Storage¶

Base classes and types for persistent key-value stores.

Stores provide long-term memory that persists across threads and conversations. Supports hierarchical namespaces, key-value storage, and optional vector search.

Core types

BaseStore: Store interface with sync/async operations
Item: Stored key-value pairs with metadata
Op: Get/Put/Search/List operations

Modules:

Name	Description
`batch`	Utilities for batching operations in a background task.
`embed`	Utilities for working with embedding functions and LangChain's Embeddings interface.

Classes:

Name	Description
`Embeddings`	Interface for embedding models.
`Item`	Represents a stored item with metadata.
`GetOp`	Operation to retrieve a specific item by its namespace and key.
`SearchOp`	Operation to search for items within a specified namespace hierarchy.
`MatchCondition`	Represents a pattern for matching namespaces in the store.
`ListNamespacesOp`	Operation to list and filter namespaces in the store.
`PutOp`	Operation to store, update, or delete an item in the store.
`BaseStore`	Abstract base class for persistent key-value stores.

Functions:

Name	Description
`ensure_embeddings`	Ensure that an embedding function conforms to LangChain's Embeddings interface.
`get_text_at_path`	Extract text from an object using a path expression or pre-tokenized path.
`tokenize_path`	Tokenize a path into components.

Attributes:

Name	Type	Description
`NamespacePath`		A tuple representing a namespace path that can include wildcards.
`NamespaceMatchType`		Specifies how to match namespace paths.

NamespacePath `module-attribute` ¶

NamespacePath = tuple[Union[str, Literal['*']], ...]

A tuple representing a namespace path that can include wildcards.

Examples

("users",)  # Exact users namespace
("documents", "*")  # Any sub-namespace under documents
("cache", "*", "v1")  # Any cache category with v1 version

NamespaceMatchType `module-attribute` ¶

NamespaceMatchType = Literal['prefix', 'suffix']

Specifies how to match namespace paths.

Values

"prefix": Match from the start of the namespace "suffix": Match from the end of the namespace

Embeddings ¶

Bases: ABC

Interface for embedding models.

This is an interface meant for implementing text embedding models.

Text embedding models are used to map text to a vector (a point in n-dimensional space).

Texts that are similar will usually be mapped to points that are close to each other in this space. The exact details of what's considered "similar" and how "distance" is measured in this space are dependent on the specific embedding model.

This abstraction contains a method for embedding a list of documents and a method for embedding a query text. The embedding of a query text is expected to be a single vector, while the embedding of a list of documents is expected to be a list of vectors.

Usually the query embedding is identical to the document embedding, but the abstraction allows treating them independently.

In addition to the synchronous methods, this interface also provides asynchronous versions of the methods.

By default, the asynchronous methods are implemented using the synchronous methods; however, implementations may choose to override the asynchronous methods with an async native implementation for performance reasons.

Methods:

Name	Description
`embed_documents`	Embed search docs.
`embed_query`	Embed query text.
`aembed_documents`	Asynchronous Embed search docs.
`aembed_query`	Asynchronous Embed query text.

embed_documents `abstractmethod` ¶

embed_documents(texts: list[str]) -> list[list[float]]

Embed search docs.

Parameters:

Name	Type	Description	Default
`texts`	`list[str]`	List of text to embed.	required

Returns:

Type	Description
`list[list[float]]`	List of embeddings.

embed_query `abstractmethod` ¶

embed_query(text: str) -> list[float]

Embed query text.

Parameters:

Name	Type	Description	Default
`text`	`str`	Text to embed.	required

Returns:

Type	Description
`list[float]`	Embedding.

aembed_documents `async` ¶

aembed_documents(texts: list[str]) -> list[list[float]]

Asynchronous Embed search docs.

Parameters:

Name	Type	Description	Default
`texts`	`list[str]`	List of text to embed.	required

Returns:

Type	Description
`list[list[float]]`	List of embeddings.

aembed_query `async` ¶

aembed_query(text: str) -> list[float]

Asynchronous Embed query text.

Parameters:

Name	Type	Description	Default
`text`	`str`	Text to embed.	required

Returns:

Type	Description
`list[float]`	Embedding.

NotProvided ¶

Sentinel singleton.

Item ¶

Represents a stored item with metadata.

Parameters:

Name	Type	Description	Default
`value`	`dict[str, Any]`	The stored data as a dictionary. Keys are filterable.	required
`key`	`str`	Unique identifier within the namespace.	required
`namespace`	`tuple[str, ...]`	Hierarchical path defining the collection in which this document resides. Represented as a tuple of strings, allowing for nested categorization. For example: ("documents", 'user123')	required
`created_at`	`datetime`	Timestamp of item creation.	required
`updated_at`	`datetime`	Timestamp of last update.	required

SearchItem ¶

Bases: Item

Represents an item returned from a search operation with additional metadata.

Methods:

Name	Description
`__init__`	Initialize a result item.

init ¶

__init__(
    namespace: tuple[str, ...],
    key: str,
    value: dict[str, Any],
    created_at: datetime,
    updated_at: datetime,
    score: Optional[float] = None,
) -> None

Initialize a result item.

Parameters:

Name	Type	Description	Default
`namespace`	`tuple[str, ...]`	Hierarchical path to the item.	required
`key`	`str`	Unique identifier within the namespace.	required
`value`	`dict[str, Any]`	The stored value.	required
`created_at`	`datetime`	When the item was first created.	required
`updated_at`	`datetime`	When the item was last updated.	required
`score`	`Optional[float]`	Relevance/similarity score if from a ranked operation.	`None`

GetOp ¶

Bases: NamedTuple

Operation to retrieve a specific item by its namespace and key.

This operation allows precise retrieval of stored items using their full path (namespace) and unique identifier (key) combination.

Examples

Basic item retrieval:

GetOp(namespace=("users", "profiles"), key="user123")
GetOp(namespace=("cache", "embeddings"), key="doc456")

Attributes:

Name	Type	Description
`namespace`	`tuple[str, ...]`	Hierarchical path that uniquely identifies the item's location.
`key`	`str`	Unique identifier for the item within its specific namespace.
`refresh_ttl`	`bool`	Whether to refresh TTLs for the returned item.

namespace `instance-attribute` ¶

namespace: tuple[str, ...]

Hierarchical path that uniquely identifies the item's location.

Examples

("users",)  # Root level users namespace
("users", "profiles")  # Profiles within users namespace

key `instance-attribute` ¶

key: str

Unique identifier for the item within its specific namespace.

Examples

"user123"  # For a user profile
"doc456"  # For a document

refresh_ttl `class-attribute` `instance-attribute` ¶

refresh_ttl: bool = True

Whether to refresh TTLs for the returned item.

If no TTL was specified for the original item(s), or if TTL support is not enabled for your adapter, this argument is ignored.

SearchOp ¶

Bases: NamedTuple

Operation to search for items within a specified namespace hierarchy.

This operation supports both structured filtering and natural language search within a given namespace prefix. It provides pagination through limit and offset parameters.

Note

Natural language search support depends on your store implementation.

Examples

Search with filters and pagination:

SearchOp(
    namespace_prefix=("documents",),
    filter={"type": "report", "status": "active"},
    limit=5,
    offset=10
)

Natural language search:

SearchOp(
    namespace_prefix=("users", "content"),
    query="technical documentation about APIs",
    limit=20
)

Attributes:

Name	Type	Description
`namespace_prefix`	`tuple[str, ...]`	Hierarchical path prefix defining the search scope.
`filter`	`Optional[dict[str, Any]]`	Key-value pairs for filtering results based on exact matches or comparison operators.
`limit`	`int`	Maximum number of items to return in the search results.
`offset`	`int`	Number of matching items to skip for pagination.
`query`	`Optional[str]`	Natural language search query for semantic search capabilities.
`refresh_ttl`	`bool`	Whether to refresh TTLs for the returned item.

namespace_prefix `instance-attribute` ¶

namespace_prefix: tuple[str, ...]

Hierarchical path prefix defining the search scope.

Examples

()  # Search entire store
("documents",)  # Search all documents
("users", "content")  # Search within user content

filter `class-attribute` `instance-attribute` ¶

filter: Optional[dict[str, Any]] = None

Key-value pairs for filtering results based on exact matches or comparison operators.

