diff --git a/hindsight-docs/blog/2026-02-09-resolving-memory-conflicts.md b/hindsight-docs/blog/2026-02-09-resolving-memory-conflicts.md
index 30ef48c9..9a1023c8 100644
--- a/hindsight-docs/blog/2026-02-09-resolving-memory-conflicts.md
+++ b/hindsight-docs/blog/2026-02-09-resolving-memory-conflicts.md
@@ -4,6 +4,7 @@ description: Learn how Hindsight handles contradictory information by tracking t
 authors: [hindsight]
 image: /img/blog/2026-02-09/consolidation-pipeline.png
 date: 2026-02-09
+hide_table_of_contents: true
 ---
 
 # How We Solved Memory Conflicts in Hindsight
diff --git a/hindsight-docs/blog/2026-02-13-version-0-4-11.md b/hindsight-docs/blog/2026-02-13-version-0-4-11.md
index daf10b08..9c07ba25 100644
--- a/hindsight-docs/blog/2026-02-13-version-0-4-11.md
+++ b/hindsight-docs/blog/2026-02-13-version-0-4-11.md
@@ -3,6 +3,7 @@ title: "What's new in Hindsight 0.4.11"
 description: New features and improvements in Hindsight 0.4.11
 authors: [hindsight]
 date: 2026-02-13
+hide_table_of_contents: true
 ---
 
 Hindsight 0.4.11 focuses on production-ready deployments with improved flexibility and observability.
diff --git a/hindsight-docs/blog/2026-02-18-version-0-4-12.md b/hindsight-docs/blog/2026-02-18-version-0-4-12.md
index 6b99181d..dd6128a9 100644
--- a/hindsight-docs/blog/2026-02-18-version-0-4-12.md
+++ b/hindsight-docs/blog/2026-02-18-version-0-4-12.md
@@ -3,6 +3,7 @@ title: "What's new in Hindsight 0.4.12"
 description: New features and improvements in Hindsight 0.4.12
 authors: [hindsight]
 date: 2026-02-18
+hide_table_of_contents: true
 ---
 
 Hindsight 0.4.12 expands what you can ingest, cuts ingestion costs, and broadens where you can run it.
diff --git a/hindsight-docs/blog/2026-02-26-chat.md b/hindsight-docs/blog/2026-02-26-chat.md
index fa38a6d8..598cf618 100644
--- a/hindsight-docs/blog/2026-02-26-chat.md
+++ b/hindsight-docs/blog/2026-02-26-chat.md
@@ -1,11 +1,13 @@
 ---
-title: "I Gave My Vercel Chat SDK Bot a Memory. Now It Remembers Users Across Slack and Discord."
+title: "Your Vercel Chat SDK bot forgets everything. Hindsight fixes that."
 authors: [hindsight]
 date: 2026-02-26
 tags: [chat-sdk, slack, discord, typescript, memory]
+image: /img/blog/vercel-chat.png
 ---
 
-# I Gave My Vercel Chat SDK Bot a Memory. Now It Remembers Users Across Slack and Discord.
+
+![Consolidation Pipeline](/img/blog/vercel-chat.png)
 
 ## TL;DR
 
diff --git a/hindsight-docs/docs/sdks/integrations/chat.md b/hindsight-docs/docs/sdks/integrations/chat.md
index 8b024f52..a35b3b18 100644
--- a/hindsight-docs/docs/sdks/integrations/chat.md
+++ b/hindsight-docs/docs/sdks/integrations/chat.md
@@ -4,7 +4,7 @@ sidebar_position: 5
 
 # Vercel Chat SDK
 
-The `@vectorize-io/hindsight-chat` package gives your [Vercel Chat SDK](https://github.com/vercel/chat) bots persistent, per-user memory with a single handler wrapper. Works with Slack, Discord, Teams, Google Chat, GitHub, and Linear.
+We built `@vectorize-io/hindsight-chat` to give [Vercel Chat SDK](https://github.com/vercel/chat) bots persistent, per-user memory with a single handler wrapper. The integration works across Slack, Discord, Teams, Google Chat, GitHub, and Linear — no custom plumbing required.
 
 ## Installation
 
@@ -60,7 +60,7 @@ chat.onNewMention(
 
 ### `withHindsightChat(options, handler)`
 
-Returns a standard Chat SDK handler `(thread, message) => Promise<void>`.
+`withHindsightChat` wraps your existing Chat SDK handler and injects memory context automatically. It returns a standard handler `(thread, message) => Promise<void>` so it drops in without changing your handler signature.
 
 #### Options
 
@@ -80,7 +80,7 @@ Returns a standard Chat SDK handler `(thread, message) => Promise<void>`.
 
 ### Context (`ctx`)
 
-The third argument passed to your handler:
+We inject a third `ctx` argument into your handler that exposes the full Hindsight memory API scoped to the current user's bank:
 
 | Property/Method | Description |
 |----------------|-------------|
@@ -160,4 +160,4 @@ chat.onNewMention(
 
 ## Error Handling
 
-Memory failures never break your bot. Auto-recall and auto-retain errors are logged as warnings and the handler continues with empty memories. Manual `ctx.retain()`, `ctx.recall()`, and `ctx.reflect()` calls propagate errors normally so you can handle them as needed.
+We designed the integration so that memory failures never break your bot. Auto-recall and auto-retain errors are caught internally, logged as warnings, and the handler continues with empty memories. Manual `ctx.retain()`, `ctx.recall()`, and `ctx.reflect()` calls propagate errors normally so you can handle them as needed.
diff --git a/hindsight-docs/docusaurus.config.ts b/hindsight-docs/docusaurus.config.ts
index 3185b103..9daef52e 100644
--- a/hindsight-docs/docusaurus.config.ts
+++ b/hindsight-docs/docusaurus.config.ts
@@ -117,8 +117,7 @@ const config: Config = {
           blogTitle: 'Hindsight Blog',
           blogDescription: 'Updates, insights, and deep dives into agent memory',
           postsPerPage: 10,
-          blogSidebarTitle: 'Recent posts',
-          blogSidebarCount: 'ALL',
+          blogSidebarCount: 0,
         },
         theme: {
           customCss: './src/css/custom.css',
@@ -235,8 +234,7 @@ const config: Config = {
           className: 'navbar-item-resources',
           items: [
             {
-              type: 'doc',
-              docId: 'cookbook/index',
+              to: '/cookbook',
               label: 'Cookbook',
             },
             {
@@ -247,22 +245,22 @@ const config: Config = {
               to: '/api-reference',
               label: 'API Reference',
             },
+            {
+              href: 'https://join.slack.com/t/hindsight-space/shared_invite/zt-3nhbm4w29-LeSJ5Ixi6j8PdiYOCPlOgg',
+              label: 'Community',
+            },
           ],
         },
         {
           href: 'https://ui.hindsight.vectorize.io/signup',
           position: 'right',
-          label: 'Hindsight Cloud',
+          label: 'Cloud',
           className: 'navbar-item-cloud',
         },
         {
           type: 'docsVersionDropdown',
           position: 'right',
-        },
-        {
-          href: 'https://join.slack.com/t/hindsight-space/shared_invite/zt-3nhbm4w29-LeSJ5Ixi6j8PdiYOCPlOgg',
-          position: 'right',
-          label: 'Community',
+          className: 'navbar-item-version',
         },
         {
           href: 'https://github.com/vectorize-io/hindsight',
diff --git a/hindsight-docs/sidebars.ts b/hindsight-docs/sidebars.ts
index b0b16fba..817b26bf 100644
--- a/hindsight-docs/sidebars.ts
+++ b/hindsight-docs/sidebars.ts
@@ -234,13 +234,6 @@ const sidebars: SidebarsConfig = {
       ],
     },
   ],
-  cookbookSidebar: [
-    {
-      type: 'doc',
-      id: 'cookbook/index',
-      label: 'Cookbook',
-    },
-  ],
 };
 
 export default sidebars;
diff --git a/hindsight-docs/src/components/CookbookGrid.module.css b/hindsight-docs/src/components/CookbookGrid.module.css
new file mode 100644
index 00000000..7a09eac1
--- /dev/null
+++ b/hindsight-docs/src/components/CookbookGrid.module.css
@@ -0,0 +1,115 @@
+.grid {
+  display: grid;
+  grid-template-columns: repeat(3, 1fr);
+  gap: 1.25rem;
+  margin-bottom: 3rem;
+}
+
+@media (max-width: 996px) {
+  .grid {
+    grid-template-columns: repeat(2, 1fr);
+  }
+}
+
+@media (max-width: 640px) {
+  .grid {
+    grid-template-columns: 1fr;
+  }
+}
+
+/* Card */
+.card {
+  display: flex;
+  flex-direction: column;
+  border-radius: 10px;
+  border: 1px solid var(--ifm-color-emphasis-200);
+  border-top: 3px solid transparent;
+  border-image: linear-gradient(90deg, #0074d9, #009296) 1;
+  background: var(--ifm-background-surface-color);
+  text-decoration: none !important;
+  color: inherit;
+  transition: box-shadow 0.2s ease, transform 0.2s ease;
+  overflow: hidden;
+}
+
+[data-theme='dark'] .card {
+  background: #1c1c1e;
+  border-color: rgba(255, 255, 255, 0.07);
+}
+
+.card:hover {
+  transform: translateY(-2px);
+  box-shadow: 0 6px 24px rgba(0, 116, 217, 0.12);
+  text-decoration: none !important;
+  color: inherit;
+}
+
+/* Body */
+.cardBody {
+  display: flex;
+  flex-direction: column;
+  flex: 1;
+  padding: 1.25rem 1.25rem 1.25rem;
+  gap: 0.4rem;
+}
+
+.cardTitle {
+  font-size: 1rem;
+  font-weight: 700;
+  line-height: 1.4;
+  margin: 0;
+  color: var(--ifm-heading-color);
+  letter-spacing: -0.01em;
+  border-bottom: none !important;
+  border-image: none !important;
+}
+
+.cardDescription {
+  font-size: 0.82rem;
+  font-family: 'JetBrains Mono', 'Fira Code', 'SF Mono', Monaco, Consolas, monospace;
+  color: var(--ifm-color-emphasis-700);
+  line-height: 1.6;
+  margin: 0;
+  flex: 1;
+  display: -webkit-box;
+  -webkit-line-clamp: 2;
+  -webkit-box-orient: vertical;
+  overflow: hidden;
+}
+
+.cardFooter {
+  display: flex;
+  align-items: center;
+  flex-wrap: wrap;
+  gap: 0.4rem;
+  margin-top: 0.75rem;
+  padding-top: 0.65rem;
+  border-top: 1px solid var(--ifm-color-emphasis-100);
+}
+
+[data-theme='dark'] .cardFooter {
+  border-top-color: rgba(255, 255, 255, 0.06);
+}
+
+.cardTopic {
+  font-size: 0.72rem;
+  font-weight: 600;
+  color: var(--ifm-color-primary);
+  text-transform: uppercase;
+  letter-spacing: 0.05em;
+}
+
+.cardSdk {
+  font-size: 0.72rem;
+  font-weight: 500;
+  font-family: 'JetBrains Mono', 'Fira Code', monospace;
+  color: var(--ifm-color-emphasis-700);
+  background: var(--ifm-color-emphasis-100);
+  padding: 0.1rem 0.45rem;
+  border-radius: 4px;
+}
+
+[data-theme='dark'] .cardSdk {
+  background: rgba(255, 255, 255, 0.07);
+  color: var(--ifm-color-emphasis-600);
+}
diff --git a/hindsight-docs/src/components/CookbookGrid.tsx b/hindsight-docs/src/components/CookbookGrid.tsx
new file mode 100644
index 00000000..81a7a6f7
--- /dev/null
+++ b/hindsight-docs/src/components/CookbookGrid.tsx
@@ -0,0 +1,44 @@
+import React from 'react';
+import Link from '@docusaurus/Link';
+import styles from './CookbookGrid.module.css';
+
+export interface CookbookCard {
+  title: string;
+  href: string;
+  description?: string;
+  tags?: {
+    sdk?: string;
+    topic?: string;
+  };
+}
+
+interface CookbookGridProps {
+  items: CookbookCard[];
+}
+
+function Card({title, href, description, tags}: CookbookCard) {
+  return (
+    <Link to={href} className={styles.card}>
+      <div className={styles.cardBody}>
+        <h3 className={styles.cardTitle}>{title}</h3>
+        {description && <p className={styles.cardDescription}>{description}</p>}
+        {(tags?.topic || tags?.sdk) && (
+          <div className={styles.cardFooter}>
+            {tags.topic && <span className={styles.cardTopic}>{tags.topic}</span>}
+            {tags.sdk && <span className={styles.cardSdk}>{tags.sdk}</span>}
+          </div>
+        )}
+      </div>
+    </Link>
+  );
+}
+
+export default function CookbookGrid({items}: CookbookGridProps) {
+  return (
+    <div className={styles.grid}>
+      {items.map((item) => (
+        <Card key={item.href} {...item} />
+      ))}
+    </div>
+  );
+}
diff --git a/hindsight-docs/src/components/RecipeCarousel.module.css b/hindsight-docs/src/components/RecipeCarousel.module.css
deleted file mode 100644
index 32b5b9d4..00000000
--- a/hindsight-docs/src/components/RecipeCarousel.module.css
+++ /dev/null
@@ -1,181 +0,0 @@
-.carouselSection {
-  margin: 3rem 0;
-}
-
-.sectionTitle {
-  font-size: 1.75rem;
-  margin-bottom: 1.5rem;
-  font-weight: 600;
-  color: var(--ifm-font-color-base);
-}
-
-.carousel {
-  /* Grid layout */
-}
-
-.carouselTrack {
-  display: grid;
-  grid-template-columns: repeat(auto-fill, minmax(320px, 1fr));
-  gap: 1.25rem;
-}
-
-.card {
-  padding: 1.5rem;
-  border-radius: 12px;
-  border: 2px solid var(--card-border, var(--ifm-color-emphasis-300));
-  text-decoration: none;
-  color: inherit;
-  display: flex;
-  flex-direction: column;
-  justify-content: space-between;
-  gap: 1rem;
-  transition: all 0.2s ease;
-  position: relative;
-  overflow: hidden;
-  min-height: 200px;
-}
-
-/* Alternating style: Odd cards = white/solid, Even cards = colored gradient */
-
-/* ODD CARDS - White/Solid background */
-.card:nth-child(odd) {
-  background: #ffffff;
-}
-
-.card:nth-child(odd):hover {
-  box-shadow: 0 8px 24px rgba(0, 0, 0, 0.1);
-  transform: translateY(-2px);
-}
-
-/* EVEN CARDS - Colored gradients (cycle through 4 colors) */
-.card:nth-child(4n+2) {
-  background: linear-gradient(135deg, rgba(0, 116, 217, 0.08) 0%, rgba(0, 146, 150, 0.08) 100%);
-}
-
-.card:nth-child(4n+4) {
-  background: linear-gradient(135deg, rgba(99, 102, 241, 0.08) 0%, rgba(168, 85, 247, 0.08) 100%);
-}
-
-.card:nth-child(4n+6) {
-  background: linear-gradient(135deg, rgba(16, 185, 129, 0.08) 0%, rgba(5, 150, 105, 0.08) 100%);
-}
-
-.card:nth-child(4n+8) {
-  background: linear-gradient(135deg, rgba(245, 158, 11, 0.08) 0%, rgba(217, 119, 6, 0.08) 100%);
-}
-
-.card:nth-child(even):hover {
-  box-shadow: 0 8px 24px rgba(0, 0, 0, 0.15);
-  transform: translateY(-2px);
-}
-
-/* ============================================
-   DARK MODE
-   ============================================ */
-
-/* ODD CARDS - Dark solid background */
-[data-theme='dark'] .card:nth-child(odd) {
-  background: var(--ifm-background-surface-color);
-}
-
-[data-theme='dark'] .card {
-  border-color: var(--card-border-dark, var(--ifm-color-emphasis-300));
-}
-
-[data-theme='dark'] .card:nth-child(odd):hover {
-  box-shadow: 0 8px 24px rgba(0, 0, 0, 0.6);
-}
-
-/* EVEN CARDS - Colored gradients (more vibrant in dark mode) */
-[data-theme='dark'] .card:nth-child(4n+2) {
-  background: linear-gradient(135deg, rgba(59, 130, 246, 0.15) 0%, rgba(20, 184, 166, 0.15) 100%);
-}
-
-[data-theme='dark'] .card:nth-child(4n+4) {
-  background: linear-gradient(135deg, rgba(139, 92, 246, 0.15) 0%, rgba(217, 70, 239, 0.15) 100%);
-}
-
-[data-theme='dark'] .card:nth-child(4n+6) {
-  background: linear-gradient(135deg, rgba(16, 185, 129, 0.15) 0%, rgba(132, 204, 22, 0.15) 100%);
-}
-
-[data-theme='dark'] .card:nth-child(4n+8) {
-  background: linear-gradient(135deg, rgba(251, 146, 60, 0.15) 0%, rgba(239, 68, 68, 0.15) 100%);
-}
-
-[data-theme='dark'] .card:nth-child(even):hover {
-  box-shadow: 0 8px 24px rgba(0, 0, 0, 0.8);
-}
-
-.cardContent {
-  display: flex;
-  flex-direction: column;
-  gap: 0.65rem;
-  flex-grow: 1;
-}
-
-.cardFooter {
-  display: flex;
-  align-items: center;
-  justify-content: space-between;
-  margin-top: auto;
-}
-
-.cardTitle {
-  font-size: 1.05rem;
-  font-weight: 600;
-  color: var(--ifm-font-color-base);
-  line-height: 1.4;
-}
-
-.cardDescription {
-  font-size: 0.9rem;
-  color: var(--ifm-color-emphasis-800);
-  margin: 0;
-  line-height: 1.6;
-}
-
-[data-theme='dark'] .cardDescription {
-  color: var(--ifm-color-emphasis-600);
-}
-
-.cardTags {
-  display: flex;
-  flex-wrap: nowrap;
-  gap: 0.75rem;
-  align-items: center;
-  flex: 1;
-}
-
-.tag {
-  font-size: 0.72rem;
-  padding: 0.35rem 0.75rem;
-  border-radius: 6px;
-  background: var(--tag-bg);
-  color: var(--tag-text);
-  font-weight: 600;
-  border: 1px solid var(--tag-border);
-  white-space: nowrap;
-  flex-shrink: 0;
-}
-
-[data-theme='dark'] .tag {
-  background: var(--tag-bg-dark);
-  color: var(--tag-text-dark);
-  border-color: var(--tag-border-dark);
-}
-
-.cardLink {
-  color: var(--ifm-color-primary);
-  font-weight: 600;
-  flex-shrink: 0;
-  font-size: 1.25rem;
-  line-height: 1;
-  opacity: 0.7;
-  transition: opacity 0.2s ease;
-  margin-left: 1rem;
-}
-
-.card:hover .cardLink {
-  opacity: 1;
-}
diff --git a/hindsight-docs/src/components/RecipeCarousel.tsx b/hindsight-docs/src/components/RecipeCarousel.tsx
deleted file mode 100644
index 4044cd7c..00000000
--- a/hindsight-docs/src/components/RecipeCarousel.tsx
+++ /dev/null
@@ -1,171 +0,0 @@
-import React from 'react';
-import Link from '@docusaurus/Link';
-import styles from './RecipeCarousel.module.css';
-
-export interface RecipeCard {
-  title: string;
-  href: string;
-  tags?: {
-    sdk?: string;       // Package name: "hindsight-python", "hindsight-nodejs", "litellm-python", "ai-sdk", etc.
-    topic?: string;     // "Learning", "Quick Start", "Recommendation", "Chat"
-  };
-  description?: string;
-}
-
-interface RecipeCarouselProps {
-  title: string;
-  items: RecipeCard[];
-}
-
-// Language icons using inline SVG data URIs or image paths
-const LANGUAGE_ICONS: Record<string, string> = {
-  Python: "data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 24 24'%3E%3Cpath fill='%233776ab' d='M14.25.18l.9.2.73.26.59.3.45.32.34.34.25.34.16.33.1.3.04.26.02.2-.01.13V8.5l-.05.63-.13.55-.21.46-.26.38-.3.31-.33.25-.35.19-.35.14-.33.1-.3.07-.26.04-.21.02H8.77l-.69.05-.59.14-.5.22-.41.27-.33.32-.27.35-.2.36-.15.37-.1.35-.07.32-.04.27-.02.21v3.06H3.17l-.21-.03-.28-.07-.32-.12-.35-.18-.36-.26-.36-.36-.35-.46-.32-.59-.28-.73-.21-.88-.14-1.05-.05-1.23.06-1.22.16-1.04.24-.87.32-.71.36-.57.4-.44.42-.33.42-.24.4-.16.36-.1.32-.05.24-.01h.16l.06.01h8.16v-.83H6.18l-.01-2.75-.02-.37.05-.34.11-.31.17-.28.25-.26.31-.23.38-.2.44-.18.51-.15.58-.12.64-.1.71-.06.77-.04.84-.02 1.27.05zm-6.3 1.98l-.23.33-.08.41.08.41.23.34.33.22.41.09.41-.09.33-.22.23-.34.08-.41-.08-.41-.23-.33-.33-.22-.41-.09-.41.09zm13.09 3.95l.28.06.32.12.35.18.36.27.36.35.35.47.32.59.28.73.21.88.14 1.04.05 1.23-.06 1.23-.16 1.04-.24.86-.32.71-.36.57-.4.45-.42.33-.42.24-.4.16-.36.09-.32.05-.24.02-.16-.01h-8.22v.82h5.84l.01 2.76.02.36-.05.34-.11.31-.17.29-.25.25-.31.24-.38.2-.44.17-.51.15-.58.13-.64.09-.71.07-.77.04-.84.01-1.27-.04-1.07-.14-.9-.2-.73-.25-.59-.3-.45-.33-.34-.34-.25-.34-.16-.33-.1-.3-.04-.25-.02-.2.01-.13v-5.34l.05-.64.13-.54.21-.46.26-.38.3-.32.33-.24.35-.2.35-.14.33-.1.3-.06.26-.04.21-.02.13-.01h5.84l.69-.05.59-.14.5-.21.41-.28.33-.32.27-.35.2-.36.15-.36.1-.35.07-.32.04-.28.02-.21V6.07h2.09l.14.01zm-6.47 14.25l-.23.33-.08.41.08.41.23.33.33.23.41.08.41-.08.33-.23.23-.33.08-.41-.08-.41-.23-.33-.33-.23-.41-.08-.41.08z'/%3E%3C/svg%3E",
-  'Node.js': "data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 24 24'%3E%3Cpath fill='%23339933' d='M11.998 0c-.27 0-.54.07-.772.202L2.428 5.05C1.983 5.321 1.7 5.802 1.7 6.32v11.36c0 .518.283 1 .728 1.27l2.375 1.371c.64.321 1.094.32 1.468.32 1.203 0 1.89-.73 1.89-1.996V7.362c0-.146-.117-.264-.262-.264H7.11c-.146 0-.263.118-.263.264v11.283c0 .876-.906 1.753-2.38 1.01L2.103 18.28c-.046-.026-.073-.08-.073-.132V6.754c0-.051.027-.106.073-.132l8.798-5.08c.044-.026.102-.026.145 0l8.798 5.08c.046.026.074.081.074.132v11.394c0 .051-.028.106-.074.132l-8.798 5.08c-.043.026-.101.026-.144 0l-2.248-1.336c-.064-.037-.144-.04-.21-.011-.55.307-.658.373-1.177.45-.12.019-.301.06.073.276l2.93 1.738c.23.133.49.202.772.202s.542-.069.772-.202l8.798-5.08c.476-.27.772-.772.772-1.27V6.32c0-.518-.296-.999-.772-1.27L12.77.202C12.538.07 12.268 0 11.998 0zm2.657 6.343c-2.432 0-2.945.953-2.945 2.146 0 .145.117.263.263.263h.788c.131 0 .24-.095.261-.221.177-.718.708-1.08 1.633-1.08.738 0 1.177.168 1.177.803 0 .325-.128.567-.678.73l-1.69.419c-.899.223-1.47.756-1.47 1.636 0 1.076.905 1.715 2.423 1.715 1.704 0 2.55-.593 2.656-1.866.006-.073-.018-.144-.066-.197-.047-.053-.114-.083-.186-.083h-.791c-.123 0-.23.089-.258.207-.286.644-.98.849-1.817.849-.65 0-1.16-.207-1.16-.725 0-.325.144-.424.903-.609l1.476-.367c.898-.223 1.462-.72 1.462-1.613 0-1.12-.937-1.787-2.574-1.787z'/%3E%3C/svg%3E",
-  TypeScript: "data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 24 24'%3E%3Cpath fill='%233178c6' d='M1.125 0C.502 0 0 .502 0 1.125v21.75C0 23.498.502 24 1.125 24h21.75c.623 0 1.125-.502 1.125-1.125V1.125C24 .502 23.498 0 22.875 0zm17.363 9.75c.612 0 1.154.037 1.627.111.472.074.914.187 1.323.34v2.458c-.444-.223-.935-.39-1.473-.501-.539-.111-1.09-.167-1.655-.167-.562 0-1.011.062-1.349.187-.338.124-.507.335-.507.632 0 .234.095.42.285.558.19.138.503.275.94.411l1.503.434c.915.262 1.577.609 1.984 1.04.408.432.612.998.612 1.699 0 .915-.35 1.638-1.05 2.168-.7.53-1.667.795-2.9.795-.591 0-1.178-.051-1.76-.153-.582-.102-1.13-.258-1.645-.468v-2.503c.544.287 1.09.507 1.637.66.546.153 1.084.23 1.613.23.609 0 1.071-.073 1.386-.219.315-.146.472-.369.472-.669 0-.262-.106-.471-.318-.628-.212-.157-.551-.306-1.017-.447l-1.42-.395c-.877-.234-1.515-.563-1.916-.985-.4-.422-.6-.98-.6-1.673 0-.857.348-1.545 1.044-2.063.696-.518 1.633-.777 2.811-.777zm-13.6 1.77H8.45l-.031 4.18c0 .754-.13 1.314-.39 1.68-.26.367-.65.55-1.168.55-.286 0-.56-.037-.822-.11-.262-.074-.506-.173-.733-.297v1.818c.319.111.665.187 1.038.228.373.04.736.06 1.089.06.924 0 1.623-.247 2.097-.74.474-.494.711-1.254.711-2.28V11.52z'/%3E%3C/svg%3E",
-  Go: "/img/icons/golang.png",
-};
-
-// Get language icon based on package name
-function getPackageIcon(packageName: string): string | undefined {
-  // If it starts with @vectorize-io, it's Node.js
-  if (packageName.startsWith('@vectorize-io')) {
-    return LANGUAGE_ICONS['Node.js'];
-  }
-  // If it ends with -go or contains go-, it's Go
-  if (packageName.endsWith('-go') || packageName.includes('go-')) {
-    return LANGUAGE_ICONS.Go;
-  }
-  // Otherwise assume Python
-  return LANGUAGE_ICONS.Python;
-}
-
-// Generate color scheme from tag text using hash
-function getTagColor(tag: string): any {
-  // Hash function to get consistent color from string
-  let hash = 0;
-  for (let i = 0; i < tag.length; i++) {
-    hash = tag.charCodeAt(i) + ((hash << 5) - hash);
-  }
-
-  // 12 vibrant color palettes with better contrast
-  const palettes = [
-    { h: 340, s: 75, l: 50 }, // Pink
-    { h: 291, s: 65, l: 45 }, // Purple
-    { h: 262, s: 55, l: 48 }, // Deep Purple
-    { h: 231, s: 50, l: 50 }, // Indigo
-    { h: 207, s: 80, l: 50 }, // Blue
-    { h: 199, s: 85, l: 45 }, // Light Blue
-    { h: 187, s: 70, l: 45 }, // Cyan
-    { h: 174, s: 70, l: 50 }, // Teal
-    { h: 142, s: 65, l: 45 }, // Green
-    { h: 88, s: 55, l: 48 },  // Light Green
-    { h: 38, s: 85, l: 50 },  // Orange
-    { h: 14, s: 85, l: 50 },  // Deep Orange
-  ];
-
-  const palette = palettes[Math.abs(hash) % palettes.length];
-  const { h, s, l } = palette;
-
-  return {
-    // Light mode: subtle background, darker text for contrast
-    bg: `hsla(${h}, ${s}%, ${l}%, 0.15)`,
-    text: `hsl(${h}, ${Math.min(s + 10, 90)}%, ${Math.max(l - 25, 25)}%)`,
-    border: `hsla(${h}, ${s}%, ${l}%, 0.35)`,
-    // Dark mode: more vibrant background, lighter text
-    bgDark: `hsla(${h}, ${Math.max(s - 10, 50)}%, ${l}%, 0.25)`,
-    textDark: `hsl(${h}, ${Math.max(s - 15, 40)}%, ${Math.min(l + 35, 85)}%)`,
-    borderDark: `hsla(${h}, ${s}%, ${l}%, 0.4)`,
-  };
-}
-
-export default function RecipeCarousel({ title, items }: RecipeCarouselProps): React.ReactElement {
-  // Generate ID from title for anchor links
-  const sectionId = title.toLowerCase().replace(/\s+/g, '-');
-
-  return (
-    <div className={styles.carouselSection} id={sectionId}>
-      <h2 className={styles.sectionTitle}>{title}</h2>
-      <div className={styles.carousel}>
-        <div className={styles.carouselTrack}>
-          {items.map((item, index) => {
-            // Get topic color for card border
-            const topicColors = item.tags?.topic ? getTagColor(item.tags.topic) : null;
-
-            return (
-              <Link
-                key={index}
-                to={item.href}
-                className={styles.card}
-                style={{
-                  '--card-border': topicColors?.border,
-                  '--card-border-dark': topicColors?.borderDark,
-                } as React.CSSProperties}
-              >
-                <div className={styles.cardContent}>
-                  <span className={styles.cardTitle}>{item.title}</span>
-                  {item.description && (
-                    <p className={styles.cardDescription}>{item.description}</p>
-                  )}
-                </div>
-                <div className={styles.cardFooter}>
-                  {item.tags && (
-                    <div className={styles.cardTags}>
-                      {item.tags.sdk && (() => {
-                        const colors = getTagColor(item.tags.sdk);
-                        const icon = getPackageIcon(item.tags.sdk);
-                        return (
-                          <span
-                            className={styles.tag}
-                            style={{
-                              display: 'flex',
-                              alignItems: 'center',
-                              gap: '0.4rem',
-                              '--tag-bg': colors.bg,
-                              '--tag-text': colors.text,
-                              '--tag-border': colors.border,
-                              '--tag-bg-dark': colors.bgDark,
-                              '--tag-text-dark': colors.textDark,
-                              '--tag-border-dark': colors.borderDark,
-                            } as React.CSSProperties}
-                          >
-                            {icon && (
-                              <img
-                                src={icon}
-                                alt=""
-                                style={{ width: '13px', height: '13px', flexShrink: 0 }}
-                              />
-                            )}
-                            {item.tags.sdk}
-                          </span>
-                        );
-                      })()}
-                      {item.tags.topic && (() => {
-                        const colors = getTagColor(item.tags.topic);
-                        return (
-                          <span
-                            className={styles.tag}
-                            style={{
-                              '--tag-bg': colors.bg,
-                              '--tag-text': colors.text,
-                              '--tag-border': colors.border,
-                              '--tag-bg-dark': colors.bgDark,
-                              '--tag-text-dark': colors.textDark,
-                              '--tag-border-dark': colors.borderDark,
-                            } as React.CSSProperties}
-                          >
-                            {item.tags.topic}
-                          </span>
-                        );
-                      })()}
-                    </div>
-                  )}
-                  <span className={styles.cardLink}>→</span>
-                </div>
-              </Link>
-            );
-          })}
-        </div>
-      </div>
-    </div>
-  );
-}
diff --git a/hindsight-docs/src/components/SkillBanner.module.css b/hindsight-docs/src/components/SkillBanner.module.css
index b92988cf..b6e519be 100644
--- a/hindsight-docs/src/components/SkillBanner.module.css
+++ b/hindsight-docs/src/components/SkillBanner.module.css
@@ -92,6 +92,43 @@
   display: block;
 }
 
+.titleRow {
+  display: flex;
+  align-items: center;
+  gap: 8px;
+}
+
+.copyPageButton {
+  display: flex;
+  align-items: center;
+  gap: 4px;
+  background: rgba(255, 255, 255, 0.12);
+  border: 1px solid rgba(255, 255, 255, 0.2);
+  border-radius: 4px;
+  padding: 3px 7px;
+  color: rgba(255, 255, 255, 0.85);
+  font-size: 10px;
+  font-weight: 600;
+  cursor: pointer;
+  white-space: nowrap;
+  flex-shrink: 0;
+  transition: all 0.2s ease;
+}
+
+.copyPageButton:hover {
+  background: rgba(255, 255, 255, 0.22);
+  color: white;
+}
+
+.copyPageButton:active {
+  transform: scale(0.95);
+}
+
+.copyPageButton.copyPageCopied {
+  background: rgba(255, 255, 255, 0.22);
+  color: white;
+}
+
 /* Dark mode - make it stand out even more */
 html[data-theme='dark'] .banner {
   box-shadow: 0 4px 12px rgba(0, 116, 217, 0.2),
diff --git a/hindsight-docs/src/components/SkillBanner.tsx b/hindsight-docs/src/components/SkillBanner.tsx
index 004ce1f7..f7fae2e2 100644
--- a/hindsight-docs/src/components/SkillBanner.tsx
+++ b/hindsight-docs/src/components/SkillBanner.tsx
@@ -1,39 +1,138 @@
-import React, { useState } from 'react';
+import React, { useState, useCallback } from 'react';
 import styles from './SkillBanner.module.css';
 
+function extractMarkdown(element: Element): string {
+  let text = '';
+
+  const processNode = (node: Node): string => {
+    if (node.nodeType === Node.TEXT_NODE) {
+      return node.textContent || '';
+    }
+    if (node.nodeType === Node.ELEMENT_NODE) {
+      const el = node as Element;
+      const tagName = el.tagName.toLowerCase();
+      const children = Array.from(el.childNodes).map(processNode).join('');
+      switch (tagName) {
+        case 'h1': return `# ${children}\n\n`;
+        case 'h2': return `## ${children}\n\n`;
+        case 'h3': return `### ${children}\n\n`;
+        case 'h4': return `#### ${children}\n\n`;
+        case 'h5': return `##### ${children}\n\n`;
+        case 'h6': return `###### ${children}\n\n`;
+        case 'p': return `${children}\n\n`;
+        case 'ul': return `${children}\n`;
+        case 'ol': return `${children}\n`;
+        case 'li': {
+          const parent = el.parentElement;
+          const isOrdered = parent?.tagName.toLowerCase() === 'ol';
+          if (isOrdered) {
+            const index = Array.from(parent?.children || []).indexOf(el) + 1;
+            return `${index}. ${children}\n`;
+          }
+          return `- ${children}\n`;
+        }
+        case 'code': {
+          const isBlock = el.parentElement?.tagName.toLowerCase() === 'pre';
+          if (isBlock) {
+            const lang = el.className.replace('language-', '');
+            return `\`\`\`${lang}\n${children}\n\`\`\`\n\n`;
+          }
+          return `\`${children}\``;
+        }
+        case 'pre': return children;
+        case 'blockquote': return children.split('\n').map(line => `> ${line}`).join('\n') + '\n\n';
+        case 'a': return `[${children}](${el.getAttribute('href') || ''})`;
+        case 'strong': case 'b': return `**${children}**`;
+        case 'em': case 'i': return `*${children}*`;
+        case 'br': return '\n';
+        case 'hr': return '---\n\n';
+        case 'table': return `${children}\n`;
+        case 'thead': case 'tbody': return children;
+        case 'tr': return `${children}|\n`;
+        case 'th': case 'td': return `| ${children} `;
+        case 'img': return `![${el.getAttribute('alt') || ''}](${el.getAttribute('src') || ''})`;
+        default: return children;
+      }
+    }
+    return '';
+  };
+
+  Array.from(element.childNodes).forEach(node => { text += processNode(node); });
+  return text;
+}
+
 export default function SkillBanner(): JSX.Element {
-  const [copied, setCopied] = useState(false);
+  const [commandCopied, setCommandCopied] = useState(false);
+  const [pageCopied, setPageCopied] = useState(false);
   const command = 'npx skills add https://github.com/vectorize-io/hindsight --skill hindsight-docs';
 
