip or [Homebrew](https://brew.sh/):
 
-=== "pip"
-
+=== "pip" 
     ```bash
     pip install langgraph-cli
     ```
 
 === "Homebrew"
-
     ```bash
     brew install langgraph-cli
     ```
-:::
-
-:::js
-
-## Installation
-
-The LangGraph.js CLI can be installed from the NPM registry:
-
-=== "npx"
-    ```bash
-    npx @langchain/langgraph-cli
-    ```
-
-=== "npm"
-    ```bash
-    npm install @langchain/langgraph-cli
-    ```
-
-=== "yarn"
-    ```bash
-    yarn add @langchain/langgraph-cli
-    ```
-
-=== "pnpm"
-    ```bash
-    pnpm add @langchain/langgraph-cli
-    ```
-
-=== "bun"
-    ```bash
-    bun add @langchain/langgraph-cli
-    ```
-:::
 
 ## Commands
 
 LangGraph CLI provides the following core functionality:
 
-| Command                                                        | Description                                                                                                                                                                                                                                                                            |
-| -------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| [`langgraph build`](../cloud/reference/cli.md#build)           | Builds a Docker image for the [LangGraph API server](./langgraph_server.md) that can be directly deployed.                                                                                                                                                                             |
-| [`langgraph dev`](../cloud/reference/cli.md#dev)               | Starts a lightweight development server that requires no Docker installation. This server is ideal for rapid development and testing.                                                                                                                                                  |
+| Command | Description |
+| -------- | -------|
+| [`langgraph build`](../cloud/reference/cli.md#build) | Builds a Docker image for the [LangGraph API server](./langgraph_server.md) that can be directly deployed. |
+| [`langgraph dev`](../cloud/reference/cli.md#dev) | Starts a lightweight development server that requires no Docker installation. This server is ideal for rapid development and testing. This is available in version 0.1.55 and up.
 | [`langgraph dockerfile`](../cloud/reference/cli.md#dockerfile) | Generates a [Dockerfile](https://docs.docker.com/reference/dockerfile/) that can be used to build images for and deploy instances of the [LangGraph API server](./langgraph_server.md). This is useful if you want to further customize the dockerfile or deploy in a more custom way. |
-| [`langgraph up`](../cloud/reference/cli.md#up)                 | Starts an instance of the [LangGraph API server](./langgraph_server.md) locally in a docker container. This requires the docker server to be running locally. It also requires a LangSmith API key for local development or a license key for production use.                          |
+| [`langgraph up`](../cloud/reference/cli.md#up) | Starts an instance of the [LangGraph API server](./langgraph_server.md) locally in a docker container. This requires the docker server to be running locally. It also requires a LangSmith API key for local development or a license key for production use. | 
 
 For more information, see the [LangGraph CLI Reference](../cloud/reference/cli.md).

docs/docs/concepts/langgraph_cloud.md

@@ -11,11 +11,11 @@ To deploy a [LangGraph Server](../concepts/langgraph_server.md), follow the how-
 
 The Cloud SaaS deployment option is a fully managed model for deployment where we manage the [control plane](./langgraph_control_plane.md) and [data plane](./langgraph_data_plane.md) in our cloud.