The filter supports both exact matches and operator-based comparisons.

Supported Operators

$eq: Equal to (same as direct value comparison)
$ne: Not equal to
$gt: Greater than
$gte: Greater than or equal to
$lt: Less than
$lte: Less than or equal to

Examples

Simple exact match:

{"status": "active"}

Comparison operators:

{"score": {"$gt": 4.99}}  # Score greater than 4.99

Multiple conditions:

{
    "score": {"$gte": 3.0},
    "color": "red"
}

limit `class-attribute` `instance-attribute` ¶

limit: int = 10

Maximum number of items to return in the search results.

offset `class-attribute` `instance-attribute` ¶

offset: int = 0

Number of matching items to skip for pagination.

query `class-attribute` `instance-attribute` ¶

query: Optional[str] = None

Natural language search query for semantic search capabilities.

Examples

"technical documentation about REST APIs"
"machine learning papers from 2023"

refresh_ttl `class-attribute` `instance-attribute` ¶

refresh_ttl: bool = True

Whether to refresh TTLs for the returned item.

If no TTL was specified for the original item(s), or if TTL support is not enabled for your adapter, this argument is ignored.

MatchCondition ¶

Bases: NamedTuple

Represents a pattern for matching namespaces in the store.

This class combines a match type (prefix or suffix) with a namespace path pattern that can include wildcards to flexibly match different namespace hierarchies.

Examples

Prefix matching:

MatchCondition(match_type="prefix", path=("users", "profiles"))

Suffix matching with wildcard:

MatchCondition(match_type="suffix", path=("cache", "*"))

Simple suffix matching:

MatchCondition(match_type="suffix", path=("v1",))

Attributes:

Name	Type	Description
`match_type`	`NamespaceMatchType`	Type of namespace matching to perform.
`path`	`NamespacePath`	Namespace path pattern that can include wildcards.

match_type `instance-attribute` ¶

match_type: NamespaceMatchType

Type of namespace matching to perform.

path `instance-attribute` ¶

path: NamespacePath

Namespace path pattern that can include wildcards.

ListNamespacesOp ¶

Bases: NamedTuple

Operation to list and filter namespaces in the store.

This operation allows exploring the organization of data, finding specific collections, and navigating the namespace hierarchy.

Examples

List all namespaces under the "documents" path:

ListNamespacesOp(
    match_conditions=(MatchCondition(match_type="prefix", path=("documents",)),),
    max_depth=2
)

List all namespaces that end with "v1":

ListNamespacesOp(
    match_conditions=(MatchCondition(match_type="suffix", path=("v1",)),),
    limit=50
)

Attributes:

Name	Type	Description
`match_conditions`	`Optional[tuple[MatchCondition, ...]]`	Optional conditions for filtering namespaces.
`max_depth`	`Optional[int]`	Maximum depth of namespace hierarchy to return.
`limit`	`int`	Maximum number of namespaces to return.
`offset`	`int`	Number of namespaces to skip for pagination.

match_conditions `class-attribute` `instance-attribute` ¶

match_conditions: Optional[tuple[MatchCondition, ...]] = (
    None
)

Optional conditions for filtering namespaces.

Examples

All user namespaces:

(MatchCondition(match_type="prefix", path=("users",)),)

All namespaces that start with "docs" and end with "draft":

(
    MatchCondition(match_type="prefix", path=("docs",)),
    MatchCondition(match_type="suffix", path=("draft",))
)

max_depth `class-attribute` `instance-attribute` ¶

max_depth: Optional[int] = None

Maximum depth of namespace hierarchy to return.

Note

Namespaces deeper than this level will be truncated.

limit `class-attribute` `instance-attribute` ¶

limit: int = 100

Maximum number of namespaces to return.

offset `class-attribute` `instance-attribute` ¶

offset: int = 0

Number of namespaces to skip for pagination.

PutOp ¶

Bases: NamedTuple

Operation to store, update, or delete an item in the store.

This class represents a single operation to modify the store's contents, whether adding new items, updating existing ones, or removing them.

Attributes:

Name	Type	Description
`namespace`	`tuple[str, ...]`	Hierarchical path that identifies the location of the item.
`key`	`str`	Unique identifier for the item within its namespace.
`value`	`Optional[dict[str, Any]]`	The data to store, or None to mark the item for deletion.
`index`	`Optional[Union[Literal[False], list[str]]]`	Controls how the item's fields are indexed for search operations.
`ttl`	`Optional[float]`	Controls the TTL (time-to-live) for the item in minutes.

namespace `instance-attribute` ¶

namespace: tuple[str, ...]

Hierarchical path that identifies the location of the item.

The namespace acts as a folder-like structure to organize items. Each element in the tuple represents one level in the hierarchy.

Examples

Root level documents

("documents",)

User-specific documents

("documents", "user123")

Nested cache structure

("cache", "embeddings", "v1")

key `instance-attribute` ¶

key: str

Unique identifier for the item within its namespace.

The key must be unique within the specific namespace to avoid conflicts. Together with the namespace, it forms a complete path to the item.

Example

If namespace is ("documents", "user123") and key is "report1", the full path would effectively be "documents/user123/report1"

value `instance-attribute` ¶

value: Optional[dict[str, Any]]

The data to store, or None to mark the item for deletion.

The value must be a dictionary with string keys and JSON-serializable values. Setting this to None signals that the item should be deleted.

Example

{ "field1": "string value", "field2": 123, "nested": {"can": "contain", "any": "serializable data"} }

index `class-attribute` `instance-attribute` ¶

index: Optional[Union[Literal[False], list[str]]] = None

Controls how the item's fields are indexed for search operations.

Indexing configuration determines how the item can be found through search

None (default): Uses the store's default indexing configuration (if provided)
False: Disables indexing for this item
list[str]: Specifies which json path fields to index for search

The item remains accessible through direct get() operations regardless of indexing. When indexed, fields can be searched using natural language queries through vector similarity search (if supported by the store implementation).

Path Syntax

Simple field access: "field"
Nested fields: "parent.child.grandchild"
Array indexing:
Specific index: "array[0]"
Last element: "array[-1]"
All elements (each individually): "array[*]"

Examples

None - Use store defaults (whole item)
list[str] - List of fields to index

[
    "metadata.title",                    # Nested field access
    "context[*].content",                # Index content from all context as separate vectors
    "authors[0].name",                   # First author's name
    "revisions[-1].changes",             # Most recent revision's changes
    "sections[*].paragraphs[*].text",    # All text from all paragraphs in all sections
    "metadata.tags[*]",                  # All tags in metadata
]

ttl `class-attribute` `instance-attribute` ¶

ttl: Optional[float] = None

Controls the TTL (time-to-live) for the item in minutes.

If provided, and if the store you are using supports this feature, the item will expire this many minutes after it was last accessed. The expiration timer refreshes on both read operations (get/search) and write operations (put/update). When the TTL expires, the item will be scheduled for deletion on a best-effort basis. Defaults to None (no expiration).

InvalidNamespaceError ¶

Bases: ValueError

Provided namespace is invalid.

TTLConfig ¶

Bases: TypedDict

Configuration for TTL (time-to-live) behavior in the store.

Attributes:

Name	Type	Description
`refresh_on_read`	`bool`	Default behavior for refreshing TTLs on read operations (GET and SEARCH).
`default_ttl`	`Optional[float]`	Default TTL (time-to-live) in minutes for new items.
`sweep_interval_minutes`	`Optional[int]`	Interval in minutes between TTL sweep operations.

refresh_on_read `instance-attribute` ¶

refresh_on_read: bool

Default behavior for refreshing TTLs on read operations (GET and SEARCH).

If True, TTLs will be refreshed on read operations (get/search) by default. This can be overridden per-operation by explicitly setting refresh_ttl. Defaults to True if not configured.

default_ttl `instance-attribute` ¶

default_ttl: Optional[float]

Default TTL (time-to-live) in minutes for new items.

If provided, new items will expire after this many minutes after their last access. The expiration timer refreshes on both read and write operations. Defaults to None (no expiration).

sweep_interval_minutes `instance-attribute` ¶

sweep_interval_minutes: Optional[int]

Interval in minutes between TTL sweep operations.

