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
module-attribute
¶
module-attribute
¶
Specifies how to match namespace paths.
Values
"prefix": Match from the start of the namespace "suffix": Match from the end of the namespace
Bases:
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.
abstractmethod
¶
Embed search docs.
Parameters:
-
texts(list [str ]List of text to embed.
Returns:
-
list [list [float ]]List of embeddings.
abstractmethod
¶
Embed query text.
Parameters:
-
text(str Text to embed.
Returns:
-
list [float ]Embedding.
async
¶
Asynchronous Embed search docs.
Parameters:
-
texts(list [str ]List of text to embed.
Returns:
-
list [list [float ]]List of embeddings.
async
¶
Asynchronous Embed query text.
Parameters:
-
text(str Text to embed.
Returns:
-
list [float ]Embedding.
Sentinel singleton.
Represents a stored item with metadata.
Parameters:
-
value(dict [str ,Any ]The stored data as a dictionary. Keys are filterable.
-
key(str Unique identifier within the namespace.
-
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')
-
created_at(datetime Timestamp of item creation.
-
updated_at(datetime Timestamp of last update.
Bases:
Represents an item returned from a search operation with additional metadata.
__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:
-
namespace(tuple [str , ...]Hierarchical path to the item.
-
key(str Unique identifier within the namespace.
-
value(dict [str ,Any ]The stored value.
-
created_at(datetime When the item was first created.
-
updated_at(datetime When the item was last updated.
-
score(Optional [float ]None) –Relevance/similarity score if from a ranked operation.
Bases:
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:
Bases:
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:
instance-attribute
¶
class-attribute
instance-attribute
¶
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
class-attribute
instance-attribute
¶
Maximum number of items to return in the search results.
class-attribute
instance-attribute
¶
Number of matching items to skip for pagination.
class-attribute
instance-attribute
¶
Natural language search query for semantic search capabilities.
Examples
- "technical documentation about REST APIs"
- "machine learning papers from 2023"
class-attribute
instance-attribute
¶
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.
Bases:
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:
Suffix matching with wildcard:
Simple suffix matching:
Bases:
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":
class-attribute
instance-attribute
¶
Optional conditions for filtering namespaces.
class-attribute
instance-attribute
¶
Maximum depth of namespace hierarchy to return.
Note
Namespaces deeper than this level will be truncated.
class-attribute
instance-attribute
¶
Number of namespaces to skip for pagination.
Bases:
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.
instance-attribute
¶
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.
instance-attribute
¶
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"
instance-attribute
¶
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"} }
class-attribute
instance-attribute
¶
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
]
class-attribute
instance-attribute
¶
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).
Bases:
Provided namespace is invalid.
Bases:
Configuration for TTL (time-to-live) behavior in the store.
instance-attribute
¶
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.
instance-attribute
¶
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).
instance-attribute
¶
Interval in minutes between TTL sweep operations.
If provided, the store will periodically delete expired items based on TTL. Defaults to None (no sweeping).
Bases:
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.
instance-attribute
¶
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
}
)
instance-attribute
¶
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")
Bases:
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.
abstractmethod
¶
Execute multiple operations synchronously in a single batch.
Parameters:
-
ops(Iterable [Op ]An iterable of operations to execute.
Returns:
-
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.
abstractmethod
async
¶
Execute multiple operations asynchronously in a single batch.
Parameters:
-
ops(Iterable [Op ]An iterable of operations to execute.
Returns:
-
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(
namespace: tuple [str , ...],
key: str ,
*,
refresh_ttl: Optional [bool ] = None
) -> Optional [Item ]
Retrieve a single item.
Parameters:
-
namespace(tuple [str , ...]Hierarchical path for the item.
-
key(str Unique identifier within the namespace.
-
refresh_ttl(Optional [bool ]None) –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.
Returns:
-
Optional [Item ]The retrieved item or None if not found.