-  const handleCopy = async () => {
+  const handleCopyCommand = async () => {
     try {
       await navigator.clipboard.writeText(command);
-      setCopied(true);
-      setTimeout(() => setCopied(false), 2000);
+      setCommandCopied(true);
+      setTimeout(() => setCommandCopied(false), 2000);
     } catch (err) {
       console.error('Failed to copy:', err);
     }
   };
 
+  const handleCopyPage = useCallback(async () => {
+    try {
+      const contentElement = document.querySelector('.markdown');
+      if (!contentElement) return;
+      const title = document.querySelector('h1')?.textContent;
+      let markdown = title ? `# ${title}\n\n` : '';
+      const contentToCopy = Array.from(contentElement.children)
+        .filter(child => !(child.tagName === 'H1' && child.textContent === title))
+        .map(child => extractMarkdown(child))
+        .join('');
+      markdown += contentToCopy;
+      markdown = markdown.replace(/\n{3,}/g, '\n\n').trim();
+      await navigator.clipboard.writeText(markdown);
+      setPageCopied(true);
+      setTimeout(() => setPageCopied(false), 2000);
+    } catch (error) {
+      console.error('Failed to copy page content:', error);
+    }
+  }, []);
+
   return (
     <div className={styles.container}>
       <div className={styles.banner}>
         <div className={styles.icon}>🤖</div>
         <div className={styles.content}>
-          <div className={styles.title}>
-            Using a coding agent? Install the docs skill for instant access
+          <div className={styles.titleRow}>
+            <div className={styles.title}>
+              Using a coding agent? Run this to install the Hindsight docs skill:
+            </div>
+            <button
+              className={`${styles.copyPageButton} ${pageCopied ? styles.copyPageCopied : ''}`}
+              onClick={handleCopyPage}
+              aria-label="Export page as markdown"
+              title={pageCopied ? 'Copied!' : 'Export page as markdown'}
+            >
+              {pageCopied ? (
+                <svg width="12" height="12" viewBox="0 0 24 24" fill="none" stroke="currentColor" strokeWidth="2.5">
+                  <polyline points="20 6 9 17 4 12"></polyline>
+                </svg>
+              ) : (
+                <svg width="12" height="12" viewBox="0 0 16 16" fill="currentColor">
+                  <path d="M4 2a2 2 0 0 1 2-2h8a2 2 0 0 1 2 2v8a2 2 0 0 1-2 2H6a2 2 0 0 1-2-2V2zm2-1a1 1 0 0 0-1 1v8a1 1 0 0 0 1 1h8a1 1 0 0 0 1-1V2a1 1 0 0 0-1-1H6z"/>
+                  <path d="M2 5a1 1 0 0 0-1 1v8a1 1 0 0 0 1 1h8a1 1 0 0 0 1-1v-1h1v1a2 2 0 0 1-2 2H2a2 2 0 0 1-2-2V6a2 2 0 0 1 2-2h1v1H2z"/>
+                </svg>
+              )}
+              <span>{pageCopied ? 'Copied!' : 'export this page as .md'}</span>
+            </button>
           </div>
           <div className={styles.commandWrapper}>
-            <code className={styles.command}>
-              {command}
-            </code>
+            <code className={styles.command}>{command}</code>
             <button
               className={styles.copyButton}
-              onClick={handleCopy}
+              onClick={handleCopyCommand}
               aria-label="Copy command"
-              title={copied ? 'Copied!' : 'Copy to clipboard'}
+              title={commandCopied ? 'Copied!' : 'Copy to clipboard'}
             >
-              {copied ? (
+              {commandCopied ? (
                 <svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" strokeWidth="2">
                   <polyline points="20 6 9 17 4 12"></polyline>
                 </svg>
diff --git a/hindsight-docs/src/css/custom.css b/hindsight-docs/src/css/custom.css
index 4f62828d..9ef4a8a8 100644
--- a/hindsight-docs/src/css/custom.css
+++ b/hindsight-docs/src/css/custom.css
@@ -94,65 +94,6 @@
   transition: background-color 0.15s ease;
 }
 
-/* Navbar icons (desktop only) */
-@media (min-width: 1400px) {
-  .navbar-item-developer::before,
-  .navbar-item-sdks::before,
-  .navbar-item-api::before,
-  .navbar-item-cookbook::before,
-  .navbar-item-changelog::before {
-    display: inline-block;
-    width: 16px;
-    height: 16px;
-    margin-right: 6px;
-    vertical-align: middle;
-    background-size: contain;
-    background-repeat: no-repeat;
-    background-position: center;
-    content: '';
-  }
-
-  .navbar-item-developer::before {
-    background-image: url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 256 256'%3E%3Cpath fill='%23666' d='M71.68 97.22 34.74 128l36.94 30.78a12 12 0 1 1-15.36 18.44l-48-40a12 12 0 0 1 0-18.44l48-40a12 12 0 0 1 15.36 18.44Zm176 21.56-48-40a12 12 0 1 0-15.36 18.44L221.26 128l-36.94 30.78a12 12 0 1 0 15.36 18.44l48-40a12 12 0 0 0 0-18.44ZM164.1 28.72a12 12 0 0 0-15.38 7.18l-64 176a12 12 0 0 0 7.18 15.37 11.79 11.79 0 0 0 4.1.73 12 12 0 0 0 11.28-7.9l64-176a12 12 0 0 0-7.18-15.38Z'/%3E%3C/svg%3E");
-  }
-
-  .navbar-item-sdks::before {
-    background-image: url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 256 256'%3E%3Cpath fill='%23666' d='m225.6 62.64-88-48.17a19.91 19.91 0 0 0-19.2 0l-88 48.17A20 20 0 0 0 20 80.19v95.62a20 20 0 0 0 10.4 17.55l88 48.17a19.89 19.89 0 0 0 19.2 0l88-48.17a20 20 0 0 0 10.4-17.55V80.19a20 20 0 0 0-10.4-17.55ZM128 36.57 200 76l-72 39.42L56 76ZM44 96.82l72 39.43v76.89l-72-39.42Zm96 116.32v-76.89l72-39.43v76.89Z'/%3E%3C/svg%3E");
-  }
-
-  .navbar-item-api::before {
-    background-image: url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 256 256'%3E%3Cpath fill='%23666' d='M180.49 143.51a12 12 0 0 1 0 17l-24 24a12 12 0 0 1-17-17L155 152l-15.52-15.51a12 12 0 0 1 17-17ZM112.49 120.49a12 12 0 0 0-17 0l-24 24a12 12 0 0 0 0 17l24 24a12 12 0 0 0 17-17L97 153l15.52-15.51a12 12 0 0 0-.03-17ZM220 88v24a12 12 0 0 1-24 0v-16h-44a12 12 0 0 1-12-12V40H60v68a12 12 0 0 1-24 0V40a20 20 0 0 1 20-20h96a12 12 0 0 1 8.49 3.52l56 56A12 12 0 0 1 220 88Zm-60-8h23L160 57Zm-4 132H60v-12a12 12 0 0 0-24 0v12a20 20 0 0 0 20 20h100a12 12 0 0 0 0-24Zm64-44a12 12 0 0 0-12 12v36h-44a12 12 0 0 0 0 24h44a20 20 0 0 0 20-20v-40a12 12 0 0 0-8-12Z'/%3E%3C/svg%3E");
-  }
-
-  .navbar-item-cookbook::before {
-    background-image: url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 256 256'%3E%3Cpath fill='%23666' d='M224 44H160a43.86 43.86 0 0 0-32 13.85A43.86 43.86 0 0 0 96 44H32a20 20 0 0 0-20 20v128a20 20 0 0 0 20 20h64a20 20 0 0 1 20 20 12 12 0 0 0 24 0 20 20 0 0 1 20-20h64a20 20 0 0 0 20-20V64a20 20 0 0 0-20-20ZM96 188H36V68h60a20 20 0 0 1 20 20v108.69A43.74 43.74 0 0 0 96 188Zm124 0h-60a43.74 43.74 0 0 0-20 8.69V88a20 20 0 0 1 20-20h60Z'/%3E%3C/svg%3E");
-  }
-
-  .navbar-item-changelog::before {
-    background-image: url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 256 256'%3E%3Cpath fill='%23666' d='M140 80v41.21l34.17 20.5a12 12 0 1 1-12.34 20.58l-40-24A12 12 0 0 1 116 128V80a12 12 0 0 1 24 0Zm-12-52a99.38 99.38 0 0 0-70.76 29.34c-4.69 4.74-9 9.37-13.24 14V64a12 12 0 0 0-24 0v40a12 12 0 0 0 12 12h40a12 12 0 0 0 0-24H53.41c4.24-5.95 8.53-11.93 13.49-16.95A76 76 0 1 1 52 128a12 12 0 0 0-24 0 100 100 0 1 0 100-100Z'/%3E%3C/svg%3E");
-  }
-
-  /* Dark mode icons */
-  [data-theme='dark'] .navbar-item-developer::before {
-    background-image: url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 256 256'%3E%3Cpath fill='%23ccc' d='M71.68 97.22 34.74 128l36.94 30.78a12 12 0 1 1-15.36 18.44l-48-40a12 12 0 0 1 0-18.44l48-40a12 12 0 0 1 15.36 18.44Zm176 21.56-48-40a12 12 0 1 0-15.36 18.44L221.26 128l-36.94 30.78a12 12 0 1 0 15.36 18.44l48-40a12 12 0 0 0 0-18.44ZM164.1 28.72a12 12 0 0 0-15.38 7.18l-64 176a12 12 0 0 0 7.18 15.37 11.79 11.79 0 0 0 4.1.73 12 12 0 0 0 11.28-7.9l64-176a12 12 0 0 0-7.18-15.38Z'/%3E%3C/svg%3E");
-  }
-
-  [data-theme='dark'] .navbar-item-sdks::before {
-    background-image: url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 256 256'%3E%3Cpath fill='%23ccc' d='m225.6 62.64-88-48.17a19.91 19.91 0 0 0-19.2 0l-88 48.17A20 20 0 0 0 20 80.19v95.62a20 20 0 0 0 10.4 17.55l88 48.17a19.89 19.89 0 0 0 19.2 0l88-48.17a20 20 0 0 0 10.4-17.55V80.19a20 20 0 0 0-10.4-17.55ZM128 36.57 200 76l-72 39.42L56 76ZM44 96.82l72 39.43v76.89l-72-39.42Zm96 116.32v-76.89l72-39.43v76.89Z'/%3E%3C/svg%3E");
-  }
-
-  [data-theme='dark'] .navbar-item-api::before {
-    background-image: url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 256 256'%3E%3Cpath fill='%23ccc' d='M180.49 143.51a12 12 0 0 1 0 17l-24 24a12 12 0 0 1-17-17L155 152l-15.52-15.51a12 12 0 0 1 17-17ZM112.49 120.49a12 12 0 0 0-17 0l-24 24a12 12 0 0 0 0 17l24 24a12 12 0 0 0 17-17L97 153l15.52-15.51a12 12 0 0 0-.03-17ZM220 88v24a12 12 0 0 1-24 0v-16h-44a12 12 0 0 1-12-12V40H60v68a12 12 0 0 1-24 0V40a20 20 0 0 1 20-20h96a12 12 0 0 1 8.49 3.52l56 56A12 12 0 0 1 220 88Zm-60-8h23L160 57Zm-4 132H60v-12a12 12 0 0 0-24 0v12a20 20 0 0 0 20 20h100a12 12 0 0 0 0-24Zm64-44a12 12 0 0 0-12 12v36h-44a12 12 0 0 0 0 24h44a20 20 0 0 0 20-20v-40a12 12 0 0 0-8-12Z'/%3E%3C/svg%3E");
-  }
-
-  [data-theme='dark'] .navbar-item-cookbook::before {
-    background-image: url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 256 256'%3E%3Cpath fill='%23ccc' d='M224 44H160a43.86 43.86 0 0 0-32 13.85A43.86 43.86 0 0 0 96 44H32a20 20 0 0 0-20 20v128a20 20 0 0 0 20 20h64a20 20 0 0 1 20 20 12 12 0 0 0 24 0 20 20 0 0 1 20-20h64a20 20 0 0 0 20-20V64a20 20 0 0 0-20-20ZM96 188H36V68h60a20 20 0 0 1 20 20v108.69A43.74 43.74 0 0 0 96 188Zm124 0h-60a43.74 43.74 0 0 0-20 8.69V88a20 20 0 0 1 20-20h60Z'/%3E%3C/svg%3E");
-  }
-
-  [data-theme='dark'] .navbar-item-changelog::before {
-    background-image: url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 256 256'%3E%3Cpath fill='%23ccc' d='M140 80v41.21l34.17 20.5a12 12 0 1 1-12.34 20.58l-40-24A12 12 0 0 1 116 128V80a12 12 0 0 1 24 0Zm-12-52a99.38 99.38 0 0 0-70.76 29.34c-4.69 4.74-9 9.37-13.24 14V64a12 12 0 0 0-24 0v40a12 12 0 0 0 12 12h40a12 12 0 0 0 0-24H53.41c4.24-5.95 8.53-11.93 13.49-16.95A76 76 0 1 1 52 128a12 12 0 0 0-24 0 100 100 0 1 0 100-100Z'/%3E%3C/svg%3E");
-  }
-}
 
 /* GitHub icon link */
 .header-github-link::before {
@@ -212,11 +153,7 @@
     height: 24px !important;
   }
 
-  /* Hide all desktop navbar items except logo and toggle */
-  .navbar__items--right > .navbar__item {
-    display: none !important;
-  }
-
+  /* Hide left navbar links (accessible via hamburger) */
   .navbar__items--left > .navbar__link {
     display: none !important;
   }
@@ -301,6 +238,13 @@
   }
 }
 
+/* Truly mobile (<= 996px): hide right navbar items too */
+@media (max-width: 996px) {
+  .navbar__items--right > .navbar__item {
+    display: none !important;
+  }
+}
+
 /* Mobile sidebar - show right navbar items */
 @media (max-width: 1399px) {
   /* Ensure right-side items are visible in mobile sidebar */
@@ -589,16 +533,6 @@ div[class*="codeBlockContent"] .prism-code {
   max-width: 100%;
 }
 
-/* Force blog posts to be wider - aggressive override */
-body[class*="blog"] .container,
-body[class*="blog"] main .container {
-  max-width: 100% !important;
-}
-
-body[class*="blog"] article {
-  max-width: 1200px !important;
-  margin: 0 auto !important;
-}
 
 /* Page title with gradient */
 article h1,
@@ -1336,6 +1270,48 @@ ul[class*="suggestion"] {
   display: none !important;
 }
 
+/* ===== Blog Post & Cookbook Typography ===== */
+
+/* Shared: larger body text, looser line-height */
+html.blog-post-page article p,
+html.blog-post-page article li,
+html.blog-post-page article blockquote p,
+html.mdx-wrapper article p,
+html.mdx-wrapper article li,
+html.mdx-wrapper article blockquote p,
+html.docs-wrapper article p,
+html.docs-wrapper article li,
+html.docs-wrapper article blockquote p {
+  font-size: 1rem;
+  line-height: 1.85;
+}
+
+/* Blog only: monospace body font — Supermemory-style */
+html.blog-post-page article p,
+html.blog-post-page article li,
+html.blog-post-page article blockquote p {
+  font-family: 'JetBrains Mono', 'Fira Code', 'SF Mono', Monaco, Consolas, monospace;
+}
+
+/* Shared: bump heading sizes */
+html.blog-post-page article h1,
+html.mdx-wrapper article h1,
+html.docs-wrapper article h1 {
+  font-size: 2.5rem;
+}
+
+html.blog-post-page article h2,
+html.mdx-wrapper article h2,
+html.docs-wrapper article h2 {
+  font-size: 1.6rem;
+}
+
+html.blog-post-page article h3,
+html.mdx-wrapper article h3,
+html.docs-wrapper article h3 {
+  font-size: 1.2rem;
+}
+
 /* ===== Blog Styling ===== */
 
 /* Blog author text visible in light mode */
@@ -1384,75 +1360,3 @@ ul[class*="suggestion"] {
   }
 }
 
-
-/* ============================================
-   Cookbook: Hide sidebar for OpenAI-style layout
-   ============================================ */
-
-/* Hide sidebar completely on cookbook pages - use multiple selectors for reliability */
-[class*="docPage"] aside[class*="docSidebarContainer"],
-aside[class*="docSidebarContainer"]:has(+ * .cookbook-page),
-body:has(.cookbook-page) aside[class*="docSidebarContainer"],
-.hidden-sidebar aside,
-article[id="cookbook-index"] ~ aside,
-div:has(> article[id="cookbook-index"]) aside {
-  display: none !important;
-  width: 0 !important;
-  min-width: 0 !important;
-}
-
-/* Make main wrapper full width */
-body:has(.cookbook-page) .main-wrapper,
-.hidden-sidebar ~ * .main-wrapper,
-div:has(> article[id="cookbook-index"]) {
-  max-width: 100% !important;
-}
-
-/* Make doc page container full width */
-body:has(.cookbook-page) [class*="docMainContainer"],
-.hidden-sidebar [class*="docMainContainer"],
-div:has(> .cookbook-page) > div {
-  max-width: 100% !important;
-}
-
-/* Make the content column full width */
-body:has(.cookbook-page) [class*="docItemCol"],
-.hidden-sidebar [class*="docItemCol"],
-.cookbook-page ~ * [class*="col"] {
-  max-width: 100% !important;
-  flex: 1 1 100% !important;
-}
-
-/* Container adjustments */
-.cookbook-page .container,
-body:has(.cookbook-page) .container {
-  max-width: 1400px !important;
-  padding-left: 2rem !important;
-  padding-right: 2rem !important;
-}
-
-/* Responsive adjustments */
-@media (max-width: 996px) {
-  .cookbook-page .container {
-    padding-left: 1rem !important;
-    padding-right: 1rem !important;
-  }
-}
-
-/* Additional fallback selectors for hiding cookbook sidebar */
-[data-route="/cookbook"] aside,
-[data-route="/cookbook/"] aside,
-div[class*="docPage"]:has(article[id*="cookbook"]) > aside:first-child {
-  display: none !important;
-}
-
-/* Force full width on cookbook route */
-[data-route="/cookbook"] div[class*="docRoot"],
-[data-route="/cookbook/"] div[class*="docRoot"] {
-  grid-template-columns: 0 auto !important;
-}
-
-[data-route="/cookbook"] main,
-[data-route="/cookbook/"] main {
-  max-width: 100% !important;
-}
diff --git a/hindsight-docs/docs/cookbook/applications/chat-memory.md b/hindsight-docs/src/pages/cookbook/applications/chat-memory.md
similarity index 100%
rename from hindsight-docs/docs/cookbook/applications/chat-memory.md
rename to hindsight-docs/src/pages/cookbook/applications/chat-memory.md
diff --git a/hindsight-docs/docs/cookbook/applications/chat-sdk-multi-platform.md b/hindsight-docs/src/pages/cookbook/applications/chat-sdk-multi-platform.md
similarity index 100%
rename from hindsight-docs/docs/cookbook/applications/chat-sdk-multi-platform.md
rename to hindsight-docs/src/pages/cookbook/applications/chat-sdk-multi-platform.md
diff --git a/hindsight-docs/docs/cookbook/applications/crewai-memory.md b/hindsight-docs/src/pages/cookbook/applications/crewai-memory.md
similarity index 100%
rename from hindsight-docs/docs/cookbook/applications/crewai-memory.md
rename to hindsight-docs/src/pages/cookbook/applications/crewai-memory.md
diff --git a/hindsight-docs/docs/cookbook/applications/deliveryman-demo.md b/hindsight-docs/src/pages/cookbook/applications/deliveryman-demo.md
similarity index 100%
rename from hindsight-docs/docs/cookbook/applications/deliveryman-demo.md
rename to hindsight-docs/src/pages/cookbook/applications/deliveryman-demo.md
diff --git a/hindsight-docs/docs/cookbook/applications/go-memory-service.md b/hindsight-docs/src/pages/cookbook/applications/go-memory-service.md
similarity index 100%
rename from hindsight-docs/docs/cookbook/applications/go-memory-service.md
rename to hindsight-docs/src/pages/cookbook/applications/go-memory-service.md
diff --git a/hindsight-docs/docs/cookbook/applications/hindsight-litellm-demo.md b/hindsight-docs/src/pages/cookbook/applications/hindsight-litellm-demo.md
similarity index 100%
rename from hindsight-docs/docs/cookbook/applications/hindsight-litellm-demo.md
rename to hindsight-docs/src/pages/cookbook/applications/hindsight-litellm-demo.md
diff --git a/hindsight-docs/docs/cookbook/applications/hindsight-tool-learning-demo.md b/hindsight-docs/src/pages/cookbook/applications/hindsight-tool-learning-demo.md
similarity index 100%
rename from hindsight-docs/docs/cookbook/applications/hindsight-tool-learning-demo.md
rename to hindsight-docs/src/pages/cookbook/applications/hindsight-tool-learning-demo.md
diff --git a/hindsight-docs/docs/cookbook/applications/openai-fitness-coach.md b/hindsight-docs/src/pages/cookbook/applications/openai-fitness-coach.md
similarity index 100%
rename from hindsight-docs/docs/cookbook/applications/openai-fitness-coach.md
rename to hindsight-docs/src/pages/cookbook/applications/openai-fitness-coach.md
diff --git a/hindsight-docs/docs/cookbook/applications/sanity-blog-memory.md b/hindsight-docs/src/pages/cookbook/applications/sanity-blog-memory.md
similarity index 100%
rename from hindsight-docs/docs/cookbook/applications/sanity-blog-memory.md
rename to hindsight-docs/src/pages/cookbook/applications/sanity-blog-memory.md
diff --git a/hindsight-docs/docs/cookbook/applications/stancetracker.md b/hindsight-docs/src/pages/cookbook/applications/stancetracker.md
similarity index 100%
rename from hindsight-docs/docs/cookbook/applications/stancetracker.md
rename to hindsight-docs/src/pages/cookbook/applications/stancetracker.md
diff --git a/hindsight-docs/docs/cookbook/applications/taste-ai.md b/hindsight-docs/src/pages/cookbook/applications/taste-ai.md
similarity index 100%
rename from hindsight-docs/docs/cookbook/applications/taste-ai.md
rename to hindsight-docs/src/pages/cookbook/applications/taste-ai.md
diff --git a/hindsight-docs/docs/cookbook/index.mdx b/hindsight-docs/src/pages/cookbook/index.mdx
similarity index 89%
rename from hindsight-docs/docs/cookbook/index.mdx
rename to hindsight-docs/src/pages/cookbook/index.mdx
index 71479204..e2a47aa7 100644
--- a/hindsight-docs/docs/cookbook/index.mdx
+++ b/hindsight-docs/src/pages/cookbook/index.mdx
@@ -1,25 +1,32 @@
 ---
-sidebar_position: 1
+title: Cookbook
 hide_table_of_contents: true
-pagination_next: null
-pagination_prev: null
-custom_edit_url: null
-sidebar_class_name: hidden-sidebar
 ---
 
-import RecipeCarousel from '@site/src/components/RecipeCarousel';
+import CookbookGrid from '@site/src/components/CookbookGrid';
 
-<div className="cookbook-page">
+<div>
 
-# Cookbook
+<div style={{textAlign: 'center', marginBottom: '3.5rem'}}>
+  <h1 style={{
+    fontSize: '3rem',
+    fontWeight: 800,
+    background: 'linear-gradient(135deg, #0074d9, #009296)',
+    WebkitBackgroundClip: 'text',
+    WebkitTextFillColor: 'transparent',
+    backgroundClip: 'text',
+    letterSpacing: '-0.03em',
+    lineHeight: 1.15,
+    marginBottom: '0.75rem',
+  }}>Cookbook</h1>
+  <p style={{fontSize: '1.05rem', color: 'var(--ifm-color-emphasis-600)', maxWidth: 520, margin: '0 auto', lineHeight: 1.7}}>
+    Practical examples and complete applications built with Hindsight.
+  </p>
+</div>
 
-Learn how to build with Hindsight through practical examples:
+## Recipes
 
-- **[Recipes](#recipes)** - Step-by-step guides and patterns for common use cases
-- **[Applications](#applications)** - Complete, runnable applications demonstrating Hindsight integration
-
-<RecipeCarousel
-  title="Recipes"
+<CookbookGrid
   items={[
     {
       title: "Hindsight Quickstart",
@@ -90,8 +97,9 @@ Learn how to build with Hindsight through practical examples:
   ]}
 />
 
-<RecipeCarousel
-  title="Applications"
+## Applications
+
+<CookbookGrid
   items={[
     {
       title: "Chat Memory App",
diff --git a/hindsight-docs/docs/cookbook/recipes/fitness_tracker.md b/hindsight-docs/src/pages/cookbook/recipes/fitness_tracker.md
similarity index 100%
rename from hindsight-docs/docs/cookbook/recipes/fitness_tracker.md
rename to hindsight-docs/src/pages/cookbook/recipes/fitness_tracker.md
diff --git a/hindsight-docs/docs/cookbook/recipes/healthcare_assistant.md b/hindsight-docs/src/pages/cookbook/recipes/healthcare_assistant.md
similarity index 100%
rename from hindsight-docs/docs/cookbook/recipes/healthcare_assistant.md
rename to hindsight-docs/src/pages/cookbook/recipes/healthcare_assistant.md
diff --git a/hindsight-docs/docs/cookbook/recipes/litellm-memory-demo.md b/hindsight-docs/src/pages/cookbook/recipes/litellm-memory-demo.md
similarity index 100%
rename from hindsight-docs/docs/cookbook/recipes/litellm-memory-demo.md
rename to hindsight-docs/src/pages/cookbook/recipes/litellm-memory-demo.md
diff --git a/hindsight-docs/docs/cookbook/recipes/movie_recommendation.md b/hindsight-docs/src/pages/cookbook/recipes/movie_recommendation.md
similarity index 100%
rename from hindsight-docs/docs/cookbook/recipes/movie_recommendation.md
rename to hindsight-docs/src/pages/cookbook/recipes/movie_recommendation.md
diff --git a/hindsight-docs/docs/cookbook/recipes/per-user-memory.md b/hindsight-docs/src/pages/cookbook/recipes/per-user-memory.md
similarity index 100%
rename from hindsight-docs/docs/cookbook/recipes/per-user-memory.md
rename to hindsight-docs/src/pages/cookbook/recipes/per-user-memory.md
diff --git a/hindsight-docs/docs/cookbook/recipes/personal_assistant.md b/hindsight-docs/src/pages/cookbook/recipes/personal_assistant.md
similarity index 100%
rename from hindsight-docs/docs/cookbook/recipes/personal_assistant.md
rename to hindsight-docs/src/pages/cookbook/recipes/personal_assistant.md
diff --git a/hindsight-docs/docs/cookbook/recipes/personalized_search.md b/hindsight-docs/src/pages/cookbook/recipes/personalized_search.md
similarity index 100%
rename from hindsight-docs/docs/cookbook/recipes/personalized_search.md
rename to hindsight-docs/src/pages/cookbook/recipes/personalized_search.md
diff --git a/hindsight-docs/docs/cookbook/recipes/quickstart.md b/hindsight-docs/src/pages/cookbook/recipes/quickstart.md
similarity index 100%
rename from hindsight-docs/docs/cookbook/recipes/quickstart.md
rename to hindsight-docs/src/pages/cookbook/recipes/quickstart.md
diff --git a/hindsight-docs/docs/cookbook/recipes/study_buddy.md b/hindsight-docs/src/pages/cookbook/recipes/study_buddy.md
similarity index 100%
rename from hindsight-docs/docs/cookbook/recipes/study_buddy.md
rename to hindsight-docs/src/pages/cookbook/recipes/study_buddy.md
diff --git a/hindsight-docs/docs/cookbook/recipes/support-agent-shared-knowledge.md b/hindsight-docs/src/pages/cookbook/recipes/support-agent-shared-knowledge.md
similarity index 100%
rename from hindsight-docs/docs/cookbook/recipes/support-agent-shared-knowledge.md
rename to hindsight-docs/src/pages/cookbook/recipes/support-agent-shared-knowledge.md
diff --git a/hindsight-docs/docs/cookbook/recipes/tool-learning-demo.md b/hindsight-docs/src/pages/cookbook/recipes/tool-learning-demo.md
similarity index 100%
rename from hindsight-docs/docs/cookbook/recipes/tool-learning-demo.md
rename to hindsight-docs/src/pages/cookbook/recipes/tool-learning-demo.md
diff --git a/hindsight-docs/src/theme/BlogLayout/index.tsx b/hindsight-docs/src/theme/BlogLayout/index.tsx
new file mode 100644
index 00000000..ea934b72
--- /dev/null
+++ b/hindsight-docs/src/theme/BlogLayout/index.tsx
@@ -0,0 +1,16 @@
+import React, {type ReactNode} from 'react';
+import Layout from '@theme/Layout';
+import type {Props} from '@theme/BlogLayout';
+
+// eslint-disable-next-line @typescript-eslint/no-unused-vars
+export default function BlogLayout({sidebar: _sidebar, toc: _toc, children, ...layoutProps}: Props): ReactNode {
+  return (
+    <Layout {...layoutProps}>
+      <div className="container margin-vert--lg">
+        <div className="row">
+          <main className="col col--8 col--offset-2">{children}</main>
+        </div>
+      </div>
+    </Layout>
+  );
+}
diff --git a/hindsight-docs/src/theme/BlogListPage/index.tsx b/hindsight-docs/src/theme/BlogListPage/index.tsx
new file mode 100644
index 00000000..367ae894
--- /dev/null
+++ b/hindsight-docs/src/theme/BlogListPage/index.tsx
@@ -0,0 +1,78 @@
+import React from 'react';
+import Link from '@docusaurus/Link';
+import Layout from '@theme/Layout';
+import type {Props} from '@theme/BlogListPage';
+import type {PropBlogPostContent} from '@docusaurus/plugin-content-blog';
+import styles from './styles.module.css';
+
+function formatDate(dateString: string): string {
+  const date = new Date(dateString);
+  return date.toLocaleDateString('en-US', {month: 'short', day: 'numeric', year: 'numeric'});
+}
+
+function BlogCard({content}: {content: PropBlogPostContent}) {
+  const {metadata, assets} = content;
+  const {title, description, date, readingTime, permalink, frontMatter} = metadata;
+  const image = assets.image ?? frontMatter.image ?? '/img/blog-default.jpg';
+
+  return (
+    <Link to={permalink} className={styles.card}>
+      <div className={styles.cardImageWrapper}>
+        {image ? (
+          <img src={image} alt={title} className={styles.cardImage} />
+        ) : (
+          <div className={styles.cardImagePlaceholder} />
+        )}
+      </div>
+      <div className={styles.cardBody}>
+        <h2 className={styles.cardTitle}>{title}</h2>
+        {description && <p className={styles.cardDescription}>{description}</p>}
+        <div className={styles.cardFooter}>
+          <span className={styles.cardDate}>{formatDate(date)}</span>
+          {readingTime !== undefined && (
+            <span className={styles.cardReadTime}>{Math.ceil(readingTime)} min read</span>
+          )}
+        </div>
+      </div>
+    </Link>
+  );
+}
+
+export default function BlogListPage({items, metadata}: Props): React.ReactElement {
+  const {blogTitle, blogDescription, totalPages, page, nextPage, previousPage} = metadata;
+
+  return (
+    <Layout title={blogTitle} description={blogDescription}>
+      <main className={styles.blogPage}>
+        <header className={styles.header}>
+          <h1 className={styles.headerTitle}>{blogTitle}</h1>
+          {blogDescription && <p className={styles.headerSubtitle}>{blogDescription}</p>}
+        </header>
+
+        <div className={styles.grid}>
+          {items.map(({content: BlogPostContent}) => (
+            <BlogCard key={BlogPostContent.metadata.permalink} content={BlogPostContent} />
+          ))}
+        </div>
+
+        {totalPages > 1 && (
+          <nav className={styles.pagination}>
+            {previousPage && (
+              <Link to={previousPage} className={styles.paginationButton}>
+                ← Previous
+              </Link>
+            )}
+            <span className={styles.paginationInfo}>
+              Page {page} of {totalPages}
+            </span>
+            {nextPage && (
+              <Link to={nextPage} className={styles.paginationButton}>
+                Next →
+              </Link>
+            )}
+          </nav>
+        )}
+      </main>
+    </Layout>
+  );
+}
diff --git a/hindsight-docs/src/theme/BlogListPage/styles.module.css b/hindsight-docs/src/theme/BlogListPage/styles.module.css
new file mode 100644
index 00000000..220b8be2
--- /dev/null
+++ b/hindsight-docs/src/theme/BlogListPage/styles.module.css
@@ -0,0 +1,185 @@
+.blogPage {
+  max-width: 1280px;
+  margin: 0 auto;
+  padding: 4rem 2rem 6rem;
+}
+
+/* ── Header ─────────────────────────────────────── */
+.header {
+  text-align: center;
+  margin-bottom: 4rem;
+}
+
+.headerTitle {
+  font-size: 3rem;
+  font-weight: 800;
+  background: linear-gradient(135deg, #0074d9, #009296);
+  -webkit-background-clip: text;
+  -webkit-text-fill-color: transparent;
+  background-clip: text;
+  margin-bottom: 0.75rem;
+  line-height: 1.15;
+  letter-spacing: -0.03em;
+}
+
+.headerSubtitle {
+  font-size: 1.05rem;
+  color: var(--ifm-color-emphasis-600);
+  max-width: 520px;
+  margin: 0 auto;
+  line-height: 1.7;
+}
+
+/* ── Grid ────────────────────────────────────────── */
+.grid {
+  display: grid;
+  grid-template-columns: repeat(3, 1fr);
+  gap: 2rem;
+}
+
+@media (max-width: 996px) {
+  .grid {
+    grid-template-columns: repeat(2, 1fr);
+    gap: 1.5rem;
+  }
+}
+
+@media (max-width: 640px) {
+  .grid {
+    grid-template-columns: 1fr;
+    gap: 2rem;
+  }
+
+  .headerTitle {
+    font-size: 2.25rem;
+  }
+}
+
+/* ── Card ────────────────────────────────────────── */
+.card {
+  display: flex;
+  flex-direction: column;
+  text-decoration: none !important;
+  color: inherit;
+  border-radius: 0;
+  background: transparent;
+  transition: opacity 0.2s ease;
+}
+
+.card:hover {
+  text-decoration: none !important;
+  color: inherit;
+  opacity: 0.9;
+}
+
+.card:hover .cardImage {
+  transform: none;
+}
+
+/* ── Card image ──────────────────────────────────── */
+.cardImageWrapper {
+  aspect-ratio: 16 / 9;
+  overflow: hidden;
+  width: 100%;
+  border-radius: 4px;
+  background: #111;
+}
+
+.cardImage {
+  width: 100%;
+  height: 100%;
+  object-fit: cover;
+  display: block;
+  transition: transform 0.4s ease;
+}
+
+.cardImagePlaceholder {
+  width: 100%;
+  height: 100%;
+  background: linear-gradient(135deg, #0074d9 0%, #009296 100%);
+  opacity: 0.35;
+}
+
+/* ── Card body ───────────────────────────────────── */
+.cardBody {
+  display: flex;
+  flex-direction: column;
+  flex: 1;
+  padding: 1rem 0 0;
+  gap: 0.5rem;
+}
+
+.cardTitle {
+  font-size: 1.25rem;
+  font-weight: 700;
+  line-height: 1.35;
+  margin: 0;
+  color: var(--ifm-heading-color);
+  letter-spacing: -0.02em;
+}
+
+.cardDescription {
+  font-size: 0.85rem;
+  font-family: 'JetBrains Mono', 'Fira Code', 'SF Mono', Monaco, Consolas, monospace;
+  color: var(--ifm-color-emphasis-500);
+  line-height: 1.65;
+  margin: 0;
+  flex: 1;
+  display: -webkit-box;
+  -webkit-line-clamp: 2;
+  -webkit-box-orient: vertical;
+  overflow: hidden;
+}
+
+.cardFooter {
+  display: flex;
+  align-items: center;
+  gap: 0.35rem;
+  margin-top: 0.5rem;
+}
+
+.cardDate {
+  font-size: 0.78rem;
+  color: var(--ifm-color-emphasis-400);
+  font-weight: 500;
+}
+
+.cardReadTime {
+  font-size: 0.78rem;
+  color: var(--ifm-color-emphasis-400);
+}
+
+.cardReadTime::before {
+  content: '·';
+  margin-right: 0.35rem;
+}
+
+/* ── Pagination ──────────────────────────────────── */
+.pagination {
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  gap: 1.5rem;
+  margin-top: 4rem;
+}
+
+.paginationButton {
+  padding: 0.5rem 1.25rem;
+  border: 1px solid var(--ifm-color-emphasis-300);
+  border-radius: 8px;
+  font-size: 0.9rem;
+  font-weight: 500;
+  color: var(--ifm-color-primary);
+  text-decoration: none !important;
+  transition: background 0.15s ease, border-color 0.15s ease;
+}
+
+.paginationButton:hover {
+  background: var(--ifm-color-primary-lightest);
+  border-color: var(--ifm-color-primary);
+}
+
+.paginationInfo {
+  font-size: 0.875rem;
+  color: var(--ifm-color-emphasis-600);
+}
diff --git a/hindsight-docs/src/theme/DocItem/Content/index.tsx b/hindsight-docs/src/theme/DocItem/Content/index.tsx
index 97a084e5..acbd6646 100644
--- a/hindsight-docs/src/theme/DocItem/Content/index.tsx
+++ b/hindsight-docs/src/theme/DocItem/Content/index.tsx
@@ -2,20 +2,9 @@ import React from 'react';
 import DocItemContent from '@theme-original/DocItem/Content';
 import type DocItemContentType from '@theme/DocItem/Content';
 import type { WrapperProps } from '@docusaurus/types';
-import CopyPageButton from '@site/src/components/CopyPageButton';
-import styles from './styles.module.css';
 
