"""The Async LangSmith Client."""

from __future__ import annotations

import asyncio
import datetime
import json
import logging
import random
import uuid
import warnings
from collections.abc import AsyncGenerator, AsyncIterator, Mapping, Sequence
from functools import partial
from typing import (
    TYPE_CHECKING,
    Any,
    Literal,
    Optional,
    Union,
    cast,
)

import httpx

import langsmith._openapi_client as _langsmith_api_module
from langsmith.client import _get_openapi_base_url

if TYPE_CHECKING:
    from langsmith._openapi_client.resources.runs import AsyncRunsResource

from langsmith import client as ls_client
from langsmith import schemas as ls_schemas
from langsmith import utils as ls_utils
from langsmith._internal import _profiles
from langsmith._internal._backend_version import _check_backend_version
from langsmith._internal._hub import (
    HUB,
    REPO_HANDLE_PATTERN,
    build_commit_url,
    platform_hub_path,
    validate_parent_commit,
)
from langsmith.prompt_cache import AsyncPromptCache, async_prompt_cache_singleton

logger = logging.getLogger(__name__)

ID_TYPE = Union[uuid.UUID, str]

if TYPE_CHECKING:
    from langsmith._openapi_client.resources.datasets.datasets import (
        AsyncDatasetsResource,
    )
    from langsmith._openapi_client.resources.online_evaluators import (
        AsyncOnlineEvaluatorsResource as AsyncEvaluatorsResource,
    )
    from langsmith._openapi_client.resources.public.public import (
        AsyncPublicResource,
    )
    from langsmith._openapi_client.resources.sandboxes.sandboxes import (
        AsyncSandboxesResource,
    )
    from langsmith._openapi_client.resources.threads import AsyncThreadsResource
    from langsmith._openapi_client.resources.traces import AsyncTracesResource


class AsyncClient:
    """Async Client for interacting with the LangSmith API."""

    __slots__ = (
        "_retry_config",
        "_client",
        "_web_url",
        "_settings",
        "_cache",
        "_custom_headers",
        "_api_key",
        "_oauth_access_token",
        "_workspace_id",
        "_profile_auth",
        "_profile_auth_headers",
        "_langsmith_api",
        "_info",
    )

    _custom_headers: dict[str, str]
    _api_key: Optional[str]
    _oauth_access_token: Optional[str]
    _workspace_id: Optional[str]
    _profile_auth: Optional[_profiles.ProfileAuth]
    _profile_auth_headers: dict[str, str]
    _info: Optional[ls_schemas.LangSmithInfo]

    def _compute_headers(self) -> dict[str, str]:
        headers = {**self._custom_headers}
        # Required headers that should not be overridden
        headers["Content-Type"] = "application/json"
        if self._api_key:
            headers[ls_client.X_API_KEY] = self._api_key
        elif self._profile_auth_headers:
            headers.update(self._profile_auth_headers)
        elif self._oauth_access_token:
            headers["Authorization"] = f"Bearer {self._oauth_access_token}"
        if self._workspace_id:
            headers["X-Tenant-Id"] = self._workspace_id
        return headers

    @property
    def headers(self) -> dict[str, str]:
        """Return the custom headers used for API requests."""
        return self._custom_headers

    @headers.setter
    def headers(self, value: Optional[dict[str, str]]) -> None:
        self._custom_headers = value or {}
        self._client.headers = httpx.Headers(self._compute_headers())

    @property
    def _headers(self) -> dict[str, str]:
        """Return the merged headers used for API requests."""
        return dict(self._client.headers)

    @property
    def api_key(self) -> Optional[str]:
        """Return the API key used for authentication."""
        return self._api_key

    @api_key.setter
    def api_key(self, value: Optional[str]) -> None:
        self._api_key = value
        self._client.headers = httpx.Headers(self._compute_headers())

    @property
    def workspace_id(self) -> Optional[str]:
        """Return the workspace ID used for API requests."""
        return self._workspace_id

    @workspace_id.setter
    def workspace_id(self, value: Optional[str]) -> None:
        self._workspace_id = ls_utils.get_workspace_id(value)
        self._client.headers = httpx.Headers(self._compute_headers())

    async def info(self) -> ls_schemas.LangSmithInfo:
        """Get information about the LangSmith server."""
        if self._info is not None:
            return self._info
        try:
            response = await self._arequest_with_retries(
                "GET",
                "/info",
                headers={"Accept": "application/json"},
            )
            ls_utils.raise_for_status_with_text(response)
            self._info = ls_schemas.LangSmithInfo(**response.json())
        except BaseException as e:
            logger.warning(f"Failed to get info from {self._api_url}: {repr(e)}")
            self._info = ls_schemas.LangSmithInfo()
        return self._info

    def __init__(
        self,
        api_url: Optional[str] = None,
        api_key: Optional[str] = None,
        timeout_ms: Optional[
            Union[
                int, tuple[Optional[int], Optional[int], Optional[int], Optional[int]]
            ]
        ] = None,
        retry_config: Optional[Mapping[str, Any]] = None,
        web_url: Optional[str] = None,
        headers: Optional[dict[str, str]] = None,
        workspace_id: Optional[str] = None,
        disable_prompt_cache: bool = False,
        cache: Optional[Union[bool, AsyncPromptCache]] = None,
    ):
        """Initialize the async client.

        Args:
            api_url: URL for the LangSmith API.
            api_key: API key for the LangSmith API.
            timeout_ms: Timeout for requests in milliseconds.
            retry_config: Retry configuration.
            web_url: URL for the LangSmith web app.
            headers: Additional HTTP headers to include in all requests.

                These headers will be merged with the default headers
                (Content-Type, x-api-key, etc.). Custom headers will not override
                the default required headers.
            workspace_id: The workspace ID. Required for org-scoped API keys.
            disable_prompt_cache: Disable prompt caching for this client.
            cache: **[Deprecated]** Control prompt caching behavior.

                This parameter is deprecated. Use `configure_global_async_prompt_cache()` to
                configure caching, or `disable_prompt_cache=True` to disable it.

                - `True`: Enable caching with the global singleton
                - `False`: Disable caching (equivalent to `disable_prompt_cache=True`)
                - `AsyncCache(...)`/`AsyncPromptCache(...)`: Use a custom cache instance
        """
        self._retry_config = retry_config or {"max_retries": 3}
        self._custom_headers = headers or {}
        env_api_url = ls_client._get_langsmith_env_var_uncached("ENDPOINT")
        env_api_key = ls_client._get_langsmith_env_var_uncached("API_KEY")
        env_workspace_id = ls_client._get_langsmith_env_var_uncached("WORKSPACE_ID")
        profile_config = _profiles.load_profile_client_config()
        api_url_ = (
            api_url if api_url is not None else env_api_url or profile_config.api_url
        )
        explicit_or_env_api_key = api_key if api_key is not None else env_api_key
        profile_auth_enabled = api_key is None and env_api_key is None
        use_profile_oauth = profile_auth_enabled and profile_config.has_oauth
        api_key_ = (
            explicit_or_env_api_key
            if explicit_or_env_api_key is not None
            else None
            if use_profile_oauth
            else profile_config.api_key
        )
        self._oauth_access_token = (
            profile_config.oauth_access_token if use_profile_oauth else None
        )
        workspace_id_ = (
            workspace_id
            if workspace_id is not None
            else env_workspace_id or profile_config.workspace_id
        )
        api_key = ls_utils.get_api_key(api_key_)
        api_url = ls_utils.get_api_url(api_url_)
        self._workspace_id = ls_utils.get_workspace_id(workspace_id_)
        self._profile_auth = None
        self._profile_auth_headers = {}
        if use_profile_oauth:
            self._profile_auth = _profiles.ProfileAuth(
                profile_config,
                api_key_header=ls_client.X_API_KEY,
            )
            self._profile_auth_headers = self._profile_auth.current_auth_headers()
            self._oauth_access_token = self._profile_auth.oauth_access_token
        self._api_key = api_key
        _headers = self._compute_headers()
        ls_client._validate_api_key_if_hosted(
            api_url,
            api_key
            or self._oauth_access_token
            or (
                "profile-auth"
                if self._profile_auth is not None and self._profile_auth.has_auth
                else None
            ),
        )

        if isinstance(timeout_ms, int):
            timeout_: Union[tuple, float] = (timeout_ms / 1000, None, None, None)
        elif isinstance(timeout_ms, tuple):
            timeout_ = tuple([t / 1000 if t is not None else None for t in timeout_ms])
        else:
            timeout_ = 10
        self._client = httpx.AsyncClient(
            base_url=api_url, headers=_headers, timeout=timeout_
        )
        self._web_url = web_url
        self._settings: Optional[ls_schemas.LangSmithSettings] = None
        self._info: Optional[ls_schemas.LangSmithInfo] = None

        # Initialize prompt cache
        # Handle backwards compatibility for deprecated `cache` parameter
        if cache is not None and disable_prompt_cache:
            import warnings

            warnings.warn(
                "Both 'cache' and 'disable_prompt_cache' were provided. "
                "The 'cache' parameter is deprecated and will be removed in a future version. "
                "Using 'cache' parameter value.",
                DeprecationWarning,
                stacklevel=2,
            )

        if cache is not None:
            import warnings

            warnings.warn(
                "The 'cache' parameter is deprecated and will be removed in a future version. "
                "Use 'configure_global_async_prompt_cache()' to configure the global cache, or "
                "'disable_prompt_cache=True' to disable caching for this client.",
                DeprecationWarning,
                stacklevel=2,
            )
            # Handle old cache parameter
            if cache is False:
                self._cache: Optional[AsyncPromptCache] = None
            elif cache is True:
                self._cache = async_prompt_cache_singleton
            else:
                # Custom AsyncPromptCache instance provided
                self._cache = cache
        elif not disable_prompt_cache:
            # Use the global singleton instance
            self._cache = async_prompt_cache_singleton
        else:
            self._cache = None

        self._langsmith_api = _langsmith_api_module.AsyncLangsmith(
            api_key=self._api_key,
            tenant_id=self._workspace_id,
            base_url=_get_openapi_base_url(str(self._client.base_url)),
            timeout=self._client.timeout,
            default_headers=_headers or None,
        )

    # ------------------------------------------------------------------
    # Stainless v2 resource accessors
    # Only resources that target /v2/ endpoints are exposed here.
    # @property is used (not @cached_property) because __slots__ disables
    # __dict__; the stainless client caches each resource internally.
    # ------------------------------------------------------------------

    @property
    def runs(self) -> AsyncRunsResource:
        """Access the runs resource."""
        return self._langsmith_api.runs

    @property
    def evaluators(self) -> AsyncEvaluatorsResource:
        """Access the evaluator resource."""
        return self._langsmith_api.online_evaluators

    @property
    def sandboxes(self) -> AsyncSandboxesResource:
        """Access the sandboxes resource (registries, snapshots, boxes)."""
        return self._langsmith_api.sandboxes

    @property
    def datasets(self) -> AsyncDatasetsResource:
        """Access the v2 datasets resource (experiment_runs, etc.)."""
        return self._langsmith_api.datasets

    @property
    def threads(self) -> AsyncThreadsResource:
        """Access the threads resource (query, stats, list_traces)."""
        return self._langsmith_api.threads

    @property
    def traces(self) -> AsyncTracesResource:
        """Access the traces resource (query, list_runs)."""
        return self._langsmith_api.traces

    @property
    def public(self) -> AsyncPublicResource:
        """Access the public shared-run resource."""
        return self._langsmith_api.public

    async def __aenter__(self) -> AsyncClient:
        """Enter the async client."""
        if self._cache is not None:
            await self._cache.start()
        info = await self.info()
        _check_backend_version(info.version)
        return self

    async def __aexit__(self, exc_type, exc_val, exc_tb):
        """Exit the async client."""
        await self.aclose()

    async def aclose(self):
        """Close the async client."""
        if self._cache is not None:
            await self._cache.stop()
        await self._client.aclose()

    def __repr__(self) -> str:
        """Return a string representation of the instance.

