Iris docs: GA read-through feedback, Playbooks/Lens/Identity Service self-managed guides, LangCache out

content/develop/ai/_index.md

@@ -68,7 +68,7 @@ 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 services, available fully managed on Redis Cloud or self-managed on your own infrastructure:
 
-- [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.
+- [LangCache]({{< relref "/develop/ai/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 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.
@@ -80,7 +80,7 @@ The [Context Engine]({{< relref "/develop/ai/context-engine" >}}) (Redis Iris) i
 1. [**Search with vectors**]({{< relref "develop/ai/search-and-query/vectors#search-with-vectors" >}}): Redis supports several advanced querying strategies with vector fields including k-nearest neighbor ([KNN]({{< relref "develop/ai/search-and-query/vectors#knn-vector-search" >}})), [vector range queries]({{< relref "develop/ai/search-and-query/vectors#vector-range-queries" >}}), and [metadata filters]({{< relref "develop/ai/search-and-query/vectors#filters" >}}).
 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 semantic caching**]({{< relref "/develop/ai/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. [**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.
@@ -137,7 +137,7 @@ AI agents can act autonomously to plan and execute tasks for the user.
 #### Context Engine
 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 LangCache]({{< relref "/develop/ai/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 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.

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

@@ -89,7 +89,7 @@ You can build each part of an agent's context layer yourself from Redis data str
 | Agent need | Build it yourself with Redis | Redis Iris service |
 |:--|:--|:--|
 | 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. |
+| Avoid repeat LLM calls | Vector search plus your own cache logic | [LangCache](/content/develop/ai/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 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. |
 
@@ -310,7 +310,7 @@ Production-ready agents include built-in reliability features:
 ### Performance optimization
 
 - Cache frequently accessed information
-- Cache LLM responses semantically, so paraphrased questions skip the model call. [LangCache](/content/develop/ai/context-engine/langcache/_index.md) does this as a managed service
+- Cache LLM responses semantically, so paraphrased questions skip the model call. [LangCache](/content/develop/ai/langcache/_index.md) does this as a managed service
 - Use efficient data structures for fast retrieval
 - Scale resources based on demand
 
@@ -343,7 +343,7 @@ Production-ready agents include built-in reliability features:
 
 ### Cost Optimization
 
-- LLM cost management: Use appropriate models for different tasks, and serve repeat questions from a semantic cache such as [LangCache](/content/develop/ai/context-engine/langcache/_index.md)
+- LLM cost management: Use appropriate models for different tasks, and serve repeat questions from a semantic cache such as [LangCache](/content/develop/ai/langcache/_index.md)
 - Redis memory optimization: Efficient data structures and TTL policies
 - API rate limiting: Prevent excessive external API calls
 - Resource monitoring: Track and optimize compute and storage costs
@@ -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) that run in your browser
+- Interactive demos for [Agent Memory](/content/develop/ai/context-engine/agent-memory/interactive-demo.md), [LangCache](/content/develop/ai/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

@@ -15,28 +15,30 @@ weight: 30
 
 Redis Iris eliminates the infrastructure burden of building context-aware AI agents.
 
+## Explore Redis Iris
+
 <div class="grid grid-cols-1 md:grid-cols-3 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-iris-64-duotone.png" title="SDK and CLI setup" description="Install redis-iris and connect it to Redis Cloud or a self-managed deployment" url="/develop/ai/context-engine/sdk-and-cli" >}}
   {{< 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-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-playbooks" >}}
-  {{< tile-card icon="images/dev/icons/icon-redis-iris-64-duotone.png" title="Lens" description="Debug agent answers by tracing the context behind each turn" url="/develop/ai/context-engine/lens" >}}
+  {{< 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="Intent Router" description="Classify requests into intents and route them to the right tool or agent" url="/develop/ai/context-engine/intent-router" >}}
-  {{< tile-card icon="images/dev/icons/icon-redis-iris-64-duotone.png" title="SDK and CLI setup" description="Install redis-iris and connect it to Redis Cloud or a self-managed deployment" url="/develop/ai/context-engine/sdk-and-cli" >}}
+  {{< tile-card icon="images/dev/icons/icon-redis-iris-64-duotone.png" title="Lens (beta)" description="Debug agent answers by tracing the context behind each turn" url="/develop/ai/context-engine/lens" >}}
   {{< tile-card icon="images/dev/icons/icon-redis-code-64-duotone.png" title="Agent Mesh (beta)" description="Share agent-to-agent task state across Python replicas and resume event delivery" url="/develop/ai/context-engine/agent-mesh" >}}
 </div>
 
 ## What is Redis Iris?
 
 Redis Iris is a production-ready context engine for AI agents that:
 