 type Props = WrapperProps<typeof DocItemContentType>;
 
 export default function DocItemContentWrapper(props: Props): JSX.Element {
-  return (
-    <>
-      <div className={styles.docItemHeader}>
-        <div className={styles.docItemActions}>
-          <CopyPageButton />
-        </div>
-      </div>
-      <DocItemContent {...props} />
-    </>
-  );
-}
\ No newline at end of file
+  return <DocItemContent {...props} />;
+}
diff --git a/hindsight-docs/static/img/blog-default.jpg b/hindsight-docs/static/img/blog-default.jpg
new file mode 100644
index 00000000..8129311c
Binary files /dev/null and b/hindsight-docs/static/img/blog-default.jpg differ
diff --git a/hindsight-docs/static/img/blog/vercel-chat.png b/hindsight-docs/static/img/blog/vercel-chat.png
new file mode 100644
index 00000000..27681ec6
Binary files /dev/null and b/hindsight-docs/static/img/blog/vercel-chat.png differ
diff --git a/hindsight-docs/static/img/undraw_docusaurus_mountain.svg b/hindsight-docs/static/img/undraw_docusaurus_mountain.svg
deleted file mode 100644
index af961c49..00000000
--- a/hindsight-docs/static/img/undraw_docusaurus_mountain.svg
+++ /dev/null
@@ -1,171 +0,0 @@
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-  <title>Easy to Use</title>
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diff --git a/hindsight-docs/versioned_docs/version-0.3/cookbook/applications/openai-fitness-coach.md b/hindsight-docs/versioned_docs/version-0.3/cookbook/applications/openai-fitness-coach.md
deleted file mode 100644
index 845e45d8..00000000
--- a/hindsight-docs/versioned_docs/version-0.3/cookbook/applications/openai-fitness-coach.md
+++ /dev/null
@@ -1,315 +0,0 @@
----
-sidebar_position: 1
----
-
-# OpenAI Agent + Hindsight Memory Integration
-
-
-:::info Complete Application
-This is a complete, runnable application demonstrating Hindsight integration.
-[**View source on GitHub →**](https://github.com/vectorize-io/hindsight-cookbook/tree/main/openai-fitness-coach)
-:::
-
-
-A fitness coach example demonstrating how to use **OpenAI Agents** with **Hindsight as a memory backend**.
-
-## What This Demonstrates
-
-This example showcases:
-
-- **OpenAI Assistants** handling conversation logic
-- **Hindsight** providing sophisticated memory storage & retrieval
-- **Function calling** to bridge them together
-- **Streaming responses** for real-time interaction (enabled by default)
-- **Bidirectional memory** - both user data AND coach observations stored
-- **System-level post-processing** - automatic opinion storage for reliability
-- **Temporal-semantic memory** queries via function tools
-- **Enhanced preference learning** - coach learns and respects user likes/dislikes
-- **Real-world integration pattern** for adding memory to AI agents
-
-## Architecture
-
-```
-User: "I ran 5K today, don't like tempo runs"
-    |
-OpenAI Assistant
-    |
-Function Call: store_memory(workout + preference)
-    |
-Hindsight API (stores as world/agent)
-    |
-OpenAI Assistant: "What should I focus on?"
-    |
-Function Call: retrieve_memories("workouts and preferences")
-    |
-Hindsight API (returns workouts + preferences)
-    |
-OpenAI Assistant (analyzes, gives advice)
-    |
-Function Call: store_memory(advice as opinion)
-    |
-Hindsight API (stores coach's observation)
-    |
-Personalized Answer
-```
-
-## Key Difference from Standard Demo
-
-| Component | Standard Demo | OpenAI Integration |
-|-----------|---------------|-------------------|
-| **Conversation** | Hindsight `/think` endpoint | OpenAI Assistant API |
-| **Memory** | Hindsight (built-in) | Hindsight (via function calling) |
-| **LLM** | Configured in Hindsight | OpenAI GPT-4 |
-| **Opinion Formation** | Automatic in `/think` | Explicit via `store_memory(type="opinion")` |
-| **Best For** | Hindsight-native apps | Integrating memory into existing OpenAI agents |
-
-## Quick Start
-
-### Prerequisites
-
-1. **OpenAI API Key**
-   ```bash
-   export OPENAI_API_KEY=your_openai_api_key
-   ```
-
-2. **Hindsight API running**
-   ```bash
-   # Follow Hindsight setup instructions to start the API
-   # Default: http://localhost:8888
-   ```
-
-3. **Install dependencies**
-   ```bash
-   pip install openai requests
-   ```
-
-### Run the Conversational Demo
-
-```bash
-cd openai-fitness-coach
-export OPENAI_API_KEY=your_key_here
-python demo_conversational.py
-```
-
-The demo showcases:
-1. **Natural language workout logging** - Tell the coach what you did conversationally
-2. **Preference learning** - Express likes/dislikes and watch the coach adapt
-3. **Goal tracking** - Set goals, track progress, achieve milestones
-4. **Bidirectional memory** - Both your activities AND coach's advice are stored
-5. **Streaming responses** - See responses appear in real-time
-6. **7 interactive phases** - From goal setting to achievement recognition
-
-The demo uses a separate agent (`fitness-coach-demo`) to avoid mixing with real data.
-
-## Usage
-
-### Chat with Your Coach
-
-**Interactive mode:**
-```bash
-python openai_coach.py
-```
-
-**Single question:**
-```bash
-python openai_coach.py "What did I do for training this week?"
-```
-
-## How It Works
-
-### 1. Memory Tools (`memory_tools.py`)
-
-Defines function tools that the OpenAI Agent can call:
-
-```python
-retrieve_memories(query, fact_types, top_k)
-search_workouts(after_date, before_date, workout_type)
-get_nutrition_summary(after_date, before_date)
-get_user_goals()
-get_coach_opinions(about)
-```
-
-Each function makes API calls to Hindsight to fetch relevant memories.
-
-### 2. OpenAI Agent (`openai_coach.py`)
-
-Creates an OpenAI Assistant with:
-- Fitness coaching instructions
-- Access to memory function tools
-- Conversation management
-
-When you ask a question:
-1. User message is sent to OpenAI Assistant
-2. Assistant decides which memory functions to call
-3. Functions fetch data from Hindsight
-4. Assistant generates response using retrieved context
-
-### 3. Function Calling Flow
-
-```python
-# User asks: "What did I run this week?"
-
-# OpenAI Assistant decides to call:
-search_workouts(
-    after_date="2024-11-18",
-    workout_type="running"
-)
-
-# Function retrieves from Hindsight:
-{
-  "results": [
-    {"text": "User completed 45-minute cardio workout: running..."},
-    {"text": "User completed 60-minute cardio workout: running..."}
-  ]
-}
-
-# OpenAI Assistant generates response:
-"This week you've done two runs: a 45-minute run on Monday
-and a longer 60-minute run on Wednesday. Great consistency!"
-```
-
-## Example Questions
-
-Try asking:
-
-```bash
-python openai_coach.py "What does my training look like this week?"
-python openai_coach.py "Based on my workouts, should I rest today?"
-python openai_coach.py "How is my nutrition supporting my goals?"
-python openai_coach.py "What's my progress toward my goal?"
-python openai_coach.py "Compare my training this month to last month"
-```
-
-The agent will automatically:
-1. Identify what memories it needs
-2. Call the appropriate function tools
-3. Retrieve data from Hindsight
-4. Generate a personalized response
-
-## Memory Types Retrieved
-
-The OpenAI Agent can retrieve different memory types from Hindsight:
-
-- **World Facts** (`fact_type: "world"`): Workouts, meals, activities
-- **Agent Facts** (`fact_type: "agent"`): Goals, intentions
-- **Opinions** (`fact_type: "opinion"`): Coach's observations about patterns
-
-## Customization
-
-### Add New Function Tools
-
-Edit `memory_tools.py` to add new capabilities:
-
-```python
-def get_weekly_summary(week_offset: int = 0):
-    """Get a summary of a specific week."""
-    # Implementation
-    pass
-
-# Add to MEMORY_TOOLS list
-MEMORY_TOOLS.append({
-    "type": "function",
-    "function": {
-        "name": "get_weekly_summary",
-        "description": "Get training summary for a specific week",
-        # ... parameters
-    }
-})
-
-# Add to FUNCTION_MAP
-FUNCTION_MAP["get_weekly_summary"] = get_weekly_summary
-```
-
-### Modify Assistant Instructions
-
-Edit `openai_coach.py` to change the coach's personality or behavior:
-
-```python
-assistant = client.beta.assistants.create(
-    name="Your Custom Coach",
-    instructions="Your custom instructions here...",
-    model="gpt-4o-mini",
-    tools=MEMORY_TOOLS
-)
-```
-
-## Use Cases
-
-This pattern works for any application that needs memory:
-
-1. **Customer Support Agents** - Remember past conversations and issues
-2. **Personal Assistants** - Remember preferences, schedules, past decisions
-3. **Educational Tutors** - Track learning progress over time
-4. **Health Coaches** - Monitor habits, progress, goals (like this example)
-5. **Sales Assistants** - Remember customer interactions and preferences
-
-## Integration Pattern
-
-**To add Hindsight memory to your own OpenAI Agent:**
-
-1. Define function tools that call Hindsight API
-2. Register them with your OpenAI Assistant
-3. Implement function handlers to execute Hindsight queries
-4. Let OpenAI Assistant decide when to retrieve memories
-
-The key benefit: **Separation of concerns**
-- OpenAI = Conversation logic
-- Hindsight = Memory storage, retrieval, temporal queries, entity linking
-
-## When to Use This vs. Standard Hindsight
-
-**Use OpenAI + Hindsight (this example) when:**
-- You want OpenAI's conversation capabilities
-- You're already using OpenAI Agents
-- You want explicit control over when to retrieve memories
-- You want to combine Hindsight with other OpenAI features
-
-**Use Hindsight directly when:**
-- You want a complete memory-first solution
-- You want automatic memory retrieval and opinion formation
-- You want to use different LLM providers (not just OpenAI)
-- You want the `/think` endpoint's integrated approach
-
-## Learning Points
-
-After running this demo, you'll understand:
-
-1. How to add sophisticated memory to any OpenAI Agent
-2. How function calling bridges LLMs and memory systems
-3. How temporal-semantic queries work via function tools
-4. Real-world pattern for LLM + memory integration
-
-## Core Files
-
-- `demo_conversational.py` - Conversational demo showcasing preference learning and goal tracking
-- `openai_coach.py` - OpenAI Assistant wrapper with streaming and memory integration
-- `memory_tools.py` - Function calling tools that bridge to Hindsight API
-- `.openai_assistant_id` - Saved assistant ID (auto-generated, gitignored)
-
-## Common Issues
-
-**"OPENAI_API_KEY not set"**
-```bash
-export OPENAI_API_KEY=your_api_key_here
-```
-
-**"Agent not found"**
-- Make sure the Hindsight fitness-coach agent exists
-
-**"Connection refused"**
-- Make sure Hindsight API is running on localhost:8888
-
-## Next Steps
-
-1. Run the demo to see it in action
-2. Try chatting with the coach: `python openai_coach.py`
-3. Log your own workouts and meals
-4. Experiment with different questions
-5. Add custom function tools for your use case
-
----
-
-**Built with:**
-- OpenAI Assistants API
-- Hindsight (temporal-semantic memory)
-- Function calling for integration
diff --git a/hindsight-docs/versioned_docs/version-0.3/cookbook/index.mdx b/hindsight-docs/versioned_docs/version-0.3/cookbook/index.mdx
deleted file mode 100644
index 27e96d04..00000000
--- a/hindsight-docs/versioned_docs/version-0.3/cookbook/index.mdx
+++ /dev/null
@@ -1,27 +0,0 @@
----
-sidebar_position: 1
----
-
-import RecipeCarousel from '@site/src/components/RecipeCarousel';
-
-# Cookbook
-
-Practical patterns, recipes, and complete applications for building with Hindsight.
-
-<RecipeCarousel
-  title="Recipes"
-  items={[
-    { title: "Hindsight Quickstart", href: "/cookbook/recipes/quickstart" },
-    { title: "Per-User Memory", href: "/cookbook/recipes/per-user-memory" },
-    { title: "Support Agent with Shared Knowledge", href: "/cookbook/recipes/support-agent-shared-knowledge" },
-    { title: "Memory with LiteLLM", href: "/cookbook/recipes/litellm-memory-demo" },
-    { title: "Routing Tool Learning", href: "/cookbook/recipes/tool-learning-demo" }
-  ]}
-/>
-
-<RecipeCarousel
-  title="Applications"
-  items={[
-    { title: "OpenAI Agent + Hindsight Memory Integration", href: "/cookbook/applications/openai-fitness-coach" }
-  ]}
-/>
diff --git a/hindsight-docs/versioned_docs/version-0.3/cookbook/recipes/litellm-memory-demo.md b/hindsight-docs/versioned_docs/version-0.3/cookbook/recipes/litellm-memory-demo.md
deleted file mode 100644
index d633f706..00000000
--- a/hindsight-docs/versioned_docs/version-0.3/cookbook/recipes/litellm-memory-demo.md
+++ /dev/null
@@ -1,187 +0,0 @@
----
-sidebar_position: 4
----
-
-# Memory with LiteLLM
-
-
-:::tip Run this notebook
-This recipe is available as an interactive Jupyter notebook.
-[**Open in GitHub →**](https://github.com/vectorize-io/hindsight-cookbook/blob/main/notebooks/04-litellm-memory-demo.ipynb)
-:::
-
-
-This notebook demonstrates how to add persistent memory to any LLM app using the `hindsight-litellm` package. Memory storage and injection happen automatically via LiteLLM callbacks - no manual memory management needed!
-
-**Key features demonstrated:**
-1. `configure()` + `enable()` - Set up automatic memory integration
-2. Automatic storage - Conversations are stored after each LLM call
-3. Automatic injection - Relevant memories are injected into prompts
-
-The `hindsight-litellm` package hooks into LiteLLM's callback system to:
-- Store each conversation after successful LLM responses
-- Inject relevant memories into the system prompt before LLM calls
-
-## Prerequisites
-
-Make sure you have Hindsight running:
-
-```bash
-export OPENAI_API_KEY=your-key
-
-docker run --rm -it --pull always -p 8888:8888 -p 9999:9999 \
-  -e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
-  -e HINDSIGHT_API_LLM_MODEL=gpt-4o-mini \
-  -v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
-  ghcr.io/vectorize-io/hindsight:latest
-```
-
-- API: http://localhost:8888
-- UI: http://localhost:9999
-
-## Installation
-
-
-```python
-!pip install hindsight-litellm litellm nest_asyncio python-dotenv -U -q
-```
-
-## Setup
-
-
-```python
-import os
-import uuid
-import time
-import logging
-import nest_asyncio
-from dotenv import load_dotenv
-
-# Apply nest_asyncio for Jupyter compatibility
-nest_asyncio.apply()
-
-# Load environment variables
-load_dotenv()
-
-# Configure logging
-logging.basicConfig(level=logging.INFO)
-logging.getLogger("LiteLLM").setLevel(logging.WARNING)
-logging.getLogger("LiteLLM Router").setLevel(logging.WARNING)
-logging.getLogger("LiteLLM Proxy").setLevel(logging.WARNING)
-
-# Import hindsight_litellm
-import hindsight_litellm
-
-# Configuration
-HINDSIGHT_API_URL = os.getenv("HINDSIGHT_API_URL", "http://localhost:8888")
-
-# Check for API key
-if not os.getenv("OPENAI_API_KEY"):
-    print("Warning: OPENAI_API_KEY not set")
-```
-
-## Configure and Enable Automatic Memory
-
-This is all you need! After this, all LiteLLM calls will automatically:
-- Have relevant memories injected into the prompt
-- Store conversations to Hindsight after the response
-
-
-```python
-# Generate a unique bank_id for this demo session
-bank_id = f"demo-{uuid.uuid4().hex[:8]}"
-print(f"Using bank_id: {bank_id}")
-
-# Configure and enable hindsight
-hindsight_litellm.configure(
-    hindsight_api_url=HINDSIGHT_API_URL,
-    bank_id=bank_id,
-    store_conversations=True,  # Automatically store conversations
-    inject_memories=True,       # Automatically inject relevant memories
-    verbose=True,               # Enable logging to debug memory operations
-)
-hindsight_litellm.enable()
-
-print("Hindsight memory integration enabled!")
-```
-
-## Conversation 1: User Introduces Themselves
-
-In this first conversation, the user shares some information about themselves. This will be automatically stored to Hindsight memory.
-
-
-```python
-user_message_1 = "Hi! I'm Alex and I work at Google as a software engineer. I love Python and machine learning."
-print(f"User: {user_message_1}\n")
-
-# Use hindsight_litellm.completion() directly
-response_1 = hindsight_litellm.completion(
-    model="gpt-4o-mini",
-    messages=[
-        {"role": "system", "content": "You are a helpful assistant."},
-        {"role": "user", "content": user_message_1}
-    ],
-)
-
-assistant_response_1 = response_1.choices[0].message.content
-print(f"Assistant: {assistant_response_1}")
-print("\n(Conversation automatically stored to Hindsight)")
-```
-
-## Wait for Memory Processing
-
-Hindsight needs a few seconds to process and extract facts from the conversation.
-
-
-```python
-print("Waiting 12 seconds for memory processing...")
-time.sleep(12)
-print("Done!")
-```
-
-## Conversation 2: Test Memory-Augmented Response
-
-Now we start a fresh conversation and ask what the assistant remembers. The memories from the previous conversation will be automatically injected into the prompt!
-
-
-```python
-user_message_2 = "What do you know about me? What programming language should I use for my next project?"
-print(f"User: {user_message_2}\n")
-
-# Memories are automatically injected before this call!
-response_2 = hindsight_litellm.completion(
-    model="gpt-4o-mini",
-    messages=[
-        {"role": "system", "content": "You are a helpful assistant."},
-        {"role": "user", "content": user_message_2}
-    ],
-)
-
-print(f"Assistant: {response_2.choices[0].message.content}")
-```
-
-## Summary
-
-The assistant should have remembered that Alex:
-- Works at Google as a software engineer
-- Loves Python and machine learning
-
-And it should have recommended Python based on that knowledge!
-
-
-```python
-print(f"Memories stored in bank: {bank_id}")
-print(f"View in UI: http://localhost:9999/banks/{bank_id}")
-```
-
-## Cleanup
-
-
-```python
-hindsight_litellm.cleanup()
-
-# Optional: delete the bank
-import requests
-response = requests.delete(f"{HINDSIGHT_API_URL}/v1/default/banks/{bank_id}")
-print(f"Deleted bank: {response.json()}")
-```
diff --git a/hindsight-docs/versioned_docs/version-0.3/cookbook/recipes/per-user-memory.md b/hindsight-docs/versioned_docs/version-0.3/cookbook/recipes/per-user-memory.md
deleted file mode 100644
index 9a7e79b2..00000000
--- a/hindsight-docs/versioned_docs/version-0.3/cookbook/recipes/per-user-memory.md
+++ /dev/null
@@ -1,247 +0,0 @@
----
-sidebar_position: 2
----
-
-# Per-User Memory
-
-
-:::tip Run this notebook
-This recipe is available as an interactive Jupyter notebook.
-[**Open in GitHub →**](https://github.com/vectorize-io/hindsight-cookbook/blob/main/notebooks/02-per-user-memory.ipynb)
-:::
-
-
-The simplest pattern: give your agent persistent memory for each user. The agent remembers past conversations, user preferences, and context across sessions.
-
-## The Problem
-
-Without memory, every conversation starts from scratch:
-
-```
-Session 1: "I prefer dark mode and use Python"
-Session 2: "What's my preferred language?" → Agent doesn't know
-```
-
-## The Solution: One Bank Per User
-
-```
-┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
-│   User A Bank   │     │   User B Bank   │     │   User C Bank   │
-│                 │     │                 │     │                 │
-│  - Conversations│     │  - Conversations│     │  - Conversations│
-│  - Preferences  │     │  - Preferences  │     │  - Preferences  │
-│  - Context      │     │  - Context      │     │  - Context      │
-└─────────────────┘     └─────────────────┘     └─────────────────┘
-        │                       │                       │
-   100% isolated          100% isolated           100% isolated
-```
-
-Each user gets their own memory bank. Complete isolation, simple mental model.
-
-
-```python
-!pip install hindsight-client nest_asyncio openai python-dotenv -U
-```
-
-## 1. Create a Bank When User Signs Up
-
-
-```python
-# Jupyter notebooks already run an asyncio event loop. The hindsight client 
-# uses loop.run_until_complete() internally, but Python doesn't allow nested 
-# event loops by default. nest_asyncio patches this to allow nesting.
-import nest_asyncio
-nest_asyncio.apply()
-
-import os
-from dotenv import load_dotenv
-from openai import OpenAI as OpenAIClient
-
-# Load environment variables from .env file
-# Copy .env.example to .env and fill in your values
-load_dotenv()
-
-# Configuration (override with env vars if set)
-HINDSIGHT_API_URL = os.getenv("HINDSIGHT_API_URL", "http://localhost:8888")
-HINDSIGHT_UI_URL = os.getenv("HINDSIGHT_UI_URL", "http://localhost:9999")
-
-from hindsight_client import Hindsight
-
-client = Hindsight(base_url=HINDSIGHT_API_URL)
-llm = OpenAIClient()  # Uses OPENAI_API_KEY from .env
-
-def on_user_signup(user_id: str):
-    client.create_bank(
-        bank_id=f"user-{user_id}",
-        name=f"Memory for {user_id}"
-    )
-    print(f"View bank: {HINDSIGHT_UI_URL}/banks/user-{user_id}?view=documents")
-```
-
-## 2. Manage Conversation Sessions
-
-Use `document_id` to group messages belonging to the same conversation. When you retain with the same `document_id`, Hindsight replaces the previous version (upsert behavior), keeping the memory up-to-date as the conversation evolves.
-
-
-```python
-import uuid
-import json
-
-class ConversationSession:
-    def __init__(self, user_id: str):
-        self.user_id = user_id
-        self.session_id = str(uuid.uuid4())  # Unique ID for this conversation
-        self.messages = []
-
-    def add_message(self, role: str, content: str):
-        self.messages.append({"role": role, "content": content})
-
-    def save(self, client: Hindsight):
-        """Save the entire conversation. Replaces previous version if session_id exists."""
-        # Convert messages to string format for retain
-        content = "\n".join([f"{m['role']}: {m['content']}" for m in self.messages])
-        client.retain(
-            bank_id=f"user-{self.user_id}",
-            content=content,
-            document_id=self.session_id  # Same ID = upsert (replace old version)
-        )
-```
-
-## 3. Recall Context Before Responding
-
-
-```python
-def get_context(user_id: str, query: str):
-    result = client.recall(
-        bank_id=f"user-{user_id}",
-        query=query
-    )
-    return result.results
-```
-
-## 4. Complete Agent Loop
-
-
-```python
-def format_results(results):
-    """Format recall results for the prompt."""
-    if not results:
-        return "No relevant memories found."
-    return "\n".join([f"- {r.text}" for r in results])
-
-def format_messages(messages):
-    """Format conversation messages for the prompt."""
-    return "\n".join([f"{m['role']}: {m['content']}" for m in messages])
-
-def handle_message(session: ConversationSession, user_message: str):
-    # 1. Add user message to session
-    session.add_message("user", user_message)
-
-    # 2. Recall relevant context from past conversations
-    context = client.recall(
-        bank_id=f"user-{session.user_id}",
-        query=user_message
-    )
-
-    # 3. Build system prompt with memory
-    system_prompt = f"""You are a helpful assistant with memory of past conversations.
-
-## What you remember about this user
-{format_results(context.results)}
-
-Respond helpfully and reference relevant memories when appropriate."""
-
-    # 4. Generate response using OpenAI
-    response = llm.chat.completions.create(
-        model="gpt-4o-mini",
-        messages=[
-            {"role": "system", "content": system_prompt},
-            *[{"role": m["role"], "content": m["content"]} for m in session.messages]
-        ]
-    )
-    assistant_response = response.choices[0].message.content
-
-    # 5. Add assistant response to session
-    session.add_message("assistant", assistant_response)
-
-    # 6. Save the updated conversation (upserts based on session_id)
-    session.save(client)
-
-    print(f"User: {user_message}")
-    print(f"Assistant: {assistant_response}\n")
-    
-    return assistant_response
-```
-
-## 5. Starting a New Conversation
-
-
-```python
-# Create the user's bank
-on_user_signup("alice")
-
-# Each new conversation gets a new session with a unique ID
-session = ConversationSession(user_id="alice")
-
-# Multiple exchanges in the same conversation
-handle_message(session, "Hi! I'm working on a Python project")
-handle_message(session, "Can you help me with async/await?")
-
-# View the stored conversation in the UI.
-# Each message updates the same document (via document_id), so you'll see
-# the full conversation history in a single document rather than separate entries.
-print(f"\nView documents: {HINDSIGHT_UI_URL}/banks/user-alice?view=documents")
-```
-
-## How Document ID Works
-
-The `document_id` parameter is key to managing evolving conversations:
-
-| Scenario | Behavior |
-|----------|----------|
-| First retain with `document_id="session_123"` | Creates new document |
-| Retain again with same `document_id="session_123"` | **Replaces** previous version (upsert) |
-| Retain with different `document_id="session_456"` | Creates separate document |
-| Retain without `document_id` | Creates new document each time |
-
-This upsert behavior means:
-- You always retain the **full conversation** state
-- Facts are re-extracted from the complete conversation
-- No duplicate or stale facts from old versions
-- Memory stays consistent as conversations evolve
-
-## What Gets Remembered
-
-Hindsight automatically extracts and connects:
-
-- **Facts**: "User prefers Python", "User is building a CLI tool"
-- **Entities**: People, projects, technologies mentioned
-- **Relationships**: How entities relate to each other
-- **Temporal context**: When things happened
-
-You don't need to manually extract or structure this - just retain the conversations.
-
-## When to Use This Pattern
-
-**Good fit:**
-- Chatbots and assistants
-- Personal AI companions
-- Any 1:1 user-to-agent interaction
-
-**Consider adding shared knowledge if:**
-- You have product docs or FAQs to reference
-- Multiple users need access to the same information
-- See the Support Agent with Shared Knowledge notebook
-
-## Cleanup
-
-Delete the banks created during this notebook:
-
-
-```python
-import requests
-
-# Delete the user-alice bank
-response = requests.delete(f"{HINDSIGHT_API_URL}/v1/default/banks/user-alice")
-print(f"Deleted user-alice: {response.json()}")
-```
diff --git a/hindsight-docs/versioned_docs/version-0.3/cookbook/recipes/quickstart.md b/hindsight-docs/versioned_docs/version-0.3/cookbook/recipes/quickstart.md
deleted file mode 100644
index cea1cd77..00000000
--- a/hindsight-docs/versioned_docs/version-0.3/cookbook/recipes/quickstart.md
+++ /dev/null
@@ -1,162 +0,0 @@
----
-sidebar_position: 1
----
-
-# Hindsight Quickstart
-
-
-:::tip Run this notebook
-This recipe is available as an interactive Jupyter notebook.
-[**Open in GitHub →**](https://github.com/vectorize-io/hindsight-cookbook/blob/main/notebooks/01-quickstart.ipynb)
-:::
-
-
-This notebook covers the basics of using Hindsight:
-- **Retain**: Store information in memory
-- **Recall**: Retrieve memories matching a query
-- **Reflect**: Generate insights from memories
-
-## Prerequisites
-
-Make sure you have Hindsight running. The easiest way is via Docker:
-
-```bash
-export OPENAI_API_KEY=your-key
-
-docker run --rm -it --pull always -p 8888:8888 -p 9999:9999 \
-  -e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
-  -e HINDSIGHT_API_LLM_MODEL=gpt-4o-mini \
-  -v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
-  ghcr.io/vectorize-io/hindsight:latest
-```
-
-- API: http://localhost:8888
-- UI: http://localhost:9999
-
-## Installation
-
-Install the Hindsight Python client:
-
-
-```python
-!pip install hindsight-client nest_asyncio python-dotenv -U
-```
-
-## Connect to Hindsight
-
-
-```python
-# Jupyter notebooks already run an asyncio event loop. The hindsight client 
-# uses loop.run_until_complete() internally, but Python doesn't allow nested 
-# event loops by default. nest_asyncio patches this to allow nesting.
-import nest_asyncio
-nest_asyncio.apply()
-
-import os
-from dotenv import load_dotenv
-
-# Load environment variables from .env file
-# Copy .env.example to .env and fill in your values
-load_dotenv()
-
-# Configuration (override with env vars if set)
-HINDSIGHT_API_URL = os.getenv("HINDSIGHT_API_URL", "http://localhost:8888")
-HINDSIGHT_UI_URL = os.getenv("HINDSIGHT_UI_URL", "http://localhost:9999")
-
-from hindsight_client import Hindsight
-
-client = Hindsight(base_url=HINDSIGHT_API_URL)
-```
-
-## Retain: Store Information
-
-The `retain` operation is used to push new memories into Hindsight. It tells Hindsight to _retain_ the information you pass in.
-
-Behind the scenes, the retain operation uses an LLM to extract key facts, temporal data, entities, and relationships.
-
-
-```python
-# Simple retain
-client.retain(
-    bank_id="my-bank",
-    content="Alice works at Google as a software engineer"
-)
-
-# View the stored document in the UI:
-print(f"View documents: {HINDSIGHT_UI_URL}/banks/my-bank?view=documents")
-```
-
-
-```python
-# Retain with context and timestamp
-client.retain(
-    bank_id="my-bank",
-    content="Alice got promoted to senior engineer",
-    context="career update",
-    timestamp="2025-06-15T10:00:00Z"
-)
-```
-
-## Recall: Retrieve Memories
-
-The `recall` operation retrieves memories matching a query. It performs 4 retrieval strategies in parallel:
-- **Semantic**: Vector similarity
-- **Keyword**: BM25 exact matching
-- **Graph**: Entity/temporal/causal links
-- **Temporal**: Time range filtering
-
-
-```python
-# Simple recall
-results = client.recall(bank_id="my-bank", query="What does Alice do?")
-
-print("Memories:")
-for r in results.results:
-    print(f"  - {r.text}")
-```
-
-
-```python
-# Temporal recall
-results = client.recall(bank_id="my-bank", query="What happened in June?")
-
-print("Memories:")
-for r in results.results:
-    print(f"  - {r.text}")
-```
-
-## Reflect: Generate Insights
-
-The `reflect` operation performs a more thorough analysis of existing memories. This allows the agent to form new connections between memories which are then persisted as opinions and/or observations.
-
-Example use cases:
-- An AI Project Manager reflecting on what risks need to be mitigated
-- A Sales Agent reflecting on why certain outreach messages have gotten responses
-- A Support Agent reflecting on opportunities where customers have unanswered questions
-
-
-```python
-response = client.reflect(bank_id="my-bank", query="What should I know about Alice?")
-print(response)
-```
-
-## Memory Types
-
-Hindsight organizes memory into four networks to mimic human memory:
-
-- **World**: Facts about the world ("The stove gets hot")
-- **Experiences**: Agent's own experiences ("I touched the stove and it really hurt")
-- **Opinion**: Beliefs with confidence scores ("I shouldn't touch the stove again" - .99 confidence)
-- **Observation**: Complex mental models derived by reflecting on facts and experiences
-
-## Cleanup
-
-Delete the bank created during this notebook:
-
-
-```python
-import requests
-
-response = requests.delete(f"{HINDSIGHT_API_URL}/v1/default/banks/my-bank")
-print(f"Deleted my-bank: {response.json()}")
-```
diff --git a/hindsight-docs/versioned_docs/version-0.3/cookbook/recipes/support-agent-shared-knowledge.md b/hindsight-docs/versioned_docs/version-0.3/cookbook/recipes/support-agent-shared-knowledge.md
deleted file mode 100644
index 3bf5ba11..00000000
--- a/hindsight-docs/versioned_docs/version-0.3/cookbook/recipes/support-agent-shared-knowledge.md
+++ /dev/null
@@ -1,315 +0,0 @@
----
-sidebar_position: 3
----
-
-# Support Agent with Shared Knowledge
-
-
-:::tip Run this notebook
-This recipe is available as an interactive Jupyter notebook.
-[**Open in GitHub →**](https://github.com/vectorize-io/hindsight-cookbook/blob/main/notebooks/03-support-agent-shared-knowledge.ipynb)
-:::
-
-
-This pattern shows how to build a support agent that combines **per-user memory** with **shared product knowledge** (RAG), giving users personalized support while leveraging a single source of truth for documentation.
-
-## The Problem
-
-You're building a support agent that needs to:
-- Remember each user's history, preferences, and past issues
-- Access shared product documentation
-- Keep user data completely isolated from other users
-
-A naive approach would index product docs into each user's memory bank, but this is expensive and wasteful (N copies for N users).
-
-## The Solution: Multi-Bank Architecture
-
-```
-┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
-│   User A Bank   │     │   User B Bank   │     │  Shared Docs    │
-│                 │     │                 │     │     Bank        │
-│  - Conversations│     │  - Conversations│     │                 │
-│  - Preferences  │     │  - Preferences  │     │  - Product docs │
-│  - Past issues  │     │  - Past issues  │     │  - FAQs         │
-│  - Solutions    │     │  - Solutions    │     │  - Guides       │
-└────────┬────────┘     └────────┬────────┘     └────────┬────────┘
-         │                       │                       │
-         └───────────────────────┴───────────────────────┘
-                                 │
-                           Agent queries
-                           multiple banks
-```
-
-**Key benefits:**
-- Product docs indexed once, shared by all users
-- User memory is 100% isolated
-- Simple mental model, no complex filtering
-
-
-```python
-!pip install hindsight-client nest_asyncio openai python-dotenv -U
-```
-
-## 1. Set Up Memory Banks
-
-Create three types of banks:
-
-
-```python
-# Jupyter notebooks already run an asyncio event loop. The hindsight client 
-# uses loop.run_until_complete() internally, but Python doesn't allow nested 
-# event loops by default. nest_asyncio patches this to allow nesting.
-import nest_asyncio
-nest_asyncio.apply()
-
-import os
-from dotenv import load_dotenv
-from openai import OpenAI as OpenAIClient
-
-# Load environment variables from .env file
-# Copy .env.example to .env and fill in your values
-load_dotenv()
-
-# Configuration (override with env vars if set)
-HINDSIGHT_API_URL = os.getenv("HINDSIGHT_API_URL", "http://localhost:8888")
-HINDSIGHT_UI_URL = os.getenv("HINDSIGHT_UI_URL", "http://localhost:9999")
-
-from hindsight_client import Hindsight
-
-client = Hindsight(base_url=HINDSIGHT_API_URL)
-llm = OpenAIClient()  # Uses OPENAI_API_KEY from .env
-
-# Shared knowledge bank (created once)
-shared_bank = client.create_bank(
-    bank_id="product-docs",
-    name="Product Documentation"
-)
-
-# Per-user banks (created when user signs up)
-def create_user_bank(user_id: str):