If provided, the store will periodically delete expired items based on TTL. Defaults to None (no sweeping).

IndexConfig ¶

Bases: TypedDict

Configuration for indexing documents for semantic search in the store.

If not provided to the store, the store will not support vector search. In that case, all index arguments to put() and aput() operations will be ignored.

Attributes:

Name	Type	Description
`dims`	`int`	Number of dimensions in the embedding vectors.
`embed`	`Union[Embeddings, EmbeddingsFunc, AEmbeddingsFunc, str]`	Optional function to generate embeddings from text.
`fields`	`Optional[list[str]]`	Fields to extract text from for embedding generation.

dims `instance-attribute` ¶

dims: int

Number of dimensions in the embedding vectors.

Common embedding models have the following dimensions

openai:text-embedding-3-large: 3072
openai:text-embedding-3-small: 1536
openai:text-embedding-ada-002: 1536
cohere:embed-english-v3.0: 1024
cohere:embed-english-light-v3.0: 384
cohere:embed-multilingual-v3.0: 1024
cohere:embed-multilingual-light-v3.0: 384

embed `instance-attribute` ¶

embed: Union[
    Embeddings, EmbeddingsFunc, AEmbeddingsFunc, str
]

Optional function to generate embeddings from text.

Can be specified in three ways

A LangChain Embeddings instance
A synchronous embedding function (EmbeddingsFunc)
An asynchronous embedding function (AEmbeddingsFunc)
A provider string (e.g., "openai:text-embedding-3-small")

Examples

Using LangChain's initialization with InMemoryStore:

from langchain.embeddings import init_embeddings
from langgraph.store.memory import InMemoryStore

store = InMemoryStore(
    index={
        "dims": 1536,
        "embed": init_embeddings("openai:text-embedding-3-small")
    }
)

Using a custom embedding function with InMemoryStore:

from openai import OpenAI
from langgraph.store.memory import InMemoryStore

client = OpenAI()

def embed_texts(texts: list[str]) -> list[list[float]]:
    response = client.embeddings.create(
        model="text-embedding-3-small",
        input=texts
    )
    return [e.embedding for e in response.data]

store = InMemoryStore(
    index={
        "dims": 1536,
        "embed": embed_texts
    }
)

Using an asynchronous embedding function with InMemoryStore:

from openai import AsyncOpenAI
from langgraph.store.memory import InMemoryStore

client = AsyncOpenAI()

async def aembed_texts(texts: list[str]) -> list[list[float]]:
    response = await client.embeddings.create(
        model="text-embedding-3-small",
        input=texts
    )
    return [e.embedding for e in response.data]

store = InMemoryStore(
    index={
        "dims": 1536,
        "embed": aembed_texts
    }
)

fields `instance-attribute` ¶

fields: Optional[list[str]]

Fields to extract text from for embedding generation.

Controls which parts of stored items are embedded for semantic search. Follows JSON path syntax:

- ["$"]: Embeds the entire JSON object as one vector  (default)
- ["field1", "field2"]: Embeds specific top-level fields
- ["parent.child"]: Embeds nested fields using dot notation
- ["array[*].field"]: Embeds field from each array element separately

Note

You can always override this behavior when storing an item using the index parameter in the put or aput operations.

Examples

# Embed entire document (default)
fields=["$"]

# Embed specific fields
fields=["text", "summary"]

# Embed nested fields
fields=["metadata.title", "content.body"]

# Embed from arrays
fields=["messages[*].content"]  # Each message content separately
fields=["context[0].text"]      # First context item's text

Note

Fields missing from a document are skipped
Array notation creates separate embeddings for each element
Complex nested paths are supported (e.g., "a.b[*].c.d")

BaseStore ¶

Bases: ABC

Abstract base class for persistent key-value stores.

Stores enable persistence and memory that can be shared across threads, scoped to user IDs, assistant IDs, or other arbitrary namespaces. Some implementations may support semantic search capabilities through an optional index configuration.

Note

Semantic search capabilities vary by implementation and are typically disabled by default. Stores that support this feature can be configured by providing an index configuration at creation time. Without this configuration, semantic search is disabled and any index arguments to storage operations will have no effect.

Similarly, TTL (time-to-live) support is disabled by default. Subclasses must explicitly set supports_ttl = True to enable this feature.

Methods:

Name	Description
`batch`	Execute multiple operations synchronously in a single batch.
`abatch`	Execute multiple operations asynchronously in a single batch.
`get`	Retrieve a single item.
`search`	Search for items within a namespace prefix.
`put`	Store or update an item in the store.
`delete`	Delete an item.
`list_namespaces`	List and filter namespaces in the store.
`aget`	Asynchronously retrieve a single item.
`asearch`	Asynchronously search for items within a namespace prefix.
`aput`	Asynchronously store or update an item in the store.
`adelete`	Asynchronously delete an item.
`alist_namespaces`	List and filter namespaces in the store asynchronously.

batch `abstractmethod` ¶

batch(ops: Iterable[Op]) -> list[Result]

Execute multiple operations synchronously in a single batch.

Parameters:

Name	Type	Description	Default
`ops`	`Iterable[Op]`	An iterable of operations to execute.	required

Returns:

Type	Description
`list[Result]`	A list of results, where each result corresponds to an operation in the input.
`list[Result]`	The order of results matches the order of input operations.

abatch `abstractmethod` `async` ¶

abatch(ops: Iterable[Op]) -> list[Result]

Execute multiple operations asynchronously in a single batch.

Parameters:

Name	Type	Description	Default
`ops`	`Iterable[Op]`	An iterable of operations to execute.	required

Returns:

Type	Description
`list[Result]`	A list of results, where each result corresponds to an operation in the input.
`list[Result]`	The order of results matches the order of input operations.

get ¶

get(
    namespace: tuple[str, ...],
    key: str,
    *,
    refresh_ttl: Optional[bool] = None
) -> Optional[Item]

Retrieve a single item.

Parameters:

Name	Type	Description	Default
`namespace`	`tuple[str, ...]`	Hierarchical path for the item.	required
`key`	`str`	Unique identifier within the namespace.	required
`refresh_ttl`	`Optional[bool]`	Whether to refresh TTLs for the returned item. If None (default), uses the store's default refresh_ttl setting. If no TTL is specified, this argument is ignored.	`None`

Returns:

Type	Description
`Optional[Item]`	The retrieved item or None if not found.

search ¶

search(
    namespace_prefix: tuple[str, ...],
    /,
    *,
    query: Optional[str] = None,
    filter: Optional[dict[str, Any]] = None,
    limit: int = 10,
    offset: int = 0,
    refresh_ttl: Optional[bool] = None,
) -> list[SearchItem]

Search for items within a namespace prefix.

Parameters:

Name	Type	Description	Default
`namespace_prefix`	`tuple[str, ...]`	Hierarchical path prefix to search within.	required
`query`	`Optional[str]`	Optional query for natural language search.	`None`
`filter`	`Optional[dict[str, Any]]`	Key-value pairs to filter results.	`None`
`limit`	`int`	Maximum number of items to return.	`10`
`offset`	`int`	Number of items to skip before returning results.	`0`
`refresh_ttl`	`Optional[bool]`	Whether to refresh TTLs for the returned items. If no TTL is specified, this argument is ignored.	`None`

Returns:

Type	Description
`list[SearchItem]`	List of items matching the search criteria.

Examples

Basic filtering:

# Search for documents with specific metadata
results = store.search(
    ("docs",),
    filter={"type": "article", "status": "published"}
)

Natural language search (requires vector store implementation):

# Initialize store with embedding configuration
store = YourStore( # e.g., InMemoryStore, AsyncPostgresStore
    index={
        "dims": 1536,  # embedding dimensions
        "embed": your_embedding_function,  # function to create embeddings
        "fields": ["text"]  # fields to embed. Defaults to ["$"]
    }
)

# Search for semantically similar documents
results = store.search(
    ("docs",),
    query="machine learning applications in healthcare",
    filter={"type": "research_paper"},
    limit=5
)

Note: Natural language search support depends on your store implementation and requires proper embedding configuration.

put ¶

put(
    namespace: tuple[str, ...],
    key: str,
    value: dict[str, Any],
    index: Optional[
        Union[Literal[False], list[str]]
    ] = None,
    *,
    ttl: Union[Optional[float], NotProvided] = NOT_PROVIDED
) -> None

Store or update an item in the store.