 
-|                                    | [Control plane](../concepts/langgraph_control_plane.md)                                                                                     | [Data plane](../concepts/langgraph_data_plane.md)                                                                                                   |
-| ---------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------- |
-| **What is it?**                    | <ul><li>Control plane UI for creating deployments and revisions</li><li>Control plane APIs for creating deployments and revisions</li></ul> | <ul><li>Data plane "listener" for reconciling deployments with control plane state</li><li>LangGraph Servers</li><li>Postgres, Redis, etc</li></ul> |
-| **Where is it hosted?**            | LangChain's cloud                                                                                                                           | LangChain's cloud                                                                                                                                   |
-| **Who provisions and manages it?** | LangChain                                                                                                                                   | LangChain                                                                                                                                           |
+|                   | [Control plane](../concepts/langgraph_control_plane.md) | [Data plane](../concepts/langgraph_data_plane.md) |
+|-------------------|-------------------|------------|
+| **What is it?** | <ul><li>Control plane UI for creating deployments and revisions</li><li>Control plane APIs for creating deployments and revisions</li></ul> | <ul><li>Data plane "listener" for reconciling deployments with control plane state</li><li>LangGraph Servers</li><li>Postgres, Redis, etc</li></ul> |
+| **Where is it hosted?** | LangChain's cloud | LangChain's cloud |
+| **Who provisions and manages it?** | LangChain | LangChain |
 
 ## Architecture
 

docs/docs/concepts/langgraph_components.md

@@ -10,4 +10,4 @@ The LangGraph Platform consists of components that work together to support the
 - [LangGraph control plane](./langgraph_control_plane.md): The LangGraph Control Plane refers to the Control Plane UI where users create and update LangGraph Servers and the Control Plane APIs that support the UI experience.
 - [LangGraph data plane](./langgraph_data_plane.md): The LangGraph Data Plane refers to LangGraph Servers, the corresponding infrastructure for each server, and the "listener" application that continuously polls for updates from the LangGraph Control Plane.
 
-![LangGraph components](img/lg_platform.png)
+![LangGraph components](img/lg_platform.png)

docs/docs/concepts/langgraph_control_plane.md

@@ -26,7 +26,7 @@ The Control Plane UI is embedded in [LangSmith](https://docs.smith.langchain.com
 
 ## Control Plane API
 
-This section describes the data model of the control plane API. The API is used to create, update, and delete deployments. See the [control plane API reference](../cloud/reference/api/api_ref_control_plane.md) for more details.
+This section describes data model of the control plane API. The API is used to create, update, and delete deployments. However, they are not publicly accessible.
 
 ### Deployment
 
@@ -34,7 +34,11 @@ A deployment is an instance of a LangGraph Server. A single deployment can have
 
 ### Revision
 
-A revision is an iteration of a deployment. When a new deployment is created, an initial revision is automatically created. To deploy code changes or update secrets for a deployment, a new revision must be created.
+A revision is an iteration of a deployment. When a new deployment is created, an initial revision is automatically created. To deploy code changes or update environment variables for a deployment, a new revision must be created.
+
+### Environment Variable
+
+Environment variables are set for a deployment. All environment variables are stored as secrets (i.e. saved in a secrets store).
 
 ## Control Plane Features
 
@@ -44,42 +48,23 @@ This section describes various features of the control plane.
 
 For simplicity, the control plane offers two deployment types with different resource allocations: `Development` and `Production`.
 
-| **Deployment Type** | **CPU/Memory**  | **Scaling**       | **Database**                                                                     |
-| ------------------- | --------------- | ----------------- | -------------------------------------------------------------------------------- |
-| Development         | 1 CPU, 1 GB RAM | Up to 1 replica   | 10 GB disk, no backups                                                           |
-| Production          | 2 CPU, 2 GB RAM | Up to 10 replicas | Autoscaling disk, automatic backups, highly available (multi-zone configuration) |
+| **Deployment Type** | **CPU/Memory**  | **Scaling**         | **Database**                                                                     |
+|---------------------|-----------------|---------------------|----------------------------------------------------------------------------------|
+| Development         | 1 CPU, 1 GB RAM | Up to 1 container   | 10 GB disk, no backups                                                           |
+| Production          | 2 CPU, 2 GB RAM | Up to 10 containers | Autoscaling disk, automatic backups, highly available (multi-zone configuration) |
 
-CPU and memory resources are per replica.
+CPU and memory resources are per container.
 
 !!! warning "Immutable Deployment Type"
 
     Once a deployment is created, the deployment type cannot be changed.