-    return client.create_bank(
-        bank_id=f"user-{user_id}",
-        name=f"Memory for {user_id}"
-    )
-```
-
-## 2. Index Product Documentation
-
-Index your product docs into the shared bank (do this once, or on doc updates):
-
-
-```python
-# Index product documentation - retain each doc separately
-client.retain(
-    bank_id="product-docs",
-    content="# Pricing Tiers\n\nBasic: $10/mo, Pro: $25/mo, Enterprise: Contact us"
-)
-
-client.retain(
-    bank_id="product-docs",
-    content="# Getting Started\n\nTo set up your account, visit the dashboard and click 'New Project'"
-)
-
-# View the stored documents in the UI:
-print(f"View documents: {HINDSIGHT_UI_URL}/banks/product-docs?view=documents")
-```
-
-## 3. Store User Conversations
-
-After each support interaction, retain it in the user's bank:
-
-
-```python
-def save_conversation(user_id: str, messages: list):
-    # Convert messages to string format
-    content = "\n".join([f"{m['role']}: {m['content']}" for m in messages])
-    client.retain(
-        bank_id=f"user-{user_id}",
-        content=content
-    )
-```
-
-## 4. Query Multiple Banks at Support Time
-
-When handling a user query, retrieve context from both banks:
-
-
-```python
-def get_support_context(user_id: str, query: str):
-    # Get user's personal context
-    user_context = client.recall(
-        bank_id=f"user-{user_id}",
-        query=query
-    )
-
-    # Get relevant product documentation
-    docs_context = client.recall(
-        bank_id="product-docs",
-        query=query
-    )
-
-    return {
-        "user_history": user_context.results,
-        "documentation": docs_context.results
-    }
-```
-
-## 5. Build the Agent Prompt
-
-Combine both contexts in your agent's prompt:
-
-
-```python
-def format_results(results):
-    """Format recall results for the prompt."""
-    if not results:
-        return "No relevant information found."
-    return "\n".join([f"- {r.text}" for r in results])
-
-def build_prompt(query: str, context: dict) -> str:
-    return f"""You are a helpful support agent.
-
-## User's History
-{format_results(context["user_history"])}
-
-## Product Documentation
-{format_results(context["documentation"])}
-
-## Current Question
-{query}
-
-Use the user's history to personalize your response and the documentation
-for accurate product information. If you find a solution, remember it for
-future reference.
-"""
-```
-
-## Promoting Learnings to Shared Knowledge
-
-When the agent discovers a solution that's not in the docs, you can optionally promote it to a "learnings" bank:
-
-```
-┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
-│   User A Bank   │     │  Shared Docs    │     │    Learnings    │
-│                 │     │     Bank        │     │      Bank       │
-│  - Conversations│     │                 │     │                 │
-│  - Preferences  │     │  - Product docs │     │  - Verified     │
-│  - Past issues  │     │  - FAQs         │     │    solutions    │
-│  - Solutions    │     │  - Guides       │     │  - Workarounds  │
-└────────┬────────┘     └────────┬────────┘     └────────┬────────┘
-         │                       │                       │
-         └───────────────────────┴───────────────────────┘
-                                 │
-                           Agent queries
-                           all three banks
-```
-
-
-```python
-# Optional: Create a curated learnings bank
-learnings_bank = client.create_bank(
-    bank_id="support-learnings",
-    name="Curated Support Learnings"
-)
-
-# After a successful resolution
-def promote_learning(insight: str):
-    client.retain(
-        bank_id="support-learnings",
-        content=insight
-    )
-```
-
-## Complete Example
-
-
-```python
-def format_results(results):
-    if not results:
-        return "No relevant information found."
-    return "\n".join([f"- {r.text}" for r in results])
-
-def handle_support_request(user_id: str, query: str):
-    # 1. Recall from user's memory
-    user_recall = client.recall(
-        bank_id=f"user-{user_id}",
-        query=query
-    )
-
-    # 2. Recall from shared docs
-    docs_recall = client.recall(
-        bank_id="product-docs",
-        query=query
-    )
-
-    # 3. Recall from learnings (optional)
-    learnings_recall = client.recall(
-        bank_id="support-learnings",
-        query=query
-    )
-
-    # 4. Build system prompt with context
-    system_prompt = f"""You are a helpful support agent. Use the context below to answer the user's question.
-
-## User's History
-{format_results(user_recall.results)}
-
-## Product Documentation
-{format_results(docs_recall.results)}
-
-## Known Solutions
-{format_results(learnings_recall.results)}
-
-Provide helpful, accurate responses based on the documentation. Reference the user's history when relevant."""
-
-    # 5. Generate response using OpenAI
-    response = llm.chat.completions.create(
-        model="gpt-4o-mini",
-        messages=[
-            {"role": "system", "content": system_prompt},
-            {"role": "user", "content": query}
-        ]
-    )
-    assistant_response = response.choices[0].message.content
-
-    # 6. Save the conversation to user's memory
-    conversation = f"user: {query}\nassistant: {assistant_response}"
-    client.retain(
-        bank_id=f"user-{user_id}",
-        content=conversation
-    )
-
-    return assistant_response
-
-# Test the function
-create_user_bank("bob")
-print("User: How do I get started?")
-result = handle_support_request("bob", "How do I get started?")
-print(f"Assistant: {result}")
-print(f"\nView user memory: {HINDSIGHT_UI_URL}/banks/user-bob?view=documents")
-```
-
-## When to Use This Pattern
-
-**Good fit:**
-- Support agents with shared documentation
-- Multi-tenant applications with shared reference data
-- Any scenario needing user isolation + shared knowledge
-
-**Consider alternatives if:**
-- You need cross-user learning (users benefiting from other users' solutions)
-- Entity relationships must span across users and docs
-
-## Cleanup
-
-Delete the banks created during this notebook:
-
-
-```python
-import requests
-
-# Delete all banks created in this notebook
-for bank_id in ["product-docs", "support-learnings", "user-bob"]:
-    response = requests.delete(f"{HINDSIGHT_API_URL}/v1/default/banks/{bank_id}")
-    print(f"Deleted {bank_id}: {response.json()}")
-```
diff --git a/hindsight-docs/versioned_docs/version-0.3/cookbook/recipes/tool-learning-demo.md b/hindsight-docs/versioned_docs/version-0.3/cookbook/recipes/tool-learning-demo.md
deleted file mode 100644
index c2e3b870..00000000
--- a/hindsight-docs/versioned_docs/version-0.3/cookbook/recipes/tool-learning-demo.md
+++ /dev/null
@@ -1,372 +0,0 @@
----
-sidebar_position: 5
----
-
-# Routing Tool Learning
-
-
-:::tip Run this notebook
-This recipe is available as an interactive Jupyter notebook.
-[**Open in GitHub →**](https://github.com/vectorize-io/hindsight-cookbook/blob/main/notebooks/05-tool-learning-demo.ipynb)
-:::
-
-
-This notebook demonstrates how Hindsight helps an LLM learn which tool to use when tool names are ambiguous. Without memory, the LLM might randomly select between similarly-named tools. With Hindsight, it learns from past interactions and consistently makes the correct choice.
-
-## The Scenario
-
-We have a task routing system with two tools:
-- `route_to_channel_alpha` - Routes to processing channel Alpha
-- `route_to_channel_omega` - Routes to processing channel Omega
-
-The tool names and descriptions are **intentionally vague**. In reality:
-- Channel Alpha handles **FINANCIAL/PAYMENT** tasks (refunds, billing, etc.)
-- Channel Omega handles **TECHNICAL/SUPPORT** tasks (bugs, features, etc.)
-
-**Without Hindsight:** The LLM guesses randomly based on vague descriptions  
-**With Hindsight:** The LLM learns from feedback which channel handles what
-
-## Prerequisites
-
-Make sure you have Hindsight running:
-
-```bash
-export OPENAI_API_KEY=your-key
-
-docker run --rm -it --pull always -p 8888:8888 -p 9999:9999 \
-  -e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
-  -e HINDSIGHT_API_LLM_MODEL=gpt-4o-mini \
-  -v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
-  ghcr.io/vectorize-io/hindsight:latest
-```
-
-## Installation
-
-
-```python
-!pip install hindsight-litellm hindsight-client litellm nest_asyncio python-dotenv -U -q
-```
-
-## Setup
-
-
-```python
-import os
-import json
-import uuid
-import time
-import logging
-import nest_asyncio
-from typing import Optional
-from dotenv import load_dotenv
-
-nest_asyncio.apply()
-load_dotenv()
-
-logging.basicConfig(level=logging.INFO)
-logging.getLogger("LiteLLM").setLevel(logging.WARNING)
-logging.getLogger("LiteLLM Router").setLevel(logging.WARNING)
-logging.getLogger("httpx").setLevel(logging.WARNING)
-
-import litellm
-import hindsight_litellm
-from hindsight_client import Hindsight
-
-HINDSIGHT_API_URL = os.getenv("HINDSIGHT_API_URL", "http://localhost:8888")
-
-if not os.getenv("OPENAI_API_KEY"):
-    print("Warning: OPENAI_API_KEY not set")
-```
-
-## Define Tools
-
-These tool definitions are **intentionally ambiguous** - the descriptions don't reveal which channel handles what type of request.
-
-
-```python
-TOOLS = [
-    {
-        "type": "function",
-        "function": {
-            "name": "route_to_channel_alpha",
-            "description": "Routes the customer request to processing channel Alpha. Use this channel for appropriate request types.",
-            "parameters": {
-                "type": "object",
-                "properties": {
-                    "request_summary": {
-                        "type": "string",
-                        "description": "A brief summary of the customer's request"
-                    },
-                    "priority": {
-                        "type": "string",
-                        "enum": ["low", "medium", "high"],
-                        "description": "Priority level of the request"
-                    }
-                },
-                "required": ["request_summary"]
-            }
-        }
-    },
-    {
-        "type": "function",
-        "function": {
-            "name": "route_to_channel_omega",
-            "description": "Routes the customer request to processing channel Omega. Use this channel for appropriate request types.",
-            "parameters": {
-                "type": "object",
-                "properties": {
-                    "request_summary": {
-                        "type": "string",
-                        "description": "A brief summary of the customer's request"
-                    },
-                    "priority": {
-                        "type": "string",
-                        "enum": ["low", "medium", "high"],
-                        "description": "Priority level of the request"
-                    }
-                },
-                "required": ["request_summary"]
-            }
-        }
-    }
-]
-```
-
-## Test Scenarios
-
-A mix of financial and technical requests to test routing accuracy.
-
-
-```python
-TEST_SCENARIOS = [
-    {
-        "type": "financial",
-        "request": "I was charged twice for my subscription last month. I need a refund for the duplicate charge.",
-        "correct_tool": "route_to_channel_alpha"
-    },
-    {
-        "type": "technical",
-        "request": "The app keeps crashing when I try to upload a file larger than 10MB. This bug is blocking my work.",
-        "correct_tool": "route_to_channel_omega"
-    },
-    {
-        "type": "financial",
-        "request": "My invoice shows an incorrect amount. The billing department needs to fix this.",
-        "correct_tool": "route_to_channel_alpha"
-    },
-    {
-        "type": "technical",
-        "request": "I'd like to request a new feature: the ability to export reports as PDF.",
-        "correct_tool": "route_to_channel_omega"
-    },
-    {
-        "type": "financial",
-        "request": "I need to update my payment method and understand why my last payment failed.",
-        "correct_tool": "route_to_channel_alpha"
-    },
-]
-```
-
-## Helper Functions
-
-
-```python
-SYSTEM_PROMPT = """You are a customer service routing agent. Your job is to route customer requests to the appropriate processing channel.
-
-You have access to two routing channels:
-- route_to_channel_alpha: Routes to channel Alpha
-- route_to_channel_omega: Routes to channel Omega
-
-Analyze the customer's request and route it to the most appropriate channel. You must call one of the routing functions to process the request.
-
-Important: Base your routing decision on what you know about each channel's purpose. If you have learned from previous interactions which channel handles specific types of requests, use that knowledge."""
-
-
-def make_routing_request(user_request: str, use_hindsight: bool, bank_id: Optional[str] = None):
-    """Make a routing request and return the tool called."""
-    messages = [
-        {"role": "system", "content": SYSTEM_PROMPT},
-        {"role": "user", "content": f"Customer Request: {user_request}"}
-    ]
-
-    if use_hindsight and bank_id:
-        response = hindsight_litellm.completion(
-            model="gpt-4o-mini",
-            messages=messages,
-            tools=TOOLS,
-            tool_choice="required",
-            temperature=0.0,
-        )
-    else:
-        response = litellm.completion(
-            model="gpt-4o-mini",
-            messages=messages,
-            tools=TOOLS,
-            tool_choice="required",
-            temperature=0.7,
-        )
-
-    if response.choices[0].message.tool_calls:
-        tool_call = response.choices[0].message.tool_calls[0]
-        return tool_call.function.name
-    return None
-
-
-def store_feedback(bank_id: str, request: str, correct_tool: str, request_type: str):
-    """Store feedback about which tool was correct for a request type."""
-    client = Hindsight(base_url=HINDSIGHT_API_URL, timeout=60.0)
-
-    feedback_content = f"""ROUTING FEEDBACK:
-Request type: {request_type}
-Customer request: "{request}"
-Correct routing: {correct_tool}
-
-LEARNED RULE: {request_type.upper()} requests (like refunds, billing, payments, charges, invoices) should ALWAYS be routed to {correct_tool}.
-This is important institutional knowledge for routing decisions."""
-
-    client.retain(
-        bank_id=bank_id,
-        content=feedback_content,
-        context=f"routing:feedback:{request_type}",
-        metadata={"request_type": request_type, "correct_tool": correct_tool}
-    )
-```
-
-## Phase 1: Without Hindsight (No Memory)
-
-The LLM has no prior knowledge about which channel handles what. With ambiguous tool descriptions, it may route incorrectly.
-
-
-```python
-print("=" * 60)
-print("PHASE 1: WITHOUT HINDSIGHT (No Memory)")
-print("=" * 60)
-
-phase1_results = []
-for i, scenario in enumerate(TEST_SCENARIOS[:3], 1):
-    print(f"\n--- Test {i}: {scenario['type'].upper()} Request ---")
-    print(f"Request: \"{scenario['request'][:60]}...\"")
-
-    tool_name = make_routing_request(scenario['request'], use_hindsight=False)
-
-    is_correct = tool_name == scenario['correct_tool']
-    phase1_results.append(is_correct)
-
-    print(f"LLM chose: {tool_name}")
-    print(f"Correct tool: {scenario['correct_tool']}")
-    print(f"Result: {'✓ CORRECT' if is_correct else '✗ INCORRECT'}")
-
-phase1_accuracy = sum(phase1_results) / len(phase1_results) * 100
-print(f"\n>>> Phase 1 Accuracy: {phase1_accuracy:.0f}% ({sum(phase1_results)}/{len(phase1_results)})")
-```
-
-## Phase 2: Teaching Phase
-
-Now we provide feedback about correct routing to build memory. This simulates a human supervisor correcting the AI's routing decisions.
-
-
-```python
-bank_id = f"tool-learning-{uuid.uuid4().hex[:8]}"
-print(f"Using bank_id: {bank_id}")
-
-# Configure and enable Hindsight
-hindsight_litellm.configure(
-    hindsight_api_url=HINDSIGHT_API_URL,
-    bank_id=bank_id,
-    store_conversations=True,
-    inject_memories=True,
-    max_memories=10,
-    recall_budget="high",
-    verbose=False,
-)
-hindsight_litellm.enable()
-
-print("\nStoring routing feedback...")
-
-feedback_examples = [
-    ("I need a refund for an incorrect charge on my account.", "route_to_channel_alpha", "financial"),
-    ("There's a bug in the system causing data loss.", "route_to_channel_omega", "technical"),
-    ("My billing statement has errors that need correction.", "route_to_channel_alpha", "financial"),
-    ("I want to request a new feature for the dashboard.", "route_to_channel_omega", "technical"),
-]
-
-for request, correct_tool, req_type in feedback_examples:
-    print(f"  Storing: {req_type.upper()} → {correct_tool}")
-    store_feedback(bank_id, request, correct_tool, req_type)
-
-print("\nWaiting 15 seconds for Hindsight to process memories...")
-time.sleep(15)
-print("Done!")
-```
-
-## Phase 3: With Hindsight (Memory-Augmented)
-
-The LLM now has access to learned routing knowledge via Hindsight. It should route requests correctly based on past feedback.
-
-
-```python
-print("=" * 60)
-print("PHASE 3: WITH HINDSIGHT (Memory-Augmented)")
-print("=" * 60)
-
-phase3_results = []
-for i, scenario in enumerate(TEST_SCENARIOS, 1):
-    print(f"\n--- Test {i}: {scenario['type'].upper()} Request ---")
-    print(f"Request: \"{scenario['request'][:60]}...\"")
-
-    tool_name = make_routing_request(
-        scenario['request'],
-        use_hindsight=True,
-        bank_id=bank_id
-    )
-
-    is_correct = tool_name == scenario['correct_tool']
-    phase3_results.append(is_correct)
-
-    print(f"LLM chose: {tool_name}")
-    print(f"Correct tool: {scenario['correct_tool']}")
-    print(f"Result: {'✓ CORRECT' if is_correct else '✗ INCORRECT'}")
-
-phase3_accuracy = sum(phase3_results) / len(phase3_results) * 100
-print(f"\n>>> Phase 3 Accuracy: {phase3_accuracy:.0f}% ({sum(phase3_results)}/{len(phase3_results)})")
-```
-
-## Summary
-
-
-```python
-print("=" * 60)
-print("SUMMARY")
-print("=" * 60)
-print(f"\nPhase 1 (No Memory):      {phase1_accuracy:.0f}% accuracy")
-print(f"Phase 3 (With Hindsight): {phase3_accuracy:.0f}% accuracy")
-
-improvement = phase3_accuracy - phase1_accuracy
-if improvement > 0:
-    print(f"\n🎉 Improvement: +{improvement:.0f}% accuracy with Hindsight!")
-elif improvement == 0:
-    print(f"\nNote: Results may vary. Run again to see learning effect.")
-else:
-    print(f"\nNote: Phase 1 got lucky! Run again to see typical behavior.")
-
-print(f"\nMemories stored in bank: {bank_id}")
-print(f"View in UI: http://localhost:9999/banks/{bank_id}")
-
-print("\n" + "=" * 60)
-print("KEY INSIGHT")
-print("=" * 60)
-print("Hindsight allows the LLM to learn from experience which tool")
-print("to use, even when tool names/descriptions are ambiguous.")
-```
-
-## Cleanup
-
-
-```python
-hindsight_litellm.cleanup()
-
-# Optional: delete the bank
-import requests
-response = requests.delete(f"{HINDSIGHT_API_URL}/v1/default/banks/{bank_id}")
-print(f"Deleted bank: {response.json()}")
-```
diff --git a/hindsight-docs/versioned_docs/version-0.4/cookbook/applications/chat-memory.md b/hindsight-docs/versioned_docs/version-0.4/cookbook/applications/chat-memory.md
deleted file mode 100644
index cf36aa9e..00000000
--- a/hindsight-docs/versioned_docs/version-0.4/cookbook/applications/chat-memory.md
+++ /dev/null
@@ -1,120 +0,0 @@
----
-sidebar_position: 1
----
-
-# Chat Memory App
-
-
-:::info Complete Application
-This is a complete, runnable application demonstrating Hindsight integration.
-[**View source on GitHub →**](https://github.com/vectorize-io/hindsight-cookbook/tree/main/applications/chat-memory)
-:::
-
-
-A demo chat application that uses Groq's `qwen/qwen3-32b` model with Hindsight for persistent per-user memory.
-
-## Features
-
-- 🧠 **Persistent Memory**: Each user gets their own memory bank that remembers conversations
-- 🚀 **Fast AI**: Powered by Groq's high-speed inference
-- 🎯 **Per-User Context**: Isolated memory per user with automatic context retrieval
-- 💬 **Real-time Chat**: Instant responses with memory-augmented context
-
-## Setup
-
-### 1. Start Hindsight API
-
-First, start the Hindsight API server using Docker:
-
-```bash
-export GROQ_API_KEY=your_groq_api_key_here
-
-# Start Hindsight with Groq as the LLM provider
-docker run --rm -it --pull always -p 8888:8888 -p 9999:9999 \
-  -e HINDSIGHT_API_LLM_PROVIDER=groq \
-  -e HINDSIGHT_API_LLM_API_KEY=$GROQ_API_KEY \
-  -e HINDSIGHT_API_LLM_MODEL="openai/gpt-oss-20b" \
-  -v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
-  ghcr.io/vectorize-io/hindsight:latest
-```
-
-- **API**: http://localhost:8888
-- **Control Plane UI**: http://localhost:9999
-
-### 2. Configure Environment
-
-Copy your Groq API key to the environment file:
-
-```bash
-# Update .env.local with your Groq API key
-echo "GROQ_API_KEY=your_groq_api_key_here" > .env.local
-echo "HINDSIGHT_API_URL=http://localhost:8888" >> .env.local
-```
-
-If you don't have one, you can get a free Groq API key here: https://console.groq.com/home
-
-### 3. Install Dependencies
-
-```bash
-npm install
-```
-
-### 4. Run the App
-
-```bash
-npm run dev
-```
-
-Open http://localhost:3000 in your browser.
-
-## How It Works
-
-1. **User Identity**: Each browser session gets a unique user ID
-2. **Memory Bank Creation**: First message creates a personal memory bank in Hindsight
-3. **Context Retrieval**: Before responding, relevant memories are retrieved
-4. **Memory Augmented Response**: Groq generates responses with memory context
-5. **Conversation Storage**: Each conversation is stored for future context
-
-## Architecture
-
-```
-User Message
-     ↓
-Next.js API Route (/api/chat)
-     ↓
-Hindsight.recall() → Get relevant memories
-     ↓
-Groq API → Generate response with memory context
-     ↓
-Hindsight.retain() → Store conversation
-     ↓
-Response to User
-```
-
-## Memory Bank Structure
-
-Each user gets their own isolated memory bank with:
-- **Name**: "Chat Memory for [userId]"
-- **Background**: Conversational AI assistant context
-- **Disposition**: Empathetic (4), Low Skepticism (2), Balanced Literalism (3)
-
-## Try It Out
-
-1. **First Conversation**: Tell the assistant about yourself
-   - "Hi! I'm a software engineer from San Francisco. I love Python and machine learning."
-
-2. **Second Conversation**: Ask what it remembers
-   - "What do you know about me?"
-   - "What programming languages do I like?"
-
-3. **Context Building**: Continue sharing preferences
-   - "I prefer VS Code over other editors"
-   - "I'm working on a React project"
-
-4. **Memory Verification**: Visit the Hindsight Control Plane at http://localhost:9999 to see stored memories
-
-## Development
-
-- **Groq Model**: Uses `qwen/qwen3-32b` for fast, high-quality responses
-- **Memory Storage**: Automatic conversation retention with context categorization
-- **Memory Retrieval**: Semantic search with 2048 token budget for relevant context
diff --git a/hindsight-docs/versioned_docs/version-0.4/cookbook/applications/deliveryman-demo.md b/hindsight-docs/versioned_docs/version-0.4/cookbook/applications/deliveryman-demo.md
deleted file mode 100644
index 866c3e21..00000000
--- a/hindsight-docs/versioned_docs/version-0.4/cookbook/applications/deliveryman-demo.md
+++ /dev/null
@@ -1,145 +0,0 @@
----
-sidebar_position: 2
----
-
-# Deliveryman Demo
-
-
-:::info Complete Application
-This is a complete, runnable application demonstrating Hindsight integration.
-[**View source on GitHub →**](https://github.com/vectorize-io/hindsight-cookbook/tree/main/applications/deliveryman-demo)
-:::
-
-
-A delivery agent simulation that demonstrates Hindsight's long-term memory capabilities. An AI agent navigates a multi-building office complex to deliver packages, learning employee locations and optimal paths over time through mental models.
-
-## Prerequisites
-
-- Python 3.11+
-- Node.js 18+
-- [uv](https://docs.astral.sh/uv/) (Python package manager)
-
-## Setup (Fresh Environment)
-
-### 1. Clone Repositories
-
-```bash
-# Clone Hindsight (memory engine)
-git clone https://github.com/anthropics/hindsight.git
-
-# Clone the cookbook (contains this demo)
-git clone https://github.com/anthropics/hindsight-cookbook.git
-```
-
-### 2. Start Hindsight API
-
-```bash
-cd hindsight
-cp .env.example .env
-```
-
-Edit `.env` with your LLM configuration:
-
-```bash
-HINDSIGHT_API_LLM_PROVIDER=groq
-HINDSIGHT_API_LLM_API_KEY=<your-groq-api-key>
-HINDSIGHT_API_LLM_MODEL=openai/gpt-oss-120b
-HINDSIGHT_API_HOST=0.0.0.0
-HINDSIGHT_API_PORT=8888
-HINDSIGHT_API_ENABLE_OBSERVATIONS=true
-
-# Retain extraction settings (improves employee/location extraction)
-HINDSIGHT_API_RETAIN_EXTRACTION_MODE=custom
-HINDSIGHT_API_RETAIN_CUSTOM_INSTRUCTIONS="Delivery agent. Remember employee locations, building layout, and optimal paths."
-
-# Embedded database storage
-PG0_DATA_DIR=/tmp/hindsight-data
-```
-
-Start the API:
-
-```bash
-./scripts/dev/start-api.sh
-# Runs on http://localhost:8888
-```
-
-### 3. Start Hindsight Control Plane (Optional)
-
-The control plane provides a web UI for inspecting memory banks, facts, and mental models.
-
-```bash
-cd hindsight
-./scripts/dev/start-control-plane.sh
-# Runs on a dynamic port (check terminal output)
-```
-
-### 4. Start Demo Backend
-
-```bash
-cd hindsight-cookbook/deliveryman-demo/backend
-
-# Create virtual environment and install dependencies
-python -m venv .venv
-source .venv/bin/activate
-pip install -r requirements.txt
-```
-
-Create `backend/.env`:
-
-```bash
-OPENAI_API_KEY=<your-openai-api-key>
-GROQ_API_KEY=<your-groq-api-key>
-HINDSIGHT_API_URL=http://localhost:8888
-LLM_MODEL=openai/gpt-4o
-```
-
-Start the backend:
-
-```bash
-./run.sh
-# Or manually:
-python -m uvicorn app.main:app --host 0.0.0.0 --port 8000 --ws wsproto --reload
-```
-
-**Note:** The `--ws wsproto` flag is required for WebSocket support. Without it, connections will fail with error 1006.
-
-### 5. Start Demo Frontend
-
-```bash
-cd hindsight-cookbook/deliveryman-demo/frontend
-npm install
-npm run dev
-# Runs on http://localhost:5173
-```
-
-### 6. Open the Demo
-
-Navigate to http://localhost:5173 in your browser.
-
-## How It Works
-
-1. The agent receives a delivery task (e.g., "Deliver Package #3954 to Victor Huang")
-2. It navigates a multi-building complex with floors, elevators, and sky bridges
-3. Along the way it encounters employees and learns their locations
-4. After each delivery, the conversation is sent to Hindsight via the **retain** API
-5. Hindsight extracts facts (employee locations, building layout) and builds **mental models**
-6. On subsequent deliveries, the agent queries Hindsight to recall what it learned
-
-## Architecture
-
-```
-Browser (5173) → Frontend (React + Phaser)
-                    ↓ WebSocket
-                 Backend (8000) → FastAPI + Delivery Agent
-                    ↓ HTTP
-                 Hindsight API (8888) → Memory Engine + PostgreSQL
-```
-
-## Troubleshooting
-
-| Problem | Solution |
-|---------|----------|
-| WebSocket error 1006 | Restart backend with `--ws wsproto` flag |
-| Mental models missing employees | Check `HINDSIGHT_API_RETAIN_EXTRACTION_MODE=custom` is set |
-| Hindsight connection refused | Verify Hindsight API is running on port 8888 |
-| Frontend shows "Disconnected" | Check backend is running on port 8000 |
diff --git a/hindsight-docs/versioned_docs/version-0.4/cookbook/applications/go-memory-service.md b/hindsight-docs/versioned_docs/version-0.4/cookbook/applications/go-memory-service.md
deleted file mode 100644
index ef0728fc..00000000
--- a/hindsight-docs/versioned_docs/version-0.4/cookbook/applications/go-memory-service.md
+++ /dev/null
@@ -1,102 +0,0 @@
----
-sidebar_position: 3
----
-
-# Go Memory-Augmented API
-
-
-:::info Complete Application
-This is a complete, runnable application demonstrating Hindsight integration.
-[**View source on GitHub →**](https://github.com/vectorize-io/hindsight-cookbook/tree/main/applications/go-memory-service)
-:::
-
-
-A Go HTTP microservice demonstrating per-user memory isolation with Hindsight. Remembers each user's tech stack, problems solved, and preferences to provide personalized assistance.
-
-## Features
-
-- 🔐 **Per-User Isolation**: Each user gets their own memory bank
-- 🧠 **Context-Aware Responses**: Uses recall + reflect for personalized answers
-- 🏃 **Fire-and-Forget Memory**: Background goroutines store interactions without blocking responses
-- 🏷️ **Tag-Based Partitioning**: Organize memories by type (projects, debugging, preferences)
-
-## Setup
-
-### 1. Start Hindsight
-
-```bash
-export OPENAI_API_KEY=your-key
-
-docker run --rm -it --pull always -p 8888:8888 -p 9999:9999 \
-  -e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
-  -e HINDSIGHT_API_LLM_MODEL=gpt-4o-mini \
-  -v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
-  ghcr.io/vectorize-io/hindsight:latest
-```
-
-### 2. Run the service
-
-```bash
-go run main.go
-```
-
-### 3. Try it out
-
-```bash
-# Store memories
-curl -s localhost:8080/learn -d '{
-  "user_id": "alice",
-  "content": "I am building a Go microservice with gRPC and PostgreSQL",
-  "tags": ["project"]
-}'
-
-curl -s localhost:8080/learn -d '{
-  "user_id": "alice",
-  "content": "I prefer structured logging with slog over zerolog",
-  "tags": ["preferences"]
-}'
-
-# Ask questions (uses recall + reflect)
-curl -s localhost:8080/ask -d '{
-  "user_id": "alice",
-  "query": "What tech stack am I using?"
-}' | jq .
-
-# Raw memory recall
-curl -s "localhost:8080/recall/alice?q=database" | jq .
-```
-
-## API Endpoints
-
-- `POST /learn` - Store new information for a user
-- `POST /ask` - Ask a question using the user's memories
-- `GET /recall/{userID}?q=query` - Direct memory recall
-- `GET /health` - Health check
-
-## Key Patterns
-
-**Per-User Banks**: Each user gets an isolated memory bank (`user-alice`, `user-bob`)
-
-**Async Memory Storage**: Interactions are stored in background goroutines:
-
-```go
-go func() {
-    bgCtx, cancel := context.WithTimeout(context.Background(), 30*time.Second)
-    defer cancel()
-
-    retainReq := hindsight.RetainRequest{
-        Items: []hindsight.MemoryItem{{
-            Content: interaction,
-            Context: *hindsight.NewNullableString(hindsight.PtrString("Q&A interaction")),
-        }},
-    }
-    client.MemoryAPI.RetainMemories(bgCtx, bankID).RetainRequest(retainReq).Execute()
-}()
-```
-
-**Tag-Based Filtering**: Partition memories within a bank by type for scoped retrieval
-
-## Learn More
-
-- [Go SDK Documentation](https://hindsight.vectorize.io/sdks/go)
-- [Hindsight Documentation](https://hindsight.vectorize.io)
diff --git a/hindsight-docs/versioned_docs/version-0.4/cookbook/applications/hindsight-litellm-demo.md b/hindsight-docs/versioned_docs/version-0.4/cookbook/applications/hindsight-litellm-demo.md
deleted file mode 100644
index 6ec29c3c..00000000
--- a/hindsight-docs/versioned_docs/version-0.4/cookbook/applications/hindsight-litellm-demo.md
+++ /dev/null
@@ -1,206 +0,0 @@
----
-sidebar_position: 4
----
-
-# Memory Approaches Comparison Demo
-
-
-:::info Complete Application
-This is a complete, runnable application demonstrating Hindsight integration.
-[**View source on GitHub →**](https://github.com/vectorize-io/hindsight-cookbook/tree/main/applications/hindsight-litellm-demo)
-:::
-
-
-Interactive Streamlit app comparing three memory approaches for LLM applications:
-
-1. **No Memory** - Each query is independent (baseline)
-2. **Full Conversation History** - Pass entire conversation (truncated to simulate context limits)
-3. **Hindsight Memory** - Intelligent semantic memory retrieval
-
-This demo showcases how Hindsight's semantic memory outperforms traditional approaches, especially as conversations grow longer.
-
-## Quick Start
-
-```bash
-# 1. Set your OpenAI API key
-export OPENAI_API_KEY=your-key
-
-# 2. Start Hindsight server
-docker run -d -p 8888:8888 -p 9999:9999 \
-  -e HINDSIGHT_API_LLM_PROVIDER=openai \
-  -e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
-  ghcr.io/vectorize-io/hindsight:latest
-
-# 3. Run the demo
-./run.sh
-```
-
-Then open http://localhost:8501 in your browser.
-
-## What This Demo Shows
-
-### The Problem with Traditional Approaches
-
-| Approach | How it Works | Limitation |
-|----------|--------------|------------|
-| **No Memory** | Each query standalone | Forgets everything between messages |
-| **Full History** | Pass all messages to LLM | Token limits cause truncation - loses early context |
-| **Hindsight** | Semantic retrieval of relevant facts | Retrieves what's relevant regardless of when it was said |
-
-### Key Insight
-
-After 5-10 messages, watch the **Full Conversation History** column start losing early context due to truncation (artificially set to 4 messages to demonstrate this quickly). Meanwhile, **Hindsight Memory** can still recall facts from the beginning because it uses semantic retrieval rather than sequential history.
-
-## Testing the Demo
-
-1. **Introduce yourself**:
-   - "Hi, I'm Sarah, a data scientist at Netflix"
-   - "I prefer Python and love machine learning"
-
-2. **Have several exchanges** about different topics
-
-3. **Test recall**:
-   - "What programming language should I use?"
-   - "What do you know about me?"
-
-Watch how the three columns respond differently as the conversation grows.
-
-## Features
-
-- **Side-by-side comparison** of all three approaches
-- **Debug panels** showing what context each approach uses
-- **Memory explorer** to search Hindsight memories directly
-- **Configurable settings** for history truncation, max memories, etc.
-- **Multi-provider support** via LiteLLM (OpenAI, Anthropic, Groq)
-
-## Prerequisites
-
-- Python 3.10+
-- Hindsight server running (Docker recommended)
-- At least one LLM API key (OpenAI recommended)
-
-## Setup
-
-### Using run.sh (Recommended)
-
-```bash
-# Set API key
-export OPENAI_API_KEY=your-key
-
-# Start Hindsight, then run:
-./run.sh
-```
-
-The script will check and install dependencies automatically.
-
-### Manual Setup
-
-```bash
-# Install dependencies
-pip install streamlit litellm
-
-# Install Hindsight packages
-pip install hindsight-client hindsight-litellm
-
-# Run the app
-streamlit run app.py
-```
-
-### Starting Hindsight Server
-
-```bash
-docker run -d -p 8888:8888 -p 9999:9999 \
-  -e HINDSIGHT_API_LLM_PROVIDER=openai \
-  -e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
-  ghcr.io/vectorize-io/hindsight:latest
-