Parameters:

Name	Type	Description	Default
`namespace`	`tuple[str, ...]`	Hierarchical path for the item, represented as a tuple of strings. Example: ("documents", "user123")	required
`key`	`str`	Unique identifier within the namespace. Together with namespace forms the complete path to the item.	required
`value`	`dict[str, Any]`	Dictionary containing the item's data. Must contain string keys and JSON-serializable values.	required
`index`	`Optional[Union[Literal[False], list[str]]]`	Controls how the item's fields are indexed for search: None (default): Use `fields` you configured when creating the store (if any) If you do not initialize the store with indexing capabilities, the `index` parameter will be ignored False: Disable indexing for this item list[str]: List of field paths to index, supporting: Nested fields: "metadata.title" Array access: "chapters[*].content" (each indexed separately) Specific indices: "authors[0].name"	`None`
`ttl`	`Union[Optional[float], NotProvided]`	Time to live in minutes. Support for this argument depends on your store adapter. If specified, the item will expire after this many minutes from when it was last accessed. None means no expiration. Expired runs will be deleted opportunistically. By default, the expiration timer refreshes on both read operations (get/search) and write operations (put/update), whenever the item is included in the operation.	`NOT_PROVIDED`

Note

Indexing support depends on your store implementation. If you do not initialize the store with indexing capabilities, the index parameter will be ignored.

Similarly, TTL support depends on the specific store implementation. Some implementations may not support expiration of items.

Examples

Store item. Indexing depends on how you configure the store.

store.put(("docs",), "report", {"memory": "Will likes ai"})

Do not index item for semantic search. Still accessible through get() and search() operations but won't have a vector representation.

store.put(("docs",), "report", {"memory": "Will likes ai"}, index=False)

Index specific fields for search.

store.put(("docs",), "report", {"memory": "Will likes ai"}, index=["memory"])

delete ¶

delete(namespace: tuple[str, ...], key: str) -> None

Delete an item.

Parameters:

Name	Type	Description	Default
`namespace`	`tuple[str, ...]`	Hierarchical path for the item.	required
`key`	`str`	Unique identifier within the namespace.	required

list_namespaces ¶

list_namespaces(
    *,
    prefix: Optional[NamespacePath] = None,
    suffix: Optional[NamespacePath] = None,
    max_depth: Optional[int] = None,
    limit: int = 100,
    offset: int = 0
) -> list[tuple[str, ...]]

List and filter namespaces in the store.

Used to explore the organization of data, find specific collections, or navigate the namespace hierarchy.

Parameters:

Name	Type	Description	Default
`prefix`	`Optional[NamespacePath]`	Filter namespaces that start with this path.	`None`
`suffix`	`Optional[NamespacePath]`	Filter namespaces that end with this path.	`None`
`max_depth`	`Optional[int]`	Return namespaces up to this depth in the hierarchy. Namespaces deeper than this level will be truncated.	`None`
`limit`	`int`	Maximum number of namespaces to return (default 100).	`100`
`offset`	`int`	Number of namespaces to skip for pagination (default 0).	`0`

Returns:

Type	Description
`list[tuple[str, ...]]`	List[Tuple[str, ...]]: A list of namespace tuples that match the criteria.
`list[tuple[str, ...]]`	Each tuple represents a full namespace path up to `max_depth`.

???+ example "Examples": Setting max_depth=3. Given the namespaces:

# Example if you have the following namespaces:
# ("a", "b", "c")
# ("a", "b", "d", "e")
# ("a", "b", "d", "i")
# ("a", "b", "f")
# ("a", "c", "f")
store.list_namespaces(prefix=("a", "b"), max_depth=3)
# [("a", "b", "c"), ("a", "b", "d"), ("a", "b", "f")]

aget `async` ¶

aget(
    namespace: tuple[str, ...],
    key: str,
    *,
    refresh_ttl: Optional[bool] = None
) -> Optional[Item]

Asynchronously retrieve a single item.

Parameters:

Name	Type	Description	Default
`namespace`	`tuple[str, ...]`	Hierarchical path for the item.	required
`key`	`str`	Unique identifier within the namespace.	required

Returns:

Type	Description
`Optional[Item]`	The retrieved item or None if not found.

asearch `async` ¶

asearch(
    namespace_prefix: tuple[str, ...],
    /,
    *,
    query: Optional[str] = None,
    filter: Optional[dict[str, Any]] = None,
    limit: int = 10,
    offset: int = 0,
    refresh_ttl: Optional[bool] = None,
) -> list[SearchItem]

Asynchronously search for items within a namespace prefix.

Parameters:

Name	Type	Description	Default
`namespace_prefix`	`tuple[str, ...]`	Hierarchical path prefix to search within.	required
`query`	`Optional[str]`	Optional query for natural language search.	`None`
`filter`	`Optional[dict[str, Any]]`	Key-value pairs to filter results.	`None`
`limit`	`int`	Maximum number of items to return.	`10`
`offset`	`int`	Number of items to skip before returning results.	`0`
`refresh_ttl`	`Optional[bool]`	Whether to refresh TTLs for the returned items. If None (default), uses the store's TTLConfig.refresh_default setting. If TTLConfig is not provided or no TTL is specified, this argument is ignored.	`None`

Returns:

Type	Description
`list[SearchItem]`	List of items matching the search criteria.

Examples

Basic filtering:

# Search for documents with specific metadata
results = await store.asearch(
    ("docs",),
    filter={"type": "article", "status": "published"}
)

Natural language search (requires vector store implementation):

# Initialize store with embedding configuration
store = YourStore( # e.g., InMemoryStore, AsyncPostgresStore
    index={
        "dims": 1536,  # embedding dimensions
        "embed": your_embedding_function,  # function to create embeddings
        "fields": ["text"]  # fields to embed
    }
)

# Search for semantically similar documents
results = await store.asearch(
    ("docs",),
    query="machine learning applications in healthcare",
    filter={"type": "research_paper"},
    limit=5
)

Note: Natural language search support depends on your store implementation and requires proper embedding configuration.

aput `async` ¶

aput(
    namespace: tuple[str, ...],
    key: str,
    value: dict[str, Any],
    index: Optional[
        Union[Literal[False], list[str]]
    ] = None,
    *,
    ttl: Union[Optional[float], NotProvided] = NOT_PROVIDED
) -> None

Asynchronously store or update an item in the store.

Parameters:

Name	Type	Description	Default
`namespace`	`tuple[str, ...]`	Hierarchical path for the item, represented as a tuple of strings. Example: ("documents", "user123")	required
`key`	`str`	Unique identifier within the namespace. Together with namespace forms the complete path to the item.	required
`value`	`dict[str, Any]`	Dictionary containing the item's data. Must contain string keys and JSON-serializable values.	required
`index`	`Optional[Union[Literal[False], list[str]]]`	Controls how the item's fields are indexed for search: None (default): Use `fields` you configured when creating the store (if any) If you do not initialize the store with indexing capabilities, the `index` parameter will be ignored False: Disable indexing for this item list[str]: List of field paths to index, supporting: Nested fields: "metadata.title" Array access: "chapters[*].content" (each indexed separately) Specific indices: "authors[0].name"	`None`
`ttl`	`Union[Optional[float], NotProvided]`	Time to live in minutes. Support for this argument depends on your store adapter. If specified, the item will expire after this many minutes from when it was last accessed. None means no expiration. Expired runs will be deleted opportunistically. By default, the expiration timer refreshes on both read operations (get/search) and write operations (put/update), whenever the item is included in the operation.	`NOT_PROVIDED`

Note

Indexing support depends on your store implementation. If you do not initialize the store with indexing capabilities, the index parameter will be ignored.

Similarly, TTL support depends on the specific store implementation. Some implementations may not support expiration of items.