 
-!!! info "Self-Hosted Deployment"
-Resources for [Self-Hosted Data Plane](../concepts/langgraph_self_hosted_data_plane.md) and [Self-Hosted Control Plane](../concepts/langgraph_self_hosted_control_plane.md) deployments can be fully customized. Deployment types are only applicable for [Cloud SaaS](../concepts/langgraph_cloud.md) deployments.
+!!! info "Resource Customization"
+    For `Production` type deployments, resources can be manually increased on a case-by-case basis depending on use case and capacity constraints. Contact support@langchain.dev to request an increase in resources.
 
-#### Production
+    For `Development` types deployments, database disk size can be manually increased on a case-by-case basis depending on use case and capacity constraints. For most use cases, [TTLs](../how-tos/ttl/configure_ttl.md) should be configured to manage disk usage. Contact support@langchain.dev to request an increase in resources.
 
-`Production` type deployments are suitable for "production" workloads. For example, select `Production` for customer-facing applications in the critical path.
-
-Resources for `Production` type deployments can be manually increased on a case-by-case basis depending on use case and capacity constraints. Contact support@langchain.dev to request an increase in resources.
-
-#### Development
-
-`Development` type deployments are suitable development and testing. For example, select `Development` for internal testing environments. `Development` type deployments are not suitable for "production" workloads.
-
-!!! danger "Preemptible Compute Infrastructure"
-`Development` type deployments (API server, queue server, and database) are provisioned on preemptible compute infrastructure. This means the compute infrastructure **may be terminated at any time without notice**. This may result in intermittent...
-
-    - Redis connection timeouts/errors
-    - Postgres connection timeouts/errors
-    - Failed or retrying background runs
-
-    This behavior is expected. Preemptible compute infrastructure **significantly reduces the cost to provision a `Development` type deployment**. By design, LangGraph Server is fault-tolerant. The implementation will automatically attempt to recover from Redis/Postgres connection errors and retry failed background runs.
-
-    `Production` type deployments are provisioned on durable compute infrastructure, not preemptible compute infrastructure.
-
-Database disk size for `Development` type deployments can be manually increased on a case-by-case basis depending on use case and capacity constraints. For most use cases, [TTLs](../how-tos/ttl/configure_ttl.md) should be configured to manage disk usage. Contact support@langchain.dev to request an increase in resources.
+    Resources for [Self-Hosted Data Plane](../concepts/langgraph_self_hosted_data_plane.md) and [Self-Hosted Control Plane](../concepts/langgraph_self_hosted_control_plane.md) deployments can be fully customized.
 
 ### Database Provisioning
 
@@ -92,7 +77,7 @@ There is no direct access to the database. All access to the database occurs thr
 The database is never deleted until the deployment itself is deleted.
 
 !!! info
-A custom Postgres instance can be configured for [Self-Hosted Data Plane](../concepts/langgraph_self_hosted_data_plane.md) and [Self-Hosted Control Plane](../concepts/langgraph_self_hosted_control_plane.md) deployments.
+    A custom Postgres instance can be configured for [Self-Hosted Data Plane](../concepts/langgraph_self_hosted_data_plane.md) and [Self-Hosted Control Plane](../concepts/langgraph_self_hosted_control_plane.md) deployments.
 
 ### Asynchronous Deployment
 
@@ -112,18 +97,11 @@ After a deployment is ready, the control plane monitors the deployment and recor
 - Number of container restarts.
 - Number of replicas (this will increase with [autoscaling](../concepts/langgraph_data_plane.md#autoscaling)).
 - [Postgres](../concepts/langgraph_data_plane.md#postgres) CPU, memory usage, and disk usage.
-- [LangGraph Server queue](../concepts/langgraph_server.md#persistence-and-task-queue) pending/active run count.
-- [LangGraph Server API](../concepts/langgraph_server.md) success response count, error response count, and latency.
 
 These metrics are displayed as charts in the Control Plane UI.
