RED-220418: Add Context Gateway developer docs

content/develop/ai/_index.md

@@ -26,7 +26,7 @@ Redis is an in-memory data platform purpose-built for the speed and structure th
 - **Vector search**: Store and query vector embeddings using KNN and range queries with metadata filters, across hashes and JSON documents
 - **Semantic caching**: Reduce LLM API costs by reusing cached responses for semantically similar prompts with LangCache
 - **Agent memory**: Give agents short-term session memory and long-term persistent memory that survives across interactions
-- **Structured data access**: Turn your business data into governed tools agents can reliably query with Context Retriever
+- **Tool and data access**: Give agents one endpoint for the tools of your MCP servers and your business data with Context Gateway
 - **Live data sync**: Stream changes from relational databases into Redis in near real time so agents always work with current information
 
 ## Why use Redis for AI and search?
@@ -70,7 +70,7 @@ The [Context Engine]({{< relref "/develop/ai/context-engine" >}}) (Redis Iris) i
 
 - [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.
 - [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.
+- [Context Gateway]({{< relref "/develop/ai/context-engine/context-gateway" >}}): Gives agents one Model Context Protocol (MCP) endpoint for the tools of any MCP server, and serves your Redis data as tools with Context Surfaces.
 - [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.
 
 ## How to's
@@ -82,7 +82,7 @@ The [Context Engine]({{< relref "/develop/ai/context-engine" >}}) (Redis Iris) i
 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. [**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. [**Connect tools and business data**]({{< relref "/develop/ai/context-engine/context-gateway" >}}): Use Context Gateway to give any agent one endpoint for your MCP servers and your Redis data.
 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.
 
 #### Learn how to index and query vector embeddings
@@ -139,7 +139,7 @@ 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 Context Retriever]({{< relref "/develop/ai/context-engine/context-retriever" >}}): Expose your business data as governed tools that agents can reliably query.
+* [Get started with Context Gateway]({{< relref "/develop/ai/context-engine/context-gateway" >}}): Give agents one MCP endpoint for the tools of your MCP servers and your Redis data.
 * [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.
 
 ## Tutorials

content/develop/ai/agent-builder/agent-concepts.md

@@ -90,10 +90,10 @@ You can build each part of an agent's context layer yourself from Redis data str
 |:--|:--|:--|
 | Remember the user across sessions | Streams, Hashes, and vector search for session and long-term memory | [Agent Memory](/content/develop/ai/context-engine/agent-memory/_index.md) stores session events, summarizes long sessions, and extracts long-term memories in the background. |
 | Avoid repeat LLM calls | Vector search plus your own cache logic | [LangCache](/content/develop/ai/context-engine/langcache/_index.md) returns a cached response when a new prompt is semantically similar to a cached one. |
-| Query business data safely | Hand-written tools or generated queries per agent | [Context Retriever](/content/develop/ai/context-engine/context-retriever/_index.md) generates tools from a data model you define once. Agents call them over MCP (Model Context Protocol), and agent keys limit what each agent can reach. |
+| Query business data safely | Hand-written tools or generated queries per agent | [Context Gateway](/content/develop/ai/context-engine/context-gateway/_index.md) generates tools from a data model you define once. Agents call them over MCP (Model Context Protocol), and agent keys limit what each agent can reach. |
 | Keep that data current | Your own sync jobs from the source database | [Data Integration](/content/develop/ai/context-engine/data-integration/_index.md) streams changes from relational databases into Redis within seconds. |
 
-LangCache, Agent Memory, and Context Retriever are currently in preview. To see where each service fits in a single request, see [how a request flows through Redis Iris](/content/develop/ai/context-engine/concepts/request-flow.md).
+LangCache and Agent Memory are currently in preview. To see where each service fits in a single request, see [how a request flows through Redis Iris](/content/develop/ai/context-engine/concepts/request-flow.md).
 
