Release 0.11.21 (#2128 )

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: marcusschiesser <17126+marcusschiesser@users.noreply.github.com>
feat: VectoryMemoryBlock (#2110 )
2026-07-01 22:14:03 -04:00 · 2025-07-22 12:23:58 +08:00 · 2025-07-22 12:18:09 +08:00 · 2025-07-22 11:30:01 +08:00 · 2025-07-21 15:40:31 -06:00 · 2025-07-21 15:30:37 -06:00
206 changed files with 6192 additions and 11306 deletions
@@ -1,5 +1,86 @@
 # @llamaindex/doc

+## 0.2.44
+
+### Patch Changes
+
+- 38da40b: feat: VectoryMemoryBlock
+- Updated dependencies [38da40b]
+  - @llamaindex/core@0.6.17
+  - @llamaindex/cloud@4.0.26
+  - llamaindex@0.11.21
+  - @llamaindex/node-parser@2.0.17
+  - @llamaindex/openai@0.4.12
+  - @llamaindex/readers@3.1.16
+  - @llamaindex/workflow@1.1.17
+
+## 0.2.43
+
+### Patch Changes
+
+- ea15e75: Minor updates in deployment docs
+
+## 0.2.42
+
+### Patch Changes
+
+- a8ec08c: fix: ensure correct message content in agent workflow
+- Updated dependencies [a8ec08c]
+- Updated dependencies [2967d57]
+  - @llamaindex/core@0.6.16
+  - @llamaindex/workflow@1.1.16
+  - @llamaindex/cloud@4.0.25
+  - llamaindex@0.11.20
+  - @llamaindex/node-parser@2.0.16
+  - @llamaindex/openai@0.4.11
+  - @llamaindex/readers@3.1.15
+
+## 0.2.41
+
+### Patch Changes
+
+- Updated dependencies [856dd8c]
+  - @llamaindex/openai@0.4.10
+
+## 0.2.40
+
+### Patch Changes
+
+- Updated dependencies [7ad3411]
+- Updated dependencies [5da5b3c]
+- Updated dependencies [a1fdb07]
+  - @llamaindex/core@0.6.15
+  - @llamaindex/workflow@1.1.15
+  - @llamaindex/openai@0.4.9
+  - @llamaindex/cloud@4.0.24
+  - llamaindex@0.11.19
+  - @llamaindex/node-parser@2.0.15
+  - @llamaindex/readers@3.1.14
+
+## 0.2.39
+
+### Patch Changes
+
+- Updated dependencies [a1b1598]
+  - @llamaindex/cloud@4.0.23
+  - llamaindex@0.11.18
+
+## 0.2.38
+
+### Patch Changes
+
+- Updated dependencies [d2be868]
+  - @llamaindex/cloud@4.0.22
+  - llamaindex@0.11.17
+
+## 0.2.37
+
+### Patch Changes
+
+- Updated dependencies [579ca0c]
+  - @llamaindex/cloud@4.0.21
+  - llamaindex@0.11.16
+
 ## 0.2.36

 ### Patch Changes
@@ -27,6 +27,33 @@ const config = {
        destination: "/docs/workflows/:path*",
        permanent: true,
      },
+      {
+        source: "/docs/llamaindex/getting_started/installation/node.mdx",
+        destination:
+          "/docs/llamaindex/getting_started/installation/server-apis.mdx",
+        permanent: true,
+      },
+      {
+        source: "/docs/llamaindex/getting_started/installation/typescript.mdx",
+        destination: "/docs/llamaindex/getting_started/installation/index.mdx",
+        permanent: true,
+      },
+      {
+        source: "/docs/llamaindex/getting_started/installation/next.mdx",
+        destination: "/docs/llamaindex/getting_started/installation/nextjs.mdx",
+        permanent: true,
+      },
+      {
+        source: "/docs/llamaindex/getting_started/installation/vite.mdx",
+        destination: "/docs/llamaindex/getting_started/installation/index.mdx",
+        permanent: true,
+      },
+      {
+        source: "/docs/llamaindex/getting_started/installation/cloudflare.mdx",
+        destination:
+          "/docs/llamaindex/getting_started/installation/serverless.mdx",
+        permanent: true,
+      },
    ];
  },
  turbopack: {
@@ -1,6 +1,6 @@
 {
  "name": "@llamaindex/doc",
-  "version": "0.2.36",
+  "version": "0.2.44",
  "private": true,
  "scripts": {
    "postinstall": "fumadocs-mdx",
@@ -1,4 +1,4 @@
-import { MockLLM } from "@llamaindex/core/utils";
+import { MockLLM } from "@llamaindex/core/llms/mock";
 import { LlamaIndexAdapter, type Message } from "ai";
 import { Settings, SimpleChatEngine, type ChatMessage } from "llamaindex";
 import { NextResponse, type NextRequest } from "next/server";
@@ -1,6 +1,6 @@
 import { AIProvider } from "@/actions";
 import { TooltipProvider } from "@/components/ui/tooltip";
-import { GoogleAnalytics } from "@next/third-parties/google";
+import { GoogleAnalytics, GoogleTagManager } from "@next/third-parties/google";
 import { RootProvider } from "fumadocs-ui/provider";
 import { Inter } from "next/font/google";
 import type { ReactNode } from "react";
@@ -36,6 +36,7 @@ export default function Layout({ children }: { children: ReactNode }) {
          LlamaIndex.TS - Build LLM-powered document agents and workflows
        </title>
      </head>
+      <GoogleTagManager gtmId="GTM-WWRFB36R" />
      <body className="flex min-h-screen flex-col">
        <TooltipProvider>
          <AIProvider>
@@ -19,3 +19,8 @@ npm run dev
 to start the development server. You can then visit [http://localhost:3000](http://localhost:3000) to see your app, which should look something like this:

 ![create-llama interface](/images/create_llama.png)
+
+## Learn more
+
+- [Learn more about `create-llama`](https://github.com/run-llama/create-llama)
+- [Want to use the same UI components? You can use our React components](https://ui.llamaindex.ai/)
@@ -17,7 +17,8 @@ npm i
 Then you can run any example in the folder with `tsx`, e.g.:

 ```bash npm2yarn
-npx tsx ./vectorIndex.ts
+export OPENAI_API_KEY=your-api-key
+npx tsx ./agents/agent/openai.ts
 ```

 ## Try examples online
@@ -1,70 +0,0 @@
---
-title: With Cloudflare Worker
-description: In this guide, you'll learn how to use LlamaIndex with CloudFlare Worker
---
-
-Before you start, make sure you have try LlamaIndex.TS in Node.js to make sure you understand the basics.
-
-<Card
-  title="Getting Started with LlamaIndex.TS in Node.js"
-  href="/docs/llamaindex/getting_started/installation/node"
-/>
-
-Also, you need have the basic understanding of <a href='https://developers.cloudflare.com/workers/'><SiCloudflareworkers className="inline mr-2" color="#F38020" />Cloudflare Worker</a>.
-
-## Adding environment variables
-
-```ts
-export default {
-  async fetch(request: Request, env: Env): Promise<Response> {
-    const { setEnvs } = await import("@llamaindex/env");
-    setEnvs(env);
-    const { OpenAIAgent } = await import("@llamaindex/openai");
-    // Start your code here
-    return new Response("Hello, world!");
-  },
-};
-```
-
-Then, you need create `.dev.vars` and add LLM api keys for the local development, such as `OPENAI_API_KEY` for OpenAI API key.
-
-<Callout type="warn">Do not commit the api key to git repository.</Callout>
-
-## Integrating with Hono
-
-```ts
-import { Hono } from "hono";
-
-type Bindings = {
-  OPENAI_API_KEY: string;
-};
-
-const app = new Hono<{
-  Bindings: Bindings;
-}>();
-
-app.post("/llm", async (c) => {
-  const { setEnvs } = await import("@llamaindex/env");
-  setEnvs(c.env);
-
-  // ...
-
-  return new Response('Hello, world!');
-})
-
-export default {
-  fetch: app.fetch,
-};
-```
-
-## Difference between Node.js and Cloudflare Worker
-
-In Cloudflare Worker and similar serverless JS environment, you need to be aware of the following differences:
-
- Some Node.js modules are not available in Cloudflare Worker, such as `node:fs`, `node:child_process`, `node:cluster`...
- You are recommend to design your code using network request, such as use `fetch` API to communicate with database, instead of a long-running process in Node.js.
- Some of LlamaIndex.TS packages are not available in Cloudflare Worker, for example `@llamaindex/readers` and `@llamaindex/huggingface`.
- The main `llamaindex` is designed to work in all JavaScript environment, including Cloudflare Worker. If you find any issue, please report to us.
- `@llamaindex/env` is a JS environment binding module, which polyfill some Node.js/Modern Web API (for example, we have a memory based `fs` module, and Crypto API polyfill). It is designed to work in all JavaScript environment, including Cloudflare Worker.
-
-
@@ -1,69 +1,177 @@
 ---
 title: Installation
-description: How to install llamaindex packages.
+description: How to install and set up LlamaIndex.TS for your project.
 ---

-To install llamaindex, run the following command:
+## Quick Start
+
+Install the core package:

 ```package-install
 npm i llamaindex
 ```

-In most cases, you'll also need an LLM package and the Workflow package to use LlamaIndex. For example, to use the OpenAI LLM with agents, you would install the following:
+In most cases, you'll also need an LLM provider and the Workflow package:

 ```package-install
 npm i @llamaindex/openai @llamaindex/workflow
 ```

-Go to [LLM APIs](/docs/llamaindex/modules/models/llms) to find out how to use other LLMs.
+## Environment Setup

+### API Keys

-## Frameworks
+Most LLM providers require API keys. Set your OpenAI key (or other provider):

-LlamaIndex supports a wide range of frameworks and runtimes. Click on the card below to learn more.
+```bash
+export OPENAI_API_KEY=your-api-key
+```
+
+Or use a `.env` file:
+
+```bash
+echo "OPENAI_API_KEY=your-api-key" > .env
+```
+
+<Callout type="warn">Never commit API keys to your repository.</Callout>
+
+### Loading Environment Variables
+
+For Node.js applications:
+
+```bash
+node --env-file .env your-script.js
+```
+
+For other environments, see the deployment-specific guides below.
+
+## TypeScript Configuration
+
+LlamaIndex.TS is built with TypeScript and provides excellent type safety. Add these settings to your `tsconfig.json`:
+
+```json5
+{
+  "compilerOptions": {
+    // Essential for module resolution
+    "moduleResolution": "bundler", // or "nodenext" | "node16" | "node"
+    
+    // Required for Web Stream API support
+    "lib": ["DOM.AsyncIterable"],
+    
+    // Recommended for better compatibility
+    "target": "es2020",
+    "module": "esnext"
+  }
+}
+```
+
+## Running your first agent
+
+### Set up
+
+If you don't already have a project, you can create a new one in a new folder:
+
+```package-install
+npm init
+npm i -D typescript @types/node
+npm i @llamaindex/openai @llamaindex/workflow llamaindex zod
+```
+
+### Run the agent
+
+Create the file `example.ts`. This code will:
+
+- Create two tools for use by the agent:
+  - A `sumNumbers` tool that adds two numbers
+  - A `divideNumbers` tool that divides numbers
+- Give an example of the data structure we wish to generate
+- Prompt the LLM with instructions and the example, plus a sample transcript
+
+<include cwd>../../examples/agents/agent/openai.ts</include>
+
+To run the code:
+
+```package-install
+npx tsx example.ts
+```
+
+You should expect output something like:
+
+```
+{
+  result: '5 + 5 is 10. Then, 10 divided by 2 is 5.',
+  state: {
+    memory: Memory {
+      messages: [Array],
+      tokenLimit: 30000,
+      shortTermTokenLimitRatio: 0.7,
+      memoryBlocks: [],
+      memoryCursor: 0,
+      adapters: [Object]
+    },
+    scratchpad: [],
+    currentAgentName: 'Agent',
+    agents: [ 'Agent' ],
+    nextAgentName: null
+  }
+}
+Done
+```
+
+## Performance Optimization
+
+### Tokenization Speed
+
+Install `gpt-tokenizer` for 60x faster tokenization (Node.js environments only):
+
+```package-install
+npm i gpt-tokenizer
+```
+
+LlamaIndex will automatically use this when available.
+
+## Deployment Guides
+
+Choose your deployment target:

 <Cards>
-	<Card title={
-		<>
-			<SiNodedotjs className="inline" color="#5FA04E" /> Node.js
-		</>
-	} href="/docs/llamaindex/getting_started/installation/node" />
-	<Card title={
-		<>
-			<SiTypescript className="inline" color="#3178C6" /> TypeScript
-		</>
-	} href="/docs/llamaindex/getting_started/installation/typescript" />
-	<Card title={
-		<>
-			<SiVite className='inline' color='#646CFF' /> Vite
-		</>
-	} href="/docs/llamaindex/getting_started/installation/vite" />
-	<Card
-		title={
-			<>
-				<SiNextdotjs className='inline' /> Next.js (React Server Component)
-			</>
-		}
-		href="/docs/llamaindex/getting_started/installation/next"
-	/>
-	<Card title={
-		<>
-			<SiCloudflareworkers className='inline' color='#F38020' /> Cloudflare Workers
-		</>
-	} href="/docs/llamaindex/getting_started/installation/cloudflare" />
+  <Card 
+    title="Server APIs & Backends" 
+    description="Express, Fastify, Koa, standalone Node.js servers"
+    href="/docs/llamaindex/getting_started/installation/server-apis" 
+  />
+  <Card 
+    title="Serverless Functions" 
+    description="Vercel, Netlify, AWS Lambda, Cloudflare Workers"
+    href="/docs/llamaindex/getting_started/installation/serverless" 
+  />
+  <Card 
+    title="Next.js Applications" 
+    description="API routes, server components, edge runtime"
+    href="/docs/llamaindex/getting_started/installation/nextjs" 
+  />
+  <Card 
+    title="Troubleshooting" 
+    description="Common issues, bundle optimization, compatibility"
+    href="/docs/llamaindex/getting_started/installation/troubleshooting" 
+  />
 </Cards>

-## What's next?
+## LLM/Embedding Providers
+
+Go to [LLM APIs](/docs/llamaindex/modules/models/llms) and [Embedding APIs](/docs/llamaindex/modules/models/embeddings) to find out how to use different LLM and embedding providers beyond OpenAI.
+
+## What's Next?