Examples

Store item. Indexing depends on how you configure the store.

await store.aput(("docs",), "report", {"memory": "Will likes ai"})

Do not index item for semantic search. Still accessible through get() and search() operations but won't have a vector representation.

await store.aput(("docs",), "report", {"memory": "Will likes ai"}, index=False)

Index specific fields for search (if store configured to index items):

await store.aput(
    ("docs",),
    "report",
    {
        "memory": "Will likes ai",
        "context": [{"content": "..."}, {"content": "..."}]
    },
    index=["memory", "context[*].content"]
)

adelete `async` ¶

adelete(namespace: tuple[str, ...], key: str) -> None

Asynchronously delete an item.

Parameters:

Name	Type	Description	Default
`namespace`	`tuple[str, ...]`	Hierarchical path for the item.	required
`key`	`str`	Unique identifier within the namespace.	required

alist_namespaces `async` ¶

alist_namespaces(
    *,
    prefix: Optional[NamespacePath] = None,
    suffix: Optional[NamespacePath] = None,
    max_depth: Optional[int] = None,
    limit: int = 100,
    offset: int = 0
) -> list[tuple[str, ...]]

List and filter namespaces in the store asynchronously.

Used to explore the organization of data, find specific collections, or navigate the namespace hierarchy.

Parameters:

Name	Type	Description	Default
`prefix`	`Optional[NamespacePath]`	Filter namespaces that start with this path.	`None`
`suffix`	`Optional[NamespacePath]`	Filter namespaces that end with this path.	`None`
`max_depth`	`Optional[int]`	Return namespaces up to this depth in the hierarchy. Namespaces deeper than this level will be truncated to this depth.	`None`
`limit`	`int`	Maximum number of namespaces to return (default 100).	`100`
`offset`	`int`	Number of namespaces to skip for pagination (default 0).	`0`

Returns:

Type	Description
`list[tuple[str, ...]]`	List[Tuple[str, ...]]: A list of namespace tuples that match the criteria.
`list[tuple[str, ...]]`	Each tuple represents a full namespace path up to `max_depth`.

Examples

Setting max_depth=3 with existing namespaces:

# Given the following namespaces:
# ("a", "b", "c")
# ("a", "b", "d", "e")
# ("a", "b", "d", "i")
# ("a", "b", "f")
# ("a", "c", "f")

await store.alist_namespaces(prefix=("a", "b"), max_depth=3)
# Returns: [("a", "b", "c"), ("a", "b", "d"), ("a", "b", "f")]

ensure_embeddings ¶

ensure_embeddings(
    embed: Union[
        Embeddings,
        EmbeddingsFunc,
        AEmbeddingsFunc,
        str,
        None,
    ],
) -> Embeddings

Ensure that an embedding function conforms to LangChain's Embeddings interface.

This function wraps arbitrary embedding functions to make them compatible with LangChain's Embeddings interface. It handles both synchronous and asynchronous functions.

Parameters:

Name	Type	Description	Default
`embed`	`Union[Embeddings, EmbeddingsFunc, AEmbeddingsFunc, str, None]`	Either an existing Embeddings instance, or a function that converts text to embeddings. If the function is async, it will be used for both sync and async operations.	required

Returns:

Type	Description
`Embeddings`	An Embeddings instance that wraps the provided function(s).

Examples

Wrap a synchronous embedding function:

def my_embed_fn(texts):
    return [[0.1, 0.2] for _ in texts]

embeddings = ensure_embeddings(my_embed_fn)
result = embeddings.embed_query("hello")  # Returns [0.1, 0.2]

Wrap an asynchronous embedding function:

async def my_async_fn(texts):
    return [[0.1, 0.2] for _ in texts]

embeddings = ensure_embeddings(my_async_fn)
result = await embeddings.aembed_query("hello")  # Returns [0.1, 0.2]

Initialize embeddings using a provider string:

# Requires langchain>=0.3.9 and langgraph-checkpoint>=2.0.11
embeddings = ensure_embeddings("openai:text-embedding-3-small")
result = embeddings.embed_query("hello")

get_text_at_path ¶

get_text_at_path(
    obj: Any, path: Union[str, list[str]]
) -> list[str]

Extract text from an object using a path expression or pre-tokenized path.

Parameters:

Name	Type	Description	Default
`obj`	`Any`	The object to extract text from	required
`path`	`Union[str, list[str]]`	Either a path string or pre-tokenized path list.	required

Path types handled

Simple paths: "field1.field2"
Array indexing: "[0]", "[*]", "[-1]"
Wildcards: "*"
Multi-field selection: "{field1,field2}"
Nested paths in multi-field: "{field1,nested.field2}"

tokenize_path ¶

tokenize_path(path: str) -> list[str]

Tokenize a path into components.

Types handled

Simple paths: "field1.field2"
Array indexing: "[0]", "[*]", "[-1]"
Wildcards: "*"
Multi-field selection: "{field1,field2}"

Modules:

Name	Description
`aio`
`base`

Classes:

Name	Description
`AsyncPostgresStore`	Asynchronous Postgres-backed store with optional vector search using pgvector.
`PostgresStore`	Postgres-backed store with optional vector search using pgvector.

AsyncPostgresStore ¶

Bases: AsyncBatchedBaseStore, BasePostgresStore[Conn]

Asynchronous Postgres-backed store with optional vector search using pgvector.

Examples

Basic setup and usage:

from langgraph.store.postgres import AsyncPostgresStore

conn_string = "postgresql://user:pass@localhost:5432/dbname"

async with AsyncPostgresStore.from_conn_string(conn_string) as store:
    await store.setup()  # Run migrations. Done once

    # Store and retrieve data
    await store.aput(("users", "123"), "prefs", {"theme": "dark"})
    item = await store.aget(("users", "123"), "prefs")

Vector search using LangChain embeddings:

from langchain.embeddings import init_embeddings
from langgraph.store.postgres import AsyncPostgresStore

conn_string = "postgresql://user:pass@localhost:5432/dbname"

async with AsyncPostgresStore.from_conn_string(
    conn_string,
    index={
        "dims": 1536,
        "embed": init_embeddings("openai:text-embedding-3-small"),
        "fields": ["text"]  # specify which fields to embed. Default is the whole serialized value
    }
) as store:
    await store.setup()  # Run migrations. Done once

    # Store documents
    await store.aput(("docs",), "doc1", {"text": "Python tutorial"})
    await store.aput(("docs",), "doc2", {"text": "TypeScript guide"})
    await store.aput(("docs",), "doc3", {"text": "Other guide"}, index=False)  # don't index

    # Search by similarity
    results = await store.asearch(("docs",), query="programming guides", limit=2)

Using connection pooling for better performance:

from langgraph.store.postgres import AsyncPostgresStore, PoolConfig

conn_string = "postgresql://user:pass@localhost:5432/dbname"

async with AsyncPostgresStore.from_conn_string(
    conn_string,
    pool_config=PoolConfig(
        min_size=5,
        max_size=20
    )
) as store:
    await store.setup()  # Run migrations. Done once
    # Use store with connection pooling...

Warning

Make sure to: 1. Call setup() before first use to create necessary tables and indexes 2. Have the pgvector extension available to use vector search 3. Use Python 3.10+ for async functionality

Note

Semantic search is disabled by default. You can enable it by providing an index configuration when creating the store. Without this configuration, all index arguments passed to put or aput will have no effect.

Note

If you provide a TTL configuration, you must explicitly call start_ttl_sweeper() to begin the background task that removes expired items. Call stop_ttl_sweeper() to properly clean up resources when you're done with the store.

Methods:

Name	Description
`from_conn_string`	Create a new AsyncPostgresStore instance from a connection string.
`setup`	Set up the store database asynchronously.
`sweep_ttl`	Delete expired store items based on TTL.
`start_ttl_sweeper`	Periodically delete expired store items based on TTL.
`stop_ttl_sweeper`	Stop the TTL sweeper task if it's running.

from_conn_string `async` `classmethod` ¶

from_conn_string(
    conn_string: str,
    *,
    pipeline: bool = False,
    pool_config: Optional[PoolConfig] = None,
    index: Optional[PostgresIndexConfig] = None,
    ttl: Optional[TTLConfig] = None
) -> AsyncIterator[AsyncPostgresStore]

Create a new AsyncPostgresStore instance from a connection string.

