Principle:Langchain ai Langgraph Cache Backend Selection
| Attribute | Value |
|---|---|
| Knowledge Sources | LangGraph |
| Domains | Caching, Architecture |
| Last Updated | 2026-02-11 15:00 GMT |
Overview
Cache backend selection is the architectural decision of choosing between SQLite-based and Redis-based cache implementations based on deployment requirements such as persistence, distribution, and performance characteristics.
Description
LangGraph provides two cache backend implementations that share a common BaseCache interface, offering key-value storage with optional TTL (time-to-live) expiration and namespace-based key partitioning.
SqliteCache is a file-based implementation backed by SQLite. It stores cached values in a single cache table with columns for namespace, key, expiry timestamp, encoding type, and serialized data. The database uses WAL (Write-Ahead Logging) journal mode for improved concurrency and employs a threading.RLock to serialize access across threads. TTL is implemented at the application level: expired entries are lazily detected on retrieval by comparing the stored expiry against the current UTC time and are purged from the database, avoiding the need for background cleanup processes. Async methods delegate to synchronous implementations via asyncio.to_thread.
RedisCache is a distributed implementation backed by Redis. It stores serialized values using a configurable key prefix (defaulting to "langgraph:cache:") and a compact "encoding:data" binary format that embeds the encoding type name directly in the stored value. It uses Redis MGET for efficient batch retrieval and pipeline for batch writes, minimizing round trips. When a TTL is provided, values are stored with SETEX for native Redis-managed expiration. All Redis operations are wrapped in try/except blocks for graceful degradation: cache failures silently return empty results or skip writes, ensuring the application never crashes due to cache unavailability.
Both backends share the same API surface: get, set, clear (and their async counterparts aget, aset, aclear), with namespaced keys represented as (Namespace, key_str) tuples.
Usage
Use SqliteCache for local development, testing, or single-process deployments where no external cache infrastructure is available. It requires no dependencies beyond Python's standard library and persists data across process restarts.
Use RedisCache for production or multi-process deployments where cache data must be shared across multiple LangGraph instances, or where native TTL management, high-throughput access, and horizontal scalability are required.
Both backends accept an optional custom SerializerProtocol for controlling how values are serialized and deserialized.
Theoretical Basis
Cache backend selection follows the strategy pattern, where a common interface (BaseCache) allows the caching strategy to be swapped without changing client code. The choice of backend is an infrastructure decision driven by non-functional requirements rather than application logic.
The two backends represent opposite ends of the local vs. distributed trade-off spectrum. SqliteCache optimizes for simplicity and zero-dependency operation at the cost of single-process scope, while RedisCache optimizes for shared state and horizontal scalability at the cost of external infrastructure.
The graceful degradation behavior in RedisCache implements the circuit breaker principle: when the cache backend is unavailable, the system falls back to uncached behavior rather than failing. This ensures that caching remains a performance optimization rather than a reliability dependency.
TTL-based expiration follows the time-bounded validity principle, where cached data is automatically invalidated after a specified duration to prevent stale reads. The two backends implement TTL differently -- SqliteCache uses application-level lazy expiration while Redis uses native server-side expiration -- but the semantic guarantee to callers is identical.