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:
-
namespace_prefix(tuple [str , ...]Hierarchical path prefix to search within.
-
query(Optional [str ]None) –Optional query for natural language search.
-
filter(Optional [dict [str ,Any ]]None) –Key-value pairs to filter results.
-
limit(int 10) –Maximum number of items to return.
-
offset(int 0) –Number of items to skip before returning results.
-
refresh_ttl(Optional [bool ]None) –Whether to refresh TTLs for the returned items. If no TTL is specified, this argument is ignored.
Returns:
-
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(
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:
-
namespace(tuple [str , ...]Hierarchical path for the item, represented as a tuple of strings. Example: ("documents", "user123")
-
key(str Unique identifier within the namespace. Together with namespace forms the complete path to the item.
-
value(dict [str ,Any ]Dictionary containing the item's data. Must contain string keys and JSON-serializable values.
-
index(Optional [Union [Literal [False],list [str ]]]None) –Controls how the item's fields are indexed for search:
- None (default): Use
fieldsyou configured when creating the store (if any) If you do not initialize the store with indexing capabilities, theindexparameter 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 (default): Use
-
ttl(Union [Optional [float ],NotProvided ]NOT_PROVIDED 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.
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.
Do not index item for semantic search. Still accessible through get() and search() operations but won't have a vector representation.
Index specific fields for search.
Delete an item.
Parameters:
-
namespace(tuple [str , ...]Hierarchical path for the item.
-
key(str Unique identifier within the namespace.
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:
-
prefix(Optional [Tuple [str , ...]]None) –Filter namespaces that start with this path.
-
suffix(Optional [Tuple [str , ...]]None) –Filter namespaces that end with this path.
-
max_depth(Optional [int ]None) –Return namespaces up to this depth in the hierarchy. Namespaces deeper than this level will be truncated.
-
limit(int 100) –Maximum number of namespaces to return (default 100).
-
offset(int 0) –Number of namespaces to skip for pagination (default 0).
Returns:
-
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:
async
¶
aget(
namespace: tuple [str , ...],
key: str ,
*,
refresh_ttl: Optional [bool ] = None
) -> Optional [Item ]
Asynchronously retrieve a single item.
Parameters:
-
namespace(tuple [str , ...]Hierarchical path for the item.
-
key(str Unique identifier within the namespace.
Returns:
-
Optional [Item ]The retrieved item or None if not found.
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:
-
namespace_prefix(tuple [str , ...]Hierarchical path prefix to search within.
-
query(Optional [str ]None) –Optional query for natural language search.
-
filter(Optional [dict [str ,Any ]]None) –Key-value pairs to filter results.
-
limit(int 10) –Maximum number of items to return.
-
offset(int 0) –Number of items to skip before returning results.
-
refresh_ttl(Optional [bool ]None) –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.
Returns:
-
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.
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:
-
namespace(tuple [str , ...]Hierarchical path for the item, represented as a tuple of strings. Example: ("documents", "user123")
-
key(str Unique identifier within the namespace. Together with namespace forms the complete path to the item.
-
value(dict [str ,Any ]Dictionary containing the item's data. Must contain string keys and JSON-serializable values.
-
index(Optional [Union [Literal [False],list [str ]]]None) –Controls how the item's fields are indexed for search:
- None (default): Use
fieldsyou configured when creating the store (if any) If you do not initialize the store with indexing capabilities, theindexparameter 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 (default): Use
-
ttl(Union [Optional [float ],NotProvided ]NOT_PROVIDED 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.
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.
Do not index item for semantic search. Still accessible through get() and search() operations but won't have a vector representation.
Index specific fields for search (if store configured to index items):
async
¶
Asynchronously delete an item.
Parameters:
-
namespace(tuple [str , ...]Hierarchical path for the item.
-
key(str Unique identifier within the namespace.
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:
-
prefix(Optional [Tuple [str , ...]]None) –Filter namespaces that start with this path.