-- **Answers recurring questions without a model call**: Agent Playbooks return the answers and procedures your business published, matched to the agent's task by semantic similarity
 - **Adds persistent memory**: Agents remember past interactions and user preferences across sessions
-- **Routes each message**: Intent Router matches a message to the intents you defined and returns the target that handles it
+- **Answers recurring questions without a model call**: Agent Playbooks return the answers and procedures your business published, matched to the agent's task by semantic similarity
 - **Connects tools and business data**: Context Gateway gives agents one MCP endpoint for the tools of any MCP server and your Redis data
+- **Routes each message**: Intent Router matches a message to the intents you defined and returns the target that handles it
+- **Shows why an agent answered the way it did**: Lens (beta) traces each turn, from what each source returned to what entered the prompt
+- **Keeps long responses deliverable**: Agent Mesh (beta) keeps an agent's response stream resumable when the client disconnects
 - **Keeps data fresh**: Data Integration streams live changes from relational databases into Redis within seconds
-- **Shows why an agent answered the way it did**: Lens traces each turn, from what each source returned to what entered the prompt
-- **Keeps long responses deliverable**: Agent Mesh keeps an agent's response stream resumable when the client disconnects
 - **Deploys your way**: Check each service’s operator guide for its managed and self-managed deployment options.
 
 ## Why use Redis Iris?

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

@@ -41,9 +41,9 @@ Access Redis Agent Memory through the `redis-iris` Python SDK, the `iris` CLI, i
 * **Multi-session recall:** Retrieve relevant memories across conversations and filter results by owner, session, namespace, topic, or memory type.
 * **Custom memory types:** Define memory types for your business domain, with structured fields and instructions that control what Redis Agent Memory extracts.
 
-## Two-tier memory model
+## Concepts
 
-Redis Agent Memory provides two memory tiers:
+Redis Agent Memory provides two memory tiers. See the [overview](/content/develop/ai/context-engine/agent-memory/overview.md) for how they relate to sessions, owners, and namespaces.
 
 <div class="grid grid-cols-1 md:grid-cols-2 gap-6 my-6">
   <div class="p-5 border border-redis-pen-300 rounded-lg">
@@ -97,26 +97,26 @@ Agent: Good to know! I'll help you find some vegetarian-friendly restaurants in
   </div>
 </div>
 
-## Get started with Redis Agent Memory {#get-started}
+## Quick example {#get-started}
 
-Get started with Redis Agent Memory on Redis Cloud or deploy it with Redis Software.
+1. Set up an Agent Memory store:
+    - **Redis Cloud:** [Create an Agent Memory service on Redis Cloud](/content/operate/iris/agent-memory/create-service.md).
+    - **Self-managed:** [Deploy Agent Memory on Kubernetes](/content/operate/iris/agent-memory/self-managed/_index.md).
+1. Install the Redis Iris Python package, which includes the `iris` CLI:
 
-<div class="grid grid-cols-1 md:grid-cols-2 gap-6 my-8">
-  <div class="p-5 border border-redis-pen-300 rounded-lg">
-    <h3 class="text-redis-ink-900 font-semibold mb-3">Redis Cloud</h3>
-    <p>Create a managed Redis Agent Memory service and make your first requests.</p>
-    <p><a href="{{< relref "/operate/iris/agent-memory/create-service" >}}">Open the Redis Cloud setup guide</a></p>
-  </div>
-  <div class="p-5 border border-redis-pen-300 rounded-lg">
-    <h3 class="text-redis-ink-900 font-semibold mb-3">Redis Software</h3>
-    <p>Deploy Redis Agent Memory on Kubernetes with Redis Software.</p>
-    <p><a href="{{< relref "/operate/iris/agent-memory/self-managed" >}}">Open the self-managed deployment guide</a></p>
-  </div>
-</div>
+    ```bash
+    pip install 'redis-iris>=1.0.0'
+    ```
+
+1. With `IRIS_AGENT_MEMORY_URL`, `IRIS_AGENT_MEMORY_STORE_ID`, and `IRIS_AGENT_MEMORY_API_KEY` in your environment, record a conversation, then recall what Agent Memory extracted from it:
+
+{{< clients-example set="iris_agent_memory_quickstart" step="agent-memory-connect" description="" lang_filter="Python (Iris),cURL (Iris)" footer="hide" />}}
+
+{{< clients-example set="iris_agent_memory_quickstart" step="agent-memory-quickstart-save" description="" lang_filter="Python (Iris),cURL (Iris)" footer="hide" />}}
 
-### Follow the quickstart
+{{< clients-example set="iris_agent_memory_quickstart" step="agent-memory-quickstart-recall" description="" lang_filter="Python (Iris),CLI (Iris),cURL (Iris)" footer="hide" />}}
 
-After your service is ready, follow the [quickstart]({{< relref "/develop/ai/context-engine/agent-memory/quickstart" >}}). Choose Python, the Iris CLI, or cURL to explore the same travel planning scenario.
+Follow the [quickstart](/content/develop/ai/context-engine/agent-memory/quickstart.md) to explore the same travel planning scenario in Python, the Iris CLI, or cURL.
 