 ## Types of agents you can build
 
@@ -327,7 +327,7 @@ Production-ready agents include built-in reliability features:
 
 - Data encryption: Encrypt sensitive data at rest and in transit
 - Access controls: Implement proper authentication and authorization
-- Governed data access: Give agents defined tools instead of direct database access. [Context Retriever](/content/develop/ai/context-engine/context-retriever/concepts.md) generates these tools and uses access tags to filter what each agent can see
+- Governed data access: Give agents defined tools instead of direct database access. [Context Gateway](/content/develop/ai/context-engine/context-gateway/concepts.md) serves these tools through one MCP endpoint, so agents never hold database or Upstream credentials.
 - Data retention: Automatic cleanup of personal data per regulations
 - Sensitive data in memory: Keep information such as payment card numbers out of long-term memory. Agent Memory [sensitive-data exclusions](/content/operate/iris/agent-memory/create-service.md#sensitive-data-exclusions) guide automatic extraction away from it
 - Audit logging: Track all data access and modifications
@@ -377,7 +377,7 @@ Ready to build your AI agent with Redis?
 **Learn more:**
 - [Redis Iris Context Engine — Agent Memory](/content/develop/ai/context-engine/agent-memory/_index.md) for managed session and long-term agent memory
 - [Redis Iris concepts](/content/develop/ai/context-engine/concepts/_index.md) for how memory, caching, and governed data access work together
-- Interactive demos for [Agent Memory](/content/develop/ai/context-engine/agent-memory/interactive-demo.md), [LangCache](/content/develop/ai/context-engine/langcache/interactive-demo.md), and [Context Retriever](/content/develop/ai/context-engine/context-retriever/interactive-demo.md) that run in your browser
+- Interactive demos for [Agent Memory](/content/develop/ai/context-engine/agent-memory/interactive-demo.md), [LangCache](/content/develop/ai/context-engine/langcache/interactive-demo.md) that run in your browser
 - [Redis Vector Search documentation](/content/develop/ai/search-and-query/vectors/_index.md)
 - [RedisVL Python library](/content/develop/clients/redis-vl.md) for vector operations and AI workflows
 - [Redis data structures guide](/content/develop/data-types/_index.md)

content/develop/ai/context-engine/_index.md

@@ -19,7 +19,7 @@ Redis Iris eliminates the infrastructure burden of building context-aware AI age
 <div class="grid grid-cols-1 md:grid-cols-4 gap-6 my-8">
   {{< tile-card icon="images/dev/icons/icon-redis-iris-64-duotone.png" title="Concepts" description="What happens when an agent asks Redis Iris for context" url="/develop/ai/context-engine/concepts" >}}
   {{< tile-card icon="images/dev/icons/icon-redis-agent-memory-64-duotone.png" title="Agent Memory" description="Persistent short-term and long-term memory across agent interactions" url="/develop/ai/context-engine/agent-memory" >}}
-  {{< tile-card icon="images/dev/icons/icon-redis-context-retriever-64-duotone.png" title="Context Retriever" description="Governed, schema-first data access tools for agents" url="/develop/ai/context-engine/context-retriever" >}}
+  {{< tile-card icon="images/dev/icons/icon-redis-iris-64-duotone.png" title="Context Gateway" description="One MCP endpoint for the tools of any MCP server and your Redis data" url="/develop/ai/context-engine/context-gateway" >}}
   {{< tile-card icon="images/dev/icons/icon-redis-iris-64-duotone.png" title="Agent Playbooks" description="Publish reusable answers and procedures for agents" url="/develop/ai/context-engine/agent-playbook" >}}
 </div>
 
@@ -29,7 +29,7 @@ Redis Iris is a production-ready context engine for AI agents that:
 
 - **Reduces LLM costs**: Semantic caching returns cached responses for similar queries in milliseconds
 - **Adds persistent memory**: Agents remember past interactions and user preferences across sessions
-- **Structures business data access**: Context Retriever generates governed tools agents can safely call at runtime
+- **Connects tools and business data**: Context Gateway gives agents one MCP endpoint for the tools of any MCP server and your Redis data
 - **Keeps data fresh**: Data Integration streams live changes from relational databases into Redis within seconds
 - **Deploys your way**: Check each service’s operator guide for its managed and self-managed deployment options.
 

content/develop/ai/context-engine/agent-memory/interactive-demo.md

@@ -13,7 +13,7 @@ weight: 9
 
 Redis Agent Memory gives an AI agent two tiers of memory. Session memory holds the current conversation, and long-term memory holds what the agent should remember across conversations. This demo follows one customer of a food delivery app through two conversations. You choose what she says, and the demo shows what each tier stores, when it changes, and how long it lasts.
 