-# Verify it's running
-curl http://localhost:8888/health
-```
-
-## Configuration
-
-### Sidebar Options
-
-**Model Selection:**
-- Provider: OpenAI, Anthropic, Groq
-- Model: Various models per provider
-- Custom model ID support
-
-**Full History Config:**
-- Max Messages to Keep (default: 4 to demonstrate truncation)
-
-**Hindsight Config:**
-- API URL (default: http://localhost:8888)
-- Bank ID and Entity ID for memory isolation
-- Max Memories to retrieve
-- Recall Budget (low/mid/high)
-
-**Generation Settings:**
-- Temperature
-- Max Tokens
-- System Prompt
-
-## Supported Models
-
-### OpenAI
-- gpt-4o, gpt-4o-mini, gpt-4-turbo, gpt-4, gpt-3.5-turbo
-
-### Anthropic
-- claude-3-5-sonnet-20241022, claude-3-5-haiku-20241022
-- claude-3-opus-20240229, claude-3-sonnet-20240229
-
-### Groq
-- groq/llama-3.1-70b-versatile, groq/llama-3.1-8b-instant
-- groq/mixtral-8x7b-32768
-
-## Environment Variables
-
-```bash
-# Required
-export OPENAI_API_KEY=sk-...
-
-# Optional (for other providers)
-export ANTHROPIC_API_KEY=sk-ant-...
-export GROQ_API_KEY=gsk_...
-
-# Optional
-export HINDSIGHT_URL=http://localhost:8888
-```
-
-## Troubleshooting
-
-### Hindsight server not responding
-
-```bash
-# Check if running
-curl http://localhost:8888/health
-
-# Start with Docker
-docker run -d -p 8888:8888 -p 9999:9999 \
-  -e HINDSIGHT_API_LLM_PROVIDER=openai \
-  -e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
-  ghcr.io/vectorize-io/hindsight:latest
-```
-
-### hindsight-litellm not installed
-
-```bash
-pip install hindsight-litellm
-```
-
-### API key errors
-
-Make sure the appropriate API key is set:
-```bash
-export OPENAI_API_KEY=your-key
-```
-
-## Related
-
-- [Hindsight](https://github.com/vectorize-io/hindsight) - Memory infrastructure for AI applications
-- [hindsight-litellm](https://github.com/vectorize-io/hindsight/tree/main/hindsight-integrations/litellm) - LiteLLM integration package
-
-## License
-
-MIT
diff --git a/hindsight-docs/versioned_docs/version-0.4/cookbook/applications/hindsight-tool-learning-demo.md b/hindsight-docs/versioned_docs/version-0.4/cookbook/applications/hindsight-tool-learning-demo.md
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index 81d10dbf..00000000
--- a/hindsight-docs/versioned_docs/version-0.4/cookbook/applications/hindsight-tool-learning-demo.md
+++ /dev/null
@@ -1,123 +0,0 @@
----
-sidebar_position: 5
----
-
-# Tool Learning Demo
-
-
-:::info Complete Application
-This is a complete, runnable application demonstrating Hindsight integration.
-[**View source on GitHub →**](https://github.com/vectorize-io/hindsight-cookbook/tree/main/applications/hindsight-tool-learning-demo)
-:::
-
-
-An interactive Streamlit demo showing how Hindsight helps LLMs learn which tool to use when tool names are ambiguous.
-
-## The Problem
-
-When building AI agents with tool/function calling, tool names and descriptions aren't always clear. An LLM might randomly select between similarly-named tools, leading to incorrect behavior.
-
-## The Scenario
-
-This demo simulates a **customer service routing system** with two channels:
-
-| Tool | Description (What the LLM sees) | Actual Purpose (Hidden) |
-|------|--------------------------------|------------------------|
-| `route_to_channel_alpha` | "Routes to channel Alpha for appropriate request types" | Financial issues (refunds, billing, payments) |
-| `route_to_channel_omega` | "Routes to channel Omega for appropriate request types" | Technical issues (bugs, features, errors) |
-
-The descriptions are **intentionally vague**! Without prior knowledge, the LLM must guess which channel handles what.
-
-## The Solution: Learning with Hindsight
-
-With Hindsight memory:
-1. **Store routing feedback** about which channel handles which request type
-2. **Retrieve learned knowledge** when making routing decisions
-3. **Consistently route correctly** based on past experience
-
-## Quick Start
-
-### Prerequisites
-
-1. **Hindsight Server** running (Docker):
-```bash
-docker run -d -p 8888:8888 -p 9999:9999 \
-  -e HINDSIGHT_API_LLM_PROVIDER=openai \
-  -e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
-  -e HINDSIGHT_API_LLM_MODEL=gpt-4o-mini \
-  ghcr.io/vectorize-io/hindsight:latest
-```
-
-2. **OpenAI API Key**:
-```bash
-export OPENAI_API_KEY=your-key-here
-```
-
-### Run the Demo
-
-```bash
-./run.sh
-```
-
-Or manually:
-```bash
-pip install -r requirements.txt
-streamlit run app.py
-```
-
-## How to Use the Demo
-
-### Step 1: Test Without Memory (Baseline)
-
-1. Select a **Financial Request** (e.g., "I need a refund...")
-2. Click **Route Request**
-3. Observe: The "Without Hindsight" column may route incorrectly
-
-### Step 2: Route First Customer and Learn
-
-1. Route a customer → Both LLMs route simultaneously
-2. Feedback is automatically stored to Hindsight
-3. Wait ~5 seconds for Hindsight to index the memory
-
-### Step 3: Test With Memory
-
-1. Select another request (financial or technical)
-2. Click **Route Request**
-3. Observe: The "With Hindsight" column should now route correctly!
-
-### Step 4: View Statistics
-
-- See accuracy comparison between "Without Memory" vs "With Hindsight"
-- Review test history to see the improvement over time
-
-## Demo Features
-
-- **Side-by-side comparison**: See routing results with and without memory
-- **Pre-defined test requests**: Financial and technical scenarios
-- **Custom requests**: Enter your own customer requests
-- **Memory Explorer**: Query stored routing knowledge directly
-- **Live statistics**: Track accuracy improvement
-
-## Key Insight
-
-> Even when tool names and descriptions don't reveal their purpose, Hindsight allows the LLM to **learn from experience** which tool to use for which type of request.
-
-This is especially valuable for:
-- Enterprise systems with legacy tool names
-- Multi-tenant systems where tools have generic names
-- Agents that need to learn organization-specific workflows
-
-## Configuration
-
-| Setting | Default | Description |
-|---------|---------|-------------|
-| Model | gpt-4o-mini | LLM model for routing decisions |
-| Temperature (No Memory) | 0.7 | Randomness for baseline tests |
-| Hindsight API URL | http://localhost:8888 | Hindsight server URL |
-
-## Files
-
-- `app.py` - Main Streamlit application
-- `requirements.txt` - Python dependencies
-- `run.sh` - Launch script with dependency checking
-- `README.md` - This file
diff --git a/hindsight-docs/versioned_docs/version-0.4/cookbook/applications/openai-fitness-coach.md b/hindsight-docs/versioned_docs/version-0.4/cookbook/applications/openai-fitness-coach.md
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index d0971fc0..00000000
--- a/hindsight-docs/versioned_docs/version-0.4/cookbook/applications/openai-fitness-coach.md
+++ /dev/null
@@ -1,315 +0,0 @@
----
-sidebar_position: 6
----
-
-# OpenAI Agent + Hindsight Memory Integration
-
-
-:::info Complete Application
-This is a complete, runnable application demonstrating Hindsight integration.
-[**View source on GitHub →**](https://github.com/vectorize-io/hindsight-cookbook/tree/main/applications/openai-fitness-coach)
-:::
-
-
-A fitness coach example demonstrating how to use **OpenAI Agents** with **Hindsight as a memory backend**.
-
-## What This Demonstrates
-
-This example showcases:
-
-- **OpenAI Assistants** handling conversation logic
-- **Hindsight** providing sophisticated memory storage & retrieval
-- **Function calling** to bridge them together
-- **Streaming responses** for real-time interaction (enabled by default)
-- **Bidirectional memory** - both user data AND coach observations stored
-- **System-level post-processing** - automatic opinion storage for reliability
-- **Temporal-semantic memory** queries via function tools
-- **Enhanced preference learning** - coach learns and respects user likes/dislikes
-- **Real-world integration pattern** for adding memory to AI agents
-
-## Architecture
-
-```
-User: "I ran 5K today, don't like tempo runs"
-    |
-OpenAI Assistant
-    |
-Function Call: store_memory(workout + preference)
-    |
-Hindsight API (stores as world/agent)
-    |
-OpenAI Assistant: "What should I focus on?"
-    |
-Function Call: retrieve_memories("workouts and preferences")
-    |
-Hindsight API (returns workouts + preferences)
-    |
-OpenAI Assistant (analyzes, gives advice)
-    |
-Function Call: store_memory(advice as opinion)
-    |
-Hindsight API (stores coach's observation)
-    |
-Personalized Answer
-```
-
-## Key Difference from Standard Demo
-
-| Component | Standard Demo | OpenAI Integration |
-|-----------|---------------|-------------------|
-| **Conversation** | Hindsight `/think` endpoint | OpenAI Assistant API |
-| **Memory** | Hindsight (built-in) | Hindsight (via function calling) |
-| **LLM** | Configured in Hindsight | OpenAI GPT-4 |
-| **Opinion Formation** | Automatic in `/think` | Explicit via `store_memory(type="opinion")` |
-| **Best For** | Hindsight-native apps | Integrating memory into existing OpenAI agents |
-
-## Quick Start
-
-### Prerequisites
-
-1. **OpenAI API Key**
-   ```bash
-   export OPENAI_API_KEY=your_openai_api_key
-   ```
-
-2. **Hindsight API running**
-   ```bash
-   # Follow Hindsight setup instructions to start the API
-   # Default: http://localhost:8888
-   ```
-
-3. **Install dependencies**
-   ```bash
-   pip install openai requests
-   ```
-
-### Run the Conversational Demo
-
-```bash
-cd openai-fitness-coach
-export OPENAI_API_KEY=your_key_here
-python demo_conversational.py
-```
-
-The demo showcases:
-1. **Natural language workout logging** - Tell the coach what you did conversationally
-2. **Preference learning** - Express likes/dislikes and watch the coach adapt
-3. **Goal tracking** - Set goals, track progress, achieve milestones
-4. **Bidirectional memory** - Both your activities AND coach's advice are stored
-5. **Streaming responses** - See responses appear in real-time
-6. **7 interactive phases** - From goal setting to achievement recognition
-
-The demo uses a separate agent (`fitness-coach-demo`) to avoid mixing with real data.
-
-## Usage
-
-### Chat with Your Coach
-
-**Interactive mode:**
-```bash
-python openai_coach.py
-```
-
-**Single question:**
-```bash
-python openai_coach.py "What did I do for training this week?"
-```
-
-## How It Works
-
-### 1. Memory Tools (`memory_tools.py`)
-
-Defines function tools that the OpenAI Agent can call:
-
-```python
-retrieve_memories(query, fact_types, top_k)
-search_workouts(after_date, before_date, workout_type)
-get_nutrition_summary(after_date, before_date)
-get_user_goals()
-get_coach_opinions(about)
-```
-
-Each function makes API calls to Hindsight to fetch relevant memories.
-
-### 2. OpenAI Agent (`openai_coach.py`)
-
-Creates an OpenAI Assistant with:
-- Fitness coaching instructions
-- Access to memory function tools
-- Conversation management
-
-When you ask a question:
-1. User message is sent to OpenAI Assistant
-2. Assistant decides which memory functions to call
-3. Functions fetch data from Hindsight
-4. Assistant generates response using retrieved context
-
-### 3. Function Calling Flow
-
-```python
-# User asks: "What did I run this week?"
-
-# OpenAI Assistant decides to call:
-search_workouts(
-    after_date="2024-11-18",
-    workout_type="running"
-)
-
-# Function retrieves from Hindsight:
-{
-  "results": [
-    {"text": "User completed 45-minute cardio workout: running..."},
-    {"text": "User completed 60-minute cardio workout: running..."}
-  ]
-}
-
-# OpenAI Assistant generates response:
-"This week you've done two runs: a 45-minute run on Monday
-and a longer 60-minute run on Wednesday. Great consistency!"
-```
-
-## Example Questions
-
-Try asking:
-
-```bash
-python openai_coach.py "What does my training look like this week?"
-python openai_coach.py "Based on my workouts, should I rest today?"
-python openai_coach.py "How is my nutrition supporting my goals?"
-python openai_coach.py "What's my progress toward my goal?"
-python openai_coach.py "Compare my training this month to last month"
-```
-
-The agent will automatically:
-1. Identify what memories it needs
-2. Call the appropriate function tools
-3. Retrieve data from Hindsight
-4. Generate a personalized response
-
-## Memory Types Retrieved
-
-The OpenAI Agent can retrieve different memory types from Hindsight:
-
-- **World Facts** (`fact_type: "world"`): Workouts, meals, activities
-- **Agent Facts** (`fact_type: "agent"`): Goals, intentions
-- **Opinions** (`fact_type: "opinion"`): Coach's observations about patterns
-
-## Customization
-
-### Add New Function Tools
-
-Edit `memory_tools.py` to add new capabilities:
-
-```python
-def get_weekly_summary(week_offset: int = 0):
-    """Get a summary of a specific week."""
-    # Implementation
-    pass
-
-# Add to MEMORY_TOOLS list
-MEMORY_TOOLS.append({
-    "type": "function",
-    "function": {
-        "name": "get_weekly_summary",
-        "description": "Get training summary for a specific week",
-        # ... parameters
-    }
-})
-
-# Add to FUNCTION_MAP
-FUNCTION_MAP["get_weekly_summary"] = get_weekly_summary
-```
-
-### Modify Assistant Instructions
-
-Edit `openai_coach.py` to change the coach's personality or behavior:
-
-```python
-assistant = client.beta.assistants.create(
-    name="Your Custom Coach",
-    instructions="Your custom instructions here...",
-    model="gpt-4o-mini",
-    tools=MEMORY_TOOLS
-)
-```
-
-## Use Cases
-
-This pattern works for any application that needs memory:
-
-1. **Customer Support Agents** - Remember past conversations and issues
-2. **Personal Assistants** - Remember preferences, schedules, past decisions
-3. **Educational Tutors** - Track learning progress over time
-4. **Health Coaches** - Monitor habits, progress, goals (like this example)
-5. **Sales Assistants** - Remember customer interactions and preferences
-
-## Integration Pattern
-
-**To add Hindsight memory to your own OpenAI Agent:**
-
-1. Define function tools that call Hindsight API
-2. Register them with your OpenAI Assistant
-3. Implement function handlers to execute Hindsight queries
-4. Let OpenAI Assistant decide when to retrieve memories
-
-The key benefit: **Separation of concerns**
-- OpenAI = Conversation logic
-- Hindsight = Memory storage, retrieval, temporal queries, entity linking
-
-## When to Use This vs. Standard Hindsight
-
-**Use OpenAI + Hindsight (this example) when:**
-- You want OpenAI's conversation capabilities
-- You're already using OpenAI Agents
-- You want explicit control over when to retrieve memories
-- You want to combine Hindsight with other OpenAI features
-
-**Use Hindsight directly when:**
-- You want a complete memory-first solution
-- You want automatic memory retrieval and opinion formation
-- You want to use different LLM providers (not just OpenAI)
-- You want the `/think` endpoint's integrated approach
-
-## Learning Points
-
-After running this demo, you'll understand:
-
-1. How to add sophisticated memory to any OpenAI Agent
-2. How function calling bridges LLMs and memory systems
-3. How temporal-semantic queries work via function tools
-4. Real-world pattern for LLM + memory integration
-
-## Core Files
-
-- `demo_conversational.py` - Conversational demo showcasing preference learning and goal tracking
-- `openai_coach.py` - OpenAI Assistant wrapper with streaming and memory integration
-- `memory_tools.py` - Function calling tools that bridge to Hindsight API
-- `.openai_assistant_id` - Saved assistant ID (auto-generated, gitignored)
-
-## Common Issues
-
-**"OPENAI_API_KEY not set"**
-```bash
-export OPENAI_API_KEY=your_api_key_here
-```
-
-**"Agent not found"**
-- Make sure the Hindsight fitness-coach agent exists
-
-**"Connection refused"**
-- Make sure Hindsight API is running on localhost:8888
-
-## Next Steps
-
-1. Run the demo to see it in action
-2. Try chatting with the coach: `python openai_coach.py`
-3. Log your own workouts and meals
-4. Experiment with different questions
-5. Add custom function tools for your use case
-
----
-
-**Built with:**
-- OpenAI Assistants API
-- Hindsight (temporal-semantic memory)
-- Function calling for integration
diff --git a/hindsight-docs/versioned_docs/version-0.4/cookbook/applications/sanity-blog-memory.md b/hindsight-docs/versioned_docs/version-0.4/cookbook/applications/sanity-blog-memory.md
deleted file mode 100644
index fc555869..00000000
--- a/hindsight-docs/versioned_docs/version-0.4/cookbook/applications/sanity-blog-memory.md
+++ /dev/null
@@ -1,371 +0,0 @@
----
-sidebar_position: 7
----
-
-# Sanity CMS Blog Memory
-
-
-:::info Complete Application
-This is a complete, runnable application demonstrating Hindsight integration.
-[**View source on GitHub →**](https://github.com/vectorize-io/hindsight-cookbook/tree/main/applications/sanity-blog-memory)
-:::
-
-
-A Hindsight cookbook recipe demonstrating how to sync blog posts from **Sanity CMS** to Hindsight agent memory, enabling semantic search, temporal queries, and AI-powered content insights.
-
-## Features
-
-- **Blog Post Sync**: Automatically sync all blog posts from Sanity to Hindsight
-- **Document-based Upsert**: Idempotent syncing with `document_id` - re-running sync updates existing content
-- **Semantic Search**: Find related content using natural language queries
-- **Temporal Queries**: Ask "What did I write in January 2025?"
-- **Reflect for Insights**: Generate AI-powered analysis of your blog content
-- **Related Content Discovery**: Power "Related Posts" features with semantic similarity
-
-## Architecture
-
-```
-┌─────────────────┐         ┌─────────────────┐         ┌─────────────────┐
-│                 │         │                 │         │                 │
-│   Sanity CMS    │───────▶│    Sync Script  │───────▶│   Hindsight     │
-│   (Content)     │  GROQ   │   (TypeScript)  │  HTTP   │   (Memory)      │
-│                 │         │                 │         │                 │
-└─────────────────┘         └─────────────────┘         └─────────────────┘
-                                                               │
-                                                               ▼
-                                                        ┌─────────────────┐
-                                                        │                 │
-                                                        │   Your App      │
-                                                        │   - Recall      │
-                                                        │   - Reflect     │
-                                                        │                 │
-                                                        └─────────────────┘
-```
-
-## Quick Start
-
-### 1. Start Hindsight
-
-Choose your preferred LLM provider:
-
-**Option A: Using Docker Compose (Recommended)**
-
-```bash
-# Set your API key
-export OPENAI_API_KEY=sk-...
-# OR
-export GOOGLE_API_KEY=...  # Gemini (free tier available)
-# OR
-export GROQ_API_KEY=...    # Groq (free tier available)
-
-# Start Hindsight
-docker compose up -d
-```
-
-**Option B: Using Docker directly**
-
-```bash
-export OPENAI_API_KEY=sk-...
-
-docker run --rm -it --pull always -p 8888:8888 -p 9999:9999 \
-  -e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
-  -e HINDSIGHT_API_LLM_MODEL=gpt-4o-mini \
-  -v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
-  ghcr.io/vectorize-io/hindsight:latest
-```
-
-- **API**: http://localhost:8888
-- **Control Plane UI**: http://localhost:9999
-
-### 2. Configure Environment
-
-```bash
-# Copy example config
-cp .env.example .env
-
-# Edit with your values
-nano .env
-```
-
-Required settings:
-```bash
-# Hindsight
-HINDSIGHT_API_URL=http://localhost:8888
-HINDSIGHT_BANK_ID=blog-memory
-
-# Sanity CMS
-SANITY_PROJECT_ID=your-project-id
-SANITY_DATASET=production
-```
-
-### 3. Install Dependencies
-
-```bash
-npm install
-```
-
-### 4. Sync Your Blog Posts
-
-```bash
-npm run sync
-```
-
-Expected output:
-```
-=======================================
-  Sanity -> Hindsight Blog Sync
-=======================================
-
-Setting up memory bank...
-Memory bank "blog-memory" ready
-
-Fetching posts from Sanity CMS...
-Found 10 posts to sync
-
-Syncing posts to Hindsight...
-  [1/10] "Why I Chose Qwik"... done
-  [2/10] "Building AI Agents"... done
-  ...
-
-=======================================
-  Sync Complete
-=======================================
-  Synced: 10 posts
-```
-
-### 5. Query Your Content
-
-```bash
-npm run query
-```
-
-## Query Examples
-
-### Semantic Search
-
-Find related content using natural language:
-
-```typescript
-import { recallMemory } from './hindsight-client.js';
-
-// Find posts about AI agents
-const result = await recallMemory('AI agents and automation', {
-  budget: 'mid',
-  maxTokens: 2048,
-});
-
-console.log(`Found ${result.results.length} relevant posts`);
-```
-
-### Temporal Queries
-
-Ask about content from specific time periods:
-
-```typescript
-// Posts from January 2025
-const result = await recallMemory('What did I write about in January 2025?', {
-  queryTimestamp: '2025-01-31T23:59:59Z',
-});
-```
-
-### Reflect for Insights
-
-Generate AI-powered analysis of your content:
-
-```typescript
-import { reflectOnMemory } from './hindsight-client.js';
-
-// Analyze blog themes
-const insights = await reflectOnMemory(
-  'What are the main themes of my blog? What topics do I write about most?',
-  { budget: 'high' }
-);
-
-console.log(insights.text);
-```
-
-### Related Content Discovery
-
-Power your "Related Posts" feature:
-
-```typescript
-// Find posts similar to a specific article
-const related = await recallMemory(
-  'Find posts related to "Why I Chose Qwik for My Personal Website"',
-  { budget: 'mid' }
-);
-```
-
-## Memory Structure
-
-Each blog post is stored with rich metadata for optimal recall:
-
-```
-# Blog Post: {title}
-
-**Published:** {date}
-**URL:** {base_url}/blog/{slug}
-**Tags:** {tags}
-**Reading Time:** {reading_time}
-
-## Description
-{description}
-
-## Content
-{full_content}
-```
-
-Key features:
-- **document_id**: `post:{slug}` - Enables upsert on re-sync
-- **context**: `blog-post` - Categorizes the memory type
-- **timestamp**: Post publication date - Enables temporal queries
-
-## Use Cases
-
-### 1. AI-Powered Blog Search
-
-Replace keyword search with semantic understanding:
-
-```typescript
-// Old: keyword matching
-const results = posts.filter(p => p.title.includes('React'));
-
-// New: semantic understanding
-const result = await recallMemory('frontend framework tutorials');
-```
-
-### 2. Content Recommendation Engine
-
-Generate personalized recommendations:
-
-```typescript
-const recommendations = await reflectOnMemory(
-  'Based on a reader interested in "AI automation", recommend related posts'
-);
-```
-
-### 3. Writing Assistant
-
-Get topic suggestions based on your existing content:
-
-```typescript
-const suggestions = await reflectOnMemory(
-  'What topics should I write about next? What gaps exist in my content?'
-);
-```
-
-### 4. Content Analytics
-
-Analyze your blog's evolution:
-
-```typescript
-const analysis = await reflectOnMemory(
-  'How have my writing topics evolved over the past year?'
-);
-```
-
-## Configuration
-
-### Environment Variables
-
-| Variable | Description | Default |
-|----------|-------------|---------|
-| `HINDSIGHT_API_URL` | Hindsight API endpoint | `http://localhost:8888` |
-| `HINDSIGHT_BANK_ID` | Memory bank identifier | `blog-memory` |
-| `SANITY_PROJECT_ID` | Your Sanity project ID | (required) |
-| `SANITY_DATASET` | Sanity dataset name | `production` |
-| `SANITY_API_TOKEN` | Sanity API token (for private datasets) | (none) |
-| `SANITY_API_VERSION` | Sanity API version | `2024-01-09` |
-| `SITE_URL` | Your blog's base URL | `https://example.com` |
-
-### Memory Bank Disposition
-
-The memory bank is configured with disposition traits optimized for blog content:
-
-```typescript
-{
-  skepticism: 2,   // Trusting - blog content is authoritative
-  literalism: 4,   // Literal - exact content matters
-  empathy: 3,      // Balanced
-}
-```
-
-## Extending for Other CMS Platforms
-
-This pattern can be adapted for any CMS. The key components:
-
-### 1. CMS Client
-
-Replace `sanity-client.ts` with your CMS:
-
-```typescript
-// contentful-client.ts
-import { createClient } from 'contentful';
-
-export async function getAllPosts(): Promise<BlogPost[]> {
-  const client = createClient({...});
-  const entries = await client.getEntries({ content_type: 'blogPost' });
-  return entries.items.map(transformPost);
-}
-```
-
-### 2. Content Transformation
-
-Ensure your content is formatted for semantic search:
-
-```typescript
-function formatPostContent(post: BlogPost): string {
-  return `# ${post.title}
-  
-**Published:** ${post.date}
-...
-${post.content}`;
-}
-```
-
-### 3. Document ID Strategy
-
-Use a consistent document ID for upsert behavior:
-
-```typescript
-await retainBlogPost(content, {
-  documentId: `post:${post.slug}`,  // Unique, stable identifier
-  timestamp: post.date,
-});
-```
-
-## Troubleshooting
-
-### "Connection refused" error
-
-Make sure Hindsight is running:
-```bash
-docker compose up -d
-curl http://localhost:8888/health
-```
-
-### "No posts found" during sync
-
-Check your Sanity configuration:
-```bash
-# Verify project ID
-echo $SANITY_PROJECT_ID
-
-# Test GROQ query
-npx sanity query '*[_type == "post"][0..2]{title}'
-```
-
-### Slow recall/reflect responses
-
-This is normal for the first query as Hindsight builds embeddings. Subsequent queries are faster. Use `budget: 'low'` for faster responses at the cost of recall quality.
-
-## Resources
-
-- [Hindsight Documentation](https://hindsight.vectorize.io/)
-- [Hindsight GitHub](https://github.com/vectorize-io/hindsight)
-- [Sanity CMS Documentation](https://www.sanity.io/docs)
-- [Hindsight Cookbook](https://github.com/vectorize-io/hindsight-cookbook)
-
-## License
-
-MIT
diff --git a/hindsight-docs/versioned_docs/version-0.4/cookbook/applications/stancetracker.md b/hindsight-docs/versioned_docs/version-0.4/cookbook/applications/stancetracker.md
deleted file mode 100644
index 5d464017..00000000
--- a/hindsight-docs/versioned_docs/version-0.4/cookbook/applications/stancetracker.md
+++ /dev/null
@@ -1,276 +0,0 @@
----
-sidebar_position: 8
----
-
-# Stance Tracker
-
-
-:::info Complete Application
-This is a complete, runnable application demonstrating Hindsight integration.
-[**View source on GitHub →**](https://github.com/vectorize-io/hindsight-cookbook/tree/main/applications/stancetracker)
-:::
-
-
-An AI-powered application that tracks political candidates' stances on issues over time using Hindsight memory system and web scraping.
-
-## Features
-
-- **Geographic Targeting**: Track stances by country, state/province, and city
-- **Multi-Candidate Tracking**: Monitor multiple candidates simultaneously
-- **Temporal Analysis**: Historical stance tracking with configurable time ranges
-- **Automated Scraping**: Periodic content collection with configurable frequencies (hourly/daily/weekly)
-- **Stance Change Detection**: Automatic detection and highlighting of position changes
-- **Interactive Timeline**: Visual graph showing stance evolution with reference callouts
-- **Source Attribution**: All stances linked to verified sources with excerpts
-
-## Architecture
-
-### Memory System (Hindsight Integration)
-
-This app uses the Hindsight memory system from `github.com/vectorize-io/hindsight`:
-
-1. **Banks**: Each scraper agent has its own memory bank
-2. **Retain**: Stores candidate statements and web scraping results
-3. **Recall**: Semantic search to retrieve relevant memories
-4. **Reflect**: Generates contextual analysis using stored memories
-5. **Temporal Search**: Queries memories within specific time periods
-
-### Tech Stack
-
-- **Frontend**: Next.js 16, React, TypeScript, TailwindCSS
-- **Visualization**: Recharts for timeline graphs
-- **Backend**: Next.js API routes
-- **Memory**: Hindsight (from github.com/vectorize-io/hindsight)
-- **Database**: JSON file storage (no database required)
-- **Web Search**: Tavily API
-- **LLM**: OpenAI/Anthropic/Groq (configurable)
-- **Scheduling**: node-cron
-
-## Prerequisites
-
-1. **Hindsight API** running (from github.com/vectorize-io/hindsight)
-2. **API Keys**:
-   - Tavily API key (for web search)
-   - LLM provider API key (OpenAI, Anthropic, or Groq)
-
-## Setup
-
-### 1. Install Dependencies
-
-```bash
-npm install
-```
-
-### 2. Configure Environment
-
-Copy `.env.example` to `.env` and fill in your credentials:
-
-```bash
-cp .env.example .env
-```
-
-Edit `.env`:
-
-```env
-# Hindsight API (from github.com/vectorize-io/hindsight)
-HINDSIGHT_API_URL=http://localhost:8888
-
-# Tavily API (for web search)
-TAVILY_API_KEY=your_tavily_api_key_here
-
-# LLM Provider
-LLM_PROVIDER=openai  # or anthropic, groq
-LLM_API_KEY=your_llm_api_key_here
-LLM_MODEL=gpt-4-turbo-preview
-```
-
-### 3. Start Hindsight
-
-Clone and run Hindsight from github.com/vectorize-io/hindsight:
-
-```bash
-# Clone and run github.com/vectorize-io/hindsight
-cd /path/to/hindsight
-cargo run --bin hindsight-server
-```
-
-Verify Hindsight is running at `http://localhost:8888`
-
-### 4. Run the Application
-
-```bash
-npm run dev
-```
-
-Visit `http://localhost:3000`
-
-## Usage
-
-### Creating a Tracking Session
-
-1. **Set Location**: Enter country (required), state/province, and city (optional)
-2. **Choose Topic**: Specify the issue to track (e.g., "Climate Change Policy")
-3. **Add Candidates**: Enter names of candidates/politicians to track
-4. **Configure Time Range**: Set historical start/end dates for initial analysis
-5. **Set Frequency**: Choose how often to check for updates (hourly/daily/weekly)
-6. **Start Tracking**: Click "Start Tracking" to begin
-
-### Viewing Results
-
-- **Timeline Graph**: Shows confidence levels of each candidate's stance over time
-- **Stance Changes**: Red circles on the graph indicate detected position changes
-- **Click Points**: Click any point to see detailed stance information and sources
-- **Source Links**: Each stance includes links to original references
-
-### Managing Sessions
-
-- **Pause/Resume**: Temporarily stop or restart tracking
-- **Run Now**: Trigger an immediate update outside the schedule
-- **Status**: View current session status and frequency
-
-## API Endpoints
-
-### Sessions
-
-- `POST /api/sessions` - Create new tracking session
-- `GET /api/sessions?id={id}` - Get session details
-- `GET /api/sessions` - List all sessions
-- `PATCH /api/sessions` - Update session status
-
-### Stances
-
-- `POST /api/stances` - Process candidate stance
-- `GET /api/stances?sessionId={id}&candidate={name}` - Get stances
-
-### Scheduler
-
-- `POST /api/scheduler` - Control session scheduling
-  - Actions: `start`, `stop`, `run`
-
-## Hindsight Integration Examples
-
-### 1. Storing Memories
-
-```typescript
-// Store web scraping results
-await hindsightClient.retain(bankId, articleContent, {
-  context: 'web_search_result',
-  timestamp: articleDate,
-  metadata: { url: articleUrl }
-});
-```
-
-### 2. Semantic Search
-
-```typescript
-// Search for relevant memories
-const results = await hindsightClient.recall(bankId, query, {
-  budget: 'high',
-  maxTokens: 8192
-});
-```
-
-### 3. Temporal Filtering
-
-```typescript
-// Query memories up to a specific point in time
-const results = await hindsightClient.recall(bankId, query, {
-  queryTimestamp: '2024-12-01T00:00:00Z'
-});
-```
-
-### 4. Contextual Analysis
-
-```typescript
-// Generate analysis using stored memories
-const response = await hindsightClient.reflect(bankId,
-  'What is the candidate\'s stance on this issue?',
-  { budget: 'high' }
-);
-```
-
-## Production Deployment
-
-### Vercel Deployment
-
-```bash
-# Install Vercel CLI
-npm i -g vercel
-
-# Deploy
-vercel
-
-# Set environment variables in Vercel dashboard:
-# - HINDSIGHT_API_URL
-# - TAVILY_API_KEY
-# - LLM_PROVIDER
-# - LLM_API_KEY
-# - LLM_MODEL
-```
-
-**Note**: The `data/` directory for JSON storage will be ephemeral on Vercel. For production, consider using a persistent database or object storage.
-
-## Development
-
-### Project Structure
-
-```
-stancetracker/
-├── app/
-│   ├── api/          # API routes
-│   ├── globals.css   # Global styles
-│   ├── layout.tsx    # Root layout
-│   └── page.tsx      # Main page
-├── components/       # React components
-├── lib/
-│   ├── db/          # JSON database utilities
-│   ├── hindsight-client.ts    # Hindsight API client
-│   ├── llm-client.ts       # LLM provider client
-│   ├── web-scraper.ts      # Tavily web scraper
-│   ├── scraper-agent.ts    # Content scraper
-│   ├── rag-system.ts       # Memory retrieval
-│   ├── stance-extractor.ts # Stance analysis
-│   ├── stance-pipeline.ts  # Main pipeline
-│   └── scheduler.ts        # Job scheduling
-└── types/           # TypeScript types
-```
-
-### Adding New LLM Providers
-
-Edit `lib/llm-client.ts` and add a new method:
-
-```typescript
-private async newProviderComplete(messages, options) {
-  // Implementation
-}
-```
-
-## Limitations
-
-- **Web Search**: Uses Tavily API which has rate limits
-- **Source Verification**: Manual verification recommended for critical applications
-- **Stance Extraction**: LLM-based, subject to model limitations
-- **Storage**: JSON file storage is not suitable for high-scale production use
-- **Rate Limits**: Respect API rate limits for Tavily, Hindsight, and LLM providers
-
-## Future Enhancements
-
-- [ ] Real-time social media monitoring
-- [ ] Speech/video transcription analysis
-- [ ] Multi-language support
-- [ ] Sentiment analysis integration
-- [ ] Comparative analysis dashboard
-- [ ] Export to CSV/PDF
-- [ ] Email notifications for stance changes
-- [ ] Public API for third-party integrations
-
-## License
-
-MIT
-
-## Support
-
-For issues or questions, please check:
-- Hindsight documentation: `github.com/vectorize-io/hindsight/README.md`
-- Tavily API docs: https://tavily.com/
-- Project issues: Create an issue in the repository
diff --git a/hindsight-docs/versioned_docs/version-0.4/cookbook/applications/taste-ai.md b/hindsight-docs/versioned_docs/version-0.4/cookbook/applications/taste-ai.md
deleted file mode 100644
index b6883c16..00000000
--- a/hindsight-docs/versioned_docs/version-0.4/cookbook/applications/taste-ai.md
+++ /dev/null
@@ -1,122 +0,0 @@
----
-sidebar_position: 9
----
-
-# Hindsight AI SDK - Personal Chef
-
-
-:::info Complete Application
