Using LangCache for Building Agents. And no, it's not from LangChain. It’s from Redis, built for production-scale memory and recall. LangChain’s in-built caching mostly works on exact text matches. Redis LangCache, in contrast, uses semantic caching; it recalls based on meaning, not identical strings. Here’s how it works under the hood: >A user sends a prompt to your AI app. >Your app sends the prompt to LangCache via: POST /v1/caches/{cacheId}/entries/search >It calls an embedding model to generate a vector for the prompt. >It searches the cache for a semantically similar entry using those embeddings. >If a match is found (cache hit): LangCache returns the cached response instantly. >If no match is found (cache miss): Your app calls the LLM, gets a new response, then stores it back via: POST /v1/caches/{cacheId}/entries >LangCache saves the new embedding and response for future reuse. How It Differs from LangChain Caching: >LangChain’s built-in caches (like RedisCache or InMemoryCache) work only on exact string matches. >RedisSemanticCache supports embeddings, but it’s self-hosted and limited in scale. >Redis LangCache is a fully managed semantic caching service designed for production workloads. Why it matters : >Faster response times >Reduced API costs >No infrastructure management >Language-agnostic (via REST API) When to use it : >AI agents, RAG systems, & chatbots >Repetitive or similar query handling >Production-grade reliability >Auto-optimized embeddings >Detailed cache monitoring

* The opinions described in this post are my personal opinions and are not attributable to AWS ----------------------------------------- I'm excited to share my expertise on seamless database transitions at #AWSreInvent in Las Vegas, Nevada! Join me for "OPN406 | Moving to Valkey without downtime" where I'll walk you through proven strategies for migrating from Redis to @valkey_io while keeping your applications running smoothly. This chalk talk will cover compatibility assessments, migration planning, and performance optimization techniques that I've refined through real-world implementations. You'll leave with actionable insights and practical approaches you can immediately apply to your own migration projects. I look forward to sharing these valuable techniques with you and answering your questions during our interactive discussion. registration.awsevents.com/f… #valkey #inmemorycache #Database #OpenSource #LinuxFoundation

Calling all @ApacheIgnite Users! Registration for the upcoming Ignite Summit on Feb 25 is now open. Reserve your seat today! hubs.la/Q034fkvq0 #ApacheIgnite #IgniteSummit2025 #InMemoryDatabase #InMemoryComputing #InMemoryCache #DistributedDatabase

Calling all Apache Ignite Users! Registration for the upcoming Ignite Summit on Feb 25 is now open. Reserve your seat today! hubs.la/Q034fm530 #ApacheIgnite #IgniteSummit2025 #InMemoryDatabase #InMemoryComputing #InMemoryCache #DistributedDatabase

Hi there, Apollo Client maintainer here :) There is one thing in the article that needs a correction - when using `registerApolloClient`, you should use the `ApolloClient` and `InMemoryCache` classes that are exported from `"@apollo/experimental-nextjs-app-support"`, not from `"@apollo/client"` - and you don't need the `ssrMode` option. See github.com/apollographql/apo…

見てる: "InMemoryCache is not respecting the Cache-Control header · Issue #7796 · apollographql/apollo-client" github.com/apollographql/apo…

Renamed exports In the past, the `@apollo/experimental-nextjs-app-support` package had two entry points, `/rsc` and `/ssr`. This has caused a lot of confusion, and both entry points have been moved together into the base entry point, `@apollo/experimental-nextjs-app-support`. On top of that, the Apollo Client React hooks (`useQuery`, `useSuspenseQuery`, etc.) no longer need to be imported from `@apollo/experimental-nextjs-app-support`, but they can be imported directly from `@apollo/client` now, while still being streaming-compatible. Also, a few exports have been renamed: - `NextSSRApolloClient` -> `ApolloClient` - `NextSSRInMemoryCache` -> `InMemoryCache` - `resetNextSSRApolloSingletons` -> `resetApolloClientSingletons` The old entry points with the old exports still exist and each of them has a `@.deprecated` docblock explaining the new import to use.

Not a #FullStack ecosystem. It’s primarily a #Storage network. This is not competition for $ICP. Not even a runner up. They have object/file storage. Do they have the rest of these out of the box like $ICP? - #IdentityProvider - #DataPersistence - #BackEndComputeExecution - #AutomaticHorizontalScaling - #InMemoryCache - HeapMemory for WASM containers. - #StableMemory that persists across #dApp deployments (contract upgrades) - Hint nobody has this besides $ICP. - The ability to run any WASM compatible language in their #SmartContractExecutionEnvironment I think upon close inspection this is not even a comparison that can be made by reasonable people. #InternetComputer has an unfair advantage in almost every possible test.

Yeah, similar here - InMemoryCache typePolicies have a `keyFields` option, and if you set that to `false` you opt out of normalization for a type.

New release candidate: Marvin.Cache.Headers 7.0.0-rc. Support for .NET 8, switch to InMemoryCache, roundtrippable StoreKey & more. nuget.org/packages/Marvin.Ca… - enjoy :) #httpcache

InMemoryCache spends like 30-80ms writing to cache on my maxed out M2 Mac pro in dev mode: nuts.

🌵 Caching now enabled 🌵 With 3 lines of code, you can now enable caching for all LLM calls 💸💸This makes it cheaper and easier to experiment with changing only parts of a chain Supports both a temporary InMemoryCache, as well as a persistent SQLiteCache

#Saturday was fun 😺 Learnt about 1. Apollo Server and Apollo Client 2. Schema, Query, Mutation 3. InMemoryCache 4. Query Resolvers 5. Rest Data Sources Built a basic full stack application using above learning. #webdevelopment #fullstack #learning

Kod yazarken eğlenceli de olabilmeli. const client = new ApolloClient({ uri: "rickandmortyapi.com/graphql", cache: new InMemoryCache(), }); #GraphQL The Rick and Morty API. #ReactJS #VueJS #javascript #FrontEndDeveloper

🗓️ Day 31 Thx to @lukaszery, KitQL has now 2 client cache Implementations: ✅ InMemoryCache ✅ LocalStorageCache ❔What will be the next one❔ 🧡 the fact that someone can come & contribute. 🙏 you so much

#今日の積み上げ ︎︎︎︎︎︎☑︎昨日に続きApollo GraphQLの学習 ☆今日の学び InMemoryCacheとClientCacheがとにかくスゴい！ 凄さは実際に使ってもらえば実感できると思います。 #プログラミング独学 #graphql #駆け出しエンジニアとつながりたい

Apollo ClientのLocal State Management, InMemoryCacheの仕組みは破壊的イノベーションなので理解しておく必要がある / Apollo ClientのInMemoryCacheとMutationに関する調査・考察 - yigarashi のブログ yigarashi.hatenablog.com/ent…

Só para o curso -zoa Usei a solução do inMemoryCache do Apollo mesmo, usando fieldPolicies e no read do carrinho, uso Reactive Variables, assim os estados atualizam só no Client Side.

I need some help with Apollo Graphql Client. Specifically configuring inMemoryCache, and more specifically custom merge functions.