-The demo runs in your browser and doesn't connect to an Agent Memory service. Its requests and responses follow the shapes of the [Agent Memory API](/content/develop/ai/context-engine/agent-memory/api-reference.md). The customer, restaurants, and order come from the same sample data as the [Context Retriever interactive demo](/content/develop/ai/context-engine/context-retriever/interactive-demo.md).
+The demo runs in your browser and doesn't connect to an Agent Memory service. Its requests and responses follow the shapes of the [Agent Memory API](/content/develop/ai/context-engine/agent-memory/api-reference.md). The customer, restaurants, and order come from the same sample data as the [Redis Iris interactive demo](/content/develop/ai/context-engine/concepts/interactive-demo.md).
 
 {{< agent-memory-demo >}}
 

content/develop/ai/context-engine/concepts/_index.md

@@ -79,12 +79,12 @@ Run all three fully managed on Redis Cloud, or self-managed on your own infrastr
 
 - Let Iris decide what's cached and what's fresh. Don't build a separate caching layer on top of the model call.
 - Write session events as they happen. Let background extraction decide what's durable, rather than deciding yourself on every turn.
-- Model your business data as entities and relationships once. Let Context Retriever generate the tools your agent calls, instead of writing a tool per query.
+- Model your business data as entities and relationships once. Let a Context Gateway Context Surface generate the tools your agent calls, instead of writing a tool per query.
 - Scope every write and lookup by user, session, or namespace. This keeps agents and users from colliding.
 
 ## Next steps
 
 - [How a request flows through Redis Iris]({{< relref "/develop/ai/context-engine/concepts/request-flow" >}})
 - [Agent Memory overview]({{< relref "/develop/ai/context-engine/agent-memory/overview" >}})
-- [Context Retriever concepts]({{< relref "/develop/ai/context-engine/context-retriever/concepts" >}})
-- Interactive demos for [Agent Memory]({{< relref "/develop/ai/context-engine/agent-memory/interactive-demo" >}}) and [Context Retriever]({{< relref "/develop/ai/context-engine/context-retriever/interactive-demo" >}}) that run in your browser
+- [Context Gateway concepts]({{< relref "/develop/ai/context-engine/context-gateway/concepts" >}})
+- An [interactive demo]({{< relref "/develop/ai/context-engine/agent-memory/interactive-demo" >}}) of Agent Memory that runs in your browser

content/develop/ai/context-engine/concepts/interactive-demo.md

@@ -25,7 +25,7 @@ The tour runs in your browser and doesn't connect to any service. Each step link
 | Service | What it does in this request | Without it |
 |:--|:--|:--|
 | [Data Integration](/content/develop/ai/context-engine/data-integration/_index.md) | Syncs a change from PostgreSQL to Redis seconds after it happens | The agent reads last night's data and makes a promise it can't keep |
-| [Context Retriever](/content/develop/ai/context-engine/context-retriever/_index.md) | Looks up delivery times, alternatives, and past orders through governed tools | The agent can't check whether the food will arrive in time |
+| Context Retriever | Looks up delivery times, alternatives, and past orders through governed tools | The agent can't check whether the food will arrive in time |
 | [Agent Memory](/content/develop/ai/context-engine/agent-memory/_index.md) | Recalls last week's order and the customer's peanut allergy | The agent can find the order, but not the allergy |
 | [LangCache](/content/develop/ai/context-engine/langcache/_index.md) | Answers the general delivery fee question from the cache | The model answers it, which takes longer and costs output tokens |
 

content/develop/ai/context-engine/concepts/request-flow.md

@@ -14,9 +14,9 @@ bannerText: Agent Memory and Context Retriever are currently available in previe
 
 [How Redis Iris works]({{< relref "/develop/ai/context-engine/concepts" >}}) covers the mental model. This page traces the same idea through an actual request, capability by capability.
 