        Returns:
            The string representation of the instance.
        """
        return f"AsyncClient (API URL: {self._api_url})"

    @property
    def _api_url(self):
        return str(self._client.base_url)

    @property
    def _host_url(self) -> str:
        """The web host url."""
        return ls_utils.get_host_url(self._web_url, self._api_url)

    async def _ensure_profile_auth(self) -> None:
        if self._api_key or self._profile_auth is None:
            return
        if self._profile_auth.needs_refresh():
            auth_headers = await asyncio.to_thread(self._profile_auth.get_auth_headers)
        else:
            auth_headers = self._profile_auth.current_auth_headers()
        self._profile_auth_headers = auth_headers
        self._oauth_access_token = self._profile_auth.oauth_access_token
        self._client.headers = httpx.Headers(self._compute_headers())

    async def _arequest_with_retries(
        self,
        method: str,
        endpoint: str,
        *,
        stop_after_attempt: Optional[int] = None,
        retry_on: Optional[Sequence[type[BaseException]]] = None,
        **kwargs: Any,
    ) -> httpx.Response:
        """Make an async HTTP request with retries.

        Args:
            method: The HTTP method.
            endpoint: The request endpoint (appended to the API base URL).
            stop_after_attempt: The number of attempts to make. If unset, uses
                the client's configured max retries.
            retry_on: Additional exception types to retry on, beyond the
                connection/timeout/server-error set that is always retried.
            **kwargs: Forwarded to the underlying httpx request.
        """
        max_retries = (
            stop_after_attempt
            if stop_after_attempt is not None
            else cast(int, self._retry_config.get("max_retries", 3))
        )
        # Transient connection/timeout/server errors are always retried; callers
        # can opt into retrying additional types (e.g. LangSmithNotFoundError
        # when writing against a run that may not be ingested yet).
        retry_on_: tuple[type[BaseException], ...] = (
            ls_utils.LangSmithConnectionError,
            ls_utils.LangSmithRequestTimeout,
            ls_utils.LangSmithAPIError,
            *(retry_on or ()),
        )

        # Python requests library used by the normal Client filters out params with None values
        # The httpx library does not. Filter them out here to keep behavior consistent
        if "params" in kwargs:
            params = kwargs["params"]
            filtered_params = {k: v for k, v in params.items() if v is not None}
            kwargs["params"] = filtered_params

        await self._ensure_profile_auth()
        if self._profile_auth is not None and "headers" in kwargs:
            kwargs["headers"] = self._profile_auth.prepare_request_headers(
                kwargs["headers"]
            )

        for attempt in range(max_retries):
            try:
                try:
                    response = await self._client.request(method, endpoint, **kwargs)
                    ls_utils.raise_for_status_with_text(response)
                    return response
                except httpx.HTTPStatusError as e:
                    response = e.response
                    if response.status_code in {425, 500, 502, 503, 504}:
                        raise ls_utils.LangSmithAPIError(
                            f"Server error ({response.status_code}) caused failure to"
                            f" {method} {endpoint} in"
                            f" LangSmith API. {repr(e)}"
                        ) from e
                    elif response.status_code == 408:
                        raise ls_utils.LangSmithRequestTimeout(
                            f"Client took too long to send request to {method}{endpoint}"
                        ) from e
                    elif response.status_code == 429:
                        raise ls_utils.LangSmithRateLimitError(
                            f"Rate limit exceeded for {endpoint}. {repr(e)}"
                        ) from e
                    elif response.status_code == 401:
                        raise ls_utils.LangSmithAuthError(
                            f"Authentication failed for {endpoint}. {repr(e)}"
                        ) from e
                    elif response.status_code == 404:
                        raise ls_utils.LangSmithNotFoundError(
                            f"Resource not found for {endpoint}. {repr(e)}"
                        ) from e
                    elif response.status_code == 409:
                        raise ls_utils.LangSmithConflictError(
                            f"Conflict for {endpoint}. {repr(e)}"
                        ) from e
                    else:
                        raise ls_utils.LangSmithError(
                            f"Failed to {method} {endpoint} in LangSmith API. {repr(e)}"
                        ) from e
                except httpx.RequestError as e:
                    raise ls_utils.LangSmithConnectionError(
                        f"Request error: {repr(e)}"
                    ) from e
            except retry_on_:
                if attempt == max_retries - 1:
                    raise
                sleep_time = 2**attempt + (random.random() * 0.5)
                await asyncio.sleep(sleep_time)
        raise ls_utils.LangSmithAPIError(
            "Unexpected error connecting to the LangSmith API"
        )

    async def _aget_paginated_list(
        self,
        path: str,
        params: Optional[dict[str, Any]] = None,
    ) -> AsyncIterator[dict[str, Any]]:
        """Get a paginated list of items."""
        params = params or {}
        offset = params.get("offset", 0)
        params["limit"] = params.get("limit", 100)
        while True:
            params["offset"] = offset
            response = await self._arequest_with_retries("GET", path, params=params)
            items = response.json()
            if not items:
                break
            for item in items:
                yield item
            if len(items) < params["limit"]:
                break
            offset += len(items)

    async def _aget_cursor_paginated_list(
        self,
        path: str,
        *,
        body: Optional[dict] = None,
        request_method: str = "POST",
        data_key: str = "runs",
    ) -> AsyncIterator[dict]:
        """Get a cursor paginated list of items."""
        params_ = body.copy() if body else {}
        while True:
            response = await self._arequest_with_retries(
                request_method,
                path,
                content=ls_client._dumps_json(params_),
            )
            response_body = response.json()
            if not response_body:
                break
            if not response_body.get(data_key):
                break
            for run in response_body[data_key]:
                yield run
            cursors = response_body.get("cursors")
            if not cursors:
                break
            if not cursors.get("next"):
                break
            params_["cursor"] = cursors["next"]

    async def create_run(
        self,
        name: str,
        inputs: dict[str, Any],
        run_type: str,
        *,
        project_name: Optional[str] = None,
        revision_id: Optional[ls_client.ID_TYPE] = None,
        **kwargs: Any,
    ) -> None:
        """Create a run."""
        run_create = {
            "name": name,
            "id": kwargs.get("id") or uuid.uuid4(),
            "inputs": inputs,
            "run_type": run_type,
            "session_name": project_name or ls_utils.get_tracer_project(),
            "revision_id": revision_id,
            **kwargs,
        }
        await self._arequest_with_retries(
            "POST", "/runs", content=ls_client._dumps_json(run_create)
        )

    async def update_run(
        self,
        run_id: ls_client.ID_TYPE,
        **kwargs: Any,
    ) -> None:
        """Update a run."""
        data = {**kwargs, "id": ls_client._as_uuid(run_id)}
        await self._arequest_with_retries(
            "PATCH",
            f"/runs/{ls_client._as_uuid(run_id)}",
            content=ls_client._dumps_json(data),
        )

    async def read_run(self, run_id: ls_client.ID_TYPE) -> ls_schemas.Run:
        """Read a run."""
        response = await self._arequest_with_retries(
            "GET",
            f"/runs/{ls_client._as_uuid(run_id)}",
        )
        return ls_schemas.Run(**response.json())

    async def list_runs(
        self,
        *,
        project_id: Optional[
            Union[ls_client.ID_TYPE, Sequence[ls_client.ID_TYPE]]
        ] = None,
        project_name: Optional[Union[str, Sequence[str]]] = None,
        run_type: Optional[str] = None,
        trace_id: Optional[ls_client.ID_TYPE] = None,
        reference_example_id: Optional[ls_client.ID_TYPE] = None,
        query: Optional[str] = None,
        filter: Optional[str] = None,
        trace_filter: Optional[str] = None,
        tree_filter: Optional[str] = None,
        is_root: Optional[bool] = None,
        parent_run_id: Optional[ls_client.ID_TYPE] = None,
        start_time: Optional[datetime.datetime] = None,
        error: Optional[bool] = None,
        run_ids: Optional[Sequence[ls_client.ID_TYPE]] = None,
        select: Optional[Sequence[str]] = None,
        limit: Optional[int] = None,
        **kwargs: Any,
    ) -> AsyncIterator[ls_schemas.Run]:
        """List runs from the LangSmith API.

        Args:
            project_id: The ID(s) of the project to filter by.
            project_name: The name(s) of the project to filter by.
            run_type: The type of the runs to filter by.
            trace_id: The ID of the trace to filter by.
            reference_example_id: The ID of the reference example to filter by.
            query: The query string to filter by.
            filter: The filter string to filter by.
            trace_filter: Filter to apply to the ROOT run in the trace tree.

                This is meant to be used in conjunction with the regular `filter`
                parameter to let you filter runs by attributes of the root run within a
                trace.
            tree_filter: Filter to apply to OTHER runs in the trace tree, including
                sibling and child runs.

                This is meant to be used in conjunction with the regular `filter`
                parameter to let you filter runs by attributes of any run within a
                trace.
            is_root: Whether to filter by root runs.
            parent_run_id: The ID of the parent run to filter by.
            start_time: The start time to filter by.
            error: Whether to filter by error status.
            run_ids: The IDs of the runs to filter by.
            select: The fields to select.
            limit: The maximum number of runs to return.
            **kwargs: Additional keyword arguments.

        Yields:
            The runs.