-
suffix(Optional [Tuple [str , ...]]None) –Filter namespaces that end with this path.
-
max_depth(Optional [int ]None) –Return namespaces up to this depth in the hierarchy. Namespaces deeper than this level will be truncated to this depth.
-
limit(int 100) –Maximum number of namespaces to return (default 100).
-
offset(int 0) –Number of namespaces to skip for pagination (default 0).
Returns:
-
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:
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:
-
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.
Returns:
-
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:
Extract text from an object using a path expression or pre-tokenized path.
Parameters:
-
obj(Any The object to extract text from
-
path(Union [str ,list [str ]]Either a path string or pre-tokenized path list.
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 a path into components.
Types handled
- Simple paths: "field1.field2"
- Array indexing: "[0]", "[*]", "[-1]"
- Wildcards: "*"
- Multi-field selection: "{field1,field2}"
Bases:
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.
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:
-
conn_string(str The Postgres connection info string.
-
pipeline(bool False) –Whether to use AsyncPipeline (only for single connections)
-
pool_config(Optional [PoolConfig ]None) –Configuration for the connection pool. If provided, will create a connection pool and use it instead of a single connection. This overrides the
pipelineargument. -
index(Optional [PostgresIndexConfig ]None) –The embedding config.
Returns:
-
AsyncPostgresStore(AsyncIterator [AsyncPostgresStore ]A new AsyncPostgresStore instance.
async
¶
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.
async
¶
Delete expired store items based on TTL.
Returns:
-
int(int The number of deleted items.
async
¶
Periodically delete expired store items based on TTL.
Returns:
-
Task [None]Task that can be awaited or cancelled.
async
¶
Stop the TTL sweeper task if it's running.
Parameters:
-
timeout(Optional [float ]None) –Maximum time to wait for the task to stop, in seconds. If None, wait indefinitely.
Returns:
-
bool(bool True if the task was successfully stopped or wasn't running, False if the timeout was reached before the task stopped.
Bases:
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.
get(
namespace: tuple [str , ...],
key: str ,
*,
refresh_ttl: Optional [bool ] = None
) -> Optional [Item ]
Retrieve a single item.
Parameters:
-
namespace(tuple [str , ...]Hierarchical path for the item.
-
key(str Unique identifier within the namespace.
-
refresh_ttl(Optional [bool ]None) –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.
Returns:
-
Optional [Item ]The retrieved item or None if not found.
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:
-
namespace_prefix(tuple [str , ...]Hierarchical path prefix to search within.
-
query(Optional [str ]None) –Optional query for natural language search.
-
filter(Optional [dict [str ,Any ]]None) –Key-value pairs to filter results.
-
limit(int 10) –Maximum number of items to return.
-
offset(int 0) –Number of items to skip before returning results.
-
refresh_ttl(Optional [bool ]None) –Whether to refresh TTLs for the returned items. If no TTL is specified, this argument is ignored.
Returns:
-
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(
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:
-
namespace(tuple [str , ...]Hierarchical path for the item, represented as a tuple of strings. Example: ("documents", "user123")
-
key(str Unique identifier within the namespace. Together with namespace forms the complete path to the item.
-
value(dict [str ,Any ]Dictionary containing the item's data. Must contain string keys and JSON-serializable values.
-
index(Optional [Union [Literal [False],list [str ]]]None) –Controls how the item's fields are indexed for search:
- None (default): Use
fieldsyou configured when creating the store (if any) If you do not initialize the store with indexing capabilities, theindexparameter 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 (default): Use
-
ttl(Union [Optional [float ],NotProvided ]NOT_PROVIDED 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.
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.
Do not index item for semantic search. Still accessible through get() and search() operations but won't have a vector representation.
Index specific fields for search.
Delete an item.
Parameters:
-
namespace(tuple [str , ...]Hierarchical path for the item.
-
key(str Unique identifier within the namespace.
