Add Valkey for AI documentation page

topics/valkey-for-ai.md

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+---
+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/)