-Every capability in Iris manages the same limited resource: the model's context window. Semantic caching skips a model call when a similar one already ran. Agent Memory decides what's worth keeping from a conversation. Context Retriever returns exactly the data a tool call needs, instead of a raw query result. Treat "what goes into the next model call" as a budget. Manage it actively. It doesn't take care of itself once a request reaches Iris.
+Every capability in Iris manages the same limited resource: the model's context window. Semantic caching skips a model call when a similar one already ran. Agent Memory decides what's worth keeping from a conversation. Context Gateway returns the result of one tool call instead of a raw query result, and holds large results as handles. Treat "what goes into the next model call" as a budget. Manage it actively. It doesn't take care of itself once a request reaches Iris.
 
-These capabilities aren't a fixed sequence. A request can draw on any combination of them before a model call happens: semantic caching for responses to similar prompts, [Agent Memory]({{< relref "/develop/ai/context-engine/agent-memory" >}}) for session and long-term recall, and [Context Retriever]({{< relref "/develop/ai/context-engine/context-retriever" >}}) for governed access to business data. [Data Integration]({{< relref "/develop/ai/context-engine/data-integration" >}}) runs in the background, keeping that business data fresh for Context Retriever to query.
+These capabilities aren't a fixed sequence. A request can draw on any combination of them before a model call happens: semantic caching for responses to similar prompts, [Agent Memory]({{< relref "/develop/ai/context-engine/agent-memory" >}}) for session and long-term recall, and [Context Gateway]({{< relref "/develop/ai/context-engine/context-gateway" >}}) for tool calls to your MCP servers and your business data. [Data Integration]({{< relref "/develop/ai/context-engine/data-integration" >}}) runs in the background, keeping that business data fresh for Context Gateway to query.
 
 Select a node in the diagram for a description of what that capability does, with a link to its docs. Select a scenario button to trace the path a request takes.
 
@@ -57,24 +57,20 @@ nodes:
                 url: "/develop/ai/context-engine/agent-memory/interactive-demo"
         docsUrl: "/develop/ai/context-engine/agent-memory"
     contextRetriever:
-        label: "Context Retriever"
+        label: "Context Gateway"
         type: process
         col: 1
         row: 2
         description: |
-            Context Retriever: governed tool calls. Calls governed, schema-first tools to fetch business data the agent needs.
-        links:
-            demo:
-                label: "Interactive demo"
-                url: "/develop/ai/context-engine/context-retriever/interactive-demo"
-        docsUrl: "/develop/ai/context-engine/context-retriever"
+            Context Gateway: tool calls through one endpoint. Calls the tools of MCP servers and Context Surfaces to fetch the business data the agent needs.
+        docsUrl: "/develop/ai/context-engine/context-gateway"
     dataIntegration:
         label: "Data Integration"
         type: process
         col: 0
         row: 3
         description: |
-            Data Integration: keeps business data fresh. Streams changes from source databases into the data layer Context Retriever queries.
+            Data Integration: keeps business data fresh. Streams changes from source databases into the data layer Context Gateway queries.
         docsUrl: "/develop/ai/context-engine/data-integration"
     cachedResponse:
         label: "Cached response"
@@ -169,9 +165,9 @@ paths:
         description: |
             For a question that depends on earlier turns or what's known about the user, Agent Memory supplies that recall before the model call.
     context:
-        label: "Needs business data: retrieves via Context Retriever"
+        label: "Needs business data: retrieves via Context Gateway"
         description: |
-            For a question that depends on live business data, Context Retriever calls governed tools to fetch it before the model call. Data Integration keeps that data fresh.
+            For a question that depends on live business data, Context Gateway calls tools to fetch it before the model call. Data Integration keeps that data fresh.
 ```
 
 ## Two things Iris changes
@@ -183,5 +179,5 @@ paths:
 ## Next steps
 
 - [Agent Memory overview]({{< relref "/develop/ai/context-engine/agent-memory/overview" >}})
-- [Context Retriever concepts]({{< relref "/develop/ai/context-engine/context-retriever/concepts" >}})
-- Interactive demos for [Agent Memory]({{< relref "/develop/ai/context-engine/agent-memory/interactive-demo" >}}) and [Context Retriever]({{< relref "/develop/ai/context-engine/context-retriever/interactive-demo" >}}) that run in your browser
+- [Context Gateway concepts]({{< relref "/develop/ai/context-engine/context-gateway/concepts" >}})
+- An [interactive demo]({{< relref "/develop/ai/context-engine/agent-memory/interactive-demo" >}}) of Agent Memory that runs in your browser