-This is a complete, runnable application demonstrating Hindsight integration.
-[**View source on GitHub →**](https://github.com/vectorize-io/hindsight-cookbook/tree/main/applications/taste-ai)
-:::
-
-
-A personal food assistant demonstrating three key Hindsight integrations using the [Vercel AI SDK v6](https://sdk.vercel.ai/docs).
-
-## Architecture: Single Bank with User Tags
-
-This demo uses a **single Hindsight bank** (`taste-ai`) for all users, with each user's data tagged using `user:${username}`.
-
-```typescript
-// All users share the same bank
-const BANK_ID = 'taste-ai';
-
-// Each memory is tagged with the user
-await hindsightTools.retain.execute({
-  bankId: BANK_ID,
-  content: userData,
-  tags: [`user:${username}`],
-});
-```
-
-This architecture enables:
-- **Per-user queries**: Filter by `user:alice` to get personalized results
-- **Aggregated insights**: Query across all users to find popular recipes or common dietary patterns
-- **Simplified management**: One bank to maintain instead of per-user banks
-
-## Three Hindsight Integrations
-
-### 1. Meal Suggestions with Memory Recall & Reflection
-
-Uses `recall` and `reflect` tools with AI SDK's agent-based approach to gather personalized context.
-
-```typescript
-const contextResult = await generateText({
-  model: llmModel,
-  tools: {
-    recall: hindsightTools.recall,
-    reflect: hindsightTools.reflect,
-  },
-  toolChoice: 'auto',
-  prompt: `You are gathering context for personalized ${mealType} recipe suggestions.
-
-Use the recall tool to search for the user's food preferences, dislikes, and recent meals.
-Then use the reflect tool to analyze their dietary patterns and restrictions.
-
-After gathering context, summarize their preferences and recent eating patterns.`,
-});
-```
-
-The AI agent autonomously:
-- Searches memory for cuisine preferences and dietary restrictions
-- Analyzes recent protein consumption for variety
-- Identifies foods to avoid
-
-### 2. Goal Progress Tracking with Mental Models
-
-Uses mental models to automatically maintain updated insights about user progress.
-
-```typescript
-// Create a mental model that auto-refreshes after new meals
-await hindsightTools.createMentalModel.execute({
-  bankId: BANK_ID,
-  mentalModelId: getMentalModelId(username, 'goals'),
-  name: `${username}'s Goal Progress`,
-  sourceQuery: `Analyze ${username}'s dietary goals and eating patterns.
-    Describe their progress towards their stated goals (weight loss, muscle gain, etc.).`,
-  tags: [`user:${username}`],
-  autoRefresh: true, // Refreshes automatically after consolidation
-});
-
-// Query the mental model for current insights
-const result = await hindsightTools.queryMentalModel.execute({
-  bankId: BANK_ID,
-  mentalModelId: mentalModelId,
-});
-```
-
-Mental models automatically:
-- Track progress towards dietary goals
-- Update after each new meal is logged
-- Provide fresh insights without manual refresh
-
-### 3. Language Enforcement with Directives
-
-Uses directives to ensure all responses match user's language preference.
-
-```typescript
-await hindsightClient.createDirective(BANK_ID, {
-  name: `${username}'s Language Preference`,
-  content: `Always respond in ${language}. All suggestions must be in ${language}.`,
-  priority: 100,
-  tags: [`user:${username}`, 'directive:language'],
-});
-```
-
-Directives are automatically injected when mental models generate insights, ensuring consistent language across all interactions.
-
-## Running the Demo
-
-```bash
-npm install
-npm run dev
-```
-
-**Requirements:**
-- Hindsight server running at `http://localhost:8888` (or set `HINDSIGHT_URL`)
-- Node.js 18+
-
-## Learn More
-
-- [Hindsight AI SDK on npm](https://www.npmjs.com/package/@vectorize-io/hindsight-ai-sdk)
-- [AI SDK Documentation](https://sdk.vercel.ai/docs)
diff --git a/hindsight-docs/versioned_docs/version-0.4/cookbook/index.mdx b/hindsight-docs/versioned_docs/version-0.4/cookbook/index.mdx
deleted file mode 100644
index 609488f6..00000000
--- a/hindsight-docs/versioned_docs/version-0.4/cookbook/index.mdx
+++ /dev/null
@@ -1,153 +0,0 @@
----
-sidebar_position: 1
-hide_table_of_contents: true
-pagination_next: null
-pagination_prev: null
-custom_edit_url: null
-sidebar_class_name: hidden-sidebar
----
-
-import RecipeCarousel from '@site/src/components/RecipeCarousel';
-
-<div className="cookbook-page">
-
-# Cookbook
-
-Learn how to build with Hindsight through practical examples:
-
-- **Recipes** - Step-by-step guides and patterns for common use cases
-- **Applications** - Complete, runnable applications demonstrating Hindsight integration
-
-<RecipeCarousel
-  title="Recipes"
-  items={[
-    {
-      title: "Hindsight Quickstart",
-      href: "/cookbook/recipes/quickstart",
-      description: "Learn the basics: retain, recall, and reflect",
-      tags: { sdk: "hindsight-client", topic: "Quick Start" }
-    },
-    {
-      title: "Per-User Memory",
-      href: "/cookbook/recipes/per-user-memory",
-      description: "Build a chatbot with per-user memory isolation",
-      tags: { sdk: "hindsight-client", topic: "Learning" }
-    },
-    {
-      title: "Support Agent with Shared Knowledge",
-      href: "/cookbook/recipes/support-agent-shared-knowledge",
-      description: "Combine per-user memory with shared product documentation",
-      tags: { sdk: "hindsight-client", topic: "Learning" }
-    },
-    {
-      title: "Memory with LiteLLM",
-      href: "/cookbook/recipes/litellm-memory-demo",
-      description: "Add automatic memory to any LLM app using LiteLLM callbacks",
-      tags: { sdk: "hindsight-litellm", topic: "Quick Start" }
-    },
-    {
-      title: "Routing Tool Learning",
-      href: "/cookbook/recipes/tool-learning-demo",
-      description: "Teach an LLM which tool to use through feedback and memory",
-      tags: { sdk: "hindsight-litellm", topic: "Learning" }
-    },
-    {
-      title: "Fitness Coach with Hindsight Memory",
-      href: "/cookbook/recipes/fitness_tracker",
-      description: "Track workouts, diet, and progress with a personalized fitness coach",
-      tags: { sdk: "hindsight-client", topic: "Recommendation" }
-    },
-    {
-      title: "Healthcare Assistant with Hindsight Memory",
-      href: "/cookbook/recipes/healthcare_assistant",
-      description: "A supportive chatbot that remembers patient history and preferences",
-      tags: { sdk: "hindsight-client", topic: "Recommendation" }
-    },
-    {
-      title: "Movie Recommendation Assistant with Hindsight Memory",
-      href: "/cookbook/recipes/movie_recommendation",
-      description: "Get personalized movie recommendations that improve over time",
-      tags: { sdk: "hindsight-client", topic: "Recommendation" }
-    },
-    {
-      title: "Personal AI Assistant with Hindsight Memory",
-      href: "/cookbook/recipes/personal_assistant",
-      description: "A general-purpose assistant that remembers your life and preferences",
-      tags: { sdk: "hindsight-client", topic: "Recommendation" }
-    },
-    {
-      title: "Personalized Search Agent with Hindsight Memory",
-      href: "/cookbook/recipes/personalized_search",
-      description: "Search assistant that learns your location, diet, and lifestyle",
-      tags: { sdk: "hindsight-client", topic: "Recommendation" }
-    },
-    {
-      title: "Study Buddy with Hindsight Memory",
-      href: "/cookbook/recipes/study_buddy",
-      description: "Track study sessions, identify knowledge gaps, and get personalized review suggestions",
-      tags: { sdk: "hindsight-client", topic: "Learning" }
-    }
-  ]}
-/>
-
-<RecipeCarousel
-  title="Applications"
-  items={[
-    {
-      title: "Chat Memory App",
-      href: "/cookbook/applications/chat-memory",
-      description: "Real-time chat app with per-user memory using Groq and Hindsight",
-      tags: { sdk: "hindsight-client", topic: "Chat" }
-    },
-    {
-      title: "Deliveryman Demo",
-      href: "/cookbook/applications/deliveryman-demo",
-      description: "Delivery agent simulation demonstrating learning through mental models",
-      tags: { sdk: "hindsight-client", topic: "Learning" }
-    },
-    {
-      title: "Go Memory-Augmented API",
-      href: "/cookbook/applications/go-memory-service",
-      description: "Go HTTP microservice with per-user memory banks for a developer knowledge assistant",
-      tags: { sdk: "hindsight-go", topic: "Learning" }
-    },
-    {
-      title: "Memory Approaches Comparison Demo",
-      href: "/cookbook/applications/hindsight-litellm-demo",
-      description: "Interactive comparison of memory approaches: none, full history, and semantic retrieval",
-      tags: { sdk: "hindsight-litellm", topic: "Quick Start" }
-    },
-    {
-      title: "Tool Learning Demo",
-      href: "/cookbook/applications/hindsight-tool-learning-demo",
-      description: "Show how Hindsight helps LLMs learn which tool to use when names are ambiguous",
-      tags: { sdk: "hindsight-litellm", topic: "Learning" }
-    },
-    {
-      title: "OpenAI Agent + Hindsight Memory Integration",
-      href: "/cookbook/applications/openai-fitness-coach",
-      description: "Fitness coach using OpenAI Assistants with Hindsight as memory backend",
-      tags: { sdk: "hindsight-client", topic: "Recommendation" }
-    },
-    {
-      title: "Sanity CMS Blog Memory",
-      href: "/cookbook/applications/sanity-blog-memory",
-      description: "Sync Sanity CMS blog posts to Hindsight for semantic search and AI insights",
-      tags: { sdk: "hindsight-client", topic: "Learning" }
-    },
-    {
-      title: "Stance Tracker",
-      href: "/cookbook/applications/stancetracker",
-      description: "Track political candidates' stances over time with automated web scraping",
-      tags: { sdk: "hindsight-client", topic: "Recommendation" }
-    },
-    {
-      title: "Hindsight AI SDK - Personal Chef",
-      href: "/cookbook/applications/taste-ai",
-      description: "Personal food assistant with AI SDK v6 showcasing recall, mental models, and directives",
-      tags: { sdk: "@vectorize-io/hindsight-ai-sdk", topic: "Recommendation" }
-    }
-  ]}
-/>
-
-</div>
diff --git a/hindsight-docs/versioned_docs/version-0.4/cookbook/recipes/fitness_tracker.md b/hindsight-docs/versioned_docs/version-0.4/cookbook/recipes/fitness_tracker.md
deleted file mode 100644
index a411eede..00000000
--- a/hindsight-docs/versioned_docs/version-0.4/cookbook/recipes/fitness_tracker.md
+++ /dev/null
@@ -1,306 +0,0 @@
----
-sidebar_position: 6
----
-
-# Fitness Coach with Hindsight Memory
-
-
-:::tip Run this notebook
-This recipe is available as an interactive Jupyter notebook.
-[**Open in GitHub →**](https://github.com/vectorize-io/hindsight-cookbook/blob/main/notebooks/fitness_tracker.ipynb)
-:::
-
-
-A personalized fitness assistant that tracks your workouts, diet, recovery, and progress over time to give contextual advice.
-
-## Features
-- Logs workout sessions with exercises and weights
-- Tracks meals and dietary preferences
-- Monitors recovery and sleep patterns
-- Provides personalized training advice
-
-## Prerequisites
-- OpenAI API key
-- Hindsight running locally via Docker (see setup below)
-
-## Start Hindsight Locally
-
-Before running this notebook, start Hindsight in a terminal:
-
-```bash
-export OPENAI_API_KEY="your-openai-api-key"
-
-docker run --rm -it --pull always -p 8888:8888 -p 9999:9999 \
-  -e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
-  -e HINDSIGHT_API_LLM_MODEL=gpt-4o-mini \
-  -v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
-  ghcr.io/vectorize-io/hindsight:latest
-```
-
-## 1. Install Dependencies
-
-
-```python
-!pip install -q hindsight-client openai nest-asyncio
-```
-
-## 2. Configure OpenAI API Key
-
-Enter your OpenAI API key when prompted (used by both Hindsight and the demo).
-
-
-```python
-import getpass
-import os
-
-# Set OpenAI API key (used by both Hindsight and the demo)
-if not os.getenv("OPENAI_API_KEY"):
-    os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter your OpenAI API key: ")
-
-print("API key configured!")
-```
-
-## 3. Initialize Clients
-
-
-```python
-import nest_asyncio
-nest_asyncio.apply()
-
-from datetime import datetime
-from openai import OpenAI
-from hindsight_client import Hindsight
-
-# Initialize Hindsight client (connects to local Docker instance)
-hindsight = Hindsight(
-    base_url=os.getenv("HINDSIGHT_BASE_URL", "http://localhost:8888"),
-)
-
-openai_client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
-
-USER_ID = "fitness-user-demo"
-
-print("Clients initialized!")
-```
-
-## 4. Define Helper Functions
-
-
-```python
-def log_workout(workout_details: str) -> str:
-    """Log a workout session with timestamp."""
-    today = datetime.now().strftime("%B %d, %Y")
-    hindsight.retain(
-        bank_id=USER_ID,
-        content=f"{today} - WORKOUT LOG: {workout_details}",
-        metadata={"category": "workout", "date": today},
-    )
-    return f"Logged workout for {today}: {workout_details}"
-
-
-def log_meal(meal_details: str) -> str:
-    """Log a meal with timestamp."""
-    today = datetime.now().strftime("%B %d, %Y")
-    hindsight.retain(
-        bank_id=USER_ID,
-        content=f"{today} - MEAL LOG: {meal_details}",
-        metadata={"category": "nutrition", "date": today},
-    )
-    return f"Logged meal for {today}: {meal_details}"
-
-
-def log_recovery(recovery_details: str) -> str:
-    """Log recovery information (sleep, soreness, etc.)."""
-    today = datetime.now().strftime("%B %d, %Y")
-    hindsight.retain(
-        bank_id=USER_ID,
-        content=f"{today} - RECOVERY LOG: {recovery_details}",
-        metadata={"category": "recovery", "date": today},
-    )
-    return f"Logged recovery for {today}: {recovery_details}"
-
-
-def store_user_profile(profile_info: str) -> str:
-    """Store user profile information."""
-    hindsight.retain(
-        bank_id=USER_ID,
-        content=f"USER PROFILE: {profile_info}",
-        metadata={"category": "profile"},
-    )
-    return f"Stored profile info: {profile_info}"
-
-
-def fitness_coach(user_query: str) -> str:
-    """Get personalized fitness advice based on query and user history."""
-    memories = hindsight.recall(
-        bank_id=USER_ID,
-        query=f"fitness workout diet recovery goals {user_query}",
-        budget="high",
-    )
-
-    memory_context = ""
-    if memories and memories.results:
-        memory_context = "\n".join(f"- {m.text}" for m in memories.results[:10])
-
-    system_prompt = f"""You are a knowledgeable and supportive fitness coach.
-You have access to the user's workout history, diet logs, recovery notes, and personal profile.
-
-What you know about this user:
-{memory_context if memory_context else "No history recorded yet."}
-
-Provide personalized, actionable advice based on their:
-- Training history and progress
-- Dietary preferences and restrictions
-- Recovery patterns
-- Personal goals
-
-Be encouraging but realistic. Reference their specific history when relevant."""
-
-    response = openai_client.chat.completions.create(
-        model="gpt-4o-mini",
-        messages=[
-            {"role": "system", "content": system_prompt},
-            {"role": "user", "content": user_query},
-        ],
-        temperature=0.7,
-        max_tokens=600,
-    )
-
-    advice = response.choices[0].message.content
-
-    hindsight.retain(
-        bank_id=USER_ID,
-        content=f"User asked: {user_query}\nCoach advised: {advice[:200]}...",
-        metadata={"category": "coaching"},
-    )
-
-    return advice
-
-
-def get_progress_report() -> str:
-    """Generate a progress report based on workout history."""
-    report = hindsight.reflect(
-        bank_id=USER_ID,
-        query="""Analyze this user's fitness journey:
-        1. How consistent have they been with workouts?
-        2. What progress have they made (weight lifted, exercises)?
-        3. How is their recovery and sleep?
-        4. What dietary patterns do you notice?
-        5. What should they focus on next?""",
-        budget="high",
-    )
-    return report.text if hasattr(report, 'text') else str(report)
-
-print("Helper functions defined!")
-```
-
-## 5. Set Up User Profile
-
-
-```python
-print("Setting up user profile...")
-
-profile_data = [
-    "Name: Anish, Age: 26, Height: 5'10\", Weight: 72kg",
-    "Goal: Building lean muscle, started gym 6 months ago",
-    "Routine: Push-pull-legs split, 5x per week",
-    "Rest days: Wednesday and Sunday",
-    "Dietary restriction: Mild lactose intolerance, uses almond milk",
-    "Health note: Occasional knee pain, avoids deep squats",
-    "Supplements: Whey protein (lactose-free), magnesium",
-    "Sleep: Aims for 7+ hours, performance drops under 6 hours",
-]
-
-for info in profile_data:
-    store_user_profile(info)
-    print(f"  Stored: {info[:50]}...")
-```
-
-## 6. Log Workout History
-
-
-```python
-print("Logging workout history...")
-
-workouts = [
-    "Push day: Bench press 3x8 @ 60kg, overhead press 4x12, tricep dips 3x10. Felt strong.",
-    "Pull day: Deadlift 3x5 @ 80kg, barbell rows 4x10, bicep curls 3x12. Good session.",
-    "Leg day: Leg press 4x12, hamstring curls 3x12, glute bridges 3x15. Knee felt okay.",
-]
-
-for workout in workouts:
-    print(f"  {log_workout(workout)[:60]}...")
-
-print("\nLogging recent meals...")
-meals = [
-    "Post-workout: Whey shake with almond milk, banana, oats",
-    "Dinner: Grilled chicken, brown rice, steamed vegetables",
-    "Snack: Greek yogurt (lactose-free) with berries",
-]
-
-for meal in meals:
-    print(f"  {log_meal(meal)[:60]}...")
-
-print("\nLogging recovery notes...")
-recovery = [
-    "Slept 7.5 hours, feeling well rested",
-    "Some DOMS in legs from yesterday, using turmeric milk",
-]
-
-for note in recovery:
-    print(f"  {log_recovery(note)[:60]}...")
-```
-
-## 7. Talk to Your Fitness Coach
-
-
-```python
-import time
-
-print("=" * 60)
-print("  Talking to your fitness coach...")
-print("=" * 60)
-
-queries = [
-    "How much was I lifting for bench press recently?",
-    "I slept poorly last night (only 5 hours). What should I do for today's workout?",
-    "Suggest a post-workout meal that works with my dietary restrictions.",
-    "My knee has been bothering me more. Any exercise modifications?",
-]
-
-for query in queries:
-    print(f"\nUser: {query}")
-    print("-" * 40)
-    response = fitness_coach(query)
-    print(f"Coach: {response}")
-    time.sleep(1)
-```
-
-## 8. Generate Progress Report
-
-
-```python
-print("=" * 60)
-print("  Progress Report")
-print("=" * 60)
-print(get_progress_report())
-```
-
-## 9. Try Your Own Query
-
-
-```python
-your_query = "What exercises should I do today?"  # Change this!
-
-print(f"You: {your_query}")
-print("-" * 40)
-print(f"Coach: {fitness_coach(your_query)}")
-```
-
-## 10. Cleanup
-
-
-```python
-hindsight.close()
-print("Client connection closed.")
-```
diff --git a/hindsight-docs/versioned_docs/version-0.4/cookbook/recipes/healthcare_assistant.md b/hindsight-docs/versioned_docs/version-0.4/cookbook/recipes/healthcare_assistant.md
deleted file mode 100644
index 995f621b..00000000
--- a/hindsight-docs/versioned_docs/version-0.4/cookbook/recipes/healthcare_assistant.md
+++ /dev/null
@@ -1,299 +0,0 @@
----
-sidebar_position: 7
----
-
-# Healthcare Assistant with Hindsight Memory
-
-
-:::tip Run this notebook
-This recipe is available as an interactive Jupyter notebook.
-[**Open in GitHub →**](https://github.com/vectorize-io/hindsight-cookbook/blob/main/notebooks/healthcare_assistant.ipynb)
-:::
-
-
-A supportive healthcare chatbot that remembers patient history, symptoms, medications, and preferences to provide personalized guidance.
-
-## Disclaimer
-
-**This is a demo application and should NOT be used for actual medical advice. Always consult qualified healthcare professionals.**
-
-## Features
-- Tracks symptoms, medications, and allergies
-- Maintains patient history across conversations
-- Provides health information and wellness tips
-- Schedules appointments
-
-## Prerequisites
-- OpenAI API key
-- Hindsight running locally via Docker (see setup below)
-
-## Start Hindsight Locally
-
-Before running this notebook, start Hindsight in a terminal:
-
-```bash
-export OPENAI_API_KEY="your-openai-api-key"
-
-docker run --rm -it --pull always -p 8888:8888 -p 9999:9999 \
-  -e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
-  -e HINDSIGHT_API_LLM_MODEL=gpt-4o-mini \
-  -v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
-  ghcr.io/vectorize-io/hindsight:latest
-```
-
-## 1. Install Dependencies
-
-
-```python
-!pip install -q hindsight-client openai nest-asyncio
-```
-
-## 2. Configure OpenAI API Key
-
-Enter your OpenAI API key when prompted (used by both Hindsight and the demo).
-
-
-```python
-import getpass
-import os
-
-# Set OpenAI API key (used by both Hindsight and the demo)
-if not os.getenv("OPENAI_API_KEY"):
-    os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter your OpenAI API key: ")
-
-print("API key configured!")
-```
-
-## 3. Initialize Clients
-
-
-```python
-import nest_asyncio
-nest_asyncio.apply()
-
-from datetime import datetime
-import random
-from openai import OpenAI
-from hindsight_client import Hindsight
-
-# Initialize Hindsight client (connects to local Docker instance)
-hindsight = Hindsight(
-    base_url=os.getenv("HINDSIGHT_BASE_URL", "http://localhost:8888"),
-)
-
-openai_client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
-
-PATIENT_ID = "patient-demo"
-
-def get_patient_bank_id(patient_id: str) -> str:
-    return f"patient-{patient_id}"
-
-print("Clients initialized!")
-```
-
-## 4. Define Helper Functions
-
-
-```python
-def store_patient_info(patient_id: str, info: str, category: str = "general") -> str:
-    """Store patient information."""
-    bank_id = get_patient_bank_id(patient_id)
-    today = datetime.now().strftime("%B %d, %Y")
-
-    hindsight.retain(
-        bank_id=bank_id,
-        content=f"{today} - {category.upper()}: {info}",
-        metadata={"category": category, "date": today},
-    )
-
-    return f"Recorded {category}: {info}"
-
-
-def get_patient_history(patient_id: str, query: str) -> str:
-    """Retrieve relevant patient history."""
-    bank_id = get_patient_bank_id(patient_id)
-
-    memories = hindsight.recall(
-        bank_id=bank_id,
-        query=query,
-        budget="high",
-    )
-
-    if memories and memories.results:
-        return "\n".join(f"- {m.text}" for m in memories.results[:10])
-    return "No relevant history found."
-
-
-def healthcare_chat(patient_id: str, user_message: str) -> str:
-    """Chat with the healthcare assistant."""
-    bank_id = get_patient_bank_id(patient_id)
-
-    history = get_patient_history(
-        patient_id,
-        f"symptoms medications allergies conditions {user_message}"
-    )
-
-    system_prompt = f"""You are a supportive healthcare assistant chatbot.
-
-IMPORTANT DISCLAIMERS:
-- You are NOT a doctor and cannot provide medical diagnoses
-- Always recommend consulting healthcare professionals for serious concerns
-- Never prescribe medications or suggest stopping prescribed treatments
-
-Your role:
-- Listen empathetically to patient concerns
-- Remember and reference their medical history
-- Provide general health information and wellness tips
-- Help track symptoms over time
-- Remind about medications and appointments
-- Suggest when to seek professional care
-
-Patient History:
-{history}
-
-Guidelines:
-- Be warm and supportive
-- Ask clarifying questions when needed
-- Reference their history when relevant
-- Flag any concerning symptoms for professional review"""
-
-    response = openai_client.chat.completions.create(
-        model="gpt-4o-mini",
-        messages=[
-            {"role": "system", "content": system_prompt},
-            {"role": "user", "content": user_message},
-        ],
-        temperature=0.7,
-        max_tokens=600,
-    )
-
-    answer = response.choices[0].message.content
-
-    hindsight.retain(
-        bank_id=bank_id,
-        content=f"Patient concern: {user_message}\nGuidance provided: {answer[:200]}...",
-        metadata={"category": "consultation"},
-    )
-
-    return answer
-
-
-def get_health_summary(patient_id: str) -> str:
-    """Generate a health summary for the patient."""
-    bank_id = get_patient_bank_id(patient_id)
-
-    summary = hindsight.reflect(
-        bank_id=bank_id,
-        query="""Summarize this patient's health profile:
-        1. Known conditions and diagnoses
-        2. Current medications
-        3. Allergies and sensitivities
-        4. Recent symptoms reported
-        5. Lifestyle factors mentioned
-        6. Any patterns or trends in their health""",
-        budget="high",
-    )
-    return summary.text if hasattr(summary, 'text') else str(summary)
-
-
-def schedule_appointment(patient_id: str, appointment_type: str, preferred_time: str) -> str:
-    """Schedule an appointment (demo)."""
-    confirmation_id = f"APT-{random.randint(10000, 99999)}"
-
-    store_patient_info(
-        patient_id,
-        f"Appointment scheduled: {appointment_type} - Preferred time: {preferred_time} - Confirmation: {confirmation_id}",
-        category="appointment"
-    )
-
-    return f"Appointment requested: {appointment_type}\nPreferred time: {preferred_time}\nConfirmation ID: {confirmation_id}\n\nA staff member will confirm the exact time within 24 hours."
-
-print("Helper functions defined!")
-```
-
-## 5. Set Up Patient Profile
-
-
-```python
-print("Setting up patient profile...")
-
-patient_info = [
-    ("Age: 45, Male, Height: 5'11\", Weight: 185 lbs", "demographics"),
-    ("Allergy: Penicillin - causes hives", "allergies"),
-    ("Allergy: Shellfish - causes throat swelling", "allergies"),
-    ("Current medication: Lisinopril 10mg daily for blood pressure", "medications"),
-    ("Current medication: Metformin 500mg twice daily for Type 2 diabetes", "medications"),
-    ("Condition: Diagnosed with Type 2 diabetes in 2020", "conditions"),
-    ("Condition: Mild hypertension, well-controlled", "conditions"),
-    ("Family history: Father had heart disease", "family_history"),
-    ("Lifestyle: Sedentary job, trying to exercise more", "lifestyle"),
-]
-
-for info, category in patient_info:
-    result = store_patient_info(PATIENT_ID, info, category)
-    print(f"  {result}")
-```
-
-## 6. Healthcare Chat
-
-
-```python
-import time
-
-print("=" * 60)
-print("  Healthcare Chat")
-print("=" * 60)
-
-conversations = [
-    "Hi, I've been having headaches for the past few days. Should I be worried?",
-    "The headaches are mostly in the afternoon. I've also been feeling more tired than usual.",
-    "I've been checking my blood sugar and it's been a bit higher lately, around 140-150 fasting.",
-    "Can you remind me what allergies I have? I'm going to a new restaurant.",
-]
-
-for message in conversations:
-    print(f"\nPatient: {message}")
-    print("-" * 40)
-    response = healthcare_chat(PATIENT_ID, message)
-    print(f"Assistant: {response}")
-    time.sleep(1)
-```
-
-## 7. Schedule Appointment
-
-
-```python
-print("=" * 60)
-print("  Scheduling Appointment")
-print("=" * 60)
-print(schedule_appointment(PATIENT_ID, "General checkup", "Next Tuesday afternoon"))
-```
-
-## 8. Health Summary
-
-
-```python
-print("=" * 60)
-print("  Patient Health Summary")
-print("=" * 60)
-print(get_health_summary(PATIENT_ID))
-```
-
-## 9. Try Your Own Question
-
-
-```python
-your_question = "Should I adjust my Metformin dose?"  # Change this!
-
-print(f"You: {your_question}")
-print("-" * 40)
-print(f"Assistant: {healthcare_chat(PATIENT_ID, your_question)}")
-```
-
-## 10. Cleanup
-
-
-```python
-hindsight.close()
-print("Client connection closed.")
-```
diff --git a/hindsight-docs/versioned_docs/version-0.4/cookbook/recipes/litellm-memory-demo.md b/hindsight-docs/versioned_docs/version-0.4/cookbook/recipes/litellm-memory-demo.md
deleted file mode 100644
index d633f706..00000000
--- a/hindsight-docs/versioned_docs/version-0.4/cookbook/recipes/litellm-memory-demo.md
+++ /dev/null
@@ -1,187 +0,0 @@
----
-sidebar_position: 4
----
-
-# Memory with LiteLLM
-
-
-:::tip Run this notebook
-This recipe is available as an interactive Jupyter notebook.
-[**Open in GitHub →**](https://github.com/vectorize-io/hindsight-cookbook/blob/main/notebooks/04-litellm-memory-demo.ipynb)
-:::
-
-
-This notebook demonstrates how to add persistent memory to any LLM app using the `hindsight-litellm` package. Memory storage and injection happen automatically via LiteLLM callbacks - no manual memory management needed!
-
-**Key features demonstrated:**
-1. `configure()` + `enable()` - Set up automatic memory integration
-2. Automatic storage - Conversations are stored after each LLM call
-3. Automatic injection - Relevant memories are injected into prompts
-
-The `hindsight-litellm` package hooks into LiteLLM's callback system to:
-- Store each conversation after successful LLM responses
-- Inject relevant memories into the system prompt before LLM calls
-
-## Prerequisites
-
-Make sure you have Hindsight running:
-
-```bash
-export OPENAI_API_KEY=your-key
-
-docker run --rm -it --pull always -p 8888:8888 -p 9999:9999 \
-  -e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
-  -e HINDSIGHT_API_LLM_MODEL=gpt-4o-mini \
-  -v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
-  ghcr.io/vectorize-io/hindsight:latest
-```
-
-- API: http://localhost:8888
-- UI: http://localhost:9999
-
-## Installation
-
-
-```python
-!pip install hindsight-litellm litellm nest_asyncio python-dotenv -U -q
-```
-
-## Setup
-
-
-```python
-import os
-import uuid
-import time
-import logging
-import nest_asyncio
-from dotenv import load_dotenv
-
-# Apply nest_asyncio for Jupyter compatibility
-nest_asyncio.apply()
-
-# Load environment variables
-load_dotenv()
-
-# Configure logging
-logging.basicConfig(level=logging.INFO)
-logging.getLogger("LiteLLM").setLevel(logging.WARNING)
-logging.getLogger("LiteLLM Router").setLevel(logging.WARNING)
-logging.getLogger("LiteLLM Proxy").setLevel(logging.WARNING)
-
-# Import hindsight_litellm
-import hindsight_litellm
-
-# Configuration
-HINDSIGHT_API_URL = os.getenv("HINDSIGHT_API_URL", "http://localhost:8888")
-
-# Check for API key
-if not os.getenv("OPENAI_API_KEY"):
-    print("Warning: OPENAI_API_KEY not set")
-```
-
-## Configure and Enable Automatic Memory
-
-This is all you need! After this, all LiteLLM calls will automatically:
-- Have relevant memories injected into the prompt
-- Store conversations to Hindsight after the response
-
-
-```python
-# Generate a unique bank_id for this demo session
-bank_id = f"demo-{uuid.uuid4().hex[:8]}"
-print(f"Using bank_id: {bank_id}")
-
-# Configure and enable hindsight
-hindsight_litellm.configure(
-    hindsight_api_url=HINDSIGHT_API_URL,
-    bank_id=bank_id,
-    store_conversations=True,  # Automatically store conversations
-    inject_memories=True,       # Automatically inject relevant memories
-    verbose=True,               # Enable logging to debug memory operations
-)
-hindsight_litellm.enable()
-
-print("Hindsight memory integration enabled!")
-```
-
-## Conversation 1: User Introduces Themselves
-
-In this first conversation, the user shares some information about themselves. This will be automatically stored to Hindsight memory.
-
-
-```python
-user_message_1 = "Hi! I'm Alex and I work at Google as a software engineer. I love Python and machine learning."
-print(f"User: {user_message_1}\n")
-
-# Use hindsight_litellm.completion() directly
-response_1 = hindsight_litellm.completion(
-    model="gpt-4o-mini",
-    messages=[
-        {"role": "system", "content": "You are a helpful assistant."},
-        {"role": "user", "content": user_message_1}
-    ],
-)
-
-assistant_response_1 = response_1.choices[0].message.content
-print(f"Assistant: {assistant_response_1}")
-print("\n(Conversation automatically stored to Hindsight)")
-```
-
-## Wait for Memory Processing
-
-Hindsight needs a few seconds to process and extract facts from the conversation.
-
-
-```python
-print("Waiting 12 seconds for memory processing...")
-time.sleep(12)
-print("Done!")
-```
-
-## Conversation 2: Test Memory-Augmented Response
-
-Now we start a fresh conversation and ask what the assistant remembers. The memories from the previous conversation will be automatically injected into the prompt!
-
-
-```python
-user_message_2 = "What do you know about me? What programming language should I use for my next project?"
-print(f"User: {user_message_2}\n")
-
-# Memories are automatically injected before this call!
-response_2 = hindsight_litellm.completion(
-    model="gpt-4o-mini",
-    messages=[
-        {"role": "system", "content": "You are a helpful assistant."},
-        {"role": "user", "content": user_message_2}
-    ],
-)
-
-print(f"Assistant: {response_2.choices[0].message.content}")
-```
-
-## Summary
-
-The assistant should have remembered that Alex:
-- Works at Google as a software engineer
-- Loves Python and machine learning
-
-And it should have recommended Python based on that knowledge!
-
-
-```python
-print(f"Memories stored in bank: {bank_id}")
-print(f"View in UI: http://localhost:9999/banks/{bank_id}")
-```
-
-## Cleanup
-
-
-```python
-hindsight_litellm.cleanup()
-
-# Optional: delete the bank
-import requests
-response = requests.delete(f"{HINDSIGHT_API_URL}/v1/default/banks/{bank_id}")
-print(f"Deleted bank: {response.json()}")
-```
diff --git a/hindsight-docs/versioned_docs/version-0.4/cookbook/recipes/movie_recommendation.md b/hindsight-docs/versioned_docs/version-0.4/cookbook/recipes/movie_recommendation.md
deleted file mode 100644
index 1dc3bf4a..00000000
--- a/hindsight-docs/versioned_docs/version-0.4/cookbook/recipes/movie_recommendation.md
+++ /dev/null
@@ -1,246 +0,0 @@
----
-sidebar_position: 8
----
-
-# Movie Recommendation Assistant with Hindsight Memory
-
-
-:::tip Run this notebook
-This recipe is available as an interactive Jupyter notebook.
-[**Open in GitHub →**](https://github.com/vectorize-io/hindsight-cookbook/blob/main/notebooks/movie_recommendation.ipynb)
-:::
-
-
-A personalized movie recommender that remembers your preferences, watch history, and tastes to give better suggestions over time.
-
-## Features
-- Remembers favorite genres, directors, and actors
-- Tracks movies you've watched and enjoyed
-- Provides contextual recommendations based on mood
-
-## Prerequisites
-- OpenAI API key