 
 ### LangSmith Integration
 
-A [LangSmith](https://docs.smith.langchain.com/) tracing project and LangSmith API key are automatically created for each deployment. The deployment uses the API key to automatically send traces to LangSmith.
+A [LangSmith](https://docs.smith.langchain.com/) tracing project is automatically created for each deployment. The tracing project has the same name as the deployment. When creating a deployment, the `LANGCHAIN_TRACING` and `LANGSMITH_API_KEY`/`LANGCHAIN_API_KEY` environment variables do not need to be specified; they are set automatically by the control plane.
 
-- The tracing project has the same name as the deployment.
-- The API key has the description `LangGraph Platform: <deployment_name>`.
-- The API key is never revealed and cannot be deleted manually.
-- When creating a deployment, the `LANGCHAIN_TRACING` and `LANGSMITH_API_KEY`/`LANGCHAIN_API_KEY` environment variables do not need to be specified; they are set automatically by the control plane.
-
-When a deployment is deleted, the traces and the tracing project are not deleted. However, the API will be deleted when the deployment is deleted.
+When a deployment is deleted, the traces and the tracing project are not deleted.

docs/docs/concepts/langgraph_data_plane.md

@@ -50,15 +50,6 @@ Runs in a LangGraph Server may be retried for specific failures (currently only
 
 This section describes various features of the data plane.
 
-### Data Region
-
-!!! info "Only for Cloud SaaS"
-    Data regions are only applicable for [Cloud SaaS](../concepts/langgraph_cloud.md) deployments.
-
-Deployments can be created in 2 data regions: US and EU
-
-The data region for a deployment is implied by the data region of the LangSmith organization where the deployment is created. Deployments and the underlying database for the deployments cannot be migrated between data regions.
-
 ### Autoscaling
 
 [`Production` type](../concepts/langgraph_control_plane.md#deployment-types) deployments automatically scale up to 10 containers. Scaling is based on 3 metrics:
@@ -78,25 +69,25 @@ Scale down actions are delayed for 30 minutes before any action is taken. In oth
 ### Static IP Addresses
 
 !!! info "Only for Cloud SaaS"
-Static IP addresses are only available for [Cloud SaaS](../concepts/langgraph_cloud.md) deployments.
+    Static IP addresses are only available for [Cloud SaaS](../concepts/langgraph_cloud.md) deployments.
 
 All traffic from deployments created after January 6th 2025 will come through a NAT gateway. This NAT gateway will have several static IP addresses depending on the data region. Refer to the table below for the list of static IP addresses:
 
 | US             | EU             |
-| -------------- | -------------- |
+|----------------|----------------|
 | 35.197.29.146  | 34.13.192.67   |
 | 34.145.102.123 | 34.147.105.64  |
 | 34.169.45.153  | 34.90.22.166   |
 | 34.82.222.17   | 34.147.36.213  |
-| 35.227.171.135 | 34.32.137.113  |
+| 35.227.171.135 | 34.32.137.113  | 
 | 34.169.88.30   | 34.91.238.184  |
 | 34.19.93.202   | 35.204.101.241 |
 | 34.19.34.50    | 35.204.48.32   |
 
 ### Custom Postgres
 
-!!! info
-Custom Postgres instances are only available for [Self-Hosted Data Plane](../concepts/langgraph_self_hosted_data_plane.md) and [Self-Hosted Control Plane](../concepts/langgraph_self_hosted_control_plane.md) deployments.
+!!! info 
+    Custom Postgres instances are only available for [Self-Hosted Data Plane](../concepts/langgraph_self_hosted_data_plane.md) and [Self-Hosted Control Plane](../concepts/langgraph_self_hosted_control_plane.md) deployments.
 
 A custom Postgres instance can be used instead of the [one automatically created by the control plane](./langgraph_control_plane.md#database-provisioning). Specify the [`POSTGRES_URI_CUSTOM`](../cloud/reference/env_var.md#postgres_uri_custom) environment variable to use a custom Postgres instance.