 <Cards>
-	<Card
-		title="Learn LlamaIndex.TS"
-		description="Learn how to use LlamaIndex.TS by starting with one of our tutorials."
-		href="/docs/llamaindex/tutorials/rag"
-	/>
-	<Card
-		title="Show me code examples"
-		description="Explore code examples using LlamaIndex.TS."
-		href="/docs/llamaindex/getting_started/examples"
-	/>
+  <Card
+    title="Learn LlamaIndex.TS"
+    description="Learn how to use LlamaIndex.TS by starting with one of our tutorials."
+    href="/docs/llamaindex/tutorials/basic_agent"
+  />
+  <Card
+    title="Show me code examples"
+    description="Explore code examples using LlamaIndex.TS."
+    href="/docs/llamaindex/getting_started/examples"
+  />
 </Cards>
@@ -1,4 +1,4 @@
 {
  "title": "Installation",
-  "pages": ["node", "typescript", "next", "vite", "cloudflare"]
+  "pages": ["server-apis", "serverless", "nextjs", "troubleshooting"]
 }
@@ -1,41 +0,0 @@
---
-title: With Next.js
-description: In this guide, you'll learn how to use LlamaIndex with Next.js.
---
-
-Before you start, make sure you have try LlamaIndex.TS in Node.js to make sure you understand the basics.
-
-<Card
-  title="Getting Started with LlamaIndex.TS in Node.js"
-  href="/docs/llamaindex/getting_started/installation/node"
-/>
-
-## Differences between Node.js and Next.js
-
-Next.js is a React framework that has both server side compatibility and client side compatibility.
-This means that you need to be careful when using LlamaIndex.TS in Next.js.
-Don't leak the import data like API keys to the client side.
-
-Also, in Next.js, there is build time and runtime. Some computations can be done at build time like Document embedding could be done at build time for better performance.
-Where as the `llamaindex` package is working with Next.js, some provider packages like `@llamaindex/huggingface` are not working well with Next.js. This is due to the upstream dependencies used by the provider package. 
-
-Make sure to use `withLlamaIndex` to make sure that LlamaIndex.TS works well with Next.js.
-
-```js
-// next.config.mjs / next.config.ts
-import withLlamaIndex from "llamaindex/next";
-
-/** @type {import('next').NextConfig} */
-const nextConfig = {};
-
-export default withLlamaIndex(nextConfig);
-```
-
-If you see any dependency issues, you are welcome to open an issue on the GitHub.
-
-## Edge Runtime
-
-[Vercel Edge Runtime](https://edge-runtime.vercel.app/) is a subset of Node.js APIs. Similar to [Cloudflare Workers](/docs/llamaindex/getting_started/installation/cloudflare#difference-between-nodejs-and-cloudflare-worker),
-it is a serverless platform that runs your code on the edge.
-
-Not all features of Node.js are supported in Vercel Edge Runtime, so does LlamaIndex.TS, we are working on more compatibility with all JavaScript runtimes.
@@ -0,0 +1,405 @@
+---
+title: Next.js Applications
+description: Deploy LlamaIndex.TS in Next.js applications with API routes, server components, and edge runtime.
+---
+
+This guide covers integrating LlamaIndex.TS agents with Next.js applications.
+
+## Essential Configuration
+
+### Next.js Config
+
+Use `withLlamaIndex` to ensure compatibility:
+
+```javascript
+// next.config.mjs
+import withLlamaIndex from "llamaindex/next";
+
+/** @type {import('next').NextConfig} */
+const nextConfig = {
+  // Your existing config
+};
+
+export default withLlamaIndex(nextConfig);
+```
+
+## API Routes
+
+### App Router (Recommended)
+
+```typescript
+// app/api/chat/route.ts
+import { agent } from "@llamaindex/workflow";
+import { tool } from "llamaindex";
+import { openai } from "@llamaindex/openai";
+import { z } from "zod";
+import { NextRequest, NextResponse } from "next/server";
+
+// Initialize agent once (consider using a singleton pattern)
+let myAgent: any = null;
+
+async function initializeAgent() {
+  if (myAgent) return myAgent;
+  
+  try {
+    const greetTool = tool({
+      name: "greet",
+      description: "Greets a user with their name",
+      parameters: z.object({
+        name: z.string(),
+      }),
+      execute: ({ name }) => `Hello, ${name}! How can I help you today?`,
+    });
+
+    myAgent = agent({
+      tools: [greetTool],
+      llm: openai({ model: "gpt-4o-mini" }),
+    });
+    
+    return myAgent;
+  } catch (error) {
+    console.error("Failed to initialize agent:", error);
+    throw error;
+  }
+}
+
+export async function POST(request: NextRequest) {
+  try {
+    const { message } = await request.json();
+    
+    if (!message || typeof message !== 'string') {
+      return NextResponse.json(
+        { error: "Message is required and must be a string" },
+        { status: 400 }
+      );
+    }
+    
+    const agent = await initializeAgent();
+    const result = await agent.run(message);
+    
+    return NextResponse.json({ response: result.data });
+  } catch (error) {
+    console.error("Chat error:", error);
+    return NextResponse.json(
+      { error: "Internal server error" },
+      { status: 500 }
+    );
+  }
+}
+```
+
+### Pages Router (Legacy)
+
+```typescript
+// pages/api/chat.ts
+import { agent } from "@llamaindex/workflow";
+import { tool } from "llamaindex";
+import { openai } from "@llamaindex/openai";
+import { z } from "zod";
+import type { NextApiRequest, NextApiResponse } from "next";
+
+let myAgent: any = null;
+
+async function initializeAgent() {
+  if (myAgent) return myAgent;
+  
+  const timeTool = tool({
+    name: "getCurrentTime",
+    description: "Gets the current time",
+    parameters: z.object({}),
+    execute: () => new Date().toISOString(),
+  });
+
+  myAgent = agent({
+    tools: [timeTool],
+    llm: openai({ model: "gpt-4o-mini" }),
+  });
+  
+  return myAgent;
+}
+
+export default async function handler(
+  req: NextApiRequest,
+  res: NextApiResponse
+) {
+  if (req.method !== "POST") {
+    return res.status(405).json({ error: "Method not allowed" });
+  }
+  
+  try {
+    const { message } = req.body;
+    
+    const agent = await initializeAgent();
+    const result = await agent.run(message);
+    
+    res.json({ response: result.data });
+  } catch (error) {
+    console.error("Chat error:", error);
+    res.status(500).json({ error: "Internal server error" });
+  }
+}
+```
+
+## Server Components
+
+Initialize agents in server components:
+
+```typescript
+// app/chat/page.tsx
+import { agent } from "@llamaindex/workflow";
+import { tool } from "llamaindex";
+import { openai } from "@llamaindex/openai";
+import { z } from "zod";
+
+async function initializeAgent() {
+  const helpTool = tool({
+    name: "getHelp",
+    description: "Provides help information",
+    parameters: z.object({
+      topic: z.string().optional(),
+    }),
+    execute: ({ topic }) => {
+      if (topic) {
+        return `Here's help for ${topic}: This is a helpful resource about ${topic}.`;
+      }
+      return "Available topics: general, troubleshooting, api, deployment";
+    },
+  });
+
+  return agent({
+    tools: [helpTool],
+    llm: openai({ model: "gpt-4o-mini" }),
+  });
+}
+
+export default async function ChatPage() {
+  const chatAgent = await initializeAgent();
+  
+  return (
+    <div>
+      <h1>Chat Interface</h1>
+      <p>Agent initialized and ready to help!</p>
+      {/* Your chat UI components */}
+    </div>
+  );
+}
+```
+
+## Edge Runtime
+
+The Edge Runtime has limited Node.js API access:
+
+```typescript
+// app/api/chat-edge/route.ts
+import { NextRequest, NextResponse } from "next/server";
+
+export const runtime = "edge";
+
+export async function POST(request: NextRequest) {
+  const { setEnvs } = await import("@llamaindex/env");
+  setEnvs(process.env);
+  
+  try {
+    const { message } = await request.json();
+    
+    const { agent } = await import("@llamaindex/workflow");
+    const { tool } = await import("llamaindex");
+    const { openai } = await import("@llamaindex/openai");
+    const { z } = await import("zod");
+
+    const timeTool = tool({
+      name: "time",
+      description: "Gets current time",
+      parameters: z.object({}),
+      execute: () => new Date().toISOString(),
+    });
+
+    const myAgent = agent({
+      tools: [timeTool],
+      llm: openai({ model: "gpt-4o-mini" }),
+    });
+
+    const result = await myAgent.run(message);
+    return NextResponse.json({ response: result.data });
+  } catch (error) {
+    return NextResponse.json({ error: error.message }, { status: 500 });
+  }
+}
+```
+
+## Streaming Responses
+
+Implement streaming for better user experience:
+
+```typescript
+// app/api/chat-stream/route.ts
+import { agent } from "@llamaindex/workflow";
+import { tool } from "llamaindex";
+import { openai } from "@llamaindex/openai";
+import { agentStreamEvent } from "@llamaindex/workflow";
+import { NextRequest } from "next/server";
+import { z } from "zod";
+
+// Initialize agent once (consider using a singleton pattern)
+let myAgent: any = null;
+
+async function initializeAgent() {
+  if (myAgent) return myAgent;
+  
+  try {
+    const greetTool = tool({
+      name: "greet",
+      description: "Greets a user with their name",
+      parameters: z.object({
+        name: z.string(),
+      }),
+      execute: ({ name }) => `Hello, ${name}! How can I help you today?`,
+    });
+
+    myAgent = agent({
+      tools: [greetTool],
+      llm: openai({ model: "gpt-4o-mini" }),
+    });
+    
+    return myAgent;
+  } catch (error) {
+    console.error("Failed to initialize agent:", error);
+    throw error;
+  }
+}
+
+export async function POST(request: NextRequest) {
+  const { message } = await request.json();
+  
+  const stream = new ReadableStream({
+    async start(controller) {
+      try {
+        const agent = await initializeAgent();
+        const events = agent.runStream(message);
+        
+        for await (const event of events) {
+          if (agentStreamEvent.include(event)) {
+            controller.enqueue(new TextEncoder().encode(event.data.delta));
+          }
+        }
+        
+        controller.close();
+      } catch (error) {
+        controller.error(error);
+      }
+    },
+  });
+  
+  return new Response(stream, {
+    headers: {
+      "Content-Type": "text/plain",
+      "Transfer-Encoding": "chunked",
+    },
+  });
+}
+```
+
+## Client-side Integration
+
+### React Hook for API Calls
+
+```typescript
+// hooks/useAgentChat.ts
+import { useState } from "react";
+
+export function useAgentChat() {
+  const [loading, setLoading] = useState(false);
+  const [error, setError] = useState<string | null>(null);
+  const [response, setResponse] = useState<string | null>(null);
+  
+  const chat = async (message: string) => {
+    setLoading(true);
+    setError(null);
+    
+    try {
+      const res = await fetch("/api/chat", {
+        method: "POST",
+        headers: { "Content-Type": "application/json" },
+        body: JSON.stringify({ message }),
+      });
+      
+      if (!res.ok) {
+        throw new Error(`HTTP error! status: ${res.status}`);
+      }
+      
+      const data = await res.json();
+      setResponse(data.response);
+    } catch (err) {
+      setError(err instanceof Error ? err.message : "An error occurred");
+    } finally {
+      setLoading(false);
+    }
+  };
+  
+  return { chat, loading, error, response };
+}
+```
+
+### Chat Component
+
+```typescript
+// components/ChatInterface.tsx
+"use client";
+
+import { useState } from "react";
+import { useAgentChat } from "@/hooks/useAgentChat";
+
+export default function ChatInterface() {
+  const [message, setMessage] = useState("");
+  const { chat, loading, error, response } = useAgentChat();
+  
+  const handleSubmit = async (e: React.FormEvent) => {
+    e.preventDefault();
+    if (!message.trim()) return;
+    
+    await chat(message);
+    setMessage("");
+  };
+  
+  return (
+    <div className="max-w-2xl mx-auto p-4">
+      <form onSubmit={handleSubmit} className="mb-4">
+        <input
+          type="text"
+          value={message}
+          onChange={(e) => setMessage(e.target.value)}
+          placeholder="Send a message..."
+          className="w-full p-2 border rounded"
+          disabled={loading}
+        />
+        <button
+          type="submit"
+          disabled={loading || !message.trim()}
+          className="mt-2 px-4 py-2 bg-blue-500 text-white rounded disabled:opacity-50"
+        >
+          {loading ? "Thinking..." : "Send"}
+        </button>
+      </form>
+      
+      {error && (
+        <div className="p-3 mb-4 bg-red-100 border border-red-400 text-red-700 rounded">
+          Error: {error}
+        </div>
+      )}
+      
+      {response && (
+        <div className="p-3 bg-gray-100 border rounded">
+          <strong>Agent:</strong>
+          <p>{response}</p>
+        </div>
+      )}
+    </div>
+  );
+}
+```
+
+## Next Steps
+
+- Learn about [serverless deployment](/docs/llamaindex/getting_started/installation/serverless)
+- Explore [server APIs](/docs/llamaindex/getting_started/installation/server-apis)
+- Check [troubleshooting guide](/docs/llamaindex/getting_started/installation/troubleshooting) for common issues 
@@ -1,40 +0,0 @@
---
-title: With Node.js/Bun/Deno
-description: In this guide, you'll learn how to use LlamaIndex with Node.js, Bun, and Deno.
---
-
-## Adding environment variables
-
-By default, LlamaIndex uses OpenAI provider, which requires an API key. You can set the `OPENAI_API_KEY` environment variable to authenticate with OpenAI.
-
-```shell
-export OPENAI_API_KEY=your-api-key
-```
-
-Or you can use a `.env` file:
-
-```shell
-echo "OPENAI_API_KEY=your-api-key" > .env
-node --env-file .env your-script.js
-```
-
-<Callout type="warn">Do not commit the api key to git repository.</Callout>
-
-For more information, see the [How to read environment variables from Node.js](https://nodejs.org/en/learn/command-line/how-to-read-environment-variables-from-nodejs).
-
-## Performance Optimization
-
-By the default, we are using `js-tiktoken` for tokenization. You can install `gpt-tokenizer` which is then automatically used by LlamaIndex to get a 60x speedup for tokenization:
-
-```package-install
-npm i gpt-tokenizer
-```
-
-**Note**: This only works for Node.js
-
-## TypeScript support
-
-<Card
-	title="Getting Started with LlamaIndex.TS in TypeScript"
-	href="/docs/llamaindex/getting_started/installation/typescript"
-/>
@@ -0,0 +1,211 @@
+---
+title: Server APIs & Backends
+description: Deploy LlamaIndex.TS in server environments like Express, Fastify, and standalone Node.js applications.
+---
+
+This guide covers adding LlamaIndex.TS agents to traditional server environments where you have full Node.js runtime access.
+
+## Supported Runtimes
+
+LlamaIndex.TS works seamlessly with:
+
+- **Node.js** (v18+)
+- **Bun** (v1.0+)
+- **Deno** (v1.30+)
+
+## Common Server Frameworks
+
+### Express.js
+
+```typescript
+import express from 'express';
+import { agent } from '@llamaindex/workflow';
+import { tool } from 'llamaindex';
+import { openai } from '@llamaindex/openai';
+import { z } from 'zod';
+
+const app = express();
+app.use(express.json());
+
+// Initialize agent once at startup
+let myAgent: any;
+
+async function initializeAgent() {
+  // Create tools for the agent
+  const sumTool = tool({
+    name: "sum",
+    description: "Adds two numbers",
+    parameters: z.object({
+      a: z.number(),
+      b: z.number(),
+    }),
+    execute: ({ a, b }) => a + b,
+  });
+
+  const multiplyTool = tool({
+    name: "multiply",
+    description: "Multiplies two numbers",
+    parameters: z.object({
+      a: z.number(),
+      b: z.number(),
+    }),
+    execute: ({ a, b }) => a * b,
+  });
+
+  // Create the agent
+  myAgent = agent({
+    tools: [sumTool, multiplyTool],
+    llm: openai({ model: "gpt-4o-mini" }),
+  });
+}
+
+app.post('/api/chat', async (req, res) => {
+  try {
+    const { message } = req.body;
+    const result = await myAgent.run(message);
+    res.json({ response: result.data });
+  } catch (error) {
+    res.status(500).json({ error: 'Chat failed' });
+  }
+});
+
+// Initialize and start server
+initializeAgent().then(() => {
+  app.listen(3000, () => {
+    console.log('Server running on port 3000');
+  });
+});
+```
+
+### Fastify
+
+```typescript
+import Fastify from 'fastify';
+import { agent } from '@llamaindex/workflow';
+import { tool } from 'llamaindex';
+import { openai } from '@llamaindex/openai';
+import { z } from 'zod';
+
+const fastify = Fastify();
+let myAgent: any;
+
+async function initializeAgent() {
+  const sumTool = tool({
+    name: "sum",
+    description: "Adds two numbers",
+    parameters: z.object({
+      a: z.number(),
+      b: z.number(),
+    }),
+    execute: ({ a, b }) => a + b,
+  });
+
+  myAgent = agent({
+    tools: [sumTool],
+    llm: openai({ model: "gpt-4o-mini" }),
+  });
+}
+
+fastify.post('/api/chat', async (request, reply) => {
+  try {
+    const { message } = request.body as { message: string };
+    const result = await myAgent.run(message);
+    return { response: result.data };
+  } catch (error) {
+    reply.status(500).send({ error: 'Chat failed' });
+  }
+});
+
+const start = async () => {
+  await initializeAgent();
+  await fastify.listen({ port: 3000 });
+  console.log('Server running on port 3000');
+};
+
+start();
+```
+
+### Hono
+
+```typescript
+import { Hono } from "hono";
+import { agent } from "@llamaindex/workflow";
+import { tool } from "llamaindex";
+import { openai } from "@llamaindex/openai";
+import { z } from "zod";
+
+type Bindings = {
+  OPENAI_API_KEY: string;
+};
+
+const app = new Hono<{ Bindings: Bindings }>();
+
+app.post("/api/chat", async (c) => {
+  const { setEnvs } = await import("@llamaindex/env");
+  setEnvs(c.env);
+  
+  const { message } = await c.req.json();
+  
+  const greetTool = tool({
+    name: "greet",
+    description: "Greets a user",
+    parameters: z.object({
+      name: z.string(),
+    }),
+    execute: ({ name }) => `Hello, ${name}!`,
+  });
+
+  const myAgent = agent({
+    tools: [greetTool],
+    llm: openai({ model: "gpt-4o-mini" }),
+  });
+  
+  try {
+    const result = await myAgent.run(message);
+    return c.json({ response: result.data });
+  } catch (error) {
+    return c.json({ error: error.message }, 500);
+  }
+});
+
+export default app;
+```
+
+## Streaming Responses
+
+For real-time agent responses:
+
+```typescript
+import { agentStreamEvent } from "@llamaindex/workflow";
+
+app.post('/api/chat-stream', async (req, res) => {
+  const { message } = req.body;
+  
+  res.writeHead(200, {
+    'Content-Type': 'text/plain',
+    'Transfer-Encoding': 'chunked',
+  });
+  
+  try {
+    const events = myAgent.runStream(message);
+    
+    for await (const event of events) {
+      if (agentStreamEvent.include(event)) {
+        res.write(event.data.delta);
+      }
+    }
+    
+    res.end();
+  } catch (error) {
+    res.write('Error: ' + error.message);
+    res.end();
+  }
+});
+```
+
+
+## Next Steps
+
+- Learn about [serverless deployment](/docs/llamaindex/getting_started/installation/serverless)
+- Explore [Next.js integration](/docs/llamaindex/getting_started/installation/nextjs)
+- Check [troubleshooting guide](/docs/llamaindex/getting_started/installation/troubleshooting) for common issues 
@@ -0,0 +1,240 @@
+---
+title: Serverless Functions
+description: Deploy LlamaIndex.TS in serverless environments like Vercel, Netlify, AWS Lambda, and Cloudflare Workers.
+---
+
+This guide covers adding LlamaIndex.TS agents to serverless environments where you have execution time and memory constraints.
+
+## Cloudflare Workers
+
+```typescript
+export default {
+  async fetch(request: Request, env: Env): Promise<Response> {
+    const { setEnvs } = await import("@llamaindex/env");
+    setEnvs(env);
+    
+    const { agent } = await import("@llamaindex/workflow");
+    const { openai } = await import("@llamaindex/openai");
+    const { tool } = await import("llamaindex");
+    const { z } = await import("zod");
+
+    const timeTool = tool({
+      name: "getCurrentTime",
+      description: "Gets the current time",
+      parameters: z.object({}),
+      execute: () => new Date().toISOString(),
+    });
+
+    const myAgent = agent({
+      tools: [timeTool],
+      llm: openai({ model: "gpt-4o-mini" }),
+    });
+
+    try {
+      const { message } = await request.json();
+      const result = await myAgent.run(message);
+      
+      return new Response(JSON.stringify({ response: result.data }), {
+        headers: { "Content-Type": "application/json" },
+      });
+    } catch (error) {
+      return new Response(JSON.stringify({ error: error.message }), {
+        status: 500,
+        headers: { "Content-Type": "application/json" },
+      });
+    }
+  },
+};
+```
+
+
+
+## Vercel Functions
+
+### Node.js Runtime
+
+```typescript
+// pages/api/chat.ts or app/api/chat/route.ts
+import { agent } from "@llamaindex/workflow";
+import { tool } from "llamaindex";
+import { openai } from "@llamaindex/openai";
+import { z } from "zod";
+
+export default async function handler(req, res) {
+  if (req.method !== 'POST') {
+    return res.status(405).json({ error: 'Method not allowed' });
+  }
+  
+  const { message } = req.body;
+  
+  const weatherTool = tool({
+    name: "getWeather",
+    description: "Get weather information",
+    parameters: z.object({
+      city: z.string(),
+    }),
+    execute: ({ city }) => `Weather in ${city}: 72°F, sunny`,
+  });
+
+  const myAgent = agent({
+    tools: [weatherTool],
+    llm: openai({ model: "gpt-4o-mini" }),
+  });
+  
+  try {
+    const result = await myAgent.run(message);
+    res.json({ response: result.data });
+  } catch (error) {
+    res.status(500).json({ error: error.message });
+  }
+}
+```
+
+### Edge Runtime
+
+```typescript
+// app/api/chat/route.ts
+import { NextRequest, NextResponse } from "next/server";
+
+export const runtime = "edge";
+
+export async function POST(request: NextRequest) {
+  const { setEnvs } = await import("@llamaindex/env");
+  setEnvs(process.env);
+  
+  const { message } = await request.json();
+  
+  try {
+    // Use simpler tools for edge runtime
+    const { agent } = await import("@llamaindex/workflow");
+    const { tool } = await import("llamaindex");
+    const { openai } = await import("@llamaindex/openai");
+    const { z } = await import("zod");
+
+    const timeTool = tool({
+      name: "time",
+      description: "Gets current time",
+      parameters: z.object({}),
+      execute: () => new Date().toISOString(),
+    });
+
+    const myAgent = agent({
+      tools: [timeTool],
+      llm: openai({ model: "gpt-4o-mini" }),
+    });
+
+    const result = await myAgent.run(message);
+    return NextResponse.json({ response: result.data });
+  } catch (error) {
+    return NextResponse.json({ error: error.message }, { status: 500 });
+  }
+}
+```
+
+## AWS Lambda
+
+```typescript
+import { APIGatewayProxyHandler } from "aws-lambda";
+import { agent } from "@llamaindex/workflow";
+import { tool } from "llamaindex";
+import { openai } from "@llamaindex/openai";
+import { z } from "zod";
+
+export const handler: APIGatewayProxyHandler = async (event, context) => {
+  const { message } = JSON.parse(event.body || "{}");
+  
+  const calculatorTool = tool({
+    name: "calculate",
+    description: "Performs basic math",
+    parameters: z.object({
+      expression: z.string(),
+    }),
+    execute: ({ expression }) => {
+      // Simple calculator implementation
+      try {
+        return `Result: ${eval(expression)}`;
+      } catch {
+        return "Invalid expression";
+      }
+    },
+  });
+
+  const myAgent = agent({
+    tools: [calculatorTool],
+    llm: openai({ model: "gpt-4o-mini" }),
+  });
+  
+  try {
+    const result = await myAgent.run(message);
+    
+    return {
+      statusCode: 200,
+      headers: {
+        "Content-Type": "application/json",
+        "Access-Control-Allow-Origin": "*",
+      },
+      body: JSON.stringify({ response: result.data }),
+    };
+  } catch (error) {
+    return {
+      statusCode: 500,
+      body: JSON.stringify({ error: error.message }),
+    };
+  }
+};
+```
+
+## Netlify Functions
+
+```typescript
+// netlify/functions/chat.ts
+import { Handler } from "@netlify/functions";
+import { agent } from "@llamaindex/workflow";
+import { tool } from "llamaindex";
+import { openai } from "@llamaindex/openai";
+import { z } from "zod";
+
+export const handler: Handler = async (event, context) => {
+  if (event.httpMethod !== "POST") {
+    return { statusCode: 405, body: "Method Not Allowed" };
+  }
+  
+  const { message } = JSON.parse(event.body || "{}");
+  
+  const helpTool = tool({
+    name: "help",
+    description: "Provides help information",
+    parameters: z.object({
+      topic: z.string().optional(),
+    }),
+    execute: ({ topic }) => {
+      return topic ? `Help for ${topic}` : "Available help topics";
+    },
+  });
+
+  const myAgent = agent({
+    tools: [helpTool],
+    llm: openai({ model: "gpt-4o-mini" }),
+  });
+  
+  try {
+    const result = await myAgent.run(message);
+    
+    return {
+      statusCode: 200,
+      body: JSON.stringify({ response: result.data }),
+    };
+  } catch (error) {
+    return {
+      statusCode: 500,
+      body: JSON.stringify({ error: error.message }),
+    };
+  }
+};
+```
+
+## Next Steps
+
+- Learn about [Next.js integration](/docs/llamaindex/getting_started/installation/nextjs)
+- Explore [server deployment](/docs/llamaindex/getting_started/installation/server-apis)
+- Check [troubleshooting guide](/docs/llamaindex/getting_started/installation/troubleshooting) for common issues 
@@ -0,0 +1,501 @@
+---
+title: Troubleshooting
+description: Common issues and solutions when installing and deploying LlamaIndex.TS applications.
+---
+
+This guide addresses common issues you might encounter when installing and deploying LlamaIndex.TS applications across different environments.
+
+## Installation Issues
+
+### Module Resolution Errors
+
+**Problem:** Import errors or module not found errors
+
+**Solution:** Ensure your `tsconfig.json` is properly configured:
+
+```json5
+{
+  "compilerOptions": {
+    "moduleResolution": "bundler", // or "nodenext" | "node16" | "node"
+    "lib": ["DOM.AsyncIterable"],
+    "target": "es2020",
+    "module": "esnext"
+  }
+}
+```
+
+**Alternative solution:** Try different module resolution strategies:
+
+```bash
+# Clear node_modules and reinstall
+rm -rf node_modules package-lock.json
+npm install
+
+# Or try with different package manager
+pnpm install
+# or
+yarn install
+```
+
+### TypeScript Errors
+
+**Problem:** TypeScript compilation errors with LlamaIndex imports
+
+**Solution:** Ensure you have the correct TypeScript configuration:
+
+```json5
+{
+  "compilerOptions": {
+    "strict": true,
+    "skipLibCheck": true, // Skip type checking of node_modules
+    "allowSyntheticDefaultImports": true,
+    "esModuleInterop": true
+  }
+}
+```
+
+### Package Compatibility Issues
+
+**Problem:** Some packages don't work in certain environments
+
+**Common incompatibilities:**
+- `@llamaindex/readers` - May not work in serverless environments
+- `@llamaindex/huggingface` - Limited browser/edge compatibility
+- File system readers - Don't work in browser/edge environments
+
+**Solution:** Use environment-specific alternatives:
+
+```typescript
+// Instead of file system readers in serverless
+// Use remote data sources
+async function loadDocumentsFromAPI() {
+  const response = await fetch('https://api.example.com/documents');
+  const data = await response.json();
+  return data.map(doc => new Document(doc.content));
+}
+```
+
+## Runtime Issues
+
+### Memory Errors
+
+**Problem:** Out of memory errors during index creation or querying
+
+**Solution:** Optimize memory usage:
+
+```typescript
+// Batch process large document sets
+async function batchProcessDocuments(documents: Document[], batchSize = 10) {
+  const results = [];
+  
+  for (let i = 0; i < documents.length; i += batchSize) {
+    const batch = documents.slice(i, i + batchSize);
+    const batchIndex = await VectorStoreIndex.fromDocuments(batch);
+    results.push(batchIndex);
+    
+    // Optional: Add delay between batches
+    await new Promise(resolve => setTimeout(resolve, 100));
+  }
+  
+  return results;
+}
+```
+
+**For serverless environments:**
+
+```typescript
+// Use external vector stores instead of in-memory
+// TODO: Example with Pinecone, Weaviate, etc.
+// const vectorStore = new PineconeVectorStore(/* config */);
+// const index = await VectorStoreIndex.fromVectorStore(vectorStore);
+```
+
+### API Rate Limiting
+
+**Problem:** Rate limiting errors from LLM providers
+
+**Solution:** Implement retry logic with exponential backoff:
+
+```typescript
+async function queryWithRetry(queryEngine: any, question: string, maxRetries = 3) {
+  for (let i = 0; i < maxRetries; i++) {
+    try {
+      return await queryEngine.query(question);
+    } catch (error) {
+      if (error.message.includes('rate limit') && i < maxRetries - 1) {
+        const delay = Math.pow(2, i) * 1000; // Exponential backoff
+        await new Promise(resolve => setTimeout(resolve, delay));
+        continue;
+      }
+      throw error;
+    }
+  }
+}
+```
+
+### Tokenization Performance
+
+**Problem:** Slow tokenization affecting performance
+
+**Solution:** Install faster tokenizer (Node.js only):
+
+```bash
+npm install gpt-tokenizer
+```
+
+LlamaIndex will automatically use this for 60x faster tokenization.
+
+## Bundling Issues
+
+### Bundle Size Too Large
+
+**Problem:** Large bundle sizes affecting performance
+
+**Solution:** Use dynamic imports and code splitting:
+
+```typescript
+// Lazy load LlamaIndex components
+const initializeLlamaIndex = async () => {
+  const { VectorStoreIndex, SimpleDirectoryReader } = await import("llamaindex");
+  return { VectorStoreIndex, SimpleDirectoryReader };
+};
+
+// In your API route
+export async function POST(request: NextRequest) {
+  const { VectorStoreIndex, SimpleDirectoryReader } = await initializeLlamaIndex();
+  // Use the imported modules
+}
+```
+
+### Webpack/Vite Bundling Issues
+
+**Problem:** Bundler compatibility issues
+
+**Solution for Next.js:**
+
+```javascript
+// next.config.mjs
+import withLlamaIndex from "llamaindex/next";
+
+const nextConfig = {
+  webpack: (config, { isServer }) => {
+    // Custom webpack configuration if needed
+    if (!isServer) {
+      config.resolve.fallback = {
+        ...config.resolve.fallback,
+        fs: false,
+        net: false,
+        tls: false,
+      };
+    }
+    return config;
+  },
+};
+
+export default withLlamaIndex(nextConfig);
+```
+
+**Solution for Vite:**
+
+```typescript
+// vite.config.ts
+import { defineConfig } from 'vite';
+
+export default defineConfig({
+  define: {
+    global: 'globalThis',
+  },
+  resolve: {
+    alias: {
+      // Add aliases for problematic modules
+    },
+  },
+  optimizeDeps: {
+    include: ['llamaindex'],
+  },
+});
+```
+
+## Environment-Specific Issues
+
+### Node.js Version Compatibility
+
+**Problem:** Node.js version compatibility issues
+
+**Solution:** Use supported Node.js versions:
+
+```json
+{
+  "engines": {
+    "node": ">=18.0.0"
+  }
+}
+```
+
+**Check your Node.js version:**
+
+```bash
+node --version
+```
+
+### Cloudflare Workers Issues
+
+**Problem:** Module not available in Cloudflare Workers
+
+**Solution:** Use `@llamaindex/env` for environment compatibility:
+
+```typescript
+export default {
+  async fetch(request: Request, env: Env): Promise<Response> {
+    const { setEnvs } = await import("@llamaindex/env");
+    setEnvs(env);
+    
+    // Your LlamaIndex code here
+  },
+};
+```
+
+### Vercel Edge Runtime Issues
+
+**Problem:** Limited Node.js API access in Edge Runtime
+
+**Solution:** Use standard runtime or adapt code:
+
+```typescript
+// Force standard runtime
+export const runtime = "nodejs";
+
+// Or adapt for edge
+export const runtime = "edge";
+
+export async function POST(request: NextRequest) {
+  // Use edge-compatible code only
+  const { setEnvs } = await import("@llamaindex/env");
+  setEnvs(process.env);
+  
+  // Avoid file system operations
+  // Use remote data sources
+}
+```
+
+## Performance Issues
+
+### Slow Query Responses
+
+**Problem:** Slow query performance
+
+**Solution:** Implement caching and optimization:
+
+```typescript
+import { LRUCache } from 'lru-cache';
+
+const queryCache = new LRUCache<string, string>({
+  max: 100,
+  ttl: 1000 * 60 * 10, // 10 minutes
+});
+
+export async function optimizedQuery(question: string, queryEngine: any) {
+  // Check cache first
+  const cached = queryCache.get(question);
+  if (cached) return cached;
+  
+  // Query and cache result
+  const result = await queryEngine.query(question);
+  queryCache.set(question, result);
+  
+  return result;
+}
+```
+
+### Cold Start Issues
+
+**Problem:** Slow cold starts in serverless environments
+
+**Solution:** Pre-warm your functions:
+
+```typescript
+// Pre-initialize outside handler
+let cachedQueryEngine: any = null;
+
+export async function handler(event: any) {
+  if (!cachedQueryEngine) {
+    cachedQueryEngine = await initializeQueryEngine();
+  }
+  
+  // Use cached engine
+  return await cachedQueryEngine.query(question);
+}
+```
+
+## Environment Variable Issues
+
+### Missing API Keys
+
+**Problem:** API key not found or invalid
+
+**Solution:** Verify environment variable setup:
+
+```typescript
+// Check if API key is available
+if (!process.env.OPENAI_API_KEY) {
+  throw new Error('OPENAI_API_KEY environment variable is required');
+}
+
+// For debugging (remove in production)
+console.log('API Key present:', !!process.env.OPENAI_API_KEY);
+```
+
+### Environment Variable Loading
+
+**Problem:** Environment variables not loading correctly
+
+**Solution:** Use proper loading mechanisms:
+
+```typescript
+// For Node.js
+import 'dotenv/config';
+
+// For Next.js - use .env.local
+// Variables are automatically loaded
+
+// For Cloudflare Workers
+export default {
+  async fetch(request: Request, env: Env): Promise<Response> {
+    // Use env parameter, not process.env
+    const apiKey = env.OPENAI_API_KEY;
+    // ...
+  },
+};
+```
+
+## Common Error Messages
+
+### "Cannot find module 'llamaindex'"
+
+**Cause:** Package not installed or module resolution issue
+
+**Solution:**
+```bash
+npm install llamaindex
+```
+
+### "Module not found: Can't resolve 'fs'"
+
+**Cause:** File system modules used in browser/edge environment
+
+**Solution:**
+```typescript
+// Use dynamic imports with fallbacks
+const loadDocuments = async () => {
+  if (typeof window !== 'undefined') {
+    // Browser environment - use alternative
+    return await loadDocumentsFromAPI();
+  } else {
+    // Node.js environment - use file system
+    const { SimpleDirectoryReader } = await import('llamaindex');
+    return await new SimpleDirectoryReader('data').loadData();
+  }
+};
+```
+
+### "ReferenceError: global is not defined"
+
+**Cause:** Global polyfill missing in browser environments
+
+**Solution:**
+```typescript
+// Add to your app entry point
+if (typeof global === 'undefined') {
+  global = globalThis;
+}
+```
+
+### "Cannot read properties of undefined (reading 'query')"
+
+**Cause:** Query engine not properly initialized
+
+**Solution:**
+```typescript
+// Always check initialization
+if (!queryEngine) {
+  throw new Error('Query engine not initialized');
+}
+
+// Or use optional chaining
+const response = await queryEngine?.query(question);
+```
+
+## Debugging Tips
+
+### Enable Debug Logging
+
+```typescript
+// Enable debug logging
+process.env.DEBUG = "llamaindex:*";
+
+// Or specific modules
+process.env.DEBUG = "llamaindex:vector-store";
+```
+
+### Check Package Versions
+
+```bash
+npm list llamaindex
+npm list @llamaindex/openai
+```
+
+### Test in Isolation
+
+```typescript
+// Create minimal test case
+import { VectorStoreIndex } from 'llamaindex';
+
+async function testBasic() {
+  try {
+    console.log('Testing basic import...');
+    const index = new VectorStoreIndex();
+    console.log('Success!');
+  } catch (error) {
+    console.error('Error:', error);
+  }
+}
+
+testBasic();
+```
+
+## Getting Help
+
+### Before Asking for Help
+
+1. **Check this troubleshooting guide**
+2. **Search existing GitHub issues**
+3. **Try minimal reproduction**
+4. **Check your environment configuration**
+
+### When Reporting Issues
+
+Include:
+- Node.js version (`node --version`)
+- Package versions (`npm list llamaindex`)
+- Environment (Node.js, Cloudflare Workers, Vercel, etc.)
+- Minimal code reproduction
+- Full error message and stack trace
+
+### Useful Resources
+
+- [GitHub Issues](https://github.com/run-llama/LlamaIndexTS/issues)
+- [Discord Community](https://discord.gg/dGcwcsnxhU)
+- [Documentation](https://docs.llamaindex.ai/)
+
+## Next Steps
+
+If you're still experiencing issues:
+
+1. **Check specific deployment guides:**
+   - [Server APIs](/docs/llamaindex/getting_started/installation/server-apis)
+   - [Serverless Functions](/docs/llamaindex/getting_started/installation/serverless)
+   - [Next.js Applications](/docs/llamaindex/getting_started/installation/nextjs)
+
+2. **Open an issue** on GitHub with a minimal reproduction
+
+3. **Join our Discord** for community support 
@@ -1,99 +0,0 @@
---
-title: With TypeScript
-description: In this guide, you'll learn how to use LlamaIndex with TypeScript
---
-
-LlamaIndex.TS is written in TypeScript and designed to be used in TypeScript projects.
-
-We put a lot of work on strong typing to make sure you have a great typing experience with code completion such as:
-
-```ts twoslash
-import { PromptTemplate } from 'llamaindex'
-const promptTemplate = new PromptTemplate({
-  template: `Context information from multiple sources is below.
---------------------
-{context}
---------------------
-Given the information from multiple sources and not prior knowledge.
-Answer the query in the style of a Shakespeare play"
-Query: {query}
-Answer:`,
-	templateVars: ["context", "query"],
-});
-// @noErrors
-promptTemplate.format({
-	c
-//^|
-})
-```
-
-## Enable TypeScript
-
-Make sure to set [moduleResolution](https://www.typescriptlang.org/docs/handbook/modules/theory.html#module-resolution) in your `tsconfig.json` file:
-
-```json5
-{
-  compilerOptions: {
-    // ⬇️ add this line to your tsconfig.json
-    moduleResolution: "bundler", // or "nodenext" | "node16" | "node"
-  },
-}
-```
-
-We recommend using `bundler` or `nodenext`, but due to popularity of `node`, we still added support for it.
-
-## Enable AsyncIterable for `Web Stream` API
-
-Some modules uses `Web Stream` API like `ReadableStream` and `WritableStream`, you need to enable `DOM.AsyncIterable` in your `tsconfig.json`.
-
-```json5
-{
-  compilerOptions: {
-    // ⬇️ add this lib to your tsconfig.json
-    lib: ["DOM.AsyncIterable"],
-  },
-}
-```
-
-```typescript
-import { tool } from 'llamaindex'
-import { agent } from "@llamaindex/workflow";
-import { openai } from "@llamaindex/openai";
-
-Settings.llm = openai({
-  model: "gpt-4o-mini",
-});
-
-const addTool = tool({
-  name: "add", 
-  description: "Adds two numbers",
-  parameters: z.object({x: z.number(), y: z.number()}),
-  execute: ({ x, y }) => x + y,
-});
-
-const myAgent = agent({
-  tools: [addTool],
-});
-
-// Chat with the agent
-const context = myAgent.run("Hello, how are you?");
-
-for await (const event of context) {
-  if (event instanceof AgentStream) {
-    for (const chunk of event.data.delta) {
-      process.stdout.write(chunk); // stream response
-    }
-  } else {
-    console.log(event); // other events
-  }
-}
-
-```
-
-## Run TypeScript Script in Node.js
-
-We recommend to use [tsx](https://www.npmjs.com/package/tsx) to run TypeScript script in Node.js.
-
-```shell
-node --import tsx ./my-script.ts
-```
@@ -1,23 +0,0 @@
---
-title: With Vite
-description: In this guide, you'll learn how to use LlamaIndex with Vite
---
-
-Before you start, make sure you have try LlamaIndex.TS in Node.js to make sure you understand the basics.
-
-<Card
-  title="Getting Started with LlamaIndex.TS in Node.js"
-  href="/docs/llamaindex/getting_started/installation/node"
-/>
-
-Also, make sure you have a basic understanding of [Vite](https://vitejs.dev/).
-
-## Why mention Vite?
-
-Vite.js is widely used in building many web applications, like React.js, even for some native app like [Electron](https://www.electronjs.org/).
-
-However, it's not a ready-to-use solution for a Node.js-like application using Vite, as Vite is designed for web applications(run in browser).
-
-There's some plugin/framework based on Vite, like [Waku.gg](https://github.com/dai-shi/waku), or [Electron Vite](https://electron-vite.org/)
-
-For now, we have no clear solution for bundling LlamaIndex.TS with Vite, if you have any idea/solution, please let us know.
@@ -1,21 +1,118 @@
 ---
-title: What is LlamaIndex.TS
-description: LlamaIndex is the leading data framework for building LLM applications
+title: Welcome to LlamaIndex.TS
+description: LlamaIndex.TS is the leading framework for utilizing context engineering to build LLM applications in JavaScript and TypeScript.
 ---

