From fcfc99ee98c11c929f82cc6098b9db664b6c9ff8 Mon Sep 17 00:00:00 2001 From: Chaitanya Nuthalapati Date: Wed, 29 Apr 2026 14:48:13 -0700 Subject: [PATCH] Add Valkey for AI documentation page Add a central page listing repositories and frameworks that integrate with Valkey for AI workloads including LMCache, Haystack, Mem0, llm-d, LangChain/LangGraph, and Strands Agents. Fixes #434 Signed-off-by: cnu Co-Authored-By: Claude Opus 4.6 Signed-off-by: Chaitanya Nuthalapati --- topics/valkey-for-ai.md | 50 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 50 insertions(+) create mode 100644 topics/valkey-for-ai.md diff --git a/topics/valkey-for-ai.md b/topics/valkey-for-ai.md new file mode 100644 index 00000000..32474948 --- /dev/null +++ b/topics/valkey-for-ai.md @@ -0,0 +1,50 @@ +--- +title: "Valkey for AI" +description: Repositories and frameworks that integrate with Valkey for AI workloads +--- + +# Valkey for AI + +This page contains links to repositories that integrate with Valkey for AI workloads. It serves as a collection of resources for developers building AI applications. + +## [LMCache](https://github.com/LMCache/LMCache) + +KV cache management engine for LLM serving that reduces time-to-first-token and increases throughput. Valkey serves as a remote storage backend for KV cache reuse across vLLM and SGLang instances, allowing any serving engine to share cached prefill results. + +- [Valkey backend documentation](https://docs.lmcache.ai/kv_cache/storage_backends/valkey.html) +- [Valkey connector source](https://github.com/LMCache/LMCache/blob/dev/lmcache/v1/storage_backend/connector/valkey_connector.py) +- [Valkey benchmarking example](https://github.com/LMCache/LMCache/tree/dev/examples/kv_cache_reuse/remote_backends/valkey) + +## [Haystack](https://github.com/deepset-ai/haystack) + +Framework for building search and RAG pipelines. Valkey serves as a ValkeyDocumentStore for storing documents with embeddings and running vector similarity search within Haystack pipelines. + +- [valkey-haystack integration](https://github.com/deepset-ai/haystack-core-integrations) +- [Haystack documentation](https://docs.haystack.deepset.ai/) + +## [Mem0](https://github.com/mem0ai/mem0) + +Self-improving memory layer for LLM applications. Valkey serves as the storage backend for user memories, facts, and preferences with native `provider: "valkey"` support. + +- [Mem0 Valkey provider documentation](https://docs.mem0.ai/components/vectordbs/config) +- [Mem0 quickstart](https://docs.mem0.ai/getting-started/quickstart) + +## [llm-d](https://github.com/llm-d/llm-d) + +Kubernetes-native inference platform for LLM serving with state-of-the-art performance. Valkey provides the message queue for asynchronous request processing, using sorted sets for priority-based scheduling. + +- [Asynchronous processing guide](https://github.com/llm-d/llm-d/tree/main/guides/asynchronous-processing) + +## [LangChain / LangGraph](https://github.com/langchain-ai/langgraph) + +Framework for building LLM-powered applications with chains and agents. Valkey provides LLM response caching, vector search, and checkpoint persistence for LangGraph agent workflows. + +- [LangChain documentation](https://python.langchain.com/docs/integrations/providers/) +- [LangGraph documentation](https://langchain-ai.github.io/langgraph/) + +## [Strands Agents](https://github.com/strands-agents/sdk-python) + +SDK for building AI agents. Valkey provides persistent session storage for conversation history, session metadata, and agent state via the strands-valkey-session-manager package. + +- Integration: [strands-valkey-session-manager](https://github.com/jeromevdl/strands-valkey-session-manager) +- [Strands Agents documentation](https://strandsagents.com/latest/)