        Examples:
            ```python
            # List all runs in a project
            project_runs = client.list_runs(project_name="<your_project>")

            # List LLM and Chat runs in the last 24 hours
            todays_llm_runs = client.list_runs(
                project_name="<your_project>",
                start_time=datetime.now() - timedelta(days=1),
                run_type="llm",
            )

            # List root traces in a project
            root_runs = client.list_runs(project_name="<your_project>", is_root=1)

            # List runs without errors
            correct_runs = client.list_runs(project_name="<your_project>", error=False)

            # List runs and only return their inputs/outputs (to speed up the query)
            input_output_runs = client.list_runs(
                project_name="<your_project>", select=["inputs", "outputs"]
            )

            # List runs by run ID
            run_ids = [
                "a36092d2-4ad5-4fb4-9c0d-0dba9a2ed836",
                "9398e6be-964f-4aa4-8ae9-ad78cd4b7074",
            ]
            selected_runs = client.list_runs(id=run_ids)

            # List all "chain" type runs that took more than 10 seconds and had
            # `total_tokens` greater than 5000
            chain_runs = client.list_runs(
                project_name="<your_project>",
                filter='and(eq(run_type, "chain"), gt(latency, 10), gt(total_tokens, 5000))',
            )

            # List all runs called "extractor" whose root of the trace was assigned feedback "user_score" score of 1
            good_extractor_runs = client.list_runs(
                project_name="<your_project>",
                filter='eq(name, "extractor")',
                trace_filter='and(eq(feedback_key, "user_score"), eq(feedback_score, 1))',
            )

            # List all runs that started after a specific timestamp and either have "error" not equal to null or a "Correctness" feedback score equal to 0
            complex_runs = client.list_runs(
                project_name="<your_project>",
                filter='and(gt(start_time, "2023-07-15T12:34:56Z"), or(neq(error, null), and(eq(feedback_key, "Correctness"), eq(feedback_score, 0.0))))',
            )

            # List all runs where `tags` include "experimental" or "beta" and `latency` is greater than 2 seconds
            tagged_runs = client.list_runs(
                project_name="<your_project>",
                filter='and(or(has(tags, "experimental"), has(tags, "beta")), gt(latency, 2))',
            )
            ```
        """  # noqa: E501
        project_ids = []
        if isinstance(project_id, (uuid.UUID, str)):
            project_ids.append(project_id)
        elif isinstance(project_id, list):
            project_ids.extend(project_id)
        if project_name is not None:
            if isinstance(project_name, str):
                project_name = [project_name]
            projects = await asyncio.gather(
                *[self.read_project(project_name=name) for name in project_name]
            )
            project_ids.extend([project.id for project in projects])

        if select and "child_run_ids" in select:
            warnings.warn(
                "The child_run_ids field is deprecated and will be removed in following versions",
                DeprecationWarning,
            )

        body_query: dict[str, Any] = {
            "session": project_ids if project_ids else None,
            "run_type": run_type,
            "reference_example": (
                [reference_example_id] if reference_example_id else None
            ),
            "query": query,
            "filter": filter,
            "trace_filter": trace_filter,
            "tree_filter": tree_filter,
            "is_root": is_root,
            "parent_run": parent_run_id,
            "start_time": start_time.isoformat() if start_time else None,
            "error": error,
            "id": run_ids,
            "trace": trace_id,
            "select": select,
            "limit": limit,
            **kwargs,
        }
        if project_ids:
            body_query["session"] = [
                str(ls_client._as_uuid(id_)) for id_ in project_ids
            ]
        body = {k: v for k, v in body_query.items() if v is not None}
        ix = 0
        async for run in self._aget_cursor_paginated_list("/runs/query", body=body):
            yield ls_schemas.Run(**run)
            ix += 1
            if limit is not None and ix >= limit:
                break

    async def share_run(
        self, run_id: ls_client.ID_TYPE, *, share_id: Optional[ls_client.ID_TYPE] = None
    ) -> str:
        """Get a share link for a run asynchronously.

        Args:
            run_id (ID_TYPE): The ID of the run to share.
            share_id: Custom share ID.

                If not provided, a random UUID will be generated.

        Returns:
            The URL of the shared run.

        Raises:
            httpx.HTTPStatusError: If the API request fails.
        """
        run_id_ = ls_client._as_uuid(run_id, "run_id")
        data = {
            "run_id": str(run_id_),
            "share_token": str(share_id or uuid.uuid4()),
        }
        response = await self._arequest_with_retries(
            "PUT",
            f"/runs/{run_id_}/share",
            content=ls_client._dumps_json(data),
        )
        ls_utils.raise_for_status_with_text(response)
        share_token = response.json()["share_token"]
        return f"{self._host_url}/public/{share_token}/r"

    async def run_is_shared(self, run_id: ls_client.ID_TYPE) -> bool:
        """Get share state for a run asynchronously."""
        link = await self.read_run_shared_link(ls_client._as_uuid(run_id, "run_id"))
        return link is not None

    async def read_run_shared_link(self, run_id: ls_client.ID_TYPE) -> Optional[str]:
        """Retrieve the shared link for a specific run asynchronously.

        Args:
            run_id (ID_TYPE): The ID of the run.

        Returns:
            Optional[str]: The shared link for the run, or None if the link is not
            available.

        Raises:
            httpx.HTTPStatusError: If the API request fails.
        """
        response = await self._arequest_with_retries(
            "GET",
            f"/runs/{ls_client._as_uuid(run_id, 'run_id')}/share",
        )
        ls_utils.raise_for_status_with_text(response)
        result = response.json()
        if result is None or "share_token" not in result:
            return None
        return f"{self._host_url}/public/{result['share_token']}/r"

    async def create_project(
        self,
        project_name: str,
        **kwargs: Any,
    ) -> ls_schemas.TracerSession:
        """Create a project."""
        data = {"name": project_name, **kwargs}
        response = await self._arequest_with_retries(
            "POST", "/sessions", content=ls_client._dumps_json(data)
        )
        return ls_schemas.TracerSession(**response.json())

    async def read_project(
        self,
        project_name: Optional[str] = None,
        project_id: Optional[ls_client.ID_TYPE] = None,
    ) -> ls_schemas.TracerSession:
        """Read a project."""
        if project_id:
            response = await self._arequest_with_retries(
                "GET", f"/sessions/{ls_client._as_uuid(project_id)}"
            )
        elif project_name:
            response = await self._arequest_with_retries(
                "GET", "/sessions", params={"name": project_name}
            )
        else:
            raise ValueError("Either project_name or project_id must be provided")

        data = response.json()
        if isinstance(data, list):
            if not data:
                raise ls_utils.LangSmithNotFoundError(
                    f"Project {project_name} not found"
                )
            return ls_schemas.TracerSession(**data[0])
        return ls_schemas.TracerSession(**data)

    async def delete_project(
        self, *, project_name: Optional[str] = None, project_id: Optional[str] = None
    ) -> None:
        """Delete a project from LangSmith.

        Args:
            project_name: The name of the project to delete.
            project_id: The ID of the project to delete.
        """
        if project_id is None and project_name is None:
            raise ValueError("Either project_name or project_id must be provided")
        if project_id is None:
            project = await self.read_project(project_name=project_name)
            project_id = str(project.id)
        if not project_id:
            raise ValueError("Project not found")
        await self._arequest_with_retries(
            "DELETE",
            f"/sessions/{ls_client._as_uuid(project_id)}",
        )

    async def create_dataset(
        self,
        dataset_name: str,
        **kwargs: Any,
    ) -> ls_schemas.Dataset:
        """Create a dataset."""
        data = {"name": dataset_name, **kwargs}
        response = await self._arequest_with_retries(
            "POST", "/datasets", content=ls_client._dumps_json(data)
        )
        return ls_schemas.Dataset(**response.json())

    async def read_dataset(
        self,
        dataset_name: Optional[str] = None,
        dataset_id: Optional[ls_client.ID_TYPE] = None,
    ) -> ls_schemas.Dataset:
        """Read a dataset."""
        if dataset_id:
            response = await self._arequest_with_retries(
                "GET", f"/datasets/{ls_client._as_uuid(dataset_id)}"
            )
        elif dataset_name:
            response = await self._arequest_with_retries(
                "GET", "/datasets", params={"name": dataset_name}
            )
        else:
            raise ValueError("Either dataset_name or dataset_id must be provided")

        data = response.json()
        if isinstance(data, list):
            if not data:
                raise ls_utils.LangSmithNotFoundError(
                    f"Dataset {dataset_name} not found"
                )
            return ls_schemas.Dataset(**data[0])
        return ls_schemas.Dataset(**data)

    async def delete_dataset(self, dataset_id: ls_client.ID_TYPE) -> None:
        """Delete a dataset."""
        await self._arequest_with_retries(
            "DELETE",
            f"/datasets/{ls_client._as_uuid(dataset_id)}",
        )

    async def list_datasets(
        self,
        **kwargs: Any,
    ) -> AsyncIterator[ls_schemas.Dataset]:
        """List datasets."""
        async for dataset in self._aget_paginated_list("/datasets", params=kwargs):
            yield ls_schemas.Dataset(**dataset)

    async def create_example(
        self,
        inputs: dict[str, Any],
        outputs: Optional[dict[str, Any]] = None,
        dataset_id: Optional[ls_client.ID_TYPE] = None,
        dataset_name: Optional[str] = None,
        **kwargs: Any,
    ) -> ls_schemas.Example:
        """Create an example."""
        if dataset_id is None and dataset_name is None:
            raise ValueError("Either dataset_id or dataset_name must be provided")
        if dataset_id is None:
            dataset = await self.read_dataset(dataset_name=dataset_name)
            dataset_id = dataset.id

        data = {
            "inputs": inputs,
            "outputs": outputs,
            "dataset_id": str(dataset_id),
            **kwargs,
        }
        response = await self._arequest_with_retries(
            "POST", "/examples", content=ls_client._dumps_json(data)
        )
        return ls_schemas.Example(**response.json())

    async def read_example(self, example_id: ls_client.ID_TYPE) -> ls_schemas.Example:
        """Read an example."""
        response = await self._arequest_with_retries(
            "GET", f"/examples/{ls_client._as_uuid(example_id)}"
        )
        return ls_schemas.Example(**response.json())

    async def list_examples(
        self,
        *,
        dataset_id: Optional[ls_client.ID_TYPE] = None,
        dataset_name: Optional[str] = None,
        **kwargs: Any,
    ) -> AsyncIterator[ls_schemas.Example]:
        """List examples."""
        params = kwargs.copy()
        if dataset_id:
            params["dataset"] = ls_client._as_uuid(dataset_id)
        elif dataset_name:
            dataset = await self.read_dataset(dataset_name=dataset_name)
            params["dataset"] = dataset.id

        async for example in self._aget_paginated_list("/examples", params=params):
            yield ls_schemas.Example(**example)

    async def create_feedback(
        self,
        run_id: Optional[ls_client.ID_TYPE] = None,
        key: str = "unnamed",
        *,
        score: Union[float, int, bool, None] = None,
        value: Union[float, int, bool, str, dict, None] = None,
        trace_id: Optional[ls_client.ID_TYPE] = None,
        correction: Union[dict, None] = None,
        feedback_id: Optional[ls_client.ID_TYPE] = None,
        feedback_config: Optional[ls_schemas.FeedbackConfig] = None,
        feedback_source_type: Union[
            ls_schemas.FeedbackSourceType, str
        ] = ls_schemas.FeedbackSourceType.API,
        source_info: Optional[dict[str, Any]] = None,
        source_run_id: Optional[ls_client.ID_TYPE] = None,
        stop_after_attempt: int = 10,
        project_id: Optional[ls_client.ID_TYPE] = None,
        comparative_experiment_id: Optional[ls_client.ID_TYPE] = None,
        feedback_group_id: Optional[ls_client.ID_TYPE] = None,
        extra: Optional[dict] = None,
        error: Optional[bool] = None,
        session_id: Optional[ls_client.ID_TYPE] = None,
        start_time: Optional[datetime.datetime] = None,
        comment: Optional[str] = None,
        extend_trace_retention: bool = True,
        **kwargs: Any,
    ) -> ls_schemas.Feedback:
        """Create feedback for a run.