-LlamaIndex is a framework for building context-augmented generative AI applications with LLMs including agents and workflows.
+LlamaIndex.TS is a **framework for utilizing context engineering to build generative AI applications** with large language models. From rapid-prototyping RAG chatbots to deploying multi-agent workflows in production, LlamaIndex gives you everything you need — all in idiomatic TypeScript.

-The TypeScript implementation is designed for JavaScript server side applications using <SiNodedotjs className="inline" color="#5FA04E" /> Node.js, <SiDeno className="inline" color="#70FFAF" /> Deno, <SiBun className="inline" /> Bun, <SiCloudflareworkers className="inline" color="#F38020" /> Cloudflare Workers, and more.
+Built for modern JavaScript runtimes like <SiNodedotjs className="inline" color="#5FA04E" /> **Node.js**, <SiDeno className="inline" color="#70FFAF" /> **Deno**, <SiBun className="inline" /> **Bun**, <SiCloudflareworkers className="inline" color="#F38020" /> **Cloudflare Workers**, and more.

-LlamaIndex.TS provides tools for beginners, advanced users, and everyone in between.
+<div className="grid grid-cols-1 gap-4 sm:grid-cols-2 lg:grid-cols-3 my-6">
+  <a href="#introduction" className="block rounded-lg border border-gray-600/40 p-4 hover:border-gray-400 hover:bg-gray-700/20 no-underline">
+    <h3 className="mb-1 text-lg font-semibold underline">Introduction</h3>
+    <p className="text-sm text-gray-400 no-underline">Context engineering, agents &amp; workflows — what do they mean?</p>
+  </a>

-Try it out with a starter example using StackBlitz:
+  <a href="#use-cases" className="block rounded-lg border border-gray-600/40 p-4 hover:border-gray-400 hover:bg-gray-700/20 no-underline">
+    <h3 className="mb-1 text-lg font-semibold underline">Use cases</h3>
+    <p className="text-sm text-gray-400 no-underline">See what you can build with LlamaIndex.TS.</p>
+  </a>
+
+  <a href="#getting-started" className="block rounded-lg border border-gray-600/40 p-4 hover:border-gray-400 hover:bg-gray-700/20 no-underline">
+    <h3 className="mb-1 text-lg font-semibold underline">Getting started</h3>
+    <p className="text-sm text-gray-400 no-underline">Your first app in 5 lines of code.</p>
+  </a>
+
+  <a href="https://docs.cloud.llamaindex.ai/" className="block rounded-lg border border-gray-600/40 p-4 hover:border-gray-400 hover:bg-gray-700/20 no-underline" target="_blank" rel="noopener noreferrer">
+    <h3 className="mb-1 text-lg font-semibold underline">LlamaCloud</h3>
+    <p className="text-sm text-gray-400 no-underline">Managed parsing, extraction &amp; retrieval pipelines.</p>
+  </a>
+
+  <a href="#community" className="block rounded-lg border border-gray-600/40 p-4 hover:border-gray-400 hover:bg-gray-700/20 no-underline">
+    <h3 className="mb-1 text-lg font-semibold underline">Community</h3>
+    <p className="text-sm text-gray-400 no-underline">Join thousands of builders on Discord, Twitter, and more.</p>
+  </a>
+
+  <a href="#related-projects" className="block rounded-lg border border-gray-600/40 p-4 hover:border-gray-400 hover:bg-gray-700/20 no-underline">
+    <h3 className="mb-1 text-lg font-semibold underline">Related projects</h3>
+    <p className="text-sm text-gray-400 no-underline">Connectors, demos &amp; starter kits.</p>
+  </a>
+</div>
+
+## Introduction
+
+### What are agents?
+
+[Agents](/docs/llamaindex/tutorials/agents/1_setup) are LLM-powered assistants that can reason, use external tools, and take actions to accomplish tasks such as research, data extraction, and automation. 
+LlamaIndex.TS provides foundational building blocks for creating and orchestrating these agents.
+
+### What are workflows?
+
+[Workflows](/docs/llamaindex/tutorials/workflows) are multi-step, event-driven processes that combine agents, data connectors, and other tools to solve complex problems. 
+With LlamaIndex.TS you can chain together retrieval, generation, and tool-calling steps and then deploy the entire pipeline as a microservice.
+
+### What is context engineering?
+
+LLMs come pre-trained on vast public corpora, but not on **your** private or domain-specific data. 
+Context engineering bridges that gap by injecting the right pieces of your data into the LLM prompt at the right time. 
+The most popular example is [Retrieval-Augmented Generation (RAG)](/docs/llamaindex/getting_started/concepts), but the same idea powers agent memory, evaluation, extraction, summarisation, and more.
+
+LlamaIndex.TS gives you:
+
+- **Data connectors** to ingest from APIs, files, SQL, and dozens more sources.
+- **Indexes & retrievers** to store and retrieve your data for LLM consumption.
+- **Agents and Engines** to query and use chat+reasoning interfaces over your data.
+- **Workflows** for fine-grained orchestration of your data and LLM-powered agents.
+- **Observability** integrations so you can iterate with confidence.
+
+You can learn more about these concepts in our [concepts guide](/docs/llamaindex/getting_started/concepts).
+
+## Use cases
+
+Popular scenarios include:
+
+- [LLM-Powered Agents](/docs/llamaindex/tutorials/agents/1_setup)
+- [Indexing and Retrieval](/docs/llamaindex/tutorials/rag)
+- [Extracting Structured Data](/docs/llamaindex/tutorials/structured_data_extraction)
+- [Custom Orchestration with Workflows](/docs/llamaindex/tutorials/workflows)
+
+## Getting started
+
+The fastest way to get started is in StackBlitz below — no local setup required:

 <iframe
  className="w-full h-[440px]"
  aria-label="LlamaIndex.TS Starter"
-  aria-description="This is a starter example for LlamaIndex.TS, it shows the basic usage of the library."
+  aria-description="Interactive starter for LlamaIndex.TS"
  src="https://stackblitz.com/github/run-llama/LlamaIndexTS/tree/main/examples?embed=1&file=starter.ts"
 />

-You'll need an OpenAI API key to run this example. You can retrieve it from [OpenAI](https://platform.openai.com/api-keys).
+Want to learn more? We have several tutorials to get you started:
+
+- [Installation + Runtime Guide](/docs/llamaindex/getting_started/installation)
+- [Create your first agent](/docs/llamaindex/tutorials/agents/1_setup)
+- [Learn how to index data and chat with it](/docs/llamaindex/tutorials/rag)
+- [Learn how to write your own workflows and agents](/docs/llamaindex/tutorials/workflows)
+
+---
+
+## LlamaCloud
+
+Need an end-to-end managed pipeline? Check out **[LlamaCloud](https://cloud.llamaindex.ai/)**: best-in-class document parsing (LlamaParse), extraction (LlamaExtract), and indexing services with generous free tiers.
+
+---
+
+## Community
+
+- [Twitter](https://twitter.com/llama_index)
+- [Discord](https://discord.gg/dGcwcsnxhU)
+- [LinkedIn](https://www.linkedin.com/company/llamaindex/)
+
+We 💜 contributors! View our [contributing guide](https://github.com/run-llama/LlamaIndexTS/blob/main/CONTRIBUTING.md) to get started.
+
+## Related projects
+
+- [Python framework GitHub](https://github.com/run-llama/llama_index)
+- [Python docs](https://docs.llamaindex.ai/)
+- [create-llama](https://www.npmjs.com/package/create-llama) — scaffold a new project in seconds!
+- [UI Components](https://ui.llamaindex.ai/) — build chat applications with our Next.js components.
@@ -34,6 +34,7 @@ const jokeAgent = agent({
 // Run the workflow
 const result = await jokeAgent.run("Tell me something funny");
 console.log(result.data.result); // Baby Llama is called cria
+console.log(result.data.message); // { role: 'assistant', content: 'Baby Llama is called cria' }
 ```

 ### Event Streaming
@@ -106,34 +106,40 @@ const memory = createMemory({

 Long-term memory is represented as `Memory Block` objects. These objects contain information that are from previous user sessions or from the beginning of the current conversation. When memory is retrieved (by calling `getLLM`), the short-term and long-term memories are merged together within the given `tokenLimit`. 

-Currently, there are two predefined memory blocks:
+Currently, there are three predefined memory blocks:

 - `staticBlock`: A memory block that stores a static piece of information.
 - `factExtractionBlock`: A memory block that extracts facts from the chat history.
+- `vectorBlock`: A memory block that stores and retrieves chat messages from a vector database using semantic similarity search. Messages are stored individually and retrieved based on their relevance to recent conversation context. Here we've passed in the `vectorStore` to use to store and retrieve the chat messages.

 This sounds a bit complicated, but it's actually quite simple. Let's look at an example:

 ```ts
-import { createMemory, factExtractionBlock, staticBlock } from "llamaindex";
+import { createMemory, factExtractionBlock, staticBlock, vectorBlock } from "llamaindex";
+import { QdrantVectorStore } from "@llamaindex/qdrant";
+import { OpenAIEmbedding } from "@llamaindex/openai";

