redis · raphaeldelio · Jul 30, 2026 · Jul 30, 2026 · Jul 30, 2026 · Jul 30, 2026
diff --git a/content/develop/ai/_index.md b/content/develop/ai/_index.md
@@ -71,7 +71,7 @@ AI agents are autonomous systems that combine LLMs with memory, tools, and plann
 The [Context Engine]({{< relref "/develop/ai/context-engine" >}}) (Redis Iris) includes four fully-managed services available on Redis Cloud:
 
 - [LangCache]({{< relref "/develop/ai/context-engine/langcache" >}}) — Semantic caching that reduces LLM API costs and improves response times by reusing cached responses for similar queries.
-- [Agent Memory]({{< relref "/develop/ai/context-engine/agent-memory" >}}) — Two-tier persistent memory (session and long-term) for agents, available as a REST API and Python SDK.
+- [Redis Agent Memory]({{< relref "/develop/ai/context-engine/agent-memory" >}}) — Two-tier persistent memory (session and long-term) for agents, available through Python and TypeScript SDKs and a REST API.
 - [Context Retriever]({{< relref "/develop/ai/context-engine/context-retriever" >}}) — Turns your business data into structured, governed tools that agents can reliably use, defined once and reused across all agents.
 - [Data Integration]({{< relref "/develop/ai/context-engine/data-integration" >}}) — Keeps your Redis Cloud database in sync with relational databases in near real time using Change Data Capture.
 
@@ -83,7 +83,7 @@ The [Context Engine]({{< relref "/develop/ai/context-engine" >}}) (Redis Iris) i
 1. [**Configure vector queries at runtime**]({{< relref "develop/ai/search-and-query/vectors#runtime-query-parameters" >}}): Select the best filter mode to optimize query execution.
 1. [**Build an AI agent**]({{< relref "/develop/ai/agent-builder" >}}): Use the interactive agent builder to generate complete working code for conversational assistants and recommendation engines.
 1. [**Add semantic caching**]({{< relref "/develop/ai/context-engine/langcache" >}}): Reduce LLM API calls by caching and reusing responses for semantically similar queries.
-1. [**Add agent memory**]({{< relref "/develop/ai/context-engine/agent-memory" >}}): Give your agent persistent session and long-term memory using the Agent Memory REST API.
+1. [**Add agent memory**]({{< relref "/develop/ai/context-engine/agent-memory" >}}): Give your agent persistent session and long-term memory using the Redis Agent Memory REST API.
 1. [**Access structured business data**]({{< relref "/develop/ai/context-engine/context-retriever" >}}): Use Context Retriever to define your business data as governed tools that any agent can query reliably.
 1. [**Sync live data to Redis**]({{< relref "/develop/ai/context-engine/data-integration" >}}): Use Data Integration to keep your Redis Cloud database in sync with your primary relational database using Change Data Capture.
 
@@ -140,7 +140,7 @@ AI agents can act autonomously to plan and execute tasks for the user.
 The Context Engine provides managed services for agent memory and data access.
 
 * [Get started with LangCache]({{< relref "/develop/ai/context-engine/langcache" >}}) — Add semantic caching to reduce LLM costs in minutes.
-* [Get started with Agent Memory]({{< relref "/develop/ai/context-engine/agent-memory" >}}) — Add persistent two-tier memory to any agent using the REST API.
+* [Get started with Redis Agent Memory]({{< relref "/develop/ai/context-engine/agent-memory" >}}) — Add persistent two-tier memory to any agent using the REST API.
 * [Get started with Context Retriever]({{< relref "/develop/ai/context-engine/context-retriever" >}}) — Expose your business data as governed tools that agents can reliably query.
 * [Get started with Data Integration]({{< relref "/develop/ai/context-engine/data-integration" >}}) — Keep Redis in sync with your primary database so agents always have fresh data.
 

diff --git a/content/develop/ai/context-engine/_index.md b/content/develop/ai/context-engine/_index.md
@@ -16,7 +16,7 @@ Give your AI agents the context layer they need to reliably act on business data
 Redis Iris eliminates the infrastructure burden of building context-aware AI agents — persistent memory, semantic caching, governed data access, and live data sync, all on Redis Cloud.
 