        Args:
            run_id: The ID of the run to provide feedback for. At least one of
                run_id, trace_id, or project_id must be specified.
            key: The name of the feedback metric.
            score: The score to rate this run on the metric or aspect.
            value: The display value or non-numeric value for this feedback.
            trace_id: The ID of the trace that contains the run.
            correction: The proper ground truth for this run.
            feedback_id: Optional ID to assign to the feedback.
            feedback_config: Configuration specifying how to interpret this
                feedback with this key.
            feedback_source_type: The feedback source type, such as API or model.
            source_info: Information about the source of this feedback.
            source_run_id: The run that generated this feedback, if model-generated.
            stop_after_attempt: The number of times to retry the request before
                giving up.
            project_id: The project or experiment ID for project-level feedback.
            comparative_experiment_id: The comparative experiment ID for this
                feedback.
            feedback_group_id: The group ID for preference or comparative
                feedback.
            extra: Metadata for the feedback.
            error: Whether the feedback represents an error.
            session_id: The project ID of the run this feedback is for.
            start_time: The start time of the run this feedback is for.
            comment: A comment about this feedback.
            extend_trace_retention: If false, create the feedback without
                extending the trace's retention tier.
            **kwargs: Additional deprecated keyword arguments.

        Returns:
            The created feedback object.

        Raises:
            httpx.HTTPStatusError: If the API request fails.
        """  # noqa: E501
        run_id = run_id or trace_id
        if run_id is None and project_id is None:
            raise ValueError("One of run_id, trace_id, or project_id  must be provided")
        if run_id is not None and project_id is not None:
            raise ValueError(
                "project_id cannot be provided if run_id or trace_id is provided"
            )
        if kwargs:
            warnings.warn(
                "The following arguments are no longer used in the create_feedback"
                f" endpoint: {sorted(kwargs)}",
                DeprecationWarning,
            )
        if not isinstance(feedback_source_type, ls_schemas.FeedbackSourceType):
            feedback_source_type = ls_schemas.FeedbackSourceType(feedback_source_type)
        if feedback_source_type == ls_schemas.FeedbackSourceType.API:
            feedback_source: ls_schemas.FeedbackSourceBase = (
                ls_schemas.APIFeedbackSource(metadata=source_info)
            )
        elif feedback_source_type == ls_schemas.FeedbackSourceType.MODEL:
            feedback_source = ls_schemas.ModelFeedbackSource(metadata=source_info)
        else:
            raise ValueError(f"Unknown feedback source type {feedback_source_type}")
        feedback_source.metadata = (
            feedback_source.metadata if feedback_source.metadata is not None else {}
        )
        if source_run_id is not None and "__run" not in feedback_source.metadata:
            feedback_source.metadata["__run"] = {"run_id": str(source_run_id)}
        if feedback_source.metadata and "__run" in feedback_source.metadata:
            run_meta: Union[dict, Any] = feedback_source.metadata["__run"]
            if hasattr(run_meta, "model_dump") and callable(
                getattr(run_meta, "model_dump")
            ):
                run_meta = run_meta.model_dump()
            if "run_id" in run_meta:
                run_meta["run_id"] = str(
                    ls_client._as_uuid(
                        feedback_source.metadata["__run"]["run_id"],
                        "feedback_source.metadata['__run']['run_id']",
                    )
                )
            feedback_source.metadata["__run"] = run_meta

        session_id_ = ls_client._ensure_uuid(
            session_id if session_id is not None else project_id, accept_null=True
        )
        feedback = ls_schemas.FeedbackCreate(
            id=ls_client._ensure_uuid(feedback_id),
            run_id=ls_client._ensure_uuid(run_id, accept_null=True),
            trace_id=ls_client._ensure_uuid(trace_id, accept_null=True),
            key=key,
            score=ls_client._format_feedback_score(score),
            value=value,
            correction=correction,
            comment=comment,
            feedback_source=feedback_source,
            created_at=datetime.datetime.now(datetime.timezone.utc),
            modified_at=datetime.datetime.now(datetime.timezone.utc),
            feedback_config=feedback_config,
            session_id=session_id_,
            start_time=start_time,
            comparative_experiment_id=ls_client._ensure_uuid(
                comparative_experiment_id, accept_null=True
            ),
            feedback_group_id=ls_client._ensure_uuid(
                feedback_group_id, accept_null=True
            ),
            extra=extra,
            error=error,
            extend_trace_retention=extend_trace_retention,
        )
        # Retry on NotFound: the run referenced by run_id/trace_id may have been
        # submitted moments ago and not yet ingested when this write lands.
        await self._arequest_with_retries(
            "POST",
            "/feedback",
            content=ls_client._dumps_json(feedback.model_dump(exclude_none=True)),
            stop_after_attempt=stop_after_attempt,
            retry_on=(ls_utils.LangSmithNotFoundError,),
        )
        return ls_schemas.Feedback(**feedback.model_dump())

    async def create_feedback_from_token(
        self,
        token_or_url: Union[str, uuid.UUID],
        score: Union[float, int, bool, None] = None,
        *,
        value: Union[float, int, bool, str, dict, None] = None,
        correction: Union[dict, None] = None,
        comment: Union[str, None] = None,
        metadata: Optional[dict] = None,
    ) -> None:
        """Create feedback from a presigned token or URL.

        Args:
            token_or_url: The token or URL from which to create feedback.
            score: The score of the feedback.
            value: The value of the feedback.
            correction: The correction of the feedback.
            comment: The comment of the feedback.
            metadata: Additional metadata for the feedback.

        Raises:
            ValueError: If the source API URL is invalid.

        Returns:
            This method does not return anything.
        """
        source_api_url, token_uuid = ls_client._parse_token_or_url(
            token_or_url, self._api_url, num_parts=1
        )
        if source_api_url != self._api_url:
            raise ValueError(f"Invalid source API URL. {source_api_url}")
        response = await self._arequest_with_retries(
            "POST",
            f"/feedback/tokens/{ls_client._as_uuid(token_uuid)}",
            content=ls_client._dumps_json(
                {
                    "score": score,
                    "value": value,
                    "correction": correction,
                    "comment": comment,
                    "metadata": metadata,
                    # TODO: Add ID once the API supports it.
                }
            ),
        )
        ls_utils.raise_for_status_with_text(response)

    async def create_presigned_feedback_token(
        self,
        run_id: ls_client.ID_TYPE,
        feedback_key: str,
        *,
        expiration: Optional[datetime.datetime | datetime.timedelta] = None,
        feedback_config: Optional[ls_schemas.FeedbackConfig] = None,
        feedback_id: Optional[ls_client.ID_TYPE] = None,
    ) -> ls_schemas.FeedbackIngestToken:
        """Create a pre-signed URL to send feedback data to.

        This is useful for giving browser-based clients a way to upload
        feedback data directly to LangSmith without accessing the
        API key.

        Args:
            run_id (Union[UUID, str]): The ID of the run to provide feedback for.
            feedback_key: The name of the metric or aspect this feedback is about.
            expiration: The expiration time of the pre-signed URL.

                Either a datetime or a timedelta offset from now.

                Default to 3 hours.
            feedback_config: `FeedbackConfig` or `None`.

                If creating a feedback_key for the first time, this defines how the
                metric should be interpreted, such as a continuous score (w/ optional
                bounds), or distribution over categorical values.
            feedback_id: The ID of the feedback to create.

                If not provided, a new feedback will be created.

        Returns:
            The pre-signed URL for uploading feedback data.
        """
        body: dict[str, Any] = {
            "run_id": run_id,
            "feedback_key": feedback_key,
            "feedback_config": feedback_config,
            "id": feedback_id or str(uuid.uuid4()),
        }
        if expiration is None:
            body["expires_in"] = ls_schemas.TimeDeltaInput(
                days=0,
                hours=3,
                minutes=0,
            )
        elif isinstance(expiration, datetime.datetime):
            body["expires_at"] = expiration.isoformat()
        elif isinstance(expiration, datetime.timedelta):
            body["expires_in"] = ls_schemas.TimeDeltaInput(
                days=expiration.days,
                hours=expiration.seconds // 3600,
                minutes=(expiration.seconds % 3600) // 60,
            )
        else:
            raise ValueError(
                f"Invalid expiration type: {type(expiration)}. "
                "Expected datetime.datetime or datetime.timedelta."
            )

        response = await self._arequest_with_retries(
            "POST",
            "/feedback/tokens",
            content=ls_client._dumps_json(body),
        )
        return ls_schemas.FeedbackIngestToken(**response.json())

    async def read_feedback(
        self, feedback_id: ls_client.ID_TYPE
    ) -> ls_schemas.Feedback:
        """Read feedback."""
        response = await self._arequest_with_retries(
            "GET", f"/feedback/{ls_client._as_uuid(feedback_id)}"
        )
        return ls_schemas.Feedback(**response.json())

    async def list_feedback(
        self,
        *,
        run_ids: Optional[Sequence[ls_client.ID_TYPE]] = None,
        feedback_key: Optional[Sequence[str]] = None,
        feedback_source_type: Optional[Sequence[ls_schemas.FeedbackSourceType]] = None,
        limit: Optional[int] = None,
        **kwargs: Any,
    ) -> AsyncIterator[ls_schemas.Feedback]:
        """List feedback."""
        params = {
            "run": (
                [str(ls_client._as_uuid(id_)) for id_ in run_ids] if run_ids else None
            ),
            "limit": min(limit, 100) if limit is not None else 100,
            **kwargs,
        }
        if feedback_key is not None:
            params["key"] = feedback_key
        if feedback_source_type is not None:
            params["source"] = feedback_source_type
        ix = 0
        async for feedback in self._aget_paginated_list("/feedback", params=params):
            yield ls_schemas.Feedback(**feedback)
            ix += 1
            if limit is not None and ix >= limit:
                break

    async def delete_feedback(self, feedback_id: ID_TYPE) -> None:
        """Delete a feedback by ID.

        Args:
            feedback_id (Union[UUID, str]): The ID of the feedback to delete.
        """
        response = await self._arequest_with_retries(
            "DELETE", f"/feedback/{ls_client._as_uuid(feedback_id, 'feedback_id')}"
        )
        ls_utils.raise_for_status_with_text(response)

    # Annotation Queue API

    async def list_annotation_queues(
        self,
        *,
        queue_ids: Optional[list[ID_TYPE]] = None,
        name: Optional[str] = None,
        name_contains: Optional[str] = None,
        limit: Optional[int] = None,
    ) -> AsyncIterator[ls_schemas.AnnotationQueue]:
        """List the annotation queues on the LangSmith API.

        Args:
            queue_ids (Optional[List[Union[UUID, str]]]): The IDs of the queues to
                filter by.
            name: The name of the queue to filter by.
            name_contains: The substring that the queue name should contain.
            limit: The maximum number of queues to return.

        Yields:
            The annotation queues.
        """
        params: dict = {
            "ids": (
                [
                    ls_client._as_uuid(id_, f"queue_ids[{i}]")
                    for i, id_ in enumerate(queue_ids)
                ]
                if queue_ids is not None
                else None
            ),
            "name": name,
            "name_contains": name_contains,
            "limit": min(limit, 100) if limit is not None else 100,
        }
        ix = 0
        async for feedback in self._aget_paginated_list(
            "/annotation-queues", params=params
        ):
            yield ls_schemas.AnnotationQueue(**feedback)
            ix += 1
            if limit is not None and ix >= limit:
                break

    async def create_annotation_queue(
        self,
        *,
        name: str,
        description: Optional[str] = None,
        queue_id: Optional[ID_TYPE] = None,
        rubric_instructions: Optional[str] = None,
        rubric_items: Optional[list[ls_schemas.AnnotationQueueRubricItem]] = None,
    ) -> ls_schemas.AnnotationQueue:
        """Create an annotation queue on the LangSmith API.