 const memoryBlocks= [
  staticBlock({
-    id: "core_info",
    content: "My name is Logan, and I live in Saskatoon. I work at LlamaIndex.",
  }),
  factExtractionBlock({
-    id: "user-extracted_info",
    priority: 1,
    llm: llm,
    maxFacts: 50,
  }),
+  vectorBlock({
+    vectorStore: new QdrantVectorStore({ url: "http://localhost:6333" }),
+    priority: 2,
+  }),
 ];
 ```

-Here, we've setup two memory blocks:
+Here, we've setup three memory blocks:

- `core_info`: A static memory block that stores some core information about the user. This information will always be inserted into the memory. The type used is `MessageContent` to support multi-modal content.
- `extracted_info`: An extracted memory block that will extract information from the chat history. Here we've passed in the `llm` to use to extract facts from the chat history, and set the `maxFacts` to 50. If the number of extracted facts exceeds this limit, the `maxFacts` will be automatically summarized and reduced to leave room for new information.
+- `staticBlock`: A static memory block that stores some core information about the user. This information will always be inserted into the memory. The type used is `MessageContent` to support multi-modal content.
+- `factExtractionBlock`: An extracted memory block that will extract information from the chat history. Here we've passed in the `llm` to use to extract facts from the chat history, and set the `maxFacts` to 50. If the number of extracted facts exceeds this limit, the `maxFacts` will be automatically summarized and reduced to leave room for new information.
+- `vectorBlock`: A vector memory block that will store in a vector database and retrieve them from there. Messages are stored individually and retrieved based on their relevance to recent conversation context. Here we've passed in the `vectorStore` to use to store and retrieve the chat messages.

 You'll also notice that we've set the `priority` for the `factExtractionBlock` block. This is used to determine the handling when the memory blocks content (i.e. long-term memory) + short-term memory exceeds the token limit on the `Memory` object.

@@ -158,6 +164,46 @@ When memory is retrieved (using `getLLM`), the short-term and long-term memories

 The amount of short-term memory included is specified by the `shortTermTokenLimitRatio`. If it's set to `0.7`, 70% of the `tokenLimit` is used for short-term memory (not including the static memory block).

+
+#### VectorBlock Configuration Options
+
+The `vectorBlock` offers several configuration options to customize its behavior:
+
+```ts
+vectorBlock({
+  vectorStore: new QdrantVectorStore({ url: "http://localhost:6333" }),
+  priority: 2,
+  retrievalContextWindow: 5, // Number of recent messages to use for context when retrieving
+  formatTemplate: new PromptTemplate({ template: "Context: {{ context }}" }), // Custom formatting template
+  nodePostprocessors: [/* custom postprocessors */], // Apply processing to retrieved nodes
+  queryOptions: {
+    similarityTopK: 3, // Number of top similar results to return (default: 2)
+    mode: VectorStoreQueryMode.DEFAULT, // Query mode for the vector store
+    sessionFilterKey: "session_id", // Metadata key for session filtering (default: "session_id")
+    // Custom filters can be added here - session filter is automatically included
+    filters: {
+      filters: [
+        { key: "custom_field", value: "custom_value", operator: "==" }
+      ],
+      condition: "and"
+    }
+  }
+})
+```
+
+**Key Configuration Options:**
+
+- **`retrievalContextWindow`**: Number of recent messages to consider when creating the retrieval query (default: 5). A larger window provides more context but may be less precise.
+- **`formatTemplate`**: Template for formatting retrieved information before adding to memory. Defaults to a simple context template.
+- **`nodePostprocessors`**: Array of postprocessors to apply to retrieved nodes, useful for filtering or transforming results.
+- **`queryOptions.similarityTopK`**: Number of most similar messages to retrieve from the vector store (default: 2).
+- **`queryOptions.sessionFilterKey`**: Metadata key used to isolate memory between different sessions (default: "session_id").
+- **`queryOptions.filters`**: Additional metadata filters for retrieval. The session filter is automatically added to ensure memory isolation.
+
+**Session Isolation:**
+
+The vectorBlock automatically adds a session filter using the block's ID to ensure that memories from different sessions don't interfere with each other. This filter uses the `sessionFilterKey` (default: "session_id") and can be customized if needed.
+
 ## Persistence with Snapshots

 Save and restore memory state:
@@ -38,10 +38,13 @@ You should expect output something like:
 {
  result: '5 + 5 is 10. Then, 10 divided by 2 is 5.',
  state: {
-    memory: ChatMemoryBuffer {
-      chatStore: SimpleChatStore {},
-      chatStoreKey: 'chat_history',
-      tokenLimit: 750000
+    memory: Memory {
+      messages: [Array],
+      tokenLimit: 30000,
+      shortTermTokenLimitRatio: 0.7,
+      memoryBlocks: [],
+      memoryCursor: 0,
+      adapters: [Object]
    },
    scratchpad: [],
    currentAgentName: 'Agent',
@@ -1,5 +1,41 @@
 # @llamaindex/cloudflare-worker-agent-test

+## 0.0.182
+
+### Patch Changes
+
+- llamaindex@0.11.21
+
+## 0.0.181
+
+### Patch Changes
+
+- llamaindex@0.11.20
+
+## 0.0.180
+
+### Patch Changes
+
+- llamaindex@0.11.19
+
+## 0.0.179
+
+### Patch Changes
+
+- llamaindex@0.11.18
+
+## 0.0.178
+
+### Patch Changes
+
+- llamaindex@0.11.17
+
+## 0.0.177
+
+### Patch Changes
+
+- llamaindex@0.11.16
+
 ## 0.0.176

 ### Patch Changes
@@ -1,6 +1,6 @@
 {
  "name": "@llamaindex/cloudflare-worker-agent-test",
-  "version": "0.0.176",
+  "version": "0.0.182",
  "type": "module",
  "private": true,
  "scripts": {
@@ -1,5 +1,45 @@
 # @llamaindex/llama-parse-browser-test

+## 0.0.81
+
+### Patch Changes
+
+- @llamaindex/cloud@4.0.26
+
+## 0.0.80
+
+### Patch Changes
+
+- Updated dependencies [2967d57]
+  - @llamaindex/cloud@4.0.25
+
+## 0.0.79
+
+### Patch Changes
+
+- @llamaindex/cloud@4.0.24
+
+## 0.0.78
+
+### Patch Changes
+
+- Updated dependencies [a1b1598]
+  - @llamaindex/cloud@4.0.23
+
+## 0.0.77
+
+### Patch Changes
+
+- Updated dependencies [d2be868]
+  - @llamaindex/cloud@4.0.22
+
+## 0.0.76
+
+### Patch Changes
+
+- Updated dependencies [579ca0c]
+  - @llamaindex/cloud@4.0.21
+
 ## 0.0.75

 ### Patch Changes
@@ -1,7 +1,7 @@
 {
  "name": "@llamaindex/llama-parse-browser-test",
  "private": true,
-  "version": "0.0.75",
+  "version": "0.0.81",
  "type": "module",
  "scripts": {
    "dev": "vite",
@@ -1,5 +1,41 @@
 # @llamaindex/next-agent-test

+## 0.1.182
+
+### Patch Changes
+
+- llamaindex@0.11.21
+
+## 0.1.181
+
+### Patch Changes
+
+- llamaindex@0.11.20
+
+## 0.1.180
+
+### Patch Changes
+
+- llamaindex@0.11.19
+
+## 0.1.179
+
+### Patch Changes
+
+- llamaindex@0.11.18
+
+## 0.1.178
+
+### Patch Changes
+
+- llamaindex@0.11.17
+
+## 0.1.177
+
+### Patch Changes
+
+- llamaindex@0.11.16
+
 ## 0.1.176

 ### Patch Changes
@@ -1,6 +1,6 @@
 {
  "name": "@llamaindex/next-agent-test",
-  "version": "0.1.176",
+  "version": "0.1.182",
  "private": true,
  "scripts": {
    "dev": "next dev",
@@ -1,5 +1,41 @@
 # test-edge-runtime

+## 0.1.181
+
+### Patch Changes
+
+- llamaindex@0.11.21
+
+## 0.1.180
+
+### Patch Changes
+
+- llamaindex@0.11.20
+
+## 0.1.179
+
+### Patch Changes
+
+- llamaindex@0.11.19
+
+## 0.1.178
+
+### Patch Changes
+
+- llamaindex@0.11.18
+
+## 0.1.177
+
+### Patch Changes
+
+- llamaindex@0.11.17
+
+## 0.1.176
+
+### Patch Changes
+
+- llamaindex@0.11.16
+
 ## 0.1.175

 ### Patch Changes
@@ -1,6 +1,6 @@
 {
  "name": "@llamaindex/nextjs-edge-runtime-test",
-  "version": "0.1.175",
+  "version": "0.1.181",
  "private": true,
  "scripts": {
    "dev": "next dev",
@@ -1,5 +1,53 @@
 # @llamaindex/next-node-runtime

+## 0.1.51
+
+### Patch Changes
+
+- llamaindex@0.11.21
+- @llamaindex/huggingface@0.1.22
+- @llamaindex/readers@3.1.16
+
+## 0.1.50
+
+### Patch Changes
+
+- llamaindex@0.11.20
+- @llamaindex/huggingface@0.1.21
+- @llamaindex/readers@3.1.15
+
+## 0.1.49
+
+### Patch Changes
+
+- @llamaindex/huggingface@0.1.20
+
+## 0.1.48
+
+### Patch Changes
+
+- llamaindex@0.11.19
+- @llamaindex/huggingface@0.1.19
+- @llamaindex/readers@3.1.14
+
+## 0.1.47
+
+### Patch Changes
+
+- llamaindex@0.11.18
+
+## 0.1.46
+
+### Patch Changes
+
+- llamaindex@0.11.17
+
+## 0.1.45
+
+### Patch Changes
+
+- llamaindex@0.11.16
+
 ## 0.1.44

 ### Patch Changes
@@ -1,6 +1,6 @@
 {
  "name": "@llamaindex/next-node-runtime-test",
-  "version": "0.1.44",
+  "version": "0.1.51",
  "private": true,
  "scripts": {
    "dev": "next dev",
@@ -1,5 +1,41 @@
 # vite-import-llamaindex

+## 0.0.48
+
+### Patch Changes
+
+- llamaindex@0.11.21
+
+## 0.0.47
+
+### Patch Changes
+
+- llamaindex@0.11.20
+
+## 0.0.46
+
+### Patch Changes
+
+- llamaindex@0.11.19
+
+## 0.0.45
+
+### Patch Changes
+
+- llamaindex@0.11.18
+
+## 0.0.44
+
+### Patch Changes
+
+- llamaindex@0.11.17
+
+## 0.0.43
+
+### Patch Changes
+
+- llamaindex@0.11.16
+
 ## 0.0.42

 ### Patch Changes
@@ -1,7 +1,7 @@
 {
  "name": "vite-import-llamaindex",
  "private": true,
-  "version": "0.0.42",
+  "version": "0.0.48",
  "type": "module",
  "scripts": {
    "build": "vite build",
@@ -1,5 +1,41 @@
 # @llamaindex/waku-query-engine-test

+## 0.0.182
+
+### Patch Changes
+
+- llamaindex@0.11.21
+
+## 0.0.181
+
+### Patch Changes
+
+- llamaindex@0.11.20
+
+## 0.0.180
+
+### Patch Changes
+
+- llamaindex@0.11.19
+
+## 0.0.179
+
+### Patch Changes
+
+- llamaindex@0.11.18
+
+## 0.0.178
+
+### Patch Changes
+
+- llamaindex@0.11.17
+
+## 0.0.177
+
+### Patch Changes
+
+- llamaindex@0.11.16
+
 ## 0.0.176

 ### Patch Changes
@@ -1,6 +1,6 @@
 {
  "name": "@llamaindex/waku-query-engine-test",
-  "version": "0.0.176",
+  "version": "0.0.182",
  "type": "module",
  "private": true,
  "scripts": {
@@ -23,7 +23,7 @@ await test("pinecone", async (t) => {
  });

  const vectorStore = new PineconeVectorStore({
-    embeddingModel: openaiEmbedding,
+    embedModel: openaiEmbedding,
  });

  t.after(async () => {
@@ -1,5 +1,194 @@
 # examples

+## 0.3.33
+
+### Patch Changes
+
+- Updated dependencies [38da40b]
+  - @llamaindex/core@0.6.17
+  - @llamaindex/cloud@4.0.26
+  - llamaindex@0.11.21
+  - @llamaindex/node-parser@2.0.17
+  - @llamaindex/anthropic@0.3.19
+  - @llamaindex/assemblyai@0.1.16
+  - @llamaindex/clip@0.0.68
+  - @llamaindex/cohere@0.0.31
+  - @llamaindex/deepinfra@0.0.68
+  - @llamaindex/discord@0.1.16
+  - @llamaindex/google@0.3.16
+  - @llamaindex/huggingface@0.1.22
+  - @llamaindex/jinaai@0.0.28
+  - @llamaindex/mistral@0.1.17
+  - @llamaindex/mixedbread@0.0.31
+  - @llamaindex/notion@0.1.16
+  - @llamaindex/ollama@0.1.17
+  - @llamaindex/openai@0.4.12
+  - @llamaindex/perplexity@0.0.25
+  - @llamaindex/portkey-ai@0.0.59
+  - @llamaindex/replicate@0.0.59
+  - @llamaindex/bm25-retriever@0.0.6
+  - @llamaindex/astra@0.0.31
+  - @llamaindex/azure@0.1.29
+  - @llamaindex/chroma@0.0.31
+  - @llamaindex/elastic-search@0.1.17
+  - @llamaindex/firestore@1.0.24
+  - @llamaindex/milvus@0.1.26
+  - @llamaindex/mongodb@0.0.32
+  - @llamaindex/pinecone@0.1.17
+  - @llamaindex/postgres@0.0.60
+  - @llamaindex/qdrant@0.1.27
+  - @llamaindex/supabase@0.1.18
+  - @llamaindex/upstash@0.0.31
+  - @llamaindex/weaviate@0.0.32
+  - @llamaindex/vercel@0.1.17
+  - @llamaindex/voyage-ai@1.0.23
+  - @llamaindex/readers@3.1.16
+  - @llamaindex/tools@0.1.7
+  - @llamaindex/workflow@1.1.17
+  - @llamaindex/deepseek@0.0.29
+  - @llamaindex/fireworks@0.0.28
+  - @llamaindex/groq@0.0.84
+  - @llamaindex/together@0.0.28
+  - @llamaindex/vllm@0.0.54
+  - @llamaindex/xai@0.0.15
+
+## 0.3.32
+
+### Patch Changes
+
+- Updated dependencies [650eeb1]
+- Updated dependencies [a8ec08c]
+- Updated dependencies [2967d57]
+  - @llamaindex/google@0.3.15
+  - @llamaindex/core@0.6.16
+  - @llamaindex/workflow@1.1.16
+  - @llamaindex/cloud@4.0.25
+  - llamaindex@0.11.20
+  - @llamaindex/node-parser@2.0.16
+  - @llamaindex/anthropic@0.3.18
+  - @llamaindex/assemblyai@0.1.15
+  - @llamaindex/clip@0.0.67
+  - @llamaindex/cohere@0.0.30
+  - @llamaindex/deepinfra@0.0.67
+  - @llamaindex/discord@0.1.15
+  - @llamaindex/huggingface@0.1.21
+  - @llamaindex/jinaai@0.0.27
+  - @llamaindex/mistral@0.1.16
+  - @llamaindex/mixedbread@0.0.30
+  - @llamaindex/notion@0.1.15
+  - @llamaindex/ollama@0.1.16
+  - @llamaindex/openai@0.4.11
+  - @llamaindex/perplexity@0.0.24
+  - @llamaindex/portkey-ai@0.0.58
+  - @llamaindex/replicate@0.0.58
+  - @llamaindex/bm25-retriever@0.0.5
+  - @llamaindex/astra@0.0.30
+  - @llamaindex/azure@0.1.28
+  - @llamaindex/chroma@0.0.30
+  - @llamaindex/elastic-search@0.1.16
+  - @llamaindex/firestore@1.0.23
+  - @llamaindex/milvus@0.1.25
+  - @llamaindex/mongodb@0.0.31
+  - @llamaindex/pinecone@0.1.16
+  - @llamaindex/postgres@0.0.59
+  - @llamaindex/qdrant@0.1.26
+  - @llamaindex/supabase@0.1.17
+  - @llamaindex/upstash@0.0.30
+  - @llamaindex/weaviate@0.0.31
+  - @llamaindex/vercel@0.1.16
+  - @llamaindex/voyage-ai@1.0.22
+  - @llamaindex/readers@3.1.15
+  - @llamaindex/tools@0.1.6
+  - @llamaindex/deepseek@0.0.28
+  - @llamaindex/fireworks@0.0.27
+  - @llamaindex/groq@0.0.83
+  - @llamaindex/together@0.0.27
+  - @llamaindex/vllm@0.0.53
+  - @llamaindex/xai@0.0.14
+
+## 0.3.31
+
+### Patch Changes
+
+- Updated dependencies [d8f4f6a]
+- Updated dependencies [856dd8c]
+  - @llamaindex/supabase@0.1.16
+  - @llamaindex/openai@0.4.10
+  - @llamaindex/clip@0.0.66
+  - @llamaindex/deepinfra@0.0.66
+  - @llamaindex/deepseek@0.0.27
+  - @llamaindex/fireworks@0.0.26
+  - @llamaindex/groq@0.0.82
+  - @llamaindex/huggingface@0.1.20
+  - @llamaindex/jinaai@0.0.26
+  - @llamaindex/perplexity@0.0.23
+  - @llamaindex/azure@0.1.27
+  - @llamaindex/together@0.0.26
+  - @llamaindex/vllm@0.0.52
+  - @llamaindex/xai@0.0.13
+
+## 0.3.30
+
+### Patch Changes
+
+- Updated dependencies [7ad3411]
+- Updated dependencies [5da5b3c]
+- Updated dependencies [a1fdb07]
+- Updated dependencies [ddc0eaf]
+  - @llamaindex/core@0.6.15
+  - @llamaindex/tools@0.1.5
+  - @llamaindex/workflow@1.1.15
+  - @llamaindex/openai@0.4.9
+  - @llamaindex/anthropic@0.3.17
+  - @llamaindex/cloud@4.0.24
+  - llamaindex@0.11.19
+  - @llamaindex/node-parser@2.0.15
+  - @llamaindex/assemblyai@0.1.14
+  - @llamaindex/clip@0.0.65
+  - @llamaindex/cohere@0.0.29
+  - @llamaindex/deepinfra@0.0.65
+  - @llamaindex/discord@0.1.14
+  - @llamaindex/google@0.3.14
+  - @llamaindex/huggingface@0.1.19
+  - @llamaindex/jinaai@0.0.25
+  - @llamaindex/mistral@0.1.15
+  - @llamaindex/mixedbread@0.0.29
+  - @llamaindex/notion@0.1.14
+  - @llamaindex/ollama@0.1.15
+  - @llamaindex/perplexity@0.0.22
+  - @llamaindex/portkey-ai@0.0.57
+  - @llamaindex/replicate@0.0.57
+  - @llamaindex/bm25-retriever@0.0.4
+  - @llamaindex/astra@0.0.29
+  - @llamaindex/azure@0.1.26
+  - @llamaindex/chroma@0.0.29
+  - @llamaindex/elastic-search@0.1.15
+  - @llamaindex/firestore@1.0.22
+  - @llamaindex/milvus@0.1.24
+  - @llamaindex/mongodb@0.0.30
+  - @llamaindex/pinecone@0.1.15
+  - @llamaindex/postgres@0.0.58
+  - @llamaindex/qdrant@0.1.25
+  - @llamaindex/supabase@0.1.15
+  - @llamaindex/upstash@0.0.29
+  - @llamaindex/weaviate@0.0.30
+  - @llamaindex/vercel@0.1.15
+  - @llamaindex/voyage-ai@1.0.21
+  - @llamaindex/readers@3.1.14
+  - @llamaindex/deepseek@0.0.26
+  - @llamaindex/fireworks@0.0.25
+  - @llamaindex/groq@0.0.81
+  - @llamaindex/together@0.0.25
+  - @llamaindex/vllm@0.0.51
+  - @llamaindex/xai@0.0.12
+
+## 0.3.29
+
+### Patch Changes
+
+- Updated dependencies [af3f866]
+  - @llamaindex/deepseek@0.0.25
+
 ## 0.3.28

 ### Patch Changes
@@ -3,7 +3,7 @@
 */
 import { openai } from "@llamaindex/openai";
 import { agent } from "@llamaindex/workflow";
-import { getWeatherTool } from "../../deprecated/agents/utils/tools";
+import { getWeatherTool } from "../tools/tools";

 async function main() {
  const weatherAgent = agent({
@@ -24,6 +24,7 @@ async function main() {
    state: result.data.state,
  });
  console.log(`${JSON.stringify(caResult, null, 2)}`);
+  console.log("assistant message:", result.data.message);
 }

 main().catch((error) => {
@@ -1,6 +1,6 @@
 import { ollama } from "@llamaindex/ollama";
 import { agent } from "@llamaindex/workflow";
-import { getWeatherTool } from "../../deprecated/agents/utils/tools";
+import { getWeatherTool } from "../tools/tools";

 async function main() {
  const myAgent = agent({
@@ -0,0 +1,150 @@
+/**
+ * Example: Vector Memory Block
+ *
+ * This example demonstrates how to use the VectorMemoryBlock to store and retrieve
+ * conversation history using vector similarity search. The vector memory block
+ * stores messages in a vector store and can retrieve relevant context based on
+ * semantic similarity to recent messages.
+ */
+
+import { OpenAI, OpenAIEmbedding } from "@llamaindex/openai";
+import { QdrantVectorStore } from "@llamaindex/qdrant";
+import { createMemory, vectorBlock } from "llamaindex";
+
+// Set up the LLM and embedding model
+const llm = new OpenAI({ model: "gpt-4.1-mini" });
+const embedModel = new OpenAIEmbedding({ model: "text-embedding-3-small" });
+
+// Simulate a conversation with some context
+// This conversation has 8 messages, which is more than the token limit of 100 tokens (set below)
+// The last 4 messages are kept in to short term memory block (as their tokens are in the limit)
+// Whereas the first 5 messages are added to long term memory block (in here we will use the vector memory block with Qdrant)
+const CONVERSATION_TURNS = [
+  //// This is the first 5 messages that are added to long term memory block (vector memory block)
+  {
+    role: "user",
+    content: "Hi, I'm Sarah and I work as a data scientist at Google.",
+  },
+  {
+    role: "assistant",
+    content:
+      "Hello Sarah! It's great to meet you. Data science at Google must be exciting!",
+  },
+  {
+    role: "user",
+    content:
+      "Yes, I specialize in machine learning and natural language processing.",
+  },
+  {
+    role: "assistant",
+    content: "That's impressive! ML and NLP are fascinating fields.",
+  },
+  {
+    role: "user",
+    content:
+      "I have a PhD in Computer Science from Stanford, and I love hiking on weekends.",
+  },
+
+  //// This is the last 4 messages that are added to short term memory block
+  {
+    role: "assistant",
+    content:
+      "Wow, Stanford PhD! And hiking is a great way to unwind from tech work.",
+  },
+  {
+    role: "user",
+    content: "I also have two cats named Whiskers and Mittens.",
+  },
+  {
+    role: "assistant",
+    content:
+      "Cats make wonderful companions! Whiskers and Mittens are cute names.",
+  },
+  {
+    role: "user",
+    content: "Summary information about Sarah and her cats",
+  },
+];
+
+async function main() {
+  console.log("=== Vector Memory Block Example ===\n");
+
+  /**
+   * Create a vector store. You can quickly get a local instance of Qdrant running with Docker:
+   * ```bash
+   * docker pull qdrant/qdrant
+   * docker run -p 6333:6333 qdrant/qdrant
+   * ```
+   *
+   * Go to http://localhost:6333/dashboard#/collections to see your data
+   */
+  const vectorStore = new QdrantVectorStore({
+    url: "http://localhost:6333",
+    embedModel,
+  });
+
+  // Create a vector memory block using the factory function
+  const vectorMemoryBlock = vectorBlock({
+    vectorStore,
+    priority: 5,
+  });
+
+  // Create a memory store with the vector memory block
+  const memory = createMemory([], {
+    llm,
+    memoryBlocks: [vectorMemoryBlock],
+    tokenLimit: 100,
+    shortTermTokenLimitRatio: 0.7,
+  });
+
+  // Store the conversation history in the vector memory
+  console.log(`Adding ${CONVERSATION_TURNS.length} messages to the memory...`);
+  for (const message of CONVERSATION_TURNS) {
+    await memory.add(message);
+  }
+
+  // Retrieve relevant context for the current user request
+  console.log("Retrieving relevant context...");
+  const chatHistory = await memory.getLLM();
+
+  // You will see there's 1 generated context message from vector memory block, and 4 messages from short term memory block
+  console.log("Chat memory:", chatHistory);
+
+  // Now simulate the assistant responding with context
+  console.log("\nAssistant response with context:");
+  const response = await llm.chat({
+    messages: chatHistory,
+  });
+  console.log(response.message.content);
+
+  // Try adding more messages to the memory
+  const newMessages = [
+    {
+      role: "user",
+      content: "Write a long paragraph about weather in Tokyo",
+    },
+    {
+      role: "assistant",
+      content:
+        "The weather in Tokyo is sunny and warm. The temperature is around 20 degrees Celsius. The weather is very nice and the people are friendly.",
+    },
+    {
+      role: "user",
+      content: "What is the weather in Tokyo?",
+    },
+  ];
+  // Add the new messages to the memory
+  for (const message of newMessages) {
+    await memory.add(message);
+  }
+
+  // Try retrieving the new messages
+  const newChatHistory = await memory.getLLM();
+  // You can see now that new chat history will contain the nodes (separated by `\n`) in the
+  // context message that is generated by the vector memory block
+  // The number of retrieved nodes is set by `similarityTopK` in `queryOptions` of `vectorBlock`
+  // (default `similarityTopK` is 2)
+  console.log("New chat history:", newChatHistory);
+}
+
+main().catch(console.error);
@@ -1,7 +1,7 @@
-import { OpenAI } from "@llamaindex/openai";
+import { openai } from "@llamaindex/openai";

 async function main() {
-  const llm = new OpenAI({ model: "gpt-4-turbo" });
+  const llm = openai({ model: "gpt-4.1-mini" });
  const args: Parameters<typeof llm.chat>[0] = {
    additionalChatOptions: {
      tool_choice: "auto",
@@ -0,0 +1,46 @@
+import { openai } from "@llamaindex/openai";
+import { tool } from "llamaindex";
+import z from "zod";
+
+import { ChatMessage } from "llamaindex";
+
+async function main() {
+  const llm = openai({ model: "gpt-4.1-mini" });
+  const messages = [
+    {
+      content: `What's the weather like in San Francisco?`,
+      role: "user",
+    } as ChatMessage,
+  ];
+
+  let exit = false;
+  do {
+    const { stream, newMessages, toolCalls } = await llm.exec({
+      messages,
+      tools: [
+        tool({
+          name: "get_weather",
+          description: "Get the current weather for a location",
+          parameters: z.object({
+            address: z.string().describe("The address"),
+          }),
+          execute: ({ address }) => {
+            return `It's sunny in ${address}!`;
+          },
+        }),
+      ],
+      stream: true,
+    });
+    for await (const chunk of stream) {
+      process.stdout.write(chunk.delta);
+    }
+    messages.push(...newMessages());
+    // exit condition to stop the agent loop
+    // here we can also check for specific tool calls or limit the number of llm.exec calls
+    exit = toolCalls.length === 0;
+  } while (!exit);
+}
+
+(async function () {
+  await main();
+})();
@@ -0,0 +1,43 @@
+import { openai } from "@llamaindex/openai";
+import { ChatMessage, tool } from "llamaindex";
+import z from "zod";
+
+async function main() {
+  const llm = openai({ model: "gpt-4.1-mini" });
+  const messages = [
+    {
+      content: `What's the weather like in San Francisco?`,
+      role: "user",
+    } as ChatMessage,
+  ];
+
+  let exit = false;
+  do {
+    const { newMessages, toolCalls } = await llm.exec({
+      messages,
+      tools: [
+        tool({
+          name: "get_weather",
+          description: "Get the current weather for a location",
+          parameters: z.object({
+            address: z.string().describe("The address"),
+          }),
+          execute: ({ address }) => {
+            return `It's sunny in ${address}!`;
+          },
+        }),
+      ],
+    });
+    console.log(newMessages);
+    messages.push(...newMessages);
+    // exit condition to stop the agent loop
+    // here we can also check for specific tool calls or limit the number of llm.exec calls
+    exit = toolCalls.length === 0;
+  } while (!exit);
+}
+
+(async function () {
+  console.log("Starting...");
+  await main();
+  console.log("Done");
+})();
@@ -4,7 +4,7 @@ import {
  getCurrentIDTool,
  getUserInfoTool,
  getWeatherTool,
-} from "./utils/tools";
+} from "../../agents/tools/tools";

 async function main() {
  // Create an OpenAIAgent with the function tools
@@ -3,7 +3,7 @@ import {
  getCurrentIDTool,
  getUserInfoTool,
  getWeatherTool,
-} from "./utils/tools";
+} from "../../agents/tools/tools";

 async function main() {
  // Create an OpenAIAgent with the function tools
@@ -30,6 +30,12 @@ async function main() {
  );
  // and print out the text part
  console.log(textPart?.text);
+
+  const imageId = response.message.options?.image_id;
+  if (imageId) {
+    console.log("Image ID for multi-turn generation:", imageId);
+    console.log("Use this image_id in subsequent requests to modify the image");
+  }
 }

 main().catch(console.error);
@@ -0,0 +1,89 @@
+import { openaiResponses } from "@llamaindex/openai";
+import fs from "fs";
+import { MessageContentDetail } from "llamaindex";
+
+async function main() {
