Principle:Langchain ai Langchain LLM Caching
| Knowledge Sources | |
|---|---|
| Domains | Optimization, Caching |
| Last Updated | 2026-02-11 00:00 GMT |
Overview
An optimization technique that stores and retrieves previous LLM responses to avoid redundant API calls for identical inputs.
Description
LLM caching intercepts the model generation pipeline between input preparation and the actual provider API call. When enabled, it creates a cache key from the serialized messages and model parameters, then checks whether a matching response already exists. On a cache hit, it returns the stored response immediately, bypassing the API call entirely. On a cache miss, it proceeds with the API call and stores the result for future reuse.
This is particularly valuable for:
- Development and testing: Avoiding repeated API charges during iteration
- Deterministic workflows: Ensuring identical inputs produce identical outputs
- Cost optimization: Reducing API costs for repeated queries
Usage
Enable caching when the same inputs are likely to be sent multiple times and exact reproducibility is acceptable. Disable it for applications requiring fresh responses on every call (e.g., conversational agents with changing context).
Theoretical Basis
The caching mechanism follows a standard cache-aside pattern:
# Abstract algorithm (not real code)
cache_key = hash(serialized_messages + model_params)
cached_result = cache.lookup(cache_key)
if cached_result is not None:
return cached_result # Cache hit
else:
result = provider_api_call(messages)
cache.store(cache_key, result)
return result # Cache miss
LangChain supports pluggable cache backends (in-memory, SQLite, Redis) via the global llm_cache setting or per-model cache parameter.