        Args:
            name: The name of the annotation queue.
            description: The description of the annotation queue.
            queue_id (Optional[Union[UUID, str]]): The ID of the annotation queue.
            rubric_instructions: The rubric instructions for the annotation queue.
            rubric_items: The feedback configs to assign to this queue's rubric.

        Returns:
            The created annotation queue object.
        """
        body: dict[str, Any] = {
            "name": name,
            "description": description,
            "id": str(queue_id) if queue_id is not None else str(uuid.uuid4()),
            "rubric_instructions": rubric_instructions,
        }
        if rubric_items is not None:
            body["rubric_items"] = rubric_items
        response = await self._arequest_with_retries(
            "POST",
            "/annotation-queues",
            json={k: v for k, v in body.items() if v is not None},
        )
        ls_utils.raise_for_status_with_text(response)
        return ls_schemas.AnnotationQueue(
            **response.json(),
        )

    async def read_annotation_queue(
        self, queue_id: ID_TYPE
    ) -> ls_schemas.AnnotationQueue:
        """Read an annotation queue with the specified `queue_id`.

        Args:
            queue_id (Union[UUID, str]): The ID of the annotation queue to read.

        Returns:
            The annotation queue object.
        """
        # TODO: Replace when actual endpoint is added
        return await self.list_annotation_queues(queue_ids=[queue_id]).__anext__()

    async def update_annotation_queue(
        self,
        queue_id: ID_TYPE,
        *,
        name: Optional[str] = None,
        description: Optional[str] = None,
        rubric_instructions: Optional[str] = None,
        rubric_items: Optional[list[ls_schemas.AnnotationQueueRubricItem]] = None,
    ) -> None:
        """Update an annotation queue with the specified `queue_id`.

        Args:
            queue_id (Union[UUID, str]): The ID of the annotation queue to update.
            name: The new name for the annotation queue.
            description: The new description for the annotation queue.
            rubric_instructions: The new rubric instructions for the queue.
            rubric_items: The feedback configs to assign to this queue's rubric.
        """
        body: dict[str, Any] = {}
        if name is not None:
            body["name"] = name
        if description is not None:
            body["description"] = description
        if rubric_instructions is not None:
            body["rubric_instructions"] = rubric_instructions
        if rubric_items is not None:
            body["rubric_items"] = rubric_items
        response = await self._arequest_with_retries(
            "PATCH",
            f"/annotation-queues/{ls_client._as_uuid(queue_id, 'queue_id')}",
            json=body,
        )
        ls_utils.raise_for_status_with_text(response)

    async def delete_annotation_queue(self, queue_id: ID_TYPE) -> None:
        """Delete an annotation queue with the specified `queue_id`.

        Args:
            queue_id (Union[UUID, str]): The ID of the annotation queue to delete.
        """
        response = await self._arequest_with_retries(
            "DELETE",
            f"/annotation-queues/{ls_client._as_uuid(queue_id, 'queue_id')}",
            headers={"Accept": "application/json", **self._client.headers},
        )
        ls_utils.raise_for_status_with_text(response)

    async def add_runs_to_annotation_queue(
        self,
        queue_id: ID_TYPE,
        *,
        run_ids: Optional[list[ID_TYPE]] = None,
        runs: Optional[Sequence[ls_schemas.RunKey]] = None,
    ) -> None:
        """Add runs to an annotation queue with the specified `queue_id`.

        Provide exactly one of `runs` or `run_ids`:

        - `runs` (preferred): each entry carries the run's full lookup key
          (`run_id`, `session_id`, `start_time`, and an optional
          `source_proposed_example_id`). This lets the run be located directly,
          without a scan, and is required for workspaces served by SmithDB.
        - `run_ids`: a plain list of run IDs. This path will be deprecated in a
          future release; prefer `runs`.

        Args:
            queue_id (Union[UUID, str]): The ID of the annotation queue.
            run_ids (Optional[list[Union[UUID, str]]]): The IDs of the runs to be
                added to the annotation queue.
            runs (Optional[Sequence[RunKey]]): The runs to add, each with
                its full lookup key.
        """
        if runs is not None and run_ids is not None:
            raise ls_utils.LangSmithUserError(
                "Provide exactly one of `runs` or `run_ids`."
            )
        base = f"/annotation-queues/{ls_client._as_uuid(queue_id, 'queue_id')}/runs"
        json_body: Union[list[str], list[dict[str, str]]]
        if runs is not None:
            path = f"{base}/by-key"
            json_body = [
                ls_client._serialize_run_key(run, i) for i, run in enumerate(runs)
            ]
        elif run_ids is not None:
            path = base
            json_body = [
                str(ls_client._as_uuid(id_, f"run_ids[{i}]"))
                for i, id_ in enumerate(run_ids)
            ]
        else:
            raise ls_utils.LangSmithUserError(
                "Provide exactly one of `runs` or `run_ids`."
            )
        response = await self._arequest_with_retries("POST", path, json=json_body)
        ls_utils.raise_for_status_with_text(response)

    async def delete_run_from_annotation_queue(
        self, queue_id: ID_TYPE, *, run_id: ID_TYPE
    ) -> None:
        """Delete a run from an annotation queue with the specified `queue_id` and `run_id`.

        Args:
            queue_id (Union[UUID, str]): The ID of the annotation queue.
            run_id (Union[UUID, str]): The ID of the run to be added to the annotation
                queue.
        """
        response = await self._arequest_with_retries(
            "DELETE",
            f"/annotation-queues/{ls_client._as_uuid(queue_id, 'queue_id')}/runs/{ls_client._as_uuid(run_id, 'run_id')}",
        )
        ls_utils.raise_for_status_with_text(response)

    async def get_run_from_annotation_queue(
        self, queue_id: ID_TYPE, *, index: int
    ) -> ls_schemas.RunWithAnnotationQueueInfo:
        """Get a run from an annotation queue at the specified index.

        Args:
            queue_id (Union[UUID, str]): The ID of the annotation queue.
            index: The index of the run to retrieve.

        Returns:
            The run at the specified index.

        Raises:
            LangSmithNotFoundError: If the run is not found at the given index.
            LangSmithError: For other API-related errors.
        """
        base_url = f"/annotation-queues/{ls_client._as_uuid(queue_id, 'queue_id')}/run"
        response = await self._arequest_with_retries("GET", f"{base_url}/{index}")
        ls_utils.raise_for_status_with_text(response)
        return ls_schemas.RunWithAnnotationQueueInfo(**response.json())

    async def list_runs_from_annotation_queue(
        self,
        queue_id: ID_TYPE,
        *,
        status: Optional[
            Literal["needs_my_review", "needs_others_review", "completed"]
        ] = None,
        limit: Optional[int] = None,
    ) -> AsyncIterator[ls_schemas.RunWithAnnotationQueueInfo]:
        """List runs in an annotation queue with the specified `queue_id`.

        Args:
            queue_id (Union[UUID, str]): The ID of the annotation queue.
            status: Filter runs by review status. Must be one of
                ``"needs_my_review"``, ``"needs_others_review"``, or
                ``"completed"``. If None, returns runs across all review states.
            limit: The maximum number of runs to return.

        Yields:
            The runs currently in the annotation queue, with queue metadata
            (e.g. ``added_at``, ``last_reviewed_time``).
        """
        params: dict = {
            "limit": min(limit, 100) if limit is not None else 100,
        }
        if status is not None:
            params["status"] = status
        path = f"/annotation-queues/{ls_client._as_uuid(queue_id, 'queue_id')}/runs"
        ix = 0
        async for run in self._aget_paginated_list(path, params=params):
            yield ls_schemas.RunWithAnnotationQueueInfo(**run)
            ix += 1
            if limit is not None and ix >= limit:
                break

    # Feedback Config API

    async def create_feedback_config(
        self,
        feedback_key: str,
        *,
        feedback_config: ls_schemas.FeedbackConfig,
        is_lower_score_better: Optional[bool] = False,
    ) -> ls_schemas.FeedbackConfigSchema:
        """Create a feedback configuration.

        Defines how feedback with a given key should be interpreted.
        If an identical configuration already exists for the key, it is
        returned unchanged. If a different configuration already exists
        for the key, an error is raised.

        Args:
            feedback_key: The feedback key to configure.
            feedback_config: The configuration defining type, bounds,
                and categories.
            is_lower_score_better: Whether a lower score is considered
                better. Defaults to False.

        Returns:
            The created or existing feedback configuration.
        """
        body: dict[str, Any] = {
            "feedback_key": feedback_key,
            "feedback_config": feedback_config,
            "is_lower_score_better": is_lower_score_better,
        }
        response = await self._arequest_with_retries(
            "POST",
            "/feedback-configs",
            json=body,
        )
        ls_utils.raise_for_status_with_text(response)
        return ls_schemas.FeedbackConfigSchema(**response.json())

    async def list_feedback_configs(
        self,
        *,
        feedback_key: Optional[Sequence[str]] = None,
        name_contains: Optional[str] = None,
        limit: Optional[int] = None,
        offset: int = 0,
    ) -> AsyncIterator[ls_schemas.FeedbackConfigSchema]:
        """List feedback configurations.

        Args:
            feedback_key: Filter by specific feedback keys.
            name_contains: Filter by substring match on the feedback key.
            limit: The maximum number of configurations to return.
            offset: The number of configurations to skip. Defaults to 0.

        Yields:
            The feedback configurations.
        """
        params: dict[str, Any] = {
            "limit": min(limit, 100) if limit is not None else 100,
            "offset": offset,
        }
        if feedback_key is not None:
            params["key"] = feedback_key
        if name_contains is not None:
            params["name_contains"] = name_contains
        ix = 0
        async for config in self._aget_paginated_list(
            "/feedback-configs", params=params
        ):
            yield ls_schemas.FeedbackConfigSchema(**config)
            ix += 1
            if limit is not None and ix >= limit:
                break

    async def update_feedback_config(
        self,
        feedback_key: str,
        *,
        feedback_config: Optional[ls_schemas.FeedbackConfig] = None,
        is_lower_score_better: Optional[bool] = None,
    ) -> ls_schemas.FeedbackConfigSchema:
        """Update a feedback configuration.

        Only the provided fields will be updated; others remain unchanged.

        Args:
            feedback_key: The feedback key of the configuration to update.
            feedback_config: The new configuration values.
            is_lower_score_better: Whether a lower score is considered
                better.

        Returns:
            The updated feedback configuration.
        """
        body: dict[str, Any] = {
            "feedback_key": feedback_key,
        }
        if feedback_config is not None:
            body["feedback_config"] = feedback_config
        if is_lower_score_better is not None:
            body["is_lower_score_better"] = is_lower_score_better
        response = await self._arequest_with_retries(
            "PATCH",
            "/feedback-configs",
            json=body,
        )
        ls_utils.raise_for_status_with_text(response)
        return ls_schemas.FeedbackConfigSchema(**response.json())

    async def delete_feedback_config(self, feedback_key: str) -> None:
        """Delete a feedback configuration.