+  const llm = openaiResponses({
+    model: "gpt-4.1-mini",
+    builtInTools: [{ type: "image_generation" }],
+  });
+
+  // First turn: Generate initial image
+  console.log("=== First Turn: Generate initial image ===");
+  const firstResponse = await llm.chat({
+    messages: [
+      {
+        role: "user",
+        content:
+          "Generate an image of a cute tiny llama wearing a hat playing with a cat on a meadow",
+      },
+    ],
+  });
+
+  const firstContent = firstResponse.message.content as MessageContentDetail[];
+  const firstImagePart = firstContent.find((part) => part.type === "image");
+  const firstTextPart = firstContent.find((part) => part.type === "text");
+
+  // Save the first image
+  if (firstImagePart?.data) {
+    fs.writeFileSync(
+      "llama-initial.png",
+      Buffer.from(firstImagePart.data as string, "base64"),
+    );
+    console.log("First image saved as 'llama-initial.png'");
+  }
+
+  if (firstTextPart?.text) {
+    console.log("First response:", firstTextPart.text);
+  }
+
+  // Get the image_id from the response options for multi-turn
+  const imageId = firstResponse.message.options?.image_id;
+  console.log("Image ID for multi-turn:", imageId);
+
+  if (imageId) {
+    // Second turn: Modify the image using the image_id
+    console.log("\n=== Second Turn: Modify the image ===");
+    const secondResponse = await llm.chat({
+      messages: [
+        {
+          role: "user",
+          content:
+            "Generate an image of a cute tiny llama wearing a hat playing with a cat on a meadow",
+        },
+        {
+          role: "assistant",
+          content: firstContent,
+          options: { image_id: imageId },
+        },
+        {
+          role: "user",
+          content:
+            "Now add a rainbow in the background and make the llama's hat blue",
+        },
+      ],
+    });
+
+    const secondContent = secondResponse.message
+      .content as MessageContentDetail[];
+    const secondImagePart = secondContent.find((part) => part.type === "image");
+    const secondTextPart = secondContent.find((part) => part.type === "text");
+
+    // Save the modified image
+    if (secondImagePart?.data) {
+      fs.writeFileSync(
+        "llama-modified.png",
+        Buffer.from(secondImagePart.data as string, "base64"),
+      );
+      console.log("Modified image saved as 'llama-modified.png'");
+    }
+
+    if (secondTextPart?.text) {
+      console.log("Second response:", secondTextPart.text);
+    }
+  } else {
+    console.log("No image_id received, cannot perform multi-turn generation");
+  }
+}
+
+main().catch(console.error);
@@ -1,6 +1,6 @@
 {
  "name": "@llamaindex/examples",
-  "version": "0.3.28",
+  "version": "0.3.33",
  "private": true,
  "scripts": {
    "lint": "eslint .",
@@ -11,52 +11,52 @@
    "@azure/cosmos": "^4.1.1",
    "@azure/identity": "^4.4.1",
    "@azure/search-documents": "^12.1.0",
-    "@llamaindex/anthropic": "^0.3.16",
-    "@llamaindex/assemblyai": "^0.1.13",
-    "@llamaindex/astra": "^0.0.28",
-    "@llamaindex/azure": "^0.1.25",
-    "@llamaindex/bm25-retriever": "^0.0.3",
-    "@llamaindex/chroma": "^0.0.28",
-    "@llamaindex/clip": "^0.0.64",
-    "@llamaindex/cloud": "^4.0.19",
-    "@llamaindex/cohere": "^0.0.28",
-    "@llamaindex/core": "^0.6.14",
-    "@llamaindex/deepinfra": "^0.0.64",
-    "@llamaindex/deepseek": "^0.0.24",
-    "@llamaindex/discord": "^0.1.13",
-    "@llamaindex/elastic-search": "^0.1.14",
+    "@llamaindex/anthropic": "^0.3.19",
+    "@llamaindex/assemblyai": "^0.1.16",
+    "@llamaindex/astra": "^0.0.31",
+    "@llamaindex/azure": "^0.1.29",
+    "@llamaindex/bm25-retriever": "^0.0.6",
+    "@llamaindex/chroma": "^0.0.31",
+    "@llamaindex/clip": "^0.0.68",
+    "@llamaindex/cloud": "^4.0.26",
+    "@llamaindex/cohere": "^0.0.31",
+    "@llamaindex/core": "^0.6.17",
+    "@llamaindex/deepinfra": "^0.0.68",
+    "@llamaindex/deepseek": "^0.0.29",
+    "@llamaindex/discord": "^0.1.16",
+    "@llamaindex/elastic-search": "^0.1.17",
    "@llamaindex/env": "^0.1.30",
-    "@llamaindex/firestore": "^1.0.21",
-    "@llamaindex/fireworks": "^0.0.24",
-    "@llamaindex/google": "^0.3.13",
-    "@llamaindex/groq": "^0.0.80",
-    "@llamaindex/huggingface": "^0.1.18",
-    "@llamaindex/jinaai": "^0.0.24",
-    "@llamaindex/milvus": "^0.1.23",
-    "@llamaindex/mistral": "^0.1.14",
-    "@llamaindex/mixedbread": "^0.0.28",
-    "@llamaindex/mongodb": "^0.0.29",
-    "@llamaindex/node-parser": "^2.0.14",
-    "@llamaindex/notion": "^0.1.13",
-    "@llamaindex/ollama": "^0.1.14",
-    "@llamaindex/openai": "^0.4.8",
-    "@llamaindex/perplexity": "^0.0.21",
-    "@llamaindex/pinecone": "^0.1.14",
-    "@llamaindex/portkey-ai": "^0.0.56",
-    "@llamaindex/postgres": "^0.0.57",
-    "@llamaindex/qdrant": "^0.1.24",
-    "@llamaindex/readers": "^3.1.13",
-    "@llamaindex/replicate": "^0.0.56",
-    "@llamaindex/supabase": "^0.1.14",
-    "@llamaindex/together": "^0.0.24",
-    "@llamaindex/tools": "^0.1.4",
-    "@llamaindex/upstash": "^0.0.28",
-    "@llamaindex/vercel": "^0.1.14",
-    "@llamaindex/vllm": "^0.0.50",
-    "@llamaindex/voyage-ai": "^1.0.20",
-    "@llamaindex/weaviate": "^0.0.29",
-    "@llamaindex/workflow": "^1.1.14",
-    "@llamaindex/xai": "^0.0.11",
+    "@llamaindex/firestore": "^1.0.24",
+    "@llamaindex/fireworks": "^0.0.28",
+    "@llamaindex/google": "^0.3.16",
+    "@llamaindex/groq": "^0.0.84",
+    "@llamaindex/huggingface": "^0.1.22",
+    "@llamaindex/jinaai": "^0.0.28",
+    "@llamaindex/milvus": "^0.1.26",
+    "@llamaindex/mistral": "^0.1.17",
+    "@llamaindex/mixedbread": "^0.0.31",
+    "@llamaindex/mongodb": "^0.0.32",
+    "@llamaindex/node-parser": "^2.0.17",
+    "@llamaindex/notion": "^0.1.16",
+    "@llamaindex/ollama": "^0.1.17",
+    "@llamaindex/openai": "^0.4.12",
+    "@llamaindex/perplexity": "^0.0.25",
+    "@llamaindex/pinecone": "^0.1.17",
+    "@llamaindex/portkey-ai": "^0.0.59",
+    "@llamaindex/postgres": "^0.0.60",
+    "@llamaindex/qdrant": "^0.1.27",
+    "@llamaindex/readers": "^3.1.16",
+    "@llamaindex/replicate": "^0.0.59",
+    "@llamaindex/supabase": "^0.1.18",
+    "@llamaindex/together": "^0.0.28",
+    "@llamaindex/tools": "^0.1.7",
+    "@llamaindex/upstash": "^0.0.31",
+    "@llamaindex/vercel": "^0.1.17",
+    "@llamaindex/vllm": "^0.0.54",
+    "@llamaindex/voyage-ai": "^1.0.23",
+    "@llamaindex/weaviate": "^0.0.32",
+    "@llamaindex/workflow": "^1.1.17",
+    "@llamaindex/xai": "^0.0.15",
    "@notionhq/client": "^4.0.0",
    "@pinecone-database/pinecone": "^4.0.0",
    "@vercel/postgres": "^0.10.0",
@@ -65,7 +65,7 @@
    "commander": "^12.1.0",
    "dotenv": "^17.2.0",
    "js-tiktoken": "^1.0.14",
-    "llamaindex": "^0.11.14",
+    "llamaindex": "^0.11.21",
    "mongodb": "6.7.0",
    "postgres": "^3.4.4",
    "wikipedia": "^2.1.2",
@@ -15,7 +15,7 @@ async function main() {
  const vectorStore = new QdrantVectorStore({
    url: process.env.QDRANT_URL,
    apiKey: process.env.QDRANT_API_KEY,
-    embeddingModel: embedding,
+    embedModel: embedding,
    collectionName: "gemini_test",
  });
  const storageContext = await storageContextFromDefaults({ vectorStore });
@@ -16,7 +16,7 @@ async function main() {
  const vectorStore = new QdrantVectorStore({
    url: process.env.QDRANT_URL,
    apiKey: process.env.QDRANT_API_KEY,
-    embeddingModel: embedding,
+    embedModel: embedding,
    collectionName: "jina_test",
  });
  const storageContext = await storageContextFromDefaults({ vectorStore });
@@ -17,7 +17,8 @@
    "release-snapshot": "pnpm run build && changeset publish --tag snapshot",
    "new-version": "changeset version && pnpm format:write && pnpm run build",
    "new-snapshot": "pnpm run build && changeset version --snapshot",
-    "lint-staged": "lint-staged"
+    "lint-staged": "lint-staged",
+    "preinstall": "npx only-allow pnpm"
  },
  "devDependencies": {
    "@changesets/cli": "^2.27.5",
@@ -42,6 +43,11 @@
    "vitest": "^3.1.1"
  },
  "packageManager": "pnpm@10.8.1",
+  "pnpm": {
+    "overrides": {
+      "@notionhq/client": "4.0.0"
+    }
+  },
  "lint-staged": {
    "*.{js,jsx,ts,tsx}": [
      "eslint --fix",
@@ -1,5 +1,41 @@
 # @llamaindex/autotool

+## 8.0.21
+
+### Patch Changes
+
+- llamaindex@0.11.21
+
+## 8.0.20
+
+### Patch Changes
+
+- llamaindex@0.11.20
+
+## 8.0.19
+
+### Patch Changes
+
+- llamaindex@0.11.19
+
+## 8.0.18
+
+### Patch Changes
+
+- llamaindex@0.11.18
+
+## 8.0.17
+
+### Patch Changes
+
+- llamaindex@0.11.17
+
+## 8.0.16
+
+### Patch Changes
+
+- llamaindex@0.11.16
+
 ## 8.0.15

 ### Patch Changes
@@ -1,5 +1,47 @@
 # @llamaindex/autotool-01-node-example

+## 0.0.129
+
+### Patch Changes
+
+- llamaindex@0.11.21
+- @llamaindex/autotool@8.0.21
+
+## 0.0.128
+
+### Patch Changes
+
+- llamaindex@0.11.20
+- @llamaindex/autotool@8.0.20
+
+## 0.0.127
+
+### Patch Changes
+
+- llamaindex@0.11.19
+- @llamaindex/autotool@8.0.19
+
+## 0.0.126
+
+### Patch Changes
+
+- llamaindex@0.11.18
+- @llamaindex/autotool@8.0.18
+
+## 0.0.125
+
+### Patch Changes
+
+- llamaindex@0.11.17
+- @llamaindex/autotool@8.0.17
+
+## 0.0.124
+
+### Patch Changes
+
+- llamaindex@0.11.16
+- @llamaindex/autotool@8.0.16
+
 ## 0.0.123

 ### Patch Changes
@@ -13,5 +13,5 @@
  "scripts": {
    "start": "node --import tsx --import @llamaindex/autotool/node ./src/index.ts"
  },
-  "version": "0.0.123"
+  "version": "0.0.129"
 }
@@ -6,7 +6,7 @@
    "url": "git+https://github.com/run-llama/LlamaIndexTS.git",
    "directory": "packages/autotool"
  },
-  "version": "8.0.15",
+  "version": "8.0.21",
  "description": "auto transpile your JS function to LLM Agent compatible",
  "files": [
    "dist",
@@ -1,5 +1,46 @@
 # @llamaindex/cloud

+## 4.0.26
+
+### Patch Changes
+
+- Updated dependencies [38da40b]
+  - @llamaindex/core@0.6.17
+
+## 4.0.25
+
+### Patch Changes
+
+- 2967d57: Default to \_public agent url id
+- Updated dependencies [a8ec08c]
+  - @llamaindex/core@0.6.16
+
+## 4.0.24
+
+### Patch Changes
+
+- Updated dependencies [7ad3411]
+- Updated dependencies [5da5b3c]
+  - @llamaindex/core@0.6.15
+
+## 4.0.23
+
+### Patch Changes
+
+- a1b1598: fix: add generic types into agent data responses
+
+## 4.0.22
+
+### Patch Changes
+
+- d2be868: Bug fixes for new beta agent-data cloud API
+
+## 4.0.21
+
+### Patch Changes
+
+- 579ca0c: chore: bump sdk version
+
 ## 4.0.20

 ### Patch Changes
@@ -2519,7 +2519,7 @@
      "get": {
        "tags": ["Organizations"],
        "summary": "Get Organization Usage",
-        "description": "Get usage for a project",
+        "description": "Get usage for a specific organization.",
        "operationId": "get_organization_usage_api_v1_organizations__organization_id__usage_get",
        "security": [
          {
@@ -2535,15 +2535,8 @@
            "in": "path",
            "required": true,
            "schema": {
-              "anyOf": [
-                {
-                  "type": "string",
-                  "format": "uuid"
-                },
-                {
-                  "type": "null"
-                }
-              ],
+              "type": "string",
+              "format": "uuid",
              "title": "Organization Id"
            }
          },
@@ -11650,7 +11643,7 @@
    },
    "/api/v1/projects/{project_id}/agents": {
      "get": {
-        "tags": ["Llama Apps"],
+        "tags": ["Agent Deployments"],
        "summary": "List Deployments",
        "description": "List all deployments for a project.",
        "operationId": "list_deployments_api_v1_projects__project_id__agents_get",
@@ -11716,7 +11709,7 @@
    },
    "/api/v1/projects/{project_id}/agents:sync": {
      "post": {
-        "tags": ["Llama Apps"],
+        "tags": ["Agent Deployments"],
        "summary": "Sync Deployments",
        "description": "Sync deployments for a project.",
        "operationId": "sync_deployments_api_v1_projects__project_id__agents_sync_post",
@@ -11780,12 +11773,12 @@
        }
      }
    },
-    "/api/v1/billing/checkout-session": {
+    "/api/v1/classifier/classify": {
      "post": {
-        "tags": ["Billing"],
-        "summary": "Create Checkout Session",
-        "description": "Create a new checkout session.",
-        "operationId": "create_checkout_session_api_v1_billing_checkout_session_post",
+        "tags": ["Classifier", "Beta", "Classifier"],
+        "summary": "Classify Documents",
+        "description": "**[BETA]** Classify documents based on provided rules - simplified classification system.\n\n**This is a Beta feature** - API may change based on user feedback.\n\nThis endpoint supports:\n- Classifying new uploaded files\n- Classifying existing files by ID\n- Both new files and existing file IDs in one request\n\n## v0 Features:\n- **Simplified Rules**: Only `type` and `description` fields needed\n- **Matching Threshold**: Confidence-based classification with configurable threshold\n- **Smart Classification**: Filename heuristics + LLM content analysis\n- **Document Type Filtering**: Automatically filters out non-document file types\n- **Fast Processing**: Uses LlamaParse fast mode + GPT-4.1-nano\n- **Optimized Performance**: Parses each file only once for all rules\n\n## Simplified Scoring Logic:\n1. **Evaluate All Rules**: Compare document against all classification rules\n2. **Best Match Selection**: Return the highest scoring rule above matching_threshold\n3. **Unknown Classification**: Return as \"unknown\" if no rules score above threshold\n\nThis ensures optimal classification by:\n- Finding the best possible match among all rules\n- Avoiding false positives with confidence thresholds\n- Maximizing performance with single-pass file parsing\n\n## Rule Format:\n```json\n[\n  {\n    \"type\": \"invoice\",\n    \"description\": \"contains invoice number, line items, and total amount\"\n  },\n  {\n    \"type\": \"receipt\",\n    \"description\": \"purchase receipt with transaction details and payment info\"\n  }\n]\n```\n\n## Classification Process:\n1. **Metadata Heuristics** (configurable via API):\n   - **Document Type Filter**: Only process document file types (PDF, DOC, DOCX, RTF, TXT, ODT, Pages, HTML, XML, Markdown)\n   - **Filename Heuristics**: Check if rule type appears in filename\n   - **Content Analysis**: Parse document content once and use LLM for semantic matching against all rules\n2. **Result**: Returns type, confidence score, and matched rule information\n\n## API Parameters:\n- `matching_threshold` (0.1-0.99, default: 0.6): Minimum confidence threshold for acceptable matches\n- `enable_metadata_heuristic` (boolean, default: true): Enable metadata-based features\n\n## Supported Document Types:\n**Text Documents**: pdf, doc, docx, rtf, txt, odt, pages\n**Web Documents**: html, htm, xml\n**Markup**: md, markdown\n\n## Limits (Beta):\n- Maximum 100 files per request\n- Maximum 10 rules per request\n- Rule descriptions: 10-500 characters\n- Document types: 1-50 characters (alphanumeric, hyphens, underscores)\n\n**Beta Notice**: This API is subject to change. Please provide feedback!",
+        "operationId": "classify_documents_api_v1_classifier_classify_post",
        "security": [
          {
            "HTTPBearer": []
@@ -11795,6 +11788,23 @@
          }
        ],
        "parameters": [
+          {
+            "name": "project_id",
+            "in": "query",
+            "required": false,
+            "schema": {
+              "anyOf": [
+                {
+                  "type": "string",
+                  "format": "uuid"
+                },
+                {
+                  "type": "null"
+                }
+              ],
+              "title": "Project Id"
+            }
+          },
          {
            "name": "organization_id",
            "in": "query",
@@ -11832,9 +11842,9 @@
        "requestBody": {
          "required": true,
          "content": {
-            "application/json": {
+            "multipart/form-data": {
              "schema": {
-                "$ref": "#/components/schemas/CheckoutSessionCreatePayload"
+                "$ref": "#/components/schemas/Body_classify_documents_api_v1_classifier_classify_post"
              }
            }
          }
@@ -11845,8 +11855,7 @@
            "content": {
              "application/json": {
                "schema": {
-                  "type": "string",
-                  "title": "Response Create Checkout Session Api V1 Billing Checkout Session Post"
+                  "$ref": "#/components/schemas/ClassifyResponse"
                }
              }
            }
@@ -11948,55 +11957,6 @@
        }
      }
    },
-    "/api/v1/billing/webhook": {
-      "post": {
-        "tags": ["Billing"],
-        "summary": "Stripe Webhook",
-        "description": "Stripe webhook endpoint.",
-        "operationId": "stripe_webhook_api_v1_billing_webhook_post",
-        "parameters": [
-          {
-            "name": "stripe-signature",
-            "in": "header",
-            "required": false,
-            "schema": {
-              "type": "string",
-              "title": "Stripe-Signature"
-            }
-          }
-        ],
-        "responses": {
-          "200": {
-            "description": "Successful Response",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "type": "object",
-                  "additionalProperties": {
-                    "const": "success",
-                    "type": "string"
-                  },
-                  "propertyNames": {
-                    "const": "status"
-                  },
-                  "title": "Response Stripe Webhook Api V1 Billing Webhook Post"
-                }
-              }
-            }
-          },
-          "422": {
-            "description": "Validation Error",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/HTTPValidationError"
-                }
-              }
-            }
-          }
-        }
-      }
-    },
    "/api/v1/billing/downgrade-plan": {
      "post": {
        "tags": ["Billing"],
@@ -12165,64 +12125,6 @@
        }
      }
    },
-    "/api/v1/billing/metronome-webhook": {
-      "post": {
-        "tags": ["Billing"],
-        "summary": "Metronome Webhook",
-        "description": "Metronome webhook endpoint.",
-        "operationId": "metronome_webhook_api_v1_billing_metronome_webhook_post",
-        "parameters": [
-          {
-            "name": "Metronome-Webhook-Signature",
-            "in": "header",
-            "required": false,
-            "schema": {
-              "type": "string",
-              "title": "Metronome-Webhook-Signature"
-            }
-          },
-          {
-            "name": "Date",
-            "in": "header",
-            "required": false,
-            "schema": {
-              "type": "string",
-              "title": "Date"
-            }
-          }
-        ],
-        "responses": {
-          "200": {
-            "description": "Successful Response",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "type": "object",
-                  "additionalProperties": {
-                    "const": "success",
-                    "type": "string"
-                  },
-                  "propertyNames": {
-                    "const": "status"
-                  },
-                  "title": "Response Metronome Webhook Api V1 Billing Metronome Webhook Post"
-                }
-              }
-            }
-          },
-          "422": {
-            "description": "Validation Error",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/HTTPValidationError"
-                }
-              }
-            }
-          }
-        }
-      }
-    },
    "/api/v1/billing/metronome/dashboard": {
      "get": {
        "tags": ["Billing"],
@@ -15768,12 +15670,12 @@
        }
      }
    },
-    "/api/v1/beta/agent-data/": {
+    "/api/v1/beta/agent-data": {
      "post": {
        "tags": ["Beta", "Agent Data"],
        "summary": "Create Agent Data",
        "description": "Create new agent data.",
-        "operationId": "create_agent_data_api_v1_beta_agent_data__post",
+        "operationId": "create_agent_data_api_v1_beta_agent_data_post",
        "security": [
          {
            "HTTPBearer": []
@@ -18883,6 +18785,73 @@
        "required": ["start_date", "end_date"],
        "title": "BillingPeriod"
      },
+      "Body_classify_documents_api_v1_classifier_classify_post": {
+        "properties": {
+          "rules_json": {
+            "type": "string",
+            "title": "Rules Json",
+            "description": "JSON string containing classifier rules"
+          },
+          "files": {
+            "anyOf": [
+              {
+                "items": {
+                  "type": "string",
+                  "format": "binary"
+                },
+                "type": "array"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "title": "Files"
+          },
+          "file_ids": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "title": "File Ids",
+            "description": "Comma-separated list of existing file IDs"
+          },
+          "matching_threshold": {
+            "anyOf": [
+              {
+                "type": "number",
+                "maximum": 0.99,
+                "minimum": 0.1
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "title": "Matching Threshold",
+            "description": "Minimum confidence threshold for acceptable matches (0.1-0.99, default: 0.6)",
+            "default": 0.6
+          },
+          "enable_metadata_heuristic": {
+            "anyOf": [
+              {
+                "type": "boolean"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "title": "Enable Metadata Heuristic",
+            "description": "Enable metadata-based features (document filtering + content classification, default: true)",
+            "default": true
+          }
+        },
+        "type": "object",
+        "required": ["rules_json"],
+        "title": "Body_classify_documents_api_v1_classifier_classify_post"
+      },
      "Body_create_report_api_v1_reports__post": {
        "properties": {
          "name": {
@@ -19376,6 +19345,11 @@
            ],
            "title": "Max Pages"
          },
+          "merge_tables_across_pages_in_markdown": {
+            "type": "boolean",
+            "title": "Merge Tables Across Pages In Markdown",
+            "default": false
+          },
          "outlined_table_extraction": {
            "type": "boolean",
            "title": "Outlined Table Extraction",
@@ -19857,6 +19831,11 @@
            ],
            "title": "Max Pages"
          },
+          "merge_tables_across_pages_in_markdown": {
+            "type": "boolean",
+            "title": "Merge Tables Across Pages In Markdown",
+            "default": false
+          },
          "outlined_table_extraction": {
            "type": "boolean",
            "title": "Outlined Table Extraction",
@@ -20430,30 +20409,101 @@
        "type": "object",
        "title": "ChatInputParams"
      },
-      "CheckoutSessionCreatePayload": {
-        "properties": {
-          "success_url": {
-            "type": "string",
-            "minLength": 1,
-            "format": "uri",
-            "title": "Success Url"
-          },
-          "cancel_url": {
-            "type": "string",
-            "minLength": 1,
-            "format": "uri",
-            "title": "Cancel Url"
-          }
-        },
-        "type": "object",
-        "required": ["success_url", "cancel_url"],
-        "title": "CheckoutSessionCreatePayload"
-      },
      "ChunkMode": {
        "type": "string",
        "enum": ["PAGE", "DOCUMENT", "SECTION", "GROUPED_PAGES"],
        "title": "ChunkMode"
      },
+      "ClassificationResult": {
+        "properties": {
+          "file_id": {
+            "type": "string",
+            "format": "uuid",
+            "title": "File Id",
+            "description": "The ID of the classified file"
+          },
+          "type": {
+            "type": "string",
+            "title": "Type",
+            "description": "The assigned document type ('unknown' if no rules matched)",
+            "examples": ["invoice", "receipt", "contract", "unknown"]
+          },
+          "confidence": {
+            "type": "number",
+            "maximum": 1.0,
+            "minimum": 0.0,
+            "title": "Confidence",
+            "description": "Confidence score of the classification (0.0-1.0)"
+          },
+          "matched_rule": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "title": "Matched Rule",
+            "description": "Description of the rule that matched, or method used (e.g., 'auto: filename contains invoice')",
+            "examples": [
+              "contains invoice number, line items, and total",
+              "auto: filename contains 'invoice'",
+              null
+            ]
+          }
+        },
+        "type": "object",
+        "required": ["file_id", "type", "confidence", "matched_rule"],
+        "title": "ClassificationResult",
+        "description": "Result of classifying a single file.\n\nContains the classification outcome with confidence score and matched rule info."
+      },
+      "ClassifyResponse": {
+        "properties": {
+          "items": {
+            "items": {