Parameters:

Name	Type	Description	Default
`conn_string`	`str`	The Postgres connection info string.	required
`pipeline`	`bool`	Whether to use AsyncPipeline (only for single connections)	`False`
`pool_config`	`Optional[PoolConfig]`	Configuration for the connection pool. If provided, will create a connection pool and use it instead of a single connection. This overrides the `pipeline` argument.	`None`
`index`	`Optional[PostgresIndexConfig]`	The embedding config.	`None`

Returns:

Name	Type	Description
`AsyncPostgresStore`	`AsyncIterator[AsyncPostgresStore]`	A new AsyncPostgresStore instance.

setup `async` ¶

setup() -> None

Set up the store database asynchronously.

This method creates the necessary tables in the Postgres database if they don't already exist and runs database migrations. It MUST be called directly by the user the first time the store is used.

sweep_ttl `async` ¶

sweep_ttl() -> int

Delete expired store items based on TTL.

Returns:

Name	Type	Description
`int`	`int`	The number of deleted items.

start_ttl_sweeper `async` ¶

start_ttl_sweeper(
    sweep_interval_minutes: Optional[int] = None,
) -> Task[None]

Periodically delete expired store items based on TTL.

Returns:

Type	Description
`Task[None]`	Task that can be awaited or cancelled.

stop_ttl_sweeper `async` ¶

stop_ttl_sweeper(timeout: Optional[float] = None) -> bool

Stop the TTL sweeper task if it's running.

Parameters:

Name	Type	Description	Default
`timeout`	`Optional[float]`	Maximum time to wait for the task to stop, in seconds. If None, wait indefinitely.	`None`

Returns:

Name	Type	Description
`bool`	`bool`	True if the task was successfully stopped or wasn't running, False if the timeout was reached before the task stopped.

PostgresStore ¶

Bases: BaseStore, BasePostgresStore[Conn]

Postgres-backed store with optional vector search using pgvector.

Examples

Basic setup and usage:

from langgraph.store.postgres import PostgresStore
from psycopg import Connection

conn_string = "postgresql://user:pass@localhost:5432/dbname"

# Using direct connection
with Connection.connect(conn_string) as conn:
    store = PostgresStore(conn)
    store.setup() # Run migrations. Done once

    # Store and retrieve data
    store.put(("users", "123"), "prefs", {"theme": "dark"})
    item = store.get(("users", "123"), "prefs")

Or using the convenient from_conn_string helper:

from langgraph.store.postgres import PostgresStore

conn_string = "postgresql://user:pass@localhost:5432/dbname"

with PostgresStore.from_conn_string(conn_string) as store:
    store.setup()

    # Store and retrieve data
    store.put(("users", "123"), "prefs", {"theme": "dark"})
    item = store.get(("users", "123"), "prefs")

Vector search using LangChain embeddings:

from langchain.embeddings import init_embeddings
from langgraph.store.postgres import PostgresStore

conn_string = "postgresql://user:pass@localhost:5432/dbname"

with PostgresStore.from_conn_string(
    conn_string,
    index={
        "dims": 1536,
        "embed": init_embeddings("openai:text-embedding-3-small"),
        "fields": ["text"]  # specify which fields to embed. Default is the whole serialized value
    }
) as store:
    store.setup() # Do this once to run migrations

    # Store documents
    store.put(("docs",), "doc1", {"text": "Python tutorial"})
    store.put(("docs",), "doc2", {"text": "TypeScript guide"})
    store.put(("docs",), "doc2", {"text": "Other guide"}, index=False) # don't index

    # Search by similarity
    results = store.search(("docs",), query="programming guides", limit=2)

Note

Semantic search is disabled by default. You can enable it by providing an index configuration when creating the store. Without this configuration, all index arguments passed to put or aputwill have no effect.

Warning

Make sure to call setup() before first use to create necessary tables and indexes. The pgvector extension must be available to use vector search.

Note

If you provide a TTL configuration, you must explicitly call start_ttl_sweeper() to begin the background thread that removes expired items. Call stop_ttl_sweeper() to properly clean up resources when you're done with the store.

Methods:

Name	Description
`get`	Retrieve a single item.
`search`	Search for items within a namespace prefix.
`put`	Store or update an item in the store.
`delete`	Delete an item.
`list_namespaces`	List and filter namespaces in the store.
`aget`	Asynchronously retrieve a single item.
`asearch`	Asynchronously search for items within a namespace prefix.
`aput`	Asynchronously store or update an item in the store.
`adelete`	Asynchronously delete an item.
`alist_namespaces`	List and filter namespaces in the store asynchronously.
`from_conn_string`	Create a new PostgresStore instance from a connection string.
`sweep_ttl`	Delete expired store items based on TTL.
`start_ttl_sweeper`	Periodically delete expired store items based on TTL.
`stop_ttl_sweeper`	Stop the TTL sweeper thread if it's running.
`__del__`	Ensure the TTL sweeper thread is stopped when the object is garbage collected.
`setup`	Set up the store database.

get ¶

get(
    namespace: tuple[str, ...],
    key: str,
    *,
    refresh_ttl: Optional[bool] = None
) -> Optional[Item]

Retrieve a single item.

Parameters:

Name	Type	Description	Default
`namespace`	`tuple[str, ...]`	Hierarchical path for the item.	required
`key`	`str`	Unique identifier within the namespace.	required
`refresh_ttl`	`Optional[bool]`	Whether to refresh TTLs for the returned item. If None (default), uses the store's default refresh_ttl setting. If no TTL is specified, this argument is ignored.	`None`

Returns:

Type	Description
`Optional[Item]`	The retrieved item or None if not found.

search ¶

search(
    namespace_prefix: tuple[str, ...],
    /,
    *,
    query: Optional[str] = None,
    filter: Optional[dict[str, Any]] = None,
    limit: int = 10,
    offset: int = 0,
    refresh_ttl: Optional[bool] = None,
) -> list[SearchItem]

Search for items within a namespace prefix.

Parameters:

Name	Type	Description	Default
`namespace_prefix`	`tuple[str, ...]`	Hierarchical path prefix to search within.	required
`query`	`Optional[str]`	Optional query for natural language search.	`None`
`filter`	`Optional[dict[str, Any]]`	Key-value pairs to filter results.	`None`
`limit`	`int`	Maximum number of items to return.	`10`
`offset`	`int`	Number of items to skip before returning results.	`0`
`refresh_ttl`	`Optional[bool]`	Whether to refresh TTLs for the returned items. If no TTL is specified, this argument is ignored.	`None`

Returns:

Type	Description
`list[SearchItem]`	List of items matching the search criteria.

Examples

Basic filtering:

# Search for documents with specific metadata
results = store.search(
    ("docs",),
    filter={"type": "article", "status": "published"}
)

Natural language search (requires vector store implementation):

# Initialize store with embedding configuration
store = YourStore( # e.g., InMemoryStore, AsyncPostgresStore
    index={
        "dims": 1536,  # embedding dimensions
        "embed": your_embedding_function,  # function to create embeddings
        "fields": ["text"]  # fields to embed. Defaults to ["$"]
    }
)

# Search for semantically similar documents
results = store.search(
    ("docs",),
    query="machine learning applications in healthcare",
    filter={"type": "research_paper"},
    limit=5
)

Note: Natural language search support depends on your store implementation and requires proper embedding configuration.

put ¶

put(
    namespace: tuple[str, ...],
    key: str,
    value: dict[str, Any],
    index: Optional[
        Union[Literal[False], list[str]]
    ] = None,
    *,
    ttl: Union[Optional[float], NotProvided] = NOT_PROVIDED
) -> None

Store or update an item in the store.