        This performs a soft delete. The configuration can be recreated
        later with the same key.

        Args:
            feedback_key: The feedback key of the configuration to delete.
        """
        response = await self._arequest_with_retries(
            "DELETE",
            "/feedback-configs",
            params={"feedback_key": feedback_key},
        )
        ls_utils.raise_for_status_with_text(response)

    async def _get_settings(self) -> ls_schemas.LangSmithSettings:
        """Get the settings for the current tenant.

        Returns:
            dict: The settings for the current tenant.
        """
        if self._settings is None:
            response = await self._arequest_with_retries("GET", "/settings")
            ls_utils.raise_for_status_with_text(response)
            self._settings = ls_schemas.LangSmithSettings(**response.json())

        return self._settings

    async def _current_tenant_is_owner(self, owner: str) -> bool:
        """Check if the current workspace has the same handle as owner.

        Args:
            owner: The owner to check against.

        Returns:
            bool: `True` if the current tenant is the owner, `False` otherwise.
        """
        settings = await self._get_settings()
        return owner == "-" or settings.tenant_handle == owner

    async def _owner_conflict_error(
        self, action: str, owner: str
    ) -> ls_utils.LangSmithUserError:
        settings = await self._get_settings()
        return ls_utils.LangSmithUserError(
            f"Cannot {action} for another tenant.\n"
            f"Current tenant: {settings.tenant_handle},\n"
            f"Requested tenant: {owner}"
        )

    async def _get_latest_commit_hash(
        self, prompt_owner_and_name: str, limit: int = 1, offset: int = 0
    ) -> Optional[str]:
        """Get the latest commit hash for a prompt.

        Args:
            prompt_owner_and_name: The owner and name of the prompt.
            limit: The maximum number of commits to fetch.
            offset: The number of commits to skip.

        Returns:
            The latest commit hash, or `None` if no commits are found.
        """
        response = await self._arequest_with_retries(
            "GET",
            f"/commits/{prompt_owner_and_name}/",
            params={"limit": limit, "offset": offset},
        )
        commits = response.json()["commits"]
        return commits[0]["commit_hash"] if commits else None

    async def _create_commit_tags(
        self, prompt_owner_and_name: str, commit_id: str, tags: Union[str, list[str]]
    ) -> None:
        """Update tags for a prompt commit.

        Args:
            prompt_owner_and_name: The owner and name of the prompt in the format 'owner/repo'.
            commit_id: The commit ID to tag.
            tags: A single tag or list of tags to apply to the commit.

        Raises:
            requests.exceptions.HTTPError: If the request fails.
        """
        # Normalize tags to always be a list
        tag_list = [tags] if isinstance(tags, str) else tags

        # Post each tag individually since there's no bulk endpoint
        async def create_tag(tag: str):
            payload = {
                "tag_name": tag,
                "commit_id": commit_id,
            }
            response = await self._arequest_with_retries(
                "POST", f"/repos/{prompt_owner_and_name}/tags", json=payload
            )
            ls_utils.raise_for_status_with_text(response)

        await asyncio.gather(*[create_tag(tag) for tag in tag_list])

    async def _like_or_unlike_prompt(
        self, prompt_identifier: str, like: bool
    ) -> dict[str, int]:
        """Like or unlike a prompt.

        Args:
            prompt_identifier: The identifier of the prompt.
            like: True to like the prompt, False to unlike it.

        Returns:
            A dictionary with the key `'likes'` and the count of likes as the value.

        Raises:
            requests.exceptions.HTTPError: If the prompt is not found or another error
                occurs.
        """
        owner, prompt_name, _ = ls_utils.parse_prompt_identifier(prompt_identifier)
        response = await self._arequest_with_retries(
            "POST", f"/likes/{owner}/{prompt_name}", json={"like": like}
        )
        response.raise_for_status()
        return response.json()

    async def _get_prompt_url(self, prompt_identifier: str) -> str:
        """Get a URL for a prompt.

        Args:
            prompt_identifier: The identifier of the prompt.

        Returns:
            The URL for the prompt.

        """
        owner, prompt_name, commit_hash = ls_utils.parse_prompt_identifier(
            prompt_identifier
        )

        if not (await self._current_tenant_is_owner(owner)):
            return f"{self._host_url}/hub/{owner}/{prompt_name}:{commit_hash[:8]}"

        settings = await self._get_settings()
        return (
            f"{self._host_url}/prompts/{prompt_name}/{commit_hash[:8]}"
            f"?organizationId={settings.id}"
        )

    async def _prompt_exists(self, prompt_identifier: str) -> bool:
        """Check if a prompt exists.

        Args:
            prompt_identifier: The identifier of the prompt.

        Returns:
            `True` if the prompt exists, `False` otherwise.
        """
        prompt = await self.get_prompt(prompt_identifier)
        return True if prompt else False

    async def like_prompt(self, prompt_identifier: str) -> dict[str, int]:
        """Like a prompt.

        Args:
            prompt_identifier: The identifier of the prompt.

        Returns:
            A dictionary with the key `'likes'` and the count of likes as the value.
        """
        return await self._like_or_unlike_prompt(prompt_identifier, like=True)

    async def unlike_prompt(self, prompt_identifier: str) -> dict[str, int]:
        """Unlike a prompt.

        Args:
            prompt_identifier: The identifier of the prompt.

        Returns:
            A dictionary with the key `'likes'` and the count of likes as the value.
        """
        return await self._like_or_unlike_prompt(prompt_identifier, like=False)

    async def list_prompts(
        self,
        *,
        limit: int = 100,
        offset: int = 0,
        is_public: Optional[bool] = None,
        is_archived: Optional[bool] = False,
        sort_field: ls_schemas.PromptSortField = ls_schemas.PromptSortField.updated_at,
        sort_direction: Literal["desc", "asc"] = "desc",
        query: Optional[str] = None,
    ) -> ls_schemas.ListPromptsResponse:
        """List prompts with pagination.

        Args:
            limit: The maximum number of prompts to return.
            offset: The number of prompts to skip.
            is_public: Filter prompts by if they are public.
            is_archived: Filter prompts by if they are archived.
            sort_field (PromptSortField): The field to sort by.

                Defaults to `'updated_at'`.
            sort_direction: The order to sort by.
            query: Filter prompts by a search query.

        Returns:
            A response object containing the list of prompts.
        """
        params = {
            "limit": limit,
            "offset": offset,
            "is_public": (
                "true" if is_public else "false" if is_public is not None else None
            ),
            "is_archived": "true" if is_archived else "false",
            "sort_field": (
                sort_field.value
                if isinstance(sort_field, ls_schemas.PromptSortField)
                else sort_field
            ),
            "sort_direction": sort_direction,
            "query": query,
            "match_prefix": "true" if query else None,
        }

        response = await self._arequest_with_retries(
            "GET", "/repos/", params=_exclude_none(params)
        )
        return ls_schemas.ListPromptsResponse(**response.json())

    async def get_prompt(self, prompt_identifier: str) -> Optional[ls_schemas.Prompt]:
        """Get a specific prompt by its identifier.

        Args:
            prompt_identifier: The identifier of the prompt.

                The identifier should be in the format `'prompt_name'` or
                `'owner/prompt_name'`.

        Returns:
            The prompt object.

        Raises:
            requests.exceptions.HTTPError: If the prompt is not found or
                another error occurs.
        """
        owner, prompt_name, _ = ls_utils.parse_prompt_identifier(prompt_identifier)
        try:
            response = await self._arequest_with_retries(
                "GET",
                f"/repos/{owner}/{prompt_name}",
            )
            return ls_schemas.Prompt(**response.json()["repo"])
        except ls_utils.LangSmithNotFoundError:
            return None

    async def create_prompt(
        self,
        prompt_identifier: str,
        *,
        description: Optional[str] = None,
        readme: Optional[str] = None,
        tags: Optional[Sequence[str]] = None,
        is_public: bool = False,
    ) -> ls_schemas.Prompt:
        """Create a new prompt.

        Does not attach prompt object, just creates an empty prompt.

        Args:
            prompt_identifier: The identifier of the prompt.

                The identifier should be in the format of `owner/name:hash`,
                `name:hash`, `owner/name`, or `name`
            description: A description of the prompt.
            readme: A readme for the prompt.
            tags: A list of tags for the prompt.
            is_public: Whether the prompt should be public.

        Returns:
            The created `Prompt` object.

        Raises:
            ValueError: If the current tenant is not the owner.
            HTTPError: If the server request fails.
        """
        settings = await self._get_settings()
        if is_public and not settings.tenant_handle:
            raise ls_utils.LangSmithUserError(
                "Cannot create a public prompt without first\n"
                "creating a LangChain Hub handle. "
                "You can add a handle by creating a public prompt at:\n"
                "https://smith.langchain.com/prompts"
            )

        owner, prompt_name, _ = ls_utils.parse_prompt_identifier(prompt_identifier)
        if not (await self._current_tenant_is_owner(owner=owner)):
            raise (await self._owner_conflict_error("create a prompt", owner))

        json: dict[str, Union[str, bool, Sequence[str]]] = {
            "repo_handle": prompt_name,
            "description": description or "",
            "readme": readme or "",
            "tags": tags or [],
            "is_public": is_public,
        }

        response = await self._arequest_with_retries("POST", "/repos/", json=json)
        response.raise_for_status()
        return ls_schemas.Prompt(**response.json()["repo"])

    async def create_commit(
        self,
        prompt_identifier: str,
        object: Any,
        *,
        parent_commit_hash: Optional[str] = None,
        tags: Optional[str | list[str]] = None,
        description: Optional[str] = None,
    ) -> str:
        """Create a commit for an existing prompt.

        Args:
            prompt_identifier: The identifier of the prompt.
            object: The LangChain object to commit.
            parent_commit_hash: The hash of the parent commit.

                Defaults to latest commit.
            tags: A single tag or list of tags to apply to the commit.

                Defaults to `None`.
            description: Optional human-readable description for the commit
                (max 1000 chars). Defaults to `None`.

        Returns:
            The url of the prompt commit.

        Raises:
            HTTPError: If the server request fails.
            ValueError: If the prompt does not exist.
        """
        if not (await self._prompt_exists(prompt_identifier)):
            raise ls_utils.LangSmithNotFoundError(
                "Prompt does not exist, you must create it first."
            )

        # Check if object is already a serialized LangChain manifest
        prepped = ls_client.prep_obj_for_push(object)
        if isinstance(prepped, dict) and "id" in prepped and "lc" in prepped:
            manifest_dict = prepped
        else:
            try:
                from langchain_core.load import dumps
            except ImportError:
                raise ImportError(
                    "The client.create_commit function requires the langchain-core"
                    "package to run.\nInstall with `pip install langchain-core`"
                )

            json_object = dumps(prepped)
            manifest_dict = json.loads(json_object)

        owner, prompt_name, _ = ls_utils.parse_prompt_identifier(prompt_identifier)
        prompt_owner_and_name = f"{owner}/{prompt_name}"

        if parent_commit_hash == "latest" or parent_commit_hash is None:
            parent_commit_hash = await self._get_latest_commit_hash(
                prompt_owner_and_name
            )

        request_dict: dict[str, Any] = {
            "parent_commit": parent_commit_hash,
            "manifest": manifest_dict,
        }
        if description is not None:
            request_dict["description"] = description
        response = await self._arequest_with_retries(
            "POST", f"/commits/{prompt_owner_and_name}", json=request_dict
        )

        commit_json = response.json()["commit"]
        commit_hash = commit_json["commit_hash"]
        commit_id = commit_json["id"]
        if tags:
            await self._create_commit_tags(prompt_owner_and_name, commit_id, tags)

        return await self._get_prompt_url(f"{prompt_owner_and_name}:{commit_hash}")

    async def update_prompt(
        self,
        prompt_identifier: str,
        *,
        description: Optional[str] = None,
        readme: Optional[str] = None,
        tags: Optional[Sequence[str]] = None,
        is_public: Optional[bool] = None,
        is_archived: Optional[bool] = None,
    ) -> dict[str, Any]:
        """Update a prompt's metadata.