+              "$ref": "#/components/schemas/ClassificationResult"
+            },
+            "type": "array",
+            "title": "Items",
+            "description": "The list of items."
+          },
+          "next_page_token": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "title": "Next Page Token",
+            "description": "A token, which can be sent as page_token to retrieve the next page. If this field is omitted, there are no subsequent pages."
+          },
+          "total_size": {
+            "anyOf": [
+              {
+                "type": "integer"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "title": "Total Size",
+            "description": "The total number of items available. This is only populated when specifically requested. The value may be an estimate and can be used for display purposes only."
+          },
+          "unknown_count": {
+            "type": "integer",
+            "minimum": 0.0,
+            "title": "Unknown Count",
+            "description": "Number of files that couldn't be classified"
+          }
+        },
+        "type": "object",
+        "required": ["items", "unknown_count"],
+        "title": "ClassifyResponse",
+        "description": "Response model for the classify endpoint following AIP-132 pagination standard.\n\nContains classification results with pagination support and summary statistics."
+      },
      "CloudAzStorageBlobDataSource": {
        "properties": {
          "supports_access_control": {
@@ -21941,6 +21991,7 @@
          },
          "query": {
            "type": "string",
+            "minLength": 1,
            "title": "Query",
            "description": "The query to retrieve against."
          }
@@ -22457,14 +22508,12 @@
          "version_metadata": {
            "anyOf": [
              {
-                "additionalProperties": true,
-                "type": "object"
+                "$ref": "#/components/schemas/DataSourceReaderVersionMetadata"
              },
              {
                "type": "null"
              }
            ],
-            "title": "Version Metadata",
            "description": "Version metadata for the data source"
          },
          "project_id": {
@@ -22570,6 +22619,24 @@
        "title": "DataSourceCreate",
        "description": "Schema for creating a data source."
      },
+      "DataSourceReaderVersionMetadata": {
+        "properties": {
+          "reader_version": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "title": "Reader Version",
+            "description": "The version of the reader to use for this data source."
+          }
+        },
+        "type": "object",
+        "title": "DataSourceReaderVersionMetadata"
+      },
      "DataSourceUpdate": {
        "properties": {
          "name": {
@@ -22841,6 +22908,7 @@
          },
          "query": {
            "type": "string",
+            "minLength": 1,
            "title": "Query",
            "description": "The query to retrieve against."
          },
@@ -23799,7 +23867,7 @@
      },
      "ExtractMode": {
        "type": "string",
-        "enum": ["FAST", "BALANCED", "PREMIUM", "MULTIMODAL", "ACCURATE"],
+        "enum": ["FAST", "BALANCED", "PREMIUM", "MULTIMODAL"],
        "title": "ExtractMode"
      },
      "ExtractModels": {
@@ -26210,6 +26278,19 @@
            "description": "Whether to try to extract outlined tables",
            "default": false
          },
+          "mergeTablesAcrossPagesInMarkdown": {
+            "anyOf": [
+              {
+                "type": "boolean"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "title": "Mergetablesacrosspagesinmarkdown",
+            "description": "Whether to merge tables across pages in markdown",
+            "default": false
+          },
          "saveImages": {
            "anyOf": [
              {
@@ -27070,6 +27151,7 @@
              "disable_image_extraction": false,
              "invalidate_cache": false,
              "outlined_table_extraction": false,
+              "merge_tables_across_pages_in_markdown": false,
              "output_pdf_of_document": false,
              "do_not_cache": false,
              "fast_mode": false,
@@ -27246,6 +27328,18 @@
            "title": "Outlined Table Extraction",
            "default": false
          },
+          "merge_tables_across_pages_in_markdown": {
+            "anyOf": [
+              {
+                "type": "boolean"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "title": "Merge Tables Across Pages In Markdown",
+            "default": false
+          },
          "output_pdf_of_document": {
            "anyOf": [
              {
@@ -29406,6 +29500,18 @@
            "title": "Outlined Table Extraction",
            "default": false
          },
+          "merge_tables_across_pages_in_markdown": {
+            "anyOf": [
+              {
+                "type": "boolean"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "title": "Merge Tables Across Pages In Markdown",
+            "default": false
+          },
          "output_pdf_of_document": {
            "anyOf": [
              {
@@ -31334,14 +31440,12 @@
          "version_metadata": {
            "anyOf": [
              {
-                "additionalProperties": true,
-                "type": "object"
+                "$ref": "#/components/schemas/DataSourceReaderVersionMetadata"
              },
              {
                "type": "null"
              }
            ],
-            "title": "Version Metadata",
            "description": "Version metadata for the data source"
          },
          "project_id": {
@@ -32129,12 +32233,6 @@
            "title": "Data Source Project File Changed",
            "description": "Whether the data source project file has changed",
            "default": false
-          },
-          "should_migrate_pipeline_file_to_external_file_id": {
-            "type": "boolean",
-            "title": "Should Migrate Pipeline File To External File Id",
-            "description": "Whether to migrate the pipeline file to the external file id",
-            "default": false
          }
        },
        "type": "object",
@@ -34239,19 +34337,6 @@
            "title": "Name",
            "description": "A name for the role."
          },
-          "organization_id": {
-            "anyOf": [
-              {
-                "type": "string",
-                "format": "uuid"
-              },
-              {
-                "type": "null"
-              }
-            ],
-            "title": "Organization Id",
-            "description": "The organization's ID."
-          },
          "permissions": {
            "items": {
              "$ref": "#/components/schemas/Permission"
@@ -34262,7 +34347,7 @@
          }
        },
        "type": "object",
-        "required": ["id", "name", "organization_id", "permissions"],
+        "required": ["id", "name", "permissions"],
        "title": "Role",
        "description": "Schema for a role."
      },
@@ -35134,19 +35219,13 @@
            "title": "Project Ids",
            "description": "The project ID scope."
          },
-          "role_id": {
-            "type": "string",
-            "format": "uuid",
-            "title": "Role Id",
-            "description": "The role's ID."
-          },
          "role": {
            "$ref": "#/components/schemas/Role",
            "description": "The role."
          }
        },
        "type": "object",
-        "required": ["id", "user_id", "organization_id", "role_id", "role"],
+        "required": ["id", "user_id", "organization_id", "role"],
        "title": "UserOrganizationRole",
        "description": "Schema for a user's role in an organization."
      },
@@ -1,6 +1,6 @@
 {
  "name": "@llamaindex/cloud",
-  "version": "4.0.20",
+  "version": "4.0.26",
  "type": "module",
  "license": "MIT",
  "scripts": {
@@ -13,20 +13,20 @@
    "./api",
    "./reader",
    "./parse",
-    "./agent"
+    "./beta/agent"
  ],
  "exports": {
    "./openapi.json": "./openapi.json",
-    "./agent": {
+    "./beta/agent": {
      "require": {
-        "types": "./agent/dist/index.d.cts",
-        "default": "./agent/dist/index.cjs"
+        "types": "./beta/agent/dist/index.d.cts",
+        "default": "./beta/agent/dist/index.cjs"
      },
      "import": {
-        "types": "./agent/dist/index.d.ts",
-        "default": "./agent/dist/index.js"
+        "types": "./beta/agent/dist/index.d.ts",
+        "default": "./beta/agent/dist/index.js"
      },
-      "default": "./agent/dist/index.js"
+      "default": "./beta/agent/dist/index.js"
    },
    "./api": {
      "require": {
@@ -1,291 +0,0 @@
-import { createClient, createConfig } from "@hey-api/client-fetch";
-import { getEnv } from "@llamaindex/env";
-import pRetry from "p-retry";
-import {
-  createAgentDataApiV1BetaAgentDataPost,
-  deleteAgentDataApiV1BetaAgentDataItemIdDelete,
-  getAgentDataApiV1BetaAgentDataItemIdGet,
-  searchAgentDataApiV1BetaAgentDataSearchPost,
-  updateAgentDataApiV1BetaAgentDataItemIdPut,
-  type AgentData,
-} from "../client";
-import type {
-  CreateAgentDataOptions,
-  ExtractedData,
-  ExtractOptions,
-  ListAgentDataOptions,
-  TypedAgentData,
-  TypedAgentDataItems,
-  UpdateAgentDataOptions,
-} from "./types";
-
-/**
- * Async client for agent data operations
- */
-export class AgentClient {
-  private client: ReturnType<typeof createClient>;
-  private baseUrl: string;
-  private headers: Record<string, string>;
-
-  constructor(options?: { apiKey?: string; baseUrl?: string }) {
-    const apiKey = options?.apiKey || getEnv("LLAMA_CLOUD_API_KEY");
-    this.baseUrl = options?.baseUrl || "https://api.cloud.llamaindex.ai/";
-
-    this.headers = {
-      "X-SDK-Name": "llamaindex-ts",
-      ...(apiKey && { Authorization: `Bearer ${apiKey}` }),
-    };
-
-    this.client = createClient(
-      createConfig({
-        baseUrl: this.baseUrl,
-        headers: this.headers,
-      }),
-    );
-  }
-
-  /**
-   * Create new agent data
-   */
-  async create<T = unknown>(
-    options: CreateAgentDataOptions<T>,
-  ): Promise<TypedAgentData<T>> {
-    const response = await createAgentDataApiV1BetaAgentDataPost({
-      throwOnError: true,
-      body: {
-        agent_slug: options.agentSlug,
-        ...(options.collection !== undefined && {
-          collection: options.collection,
-        }),
-        data: options.data as Record<string, unknown>,
-      },
-      client: this.client,
-    });
-
-    return this.transformResponse(response.data);
-  }
-
-  /**
-   * Get agent data by ID
-   */
-  async get<T = unknown>(id: string): Promise<TypedAgentData<T> | null> {
-    try {
-      const response = await getAgentDataApiV1BetaAgentDataItemIdGet({
-        throwOnError: true,
-        path: { item_id: id },
-        client: this.client,
-      });
-
-      return this.transformResponse(response.data);
-    } catch (error) {
-      if (
-        error instanceof Error &&
-        "response" in error &&
-        (error as { response?: { status?: number } }).response?.status === 404
-      ) {
-        return null;
-      }
-      throw error;
-    }
-  }
-
-  /**
-   * Update agent data
-   */
-  async update<T = unknown>(
-    id: string,
-    options: UpdateAgentDataOptions<T>,
-  ): Promise<TypedAgentData<T>> {
-    const response = await updateAgentDataApiV1BetaAgentDataItemIdPut({
-      throwOnError: true,
-      path: { item_id: id },
-      body: {
-        data: options.data as Record<string, unknown>,
-      },
-      client: this.client,
-    });
-
-    return this.transformResponse(response.data);
-  }
-
-  /**
-   * Delete agent data
-   */
-  async delete(id: string): Promise<void> {
-    await deleteAgentDataApiV1BetaAgentDataItemIdDelete({
-      throwOnError: true,
-      path: { item_id: id },
-      client: this.client,
-    });
-  }
-
-  /**
-   * List agent data
-   */
-  async list<T = unknown>(
-    options: ListAgentDataOptions,
-  ): Promise<TypedAgentDataItems<T>> {
-    const response = await searchAgentDataApiV1BetaAgentDataSearchPost({
-      throwOnError: true,
-      body: {
-        agent_slug: options.agentSlug,
-        ...(options.collection !== undefined && {
-          collection: options.collection,
-        }),
-        ...(options.filter !== undefined && { filter: options.filter }),
-        ...(options.orderBy !== undefined && { order_by: options.orderBy }),
-        ...(options.pageSize !== undefined && { page_size: options.pageSize }),
-        ...(options.pageToken !== undefined && {
-          page_token: options.pageToken,
-        }),
-        ...(options.offset !== undefined && { offset: options.offset }),
-      },
-      client: this.client,
-    });
-
-    const result: TypedAgentDataItems<T> = {
-      items: response.data.items.map((item: AgentData) =>
-        this.transformResponse(item),
-      ),
-    };
-
-    if (
-      response.data.total_size !== null &&
-      response.data.total_size !== undefined
-    ) {
-      result.totalSize = response.data.total_size;
-    }
-
-    if (
-      response.data.next_page_token !== null &&
-      response.data.next_page_token !== undefined
-    ) {
-      result.nextPageToken = response.data.next_page_token;
-    }
-
-    return result;
-  }
-
-  /**
-   * Extract data from agent with retry logic
-   */
-  async extract<T = unknown>(
-    agentId: string,
-    input: unknown,
-    options?: ExtractOptions,
-  ): Promise<ExtractedData<T>> {
-    const extractOptions = {
-      retries: options?.retryCount || 3,
-      onFailedAttempt: (error: {
-        attemptNumber: number;
-        retriesLeft: number;
-      }) => {
-        console.log(
-          `Extraction attempt ${error.attemptNumber} failed. ${error.retriesLeft} retries left.`,
-        );
-      },
-      minTimeout: options?.retryDelay || 1000,
-      maxTimeout: options?.timeout || 30000,
-    };
-
-    return pRetry(async () => {
-      // Note: The extract endpoint might not be in the generated client yet
-      // Using the native fetch API for this endpoint
-      const response = await fetch(
-        `${this.baseUrl}/api/v1/beta/agent-data/${agentId}/extract`,
-        {
-          method: "POST",
-          body: JSON.stringify({ input }),
-          headers: {
-            "Content-Type": "application/json",
-            ...this.headers,
-          },
-        },
-      );
-
-      if (!response.ok) {
-        throw new Error(`Failed to extract data: ${response.statusText}`);
-      }
-
-      const extractedData = (await response.json()) as ExtractedData<T>;
-
-      // If status is still pending or in progress, poll for completion
-      if (
-        extractedData.status === "pending" ||
-        extractedData.status === "in_progress"
-      ) {
-        return this.pollExtraction<T>(extractedData.id, options);
-      }
-
-      return extractedData;
-    }, extractOptions);
-  }
-
-  /**
-   * Poll for extraction completion
-   */
-  private async pollExtraction<T = unknown>(
-    extractionId: string,
-    options?: ExtractOptions,
-  ): Promise<ExtractedData<T>> {
-    const pollInterval = 2000; // 2 seconds
-    const maxAttempts = Math.floor((options?.timeout || 30000) / pollInterval);
-
-    for (let i = 0; i < maxAttempts; i++) {
-      await new Promise((resolve) => setTimeout(resolve, pollInterval));
-
-      const response = await fetch(
-        `${this.baseUrl}/api/v1/extractions/${extractionId}`,
-        {
-          headers: this.headers,
-        },
-      );
-
-      if (!response.ok) {
-        throw new Error(
-          `Failed to get extraction status: ${response.statusText}`,
-        );
-      }
-
-      const extractedData = (await response.json()) as ExtractedData<T>;
-
-      if (
-        extractedData.status === "completed" ||
-        extractedData.status === "failed"
-      ) {
-        return extractedData;
-      }
-    }
-
-    throw new Error("Extraction timeout exceeded");
-  }
-
-  /**
-   * Transform API response to typed data
-   */
-  private transformResponse<T = unknown>(data: AgentData): TypedAgentData<T> {
-    const result: TypedAgentData<T> = {
-      id: data.id!,
-      agentSlug: data.agent_slug,
-      data: data.data as T,
-      createdAt: new Date(data.created_at!),
-      updatedAt: new Date(data.updated_at!),
-    };
-
-    if (data.collection !== undefined) {
-      result.collection = data.collection;
-    }
-
-    return result;
-  }
-}
-
-/**
- * Create a new AsyncAgentDataClient instance
- */
-export function createAgentDataClient(options?: {
-  apiKey?: string;
-  baseUrl?: string;
-}): AgentClient {
-  return new AgentClient(options);
-}
@@ -1,98 +0,0 @@
-/**
- * Status types for agent data processing
- */
-export enum StatusType {
-  PENDING = "pending",
-  IN_PROGRESS = "in_progress",
-  COMPLETED = "completed",
-  FAILED = "failed",
-}
-
-/**
- * Filter operation for searching/filtering agent data
- */
-export interface FilterOperation {
-  [key: string]: unknown;
-}
-
-/**
- * Base extracted data interface
- */
-export interface ExtractedData<T = unknown> {
-  id: string;
-  status: StatusType;
-  data?: T;
-  error?: string;
-  createdAt: Date;
-  updatedAt: Date;
-}
-
-/**
- * TypedAgentData interface for typed agent data
- */
-export interface TypedAgentData<T = unknown> {
-  id: string;
-  agentSlug: string;
-  collection?: string;
-  data: T;
-  createdAt: Date;
-  updatedAt: Date;
-}
-
-/**
- * Collection of typed agent data items
- */
-export interface TypedAgentDataItems<T = unknown> {
-  items: TypedAgentData<T>[];
-  totalSize?: number;
-  nextPageToken?: string;
-}
-
-/**
- * Options for creating agent data
- */
-export interface CreateAgentDataOptions<T = unknown> {
-  agentSlug: string;
-  collection?: string;
-  data: T;
-}
-
-/**
- * Options for updating agent data
- */
-export interface UpdateAgentDataOptions<T = unknown> {
-  data: T;
-}
-
-/**
- * Sort options for listing
- */
-export interface SortOptions {
-  field: string;
-  order: "asc" | "desc";
-}
-
-/**
- * Options for listing agent data
- */
-export interface ListAgentDataOptions {
-  agentSlug: string;
-  collection?: string;
-  filter?: Record<string, FilterOperation>;
-  orderBy?: string;
-  pageSize?: number;
-  pageToken?: string;
-  offset?: number;
-}
-
-/**
- * Options for extraction
- */
-export interface ExtractOptions {
-  timeout?: number;
-  retryCount?: number;
-  retryDelay?: number;
-}
-
-export type ExtractedT<T> = T;
-export type AgentDataT<T> = T;
@@ -0,0 +1,329 @@
+import { createClient, createConfig } from "@hey-api/client-fetch";
+import { getEnv } from "@llamaindex/env";
+import {
+  aggregateAgentDataApiV1BetaAgentDataAggregatePost,
+  createAgentDataApiV1BetaAgentDataPost,
+  deleteAgentDataApiV1BetaAgentDataItemIdDelete,
+  getAgentDataApiV1BetaAgentDataItemIdGet,
+  searchAgentDataApiV1BetaAgentDataSearchPost,
+  updateAgentDataApiV1BetaAgentDataItemIdPut,
+  type AgentData,
+  type AggregateGroup,
+} from "../../client";
+import type {
+  AggregateAgentDataOptions,
+  SearchAgentDataOptions,
+  TypedAgentData,
+  TypedAgentDataItems,
+  TypedAggregateGroup,
+  TypedAggregateGroupItems,
+} from "./types";
+
+/**
+ * Async client for agent data operations
+ */
+export class AgentClient<T = unknown> {
+  private client: ReturnType<typeof createClient>;
+  private baseUrl: string;
+  private headers: Record<string, string>;
+  private collection: string;
+  private agentUrlId: string;
+
+  constructor({
+    apiKey = getEnv("LLAMA_CLOUD_API_KEY"),
+    baseUrl = "https://api.cloud.llamaindex.ai/",
+    collection = "default",
+    agentUrlId = "_public",
+  }: {
+    apiKey?: string;
+    baseUrl?: string;
+    collection?: string;
+    agentUrlId?: string;
+  }) {
+    this.baseUrl = baseUrl;
+
+    this.headers = {
+      "X-SDK-Name": "llamaindex-ts",
+      ...(apiKey && { Authorization: `Bearer ${apiKey}` }),
+    };
+
+    this.client = createClient(
+      createConfig({
+        baseUrl: this.baseUrl,
+        headers: this.headers,
+      }),
+    );
+
+    this.collection = collection;
+    this.agentUrlId = agentUrlId;
+  }
+
+  /**
+   * Create new agent data
+   */
+  async createItem(data: T): Promise<TypedAgentData<T>> {
+    const response = await createAgentDataApiV1BetaAgentDataPost({
+      throwOnError: true,
+      body: {
+        agent_slug: this.agentUrlId,
+        collection: this.collection,
+        data: data as Record<string, unknown>,
+      },
+      client: this.client,
+    });
+
+    return this.transformResponse(response.data);
+  }
+
+  /**
+   * Get agent data by ID
+   */
+  async getItem(id: string): Promise<TypedAgentData<T> | null> {
+    try {
+      const response = await getAgentDataApiV1BetaAgentDataItemIdGet({
+        throwOnError: true,
+        path: { item_id: id },
+        client: this.client,
+      });
+
+      return this.transformResponse(response.data);
+    } catch (error) {
+      if (
+        error instanceof Error &&
+        "response" in error &&
+        (error as { response?: { status?: number } }).response?.status === 404
+      ) {
+        return null;
+      }
+      throw error;
+    }
+  }
+
+  /**
+   * Update agent data
+   */
+  async updateItem(id: string, data: T): Promise<TypedAgentData<T>> {
+    const response = await updateAgentDataApiV1BetaAgentDataItemIdPut({
+      throwOnError: true,
+      path: { item_id: id },
+      body: {
+        data: data as Record<string, unknown>,
+      },
+      client: this.client,
+    });
+
+    return this.transformResponse(response.data);
+  }
+
+  /**
+   * Delete agent data
+   */
+  async deleteItem(id: string): Promise<void> {
+    await deleteAgentDataApiV1BetaAgentDataItemIdDelete({
+      throwOnError: true,
+      path: { item_id: id },
+      client: this.client,
+    });
+  }
+
+  /**
+   * Search agent data
+   */
+  async search(
+    options: SearchAgentDataOptions,
+  ): Promise<TypedAgentDataItems<T>> {
+    const response = await searchAgentDataApiV1BetaAgentDataSearchPost({
+      throwOnError: true,
+      body: {
+        agent_slug: this.agentUrlId,
+        ...(this.collection !== undefined && {
+          collection: this.collection,
+        }),
+        ...(options.filter !== undefined && { filter: options.filter }),
+        ...(options.orderBy !== undefined && { order_by: options.orderBy }),
+        ...(options.pageSize !== undefined && { page_size: options.pageSize }),
+        ...(options.offset !== undefined && { offset: options.offset }),
+        ...(options.includeTotal !== undefined && {
+          include_total: options.includeTotal,
+        }),
+      },
+      client: this.client,
+    });
+
+    const result: TypedAgentDataItems<T> = {
+      items: response.data.items.map((item: AgentData) =>
+        this.transformResponse(item),
+      ),
+    };
+
+    if (
+      response.data.total_size !== null &&
+      response.data.total_size !== undefined
+    ) {
+      result.totalSize = response.data.total_size;
+    }
+
+    if (
+      response.data.next_page_token !== null &&
+      response.data.next_page_token !== undefined
+    ) {
+      result.nextPageToken = response.data.next_page_token;
+    }
+
+    return result;
+  }
+
+  /**
+   * Aggregate agent data into groups
+   */
+  async aggregate(
+    options: AggregateAgentDataOptions,
+  ): Promise<TypedAggregateGroupItems<T>> {
+    const response = await aggregateAgentDataApiV1BetaAgentDataAggregatePost({
+      throwOnError: true,
+      body: {
+        agent_slug: this.agentUrlId,
+        ...(this.collection !== undefined && {
+          collection: this.collection,
+        }),
+        ...(options.filter !== undefined && { filter: options.filter }),
+        ...(options.groupBy !== undefined && { group_by: options.groupBy }),
+        ...(options.count !== undefined && { count: options.count }),
+        ...(options.first !== undefined && { first: options.first }),
+        ...(options.orderBy !== undefined && { order_by: options.orderBy }),
+        ...(options.offset !== undefined && { offset: options.offset }),
+        ...(options.pageSize !== undefined && { page_size: options.pageSize }),
+      },
+      client: this.client,
+    });
+
+    const result: TypedAggregateGroupItems<T> = {
+      items: response.data.items.map((item) =>
+        this.transformAggregateResponse(item),
+      ),
+    };
+
+    if (
+      response.data.total_size !== null &&
+      response.data.total_size !== undefined
+    ) {
+      result.totalSize = response.data.total_size;
+    }
+
+    if (
+      response.data.next_page_token !== null &&
+      response.data.next_page_token !== undefined
+    ) {
+      result.nextPageToken = response.data.next_page_token;
+    }
+
+    return result;
+  }
+
+  /**
+   * Transform API response to typed data
+   */
+  private transformResponse(data: AgentData): TypedAgentData<T> {
+    const result: TypedAgentData<T> = {
+      id: data.id!,
+      agentUrlId: data.agent_slug,
+      data: data.data as T,
+      createdAt: new Date(data.created_at!),
+      updatedAt: new Date(data.updated_at!),
+    };
+
+    if (data.collection !== undefined) {
+      result.collection = data.collection;
+    }
+
+    return result;
+  }
+
+  /**