 
@@ -105,32 +96,33 @@ Multiple deployments can share the same Postgres instance. For example, for `Dep
 ### Custom Redis
 
 !!! info
-Custom Redis instances are only available for [Self-Hosted Data Plane](../concepts/langgraph_self_hosted_control_plane.md) and [Self-Hosted Control Plane](../concepts/langgraph_self_hosted_control_plane.md) deployments.
+    Custom Redis instances are only available for [Self-Hosted Data Plane](../concepts/langgraph_self_hosted_control_plane.md) and [Self-Hosted Control Plane](../concepts/langgraph_self_hosted_control_plane.md) deployments.
 
 A custom Redis instance can be used instead of the one automatically created by the control plane. Specify the [REDIS_URI_CUSTOM](../cloud/reference/env_var.md#redis_uri_custom) environment variable to use a custom Redis instance.
 
+
 Multiple deployments can share the same Redis instance. For example, for `Deployment A`, `REDIS_URI_CUSTOM` can be set to `redis://<hostname_1>:<port>/1` and for `Deployment B`, `REDIS_URI_CUSTOM` can be set to `redis://<hostname_1>:<port>/2`. `1` and `2` are different database numbers within the same instance, but `<hostname_1>` is shared. **The same database number cannot be used for separate deployments**.
 
 ### LangSmith Tracing
 
 LangGraph Server is automatically configured to send traces to LangSmith. See the table below for details with respect to each deployment option.
 
-| Cloud SaaS                               | Self-Hosted Data Plane                                      | Self-Hosted Control Plane                                          | Standalone Container                                                                         |
-| ---------------------------------------- | ----------------------------------------------------------- | ------------------------------------------------------------------ | -------------------------------------------------------------------------------------------- |
+| Cloud SaaS | Self-Hosted Data Plane | Self-Hosted Control Plane | Standalone Container |
+|------------|------------------------|---------------------------|----------------------|
 | Required<br><br>Trace to LangSmith SaaS. | Optional<br><br>Disable tracing or trace to LangSmith SaaS. | Optional<br><br>Disable tracing or trace to Self-Hosted LangSmith. | Optional<br><br>Disable tracing, trace to LangSmith SaaS, or trace to Self-Hosted LangSmith. |
 
 ### Telemetry
 
 LangGraph Server is automatically configured to report telemetry metadata for billing purposes. See the table below for details with respect to each deployment option.
 
-| Cloud SaaS                        | Self-Hosted Data Plane            | Self-Hosted Control Plane                                                                                                           | Standalone Container                                                                                                                |
-| --------------------------------- | --------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------- |
+| Cloud SaaS | Self-Hosted Data Plane | Self-Hosted Control Plane | Standalone Container |
+|------------|------------------------|---------------------------|----------------------|
 | Telemetry sent to LangSmith SaaS. | Telemetry sent to LangSmith SaaS. | Self-reported usage (audit) for air-gapped license key.<br><br>Telemetry sent to LangSmith SaaS for LangGraph Platform License Key. | Self-reported usage (audit) for air-gapped license key.<br><br>Telemetry sent to LangSmith SaaS for LangGraph Platform License Key. |
 
 ### Licensing
 
 LangGraph Server is automatically configured to perform license key validation. See the table below for details with respect to each deployment option.
 
-| Cloud SaaS                                          | Self-Hosted Data Plane                              | Self-Hosted Control Plane                                                                  | Standalone Container                                                                       |
-| --------------------------------------------------- | --------------------------------------------------- | ------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------ |
+| Cloud SaaS | Self-Hosted Data Plane | Self-Hosted Control Plane | Standalone Container |
+|------------|------------------------|---------------------------|----------------------|
 | LangSmith API Key validated against LangSmith SaaS. | LangSmith API Key validated against LangSmith SaaS. | Air-gapped license key or LangGraph Platform License Key validated against LangSmith SaaS. | Air-gapped license key or LangGraph Platform License Key validated against LangSmith SaaS. |