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:
-
prefix(Optional [Tuple [str , ...]]None) –Filter namespaces that start with this path.
-
suffix(Optional [Tuple [str , ...]]None) –Filter namespaces that end with this path.
-
max_depth(Optional [int ]None) –Return namespaces up to this depth in the hierarchy. Namespaces deeper than this level will be truncated.
-
limit(int 100) –Maximum number of namespaces to return (default 100).
-
offset(int 0) –Number of namespaces to skip for pagination (default 0).
Returns:
-
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:
async
¶
aget(
namespace: tuple [str , ...],
key: str ,
*,
refresh_ttl: Optional [bool ] = None
) -> Optional [Item ]
Asynchronously retrieve a single item.
Parameters:
-
namespace(tuple [str , ...]Hierarchical path for the item.
-
key(str Unique identifier within the namespace.
Returns:
-
Optional [Item ]The retrieved item or None if not found.
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:
-
namespace_prefix(tuple [str , ...]Hierarchical path prefix to search within.
-
query(Optional [str ]None) –Optional query for natural language search.
-
filter(Optional [dict [str ,Any ]]None) –Key-value pairs to filter results.
-
limit(int 10) –Maximum number of items to return.
-
offset(int 0) –Number of items to skip before returning results.
-
refresh_ttl(Optional [bool ]None) –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.
Returns:
-
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.
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:
-
namespace(tuple [str , ...]Hierarchical path for the item, represented as a tuple of strings. Example: ("documents", "user123")
-
key(str Unique identifier within the namespace. Together with namespace forms the complete path to the item.
-
value(dict [str ,Any ]Dictionary containing the item's data. Must contain string keys and JSON-serializable values.
-
index(Optional [Union [Literal [False],list [str ]]]None) –Controls how the item's fields are indexed for search:
- None (default): Use
fieldsyou configured when creating the store (if any) If you do not initialize the store with indexing capabilities, theindexparameter 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 (default): Use
-
ttl(Union [Optional [float ],NotProvided ]NOT_PROVIDED 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.
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.
Do not index item for semantic search. Still accessible through get() and search() operations but won't have a vector representation.
Index specific fields for search (if store configured to index items):
async
¶
Asynchronously delete an item.
Parameters:
-
namespace(tuple [str , ...]Hierarchical path for the item.
-
key(str Unique identifier within the namespace.
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:
-
prefix(Optional [Tuple [str , ...]]None) –Filter namespaces that start with this path.
-
suffix(Optional [Tuple [str , ...]]None) –Filter namespaces that end with this path.
-
max_depth(Optional [int ]None) –Return namespaces up to this depth in the hierarchy. Namespaces deeper than this level will be truncated to this depth.
-
limit(int 100) –Maximum number of namespaces to return (default 100).
-
offset(int 0) –Number of namespaces to skip for pagination (default 0).
Returns:
-
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:
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:
-
conn_string(str The Postgres connection info string.
-
pipeline(bool False) –whether to use Pipeline
-
pool_config(Optional [PoolArgs ]None) –Configuration for the connection pool. If provided, will create a connection pool and use it instead of a single connection. This overrides the
pipelineargument. -
index(Optional [PostgresIndexConfig ]None) –The index configuration for the store.
Returns:
-
PostgresStore(Iterator [PostgresStore ]A new PostgresStore instance.
Delete expired store items based on TTL.
Returns:
-
int(int The number of deleted items.
Periodically delete expired store items based on TTL.
Returns:
-
Future [None]Future that can be waited on or cancelled.
Stop the TTL sweeper thread if it's running.
Parameters:
-
timeout(Optional [float ]None) –Maximum time to wait for the thread to stop, in seconds. If None, wait indefinitely.
Returns:
-
bool(bool True if the thread was successfully stopped or wasn't running, False if the timeout was reached before the thread stopped.
Ensure the TTL sweeper thread is stopped when the object is garbage collected.
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.