+   * Transform API aggregate response to typed data
+   */
+  private transformAggregateResponse(
+    data: AggregateGroup,
+  ): TypedAggregateGroup<T> {
+    const result: TypedAggregateGroup<T> = {
+      groupKey: data.group_key,
+    };
+
+    if (data.count !== null && data.count !== undefined) {
+      result.count = data.count;
+    }
+
+    if (data.first_item !== null && data.first_item !== undefined) {
+      result.firstItem = data.first_item as T;
+    }
+
+    return result;
+  }
+}
+
+export interface AgentDataClientOptions<T = unknown> {
+  /** API key for the client */
+  apiKey?: string;
+  /** Base URL for the client */
+  /** Base URL of the llama cloud api */
+  baseUrl?: string;
+  /** If running in an agent runtime, optionally provide the window url to infer the agent url id */
+  windowUrl?: string;
+  /** Agent URL ID for the client, if not provided, it will be inferred from the window url, or fall back to "default" */
+  agentUrlId?: string;
+  /** Collection name for the client, defaults to "default" */
+  collection?: string;
+}
+/**
+ * Create a new AsyncAgentDataClient instance. Does it's best to infer an agent url id from environment.
+ * Pass in the window url and/or env to infer the agent url id from them.
+ * @param options - The options for the client
+ * @returns A new AgentClient instance
+ */
+export function createAgentDataClient<T = unknown>({
+  apiKey,
+  baseUrl,
+  windowUrl,
+  env,
+  agentUrlId,
+  collection = "default",
+}: {
+  apiKey?: string;
+  baseUrl?: string;
+  windowUrl?: string;
+  env?: Record<string, string>;
+  agentUrlId?: string;
+  collection?: string;
+} = {}): AgentClient<T> {
+  if (env && !agentUrlId) {
+    agentUrlId =
+      env.LLAMA_DEPLOY_DEPLOYMENT_NAME ||
+      env.NEXT_PUBLIC_LLAMA_DEPLOY_DEPLOYMENT_NAME ||
+      env.VITE_LLAMA_DEPLOY_DEPLOYMENT_NAME;
+  }
+  if (windowUrl && !agentUrlId) {
+    try {
+      const url = new URL(windowUrl);
+      const path = url.pathname;
+      const isLocalhost = // local agents should default to _public, otherwise a full deployment is required
+        url.hostname.includes("localhost") ||
+        url.hostname.includes("127.0.0.1");
+      if (path.startsWith("/deployments/") && !isLocalhost) {
+        // /deployments/<agent-url-id>/ui/ -> ["", "deployments", "<agent-url-id>", "ui"]
+        agentUrlId = path.split("/")[2];
+      }
+    } catch (error) {
+      console.warn(
+        "Failed to infer agent url id from window url, falling back to default",
+        error,
+      );
+    }
+  }
+
+  return new AgentClient({
+    ...(apiKey && { apiKey }),
+    ...(baseUrl && { baseUrl }),
+    ...(agentUrlId && { agentUrlId }),
+    collection,
+  });
+}
@@ -1,18 +1,16 @@
 export { AgentClient, createAgentDataClient } from "./client";

 export type {
-  AgentDataT,
-  CreateAgentDataOptions,
-  ExtractOptions,
+  AggregateAgentDataOptions,
+  ComparisonOperator,
  ExtractedData,
-  ExtractedT,
  FilterOperation,
-  ListAgentDataOptions,
-  SortOptions,
+  SearchAgentDataOptions,
  StatusType,
  TypedAgentData,
  TypedAgentDataItems,
-  UpdateAgentDataOptions,
+  TypedAggregateGroup,
+  TypedAggregateGroupItems,
 } from "./types";

 export { StatusType as StatusTypeEnum } from "./types";
@@ -0,0 +1,138 @@
+import type { FilterOperation as RawFilterOperation } from "../../client/types.gen";
+/**
+ * Status types for agent data processing
+ */
+export const StatusType = {
+  ERROR: "error",
+  ACCEPTED: "accepted",
+  REJECTED: "rejected",
+  PENDING_REVIEW: "pending_review",
+} as const;
+
+export type StatusType = (typeof StatusType)[keyof typeof StatusType];
+
+export const ComparisonOperator = {
+  GT: "gt",
+  GTE: "gte",
+  LT: "lt",
+  LTE: "lte",
+  EQ: "eq",
+  INCLUDES: "includes",
+} as const;
+
+export type ComparisonOperator =
+  (typeof ComparisonOperator)[keyof typeof ComparisonOperator];
+
+/**
+ * Filter operation for searching/filtering agent data
+ */
+export type FilterOperation = RawFilterOperation;
+
+/**
+ * Base extracted data interface
+ */
+export interface ExtractedData<T = unknown> {
+  /** The original data that was extracted from the document. For tracking changes. Should not be updated. */
+  original_data: T;
+  /** The latest state of the data. Will differ if data has been updated. */
+  data?: T;
+  /** The status of the extracted data. Prefer to use the StatusType values, but any string is allowed. */
+  status: StatusType | string;
+  /** Confidence scores, if any, for each primitive field in the original_data data. */
+  confidence?: Record<string, unknown>;
+  /** The ID of the file that was used to extract the data. */
+  file_id?: string;
+  /** The name of the file that was used to extract the data. */
+  file_name?: string;
+  /** The hash of the file that was used to extract the data. */
+  file_hash?: string;
+  /** Additional metadata about the extracted data, such as errors, tokens, etc. */
+  metadata?: Record<string, unknown>;
+}
+
+/**
+ * TypedAgentData interface for typed agent data
+ */
+export interface TypedAgentData<T = unknown> {
+  /** The unique ID of the agent data record. */
+  id: string;
+  /** The ID of the agent that created the data. */
+  agentUrlId: string;
+  /** The collection of the agent data. */
+  collection?: string;
+  /** The data of the agent data. Usually an ExtractedData&lt;SomeOtherType&gt; */
+  data: T;
+  /** The date and time the data was created. */
+  createdAt: Date;
+  /** The date and time the data was last updated. */
+  updatedAt: Date;
+}
+
+/**
+ * Paginated response of typed agent data items
+ */
+export interface TypedAgentDataItems<T = unknown> {
+  items: TypedAgentData<T>[];
+  totalSize?: number;
+  nextPageToken?: string;
+}
+
+/**
+ * Options for listing agent data
+ */
+export interface SearchAgentDataOptions {
+  /** Filter options for the list. */
+  filter?: Record<string, FilterOperation>;
+  /** Order by options for the list. */
+  orderBy?: string;
+  /** Page size for the list. */
+  pageSize?: number;
+  /** Offset for the list. */
+  offset?: number;
+  /**
+   * Whether to include the total number of items in the response.
+   * Should use only for first request to build total pagination, and not subsequent requests.
+   */
+  includeTotal?: boolean;
+}
+
+/**
+ * Options for aggregating agent data
+ */
+export interface AggregateAgentDataOptions {
+  /** Filter options for the aggregation. */
+  filter?: Record<string, FilterOperation>;
+  /** Fields to group by. */
+  groupBy?: string[];
+  /** Whether to count the number of items in each group. */
+  count?: boolean;
+  /** Whether to return the first item in each group. */
+  first?: boolean;
+  /** Order by options for the aggregation. */
+  orderBy?: string;
+  /** Offset for the aggregation. */
+  offset?: number;
+  /** Page size for the aggregation. */
+  pageSize?: number;
+}
+
+/**
+ * Single aggregation group result
+ */
+export interface TypedAggregateGroup<T = unknown> {
+  /** The group key values */
+  groupKey: Record<string, unknown>;
+  /** Count of items in the group */
+  count?: number;
+  /** First item in the group */
+  firstItem?: T;
+}
+
+/**
+ * Paginated response of aggregated agent data
+ */
+export interface TypedAggregateGroupItems<T = unknown> {
+  items: TypedAggregateGroup<T>[];
+  totalSize?: number;
+  nextPageToken?: string;
+}
@@ -179,6 +179,7 @@ export class LlamaParseReader extends FileReader {
  page_header_suffix?: string | undefined;
  page_footer_prefix?: string | undefined;
  page_footer_suffix?: string | undefined;
+  merge_tables_across_pages_in_markdown?: boolean | undefined;

  constructor(
    params: Partial<Omit<LlamaParseReader, "language" | "apiKey">> & {
@@ -368,6 +369,8 @@ export class LlamaParseReader extends FileReader {
      page_header_suffix: this.page_header_suffix,
      page_footer_prefix: this.page_footer_prefix,
      page_footer_suffix: this.page_footer_suffix,
+      merge_tables_across_pages_in_markdown:
+        this.merge_tables_across_pages_in_markdown,
    } satisfies {
      [Key in keyof BodyUploadFileApiParsingUploadPost]-?:
        | BodyUploadFileApiParsingUploadPost[Key]
@@ -1,5 +1,24 @@
 # @llamaindex/core

+## 0.6.17
+
+### Patch Changes
+
+- 38da40b: feat: VectoryMemoryBlock
+
+## 0.6.16
+
+### Patch Changes
+
+- a8ec08c: fix: ensure correct message content in agent workflow
+
+## 0.6.15
+
+### Patch Changes
+
+- 7ad3411: feat: add llm.exec
+- 5da5b3c: add progress callback to embeddings
+
 ## 0.6.14

 ### Patch Changes
@@ -1,7 +1,7 @@
 {
  "name": "@llamaindex/core",
  "type": "module",
-  "version": "0.6.14",
+  "version": "0.6.17",
  "description": "LlamaIndex Core Module",
  "exports": {
    "./agent": {
@@ -59,6 +59,17 @@
      },
      "default": "./llms/dist/index.js"
    },
+    "./llms/mock": {
+      "require": {
+        "types": "./llms/mock/dist/index.d.cts",
+        "default": "./llms/mock/dist/index.cjs"
+      },
+      "import": {
+        "types": "./llms/mock/dist/index.d.ts",
+        "default": "./llms/mock/dist/index.js"
+      },
+      "default": "./llms/mock/dist/index.js"
+    },
    "./decorator": {
      "require": {
        "types": "./decorator/dist/index.d.cts",
@@ -17,6 +17,7 @@ export type EmbeddingInfo = {

 export type BaseEmbeddingOptions = {
  logProgress?: boolean;
+  progressCallback?: (current: number, total: number) => void;
 };

 export abstract class BaseEmbedding extends TransformComponent<
@@ -138,9 +139,11 @@ export async function batchEmbeddings<T>(
      const embeddings = await embedFunc(curBatch);

      resultEmbeddings.push(...embeddings);
-
+      if (options?.progressCallback) {
+        options?.progressCallback?.(i + 1, queue.length);
+      }
      if (options?.logProgress) {
-        console.log(`getting embedding progress: ${i} / ${queue.length}`);
+        console.log(`getting embedding progress: ${i + 1} / ${queue.length}`);
      }

      curBatch.length = 0;
@@ -1,15 +1,20 @@
 import { extractText } from "../utils/llms";
 import { streamConverter } from "../utils/stream";
+import { callTool, getToolCallsFromResponse } from "./tool-call";
 import type {
+  ChatMessage,
  ChatResponse,
  ChatResponseChunk,
  CompletionResponse,
+  ExecResponse,
+  ExecStreamResponse,
  LLM,
  LLMChatParamsNonStreaming,
  LLMChatParamsStreaming,
  LLMCompletionParamsNonStreaming,
  LLMCompletionParamsStreaming,
  LLMMetadata,
+  PartialToolCall,
  ToolCallLLMMessageOptions,
 } from "./type";

@@ -60,13 +65,180 @@ export abstract class BaseLLM<
      AdditionalChatOptions,
      AdditionalMessageOptions
    >,
-  ): Promise<AsyncIterable<ChatResponseChunk>>;
+  ): Promise<AsyncIterable<ChatResponseChunk<AdditionalMessageOptions>>>;
  abstract chat(
    params: LLMChatParamsNonStreaming<
      AdditionalChatOptions,
      AdditionalMessageOptions
    >,
  ): Promise<ChatResponse<AdditionalMessageOptions>>;
+
+  exec(
+    params: LLMChatParamsStreaming<
+      AdditionalChatOptions,
+      AdditionalMessageOptions
+    >,
+  ): Promise<ExecStreamResponse<AdditionalMessageOptions>>;
+  exec(
+    params: LLMChatParamsNonStreaming<
+      AdditionalChatOptions,
+      AdditionalMessageOptions
+    >,
+  ): Promise<ExecResponse<AdditionalMessageOptions>>;
+  async exec(
+    params:
+      | LLMChatParamsStreaming<AdditionalChatOptions, AdditionalMessageOptions>
+      | LLMChatParamsNonStreaming<
+          AdditionalChatOptions,
+          AdditionalMessageOptions
+        >,
+  ): Promise<
+    | ExecResponse<AdditionalMessageOptions>
+    | ExecStreamResponse<AdditionalMessageOptions>
+  > {
+    if (params.stream) {
+      return this.streamExec(params);
+    }
+    const newMessages: ChatMessage<AdditionalMessageOptions>[] = [];
+    const response = await this.chat(params);
+    newMessages.push(response.message);
+    const toolCalls = getToolCallsFromResponse(response);
+    if (params.tools && toolCalls.length > 0) {
+      for (const toolCall of toolCalls) {
+        const toolResultMessage = await callTool<AdditionalMessageOptions>(
+          params.tools,
+          toolCall,
+        );
+        if (toolResultMessage) {
+          newMessages.push(toolResultMessage);
+        }
+      }
+    }
+    return {
+      newMessages,
+      toolCalls,
+    };
+  }
+
+  async streamExec(
+    params: LLMChatParamsStreaming<
+      AdditionalChatOptions,
+      AdditionalMessageOptions
+    >,
+  ): Promise<ExecStreamResponse<AdditionalMessageOptions>> {
+    const responseStream = await this.chat(params);
+    const iterator = responseStream[Symbol.asyncIterator]();
+    const first = await iterator.next();
+
+    // Set firstChunk to null if empty
+    const firstChunk = !first.done ? first.value : null;
+
+    const hasToolCallsInFirst =
+      firstChunk?.options && "toolCall" in firstChunk.options;
+
+    if (!hasToolCallsInFirst) {
+      let content = firstChunk?.delta ?? "";
+      let finished = false;
+      return {
+        stream: (async function* () {
+          if (firstChunk) {
+            yield firstChunk;
+          }
+          for await (const chunk of {
+            [Symbol.asyncIterator]: () => iterator,
+          }) {
+            content += chunk.delta;
+            yield chunk;
+          }
+          finished = true;
+        })(),
+        toolCalls: [],
+        newMessages() {
+          if (!finished) {
+            throw new Error(
+              "New messages are not ready yet. Call newMessages() after the stream is done.",
+            );
+          }
+          return content
+            ? [
+                {
+                  role: "assistant",
+                  content,
+                } as ChatMessage<AdditionalMessageOptions>,
+              ]
+            : [];
+        },
+      };
+    }
+    // Helper function to process a chunk
+    function processChunk(
+      chunk: ChatResponseChunk,
+      toolCallMap: Map<string, PartialToolCall>,
+    ): ChatResponseChunk | null {
+      if (chunk.options && "toolCall" in chunk.options) {
+        // update tool call map
+        for (const toolCall of chunk.options.toolCall as PartialToolCall[]) {
+          if (toolCall.id) {
+            toolCallMap.set(toolCall.id, toolCall);
+          }
+        }
+        // return the current full response with the tool calls
+        const toolCalls = Array.from(toolCallMap.values());
+        return {
+          ...chunk,
+          options: {
+            ...chunk.options,
+            toolCall: toolCalls,
+          },
+        };
+      }
+      return null;
+    }
+    // Collect for tool call
+    let fullResponse: ChatResponseChunk | null = null;
+    const toolCallMap = new Map<string, PartialToolCall>();
+    // Process first chunk
+    fullResponse = processChunk(firstChunk, toolCallMap);
+    // Process remaining chunks
+    while (true) {
+      const next = await iterator.next();
+      if (next.done) break;
+      const chunk = next.value;
+      const potentialFull = processChunk(chunk, toolCallMap);
+      if (potentialFull) {
+        fullResponse = potentialFull;
+      }
+    }
+    if (params.tools && fullResponse) {
+      const toolCalls = getToolCallsFromResponse(fullResponse);
+      const messages: ChatMessage<AdditionalMessageOptions>[] = [];
+      messages.push({
+        role: "assistant",
+        content: "",
+        options: {
+          toolCall: toolCalls,
+        } as AdditionalMessageOptions,
+      });
+      for (const toolCall of toolCalls) {
+        const toolResultMessage = await callTool<AdditionalMessageOptions>(
+          params.tools,
+          toolCall,
+        );
+        if (toolResultMessage) {
+          messages.push(toolResultMessage);
+        }
+      }
+      return {
+        stream: (async function* () {})(),
+        newMessages() {
+          return messages;
+        },
+        toolCalls,
+      };
+    } else {
+      throw new Error("Cannot get tool calls from response");
+    }
+  }
 }

 export abstract class ToolCallLLM<
@@ -1,5 +1,4 @@
-// TODO: move to a test package
-import { ToolCallLLM } from "../llms/base";
+import { ToolCallLLM } from "./base";
 import type {
  ChatResponse,
  ChatResponseChunk,
@@ -9,7 +8,7 @@ import type {
  LLMCompletionParamsNonStreaming,
  LLMCompletionParamsStreaming,
  LLMMetadata,
-} from "../llms/type";
+} from "./type";

 export class MockLLM extends ToolCallLLM {
  metadata: LLMMetadata;
@@ -0,0 +1,61 @@
+import { stringifyJSONToMessageContent } from "../utils";
+import type {
+  BaseTool,
+  ChatMessage,
+  ChatResponse,
+  ChatResponseChunk,
+  ToolCall,
+  ToolCallLLMMessageOptions,
+} from "./type";
+
+export const getToolCallsFromResponse = (
+  response:
+    | ChatResponse<ToolCallLLMMessageOptions>
+    | ChatResponseChunk<ToolCallLLMMessageOptions>,
+): ToolCall[] => {
+  let options;
+
+  if ("message" in response) {
+    options = response.message.options;
+  } else {
+    options = response.options;
+  }
+
+  if (options && "toolCall" in options) {
+    return (options.toolCall as ToolCall[]).map((toolCall) => ({
+      ...toolCall,
+      input:
+        // XXX: this is a hack openai returns parsed object for streaming, but not for
+        // non-streaming
+        typeof toolCall.input === "string"
+          ? JSON.parse(toolCall.input)
+          : toolCall.input,
+    }));
+  }
+  return [];
+};
+
+export const callTool = async <
+  AdditionalMessageOptions extends object = object,
+>(
+  tools: BaseTool[],
+  toolCall: ToolCall,
+): Promise<ChatMessage<AdditionalMessageOptions> | null> => {
+  const tool = tools?.find((t) => t.metadata.name === toolCall.name);
+  // TODO: consider using BaseToolWithCall instead of BaseTool to avoid checking for tool.call
+  if (tool && tool.call) {
+    const result = await tool.call(toolCall.input);
+    const toolResultMessage: ChatMessage<AdditionalMessageOptions> = {
+      role: "user",
+      content: stringifyJSONToMessageContent(result),
+      options: {
+        toolResult: {
+          id: toolCall.id,
+          result,
+        },
+      } as AdditionalMessageOptions,
+    };
+    return toolResultMessage;
+  }
+  return null;
+};
@@ -95,6 +95,22 @@ export type ChatResponseChunk<
  options?: undefined | AdditionalMessageOptions;
 };

+export interface ExecResponse<
+  AdditionalMessageOptions extends object = object,
+> {
+  newMessages: ChatMessage<AdditionalMessageOptions>[];
+  toolCalls: ToolCall[];
+}
+
+export interface ExecStreamResponse<
+  AdditionalMessageOptions extends object = object,
+> {
+  stream: AsyncIterable<ChatResponseChunk<AdditionalMessageOptions>>;
+  // this is a function as while streaming, the assistant message is not ready yet - can be called after the stream is done
+  newMessages(): ChatMessage<AdditionalMessageOptions>[];
+  toolCalls: ToolCall[];
+}
+
 export interface CompletionResponse {
  text: string;
  /**
@@ -120,9 +136,9 @@ export interface LLMChatParamsBase<
  AdditionalMessageOptions extends object = object,
 > {
  messages: ChatMessage<AdditionalMessageOptions>[];
-  additionalChatOptions?: AdditionalChatOptions;
-  tools?: BaseTool[];
-  responseFormat?: z.ZodType | object;
+  additionalChatOptions?: AdditionalChatOptions | undefined;
+  tools?: BaseTool[] | undefined;
+  responseFormat?: z.ZodType | object | undefined;
 }

 export interface LLMChatParamsStreaming<
@@ -39,7 +39,9 @@ export abstract class BaseMemoryBlock<
   *
   * @returns The memory block content as an array of ChatMessage.
   */
-  abstract get(): Promise<MemoryMessage<TAdditionalMessageOptions>[]>;
+  abstract get(
+    messages?: MemoryMessage<TAdditionalMessageOptions>[],
+  ): Promise<MemoryMessage<TAdditionalMessageOptions>[]>;

  /**
   * Store the messages in the memory block.
@@ -1,3 +1,4 @@
 export { BaseMemoryBlock } from "./base";
 export { FactExtractionMemoryBlock } from "./fact";
 export { StaticMemoryBlock } from "./static";
+export { VectorMemoryBlock } from "./vector";
@@ -0,0 +1,250 @@
+import type { BaseEmbedding } from "../../embeddings";
+import type { BaseNodePostprocessor } from "../../postprocessor";
+import { BasePromptTemplate, defaultContextSystemPrompt } from "../../prompts";
+import type { NodeWithScore } from "../../schema";
+import { MetadataMode, TextNode } from "../../schema";
+import { extractText } from "../../utils/llms";
+import type {
+  BaseVectorStore,
+  MetadataFilter,
+  VectorStoreQuery,
+} from "../../vector-store";
+import { VectorStoreQueryMode } from "../../vector-store";
+import type { MemoryMessage } from "../types";
+import { BaseMemoryBlock, type MemoryBlockOptions } from "./base";
+
+/**
+ * The options for the vector memory block.
+ */
+export type VectorMemoryBlockOptions = {
+  /**
+   * The vector store to use for retrieval.
+   */
+  vectorStore: BaseVectorStore;
+
+  /**
+   * Maximum number of messages to include for context when retrieving.
+   * @default 5
+   */
+  retrievalContextWindow?: number;
+
+  /**
+   * Template for formatting the retrieved information.
+   * @default new PromptTemplate({ template: "{{ text }}" })
+   */
+  formatTemplate?: BasePromptTemplate;
+
+  /**
+   * List of node postprocessors to apply to the retrieved nodes containing messages.
+   *
+   * @default []
+   */
+  nodePostprocessors?: BaseNodePostprocessor[];
+
+  /**
+   * Configuration options for vector store queries when retrieving memory.
+   *
+   * @default
+   * ```typescript
+   * {
+   *   similarityTopK: 2,                    // Number of top similar results to return
+   *   mode: VectorStoreQueryMode.DEFAULT,   // Query mode for the vector store
+   *   sessionFilterKey: "session_id",       // Metadata key for session filtering
+   *   filters: {
+   *     filters: [
+   *       { key: "session_id", value: "<current block id>", operator: "==" }
+   *     ],
+   *     condition: "and"
+   *   }
+   * }
+   * ```
+   *
+   * Note: A session filter is automatically added to ensure memory isolation between blocks.
+   * If custom filters are provided, the session filter will be merged with them.
+   */
+  queryOptions?: Partial<VectorMemoryBlockQueryOptions>;
+} & MemoryBlockOptions;
+
+export type VectorMemoryBlockQueryOptions = Omit<
+  VectorStoreQuery,
+  "queryEmbedding" | "queryStr"
+> & {
+  sessionFilterKey: string;
+};
+
+/**
+ * A memory block that retrieves relevant information from a vector store.
+ *
+ * This block stores conversation history in a vector store and retrieves
+ * relevant information based on the most recent messages.
+ */
+export class VectorMemoryBlock<
+  TAdditionalMessageOptions extends object = object,
+> extends BaseMemoryBlock<TAdditionalMessageOptions> {
+  private readonly vectorStore: BaseVectorStore;
+  private readonly retrievalContextWindow: number;
+  private readonly formatTemplate: BasePromptTemplate;
+  private readonly nodePostprocessors: BaseNodePostprocessor[];
+  private readonly queryOptions: VectorMemoryBlockQueryOptions;
+
+  constructor(options: VectorMemoryBlockOptions) {
+    super(options);
+
+    // Validate vector store
+    if (!options.vectorStore.storesText) {
+      throw new Error(
+        "vectorStore must store text to be used as a retrieval memory block",
+      );
+    }
+
+    this.vectorStore = options.vectorStore;
+    this.retrievalContextWindow = options.retrievalContextWindow ?? 5;
+    this.queryOptions = this.buildDefaultQueryOptions(options.queryOptions);
+    this.formatTemplate = options.formatTemplate ?? defaultContextSystemPrompt;
+    this.nodePostprocessors = options.nodePostprocessors ?? [];
+  }
+
+  get embedModel(): BaseEmbedding {
+    return this.vectorStore.embedModel;
+  }
+
+  async get(
+    messages: MemoryMessage<TAdditionalMessageOptions>[] = [],
+  ): Promise<MemoryMessage<TAdditionalMessageOptions>[]> {
+    if (messages?.length === 0) return [];
+
+    // Use the last message or a context window of messages for the query
+    let context: MemoryMessage<TAdditionalMessageOptions>[];
+    if (
+      this.retrievalContextWindow > 1 &&
+      messages.length >= this.retrievalContextWindow
+    ) {
+      context = messages.slice(-this.retrievalContextWindow);
+    } else {
+      context = messages;
+    }
+    const queryText = context
+      .map((message) => extractText(message.content))
+      .join("\n\n");
+    if (!queryText) return [];
+
+    // Create and execute the query
+    const queryEmbedding = await this.embedModel.getTextEmbedding(queryText);
+    const query: VectorStoreQuery = {
+      queryStr: queryText,
+      queryEmbedding,
+      ...this.queryOptions,
+    };
+    const results = await this.vectorStore.query(query);
+    if (!results.nodes?.length) return [];
+
+    // Create nodes with scores
+    const nodesWithScores: NodeWithScore[] = results.nodes.map(
+      (node, index) => ({
+        node,
+        score: results.similarities?.[index] ?? undefined,
+      }),
+    );
+
+    // Apply postprocessors
+    let processedNodes = nodesWithScores;
+    for (const postprocessor of this.nodePostprocessors) {
+      processedNodes = await postprocessor.postprocessNodes(
+        processedNodes,
+        queryText,
+      );
+    }
+
+    // Format the results
+    const retrievedText = processedNodes
+      .map(({ node }) => node.getContent(MetadataMode.NONE))
