Python Key-Value Libraries

This monorepo contains:

• py-key-value-aio: Async key-value store library (primary and supported).
• py-key-value-sync: Sync version (no longer planned; async-only focus).

Documentation

• Full Documentation
• Getting Started Guide
• Stores Guide
• Wrappers Guide
• Adapters Guide
• API Reference

Why use this library?

• Multiple backends: Aerospike, Azure Tables, chDB, DuckDB, DynamoDB, Elasticsearch, FileTree, Firestore, Keyring, Memcached, MongoDB, OpenSearch, PostgreSQL, Redis, RocksDB, S3, Valkey, Vault, Windows Registry, and In-memory, Disk, etc.
• TTL support: Automatic expiration handling across all store types
• Type-safe: Full type hints with Protocol-based interfaces
• Adapters: Pydantic model support, raise-on-missing behavior, etc
• Wrappers: Statistics tracking and extensible wrapper system
• Collection-based: Organize keys into logical collections/namespaces
• Pluggable architecture: Easy to add custom store implementations

Value to Framework Authors

While key-value storage is valuable for individual projects, its true power emerges when framework authors use it as a pluggable abstraction layer.

By coding your framework against the AsyncKeyValue protocol, you enable your users to choose their own storage backend without changing a single line of your framework code. Users can seamlessly switch between local caching (memory, disk) for development and distributed storage (Redis, DynamoDB, MongoDB) for production.

Real-World Example: FastMCP

FastMCP demonstrates this pattern perfectly. FastMCP framework authors use the AsyncKeyValue protocol for:

• Response caching middleware: Store and retrieve cached responses
• OAuth proxy tokens: Persist authentication tokens across sessions

FastMCP users can plug in any store implementation:

• Development: MemoryStore() for fast iteration
• Production: RedisStore() for distributed caching
• Testing: NullStore() for testing without side effects

How to Use This in Your Framework

1. Accept the protocol in your framework's initialization:

   from key_value.aio.protocols.key_value import AsyncKeyValue
   
   class YourFramework:
       def __init__(self, cache: AsyncKeyValue):
           self.cache = cache
   

2. Use simple key-value operations in your framework:

   # Store data
   await self.cache.put(
       key="session:123",
       value={"user_id": "456", "expires": "2024-01-01"},
       collection="sessions",
       ttl=3600
   )
   
   # Retrieve data
   session = await self.cache.get(key="session:123", collection="sessions")
   

3. Let users choose their backend:

   # User's code - they control the storage backend
   from your_framework import YourFramework
   from key_value.aio.stores.redis import RedisStore
   from key_value.aio.stores.memory import MemoryStore
   
   # Development
   framework = YourFramework(cache=MemoryStore())
   
   # Production
   framework = YourFramework(
       cache=RedisStore(url="redis://localhost:6379/0")
   )
   
   # Production with Redis TLS
   framework = YourFramework(
       cache=RedisStore(
           url="rediss://redis.example.com:6380/0",
           ssl_ca_certs="/etc/ssl/certs/redis-ca.pem",
       )
   )
   

By depending on py-key-value-aio instead of a specific storage backend, you give your users the flexibility to choose the right storage for their needs while keeping your framework code clean and backend-agnostic.

Why not use this library?

• Async-only: This library focuses exclusively on async/await patterns. A synchronous wrapper library is not currently planned.
• Managed Entries: Raw values are not stored in backends, a wrapper object is stored instead. This wrapper object contains the value, sometimes metadata like the TTL, and the creation timestamp. Most often it is serialized to and from JSON.
• No Live Objects: Even when using the in-memory store, "live" objects are never returned from the store. You get a dictionary or a Pydantic model, hopefully a copy of what you stored, but never the same instance in memory.
• Dislike of Bear Bros: Beartype is used for runtime type checking. Core protocol methods in store and wrapper implementations (put/get/delete/ttl and their batch variants) enforce types and will raise TypeError for violations. Other code produces warnings. You can disable all beartype checks by setting PY_KEY_VALUE_DISABLE_BEARTYPE=true or suppress warnings via the warnings module.

Installation

Quick start for Async library

Install the library with the backends you need.

# Async library
pip install py-key-value-aio

# With specific backend extras
pip install py-key-value-aio[memory]
pip install py-key-value-aio[disk]
pip install py-key-value-aio[dynamodb]
pip install py-key-value-aio[s3]
pip install py-key-value-aio[azure-tables]
pip install py-key-value-aio[elasticsearch]
pip install py-key-value-aio[firestore]
# or: aerospike, redis, mongodb, memcached, valkey, vault, registry, rocksdb,
# see below for all options

import asyncio

from key_value.aio.protocols.key_value import AsyncKeyValue
from key_value.aio.stores.memory import MemoryStore


async def example(key_value: AsyncKeyValue) -> None:
    await key_value.put(key="123", value={"name": "Alice"}, collection="users", ttl=3600)
    value = await key_value.get(key="123", collection="users")
    await key_value.delete(key="123", collection="users")


async def main():
    memory_store = MemoryStore()
    await example(key_value=memory_store)

asyncio.run(main())

Introduction to py-key-value

Protocols

• Async: key_value.aio.protocols.AsyncKeyValue — async get/put/delete/ttl and bulk variants; optional protocol segments for culling, destroying stores/collections, and enumerating keys/collections implemented by capable stores.