-- Hindsight running locally via Docker (see setup below)
-
-## Start Hindsight Locally
-
-Before running this notebook, start Hindsight in a terminal:
-
-```bash
-export OPENAI_API_KEY="your-openai-api-key"
-
-docker run --rm -it --pull always -p 8888:8888 -p 9999:9999 \
-  -e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
-  -e HINDSIGHT_API_LLM_MODEL=gpt-4o-mini \
-  -v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
-  ghcr.io/vectorize-io/hindsight:latest
-```
-
-## 1. Install Dependencies
-
-
-```python
-!pip install -q hindsight-client openai nest-asyncio
-```
-
-## 2. Configure OpenAI API Key
-
-Enter your OpenAI API key when prompted (used by both Hindsight and the demo).
-
-
-```python
-import getpass
-import os
-
-# Set OpenAI API key (used by both Hindsight and the demo)
-if not os.getenv("OPENAI_API_KEY"):
-    os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter your OpenAI API key: ")
-
-print("API key configured!")
-```
-
-## 3. Initialize Clients
-
-
-```python
-import nest_asyncio
-nest_asyncio.apply()
-
-from openai import OpenAI
-from hindsight_client import Hindsight
-
-# Initialize Hindsight client (connects to local Docker instance)
-hindsight = Hindsight(
-    base_url=os.getenv("HINDSIGHT_BASE_URL", "http://localhost:8888"),
-)
-
-# Initialize OpenAI client
-openai_client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
-
-# Unique identifier for this user's memory bank
-USER_ID = "movie-fan-demo"
-
-print("Clients initialized!")
-```
-
-## 4. Define Helper Functions
-
-These functions demonstrate the three core Hindsight operations:
-- **retain()**: Store memories
-- **recall()**: Retrieve relevant memories
-- **reflect()**: Synthesize insights from memories
-
-
-```python
-def get_recommendation(user_query: str) -> str:
-    """
-    Get a movie recommendation based on user query and remembered preferences.
-    """
-    # Recall relevant memories about this user's movie preferences
-    memories = hindsight.recall(
-        bank_id=USER_ID,
-        query=f"movie preferences tastes genres {user_query}",
-        budget="mid",
-    )
-
-    # Build context from memories
-    memory_context = ""
-    if memories and memories.results:
-        memory_context = "\n".join(
-            f"- {m.text}" for m in memories.results[:5]
-        )
-
-    # Generate recommendation with context
-    system_prompt = f"""You are a helpful movie recommendation assistant.
-You remember the user's preferences and past conversations to give personalized suggestions.
-
-What you know about this user:
-{memory_context if memory_context else "No previous preferences recorded yet."}
-
-Give thoughtful, personalized recommendations based on their tastes.
-If they mention new preferences, acknowledge them."""
-
-    response = openai_client.chat.completions.create(
-        model="gpt-4o-mini",
-        messages=[
-            {"role": "system", "content": system_prompt},
-            {"role": "user", "content": user_query},
-        ],
-        temperature=0.7,
-        max_tokens=500,
-    )
-
-    recommendation = response.choices[0].message.content
-
-    # Store this interaction for future context
-    hindsight.retain(
-        bank_id=USER_ID,
-        content=f"User asked: {user_query}\nRecommendation given: {recommendation}",
-        metadata={"category": "movie_recommendation"},
-    )
-
-    return recommendation
-
-
-def store_preference(preference: str) -> None:
-    """Store an explicit user preference."""
-    hindsight.retain(
-        bank_id=USER_ID,
-        content=f"User preference: {preference}",
-        metadata={"category": "preference"},
-    )
-    print(f"Stored preference: {preference}")
-
-
-def get_preference_summary() -> str:
-    """Get a summary of what we know about the user's movie tastes."""
-    summary = hindsight.reflect(
-        bank_id=USER_ID,
-        query="Summarize this user's movie preferences, favorite genres, actors they like, and movies they've mentioned enjoying or disliking.",
-        budget="high",
-    )
-    return summary.text if hasattr(summary, 'text') else str(summary)
-
-print("Helper functions defined!")
-```
-
-## 5. Run the Demo
-
-Watch how the assistant learns and remembers preferences across conversations.
-
-
-```python
-import time
-
-print("=" * 60)
-print("  Movie Recommendation Assistant with Memory")
-print("=" * 60)
-print()
-
-# Simulate a conversation over time
-conversations = [
-    "I'm looking for a movie to watch tonight. Any suggestions?",
-    "I really loved Inception and Interstellar. Christopher Nolan is amazing!",
-    "Can you suggest something similar to those? I like mind-bending plots.",
-    "Actually, I'm not in the mood for something heavy. Something lighter?",
-    "I watched The Grand Budapest Hotel last week and loved it!",
-    "What should I watch tonight? Remember what I like!",
-]
-
-for i, query in enumerate(conversations, 1):
-    print(f"\n[Conversation {i}]")
-    print(f"User: {query}")
-    print("-" * 40)
-
-    response = get_recommendation(query)
-    print(f"Assistant: {response}")
-    print()
-
-    time.sleep(1)
-```
-
-## 6. View Learned Preferences
-
-Use `reflect()` to synthesize what Hindsight has learned about your movie tastes.
-
-
-```python
-print("=" * 60)
-print("  What I've learned about your movie tastes:")
-print("=" * 60)
-print(get_preference_summary())
-```
-
-## 7. Try Your Own Queries
-
-Experiment with your own movie preferences!
-
-
-```python
-# Try your own query!
-your_query = "I'm in the mood for a sci-fi thriller"  # Change this!
-
-print(f"You: {your_query}")
-print("-" * 40)
-print(f"Assistant: {get_recommendation(your_query)}")
-```
-
-## 8. Cleanup
-
-Close the Hindsight client connection.
-
-
-```python
-hindsight.close()
-print("Client connection closed.")
-```
-
-
-```python
-
-```
-
-
-```python
-
-```
diff --git a/hindsight-docs/versioned_docs/version-0.4/cookbook/recipes/per-user-memory.md b/hindsight-docs/versioned_docs/version-0.4/cookbook/recipes/per-user-memory.md
deleted file mode 100644
index 9a7e79b2..00000000
--- a/hindsight-docs/versioned_docs/version-0.4/cookbook/recipes/per-user-memory.md
+++ /dev/null
@@ -1,247 +0,0 @@
----
-sidebar_position: 2
----
-
-# Per-User Memory
-
-
-:::tip Run this notebook
-This recipe is available as an interactive Jupyter notebook.
-[**Open in GitHub →**](https://github.com/vectorize-io/hindsight-cookbook/blob/main/notebooks/02-per-user-memory.ipynb)
-:::
-
-
-The simplest pattern: give your agent persistent memory for each user. The agent remembers past conversations, user preferences, and context across sessions.
-
-## The Problem
-
-Without memory, every conversation starts from scratch:
-
-```
-Session 1: "I prefer dark mode and use Python"
-Session 2: "What's my preferred language?" → Agent doesn't know
-```
-
-## The Solution: One Bank Per User
-
-```
-┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
-│   User A Bank   │     │   User B Bank   │     │   User C Bank   │
-│                 │     │                 │     │                 │
-│  - Conversations│     │  - Conversations│     │  - Conversations│
-│  - Preferences  │     │  - Preferences  │     │  - Preferences  │
-│  - Context      │     │  - Context      │     │  - Context      │
-└─────────────────┘     └─────────────────┘     └─────────────────┘
-        │                       │                       │
-   100% isolated          100% isolated           100% isolated
-```
-
-Each user gets their own memory bank. Complete isolation, simple mental model.
-
-
-```python
-!pip install hindsight-client nest_asyncio openai python-dotenv -U
-```
-
-## 1. Create a Bank When User Signs Up
-
-
-```python
-# Jupyter notebooks already run an asyncio event loop. The hindsight client 
-# uses loop.run_until_complete() internally, but Python doesn't allow nested 
-# event loops by default. nest_asyncio patches this to allow nesting.
-import nest_asyncio
-nest_asyncio.apply()
-
-import os
-from dotenv import load_dotenv
-from openai import OpenAI as OpenAIClient
-
-# Load environment variables from .env file
-# Copy .env.example to .env and fill in your values
-load_dotenv()
-
-# Configuration (override with env vars if set)
-HINDSIGHT_API_URL = os.getenv("HINDSIGHT_API_URL", "http://localhost:8888")
-HINDSIGHT_UI_URL = os.getenv("HINDSIGHT_UI_URL", "http://localhost:9999")
-
-from hindsight_client import Hindsight
-
-client = Hindsight(base_url=HINDSIGHT_API_URL)
-llm = OpenAIClient()  # Uses OPENAI_API_KEY from .env
-
-def on_user_signup(user_id: str):
-    client.create_bank(
-        bank_id=f"user-{user_id}",
-        name=f"Memory for {user_id}"
-    )
-    print(f"View bank: {HINDSIGHT_UI_URL}/banks/user-{user_id}?view=documents")
-```
-
-## 2. Manage Conversation Sessions
-
-Use `document_id` to group messages belonging to the same conversation. When you retain with the same `document_id`, Hindsight replaces the previous version (upsert behavior), keeping the memory up-to-date as the conversation evolves.
-
-
-```python
-import uuid
-import json
-
-class ConversationSession:
-    def __init__(self, user_id: str):
-        self.user_id = user_id
-        self.session_id = str(uuid.uuid4())  # Unique ID for this conversation
-        self.messages = []
-
-    def add_message(self, role: str, content: str):
-        self.messages.append({"role": role, "content": content})
-
-    def save(self, client: Hindsight):
-        """Save the entire conversation. Replaces previous version if session_id exists."""
-        # Convert messages to string format for retain
-        content = "\n".join([f"{m['role']}: {m['content']}" for m in self.messages])
-        client.retain(
-            bank_id=f"user-{self.user_id}",
-            content=content,
-            document_id=self.session_id  # Same ID = upsert (replace old version)
-        )
-```
-
-## 3. Recall Context Before Responding
-
-
-```python
-def get_context(user_id: str, query: str):
-    result = client.recall(
-        bank_id=f"user-{user_id}",
-        query=query
-    )
-    return result.results
-```
-
-## 4. Complete Agent Loop
-
-
-```python
-def format_results(results):
-    """Format recall results for the prompt."""
-    if not results:
-        return "No relevant memories found."
-    return "\n".join([f"- {r.text}" for r in results])
-
-def format_messages(messages):
-    """Format conversation messages for the prompt."""
-    return "\n".join([f"{m['role']}: {m['content']}" for m in messages])
-
-def handle_message(session: ConversationSession, user_message: str):
-    # 1. Add user message to session
-    session.add_message("user", user_message)
-
-    # 2. Recall relevant context from past conversations
-    context = client.recall(
-        bank_id=f"user-{session.user_id}",
-        query=user_message
-    )
-
-    # 3. Build system prompt with memory
-    system_prompt = f"""You are a helpful assistant with memory of past conversations.
-
-## What you remember about this user
-{format_results(context.results)}
-
-Respond helpfully and reference relevant memories when appropriate."""
-
-    # 4. Generate response using OpenAI
-    response = llm.chat.completions.create(
-        model="gpt-4o-mini",
-        messages=[
-            {"role": "system", "content": system_prompt},
-            *[{"role": m["role"], "content": m["content"]} for m in session.messages]
-        ]
-    )
-    assistant_response = response.choices[0].message.content
-
-    # 5. Add assistant response to session
-    session.add_message("assistant", assistant_response)
-
-    # 6. Save the updated conversation (upserts based on session_id)
-    session.save(client)
-
-    print(f"User: {user_message}")
-    print(f"Assistant: {assistant_response}\n")
-    
-    return assistant_response
-```
-
-## 5. Starting a New Conversation
-
-
-```python
-# Create the user's bank
-on_user_signup("alice")
-
-# Each new conversation gets a new session with a unique ID
-session = ConversationSession(user_id="alice")
-
-# Multiple exchanges in the same conversation
-handle_message(session, "Hi! I'm working on a Python project")
-handle_message(session, "Can you help me with async/await?")
-
-# View the stored conversation in the UI.
-# Each message updates the same document (via document_id), so you'll see
-# the full conversation history in a single document rather than separate entries.
-print(f"\nView documents: {HINDSIGHT_UI_URL}/banks/user-alice?view=documents")
-```
-
-## How Document ID Works
-
-The `document_id` parameter is key to managing evolving conversations:
-
-| Scenario | Behavior |
-|----------|----------|
-| First retain with `document_id="session_123"` | Creates new document |
-| Retain again with same `document_id="session_123"` | **Replaces** previous version (upsert) |
-| Retain with different `document_id="session_456"` | Creates separate document |
-| Retain without `document_id` | Creates new document each time |
-
-This upsert behavior means:
-- You always retain the **full conversation** state
-- Facts are re-extracted from the complete conversation
-- No duplicate or stale facts from old versions
-- Memory stays consistent as conversations evolve
-
-## What Gets Remembered
-
-Hindsight automatically extracts and connects:
-
-- **Facts**: "User prefers Python", "User is building a CLI tool"
-- **Entities**: People, projects, technologies mentioned
-- **Relationships**: How entities relate to each other
-- **Temporal context**: When things happened
-
-You don't need to manually extract or structure this - just retain the conversations.
-
-## When to Use This Pattern
-
-**Good fit:**
-- Chatbots and assistants
-- Personal AI companions
-- Any 1:1 user-to-agent interaction
-
-**Consider adding shared knowledge if:**
-- You have product docs or FAQs to reference
-- Multiple users need access to the same information
-- See the Support Agent with Shared Knowledge notebook
-
-## Cleanup
-
-Delete the banks created during this notebook:
-
-
-```python
-import requests
-
-# Delete the user-alice bank
-response = requests.delete(f"{HINDSIGHT_API_URL}/v1/default/banks/user-alice")
-print(f"Deleted user-alice: {response.json()}")
-```
diff --git a/hindsight-docs/versioned_docs/version-0.4/cookbook/recipes/personal_assistant.md b/hindsight-docs/versioned_docs/version-0.4/cookbook/recipes/personal_assistant.md
deleted file mode 100644
index ecc520d6..00000000
--- a/hindsight-docs/versioned_docs/version-0.4/cookbook/recipes/personal_assistant.md
+++ /dev/null
@@ -1,266 +0,0 @@
----
-sidebar_position: 9
----
-
-# Personal AI Assistant with Hindsight Memory
-
-
-:::tip Run this notebook
-This recipe is available as an interactive Jupyter notebook.
-[**Open in GitHub →**](https://github.com/vectorize-io/hindsight-cookbook/blob/main/notebooks/personal_assistant.ipynb)
-:::
-
-
-A general-purpose personal assistant that remembers your preferences, schedule, family, work context, and past conversations.
-
-## Features
-- Remembers family, work, and personal details
-- Tracks preferences and habits
-- Helps with scheduling and reminders
-- Maintains context across conversations
-
-## Prerequisites
-- OpenAI API key
-- Hindsight running locally via Docker (see setup below)
-
-## Start Hindsight Locally
-
-Before running this notebook, start Hindsight in a terminal:
-
-```bash
-export OPENAI_API_KEY="your-openai-api-key"
-
-docker run --rm -it --pull always -p 8888:8888 -p 9999:9999 \
-  -e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
-  -e HINDSIGHT_API_LLM_MODEL=gpt-4o-mini \
-  -v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
-  ghcr.io/vectorize-io/hindsight:latest
-```
-
-## 1. Install Dependencies
-
-
-```python
-!pip install -q hindsight-client openai nest-asyncio
-```
-
-## 2. Configure OpenAI API Key
-
-Enter your OpenAI API key when prompted (used by both Hindsight and the demo).
-
-
-```python
-import getpass
-import os
-
-# Set OpenAI API key (used by both Hindsight and the demo)
-if not os.getenv("OPENAI_API_KEY"):
-    os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter your OpenAI API key: ")
-
-print("API key configured!")
-```
-
-## 3. Initialize Clients
-
-
-```python
-import nest_asyncio
-nest_asyncio.apply()
-
-from datetime import datetime
-from openai import OpenAI
-from hindsight_client import Hindsight
-
-# Initialize Hindsight client (connects to local Docker instance)
-hindsight = Hindsight(
-    base_url=os.getenv("HINDSIGHT_BASE_URL", "http://localhost:8888"),
-)
-
-openai_client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
-
-USER_ID = "assistant-user-demo"
-
-print("Clients initialized!")
-```
-
-## 4. Define Helper Functions
-
-
-```python
-def remember(info: str, category: str = "general") -> str:
-    """Store information to remember."""
-    today = datetime.now().strftime("%B %d, %Y")
-
-    hindsight.retain(
-        bank_id=USER_ID,
-        content=f"{today}: {info}",
-        metadata={"category": category, "date": today},
-    )
-
-    return f"I'll remember: {info}"
-
-
-def recall_context(query: str) -> str:
-    """Recall relevant memories for context."""
-    memories = hindsight.recall(
-        bank_id=USER_ID,
-        query=query,
-        budget="high",
-    )
-
-    if memories and memories.results:
-        return "\n".join(f"- {m.text}" for m in memories.results[:8])
-    return ""
-
-
-def chat(user_message: str) -> str:
-    """Chat with the personal assistant."""
-    context = recall_context(user_message)
-
-    system_prompt = f"""You are a helpful personal AI assistant with long-term memory.
-You remember the user's preferences, schedule, family, work context, and past conversations.
-
-What you remember about this user:
-{context if context else "No memories recorded yet."}
-
-Your capabilities:
-- Remember things when asked ("Remember that...", "Don't forget...")
-- Recall past information ("What did I tell you about...", "When is...")
-- Provide personalized suggestions based on known preferences
-- Help with scheduling and reminders
-- Have natural conversations while maintaining context
-
-Be helpful, proactive, and reference relevant memories naturally."""
-
-    response = openai_client.chat.completions.create(
-        model="gpt-4o-mini",
-        messages=[
-            {"role": "system", "content": system_prompt},
-            {"role": "user", "content": user_message},
-        ],
-        temperature=0.7,
-        max_tokens=500,
-    )
-
-    answer = response.choices[0].message.content
-
-    # Check if user is asking to remember something
-    lower_msg = user_message.lower()
-    if any(phrase in lower_msg for phrase in ["remember that", "don't forget", "remind me", "note that"]):
-        hindsight.retain(
-            bank_id=USER_ID,
-            content=f"User asked to remember: {user_message}",
-            metadata={"category": "reminder"},
-        )
-
-    # Store the interaction
-    hindsight.retain(
-        bank_id=USER_ID,
-        content=f"Conversation - User: {user_message[:100]} | Assistant: {answer[:100]}",
-        metadata={"category": "conversation"},
-    )
-
-    return answer
-
-
-def get_summary(topic: str = None) -> str:
-    """Get a summary of memories."""
-    query = f"Summarize what you know about {topic}" if topic else \
-            "Summarize everything you know about this user"
-
-    summary = hindsight.reflect(
-        bank_id=USER_ID,
-        query=query,
-        budget="high",
-    )
-    return summary.text if hasattr(summary, 'text') else str(summary)
-
-print("Helper functions defined!")
-```
-
-## 5. Build Context
-
-
-```python
-print("Building context...")
-
-initial_context = [
-    ("My name is Alex and I work as a product manager at TechCorp", "personal"),
-    ("My wife's name is Sarah and we have two kids: Emma (7) and Jack (4)", "family"),
-    ("I prefer morning meetings and try to keep afternoons for deep work", "preference"),
-    ("My mom's birthday is March 15th", "event"),
-    ("I'm trying to read more - currently reading 'Atomic Habits'", "hobby"),
-    ("I have a weekly team standup every Monday at 10am", "schedule"),
-    ("I'm allergic to cats", "health"),
-    ("My favorite coffee is a flat white with oat milk", "preference"),
-    ("I'm training for a half marathon in April", "goal"),
-]
-
-for info, category in initial_context:
-    result = remember(info, category)
-    print(f"  {result}")
-```
-
-## 6. Have a Conversation
-
-
-```python
-import time
-
-print("=" * 60)
-print("  Conversation")
-print("=" * 60)
-
-conversations = [
-    "Hey, what's my wife's name again?",
-    "Remember that my Q1 review is next Thursday at 2pm",
-    "I need a gift idea for my mom's birthday",
-    "What time is my Monday standup?",
-    "Can you recommend a coffee order for me?",
-    "What books am I reading?",
-]
-
-for message in conversations:
-    print(f"\nAlex: {message}")
-    print("-" * 40)
-    response = chat(message)
-    print(f"Assistant: {response}")
-    time.sleep(1)
-```
-
-## 7. View Summary
-
-
-```python
-print("=" * 60)
-print("  What I Know About You")
-print("=" * 60)
-print(get_summary())
-```
-
-
-```python
-print("=" * 60)
-print("  Your Family")
-print("=" * 60)
-print(get_summary("family"))
-```
-
-## 8. Try Your Own Message
-
-
-```python
-your_message = "What should I focus on this month with my training?"  # Change this!
-
-print(f"You: {your_message}")
-print("-" * 40)
-print(f"Assistant: {chat(your_message)}")
-```
-
-## 9. Cleanup
-
-
-```python
-hindsight.close()
-print("Client connection closed.")
-```
diff --git a/hindsight-docs/versioned_docs/version-0.4/cookbook/recipes/personalized_search.md b/hindsight-docs/versioned_docs/version-0.4/cookbook/recipes/personalized_search.md
deleted file mode 100644
index 61ac26f5..00000000
--- a/hindsight-docs/versioned_docs/version-0.4/cookbook/recipes/personalized_search.md
+++ /dev/null
@@ -1,299 +0,0 @@
----
-sidebar_position: 10
----
-
-# Personalized Search Agent with Hindsight Memory
-
-
-:::tip Run this notebook
-This recipe is available as an interactive Jupyter notebook.
-[**Open in GitHub →**](https://github.com/vectorize-io/hindsight-cookbook/blob/main/notebooks/personalized_search.ipynb)
-:::
-
-
-A search assistant that learns your preferences, location, dietary needs, and lifestyle to provide contextually relevant search results.
-
-## Features
-- Learns location, dietary restrictions, and lifestyle
-- Personalizes search queries based on context
-- Remembers past searches and preferences
-- Integrates with Tavily for real web search (optional)
-
-## Prerequisites
-- OpenAI API key
-- Hindsight running locally via Docker (see setup below)
-- Tavily API key (optional, for real web search)
-
-## Start Hindsight Locally
-
-Before running this notebook, start Hindsight in a terminal:
-
-```bash
-export OPENAI_API_KEY="your-openai-api-key"
-
-docker run --rm -it --pull always -p 8888:8888 -p 9999:9999 \
-  -e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
-  -e HINDSIGHT_API_LLM_MODEL=gpt-4o-mini \
-  -v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
-  ghcr.io/vectorize-io/hindsight:latest
-```
-
-## 1. Install Dependencies
-
-
-```python
-# Tavily is optional - demo works with simulated results if not installed
-!pip install -q hindsight-client openai tavily-python nest-asyncio
-```
-
-## 2. Configure API Keys
-
-Enter your API keys when prompted. Tavily is optional - press Enter to skip for simulated search results.
-
-
-```python
-import getpass
-import os
-
-# Set OpenAI API key (used by both Hindsight and the demo)
-if not os.getenv("OPENAI_API_KEY"):
-    os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter your OpenAI API key: ")
-
-# Tavily is optional - for real web search
-if not os.getenv("TAVILY_API_KEY"):
-    tavily_key = getpass.getpass("Enter your Tavily API key (or press Enter to skip): ")
-    if tavily_key:
-        os.environ["TAVILY_API_KEY"] = tavily_key
-
-print("API keys configured!")
-```
-
-## 3. Initialize Clients
-
-
-```python
-import nest_asyncio
-nest_asyncio.apply()
-
-from openai import OpenAI
-from hindsight_client import Hindsight
-
-# Initialize Hindsight client (connects to local Docker instance)
-hindsight = Hindsight(
-    base_url=os.getenv("HINDSIGHT_BASE_URL", "http://localhost:8888"),
-)
-
-openai_client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
-
-# Optional: Tavily for real web search
-try:
-    from tavily import TavilyClient
-    tavily = TavilyClient(api_key=os.getenv("TAVILY_API_KEY"))
-    HAS_TAVILY = True
-    print("Tavily configured - using real web search!")
-except (ImportError, Exception) as e:
-    HAS_TAVILY = False
-    print("Note: Using simulated search results (Tavily not configured)")
-
-USER_ID = "search-user-demo"
-
-print("Clients initialized!")
-```
-
-## 4. Define Helper Functions
-
-
-```python
-def store_preference(preference: str) -> str:
-    """Store a user preference."""
-    hindsight.retain(
-        bank_id=USER_ID,
-        content=f"User preference: {preference}",
-        metadata={"category": "preference"},
-    )
-    return f"Learned: {preference}"
-
-
-def store_interaction(query: str, response: str) -> None:
-    """Store a search interaction."""
-    hindsight.retain(
-        bank_id=USER_ID,
-        content=f"Search query: {query}\nResult highlights: {response[:200]}",
-        metadata={"category": "search_history"},
-    )
-
-
-def get_user_context(query: str) -> str:
-    """Retrieve relevant user context."""
-    memories = hindsight.recall(
-        bank_id=USER_ID,
-        query=f"preferences location dietary lifestyle {query}",
-        budget="mid",
-    )
-
-    if memories and memories.results:
-        return "\n".join(f"- {m.text}" for m in memories.results[:6])
-    return ""
-
-
-def personalized_search(query: str) -> str:
-    """Perform a personalized search."""
-    user_context = get_user_context(query)
-
-    enhancement_prompt = f"""Given this user's preferences and the search query, suggest how to enhance the search.
-
-User preferences:
-{user_context if user_context else "No preferences recorded yet."}
-
-Search query: {query}
-
-Return a JSON object with:
-- "enhanced_query": The improved search query incorporating relevant preferences
-- "filters": Any specific filters to apply (e.g., "vegetarian", "within 5 miles")
-- "reasoning": Brief explanation of personalizations applied"""
-
-    enhancement = openai_client.chat.completions.create(
-        model="gpt-4o-mini",
-        messages=[{"role": "user", "content": enhancement_prompt}],
-        temperature=0.3,
-        max_tokens=300,
-    )
-
-    enhanced_info = enhancement.choices[0].message.content
-
-    # Perform the search
-    if HAS_TAVILY:
-        search_results = tavily.search(
-            query=query,
-            search_depth="advanced",
-            max_results=5,
-        )
-        results_text = "\n".join(
-            f"- {r['title']}: {r['content'][:150]}..."
-            for r in search_results.get('results', [])
-        )
-    else:
-        results_text = f"[Simulated search results for: {query}]"
-
-    response_prompt = f"""Based on the search results and user preferences, provide a personalized summary.
-
-User preferences:
-{user_context if user_context else "No preferences recorded yet."}
-
-Query: {query}
-
-Search enhancement applied:
-{enhanced_info}
-
-Search results:
-{results_text}
-
-Provide a helpful, personalized response that takes into account their preferences."""
-
-    response = openai_client.chat.completions.create(
-        model="gpt-4o-mini",
-        messages=[{"role": "user", "content": response_prompt}],
-        temperature=0.7,
-        max_tokens=500,
-    )
-
-    answer = response.choices[0].message.content
-    store_interaction(query, answer)
-
-    return answer
-
-
-def get_preference_profile() -> str:
-    """Get a summary of the user's preference profile."""
-    profile = hindsight.reflect(
-        bank_id=USER_ID,
-        query="""Summarize what we know about this user:
-        - Location and neighborhood
-        - Dietary preferences and restrictions
-        - Work style and schedule
-        - Hobbies and interests
-        - Family situation
-        - Shopping preferences""",
-        budget="high",
-    )
-    return profile.text if hasattr(profile, 'text') else str(profile)
-
-print("Helper functions defined!")
-```
-
-## 5. Build User Profile
-
-
-```python
-print("Learning user preferences...")
-
-preferences = [
-    "Lives in San Francisco, Mission District",
-    "Works remotely as a software engineer",
-    "Vegetarian, prefers organic food when possible",
-    "Has a 5-year-old daughter named Emma",
-    "Enjoys hiking and outdoor activities on weekends",
-    "Prefers quiet coffee shops for remote work",
-    "Lactose intolerant, uses oat milk",
-    "Interested in sustainable and eco-friendly products",
-    "Usually free on Tuesday and Thursday afternoons",
-    "Husband is allergic to nuts",
-]
-
-for pref in preferences:
-    result = store_preference(pref)
-    print(f"  {result}")
-```
-
-## 6. Personalized Search Results
-
-
-```python
-import time
-
-print("=" * 60)
-print("  Personalized Search Results")
-print("=" * 60)
-
-searches = [
-    "Find a good coffee shop for working remotely",
-    "Restaurant recommendations for a family dinner",
-    "Birthday gift ideas for a 5-year-old",
-]
-
-for query in searches:
-    print(f"\nSearch: {query}")
-    print("-" * 40)
-    result = personalized_search(query)
-    print(result)
-    time.sleep(1)
-```
-
-## 7. View Preference Profile
-
-
-```python
-print("=" * 60)
-print("  User Preference Profile")
-print("=" * 60)
-print(get_preference_profile())
-```
-
-## 8. Try Your Own Search
-
-
-```python
-your_search = "Best hiking trails near me"  # Change this!
-
-print(f"Search: {your_search}")
-print("-" * 40)
-print(personalized_search(your_search))
-```
-
-## 9. Cleanup
-
-
-```python
-hindsight.close()
-print("Client connection closed.")
-```
diff --git a/hindsight-docs/versioned_docs/version-0.4/cookbook/recipes/quickstart.md b/hindsight-docs/versioned_docs/version-0.4/cookbook/recipes/quickstart.md
deleted file mode 100644
index cea1cd77..00000000
--- a/hindsight-docs/versioned_docs/version-0.4/cookbook/recipes/quickstart.md
+++ /dev/null
@@ -1,162 +0,0 @@
----
-sidebar_position: 1
----
-
-# Hindsight Quickstart
-
-
-:::tip Run this notebook
-This recipe is available as an interactive Jupyter notebook.
-[**Open in GitHub →**](https://github.com/vectorize-io/hindsight-cookbook/blob/main/notebooks/01-quickstart.ipynb)
-:::
-
-
-This notebook covers the basics of using Hindsight:
-- **Retain**: Store information in memory
-- **Recall**: Retrieve memories matching a query
-- **Reflect**: Generate insights from memories
-
-## Prerequisites
-
-Make sure you have Hindsight running. The easiest way is via Docker:
-
-```bash
-export OPENAI_API_KEY=your-key
-
-docker run --rm -it --pull always -p 8888:8888 -p 9999:9999 \
-  -e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
-  -e HINDSIGHT_API_LLM_MODEL=gpt-4o-mini \
-  -v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
-  ghcr.io/vectorize-io/hindsight:latest
-```
-
-- API: http://localhost:8888
-- UI: http://localhost:9999
-
-## Installation
-
-Install the Hindsight Python client:
-
-
-```python
-!pip install hindsight-client nest_asyncio python-dotenv -U
-```
-
-## Connect to Hindsight
-
-
-```python
-# Jupyter notebooks already run an asyncio event loop. The hindsight client 
-# uses loop.run_until_complete() internally, but Python doesn't allow nested 
-# event loops by default. nest_asyncio patches this to allow nesting.
-import nest_asyncio
-nest_asyncio.apply()
-
-import os
-from dotenv import load_dotenv
-
-# Load environment variables from .env file
-# Copy .env.example to .env and fill in your values
-load_dotenv()
-
-# Configuration (override with env vars if set)
-HINDSIGHT_API_URL = os.getenv("HINDSIGHT_API_URL", "http://localhost:8888")
-HINDSIGHT_UI_URL = os.getenv("HINDSIGHT_UI_URL", "http://localhost:9999")
-
-from hindsight_client import Hindsight
-
-client = Hindsight(base_url=HINDSIGHT_API_URL)
-```
-
-## Retain: Store Information
-
-The `retain` operation is used to push new memories into Hindsight. It tells Hindsight to _retain_ the information you pass in.
-
-Behind the scenes, the retain operation uses an LLM to extract key facts, temporal data, entities, and relationships.
-
-
-```python
-# Simple retain
-client.retain(
-    bank_id="my-bank",
-    content="Alice works at Google as a software engineer"
-)
-
-# View the stored document in the UI:
-print(f"View documents: {HINDSIGHT_UI_URL}/banks/my-bank?view=documents")
-```
-
-
-```python
-# Retain with context and timestamp
-client.retain(
-    bank_id="my-bank",
-    content="Alice got promoted to senior engineer",
-    context="career update",
-    timestamp="2025-06-15T10:00:00Z"
-)
-```
-
-## Recall: Retrieve Memories
-
-The `recall` operation retrieves memories matching a query. It performs 4 retrieval strategies in parallel:
-- **Semantic**: Vector similarity
-- **Keyword**: BM25 exact matching
-- **Graph**: Entity/temporal/causal links
-- **Temporal**: Time range filtering
-
-
-```python
-# Simple recall
-results = client.recall(bank_id="my-bank", query="What does Alice do?")
-
-print("Memories:")
-for r in results.results:
-    print(f"  - {r.text}")
-```
-
-
-```python
-# Temporal recall
-results = client.recall(bank_id="my-bank", query="What happened in June?")
-
-print("Memories:")
-for r in results.results:
-    print(f"  - {r.text}")
-```
-
-## Reflect: Generate Insights
-
-The `reflect` operation performs a more thorough analysis of existing memories. This allows the agent to form new connections between memories which are then persisted as opinions and/or observations.
-
-Example use cases:
-- An AI Project Manager reflecting on what risks need to be mitigated
-- A Sales Agent reflecting on why certain outreach messages have gotten responses
-- A Support Agent reflecting on opportunities where customers have unanswered questions
-
-
-```python
-response = client.reflect(bank_id="my-bank", query="What should I know about Alice?")
-print(response)
-```
-
-## Memory Types
-
-Hindsight organizes memory into four networks to mimic human memory:
-
-- **World**: Facts about the world ("The stove gets hot")
-- **Experiences**: Agent's own experiences ("I touched the stove and it really hurt")
-- **Opinion**: Beliefs with confidence scores ("I shouldn't touch the stove again" - .99 confidence)