 ## Explore the documentation
 

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

@@ -8,7 +8,7 @@ description: Create resumable event streams with Agent Mesh through A2A or FastA
 hideListLinks: true
 linktitle: Agent Mesh
 title: Redis Agent Mesh
-weight: 40
+weight: 28
 bannerText: Redis Agent Mesh is currently in beta. Features and behavior are subject to change.
 bannerChildren: true
 ---

content/develop/ai/context-engine/agent-mesh/a2a-sdk.md

@@ -249,4 +249,4 @@ Storage and internal reader calls can raise `A2AStorageError`, `A2ACapacityExcee
 
 Task listing scans an owner's Task index and suits modest inventories per owner. It is not a search index or a consistent snapshot during concurrent writes.
 
-See the [SDK API reference](https://github.com/redis/redis-iris/blob/main/docs/core/agent-mesh-a2a.md) for signatures and the [beta limits](/content/develop/ai/context-engine/agent-mesh/_index.md#requirements-and-limits) for deployment qualification. Redis durability remains application-owned; replay does not guarantee exactly-once external effects.
+See the [beta limits](/content/develop/ai/context-engine/agent-mesh/_index.md#requirements-and-limits) for deployment qualification. Redis durability remains application-owned; replay does not guarantee exactly-once external effects.

content/develop/ai/context-engine/agent-mesh/fastapi-sse-sdk.md

@@ -543,7 +543,6 @@ The SDK retains all events until the run expires. A writer that exceeds
 The demo sets `REDIS_STREAM_DONE_TTL=3600`; replay still ends at the run's
 original lifetime. Application state in your own database has separate retention.
 
-See the [SDK reference](https://github.com/redis/redis-iris/blob/main/docs/core/agent-mesh-sse.md)
-for facade exports and upstream signatures, and the
+See the
 [beta limits](/content/develop/ai/context-engine/agent-mesh/_index.md#requirements-and-limits)
 for deployment qualification.

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

@@ -8,7 +8,7 @@ categories:
 description: Create playbooks of reusable skills and answers that agents can discover and load for a task.
 linkTitle: Agent Playbooks
 hideListLinks: true
-weight: 25
+weight: 15
 cascade:
   exampleClientOrder:
   - Python (Iris)
@@ -39,7 +39,9 @@ A FAQ gives an agent a validated answer, such as where a customer can find their
 - **Governed collaboration:** Separate reading, writing, publishing, and proposing with playbook access grants.
 - **Flexible integration:** Retrieve entries through Python, HTTP, or Model Context Protocol (MCP).
 
-## Choose what to publish
+## Concepts
+
+A playbook holds two kinds of entries:
 
 | Entry | Use it for                                                                         | What the agent receives                                              |
 | ----- | ---------------------------------------------------------------------------------- | -------------------------------------------------------------------- |
@@ -50,10 +52,24 @@ A publisher selects the version of each **entry** agents receive. Your applicati
 
 See [Redis Agent Playbooks concepts]({{< relref "/develop/ai/context-engine/agent-playbooks/concepts" >}}) for versions, publication, and review.
 
-## Get started
+## Quick example
+
+1. Set up a playbook:
+    - **Redis Cloud:** [Create a playbook](/content/operate/iris/agent-playbook/create-playbook.md).
+    - **Self-managed:** [Deploy Agent Playbooks on Kubernetes](/content/operate/iris/agent-playbook/self-managed/_index.md).
+1. Install the Redis Iris Python package, which includes the `iris` CLI:
+
+    ```bash
+    pip install 'redis-iris>=1.0.0'
+    ```
+
+1. With `IRIS_AGENT_PLAYBOOKS_URL`, `IRIS_AGENT_PLAYBOOKS_PLAYBOOK_ID`, and `IRIS_AGENT_PLAYBOOKS_API_KEY` in your environment, find the published entries that match an agent's task:
+
+{{< clients-example set="iris_playbook_quickstart" step="playbook-connect" description="" lang_filter="Python (Iris),CLI (Iris),cURL (Iris)" footer="hide" />}}
+
+{{< clients-example set="iris_playbook_quickstart" step="playbook-quickstart-discover" description="" lang_filter="Python (Iris),CLI (Iris),cURL (Iris)" footer="hide" />}}
 
-1. [Create a playbook]({{< relref "/operate/iris/agent-playbook/create-playbook" >}}) on Redis Cloud.
-2. [Publish and retrieve your first FAQ]({{< relref "/develop/ai/context-engine/agent-playbooks/quickstart" >}}).
+Follow the [quickstart](/content/develop/ai/context-engine/agent-playbooks/quickstart.md) to publish your first FAQ and retrieve it.
 
 ## Manage playbook entries