The protocols offer a simple interface for your application to interact with the store:

get(key: str, collection: str | None = None) -> dict[str, Any] | None:
get_many(keys: list[str], collection: str | None = None) -> list[dict[str, Any] | None]:

put(key: str, value: dict[str, Any], collection: str | None = None, ttl: SupportsFloat | None = None) -> None:
put_many(keys: list[str], values: Sequence[dict[str, Any]], collection: str | None = None, ttl: SupportsFloat | None = None) -> None:

delete(key: str, collection: str | None = None) -> bool:
delete_many(keys: list[str], collection: str | None = None) -> int:

ttl(key: str, collection: str | None = None) -> tuple[dict[str, Any] | None, float | None]:
ttl_many(keys: list[str], collection: str | None = None) -> list[tuple[dict[str, Any] | None, float | None]]:

Stores

The library provides multiple store implementations organized into three categories:

• Local stores: In-memory and disk-based storage (Memory, Disk, RocksDB, etc.)
• Secret stores: Secure OS-level storage for sensitive data (Keyring, Vault)
• Distributed stores: Network-based storage for multi-node apps (Redis, DynamoDB, S3, Azure Tables, MongoDB, etc.)

Each store has a stability rating indicating likelihood of backwards-incompatible changes. Stable stores (Redis, Valkey, Disk, Keyring) are recommended for long-term storage.

📚 View all stores, installation guides, and examples →

Adapters

Adapters provide specialized interfaces for working with stores. Unlike wrappers, they don't implement the protocol but instead offer alternative APIs for specific use cases:

• DataclassAdapter: Type-safe dataclass storage with automatic validation
• PydanticAdapter: Type-safe Pydantic model storage with serialization
• RaiseOnMissingAdapter: Raise exceptions instead of returning None for missing keys

📚 View all adapters with examples →

Quick example - PydanticAdapter for type-safe storage:

import asyncio
from pydantic import BaseModel

from key_value.aio.adapters.pydantic import PydanticAdapter
from key_value.aio.stores.memory import MemoryStore

class User(BaseModel):
    name: str
    email: str

async def example():
    memory_store: MemoryStore = MemoryStore()

    user_adapter: PydanticAdapter[User] = PydanticAdapter(
        key_value=memory_store,
        pydantic_model=User,
        default_collection="users",
    )

    new_user: User = User(name="John Doe", email="john.doe@example.com")
    
    # Directly store the User model
    await user_adapter.put(
        key="john-doe",
        value=new_user,
    )

    # Retrieve the User model
    existing_user: User | None = await user_adapter.get(
        key="john-doe",
    )

asyncio.run(example())

Wrappers

Wrappers add functionality to stores while implementing the protocol themselves, allowing them to be stacked and used anywhere a store is expected. Available wrappers include:

• Performance: Compression, Caching (Passthrough), Statistics, Timeout
• Security: Encryption (Fernet), ReadOnly
• Reliability: Retry, Fallback
• Routing: CollectionRouting, Routing, SingleCollection
• Organization: PrefixKeys, PrefixCollections
• Constraints: LimitSize, TTLClamp, DefaultValue
• Observability: Logging, Statistics

📚 View all wrappers with examples →

Wrappers can be stacked for complex functionality:

# Create a retriable redis store with timeout protection that is monitored,
# with compressed values, and a fallback to memory store! This probably isn't
# a good idea but you can do it!
store = 
LoggingWrapper(
    CompressionWrapper(
        FallbackWrapper(
            primary_key_value=RetryWrapper(
                TimeoutWrapper(
                    key_value=redis_store,
                )
            ),
            fallback_key_value=memory_store,
        )
    )
)

Wrappers are applied in order, so the outermost wrapper is applied first and the innermost wrapper is applied last. Keep this in mind when chaining wrappers!

Atomicity / Consistency

We aim for consistent semantics across basic key-value operations. Guarantees may vary by backend (especially distributed systems) and for bulk or management operations.

Advanced Patterns

Adapters, stores, and wrappers can be combined in a variety of ways as needed.

The following example simulates a consumer of your service providing an Elasticsearch store and forcing all data into a single collection. They pass this wrapped store to your service and you further wrap it in a statistics wrapper (for metrics/monitoring) and a pydantic adapter, to simplify the application's usage.

import asyncio
from pydantic import BaseModel

from key_value.aio.adapters.pydantic import PydanticAdapter
from key_value.aio.wrappers.single_collection import SingleCollectionWrapper
from key_value.aio.wrappers.statistics import StatisticsWrapper
from key_value.aio.stores.elasticsearch import ElasticsearchStore


class User(BaseModel):
    name: str
    email: str

elasticsearch_store: ElasticsearchStore = ElasticsearchStore(
    url="https://localhost:9200", api_key="your-api-key", index="kv-store"
)

single_collection: SingleCollectionWrapper = SingleCollectionWrapper(
    key_value=elasticsearch_store, single_collection="users",
    default_collection="one-collection"
)


async def main(key_value: AsyncKeyValue):
    statistics_wrapper = StatisticsWrapper(key_value=key_value)
    users = PydanticAdapter(key_value=statistics_wrapper, pydantic_model=User)

    await users.put(
        key="u1", value=User(name="Jane", email="j@example.com"),
        collection="ignored"
    )
    user = await users.get(key="u1", collection="ignored")
    _ = statistics_wrapper.statistics  # access metrics


asyncio.run(main(key_value=single_collection))

Project links

• Full Documentation
• API Reference

Contributions welcome but may not be accepted. File an issue before submitting a pull request. If you do not get agreement on your proposal before making a pull request you may have a bad time.

Apache 2.0 licensed.