-- **Observation**: Complex mental models derived by reflecting on facts and experiences
-
-## Cleanup
-
-Delete the bank created during this notebook:
-
-
-```python
-import requests
-
-response = requests.delete(f"{HINDSIGHT_API_URL}/v1/default/banks/my-bank")
-print(f"Deleted my-bank: {response.json()}")
-```
diff --git a/hindsight-docs/versioned_docs/version-0.4/cookbook/recipes/study_buddy.md b/hindsight-docs/versioned_docs/version-0.4/cookbook/recipes/study_buddy.md
deleted file mode 100644
index 3174c210..00000000
--- a/hindsight-docs/versioned_docs/version-0.4/cookbook/recipes/study_buddy.md
+++ /dev/null
@@ -1,335 +0,0 @@
----
-sidebar_position: 11
----
-
-# Study Buddy with Hindsight Memory
-
-
-:::tip Run this notebook
-This recipe is available as an interactive Jupyter notebook.
-[**Open in GitHub →**](https://github.com/vectorize-io/hindsight-cookbook/blob/main/notebooks/study_buddy.ipynb)
-:::
-
-
-A personalized study assistant that tracks what you've learned, identifies knowledge gaps, and helps with spaced repetition.
-
-## Features
-- Tracks study sessions and topics covered
-- Monitors confidence levels per topic
-- Identifies knowledge gaps
-- Suggests topics for spaced repetition review
-
-## Prerequisites
-- OpenAI API key
-- Hindsight running locally via Docker (see setup below)
-
-## Start Hindsight Locally
-
-Before running this notebook, start Hindsight in a terminal:
-
-```bash
-export OPENAI_API_KEY="your-openai-api-key"
-
-docker run --rm -it --pull always -p 8888:8888 -p 9999:9999 \
-  -e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
-  -e HINDSIGHT_API_LLM_MODEL=gpt-4o-mini \
-  -v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
-  ghcr.io/vectorize-io/hindsight:latest
-```
-
-## 1. Install Dependencies
-
-
-```python
-!pip install -q hindsight-client openai nest-asyncio
-```
-
-## 2. Configure OpenAI API Key
-
-Enter your OpenAI API key when prompted (used by both Hindsight and the demo).
-
-
-```python
-import getpass
-import os
-
-# Set OpenAI API key (used by both Hindsight and the demo)
-if not os.getenv("OPENAI_API_KEY"):
-    os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter your OpenAI API key: ")
-
-print("API key configured!")
-```
-
-## 3. Initialize Clients
-
-
-```python
-import nest_asyncio
-nest_asyncio.apply()
-
-from datetime import datetime
-from openai import OpenAI
-from hindsight_client import Hindsight
-
-# Initialize Hindsight client (connects to local Docker instance)
-hindsight = Hindsight(
-    base_url=os.getenv("HINDSIGHT_BASE_URL", "http://localhost:8888"),
-)
-
-openai_client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
-
-USER_ID = "student-demo"
-
-print("Clients initialized!")
-```
-
-## 4. Define Helper Functions
-
-
-```python
-def record_study_session(topic: str, notes: str, confidence: str = "medium") -> str:
-    """Record a study session with topic, notes, and self-assessed confidence."""
-    today = datetime.now().strftime("%B %d, %Y")
-
-    content = f"""{today} - STUDY SESSION
-Topic: {topic}
-Confidence Level: {confidence}
-Notes: {notes}"""
-
-    hindsight.retain(
-        bank_id=USER_ID,
-        content=content,
-        metadata={
-            "category": "study_session",
-            "topic": topic,
-            "confidence": confidence,
-            "date": today,
-        },
-    )
-
-    return f"Recorded study session on '{topic}' (confidence: {confidence})"
-
-
-def record_question(topic: str, question: str, understood: bool) -> str:
-    """Record a question asked during study."""
-    today = datetime.now().strftime("%B %d, %Y")
-
-    content = f"""{today} - QUESTION
-Topic: {topic}
-Question: {question}
-Understood: {"Yes" if understood else "No - needs review"}"""
-
-    hindsight.retain(
-        bank_id=USER_ID,
-        content=content,
-        metadata={
-            "category": "question",
-            "topic": topic,
-            "understood": str(understood),
-        },
-    )
-
-    return f"Recorded question on '{topic}'"
-
-
-def study_buddy(user_query: str) -> str:
-    """Interact with the study buddy."""
-    memories = hindsight.recall(
-        bank_id=USER_ID,
-        query=f"study session topic notes questions {user_query}",
-        budget="high",
-    )
-
-    memory_context = ""
-    if memories and memories.results:
-        memory_context = "\n".join(f"- {m.text}" for m in memories.results[:8])
-
-    system_prompt = f"""You are a helpful study buddy and tutor.
-You have access to the student's study history, including:
-- Topics they've studied and their notes
-- Their self-assessed confidence levels
-- Questions they've asked and whether they understood the answers
-
-Study History:
-{memory_context if memory_context else "No study history recorded yet."}
-
-Your role:
-1. Answer questions about topics they're studying
-2. Identify knowledge gaps based on their history
-3. Suggest topics to review (spaced repetition)
-4. Provide encouragement and study tips
-5. Connect new concepts to things they've already learned
-
-Be supportive and pedagogical. Reference their previous learning when relevant."""
-
-    response = openai_client.chat.completions.create(
-        model="gpt-4o-mini",
-        messages=[
-            {"role": "system", "content": system_prompt},
-            {"role": "user", "content": user_query},
-        ],
-        temperature=0.7,
-        max_tokens=800,
-    )
-
-    answer = response.choices[0].message.content
-
-    hindsight.retain(
-        bank_id=USER_ID,
-        content=f"Student asked: {user_query}\nExplanation given: {answer[:300]}...",
-        metadata={"category": "tutoring"},
-    )
-
-    return answer
-
-
-def get_review_suggestions() -> str:
-    """Get suggestions for topics to review."""
-    suggestions = hindsight.reflect(
-        bank_id=USER_ID,
-        query="""Analyze this student's study history and suggest:
-        1. Topics with low confidence that need more review
-        2. Topics studied a while ago that should be revisited
-        3. Questions that weren't fully understood
-        4. Connections between topics they might have missed
-
-        Prioritize by what would most improve their understanding.""",
-        budget="high",
-    )
-    return suggestions.text if hasattr(suggestions, 'text') else str(suggestions)
-
-
-def get_knowledge_summary(topic: str = None) -> str:
-    """Get a summary of what the student knows."""
-    query = f"Summarize what this student knows about {topic}" if topic else \
-            "Summarize this student's overall knowledge and progress"
-
-    summary = hindsight.reflect(
-        bank_id=USER_ID,
-        query=query,
-        budget="high",
-    )
-    return summary.text if hasattr(summary, 'text') else str(summary)
-
-print("Helper functions defined!")
-```
-
-## 5. Record Study Sessions
-
-
-```python
-print("Recording study sessions...")
-
-sessions = [
-    {
-        "topic": "Classical Mechanics - Newton's Laws",
-        "notes": "Covered F=ma, action-reaction pairs, inertia. Solved problems on inclined planes.",
-        "confidence": "high",
-    },
-    {
-        "topic": "Classical Mechanics - Conservation of Momentum",
-        "notes": "Elastic vs inelastic collisions. Struggled with 2D collision problems.",
-        "confidence": "low",
-    },
-    {
-        "topic": "Classical Mechanics - Generalized Coordinates",
-        "notes": "Introduction to Lagrangian mechanics. Degrees of freedom concept.",
-        "confidence": "medium",
-    },
-    {
-        "topic": "Waves - Simple Harmonic Motion",
-        "notes": "SHM equations, period, frequency. Connected to springs and pendulums.",
-        "confidence": "high",
-    },
-    {
-        "topic": "Waves - Frequency Domain",
-        "notes": "Started Fourier transforms. Math is confusing, need more practice.",
-        "confidence": "low",
-    },
-]
-
-for session in sessions:
-    result = record_study_session(**session)
-    print(f"  {result}")
-```
-
-## 6. Record Questions
-
-
-```python
-print("Recording questions...")
-
-questions = [
-    ("Conservation of Momentum", "Why is momentum conserved in collisions?", True),
-    ("Conservation of Momentum", "How do I solve 2D collision problems?", False),
-    ("Generalized Coordinates", "What's the advantage of Lagrangian over Newtonian?", True),
-    ("Frequency Domain", "When do I use Fourier transforms vs Laplace?", False),
-]
-
-for topic, question, understood in questions:
-    result = record_question(topic, question, understood)
-    print(f"  {result}")
-```
-
-## 7. Interactive Study Session
-
-
-```python
-import time
-
-print("=" * 60)
-print("  Study Session")
-print("=" * 60)
-
-queries = [
-    "Can you explain generalized coordinates again? I remember we covered it but I'm fuzzy on the details.",
-    "What topics should I review before my exam next week?",
-    "I'm still confused about 2D collision problems. Can you walk me through an example?",
-]
-
-for query in queries:
-    print(f"\nStudent: {query}")
-    print("-" * 40)
-    response = study_buddy(query)
-    print(f"Study Buddy: {response}")
-    time.sleep(1)
-```
-
-## 8. Get Review Suggestions
-
-
-```python
-print("=" * 60)
-print("  Recommended Review Topics")
-print("=" * 60)
-print(get_review_suggestions())
-```
-
-## 9. Knowledge Summary
-
-
-```python
-print("=" * 60)
-print("  Knowledge Summary")
-print("=" * 60)
-print(get_knowledge_summary())
-```
-
-## 10. Try Your Own Question
-
-
-```python
-your_question = "What are my biggest knowledge gaps right now?"  # Change this!
-
-print(f"You: {your_question}")
-print("-" * 40)
-print(f"Study Buddy: {study_buddy(your_question)}")
-```
-
-## 11. Cleanup
-
-
-```python
-hindsight.close()
-print("Client connection closed.")
-```
diff --git a/hindsight-docs/versioned_docs/version-0.4/cookbook/recipes/support-agent-shared-knowledge.md b/hindsight-docs/versioned_docs/version-0.4/cookbook/recipes/support-agent-shared-knowledge.md
deleted file mode 100644
index 3bf5ba11..00000000
--- a/hindsight-docs/versioned_docs/version-0.4/cookbook/recipes/support-agent-shared-knowledge.md
+++ /dev/null
@@ -1,315 +0,0 @@
----
-sidebar_position: 3
----
-
-# Support Agent with Shared Knowledge
-
-
-:::tip Run this notebook
-This recipe is available as an interactive Jupyter notebook.
-[**Open in GitHub →**](https://github.com/vectorize-io/hindsight-cookbook/blob/main/notebooks/03-support-agent-shared-knowledge.ipynb)
-:::
-
-
-This pattern shows how to build a support agent that combines **per-user memory** with **shared product knowledge** (RAG), giving users personalized support while leveraging a single source of truth for documentation.
-
-## The Problem
-
-You're building a support agent that needs to:
-- Remember each user's history, preferences, and past issues
-- Access shared product documentation
-- Keep user data completely isolated from other users
-
-A naive approach would index product docs into each user's memory bank, but this is expensive and wasteful (N copies for N users).
-
-## The Solution: Multi-Bank Architecture
-
-```
-┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
-│   User A Bank   │     │   User B Bank   │     │  Shared Docs    │
-│                 │     │                 │     │     Bank        │
-│  - Conversations│     │  - Conversations│     │                 │
-│  - Preferences  │     │  - Preferences  │     │  - Product docs │
-│  - Past issues  │     │  - Past issues  │     │  - FAQs         │
-│  - Solutions    │     │  - Solutions    │     │  - Guides       │
-└────────┬────────┘     └────────┬────────┘     └────────┬────────┘
-         │                       │                       │
-         └───────────────────────┴───────────────────────┘
-                                 │
-                           Agent queries
-                           multiple banks
-```
-
-**Key benefits:**
-- Product docs indexed once, shared by all users
-- User memory is 100% isolated
-- Simple mental model, no complex filtering
-
-
-```python
-!pip install hindsight-client nest_asyncio openai python-dotenv -U
-```
-
-## 1. Set Up Memory Banks
-
-Create three types of banks:
-
-
-```python
-# Jupyter notebooks already run an asyncio event loop. The hindsight client 
-# uses loop.run_until_complete() internally, but Python doesn't allow nested 
-# event loops by default. nest_asyncio patches this to allow nesting.
-import nest_asyncio
-nest_asyncio.apply()
-
-import os
-from dotenv import load_dotenv
-from openai import OpenAI as OpenAIClient
-
-# Load environment variables from .env file
-# Copy .env.example to .env and fill in your values
-load_dotenv()
-
-# Configuration (override with env vars if set)
-HINDSIGHT_API_URL = os.getenv("HINDSIGHT_API_URL", "http://localhost:8888")
-HINDSIGHT_UI_URL = os.getenv("HINDSIGHT_UI_URL", "http://localhost:9999")
-
-from hindsight_client import Hindsight
-
-client = Hindsight(base_url=HINDSIGHT_API_URL)
-llm = OpenAIClient()  # Uses OPENAI_API_KEY from .env
-
-# Shared knowledge bank (created once)
-shared_bank = client.create_bank(
-    bank_id="product-docs",
-    name="Product Documentation"
-)
-
-# Per-user banks (created when user signs up)
-def create_user_bank(user_id: str):
-    return client.create_bank(
-        bank_id=f"user-{user_id}",
-        name=f"Memory for {user_id}"
-    )
-```
-
-## 2. Index Product Documentation
-
-Index your product docs into the shared bank (do this once, or on doc updates):
-
-
-```python
-# Index product documentation - retain each doc separately
-client.retain(
-    bank_id="product-docs",
-    content="# Pricing Tiers\n\nBasic: $10/mo, Pro: $25/mo, Enterprise: Contact us"
-)
-
-client.retain(
-    bank_id="product-docs",
-    content="# Getting Started\n\nTo set up your account, visit the dashboard and click 'New Project'"
-)
-
-# View the stored documents in the UI:
-print(f"View documents: {HINDSIGHT_UI_URL}/banks/product-docs?view=documents")
-```
-
-## 3. Store User Conversations
-
-After each support interaction, retain it in the user's bank:
-
-
-```python
-def save_conversation(user_id: str, messages: list):
-    # Convert messages to string format
-    content = "\n".join([f"{m['role']}: {m['content']}" for m in messages])
-    client.retain(
-        bank_id=f"user-{user_id}",
-        content=content
-    )
-```
-
-## 4. Query Multiple Banks at Support Time
-
-When handling a user query, retrieve context from both banks:
-
-
-```python
-def get_support_context(user_id: str, query: str):
-    # Get user's personal context
-    user_context = client.recall(
-        bank_id=f"user-{user_id}",
-        query=query
-    )
-
-    # Get relevant product documentation
-    docs_context = client.recall(
-        bank_id="product-docs",
-        query=query
-    )
-
-    return {
-        "user_history": user_context.results,
-        "documentation": docs_context.results
-    }
-```
-
-## 5. Build the Agent Prompt
-
-Combine both contexts in your agent's prompt:
-
-
-```python
-def format_results(results):
-    """Format recall results for the prompt."""
-    if not results:
-        return "No relevant information found."
-    return "\n".join([f"- {r.text}" for r in results])
-
-def build_prompt(query: str, context: dict) -> str:
-    return f"""You are a helpful support agent.
-
-## User's History
-{format_results(context["user_history"])}
-
-## Product Documentation
-{format_results(context["documentation"])}
-
-## Current Question
-{query}
-
-Use the user's history to personalize your response and the documentation
-for accurate product information. If you find a solution, remember it for
-future reference.
-"""
-```
-
-## Promoting Learnings to Shared Knowledge
-
-When the agent discovers a solution that's not in the docs, you can optionally promote it to a "learnings" bank:
-
-```
-┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
-│   User A Bank   │     │  Shared Docs    │     │    Learnings    │
-│                 │     │     Bank        │     │      Bank       │
-│  - Conversations│     │                 │     │                 │
-│  - Preferences  │     │  - Product docs │     │  - Verified     │
-│  - Past issues  │     │  - FAQs         │     │    solutions    │
-│  - Solutions    │     │  - Guides       │     │  - Workarounds  │
-└────────┬────────┘     └────────┬────────┘     └────────┬────────┘
-         │                       │                       │
-         └───────────────────────┴───────────────────────┘
-                                 │
-                           Agent queries
-                           all three banks
-```
-
-
-```python
-# Optional: Create a curated learnings bank
-learnings_bank = client.create_bank(
-    bank_id="support-learnings",
-    name="Curated Support Learnings"
-)
-
-# After a successful resolution
-def promote_learning(insight: str):
-    client.retain(
-        bank_id="support-learnings",
-        content=insight
-    )
-```
-
-## Complete Example
-
-
-```python
-def format_results(results):
-    if not results:
-        return "No relevant information found."
-    return "\n".join([f"- {r.text}" for r in results])
-
-def handle_support_request(user_id: str, query: str):
-    # 1. Recall from user's memory
-    user_recall = client.recall(
-        bank_id=f"user-{user_id}",
-        query=query
-    )
-
-    # 2. Recall from shared docs
-    docs_recall = client.recall(
-        bank_id="product-docs",
-        query=query
-    )
-
-    # 3. Recall from learnings (optional)
-    learnings_recall = client.recall(
-        bank_id="support-learnings",
-        query=query
-    )
-
-    # 4. Build system prompt with context
-    system_prompt = f"""You are a helpful support agent. Use the context below to answer the user's question.
-
-## User's History
-{format_results(user_recall.results)}
-
-## Product Documentation
-{format_results(docs_recall.results)}
-
-## Known Solutions
-{format_results(learnings_recall.results)}
-
-Provide helpful, accurate responses based on the documentation. Reference the user's history when relevant."""
-
-    # 5. Generate response using OpenAI
-    response = llm.chat.completions.create(
-        model="gpt-4o-mini",
-        messages=[
-            {"role": "system", "content": system_prompt},
-            {"role": "user", "content": query}
-        ]
-    )
-    assistant_response = response.choices[0].message.content
-
-    # 6. Save the conversation to user's memory
-    conversation = f"user: {query}\nassistant: {assistant_response}"
-    client.retain(
-        bank_id=f"user-{user_id}",
-        content=conversation
-    )
-
-    return assistant_response
-
-# Test the function
-create_user_bank("bob")
-print("User: How do I get started?")
-result = handle_support_request("bob", "How do I get started?")
-print(f"Assistant: {result}")
-print(f"\nView user memory: {HINDSIGHT_UI_URL}/banks/user-bob?view=documents")
-```
-
-## When to Use This Pattern
-
-**Good fit:**
-- Support agents with shared documentation
-- Multi-tenant applications with shared reference data
-- Any scenario needing user isolation + shared knowledge
-
-**Consider alternatives if:**
-- You need cross-user learning (users benefiting from other users' solutions)
-- Entity relationships must span across users and docs
-
-## Cleanup
-
-Delete the banks created during this notebook:
-
-
-```python
-import requests
-
-# Delete all banks created in this notebook
-for bank_id in ["product-docs", "support-learnings", "user-bob"]:
-    response = requests.delete(f"{HINDSIGHT_API_URL}/v1/default/banks/{bank_id}")
-    print(f"Deleted {bank_id}: {response.json()}")
-```
diff --git a/hindsight-docs/versioned_docs/version-0.4/cookbook/recipes/tool-learning-demo.md b/hindsight-docs/versioned_docs/version-0.4/cookbook/recipes/tool-learning-demo.md
deleted file mode 100644
index c2e3b870..00000000
--- a/hindsight-docs/versioned_docs/version-0.4/cookbook/recipes/tool-learning-demo.md
+++ /dev/null
@@ -1,372 +0,0 @@
----
-sidebar_position: 5
----
-
-# Routing Tool Learning
-
-
-:::tip Run this notebook
-This recipe is available as an interactive Jupyter notebook.
-[**Open in GitHub →**](https://github.com/vectorize-io/hindsight-cookbook/blob/main/notebooks/05-tool-learning-demo.ipynb)
-:::
-
-
-This notebook demonstrates how Hindsight helps an LLM learn which tool to use when tool names are ambiguous. Without memory, the LLM might randomly select between similarly-named tools. With Hindsight, it learns from past interactions and consistently makes the correct choice.
-
-## The Scenario
-
-We have a task routing system with two tools:
-- `route_to_channel_alpha` - Routes to processing channel Alpha
-- `route_to_channel_omega` - Routes to processing channel Omega
-
-The tool names and descriptions are **intentionally vague**. In reality:
-- Channel Alpha handles **FINANCIAL/PAYMENT** tasks (refunds, billing, etc.)
-- Channel Omega handles **TECHNICAL/SUPPORT** tasks (bugs, features, etc.)
-
-**Without Hindsight:** The LLM guesses randomly based on vague descriptions  
-**With Hindsight:** The LLM learns from feedback which channel handles what
-
-## Prerequisites
-
-Make sure you have Hindsight running:
-
-```bash
-export OPENAI_API_KEY=your-key
-
-docker run --rm -it --pull always -p 8888:8888 -p 9999:9999 \
-  -e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
-  -e HINDSIGHT_API_LLM_MODEL=gpt-4o-mini \
-  -v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
-  ghcr.io/vectorize-io/hindsight:latest
-```
-
-## Installation
-
-
-```python
-!pip install hindsight-litellm hindsight-client litellm nest_asyncio python-dotenv -U -q
-```
-
-## Setup
-
-
-```python
-import os
-import json
-import uuid
-import time
-import logging
-import nest_asyncio
-from typing import Optional
-from dotenv import load_dotenv
-
-nest_asyncio.apply()
-load_dotenv()
-
-logging.basicConfig(level=logging.INFO)
-logging.getLogger("LiteLLM").setLevel(logging.WARNING)
-logging.getLogger("LiteLLM Router").setLevel(logging.WARNING)
-logging.getLogger("httpx").setLevel(logging.WARNING)
-
-import litellm
-import hindsight_litellm
-from hindsight_client import Hindsight
-
-HINDSIGHT_API_URL = os.getenv("HINDSIGHT_API_URL", "http://localhost:8888")
-
-if not os.getenv("OPENAI_API_KEY"):
-    print("Warning: OPENAI_API_KEY not set")
-```
-
-## Define Tools
-
-These tool definitions are **intentionally ambiguous** - the descriptions don't reveal which channel handles what type of request.
-
-
-```python
-TOOLS = [
-    {
-        "type": "function",
-        "function": {
-            "name": "route_to_channel_alpha",
-            "description": "Routes the customer request to processing channel Alpha. Use this channel for appropriate request types.",
-            "parameters": {
-                "type": "object",
-                "properties": {
-                    "request_summary": {
-                        "type": "string",
-                        "description": "A brief summary of the customer's request"
-                    },
-                    "priority": {
-                        "type": "string",
-                        "enum": ["low", "medium", "high"],
-                        "description": "Priority level of the request"
-                    }
-                },
-                "required": ["request_summary"]
-            }
-        }
-    },
-    {
-        "type": "function",
-        "function": {
-            "name": "route_to_channel_omega",
-            "description": "Routes the customer request to processing channel Omega. Use this channel for appropriate request types.",
-            "parameters": {
-                "type": "object",
-                "properties": {
-                    "request_summary": {
-                        "type": "string",
-                        "description": "A brief summary of the customer's request"
-                    },
-                    "priority": {
-                        "type": "string",
-                        "enum": ["low", "medium", "high"],
-                        "description": "Priority level of the request"
-                    }
-                },
-                "required": ["request_summary"]
-            }
-        }
-    }
-]
-```
-
-## Test Scenarios
-
-A mix of financial and technical requests to test routing accuracy.
-
-
-```python
-TEST_SCENARIOS = [
-    {
-        "type": "financial",
-        "request": "I was charged twice for my subscription last month. I need a refund for the duplicate charge.",
-        "correct_tool": "route_to_channel_alpha"
-    },
-    {
-        "type": "technical",
-        "request": "The app keeps crashing when I try to upload a file larger than 10MB. This bug is blocking my work.",
-        "correct_tool": "route_to_channel_omega"
-    },
-    {
-        "type": "financial",
-        "request": "My invoice shows an incorrect amount. The billing department needs to fix this.",
-        "correct_tool": "route_to_channel_alpha"
-    },
-    {
-        "type": "technical",
-        "request": "I'd like to request a new feature: the ability to export reports as PDF.",
-        "correct_tool": "route_to_channel_omega"
-    },
-    {
-        "type": "financial",
-        "request": "I need to update my payment method and understand why my last payment failed.",
-        "correct_tool": "route_to_channel_alpha"
-    },
-]
-```
-
-## Helper Functions
-
-
-```python
-SYSTEM_PROMPT = """You are a customer service routing agent. Your job is to route customer requests to the appropriate processing channel.
-
-You have access to two routing channels:
-- route_to_channel_alpha: Routes to channel Alpha
-- route_to_channel_omega: Routes to channel Omega
-
-Analyze the customer's request and route it to the most appropriate channel. You must call one of the routing functions to process the request.
-
-Important: Base your routing decision on what you know about each channel's purpose. If you have learned from previous interactions which channel handles specific types of requests, use that knowledge."""
-
-
-def make_routing_request(user_request: str, use_hindsight: bool, bank_id: Optional[str] = None):
-    """Make a routing request and return the tool called."""
-    messages = [
-        {"role": "system", "content": SYSTEM_PROMPT},
-        {"role": "user", "content": f"Customer Request: {user_request}"}
-    ]
-
-    if use_hindsight and bank_id:
-        response = hindsight_litellm.completion(
-            model="gpt-4o-mini",
-            messages=messages,
-            tools=TOOLS,
-            tool_choice="required",
-            temperature=0.0,
-        )
-    else:
-        response = litellm.completion(
-            model="gpt-4o-mini",
-            messages=messages,
-            tools=TOOLS,
-            tool_choice="required",
-            temperature=0.7,
-        )
-
-    if response.choices[0].message.tool_calls:
-        tool_call = response.choices[0].message.tool_calls[0]
-        return tool_call.function.name
-    return None
-
-
-def store_feedback(bank_id: str, request: str, correct_tool: str, request_type: str):
-    """Store feedback about which tool was correct for a request type."""
-    client = Hindsight(base_url=HINDSIGHT_API_URL, timeout=60.0)
-
-    feedback_content = f"""ROUTING FEEDBACK:
-Request type: {request_type}
-Customer request: "{request}"
-Correct routing: {correct_tool}
-
-LEARNED RULE: {request_type.upper()} requests (like refunds, billing, payments, charges, invoices) should ALWAYS be routed to {correct_tool}.
-This is important institutional knowledge for routing decisions."""
-
-    client.retain(
-        bank_id=bank_id,
-        content=feedback_content,
-        context=f"routing:feedback:{request_type}",
-        metadata={"request_type": request_type, "correct_tool": correct_tool}
-    )
-```
-
-## Phase 1: Without Hindsight (No Memory)
-
-The LLM has no prior knowledge about which channel handles what. With ambiguous tool descriptions, it may route incorrectly.
-
-
-```python
-print("=" * 60)
-print("PHASE 1: WITHOUT HINDSIGHT (No Memory)")
-print("=" * 60)
-
-phase1_results = []
-for i, scenario in enumerate(TEST_SCENARIOS[:3], 1):
-    print(f"\n--- Test {i}: {scenario['type'].upper()} Request ---")
-    print(f"Request: \"{scenario['request'][:60]}...\"")
-
-    tool_name = make_routing_request(scenario['request'], use_hindsight=False)
-
-    is_correct = tool_name == scenario['correct_tool']
-    phase1_results.append(is_correct)
-
-    print(f"LLM chose: {tool_name}")
-    print(f"Correct tool: {scenario['correct_tool']}")
-    print(f"Result: {'✓ CORRECT' if is_correct else '✗ INCORRECT'}")
-
-phase1_accuracy = sum(phase1_results) / len(phase1_results) * 100
-print(f"\n>>> Phase 1 Accuracy: {phase1_accuracy:.0f}% ({sum(phase1_results)}/{len(phase1_results)})")
-```
-
-## Phase 2: Teaching Phase
-
-Now we provide feedback about correct routing to build memory. This simulates a human supervisor correcting the AI's routing decisions.
-
-
-```python
-bank_id = f"tool-learning-{uuid.uuid4().hex[:8]}"
-print(f"Using bank_id: {bank_id}")
-
-# Configure and enable Hindsight
-hindsight_litellm.configure(
-    hindsight_api_url=HINDSIGHT_API_URL,
-    bank_id=bank_id,
-    store_conversations=True,
-    inject_memories=True,
-    max_memories=10,
-    recall_budget="high",
-    verbose=False,
-)
-hindsight_litellm.enable()
-
-print("\nStoring routing feedback...")
-
-feedback_examples = [
-    ("I need a refund for an incorrect charge on my account.", "route_to_channel_alpha", "financial"),
-    ("There's a bug in the system causing data loss.", "route_to_channel_omega", "technical"),
-    ("My billing statement has errors that need correction.", "route_to_channel_alpha", "financial"),
-    ("I want to request a new feature for the dashboard.", "route_to_channel_omega", "technical"),
-]
-
-for request, correct_tool, req_type in feedback_examples:
-    print(f"  Storing: {req_type.upper()} → {correct_tool}")
-    store_feedback(bank_id, request, correct_tool, req_type)
-
-print("\nWaiting 15 seconds for Hindsight to process memories...")
-time.sleep(15)
-print("Done!")
-```
-
-## Phase 3: With Hindsight (Memory-Augmented)
-
-The LLM now has access to learned routing knowledge via Hindsight. It should route requests correctly based on past feedback.
-
-
-```python
-print("=" * 60)
-print("PHASE 3: WITH HINDSIGHT (Memory-Augmented)")
-print("=" * 60)
-
-phase3_results = []
-for i, scenario in enumerate(TEST_SCENARIOS, 1):
-    print(f"\n--- Test {i}: {scenario['type'].upper()} Request ---")
-    print(f"Request: \"{scenario['request'][:60]}...\"")
-
-    tool_name = make_routing_request(
-        scenario['request'],
-        use_hindsight=True,
-        bank_id=bank_id
-    )
-
-    is_correct = tool_name == scenario['correct_tool']
-    phase3_results.append(is_correct)
-
-    print(f"LLM chose: {tool_name}")
-    print(f"Correct tool: {scenario['correct_tool']}")
-    print(f"Result: {'✓ CORRECT' if is_correct else '✗ INCORRECT'}")
-
-phase3_accuracy = sum(phase3_results) / len(phase3_results) * 100
-print(f"\n>>> Phase 3 Accuracy: {phase3_accuracy:.0f}% ({sum(phase3_results)}/{len(phase3_results)})")
-```
-
-## Summary
-
-
-```python
-print("=" * 60)
-print("SUMMARY")
-print("=" * 60)
-print(f"\nPhase 1 (No Memory):      {phase1_accuracy:.0f}% accuracy")
-print(f"Phase 3 (With Hindsight): {phase3_accuracy:.0f}% accuracy")
-
-improvement = phase3_accuracy - phase1_accuracy
-if improvement > 0:
-    print(f"\n🎉 Improvement: +{improvement:.0f}% accuracy with Hindsight!")
-elif improvement == 0:
-    print(f"\nNote: Results may vary. Run again to see learning effect.")
-else:
-    print(f"\nNote: Phase 1 got lucky! Run again to see typical behavior.")
-
-print(f"\nMemories stored in bank: {bank_id}")
-print(f"View in UI: http://localhost:9999/banks/{bank_id}")
-
-print("\n" + "=" * 60)
-print("KEY INSIGHT")
-print("=" * 60)
-print("Hindsight allows the LLM to learn from experience which tool")
-print("to use, even when tool names/descriptions are ambiguous.")
-```
-
-## Cleanup
-
-
-```python
-hindsight_litellm.cleanup()
-
-# Optional: delete the bank
-import requests
-response = requests.delete(f"{HINDSIGHT_API_URL}/v1/default/banks/{bank_id}")
-print(f"Deleted bank: {response.json()}")
-```
diff --git a/hindsight-docs/versioned_sidebars/version-0.3-sidebars.json b/hindsight-docs/versioned_sidebars/version-0.3-sidebars.json
index ca17c3d1..a7610695 100644
--- a/hindsight-docs/versioned_sidebars/version-0.3-sidebars.json
+++ b/hindsight-docs/versioned_sidebars/version-0.3-sidebars.json
@@ -187,56 +187,5 @@
         }
       ]
     }
-  ],
-  "cookbookSidebar": [
-    {
-      "type": "doc",
-      "id": "cookbook/index",
-      "label": "Overview"
-    },
-    {
-      "type": "category",
-      "label": "Recipes",
-      "collapsible": false,
-      "items": [
-        {
-          "type": "doc",
-          "id": "cookbook/recipes/quickstart",
-          "label": "Hindsight Quickstart"
-        },
-        {
-          "type": "doc",
-          "id": "cookbook/recipes/per-user-memory",
-          "label": "Per-User Memory"
-        },
-        {
-          "type": "doc",
-          "id": "cookbook/recipes/support-agent-shared-knowledge",
-          "label": "Support Agent with Shared Knowledge"
-        },
-        {
-          "type": "doc",
-          "id": "cookbook/recipes/litellm-memory-demo",
-          "label": "Memory with LiteLLM"
-        },
-        {
-          "type": "doc",
-          "id": "cookbook/recipes/tool-learning-demo",
-          "label": "Routing Tool Learning"
-        }
-      ]
-    },
-    {
-      "type": "category",
-      "label": "Applications",
-      "collapsible": false,
-      "items": [
-        {
-          "type": "doc",
-          "id": "cookbook/applications/openai-fitness-coach",
-          "label": "OpenAI Agent + Hindsight Memory Integration"
-        }
-      ]
-    }
   ]
-}
+}
\ No newline at end of file
diff --git a/hindsight-docs/versioned_sidebars/version-0.4-sidebars.json b/hindsight-docs/versioned_sidebars/version-0.4-sidebars.json
index 32e3e619..0f86ed1c 100644
--- a/hindsight-docs/versioned_sidebars/version-0.4-sidebars.json
+++ b/hindsight-docs/versioned_sidebars/version-0.4-sidebars.json
@@ -231,12 +231,5 @@
         }
       ]
     }
-  ],
-  "cookbookSidebar": [
-    {
-      "type": "doc",
-      "id": "cookbook/index",
-      "label": "Cookbook"
-    }
   ]
-}
+}
\ No newline at end of file