 <div class="grid grid-cols-1 md:grid-cols-3 gap-6 my-8">
-  {{< image-card image="images/ai-brain.svg" alt="Agent Memory icon" title="Agent Memory — Persistent short-term and long-term memory across agent interactions" url="/develop/ai/context-engine/agent-memory" >}}
+  {{< image-card image="images/ai-brain.svg" alt="Redis Agent Memory icon" title="Redis Agent Memory — Persistent short-term and long-term memory across agent interactions" url="/develop/ai/context-engine/agent-memory" >}}
   {{< image-card image="images/ai-LLM-memory.svg" alt="LangCache icon" title="LangCache — Semantic caching to reduce LLM costs and improve response times" url="/develop/ai/context-engine/langcache" >}}
   {{< image-card image="images/ai-cube.svg" alt="Context Retriever icon" title="Context Retriever — Governed, schema-first data access tools for agents" url="/develop/ai/context-engine/context-retriever" >}}
 </div>
@@ -49,7 +49,7 @@ Redis Iris is a production-ready context engine for AI agents that:
     <h3 class="text-redis-ink-900 font-semibold mb-3">For developers</h3>
     <ul class="space-y-1 text-redis-pen-600">
       <li>Four fully-managed services — no infrastructure to build or maintain</li>
-      <li>REST API and Python/JS SDKs for all services</li>
+      <li>Python and JavaScript SDKs and REST APIs for all services</li>
       <li>Define your data model once, reuse it across all agents</li>
       <li>Available on Redis Cloud with no database setup required</li>
     </ul>
@@ -77,16 +77,16 @@ POST /v1/caches/{cacheId}/entries
 }
 ```
 
-See [LangCache API examples]({{< relref "/develop/ai/context-engine/langcache/api-examples" >}}) and [Agent Memory API examples]({{< relref "/develop/ai/context-engine/agent-memory/api-examples" >}}) for more.
+See [LangCache API examples]({{< relref "/develop/ai/context-engine/langcache/api-examples" >}}) and the [Redis Agent Memory REST API quickstart]({{< relref "/develop/ai/context-engine/agent-memory/rest-api-quickstart" >}}) for more.
 
 Redis Iris context engine includes four services:
 
 - **[LangCache]({{< relref "/develop/ai/context-engine/langcache" >}})**: A semantic caching service that stores and reuses LLM responses for similar queries, reducing API costs and improving response latency.
-- **[Agent Memory]({{< relref "/develop/ai/context-engine/agent-memory" >}})**: A persistent memory service that maintains short-term session memory and long-term memory across agent interactions.
+- **[Redis Agent Memory]({{< relref "/develop/ai/context-engine/agent-memory" >}})**: A persistent memory service that maintains short-term session memory and long-term memory across agent interactions.
 - **[Context Retriever]({{< relref "/develop/ai/context-engine/context-retriever" >}})**: Turns your business data into structured tools that AI agents can safely and reliably use, defined once and reused across all agents.
 - **[Data integration]({{< relref "/develop/ai/context-engine/data-integration" >}})**: Syncs live data from your existing relational databases into Redis Cloud so agents always have access to fresh, accurate business data.
 
-All four services are available on [Redis Cloud]({{< relref "/operate/rc/context-engine" >}}) using the REST API, with no database setup or management required.
+All four services are available on [Redis Cloud]({{< relref "/operate/iris" >}}) using the REST API, with no database setup or management required.
 
 ## LangCache
 
@@ -103,7 +103,7 @@ LangCache works well for AI assistants, chatbots, RAG applications, AI agents, a
 
 [Get started with LangCache]({{< relref "/develop/ai/context-engine/langcache" >}})
 
-## Agent Memory
+## Redis Agent Memory
 
 [Redis Agent Memory]({{< relref "/develop/ai/context-engine/agent-memory" >}}) gives AI agents a structured, persistent memory layer using a two-tier model:
 
@@ -112,7 +112,7 @@ LangCache works well for AI assistants, chatbots, RAG applications, AI agents, a
 
 Promotion from session memory to long-term memory is automatic and non-blocking. As a conversation progresses, the service asynchronously extracts and stores important information in the background, keeping agent interactions responsive. You can also create long-term memories directly using the API for bulk imports or external knowledge sources.
 
-Agent Memory is available as a REST API and Python SDK.
+Redis Agent Memory is available through Python and TypeScript SDKs and a REST API.
 
 [Get started with Redis Agent Memory]({{< relref "/develop/ai/context-engine/agent-memory" >}})