+      .join("\n\n");
+
+    const formattedText = this.formatTemplate.format({
+      context: retrievedText,
+    });
+
+    // Return as memory message
+    return [
+      {
+        id: this.id,
+        role: "memory",
+        content: formattedText,
+      } as MemoryMessage<TAdditionalMessageOptions>,
+    ];
+  }
+
+  async put(
+    messages: MemoryMessage<TAdditionalMessageOptions>[],
+  ): Promise<void> {
+    if (messages.length === 0) return;
+
+    // Format messages with role, text content, and additional info
+    const texts: string[] = [];
+
+    for (const message of messages) {
+      const text = extractText(message.content);
+      if (!text) continue;
+
+      let messageText = text;
+
+      // Add additional info if present
+      const additionalInfo = (message.options ?? {}) as Record<string, unknown>;
+      if (Object.keys(additionalInfo).length > 0) {
+        messageText += `\nAdditional Info: (${JSON.stringify(additionalInfo)})`;
+      }
+
+      texts.push(`<message role='${message.role}'>${messageText}</message>`);
+    }
+
+    if (texts.length === 0) return;
+
+    // Create text node with session metadata
+    const textNode = new TextNode({
+      text: texts.join("\n"),
+      metadata: { [this.queryOptions.sessionFilterKey]: this.id },
+    });
+
+    // Get embedding for the text
+    textNode.embedding = await this.embedModel.getTextEmbedding(textNode.text);
+
+    // Add to vector store
+    await this.vectorStore.add([textNode]);
+  }
+
+  private buildDefaultQueryOptions(
+    options: Partial<VectorMemoryBlockQueryOptions> | undefined,
+  ): VectorMemoryBlockQueryOptions {
+    const {
+      similarityTopK = 2,
+      mode = VectorStoreQueryMode.DEFAULT,
+      sessionFilterKey = "session_id",
+    } = options ?? {};
+
+    let filters = options?.filters;
+
+    const sessionFilter: MetadataFilter = {
+      key: sessionFilterKey,
+      value: this.id,
+      operator: "==",
+    };
+
+    if (filters) {
+      // Only add session_id filter if it doesn't exist in the filters list
+      const sessionIdFilterExists = filters.filters.some(
+        (filter) => filter.key === sessionFilterKey,
+      );
+      if (!sessionIdFilterExists) {
+        filters.filters.push(sessionFilter);
+      }
+    } else {
+      // If no filters are provided, add the session_id filter
+      filters = {
+        filters: [sessionFilter],
+        condition: "and",
+      };
+    }
+
+    return { ...options, similarityTopK, mode, sessionFilterKey, filters };
+  }
+}
@@ -8,6 +8,10 @@ import {
  StaticMemoryBlock,
  type StaticMemoryBlockOptions,
 } from "./block/static";
+import {
+  VectorMemoryBlock,
+  type VectorMemoryBlockOptions,
+} from "./block/vector";
 import { DEFAULT_TOKEN_LIMIT, Memory, type MemoryOptions } from "./memory";
 import type { MemoryMessage } from "./types";

@@ -115,6 +119,17 @@ export function factExtractionBlock<TMessageOptions extends object = object>(
  return new FactExtractionMemoryBlock<TMessageOptions>(options);
 }

+/**
+ * create a VectorMemoryBlock
+ * @param options - Configuration options for the vector memory block
+ * @returns A new VectorMemoryBlock instance
+ */
+export function vectorBlock<TMessageOptions extends object = object>(
+  options: VectorMemoryBlockOptions,
+): VectorMemoryBlock<TMessageOptions> {
+  return new VectorMemoryBlock<TMessageOptions>(options);
+}
+
 /**
 * Creates a new Memory instance from a snapshot
 * @param snapshot The snapshot to load from
@@ -31,6 +31,13 @@ export type MemoryOptions<TMessageOptions extends object = object> = {
   * Used internally for memory restoration from snapshots.
   */
  memoryCursor?: number;
+
+  /**
+   * The default LLM to use for memory retrieval.
+   * If not provided, the default `Settings.llm` will be used.
+   * This default LLM can be overridden by the LLM passed in the `getLLM` method.
+   */
+  llm?: LLM | undefined;
 };

 export class Memory<
@@ -65,6 +72,10 @@ export class Memory<
   * The cursor for the messages that have been processed into long-term memory.
   */
  private memoryCursor: number = 0;
+  /**
+   * The default LLM to use for memory retrieval.
+   */
+  private llm: LLM | undefined;

  constructor(
    messages: MemoryMessage<TMessageOptions>[] = [],
@@ -76,6 +87,7 @@ export class Memory<
      options.shortTermTokenLimitRatio ?? DEFAULT_SHORT_TERM_TOKEN_LIMIT_RATIO;
    this.memoryBlocks = options.memoryBlocks ?? [];
    this.memoryCursor = options.memoryCursor ?? 0;
+    this.initLLM(options.llm);

    this.adapters = {
      ...options.customAdapters,
@@ -84,6 +96,15 @@ export class Memory<
    } as TAdapters & BuiltinAdapters<TMessageOptions>;
  }

+  private initLLM(llm: LLM | undefined) {
+    // safe initialize LLM without throwing error if Settings.llm hasn't been set yet
+    try {
+      this.llm = llm ?? Settings.llm;
+    } catch (error) {
+      this.llm = undefined;
+    }
+  }
+
  /**
   * Add a message to the memory
   * @param message - The message to add to the memory
@@ -160,12 +181,13 @@ export class Memory<
  /**
   * Get the messages from the memory, optionally including transient messages.
   * only return messages that are within context window of the LLM
-   * @param llm - To fit the result messages to the context window of the LLM. If not provided, the default token limit will be used.
+   * @param llm - To fit the result messages to the context window of the LLM  (fallback to default llm if not provided).
+   * If llm is not specified in both the constructor and the method, the default token limit will be used.
   * @param transientMessages - Optional transient messages to include.
   * @returns The messages from the memory, optionally including transient messages.
   */
  async getLLM(
-    llm?: LLM,
+    llm: LLM | undefined = this.llm,
    transientMessages?: ChatMessage<TMessageOptions>[],
  ): Promise<ChatMessage[]> {
    // Priority of result messages:
@@ -176,11 +198,20 @@ export class Memory<
      ? Math.ceil(contextWindow * DEFAULT_TOKEN_LIMIT_RATIO)
      : this.tokenLimit;

+    let blockInputMessages = this.messages;
+    if (transientMessages && transientMessages.length > 0) {
+      blockInputMessages = [
+        ...this.messages,
+        ...transientMessages.map((m) => this.adapters.llamaindex.toMemory(m)),
+      ];
+    }
+
    // Start with fixed block messages (priority=0)
    // as it must always be included in the retrieval result
    const messages = await this.getMemoryBlockMessages(
      this.memoryBlocks.filter((block) => block.priority === 0),
      tokenLimit,
+      blockInputMessages,
    );
    // remaining token limit for short-term and memory blocks content
    const remainingTokenLimit =
@@ -207,6 +238,7 @@ export class Memory<
    const longTermBlockMessages = await this.getMemoryBlockMessages(
      longTermBlocks,
      memoryBlocksTokenLimit,
+      blockInputMessages,
    );
    messages.push(...longTermBlockMessages);

@@ -252,6 +284,7 @@ export class Memory<
  private async getMemoryBlockMessages(
    blocks: BaseMemoryBlock<TMessageOptions>[],
    tokenLimit?: number,
+    messages?: MemoryMessage<TMessageOptions>[],
  ): Promise<ChatMessage<TMessageOptions>[]> {
    if (blocks.length === 0) {
      return [];
@@ -265,7 +298,7 @@ export class Memory<
    let addedTokenCount = 0;
    for (const block of sortedBlocks) {
      try {
-        const content = await block.get();
+        const content = await block.get(messages);
        for (const message of content) {
          const chatMessage = this.adapters.llamaindex.fromMemory(message);
          const messageTokenCount = this.countMessagesToken([chatMessage]);
@@ -56,10 +56,45 @@ export function prettifyError(error: unknown): string {
  }
 }

+/**
+ * Returns a stringfied JSON with double quotes removed.
+ *
+ * @param value - The JSON value to stringify
+ * @returns The stringified JSON with no double quotes
+ */
 export function stringifyJSONToMessageContent(value: JSONValue): string {
  return JSON.stringify(value, null, 2).replace(/"([^"]*)"/g, "$1");
 }

+export function assertIsJSONValue(value: unknown): asserts value is JSONValue {
+  if (
+    typeof value === "string" ||
+    typeof value === "number" ||
+    typeof value === "boolean"
+  ) {
+    return;
+  }
+
+  if (Array.isArray(value)) {
+    for (const item of value) {
+      assertIsJSONValue(item);
+    }
+    return;
+  }
+
+  if (typeof value === "object" && value !== null) {
+    for (const [key, val] of Object.entries(value)) {
+      if (typeof key !== "string") {
+        throw new Error(`Invalid object key: ${key}`);
+      }
+      assertIsJSONValue(val);
+    }
+    return;
+  }
+
+  throw new Error(`Value is not a valid JSONValue: ${String(value)}`);
+}
+
 export {
  extractDataUrlComponents,
  extractImage,
@@ -70,8 +105,6 @@ export {
  toToolDescriptions,
 } from "./llms";

-export { MockLLM } from "./mock";
-
 export * from "./encoding";
 export { objectEntries } from "./object-entries";
 export * from "./stream";
@@ -101,7 +101,9 @@ export type VectorStoreByType = {
 };

 export type VectorStoreBaseParams = {
+  // @deprecated: use embedModel instead
  embeddingModel?: BaseEmbedding | undefined;
+  embedModel?: BaseEmbedding | undefined;
 };

 export abstract class BaseVectorStore<Client = unknown, T = unknown> {
@@ -117,7 +119,8 @@ export abstract class BaseVectorStore<Client = unknown, T = unknown> {
  ): Promise<VectorStoreQueryResult>;

  protected constructor(params?: VectorStoreBaseParams) {
-    this.embedModel = params?.embeddingModel ?? Settings.embedModel;
+    this.embedModel =
+      params?.embedModel ?? params?.embeddingModel ?? Settings.embedModel;
  }
 }

@@ -1,5 +1,5 @@
 import { LLMAgent, validateAgentParams } from "@llamaindex/core/agent";
-import { MockLLM } from "@llamaindex/core/utils";
+import { MockLLM } from "@llamaindex/core/llms/mock";
 import { expect, test } from "vitest";
 import { ZodError } from "zod";

@@ -1,4 +1,9 @@
-import { truncateMaxTokens } from "@llamaindex/core/embeddings";
+import {
+  BaseEmbedding,
+  batchEmbeddings,
+  truncateMaxTokens,
+  type BaseEmbeddingOptions,
+} from "@llamaindex/core/embeddings";
 import { Tokenizers, tokenizers } from "@llamaindex/env/tokenizers";
 import { describe, expect, test } from "vitest";

@@ -27,3 +32,77 @@ describe("truncateMaxTokens", () => {
    expect(t.includes("�")).toBe(false);
  });
 });
+
+describe("BaseEmbedding progressCallback", () => {
+  const mockEmbedFunc = async (text: string): Promise<number[]> => {
+    return Array.from({ length: 10 }, () => Math.random());
+  };
+  const mockBatchEmbedFunc = async (
+    texts: string[],
+  ): Promise<Array<number[]>> => {
+    return await Promise.all(texts.map(mockEmbedFunc));
+  };
+  const mockProgressCallback = (current: number, total: number) => {
+    console.log(`Progress: ${current}/${total}`);
+  };
+  const mockLogProgress = true;
+
+  const mockOptions = {
+    logProgress: mockLogProgress,
+    progressCallback: mockProgressCallback,
+  };
+
+  class MockEmbedding extends BaseEmbedding {
+    constructor(options: BaseEmbeddingOptions) {
+      super();
+      this.options = options;
+    }
+
+    private options: BaseEmbeddingOptions;
+
+    async getTextEmbedding(text: string): Promise<number[]> {
+      return await mockEmbedFunc(text);
+    }
+
+    getTextEmbeddings = async (texts: string[]): Promise<Array<number[]>> => {
+      return await mockBatchEmbedFunc(texts);
+    };
+
+    async getTextEmbeddingsBatch(
+      texts: string[],
+      options?: BaseEmbeddingOptions,
+    ): Promise<Array<number[]>> {
+      const mergedOptions = { ...this.options, ...options };
+
+      expect(mergedOptions.progressCallback).toBeDefined();
+
+      return await batchEmbeddings(
+        texts,
+        this.getTextEmbeddings,
+        this.embedBatchSize,
+        mergedOptions,
+      );
+    }
+  }
+
+  test("should call progressCallback with correct values", async () => {
+    // Import and use a real embedding class instead
+
+    const progressCalls: Array<{ current: number; total: number }> = [];
+    const progressCallback = (current: number, total: number) => {
+      progressCalls.push({ current, total });
+    };
+    const texts = ["text1", "text2", "text3"];
+    const embedding = new MockEmbedding({ progressCallback: progressCallback });
+    embedding.embedBatchSize = 1; // Set batch size to 1 for testing
+    // so that progressCallback is called for each item
+    // (otherwise, we'd only get a callback for 3/3, which is fine but less clear)
+    await embedding.getTextEmbeddingsBatch(texts);
+
+    expect(progressCalls).toEqual([
+      { current: 1, total: 3 },
+      { current: 2, total: 3 },
+      { current: 3, total: 3 },
+    ]);
+  });
+});
@@ -0,0 +1,43 @@
+import { MockLLM } from "@llamaindex/core/llms/mock";
+import { describe, expect, it } from "vitest";
+
+// TODO: add tests for tool calls
+describe("BaseLLM exec", () => {
+  it("should stream text response when no tool call is made", async () => {
+    const responseMessage = "This is a response message while streaming";
+
+    const llm = new MockLLM({ responseMessage });
+
+    const { stream, newMessages, toolCalls } = await llm.exec({
+      messages: [{ content: "Hi", role: "user" }],
+      stream: true,
+    });
+
+    expect(() => newMessages()).toThrowError();
+
+    const chunks = [];
+    for await (const chunk of stream) {
+      chunks.push(chunk);
+    }
+
+    expect(chunks.map((c) => c.delta).join("")).toBe(responseMessage);
+    expect(toolCalls).toEqual([]);
+    expect(newMessages()).toEqual([
+      { content: responseMessage, role: "assistant" },
+    ]);
+  });
+  it("should return text response when no tool call is made", async () => {
+    const responseMessage = "This is a response message";
+
+    const llm = new MockLLM({ responseMessage });
+
+    const { newMessages, toolCalls } = await llm.exec({
+      messages: [{ content: "Hi", role: "user" }],
+    });
+
+    expect(newMessages).toEqual([
+      { content: responseMessage, role: "assistant" },
+    ]);
+    expect(toolCalls).toEqual([]);
+  });
+});
@@ -1,7 +1,7 @@
 import { Settings } from "@llamaindex/core/global";
 import type { ChatMessage, LLM } from "@llamaindex/core/llms";
+import { MockLLM } from "@llamaindex/core/llms/mock";
 import { createMemory, Memory, staticBlock } from "@llamaindex/core/memory";
-import { MockLLM } from "@llamaindex/core/utils";
 import type { Tokenizer } from "@llamaindex/env/tokenizers";
 import {
  afterAll,
@@ -1,6 +1,6 @@
 import { SimpleChatEngine } from "@llamaindex/core/chat-engine";
+import { MockLLM } from "@llamaindex/core/llms/mock";
 import { Memory } from "@llamaindex/core/memory";
-import { MockLLM } from "@llamaindex/core/utils";
 import { describe, expect, test } from "vitest";

 describe("SimpleChatEngine", () => {
@@ -1,5 +1,41 @@
 # @llamaindex/experimental

+## 0.0.198
+
+### Patch Changes
+
+- llamaindex@0.11.21
+
+## 0.0.197
+
+### Patch Changes
+
+- llamaindex@0.11.20
+
+## 0.0.196
+
+### Patch Changes
+
+- llamaindex@0.11.19
+
+## 0.0.195
+
+### Patch Changes
+
+- llamaindex@0.11.18
+
+## 0.0.194
+
+### Patch Changes
+
+- llamaindex@0.11.17
+
+## 0.0.193
+
+### Patch Changes
+
+- llamaindex@0.11.16
+
 ## 0.0.192

 ### Patch Changes
@@ -1,7 +1,7 @@
 {
  "name": "@llamaindex/experimental",
  "description": "Experimental package for LlamaIndexTS",
-  "version": "0.0.192",
+  "version": "0.0.198",
  "type": "module",
  "types": "dist/type/index.d.ts",
  "main": "dist/cjs/index.js",
@@ -1,5 +1,58 @@
 # llamaindex

+## 0.11.21
+
+### Patch Changes
+
+- Updated dependencies [38da40b]
+  - @llamaindex/core@0.6.17
+  - @llamaindex/cloud@4.0.26
+  - @llamaindex/node-parser@2.0.17
+  - @llamaindex/workflow@1.1.17
+
+## 0.11.20
+
+### Patch Changes
+
+- Updated dependencies [a8ec08c]
+- Updated dependencies [2967d57]
+  - @llamaindex/core@0.6.16
+  - @llamaindex/workflow@1.1.16
+  - @llamaindex/cloud@4.0.25
+  - @llamaindex/node-parser@2.0.16
+
+## 0.11.19
+
+### Patch Changes
+
+- Updated dependencies [7ad3411]
+- Updated dependencies [5da5b3c]
+  - @llamaindex/core@0.6.15
+  - @llamaindex/workflow@1.1.15
+  - @llamaindex/cloud@4.0.24
+  - @llamaindex/node-parser@2.0.15
+
+## 0.11.18
+
+### Patch Changes
+
+- Updated dependencies [a1b1598]
+  - @llamaindex/cloud@4.0.23
+
+## 0.11.17
+
+### Patch Changes
+
+- Updated dependencies [d2be868]
+  - @llamaindex/cloud@4.0.22
+
+## 0.11.16
+
+### Patch Changes
+
+- Updated dependencies [579ca0c]
+  - @llamaindex/cloud@4.0.21
+
 ## 0.11.15

 ### Patch Changes
@@ -1,6 +1,6 @@
 {
  "name": "llamaindex",
-  "version": "0.11.15",
+  "version": "0.11.21",
  "license": "MIT",
  "type": "module",
  "keywords": [
@@ -272,7 +272,7 @@ export class SimpleVectorStore extends BaseVectorStore {

  static async fromPersistPath(
    persistPath: string,
-    embeddingModel?: BaseEmbedding,
+    embedModel?: BaseEmbedding,
  ): Promise<SimpleVectorStore> {
    const dirPath = path.dirname(persistPath);
    if (!(await exists(dirPath))) {
@@ -300,20 +300,20 @@ export class SimpleVectorStore extends BaseVectorStore {
    data.textIdToRefDocId = dataDict.textIdToRefDocId ?? {};
    // @ts-expect-error TS2322
    data.metadataDict = dataDict.metadataDict ?? {};
-    const store = new SimpleVectorStore({ data, embeddingModel });
+    const store = new SimpleVectorStore({ data, embedModel });
    store.persistPath = persistPath;
    return store;
  }

  static fromDict(
    saveDict: SimpleVectorStoreData,
-    embeddingModel?: BaseEmbedding,
+    embedModel?: BaseEmbedding,
  ): SimpleVectorStore {
    const data = new SimpleVectorStoreData();
    data.embeddingDict = saveDict.embeddingDict;
    data.textIdToRefDocId = saveDict.textIdToRefDocId;
    data.metadataDict = saveDict.metadataDict;
-    return new SimpleVectorStore({ data, embeddingModel });
+    return new SimpleVectorStore({ data, embedModel });
  }

  toDict(): SimpleVectorStoreData {
@@ -1,5 +1,31 @@
 # @llamaindex/core-test

+## 0.1.13
+
+### Patch Changes
+
+- @llamaindex/openai@0.4.12
+
+## 0.1.12
+
+### Patch Changes
+
+- @llamaindex/openai@0.4.11
+
+## 0.1.11
+
+### Patch Changes
+
+- Updated dependencies [856dd8c]
+  - @llamaindex/openai@0.4.10
+
+## 0.1.10
+
+### Patch Changes
+
+- Updated dependencies [a1fdb07]
+  - @llamaindex/openai@0.4.9
+
 ## 0.1.9

 ### Patch Changes
@@ -1,7 +1,7 @@
 {
  "name": "@llamaindex/llamaindex-test",
  "private": true,
-  "version": "0.1.9",
+  "version": "0.1.13",
  "type": "module",
  "scripts": {
    "test": "vitest run"
@@ -59,7 +59,7 @@ describe("SimpleVectorStore", () => {
      }),
    ];
    store = new SimpleVectorStore({
-      embeddingModel: {} as BaseEmbedding, // Mocking the embedModel
+      embedModel: {} as BaseEmbedding, // Mocking the embedModel
      data: {
        embeddingDict: {},
        textIdToRefDocId: {},
@@ -1,5 +1,27 @@
 # @llamaindex/node-parser

+## 2.0.17
+
+### Patch Changes
+
+- Updated dependencies [38da40b]
+  - @llamaindex/core@0.6.17
+
+## 2.0.16
+
+### Patch Changes
+
+- Updated dependencies [a8ec08c]
+  - @llamaindex/core@0.6.16
+
+## 2.0.15
+
+### Patch Changes
+
+- Updated dependencies [7ad3411]
+- Updated dependencies [5da5b3c]
+  - @llamaindex/core@0.6.15
+
 ## 2.0.14

 ### Patch Changes
@@ -1,6 +1,6 @@
 {
  "name": "@llamaindex/node-parser",
-  "version": "2.0.14",
+  "version": "2.0.17",
  "description": "Node parser for LlamaIndex",
  "type": "module",
  "exports": {
@@ -1,5 +1,28 @@
 # @llamaindex/anthropic

+## 0.3.19
+
+### Patch Changes
+
+- Updated dependencies [38da40b]
+  - @llamaindex/core@0.6.17
+
+## 0.3.18
+
+### Patch Changes
+
+- Updated dependencies [a8ec08c]
+  - @llamaindex/core@0.6.16
+
+## 0.3.17
+
+### Patch Changes
+
+- ddc0eaf: anthropic: stream partial tool calls
+- Updated dependencies [7ad3411]
+- Updated dependencies [5da5b3c]
+  - @llamaindex/core@0.6.15
+
 ## 0.3.16

 ### Patch Changes
@@ -1,7 +1,7 @@
 {
  "name": "@llamaindex/anthropic",
  "description": "Anthropic Adapter for LlamaIndex",
-  "version": "0.3.16",
+  "version": "0.3.19",
  "type": "module",
  "main": "./dist/index.cjs",
  "module": "./dist/index.js",
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
github-actions[bot]	e4c7113614	Release 0.11.21 (#2128 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: marcusschiesser <17126+marcusschiesser@users.noreply.github.com>	2025-07-22 12:23:58 +08:00
Thuc Pham	38da40bc98	feat: VectoryMemoryBlock (#2110 ) Co-authored-by: Marcus Schiesser <mail@marcusschiesser.de>	2025-07-22 12:18:09 +08:00
Marcus Schiesser	4d50ca4d84	chore: add streamchat test (#2122 )	2025-07-22 11:30:01 +08:00
github-actions[bot]	8b5253a297	Release (#2127 )	2025-07-21 15:40:31 -06:00
Logan	ea15e75c89	deployment docs nits (#2126 )	2025-07-21 15:30:37 -06:00
github-actions[bot]	3be87d4670	Release 0.11.20 (#2121 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: himself65 <14026360+himself65@users.noreply.github.com>	2025-07-21 09:37:44 -07:00
Terence Sim	94da13db0d	fix: azure openai streamchat empty delta throw TypeError (#2118 ) Co-authored-by: Terence Sim <40583743+InTheAxis@users.noreply.github.com>	2025-07-21 09:16:09 -07:00
Terence Sim	acd50ea99f	chore: replaced console.log with logger type from @llamaindex/env (#2123 ) Co-authored-by: Terence Sim <40583743+InTheAxis@users.noreply.github.com>	2025-07-21 09:14:06 -07:00
Adrian Lyjak	2967d57ac0	feat: default to _public agent data (#2117 )	2025-07-21 09:07:15 -07:00
Thuc Pham	a8ec08c682	fix: ensure correct message content in agent workflow (#2114 ) Co-authored-by: Marcus Schiesser <mail@marcusschiesser.de>	2025-07-21 15:13:27 +08:00
Terence Sim	678b327051	feat: added apac bedrock models (#2119 ) Co-authored-by: Marcus Schiesser <mail@marcusschiesser.de>	2025-07-21 12:13:37 +08:00
Jeremy B. Merrill	650eeb1df3	fix: GeminiEmbedding should send batches of max 100 (#2099 ) Co-authored-by: Marcus Schiesser <marcus.schiesser@googlemail.com>	2025-07-21 12:12:42 +08:00
Laurie Voss	50f6747758	Instrumenting with Google Tag Manager (in addition to Google Analytics) (#2116 )	2025-07-20 13:18:09 -07:00
github-actions[bot]	12414a6836	Release (#2113 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: marcusschiesser <17126+marcusschiesser@users.noreply.github.com>	2025-07-18 13:54:38 +08:00
Marcus Schiesser	856dd8cca8	fix: assume new models are function call models (#2112 )	2025-07-18 12:52:43 +08:00
Jerry Cheng	d8f4f6a859	Update SupabaseVectorStore.ts to fix score calculating error (#2109 ) Co-authored-by: Marcus Schiesser <marcus.schiesser@googlemail.com>	2025-07-18 12:48:47 +08:00
Logan	f594d7034f	revamp getting started flow and main index page (#2079 ) Co-authored-by: Thuc Pham <51660321+thucpn@users.noreply.github.com> Co-authored-by: Marcus Schiesser <mail@marcusschiesser.de> Co-authored-by: thucpn <thucsh2@gmail.com>	2025-07-17 16:27:28 +08:00
github-actions[bot]	c1c58feed2	Release 0.11.19 (#2105 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: marcusschiesser <17126+marcusschiesser@users.noreply.github.com>	2025-07-17 15:44:22 +08:00
Marcus Schiesser	7ad3411766	feat: add llm.exec (#2078 )	2025-07-17 15:36:56 +08:00
Neha Prasad	a1fdb07b96	feat: multi-turn image generation support (#2106 ) Co-authored-by: Marcus Schiesser <marcus.schiesser@googlemail.com>	2025-07-17 10:30:39 +08:00
Jeremy B. Merrill	5da5b3c89c	feat: add progress callback to embeddings (#2098 ) Co-authored-by: Marcus Schiesser <mail@marcusschiesser.de>	2025-07-16 13:49:49 +08:00
r3rer3	ddc0eafbaa	feat(anthropic): stream partial tool calls (#2100 )	2025-07-15 10:06:17 -07:00
github-actions[bot]	1782554488	Release 0.11.18 (#2103 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>	2025-07-14 15:53:20 -07:00
Adrian Lyjak	a1b1598bc6	fix(cloud): add generic types into agent data responses (#2102 ) Co-authored-by: Alex Yang <himself65@outlook.com>	2025-07-14 12:01:56 -07:00
Terry Zhao	b02847ae91	fix(notion): resolve @notionhq/client dependency conflict (#2097 )	2025-07-12 11:04:06 -07:00
Alex Yang	50acb4821e	feat(cloud): use camelCase (#2096 )	2025-07-12 10:59:46 -07:00
github-actions[bot]	47a5b94b0c	Release 0.11.17 (#2095 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>	2025-07-11 21:57:02 -07:00
Alex Yang	d2be868b93	feat(cloud): missing agent api (#2094 )	2025-07-11 20:45:22 -07:00
github-actions[bot]	50d42c4129	Release 0.11.16 (#2093 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>	2025-07-11 20:13:37 -07:00
github-actions[bot]	848b97d4d0	Release 0.11.16 (#2092 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>	2025-07-11 18:19:17 -07:00
Alex Yang	c5796b8d2d	fix: only allow pnpm (#2091 )	2025-07-11 18:17:47 -07:00
Alex Yang	579ca0cf60	chore: bump sdk version (#2090 )	2025-07-11 18:10:15 -07:00
Alex Yang	f7e670c8d9	fix: sdk type improvement (#2089 )	2025-07-11 17:56:41 -07:00
Alex Yang	9ff971435c	fix(cloud): agent sdk (#2088 )	2025-07-11 17:41:25 -07:00
github-actions[bot]	7c9d0e24c4	Release (#2086 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: marcusschiesser <17126+marcusschiesser@users.noreply.github.com>	2025-07-11 12:30:04 -07:00
NIEDASEN	af3f86694b	feat: add supportToolCall getter to DeepSeekLLM class (#2085 ) Co-authored-by: Marcus Schiesser <marcus.schiesser@googlemail.com>	2025-07-11 16:11:22 +08:00