Parameters:

Name	Type	Description	Default
`namespace`	`tuple[str, ...]`	Hierarchical path for the item, represented as a tuple of strings. Example: ("documents", "user123")	required
`key`	`str`	Unique identifier within the namespace. Together with namespace forms the complete path to the item.	required
`value`	`dict[str, Any]`	Dictionary containing the item's data. Must contain string keys and JSON-serializable values.	required
`index`	`Optional[Union[Literal[False], list[str]]]`	Controls how the item's fields are indexed for search: None (default): Use `fields` you configured when creating the store (if any) If you do not initialize the store with indexing capabilities, the `index` parameter will be ignored False: Disable indexing for this item list[str]: List of field paths to index, supporting: Nested fields: "metadata.title" Array access: "chapters[*].content" (each indexed separately) Specific indices: "authors[0].name"	`None`
`ttl`	`Union[Optional[float], NotProvided]`	Time to live in minutes. Support for this argument depends on your store adapter. If specified, the item will expire after this many minutes from when it was last accessed. None means no expiration. Expired runs will be deleted opportunistically. By default, the expiration timer refreshes on both read operations (get/search) and write operations (put/update), whenever the item is included in the operation.	`NOT_PROVIDED`

Note

Indexing support depends on your store implementation. If you do not initialize the store with indexing capabilities, the index parameter will be ignored.

Similarly, TTL support depends on the specific store implementation. Some implementations may not support expiration of items.

Examples

Store item. Indexing depends on how you configure the store.

store.put(("docs",), "report", {"memory": "Will likes ai"})

Do not index item for semantic search. Still accessible through get() and search() operations but won't have a vector representation.

store.put(("docs",), "report", {"memory": "Will likes ai"}, index=False)

Index specific fields for search.

store.put(("docs",), "report", {"memory": "Will likes ai"}, index=["memory"])

delete ¶

delete(namespace: tuple[str, ...], key: str) -> None

Delete an item.

Parameters:

Name	Type	Description	Default
`namespace`	`tuple[str, ...]`	Hierarchical path for the item.	required
`key`	`str`	Unique identifier within the namespace.	required

list_namespaces ¶

list_namespaces(
    *,
    prefix: Optional[NamespacePath] = None,
    suffix: Optional[NamespacePath] = None,
    max_depth: Optional[int] = None,
    limit: int = 100,
    offset: int = 0
) -> list[tuple[str, ...]]

List and filter namespaces in the store.

Used to explore the organization of data, find specific collections, or navigate the namespace hierarchy.

Parameters:

Name	Type	Description	Default
`prefix`	`Optional[NamespacePath]`	Filter namespaces that start with this path.	`None`
`suffix`	`Optional[NamespacePath]`	Filter namespaces that end with this path.	`None`
`max_depth`	`Optional[int]`	Return namespaces up to this depth in the hierarchy. Namespaces deeper than this level will be truncated.	`None`
`limit`	`int`	Maximum number of namespaces to return (default 100).	`100`
`offset`	`int`	Number of namespaces to skip for pagination (default 0).	`0`

Returns:

Type	Description
`list[tuple[str, ...]]`	List[Tuple[str, ...]]: A list of namespace tuples that match the criteria.
`list[tuple[str, ...]]`	Each tuple represents a full namespace path up to `max_depth`.

???+ example "Examples": Setting max_depth=3. Given the namespaces:

# Example if you have the following namespaces:
# ("a", "b", "c")
# ("a", "b", "d", "e")
# ("a", "b", "d", "i")
# ("a", "b", "f")
# ("a", "c", "f")
store.list_namespaces(prefix=("a", "b"), max_depth=3)
# [("a", "b", "c"), ("a", "b", "d"), ("a", "b", "f")]

aget `async` ¶

aget(
    namespace: tuple[str, ...],
    key: str,
    *,
    refresh_ttl: Optional[bool] = None
) -> Optional[Item]

Asynchronously retrieve a single item.

Parameters:

Name	Type	Description	Default
`namespace`	`tuple[str, ...]`	Hierarchical path for the item.	required
`key`	`str`	Unique identifier within the namespace.	required

Returns:

Type	Description
`Optional[Item]`	The retrieved item or None if not found.

asearch `async` ¶

asearch(
    namespace_prefix: tuple[str, ...],
    /,
    *,
    query: Optional[str] = None,
    filter: Optional[dict[str, Any]] = None,
    limit: int = 10,
    offset: int = 0,
    refresh_ttl: Optional[bool] = None,
) -> list[SearchItem]

Asynchronously search for items within a namespace prefix.

Parameters:

Name	Type	Description	Default
`namespace_prefix`	`tuple[str, ...]`	Hierarchical path prefix to search within.	required
`query`	`Optional[str]`	Optional query for natural language search.	`None`
`filter`	`Optional[dict[str, Any]]`	Key-value pairs to filter results.	`None`
`limit`	`int`	Maximum number of items to return.	`10`
`offset`	`int`	Number of items to skip before returning results.	`0`
`refresh_ttl`	`Optional[bool]`	Whether to refresh TTLs for the returned items. If None (default), uses the store's TTLConfig.refresh_default setting. If TTLConfig is not provided or no TTL is specified, this argument is ignored.	`None`

Returns:

Type	Description
`list[SearchItem]`	List of items matching the search criteria.

Examples

Basic filtering:

# Search for documents with specific metadata
results = await store.asearch(
    ("docs",),
    filter={"type": "article", "status": "published"}
)

Natural language search (requires vector store implementation):

# Initialize store with embedding configuration
store = YourStore( # e.g., InMemoryStore, AsyncPostgresStore
    index={
        "dims": 1536,  # embedding dimensions
        "embed": your_embedding_function,  # function to create embeddings
        "fields": ["text"]  # fields to embed
    }
)

# Search for semantically similar documents
results = await store.asearch(
    ("docs",),
    query="machine learning applications in healthcare",
    filter={"type": "research_paper"},
    limit=5
)

Note: Natural language search support depends on your store implementation and requires proper embedding configuration.

aput `async` ¶

aput(
    namespace: tuple[str, ...],
    key: str,
    value: dict[str, Any],
    index: Optional[
        Union[Literal[False], list[str]]
    ] = None,
    *,
    ttl: Union[Optional[float], NotProvided] = NOT_PROVIDED
) -> None

Asynchronously store or update an item in the store.

Parameters:

Name	Type	Description	Default
`namespace`	`tuple[str, ...]`	Hierarchical path for the item, represented as a tuple of strings. Example: ("documents", "user123")	required
`key`	`str`	Unique identifier within the namespace. Together with namespace forms the complete path to the item.	required
`value`	`dict[str, Any]`	Dictionary containing the item's data. Must contain string keys and JSON-serializable values.	required
`index`	`Optional[Union[Literal[False], list[str]]]`	Controls how the item's fields are indexed for search: None (default): Use `fields` you configured when creating the store (if any) If you do not initialize the store with indexing capabilities, the `index` parameter will be ignored False: Disable indexing for this item list[str]: List of field paths to index, supporting: Nested fields: "metadata.title" Array access: "chapters[*].content" (each indexed separately) Specific indices: "authors[0].name"	`None`
`ttl`	`Union[Optional[float], NotProvided]`	Time to live in minutes. Support for this argument depends on your store adapter. If specified, the item will expire after this many minutes from when it was last accessed. None means no expiration. Expired runs will be deleted opportunistically. By default, the expiration timer refreshes on both read operations (get/search) and write operations (put/update), whenever the item is included in the operation.	`NOT_PROVIDED`

Note

Indexing support depends on your store implementation. If you do not initialize the store with indexing capabilities, the index parameter will be ignored.

Similarly, TTL support depends on the specific store implementation. Some implementations may not support expiration of items.

Examples

Store item. Indexing depends on how you configure the store.

await store.aput(("docs",), "report", {"memory": "Will likes ai"})

Do not index item for semantic search. Still accessible through get() and search() operations but won't have a vector representation.

await store.aput(("docs",), "report", {"memory": "Will likes ai"}, index=False)

Index specific fields for search (if store configured to index items):

await store.aput(
    ("docs",),
    "report",
    {
        "memory": "Will likes ai",
        "context": [{"content": "..."}, {"content": "..."}]
    },
    index=["memory", "context[*].content"]
)

adelete `async` ¶

adelete(namespace: tuple[str, ...], key: str) -> None

Asynchronously delete an item.