        To update the content of a prompt, use push_prompt or create_commit instead.

        Args:
            prompt_identifier: The identifier of the prompt to update.
            description: New description for the prompt.
            readme: New readme for the prompt.
            tags: New list of tags for the prompt.
            is_public: New public status for the prompt.
            is_archived: New archived status for the prompt.

        Returns:
            The updated prompt data as returned by the server.

        Raises:
            ValueError: If the `prompt_identifier` is empty.
            HTTPError: If the server request fails.
        """
        settings = await self._get_settings()
        if is_public and not settings.tenant_handle:
            raise ValueError(
                "Cannot create a public prompt without first\n"
                "creating a LangChain Hub handle. "
                "You can add a handle by creating a public prompt at:\n"
                "https://smith.langchain.com/prompts"
            )

        json: dict[str, Union[str, bool, Sequence[str]]] = {}

        if description is not None:
            json["description"] = description
        if readme is not None:
            json["readme"] = readme
        if is_public is not None:
            json["is_public"] = is_public
        if is_archived is not None:
            json["is_archived"] = is_archived
        if tags is not None:
            json["tags"] = tags

        owner, prompt_name, _ = ls_utils.parse_prompt_identifier(prompt_identifier)
        response = await self._arequest_with_retries(
            "PATCH", f"/repos/{owner}/{prompt_name}", json=json
        )
        response.raise_for_status()
        return response.json()

    async def delete_prompt(self, prompt_identifier: str) -> None:
        """Delete a prompt.

        Args:
            prompt_identifier: The identifier of the prompt to delete.

        Returns:
            `True` if the prompt was successfully deleted, `False` otherwise.

        Raises:
            ValueError: If the current tenant is not the owner of the prompt.
        """
        owner, prompt_name, _ = ls_utils.parse_prompt_identifier(prompt_identifier)
        if not (await self._current_tenant_is_owner(owner)):
            raise (await self._owner_conflict_error("delete a prompt", owner))

        response = await self._arequest_with_retries(
            "DELETE", f"/repos/{owner}/{prompt_name}"
        )
        response.raise_for_status()

    def _get_cache_key(
        self, prompt_identifier: str, include_model: Optional[bool] = False
    ) -> str:
        """Generate a cache key for a prompt.

        Args:
            prompt_identifier: The prompt identifier.
            include_model: Whether model info is included.

        Returns:
            The cache key string.
        """
        suffix = ":with_model" if include_model else ""
        return f"{prompt_identifier}{suffix}"

    async def _afetch_prompt_from_api(
        self,
        prompt_identifier: str,
        include_model: Optional[bool] = False,
    ) -> ls_schemas.PromptCommit:
        """Fetch a prompt from the API (no caching).

        Args:
            prompt_identifier: The prompt identifier.
            include_model: Whether to include model information.

        Returns:
            The `PromptCommit` object.
        """
        owner, prompt_name, commit_hash = ls_utils.parse_prompt_identifier(
            prompt_identifier
        )
        response = await self._arequest_with_retries(
            "GET",
            (
                f"/commits/{owner}/{prompt_name}/{commit_hash}"
                f"{'?include_model=true' if include_model else ''}"
            ),
        )
        return ls_schemas.PromptCommit(
            **{"owner": owner, "repo": prompt_name, **response.json()}
        )

    async def pull_prompt_commit(
        self,
        prompt_identifier: str,
        *,
        include_model: Optional[bool] = False,
        skip_cache: bool = False,
        dangerously_pull_public_prompt: bool = False,
    ) -> ls_schemas.PromptCommit:
        """Pull a prompt object from the LangSmith API.

        Public prompts referenced by owner/name cross a trust boundary because the
        prompt manifest may contain serialized LangChain objects and configuration
        that affect runtime behavior. For example, a prompt can intentionally
        configure a model with a custom base URL, headers, model name, or other
        constructor arguments. These are supported features, but they also mean
        the prompt contents should be treated as executable configuration rather
        than plain text.

        Set `dangerously_pull_public_prompt=True` only after reviewing and
        trusting the prompt contents, not merely the publishing account. Prompts
        from your own or your organization's account can still be unsafe if that
        account or prompt was compromised.

        Args:
            prompt_identifier: The identifier of the prompt.
            include_model: Whether to include model information.
            skip_cache: Whether to skip the prompt cache. Defaults to `False`.
            dangerously_pull_public_prompt: Set to `True` to allow pulling a
                public prompt by owner/name, for example `username/promptname`.
                Defaults to `False`.

        Returns:
            The prompt object.

        Raises:
            ValueError: If no commits are found for the prompt.
        """
        ls_client._validate_public_prompt_pull(
            prompt_identifier,
            dangerously_pull_public_prompt=dangerously_pull_public_prompt,
        )

        # Create refresh function bound to this specific prompt
        refresh_func = partial(
            self._afetch_prompt_from_api, prompt_identifier, include_model
        )

        # Try cache first if enabled
        if not skip_cache and self._cache is not None:
            cache_key = self._get_cache_key(prompt_identifier, include_model)
            cached = self._cache.get(cache_key, refresh_func)
            if cached is not None:
                return cached

        # Cache miss or cache disabled - fetch from API
        result = await refresh_func()

        # Store in cache
        if not skip_cache and self._cache is not None:
            cache_key = self._get_cache_key(prompt_identifier, include_model)
            await self._cache.aset(cache_key, result, refresh_func)

        return result

    async def list_prompt_commits(
        self,
        prompt_identifier: str,
        *,
        limit: Optional[int] = None,
        offset: int = 0,
        include_model: bool = False,
    ) -> AsyncGenerator[ls_schemas.ListedPromptCommit, None]:
        """List commits for a given prompt.

        Args:
            prompt_identifier: The identifier of the prompt in the format `owner/repo_name`.
            limit: The maximum number of commits to return.

                If `None`, returns all commits.
            offset: The number of commits to skip before starting to return results.
            include_model: Whether to include the model information in the commit data.

        Yields:
            A `ListedPromptCommit` object for each commit.

        !!! note

            This method uses pagination to retrieve commits. It will make multiple API calls if necessary to retrieve all commits
            or up to the specified limit.
        """
        owner, prompt_name, _ = ls_utils.parse_prompt_identifier(prompt_identifier)

        params = {
            "limit": min(100, limit) if limit is not None else limit,
            "offset": offset,
            "include_model": include_model,
        }
        i = 0
        while True:
            params["offset"] = offset
            response = await self._arequest_with_retries(
                "GET",
                f"/commits/{owner}/{prompt_name}/",
                params=params,
            )
            val = response.json()
            items = val["commits"]
            total = val["total"]

            if not items:
                break
            for it in items:
                if limit is not None and i >= limit:
                    return  # Stop iteration if we've reached the limit
                yield ls_schemas.ListedPromptCommit(
                    **{"owner": owner, "repo": prompt_name, **it}
                )
                i += 1

            offset += len(items)
            if offset >= total:
                break

    async def pull_prompt(
        self,
        prompt_identifier: str,
        *,
        include_model: bool | None = False,
        secrets: dict[str, str] | None = None,
        secrets_from_env: bool = False,
        skip_cache: bool = False,
        dangerously_pull_public_prompt: bool = False,
    ) -> Any:
        """Pull a prompt and return it as a LangChain `PromptTemplate`.

        This method requires [`langchain-core`](https://pypi.org/project/langchain-core).

        Args:
            prompt_identifier: The identifier of the prompt.
            include_model: Whether to include model configuration in the loaded
                prompt.
            secrets: A map of secrets to use for explicit serialized LangChain secret
                references in the manifest, e.g. `{'OPENAI_API_KEY': 'sk-...'}`.

                If a manifest secret reference is not found in the map, it will be
                loaded from the environment only if `secrets_from_env` is `True`.
                Deserialized model integrations may still use their own
                environment-based credential defaults during initialization.
            secrets_from_env: Whether explicit serialized LangChain secret
                references in the manifest may be loaded from environment variables
                during deserialization.
            skip_cache: Whether to skip the prompt cache. Defaults to `False`.
            dangerously_pull_public_prompt: Set to `True` to allow pulling a
                public prompt by owner/name (for example, `username/promptname`).
                Only do this for trusted prompts. Defaults to `False`.

        Returns:
            The prompt object in the specified format.

        !!! warning "Security note"

            Pulled prompts should be treated as executable configuration, not plain
            text.

            The `secrets` and `secrets_from_env` arguments only control explicit
            serialized LangChain secret references in the manifest. They do not
            prevent deserialized model integrations from using their own
            environment-based credential defaults during initialization. For example,
            a deserialized OpenAI chat model may still use `OPENAI_API_KEY` from the
            environment if its constructor supports that default.

            Avoid pulling public prompts or prompts outside your own organization
            unless you have reviewed and trust their contents. When you do pull a
            trusted external prompt, prefer pinning to a specific commit SHA rather
            than following a mutable latest version. This is especially important
            when `include_model=True`.

        !!! warning "Behavior changed in `langsmith` 0.5.1"

            Updated to take arguments `secrets` and `secrets_from_env` which default
            to None and False, respectively.

            By default, explicit serialized LangChain secret references in a pulled
            manifest are not resolved from environment variables unless you specify
            `secrets_from_env=True`.

            These updates were made to remediate vulnerability
            [GHSA-c67j-w6g6-q2cm](https://github.com/langchain-ai/langchain/security/advisories/GHSA-c67j-w6g6-q2cm)
            in the `langchain-core` package which this method (but not the entire
            langsmith package) depends on.
        """
        prompt_object = await self.pull_prompt_commit(
            prompt_identifier,
            include_model=include_model,
            skip_cache=skip_cache,
            dangerously_pull_public_prompt=dangerously_pull_public_prompt,
        )
        return ls_client._process_prompt_manifest(
            prompt_object,
            include_model=include_model,
            secrets=secrets,
            secrets_from_env=secrets_from_env,
        )

    async def push_prompt(
        self,
        prompt_identifier: str,
        *,
        object: Optional[Any] = None,
        parent_commit_hash: str = "latest",
        is_public: Optional[bool] = None,
        description: Optional[str] = None,
        readme: Optional[str] = None,
        tags: Optional[Sequence[str]] = None,
        commit_tags: Optional[str | list[str]] = None,
        commit_description: Optional[str] = None,
    ) -> str:
        """Push a prompt to the LangSmith API.