Parameters:

Name	Type	Description	Default
`namespace`	`tuple[str, ...]`	Hierarchical path for the item.	required
`key`	`str`	Unique identifier within the namespace.	required

alist_namespaces `async` ¶

alist_namespaces(
    *,
    prefix: Optional[NamespacePath] = None,
    suffix: Optional[NamespacePath] = None,
    max_depth: Optional[int] = None,
    limit: int = 100,
    offset: int = 0
) -> list[tuple[str, ...]]

List and filter namespaces in the store asynchronously.

Used to explore the organization of data, find specific collections, or navigate the namespace hierarchy.

Parameters:

Name	Type	Description	Default
`prefix`	`Optional[NamespacePath]`	Filter namespaces that start with this path.	`None`
`suffix`	`Optional[NamespacePath]`	Filter namespaces that end with this path.	`None`
`max_depth`	`Optional[int]`	Return namespaces up to this depth in the hierarchy. Namespaces deeper than this level will be truncated to this depth.	`None`
`limit`	`int`	Maximum number of namespaces to return (default 100).	`100`
`offset`	`int`	Number of namespaces to skip for pagination (default 0).	`0`

Returns:

Type	Description
`list[tuple[str, ...]]`	List[Tuple[str, ...]]: A list of namespace tuples that match the criteria.
`list[tuple[str, ...]]`	Each tuple represents a full namespace path up to `max_depth`.

Examples

Setting max_depth=3 with existing namespaces:

# Given the following namespaces:
# ("a", "b", "c")
# ("a", "b", "d", "e")
# ("a", "b", "d", "i")
# ("a", "b", "f")
# ("a", "c", "f")

await store.alist_namespaces(prefix=("a", "b"), max_depth=3)
# Returns: [("a", "b", "c"), ("a", "b", "d"), ("a", "b", "f")]

from_conn_string `classmethod` ¶

from_conn_string(
    conn_string: str,
    *,
    pipeline: bool = False,
    pool_config: Optional[PoolConfig] = None,
    index: Optional[PostgresIndexConfig] = None,
    ttl: Optional[TTLConfig] = None
) -> Iterator[PostgresStore]

Create a new PostgresStore instance from a connection string.

Parameters:

Name	Type	Description	Default
`conn_string`	`str`	The Postgres connection info string.	required
`pipeline`	`bool`	whether to use Pipeline	`False`
`pool_config`	`Optional[PoolConfig]`	Configuration for the connection pool. If provided, will create a connection pool and use it instead of a single connection. This overrides the `pipeline` argument.	`None`
`index`	`Optional[PostgresIndexConfig]`	The index configuration for the store.	`None`

Returns:

Name	Type	Description
`PostgresStore`	`Iterator[PostgresStore]`	A new PostgresStore instance.

sweep_ttl ¶

sweep_ttl() -> int

Delete expired store items based on TTL.

Returns:

Name	Type	Description
`int`	`int`	The number of deleted items.

start_ttl_sweeper ¶

start_ttl_sweeper(
    sweep_interval_minutes: Optional[int] = None,
) -> Future[None]

Periodically delete expired store items based on TTL.

Returns:

Type	Description
`Future[None]`	Future that can be waited on or cancelled.

stop_ttl_sweeper ¶

stop_ttl_sweeper(timeout: Optional[float] = None) -> bool

Stop the TTL sweeper thread if it's running.

Parameters:

Name	Type	Description	Default
`timeout`	`Optional[float]`	Maximum time to wait for the thread to stop, in seconds. If None, wait indefinitely.	`None`

Returns:

Name	Type	Description
`bool`	`bool`	True if the thread was successfully stopped or wasn't running, False if the timeout was reached before the thread stopped.

del ¶

__del__() -> None

Ensure the TTL sweeper thread is stopped when the object is garbage collected.

setup ¶

setup() -> None

Set up the store database.

This method creates the necessary tables in the Postgres database if they don't already exist and runs database migrations. It MUST be called directly by the user the first time the store is used.

Storage¶

NamespacePath module-attribute ¶

NamespaceMatchType module-attribute ¶

Embeddings ¶

embed_documents abstractmethod ¶

embed_query abstractmethod ¶

aembed_documents async ¶

aembed_query async ¶

NotProvided ¶

Item ¶

SearchItem ¶

__init__ ¶

GetOp ¶

namespace instance-attribute ¶

key instance-attribute ¶

refresh_ttl class-attribute instance-attribute ¶

SearchOp ¶

namespace_prefix instance-attribute ¶

filter class-attribute instance-attribute ¶

limit class-attribute instance-attribute ¶

offset class-attribute instance-attribute ¶

query class-attribute instance-attribute ¶

refresh_ttl class-attribute instance-attribute ¶

MatchCondition ¶

match_type instance-attribute ¶

path instance-attribute ¶

ListNamespacesOp ¶

match_conditions class-attribute instance-attribute ¶

max_depth class-attribute instance-attribute ¶

limit class-attribute instance-attribute ¶

offset class-attribute instance-attribute ¶

PutOp ¶

namespace instance-attribute ¶

key instance-attribute ¶

value instance-attribute ¶

index class-attribute instance-attribute ¶

ttl class-attribute instance-attribute ¶

InvalidNamespaceError ¶

TTLConfig ¶

refresh_on_read instance-attribute ¶

default_ttl instance-attribute ¶

sweep_interval_minutes instance-attribute ¶

IndexConfig ¶

dims instance-attribute ¶

embed instance-attribute ¶

fields instance-attribute ¶

BaseStore ¶

batch abstractmethod ¶

abatch abstractmethod async ¶

get ¶

search ¶

put ¶

delete ¶

list_namespaces ¶

aget async ¶

asearch async ¶

aput async ¶

adelete async ¶

alist_namespaces async ¶

ensure_embeddings ¶

get_text_at_path ¶

tokenize_path ¶

AsyncPostgresStore ¶

from_conn_string async classmethod ¶

setup async ¶

sweep_ttl async ¶

start_ttl_sweeper async ¶

stop_ttl_sweeper async ¶

PostgresStore ¶

get ¶

search ¶

put ¶

delete ¶

list_namespaces ¶

aget async ¶

asearch async ¶

aput async ¶

adelete async ¶

alist_namespaces async ¶

from_conn_string classmethod ¶

NamespacePath `module-attribute` ¶

NamespaceMatchType `module-attribute` ¶

embed_documents `abstractmethod` ¶

embed_query `abstractmethod` ¶

aembed_documents `async` ¶

aembed_query `async` ¶

init ¶

namespace `instance-attribute` ¶

key `instance-attribute` ¶

refresh_ttl `class-attribute` `instance-attribute` ¶

namespace_prefix `instance-attribute` ¶

filter `class-attribute` `instance-attribute` ¶

limit `class-attribute` `instance-attribute` ¶

offset `class-attribute` `instance-attribute` ¶

query `class-attribute` `instance-attribute` ¶

refresh_ttl `class-attribute` `instance-attribute` ¶

match_type `instance-attribute` ¶

path `instance-attribute` ¶

match_conditions `class-attribute` `instance-attribute` ¶

max_depth `class-attribute` `instance-attribute` ¶

limit `class-attribute` `instance-attribute` ¶

offset `class-attribute` `instance-attribute` ¶

namespace `instance-attribute` ¶

key `instance-attribute` ¶

value `instance-attribute` ¶

index `class-attribute` `instance-attribute` ¶

ttl `class-attribute` `instance-attribute` ¶

refresh_on_read `instance-attribute` ¶

default_ttl `instance-attribute` ¶

sweep_interval_minutes `instance-attribute` ¶

dims `instance-attribute` ¶

embed `instance-attribute` ¶

fields `instance-attribute` ¶

batch `abstractmethod` ¶

abatch `abstractmethod` `async` ¶

aget `async` ¶

asearch `async` ¶

aput `async` ¶

adelete `async` ¶

alist_namespaces `async` ¶

from_conn_string `async` `classmethod` ¶

setup `async` ¶

sweep_ttl `async` ¶

start_ttl_sweeper `async` ¶

stop_ttl_sweeper `async` ¶

aget `async` ¶

asearch `async` ¶

aput `async` ¶

adelete `async` ¶

alist_namespaces `async` ¶

from_conn_string `classmethod` ¶

del ¶