        Can be used to update prompt metadata or prompt content.

        If the prompt does not exist, it will be created.

        If the prompt exists, it will be updated.

        Args:
            prompt_identifier: The identifier of the prompt.
            object: The LangChain object to push.
            parent_commit_hash: The parent commit hash.
            is_public: Whether the prompt should be public.

                If `None` (default), the current visibility status is maintained for
                existing prompts.

                For new prompts, `None` defaults to private.

                Set to `True` to make public, or `False` to make private.
            description: A description of the prompt.

                Defaults to an empty string.
            readme: A readme for the prompt.

                Defaults to an empty string.
            tags: A list of tags for the prompt.

                Defaults to an empty list.
            commit_tags: A single tag or list of tags for the prompt commit.

                Defaults to an empty list.
            commit_description: Optional human-readable description for the commit
                (max 1000 chars). Defaults to `None`.

        Returns:
            The URL of the prompt.
        """
        # Create or update prompt metadata
        if await self._prompt_exists(prompt_identifier):
            if any(
                param is not None for param in [is_public, description, readme, tags]
            ):
                await self.update_prompt(
                    prompt_identifier,
                    description=description,
                    readme=readme,
                    tags=tags,
                    is_public=is_public,
                )
        else:
            await self.create_prompt(
                prompt_identifier,
                is_public=is_public if is_public is not None else False,
                description=description,
                readme=readme,
                tags=tags,
            )

        if object is None:
            return await self._get_prompt_url(prompt_identifier=prompt_identifier)

        # Create a commit with the new manifest
        url = await self.create_commit(
            prompt_identifier,
            object,
            parent_commit_hash=parent_commit_hash,
            tags=commit_tags,
            description=commit_description,
        )
        return url

    async def pull_agent(
        self,
        identifier: str,
        *,
        version: Optional[str] = None,
    ) -> ls_schemas.AgentContext:
        """Pull an agent from Hub.

        Args:
            identifier: Repo identifier (owner/name:hash, owner/name, or name).
            version: Commit hash or tag; overrides any hash in identifier.

        Returns:
            AgentContext: The agent snapshot.
        """
        data = await self._pull_hub_directory(identifier, "agent", version=version)
        return ls_schemas.AgentContext.model_validate(data)

    async def push_agent(
        self,
        identifier: str,
        *,
        files: dict[str, Optional[ls_schemas.Entry]],
        parent_commit: Optional[str] = None,
        description: Optional[str] = None,
        readme: Optional[str] = None,
        tags: Optional[Sequence[str]] = None,
        is_public: Optional[bool] = None,
    ) -> str:
        """Push an agent to Hub, creating the repo if it does not exist."""
        return await self._push_hub_directory(
            identifier,
            "agent",
            files=files,
            parent_commit=parent_commit,
            description=description,
            readme=readme,
            tags=tags,
            is_public=is_public,
        )

    async def pull_skill(
        self,
        identifier: str,
        *,
        version: Optional[str] = None,
    ) -> ls_schemas.SkillContext:
        """Pull a skill from Hub."""
        data = await self._pull_hub_directory(identifier, "skill", version=version)
        return ls_schemas.SkillContext.model_validate(data)

    async def push_skill(
        self,
        identifier: str,
        *,
        files: dict[str, Optional[ls_schemas.Entry]],
        parent_commit: Optional[str] = None,
        description: Optional[str] = None,
        readme: Optional[str] = None,
        tags: Optional[Sequence[str]] = None,
        is_public: Optional[bool] = None,
    ) -> str:
        """Push a skill to Hub."""
        return await self._push_hub_directory(
            identifier,
            "skill",
            files=files,
            parent_commit=parent_commit,
            description=description,
            readme=readme,
            tags=tags,
            is_public=is_public,
        )

    async def delete_agent(self, identifier: str) -> None:
        """Delete an agent and its owned child file repos."""
        await self._delete_hub_directory(identifier)

    async def delete_skill(self, identifier: str) -> None:
        """Delete a skill and its owned child file repos."""
        await self._delete_hub_directory(identifier)

    async def agent_exists(self, identifier: str) -> bool:
        """Check if an agent repo exists."""
        owner, name, _ = ls_utils.parse_hub_identifier(identifier)
        return await self._hub_repo_exists(owner, name)

    async def skill_exists(self, identifier: str) -> bool:
        """Check if a skill repo exists."""
        owner, name, _ = ls_utils.parse_hub_identifier(identifier)
        return await self._hub_repo_exists(owner, name)

    async def list_agents(
        self,
        *,
        limit: int = 100,
        offset: int = 0,
        is_public: Optional[bool] = None,
        is_archived: Optional[bool] = False,
        query: Optional[str] = None,
    ) -> ls_schemas.ListPromptsResponse:
        """List agents with pagination."""
        return await self._list_hub_repos(
            "agent",
            limit=limit,
            offset=offset,
            is_public=is_public,
            is_archived=is_archived,
            query=query,
        )

    async def list_skills(
        self,
        *,
        limit: int = 100,
        offset: int = 0,
        is_public: Optional[bool] = None,
        is_archived: Optional[bool] = False,
        query: Optional[str] = None,
    ) -> ls_schemas.ListPromptsResponse:
        """List skills with pagination."""
        return await self._list_hub_repos(
            "skill",
            limit=limit,
            offset=offset,
            is_public=is_public,
            is_archived=is_archived,
            query=query,
        )

    async def _pull_hub_directory(
        self,
        identifier: str,
        repo_type: Literal["agent", "skill"],
        *,
        version: Optional[str],
    ) -> dict[str, Any]:
        """Fetch hub directory payload, merged with owner/repo from identifier."""
        owner, name, commit = ls_utils.parse_hub_identifier(identifier)
        target = (
            version if version is not None else (commit if commit != "latest" else None)
        )
        params: dict[str, Any] = {"repo_type": repo_type}
        if target:
            params["commit"] = target
        response = await self._arequest_with_retries(
            "GET",
            f"{platform_hub_path(self._api_url)}/{owner}/{name}/directories",
            params=params,
        )
        return response.json()

    async def _push_hub_directory(
        self,
        identifier: str,
        repo_type: Literal["agent", "skill"],
        *,
        files: dict[str, Any],
        parent_commit: Optional[str],
        description: Optional[str],
        readme: Optional[str],
        tags: Optional[Sequence[str]],
        is_public: Optional[bool],
    ) -> str:
        """Create a hub directory commit, creating the repo if it does not exist."""
        validate_parent_commit(parent_commit)

        owner, name, _ = ls_utils.parse_hub_identifier(identifier)
        if not (await self._current_tenant_is_owner(owner)):
            raise (await self._owner_conflict_error(f"push {repo_type}", owner))

        if await self._hub_repo_exists(owner, name):
            if any(v is not None for v in (description, readme, tags, is_public)):
                await self._update_hub_repo_metadata(
                    owner,
                    name,
                    description=description,
                    readme=readme,
                    tags=tags,
                    is_public=is_public,
                )
        else:
            if not REPO_HANDLE_PATTERN.match(name):
                raise ls_utils.LangSmithUserError(
                    f"Invalid repo_handle {name!r}: "
                    f"must match {REPO_HANDLE_PATTERN.pattern}."
                )
            await self._create_hub_repo(
                name,
                repo_type,
                description=description,
                readme=readme,
                tags=tags,
                is_public=bool(is_public),
            )

        request_files: dict[str, Optional[dict[str, Any]]] = {}
        for path, entry in files.items():
            if entry is None:
                request_files[path] = None
            else:
                request_files[path] = entry.model_dump(exclude_none=True)

        body: dict[str, Any] = {"files": request_files}
        if parent_commit is not None:
            body["parent_commit"] = parent_commit

        response = await self._arequest_with_retries(
            "POST",
            f"{platform_hub_path(self._api_url)}/{owner}/{name}/directories/commits",
            json=body,
        )
        commit_hash = response.json()["commit"]["commit_hash"]
        settings = await self._get_settings()
        return build_commit_url(self._host_url, name, commit_hash, settings.id)

    async def _delete_hub_directory(self, identifier: str) -> None:
        """Delete a hub directory repo."""
        owner, name, _ = ls_utils.parse_hub_identifier(identifier)
        if not (await self._current_tenant_is_owner(owner)):
            raise (await self._owner_conflict_error("delete", owner))
        await self._arequest_with_retries(
            "DELETE",
            f"{platform_hub_path(self._api_url)}/{owner}/{name}/directories",
        )

    async def _list_hub_repos(
        self,
        repo_type: Literal["agent", "skill"],
        *,
        limit: int,
        offset: int,
        is_public: Optional[bool],
        is_archived: Optional[bool],
        query: Optional[str],
    ) -> ls_schemas.ListPromptsResponse:
        """List hub repos filtered by type.

        Returns ``ListPromptsResponse`` because ``/repos`` is polymorphic — the
        list shape is shared across prompt, agent, and skill repos.
        """
        params: dict[str, Any] = {
            "limit": limit,
            "offset": offset,
            "repo_type": repo_type,
            "is_archived": "true" if is_archived else "false",
        }
        if is_public is not None:
            params["is_public"] = "true" if is_public else "false"
        if query:
            params["query"] = query
            params["match_prefix"] = "true"
        response = await self._arequest_with_retries("GET", HUB, params=params)
        return ls_schemas.ListPromptsResponse(**response.json())

    async def _hub_repo_exists(self, owner: str, name: str) -> bool:
        """Check if a hub repo exists."""
        try:
            await self._arequest_with_retries("GET", f"{HUB}/{owner}/{name}")
            return True
        except ls_utils.LangSmithNotFoundError:
            return False

    async def _create_hub_repo(
        self,
        name: str,
        repo_type: Literal["agent", "skill"],
        *,
        description: Optional[str],
        readme: Optional[str],
        tags: Optional[Sequence[str]],
        is_public: bool,
    ) -> None:
        """Create a new hub repo of the given type."""
        body: dict[str, Any] = {
            "repo_handle": name,
            "repo_type": repo_type,
            "is_public": is_public,
        }
        if description is not None:
            body["description"] = description
        if readme is not None:
            body["readme"] = readme
        if tags is not None:
            body["tags"] = list(tags)
        try:
            await self._arequest_with_retries("POST", "/repos/", json=body)
        except ls_utils.LangSmithConflictError:
            pass

    async def _update_hub_repo_metadata(
        self,
        owner: str,
        name: str,
        *,
        description: Optional[str],
        readme: Optional[str],
        tags: Optional[Sequence[str]],
        is_public: Optional[bool],
    ) -> None:
        """Patch hub repo metadata fields that were explicitly provided."""
        body: dict[str, Any] = {}
        if description is not None:
            body["description"] = description
        if readme is not None:
            body["readme"] = readme
        if tags is not None:
            body["tags"] = list(tags)
        if is_public is not None:
            body["is_public"] = is_public
        if body:
            await self._arequest_with_retries(
                "PATCH", f"{HUB}/{owner}/{name}", json=body
            )


def _exclude_none(d: dict) -> dict:
    """Exclude `None` values from a dictionary."""
    return {k: v for k, v in d.items() if v is not None}
